pierreqi/r_code_50k · Datasets at Hugging Face

a86eaa02de22fd7910bacb609af40fb05aae82dc

f84a075495fd8ca5e799b82e5b40f1896fa4e901

/extra/encoderExample.r

774a9d344690ad29b0e8471375005ceb54aedd03

[]

no_license

ABMI/RadiologyFeatureExtraction

d388212277af4609cad6ce6e4c7d08cae49163a7

898069202864331e84876395817c8818d7324023

refs/heads/master

2020-04-17T16:22:33.975421

2019-01-25T05:38:36

166,737,629

2

1

null

2019-01-25T05:38:37

2019-01-21T02:44:02

R

UTF-8

R

false

24,351

r

encoderExample.r

####VAE#### library(dplyr) library(keras) buildRadiologyVae <- function(dataPaths = imagePaths, vaeValidationSplit = 0.2, vaeBatchSize = 200L, vaeLatentDim = 1000L, vaeIntermediateDim = 10000L, vaeEpoch = 500L, vaeEpislonStd = 1.0, dimensionOfTarget = 2, dataFolder, originalDimension = c(128, 128), ROI2D = list(c(10:119), c(10:119)), MaxLimitUnit = 1500, samplingGenerator = FALSE) { if(dimensionOfTarget != 2) stop("Currently only dimesion of Target = 2 is avaiablbe") originalDim <- length(ROI2D[[1]]) * length(ROI2D[[2]]) K <- keras::backend() x <- keras::layer_input(shape = originalDim) h <- keras::layer_dense(x, vaeIntermediateDim, activation = "relu") z_mean <- keras::layer_dense(h, vaeLatentDim) z_log_var <- keras::layer_dense(h, vaeLatentDim) sampling <- function(arg) { z_mean <- arg[, 1:vaeLatentDim] z_log_var <- arg[, (vaeLatentDim + 1):(2 * vaeLatentDim)] epsilon <- keras::k_random_normal( shape = c(keras::k_shape(z_mean)[[1]]), mean = 0., stddev = vaeEpislonStd ) z_mean + keras::k_exp(z_log_var / 2) * epsilon } z <- keras::layer_concatenate(list(z_mean, z_log_var)) %>% keras::layer_lambda(sampling) # We instantiate these layers separately so as to reuse them later decoder_h <- keras::layer_dense(units = vaeIntermediateDim, activation = "relu") decoder_mean <- keras::layer_dense(units = originalDim, activation = "sigmoid") h_decoded <- decoder_h(z) x_decoded_mean <- decoder_mean(h_decoded) # end-to-end autoencoder vae <- keras::keras_model(x, x_decoded_mean) # encoder, from inputs to latent space encoder <- keras::keras_model(x, z_mean) # generator, from latent space to reconstruted inputs decoder_input <- keras::layer_input(shape = vaeLatentDim) h_decoded_2 <- decoder_h(decoder_input) x_decoded_mean_2 <- decoder_mean(h_decoded_2) generator <- keras::keras_model(decoder_input, x_decoded_mean_2) vae_loss <- function(x, x_decoded_mean) { xent_loss <- (originalDim / 1.0) * keras::loss_binary_crossentropy(x, x_decoded_mean) k1_loss <- -0.5 * keras::k_mean(1 + z_log_var - keras::k_square(z_mean) - keras::k_exp(z_log_var), axis = -1L) xent_loss + k1_loss } # if (!is.null(dataValidation)) dataValidation <- list(dataValidation,dataValidation) vaeEarlyStopping <- keras::callback_early_stopping(monitor = "val_loss", patience = 10, mode = "auto", min_delta = 1e-1) vae %>% keras::compile(optimizer = "rmsprop", loss = vae_loss) # Paths for data actualPaths <- apply(imagePaths, 1, function(x) file.path(dataFolder, x)) if(samplingGenerator) { # validation data valIndex <- sample(seq(actualPaths), length(actualPaths) * vaeValidationSplit) valImages <- lapply(as.array(actualPaths[valIndex]), function(x) { try({ x <- oro.dicom::dicom2nifti(oro.dicom::readDICOM(x, verbose = FALSE)) x <- EBImage::resize(x, w = originalDimension[1], h = originalDimension[2])[ROI2D[[1]], ROI2D[[2]] ] }, silent = T) }) valImages <- array(unlist(valImages), dim = c(originalDimension, length(valImages))) valImages <- valImages %>% apply(3, as.numeric) %>% t() ## Regularization the data with the max ## valImages[is.na(valImages)] <- 0 valImages <- ifelse(valImages > MaxLimitUnit, MaxLimitUnit, valImages) valImages <- valImages / MaxLimitUnit sampling_generator <- function(dataPath, batchSize, MaxLimitUnit) { function() { # gc() index <- sample(length(dataPath), batchSize, replace = FALSE) data.mat <- as.array(dataPath[index]) images <- lapply(data.mat, function(x) { try({ x <- oro.dicom::dicom2nifti(oro.dicom::readDICOM(x, verbose = FALSE)) x <- EBImage::resize(x, w = originalDimension[1], h = originalDimension[2]) [ROI2D[[1]], ROI2D[[2]] ] }, silent = T) }) images <- array(unlist(images), dim = c(ROI2D[[1]], ROI2D[[2]], length(images))) images <- images %>% apply(3, as.numeric) %>% t() # Error images[is.na(images)] <- 0 images <- ifelse(images > MaxLimitUnit, MaxLimitUnit, images) images <- images / MaxLimitUnit list(images, images) } } vae %>% keras::fit_generator( sampling_generator(actualPaths[-valIndex], vaeBatchSize, MaxLimitUnit), steps_per_epoch = length(actualPaths[-valIndex]) / vaeBatchSize , epochs = vaeEpoch , validation_data = list(valImages, valImages) , callbacks = list(vaeEarlyStopping) ) } else { data.mat <- as.array(actualPaths) images <- lapply(data.mat, function(x) { try({ x <- oro.dicom::dicom2nifti(oro.dicom::readDICOM(x, verbose = FALSE)) x <- EBImage::resize(x, w = originalDimension[1], h = originalDimension[2]) [ROI2D[[1]], ROI2D[[2]] ] }, silent = T) }) images <- array(unlist(images), dim = c(length(ROI2D[[1]]), length(ROI2D[[2]]), length(images))) images <- images %>% apply(3, as.numeric) %>% t() # error seems vector size upper 2.0 GB # saveRDS(images,file.path(dataFolder,"image64.rds")) # images<-readRDS(file.path(dataFolder,"image.rds")) images[is.na(images)] <- 0 images <- ifelse(images > MaxLimitUnit, MaxLimitUnit, images) images <- images / MaxLimitUnit vae %>% keras::fit( images, images, shuffle = TRUE, epochs = vaeEpoch, batch_size = vaeBatchSize, validation_split = vaeValidationSplit, callbacks = list(vaeEarlyStopping) ) } return(list(vae = vae, encoder = encoder, MaxLimitUnit = MaxLimitUnit, vaeBatchSize = vaeBatchSize, vaeLatentDim = vaeLatentDim)) } testVae <- function(dataPaths = imagePaths, vaeBatchSize = 200L, vaeLatentDim = 1000L, vaeIntermediateDim = 10000L, dimensionOfTarget = 2, dataFolder, originalDimension = c(128, 128), ROI2D = list(c(10:119), c(10:119)), MaxLimitUnit = 1500, samplingN = 10, vae) { if(dimensionOfTarget != 2) stop("Currently only dimesion of Target = 2 is avaiablbe") # Original dimenstion calculator originalDim <- length(ROI2D[[1]]) * length(ROI2D[[2]]) # Paths for data actualPaths <- apply(imagePaths, 1, function(x) file.path(dataFolder, x)) actualPaths <- actualPaths[seq(samplingN)] # actualPaths <- actualPaths[732:832] data.mat <- as.array(actualPaths) images <- lapply(data.mat, function(x) { try({ x <- oro.dicom::dicom2nifti(oro.dicom::readDICOM(x, verbose = FALSE)) x <- EBImage::resize(x, w = originalDimension[1], h = originalDimension[2]) [ROI2D[[1]], ROI2D[[2]] ] }, silent = T) }) images <- array(unlist(images), dim = c(length(ROI2D[[1]]), length(ROI2D[[2]]), length(images))) images <- images %>% apply(3, as.numeric) %>% t() # error seems vector size upper 2.0 GB images[is.na(images)] <- 0 images <- ifelse(images > MaxLimitUnit, MaxLimitUnit, images) images <- images / MaxLimitUnit mergeImage <- function(nifs) { resList <- NA for(i in 1:dim(nifs)[1]) { nif <- oro.nifti::as.nifti(nifs[i,,]) if(is.na(resList)) resList <- nif else resList <- abind::abind(resList, nif, along = 3) } return(resList) } originalImages <- array(unlist(images), dim = c(samplingN, length(ROI2D[[1]]), length(ROI2D[[2]]))) # Original image view # oro.nifti::image(oro.nifti::as.nifti(mergeImage(originalImages))) oro.nifti::image(oro.nifti::as.nifti(originalImages[5,,])) predicted <- predict(VAE$vae, images, batch_size = vaeBatchSize) predictedImages <- array(unlist(predicted), dim = c(samplingN, length(ROI2D[[1]]), length(ROI2D[[2]]))) # Predicted image view return(mergeImage(predictedImages)) } ####VQ-VAE#### #' This is the companion code to the post #' "Discrete Representation Learning with VQ-VAE and TensorFlow Probability" #' on the TensorFlow for R blog. #' #' https://blogs.rstudio.com/tensorflow/posts/2019-01-24-vq-vae/ #' https://github.com/rstudio/keras/pull/642/commits/b3ab58640702d0dd46e6f72b8056c0579bc0f9e2#diff-8f710ecd930d2604fb377340770cab03 # library(keras) # use_implementation("tensorflow") # library(tensorflow) # tfe_enable_eager_execution(device_policy = "silent") # # use_session_with_seed(7778, # disable_gpu = FALSE, # disable_parallel_cpu = FALSE) # # tfp <- import("tensorflow_probability") # tfd <- tfp$distributions # # library(tfdatasets) # library(dplyr) # library(glue) # library(curry) # # moving_averages <- tf$python$training$moving_averages # # # # Utilities -------------------------------------------------------- # # visualize_images <- # function(dataset, # epoch, # reconstructed_images, # random_images) { # write_png(dataset, epoch, "reconstruction", reconstructed_images) # write_png(dataset, epoch, "random", random_images) # # } # # write_png <- function(dataset, epoch, desc, images) { # png(paste0(dataset, "_epoch_", epoch, "_", desc, ".png")) # par(mfcol = c(8, 8)) # par(mar = c(0.5, 0.5, 0.5, 0.5), # xaxs = 'i', # yaxs = 'i') # for (i in 1:64) { # img <- images[i, , , 1] # img <- t(apply(img, 2, rev)) # image( # 1:28, # 1:28, # img * 127.5 + 127.5, # col = gray((0:255) / 255), # xaxt = 'n', # yaxt = 'n' # ) # } # dev.off() # # } # # # # Setup and preprocessing ------------------------------------------------- # # np <- import("numpy") # # # download from: https://github.com/rois-codh/kmnist # kuzushiji <- np$load("kmnist-train-imgs.npz") # kuzushiji <- kuzushiji$get("arr_0") # # train_images <- kuzushiji %>% # k_expand_dims() %>% # k_cast(dtype = "float32") # train_images <- train_images %>% `/`(255) # # buffer_size <- 60000 # batch_size <- 64 # num_examples_to_generate <- batch_size # # batches_per_epoch <- buffer_size / batch_size # # train_dataset <- tensor_slices_dataset(train_images) %>% # dataset_shuffle(buffer_size) %>% # dataset_batch(batch_size, drop_remainder = TRUE) # # # test # iter <- make_iterator_one_shot(train_dataset) # batch <- iterator_get_next(iter) # batch %>% dim() # # # Params ------------------------------------------------------------------ # # learning_rate <- 0.001 # latent_size <- 1 # num_codes <- 64L # code_size <- 16L # base_depth <- 32 # activation <- "elu" # beta <- 0.25 # decay <- 0.99 # input_shape <- c(28, 28, 1) # # # Models ------------------------------------------------------------------- # # default_conv <- # set_defaults(layer_conv_2d, list(padding = "same", activation = activation)) # default_deconv <- # set_defaults(layer_conv_2d_transpose, # list(padding = "same", activation = activation)) # # # Encoder ------------------------------------------------------------------ # # encoder_model <- function(name = NULL, # code_size) { # # keras_model_custom(name = name, function(self) { # self$conv1 <- default_conv(filters = base_depth, kernel_size = 5) # self$conv2 <- # default_conv(filters = base_depth, # kernel_size = 5, # strides = 2) # self$conv3 <- # default_conv(filters = 2 * base_depth, kernel_size = 5) # self$conv4 <- # default_conv( # filters = 2 * base_depth, # kernel_size = 5, # strides = 2 # ) # self$conv5 <- # default_conv( # filters = 4 * latent_size, # kernel_size = 7, # padding = "valid" # ) # self$flatten <- layer_flatten() # self$dense <- layer_dense(units = latent_size * code_size) # self$reshape <- # layer_reshape(target_shape = c(latent_size, code_size)) # # function (x, mask = NULL) { # x %>% # # output shape: 7 28 28 32 # self$conv1() %>% # # output shape: 7 14 14 32 # self$conv2() %>% # # output shape: 7 14 14 64 # self$conv3() %>% # # output shape: 7 7 7 64 # self$conv4() %>% # # output shape: 7 1 1 4 # self$conv5() %>% # # output shape: 7 4 # self$flatten() %>% # # output shape: 7 16 # self$dense() %>% # # output shape: 7 1 16 # self$reshape() # } # # }) # } # # # # Decoder ------------------------------------------------------------------ # # decoder_model <- function(name = NULL, # input_size, # output_shape) { # # keras_model_custom(name = name, function(self) { # self$reshape1 <- layer_reshape(target_shape = c(1, 1, input_size)) # self$deconv1 <- # default_deconv( # filters = 2 * base_depth, # kernel_size = 7, # padding = "valid" # ) # self$deconv2 <- # default_deconv(filters = 2 * base_depth, kernel_size = 5) # self$deconv3 <- # default_deconv( # filters = 2 * base_depth, # kernel_size = 5, # strides = 2 # ) # self$deconv4 <- # default_deconv(filters = base_depth, kernel_size = 5) # self$deconv5 <- # default_deconv(filters = base_depth, # kernel_size = 5, # strides = 2) # self$deconv6 <- # default_deconv(filters = base_depth, kernel_size = 5) # self$conv1 <- # default_conv(filters = output_shape[3], # kernel_size = 5, # activation = "linear") # # function (x, mask = NULL) { # x <- x %>% # # output shape: 7 1 1 16 # self$reshape1() %>% # # output shape: 7 7 7 64 # self$deconv1() %>% # # output shape: 7 7 7 64 # self$deconv2() %>% # # output shape: 7 14 14 64 # self$deconv3() %>% # # output shape: 7 14 14 32 # self$deconv4() %>% # # output shape: 7 28 28 32 # self$deconv5() %>% # # output shape: 7 28 28 32 # self$deconv6() %>% # # output shape: 7 28 28 1 # self$conv1() # tfd$Independent(tfd$Bernoulli(logits = x), # reinterpreted_batch_ndims = length(output_shape)) # } # }) # } # # # Vector quantizer ------------------------------------------------------------------- # # vector_quantizer_model <- # function(name = NULL, num_codes, code_size) { # # keras_model_custom(name = name, function(self) { # self$num_codes <- num_codes # self$code_size <- code_size # self$codebook <- tf$get_variable("codebook", # shape = c(num_codes, code_size), # dtype = tf$float32) # self$ema_count <- tf$get_variable( # name = "ema_count", # shape = c(num_codes), # initializer = tf$constant_initializer(0), # trainable = FALSE # ) # self$ema_means = tf$get_variable( # name = "ema_means", # initializer = self$codebook$initialized_value(), # trainable = FALSE # ) # # function (x, mask = NULL) { # # # bs * 1 * num_codes # distances <- tf$norm(tf$expand_dims(x, axis = 2L) - # tf$reshape(self$codebook, # c( # 1L, 1L, self$num_codes, self$code_size # )), # axis = 3L) # # # bs * 1 # assignments <- tf$argmin(distances, axis = 2L) # # # bs * 1 * num_codes # one_hot_assignments <- # tf$one_hot(assignments, depth = self$num_codes) # # # bs * 1 * code_size # nearest_codebook_entries <- tf$reduce_sum( # tf$expand_dims(one_hot_assignments,-1L) * # bs, 1, 64, 1 # tf$reshape(self$codebook, c( # 1L, 1L, self$num_codes, self$code_size # )), # axis = 2L # 1, 1, 64, 16 # ) # # list(nearest_codebook_entries, one_hot_assignments) # } # }) # } # # # # Update codebook ------------------------------------------------------ # # update_ema <- function(vector_quantizer, # one_hot_assignments, # codes, # decay) { # # shape = 64 # updated_ema_count <- moving_averages$assign_moving_average( # vector_quantizer$ema_count, # tf$reduce_sum(one_hot_assignments, axis = c(0L, 1L)), # decay, # zero_debias = FALSE # ) # # # 64 * 16 # updated_ema_means <- moving_averages$assign_moving_average( # vector_quantizer$ema_means, # # selects all assigned values (masking out the others) and sums them up over the batch # # (will be divided by count later) # tf$reduce_sum( # tf$expand_dims(codes, 2L) * # tf$expand_dims(one_hot_assignments, 3L), # axis = c(0L, 1L) # ), # decay, # zero_debias = FALSE # ) # # # Add small value to avoid dividing by zero # updated_ema_count <- updated_ema_count + 1e-5 # updated_ema_means <- # updated_ema_means / tf$expand_dims(updated_ema_count, axis = -1L) # # tf$assign(vector_quantizer$codebook, updated_ema_means) # } # # # # Training setup ----------------------------------------------------------- # # encoder <- encoder_model(code_size = code_size) # decoder <- decoder_model(input_size = latent_size * code_size, # output_shape = input_shape) # # vector_quantizer <- # vector_quantizer_model(num_codes = num_codes, code_size = code_size) # # optimizer <- tf$train$AdamOptimizer(learning_rate = learning_rate) # # checkpoint_dir <- "./vq_vae_checkpoints" # # checkpoint_prefix <- file.path(checkpoint_dir, "ckpt") # checkpoint <- # tf$train$Checkpoint( # optimizer = optimizer, # encoder = encoder, # decoder = decoder, # vector_quantizer_model = vector_quantizer # ) # # checkpoint$save(file_prefix = checkpoint_prefix) # # # Training loop ----------------------------------------------------------- # # num_epochs <- 20 # # for (epoch in seq_len(num_epochs)) { # # iter <- make_iterator_one_shot(train_dataset) # # total_loss <- 0 # reconstruction_loss_total <- 0 # commitment_loss_total <- 0 # prior_loss_total <- 0 # # until_out_of_range({ # # x <- iterator_get_next(iter) # # with(tf$GradientTape(persistent = TRUE) %as% tape, { # # codes <- encoder(x) # c(nearest_codebook_entries, one_hot_assignments) %<-% vector_quantizer(codes) # codes_straight_through <- codes + tf$stop_gradient(nearest_codebook_entries - codes) # decoder_distribution <- decoder(codes_straight_through) # # reconstruction_loss <- # -tf$reduce_mean(decoder_distribution$log_prob(x)) # # commitment_loss <- tf$reduce_mean(tf$square(codes - tf$stop_gradient(nearest_codebook_entries))) # # prior_dist <- tfd$Multinomial(total_count = 1, # logits = tf$zeros(c(latent_size, num_codes))) # prior_loss <- -tf$reduce_mean(tf$reduce_sum(prior_dist$log_prob(one_hot_assignments), 1L)) # # loss <- # reconstruction_loss + beta * commitment_loss + prior_loss # # }) # # encoder_gradients <- tape$gradient(loss, encoder$variables) # decoder_gradients <- tape$gradient(loss, decoder$variables) # # optimizer$apply_gradients(purrr::transpose(list( # encoder_gradients, encoder$variables # )), # global_step = tf$train$get_or_create_global_step()) # optimizer$apply_gradients(purrr::transpose(list( # decoder_gradients, decoder$variables # )), # global_step = tf$train$get_or_create_global_step()) # # update_ema(vector_quantizer, # one_hot_assignments, # codes, # decay) # # total_loss <- total_loss + loss # reconstruction_loss_total <- # reconstruction_loss_total + reconstruction_loss # commitment_loss_total <- commitment_loss_total + commitment_loss # prior_loss_total <- prior_loss_total + prior_loss # # }) # # checkpoint$save(file_prefix = checkpoint_prefix) # # cat( # glue( # "Loss (epoch): {epoch}:", # " {(as.numeric(total_loss)/trunc(buffer_size/batch_size)) %>% round(4)} loss", # " {(as.numeric(reconstruction_loss_total)/trunc(buffer_size/batch_size)) %>% round(4)} reconstruction_loss", # " {(as.numeric(commitment_loss_total)/trunc(buffer_size/batch_size)) %>% round(4)} commitment_loss", # " {(as.numeric(prior_loss_total)/trunc(buffer_size/batch_size)) %>% round(4)} prior_loss", # # ), # "\n" # ) # # # display example images (choose your frequency) # if (TRUE) { # reconstructed_images <- decoder_distribution$mean() # # (64, 1, 16) # prior_samples <- tf$reduce_sum( # # selects one of the codes (masking out 63 of 64 codes) # # (bs, 1, 64, 1) # tf$expand_dims(prior_dist$sample(num_examples_to_generate),-1L) * # # (1, 1, 64, 16) # tf$reshape(vector_quantizer$codebook, # c(1L, 1L, num_codes, code_size)), # axis = 2L # ) # decoded_distribution_given_random_prior <- # decoder(prior_samples) # random_images <- decoded_distribution_given_random_prior$mean() # visualize_images("k", epoch, reconstructed_images, random_images) # } # }

fcfe10daed18a5df8736bc21c2dfa122ead649a0

2bcd0393bd4a98f415e3ed1353e8213d226fa6dc

/clustering.R

133e351961ce521bdacd5a5e2ef9604f2734f93c

[]

no_license

kabalord/clustering

ae96e5ede932933942334232a43a1a50693a72a3

5e6b630277949a3b88dba108d41dc12e2545820c

refs/heads/master

2023-02-02T07:25:02.347324

2020-12-18T10:39:14

319,901,731

0

null

UTF-8

R

false

7,734

r

clustering.R

#installation des packages avec une fonction pour la gestion des packages list_packages_names <- c("fpc", "clusteval", "cluster") for (packname in list_packages_names){ if(packname %in% rownames(installed.packages()) == FALSE){ install.packages(packname) } } library(fpc) library(clusteval) library(cluster) #1.1 Données n1<-20 n2<-30 n3<-15 n4<-5 set.seed(123) # important : fixer la graine du générateur echantillon<-data.frame(rbind(matrix(rnorm(n1*2),ncol=2), matrix(rnorm(n2*2,mean = 4),ncol=2), matrix(c(rnorm(n3,mean = 4),rnorm(n3,mean=-4.5)),ncol=2), matrix(c(rnorm(n4,mean = -2),rnorm(n4,mean=-2.5)),ncol=2))) names(echantillon)<-c("Variable1","Variable2") #1.2 Partition #maxence fonction random_clust <- function (data, x){ sample(1:x, nrow(data), replace=T) } res <- random_clust(echantillon,3) # pour 4 classes print(res) echantillon$Classe <- res print(echantillon) quad_clust <- function(df){ med1 <- median(df$Variable1) med2 <- median(df$Variable2) df[df$Variable1<med1 & df$Variable2<med2,"Classe"] <- 1 df[df$Variable1>med1 & df$Variable2>med2,"Classe"] <- 2 df[df$Variable1>med1 & df$Variable2<med2,"Classe"] <- 3 df[df$Variable1<med1 & df$Variable2>med2,"Classe"] <- 4 return(df) } quad_clust(echantillon) #1.3 Visualisation des classes obtenues plot(x = echantillon$Variable1 , y = echantillon$Variable2 , pch = 21 , bg = echantillon$Classe , xlab = "Variable 1" , ylab = "Variable 2") #1.4 Description des classes obtenues synthese<-do.call(data.frame,aggregate(echantillon, by=list(echantillon$Classe), FUN = function(x){ return(c(Effectif = length(x), Moyenne = mean(x), Mediane = median(x), Minimum = min(x), Maximum = max(x), Ecart_type = sd(x))) })) # knitr::kable(synthese) print(synthese) #1.5 Évaluation comparaison list_packages_names <- c("fpc", "clusteval") for (packname in list_packages_names){ if(packname %in% rownames(installed.packages()) == FALSE){ install.packages(packname) } } #1.5.1 Visualisation avancée x1 <- quad_clust(echantillon) clusplot(pam(x1, 4)) plotcluster(x1, x1$Classe) x2 <- random_clust(echantillon,3) clusplot(pam(x2, 3)) plotcluster(x2, x2) #1.6 Analyse des coefficients de silhouette. si<-silhouette(echantillon$Classe,dist(echantillon[,1:2])) plot(si) print(si) #Faire de même avec les résultats de quad_clust resqc<-silhouette(x1$Classe,dist(x1[,1:2])) plot(resqc) print(resqc) #1.7 Calcul d’inertie # Fonction de calcul d'inertie # df : un data frame contenant le nuage de points # p : un vecteur de pondération inertie<-function(df,p=NULL){ # Si le vecteur de pondération n'est pas fourni # tous les poids valebt 1 if (is.null(p)){ p <- rep(1,nrow(df)) } # Calcul du centre de gravité : moyenne de chaque colonne, pondérée par p g <- ( p %*% as.matrix(df) ) / sum(p) # calcul de l'inertie iner <- 0 for (i in seq(nrow(df))){ # pour chaque point iner <- iner + sum((df[i,] - g)^2) * p[i] # ajouter à iner la distance au carré de ce point à g, pondérée par le poids du point } return(iner) } # Fonction de calcul de l'inertie intra-classe # df : toujours le data frame # cl : le vecteur des labels d'appartenance aux clusters inertie_intra<-function(df,cl){ res<-rep(0,length(unique(cl))) for (k in unique(cl)){ res[k] <- inertie(df[which(cl==k),]) } return(sum(res)) } # Fonction de clacul de l'inertie interclasse # df : toujours le data frame # clu : idem inertie_inter<-function(df,clu){ M<-matrix(rep(0,ncol(df)*length(unique(clu))),ncol = ncol(df)) for (k in unique(clu)){ M[k,]<-unname(colMeans(df[which(clu==k),])) } return(inertie(data.frame(M),unname(table(clu)))) } # ------- # MAIN # ------ # Chargement des données data(cars) # Partitionnement des données (ici par CAH) hca <- hclust(dist(cars)) clu <- cutree(hca,h=40) # Affichage print(hca) print(clu) # Affichage des inerties calculées print(inertie_intra(cars,clu)) print(inertie_inter(cars,clu)) print(inertie(cars)) print(inertie_intra(echantillon,random_clust(echantillon,3))) print(inertie_inter(echantillon,random_clust(echantillon,3))) print(inertie2(echantillon)) #1.8 Similarité #2 Clustering sur des données « réelles » avec les algorithmes des k-means et CAH #2.1 Installation du package install.packages("cluster.datasets") #2.2 Chargement des données library(cluster.datasets) data("acidosis.patients") #2.3 Première description summary(acidosis.patients) str(acidosis.patients) head(acidosis.patients,n = 4) #2.4 CAH #Représenter les données à l’aide d’une matrice de scatterplots pairs(acidosis.patients) #Centrer et réduire les données acidosis.patients.scale <- scale(acidosis.patients) #Calculer la matrice des distances euclidiennes entre individus acidosis.patients.dist <-dist(acidosis.patients) #Effectuer une CAH à partir de cette matrice acidosis.patients.hclust <- hclust(acidosis.patients.dist) plot(acidosis.patients.hclust) #Effectuer le partitionnement correspondant. Afficher la matrice de scatterplots en coloriant les points en fonction de leur classe d’appartenance dendrogram <- as.dendrogram(acidosis.patients.hclust) plot(dendrogram) rect.hclust(acidosis.patients.hclust, k=4) pairs(acidosis.patients.hclust[,4], col = c("red", "cornflowerblue", "purple", "yellow")) #2.5 k-means kmeansmet <- kmeans(acidosis.patients.scale,k=4) plot(kmeans) #3 Nombre optimal de classes install.packages(c("factoextra","NbClust")) library(factoextra) library(NbClust) # Elbow method fviz_nbclust(acidosis.patients, kmeans, method = "wss") + geom_vline(xintercept = 4, linetype = 2)+ labs(subtitle = "Elbow method") # Silhouette method fviz_nbclust(acidosis.patients, kmeans, method = "silhouette")+ labs(subtitle = "Silhouette method") # Gap statistic # nboot = 50 to keep the function speedy. # recommended value: nboot= 500 for your analysis. # Use verbose = FALSE to hide computing progression. set.seed(123) fviz_nbclust(acidosis.patients, kmeans, nstart = 25, method = "gap_stat", nboot = 50)+ labs(subtitle = "Gap statistic method") library("NbClust") nb <- NbClust(acidosis.patients, distance = "euclidean", min.nc = 2, max.nc = 10, method = "kmeans") library("factoextra") fviz_nbclust(nb) #tests setwd("/Users/walterroaserrano/Desktop/M22021/apprentissageNONsupervise") getwd() df <- read.csv("varied.csv") # Elbow method fviz_nbclust(df, kmeans, method = "wss") + geom_vline(xintercept = 4, linetype = 2)+ labs(subtitle = "Elbow method") # Silhouette method fviz_nbclust(df, kmeans, method = "silhouette")+ labs(subtitle = "Silhouette method") # Gap statistic # nboot = 50 to keep the function speedy. # recommended value: nboot= 500 for your analysis. # Use verbose = FALSE to hide computing progression. set.seed(123) fviz_nbclust(df, kmeans, nstart = 25, method = "gap_stat", nboot = 50)+ labs(subtitle = "Gap statistic method") # 7 Aides, indications et compléments # 7.1 Analyses descriptives et multivariées install.packages("ade4") library(ade4) varied <- read.csv(file = 'varied.csv',row.names=1, header=FALSE) #ACP dudi.pca(varied)

582ec2cf23bbcf2d94f18d0f87687f3def524643

2487dfa8bb23d3e1a9000dba265c416cccb69939

/man/df2matrix.Rd

82cbd967762586937cf7d7d0bdaec943d370b0cb

[]

no_license

cran/R2MLwiN

f2c5694b60e3a392ad516ab63689c642f3fc72bb

593d94db244d3fc07538aedf83fc183859b9f5fd

refs/heads/master

2023-03-21T15:14:11.554599

2023-03-14T04:40:02

17,681,793

3

1

null

UTF-8

R

false

true

1,208

rd

df2matrix.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/df2matrix.R \name{df2matrix} \alias{df2matrix} \title{Translates a data.frame, formatted for use in multiple membership modelling in MLwiN, to a matrix.} \usage{ df2matrix(data, idcols, weightcols) } \arguments{ \item{data}{A \code{\link[base]{data.frame}} object.} \item{idcols}{String vector of the identifier column names.} \item{weightcols}{String vector of the weight column names.} } \value{ An adjacency matrix as returned by \code{\link[Matrix]{sparseMatrix}}. } \description{ Translates a \code{\link[base]{data.frame}}, in a form usable by MLwiN for multiple membership models, into a \code{\link[base]{matrix}}. The data.frame needs to contain (a) columns with membership IDs (e.g. first row of which might be \code{2, 3, 5, 6, 0, 0}) and (b) columns containing weights (e.g. first row of which might be \code{0.25, 0.25, 0.25, 0.25, 0, 0}; in this example the first row of resulting matrix would be \code{0, 1, 1, 0, 1, 1}). } \seealso{ \code{\link{matrix2df}} } \author{ Zhang, Z., Charlton, C.M.J., Parker, R.M.A., Leckie, G., and Browne, W.J. (2016) Centre for Multilevel Modelling, University of Bristol, U.K. }

59451e197d068e1d67ac59b737f525a2bfa558c2

d69ce1ab7f083da591cb8608f1ebd77588d3fa5a

/man/list_accession.Rd

c09aa3404a917896c71dd3de5eea2f602d8a1c66

[]

no_license

YosefLab/ImpulseDE2

6da580082878c2a8c2913c9d378623e844388cb4

ab5282464d92ea7c06d211e42af1cbdfbe127b73

refs/heads/master

2022-10-04T18:12:18.080047

2022-09-14T09:27:18

55,623,543

29

13

null

2022-09-14T09:27:19

2016-04-06T16:45:41

R

UTF-8

R

false

true

1,826

rd

list_accession.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/srcImpulseDE2_classImpulseDE2Object.R \docType{methods} \name{list_accession} \alias{list_accession} \alias{names.ImpulseDE2Object} \alias{names,ImpulseDE2Object-method} \alias{$,ImpulseDE2Object-method} \alias{[[,ImpulseDE2Object,character,missing-method} \title{List-like accessor methods for ImpulseDE2Object} \usage{ \S4method{names}{ImpulseDE2Object}(x) \S4method{[[}{ImpulseDE2Object,character,missing}(x, i, j, ...) \S4method{$}{ImpulseDE2Object}(x, name) } \arguments{ \item{x}{(ImpulseDE2Object) ImpulseDE2 output object.} \item{i, name}{(idx or str) Name or index of core output element of ImpulseDE2Object.} \item{j}{Not used, only vectors.} \item{...}{Not used.} } \value{ Names of core output in ImpulseDE2Object. Target element from ImpulseDE2Object. Target element from ImpulseDE2Object. } \description{ Allow usage of ImpulseDE2 ouput object like a list with respect to the core output: dfImpulseDE2Results and vecDEGenes. } \examples{ lsSimulatedData <- simulateDataSetImpulseDE2( vecTimePointsA = rep(seq(1,8),3), vecTimePointsB = NULL, vecBatchesA = NULL, vecBatchesB = NULL, scaNConst = 30, scaNImp = 10, scaNLin = 10, scaNSig = 10) objectImpulseDE2 <- runImpulseDE2( matCountData = lsSimulatedData$matObservedCounts, dfAnnotation = lsSimulatedData$dfAnnotation, boolCaseCtrl = FALSE, vecConfounders = NULL, scaNProc = 1 ) names(objectImpulseDE2) # Display core output # With respect to this core output, objectImpulseDE2 # can be treated like a list. head(objectImpulseDE2[['dfImpulseDE2Results']]) head(objectImpulseDE2$dfImpulseDE2Results) head(objectImpulseDE2[['vecDEGenes']]) head(objectImpulseDE2$vecDEGenes) } \author{ David Sebastian Fischer }

85c280cab0e21c1b3da06999e098a6c5767330b6

c5f0075dbf7a226beca7d2cac63ef70bca921f8d

/Data-Mining/regression/one_useful_variable.r

0ea5cc57fe6768c12356c2cc7993a3508c344a18

[]

no_license

jaeyoung-jane-choi/2019_Indiana_University

23a271ce5294f69e08775f06437d2125c789c419

c9a7604147d9efee5bc2386cc9ae8d5240eaf450

refs/heads/main

2023-06-07T07:56:20.466645

2021-07-02T03:49:19

382,091,809

1

0

null

UTF-8

R

false

1,311

r

one_useful_variable.r

# Have a single useful variable and 99 garbage variables for prediction response # when we fit the model we see y = yhat indicating very good predition on training data. # But model performs badly when we look at new data FROM THE SAME MODEL # Ridge regression adds complexity penalty to "complexity" of regression coeffs (a) # n = 100 d = 100 # note we have as many predictor variables as observations so likely overfitting X = matrix(rnorm(n*d),nrow = n, ncol=d); y = 5*X[,1] + rnorm(n); # the relation between the response and predictors only involves 1st predictor variable! plot(X[,1],y); # can see obvious relation between first predictor and y a = solve(t(X) %*% X, t(X) %*% y); #lambda = .5; # for ridge regression #a = solve(t(X) %*% X + lambda*diag(d), t(X) %*% y); # ridge regression solves different optimization yhat = X %*% a; plot(y,yhat); # perfect prediction. we should be happy ..... ? X = matrix(rnorm(n*d),nrow = n, ncol=d); # try new data from same model y = 5*X[,1] + rnorm(n); yhat = X %*% a; plot(y,yhat); # prediction not so good ... but improves with ridge regression #cat("lambda = ", lambda, " test error = ", sum((y-yhat)^2), "normsq of a = ", sum(a*a), "\n") # for RR cat(" test error = ", sum((y-yhat)^2), "normsq of a = ", sum(a*a), "\n")

9ea7a2b13201d885a41f1fe68aaf1c6b091db15f

3fbd8c5078d5ebb28e23558b158ab74ec0f2ed6b

/man/lutInfo.Rd

0632d88ef1644c29f04e486ad7a5b37b4d08417e

[ "MIT" ]

permissive

envima/envimaR

161cf2e8a0569292ae18b0edfbb0f99900f97bd4

c8854cd06203a12cf923818728f9cff9a2e41a3d

refs/heads/master

2021-07-23T23:29:35.948757

2021-07-13T22:40:40

156,347,896

1

0

null

UTF-8

R

false

true

427

rd

lutInfo.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/helpMa.R \name{lutInfo} \alias{lutInfo} \title{Get values of default environment from internal look-up table} \usage{ lutInfo() } \value{ List containing lut content. } \description{ Get values of default environment look-up table (not required for the package but to cross-check from a user). } \details{ None } \examples{ \dontrun{ lutInfo() } }

eb6763b35ccd36a67e2646018a7fc0c711b2c7dd

a0d90bd3ecde3cbeab475095c889ac3f3c2afe05

/man/sk_utils.Rd

f63a05200cd0cc4bc8e89cae3dbf64ebe339e17b

[ "MIT" ]

permissive

abresler/sklearn

2ad843933019ed69cd7b1e12df4a2fbcd3de57e0

e51874420bb75eebd8e49aa677a30b7ec8f5bd3d

refs/heads/master

2023-08-03T18:05:48.101569

2023-07-19T13:09:25

174,732,352

7

0

null

UTF-8

R

false

true

416

rd

sk_utils.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sklearn.R \name{sk_utils} \alias{sk_utils} \title{SK Utilities \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.utils}{utility functions}} \usage{ sk_utils() } \value{ python object } \description{ SK Utilities \href{https://scikit-learn.org/stable/modules/classes.html#module-sklearn.utils}{utility functions} }

762d250f7a3d5675d1690c0adbe3c21125ae3a77

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/glycanr/examples/glyco.outliers.Rd.R

193fdee011d18dd930806ea02592cd0801dd84b4

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

228

r

glyco.outliers.Rd.R

library(glycanr) ### Name: glyco.outliers ### Title: Discover outliers in glycan data ### Aliases: glyco.outliers ### ** Examples data(mpiu) glyco.outliers(mpiu) # outliers per plate glyco.outliers(mpiu, group="Plate")

a585017b5526bf141a0a3f21112a21b163869955

6c616664e83c91416219efcf9050fa65e8c50a66

/distamp_tangara.R

50c65abcb4ae0c16eab86f0037cab9a8ad494134

[ "CC-BY-4.0", "LicenseRef-scancode-unknown-license-reference", "LicenseRef-scancode-public-domain" ]

permissive

biodiego88/Population-density-of-the-Multicolored-Tanager

16137aa1d0f5c399217c086a695c1b954e969614

ea5c78d79575c690c7b892c82b17fd7d4700c6f7

refs/heads/main

2023-03-30T18:11:54.801207

2021-04-01T16:41:35

353,763,329

0

null

ISO-8859-1

R

false

6,525

r

distamp_tangara.R

# TUTORIAL vignette("distsamp") install.packages("unmarked") install.packages("AICcmodavg") library(unmarked) library(AICcmodavg) # setiamos la carpeta donde se encuenhtran nuestros archivos setwd("C:/RAnalysis/Unmarked/distsamp/tangara") # le damos el nombre de "dists" al archivo que contiene nuestros datos dists <- read.csv("tangara_correcto.csv", header = TRUE, sep=",") # en algunos tutoriales piden darle el formato de factor a la columna que contiene las etiquetas de nuestras unidades de muestreo dists$point <- as.factor(dists$point) levels(dists$point) <- c(levels(dists$point)) levels(dists$point) # se convierten los datos de frecuencias de distancias a rangos de distancias, en este caso cada 10 mestros cp = c(0, 10, 20, 30, 40) cp # organizamos el formato de nuestros datos con la funcion formatDistData yData = formatDistData(dists, "dist", "point", cp) yData # importamos los datos de las covariables, de manera que coincidan con la organización de los datos de los conteos en cada unidad de muestreo por cada rango de distancia covs <- read.csv("tangaracovs.csv", header = TRUE, sep=",") # con la funcion unmarkedFrameDS organizamos nuestros datos para correrlos con distamp umf <- unmarkedFrameDS(y=as.matrix(yData), siteCovs=covs, survey="point", dist.breaks=c(0, 10, 20, 30, 40), unitsIn="m") summary(umf) hist(umf, xlab="distance (m)", main="", cex.lab=0.8, cex.axis=0.8) # procedemos a ajustar mnuestros datos a los modelos, iniciando con un modelo nulo con las diferentes funciones de distribución halfnormal, hazard, exp y uniforme hn_Null <- distsamp(~1~1, umf, keyfun="halfnorm", output="density", unitsOut="ha") hz_Null <- distsamp(~1~1, umf, keyfun="hazard", output="density", unitsOut="ha") exp_Null <- distsamp(~1~1, umf, keyfun="exp", output="density", unitsOut="ha") unf_Null <- distsamp(~1~1, umf, keyfun="uniform", output="density", unitsOut="ha") # a continuación probamos los modelos en los que la probabilidad de detección sin covariables y la densidad explicada por habitat con cada función de distribución hn_Nullhab <- distsamp(~1~habitat, umf, keyfun="halfnorm", output="density", unitsOut="ha") hz_Nullhab <- distsamp(~1~habitat, umf, keyfun="hazard", output="density", unitsOut="ha") exp_Nullhab <- distsamp(~1~habitat, umf, keyfun="exp", output="density", unitsOut="ha") unf_Nullhab <- distsamp(~1~habitat, umf, keyfun="uniform", output="density", unitsOut="ha") # ajustamos los modelos en los que la probabilidad de detección este explicada por tipo de habitat y la densidad sin covariables (modelo nulo) con cada función de distribución hn_habNull <- distsamp(~habitat~1, umf, keyfun="halfnorm", output="density", unitsOut="ha") hz_habNull <- distsamp(~habitat~1, umf, keyfun="hazard", output="density", unitsOut="ha") exp_habNull <- distsamp(~habitat~1, umf, keyfun="exp", output="density", unitsOut="ha") unf_habNull <- distsamp(~habitat~1, umf, keyfun="uniform", output="density", unitsOut="ha") # ajustamos los modelos en los que la probabilidad de detección y la desnidad esten explicadas por tipo de habitat con cada función de distribución hn_habhab <- distsamp(~habitat~habitat, umf, keyfun="halfnorm", output="density", unitsOut="ha") hz_habhab <- distsamp(~habitat~habitat, umf, keyfun="hazard", output="density", unitsOut="ha") exp_habhab <- distsamp(~habitat~habitat, umf, keyfun="exp", output="density", unitsOut="ha") unf_habhab <- distsamp(~habitat~habitat, umf, keyfun="uniform", output="density", unitsOut="ha") # ajuste y selección del modelo fmList <- fitList(hn_Null=hn_Null, hz_Null=hz_Null, exp_Null=exp_Null, unf_Null=unf_Null, hn_Nullhab=hn_Nullhab, hz_Nullhab=hz_Nullhab, exp_Nullhab=exp_Nullhab, unf_Nullhab=unf_Nullhab, hn_habNull=hn_habNull, hz_habNull=hz_habNull, exp_habNull=exp_habNull, unf_habNull=unf_habNull, hn_habhab=hn_habhab, hz_habhab=hz_habhab, exp_habhab=exp_habhab, unf_habhab=unf_habhab) fmList <- fitList(hn_Null=hn_Null, hz_Null=hz_Null, exp_Null=exp_Null, unf_Null=unf_Null, hn_Nullhab=hn_Nullhab, hz_Nullhab=hz_Nullhab, exp_Nullhab=exp_Nullhab, unf_Nullhab=unf_Nullhab) modSel(fmList, nullmod="hn_Null") Cand.models <- list(hn_Null, hz_Null, exp_Null, unf_Null, hn_Nullhab, hz_Nullhab, exp_Nullhab, unf_Nullhab, hn_habNull, hz_habNull, exp_habNull, unf_habNull, hn_habhab, hz_habhab, exp_habhab, unf_habhab) #nombrar los modelos Modnames <- c("hn_Null", "hz_Null", "exp_Null", "unf_Null", "hn_Nullhab", "hz_Nullhab", "exp_Nullhab", "unf_Nullhab", "hn_habNull", "hz_habNull", "exp_habNull", "unf_habNull", "hn_habhab", "hz_habhab", "exp_habhab", "unf_habhab") aictab(cand.set = Cand.models, modnames = Modnames, second.ord = TRUE, nobs = NULL, sort = TRUE) summary(hz_habhab) # al elegir el mejor modelo, manipulamos los resultados a través de la función Backtransformation para aplicar el antilogaritmo a las estimaciones, debido a que son modelos lineales generalizados de la familia Poisson en escala logaritmica backTransform(hn_Null, type="state") backTransform(hn_Null, type="det") backTransform(hz_Null, type="scale") # cuando el modelo incluye covariables, se realiza un backtransform a través de la función linearComb. Sin embargo, en este caso no fue posible backTransform(linearComb(hz_Nullhab['state'], c('BM', 'BS', 'CB', 'R'))) # por lo anterior realizamos la predicción que hace la transformación, con la función predict. Para esto creamos un formato de datos de las variables "nuevo" y luego se calcula con un nivel de 0.90 (IC 95%) nuevo = data.frame(habitat = c("R", "BS", "BM", "CB")) phz_Nullhab= predict(hz_Nullhab, type = "state", newdata = nuevo, appendData = T, level = 0.90) # Otra forma de calcular los intervalos de confianza es a través de la función confint. Cuando se realiza sobre el codigo que representa las estimaciones de predicción se hace directo. Si se hace para el modelo sin transformar, se puede realizar un exponencial del confint confint(phz_Nullhab, type='state', method='normal') exp(confint(hz_Null, type='state', method='normal', level=0.90))

3d412e5d07ca817a45d669ab726d31013e33e581

81ff4f78af4de923a71b1ac05cfebc82fb01818d

/CRAN/contrib/spaMM/R/LR.R

a2eb290447fa5965d3eba65ff538084d36a10854

[]

no_license

PRL-PRG/dyntrace-instrumented-packages

9d56a2a101c4bd9bee6bbe2ababe014d08cae1a0

6c586a26b8007dc6808865883e1dd7d2a1bf6a04

refs/heads/master

2020-03-08T15:45:59.296889

2018-04-06T09:41:52

128,220,794

0

null

UTF-8

R

false

9,349

r

LR.R

.compare_model_structures <- function(object,object2) { if (inherits(object,"HLfitlist") || inherits(object2,"HLfitlist")) { stop("This does not yet work on HLfitlist objects") } X1 <- colnames(object$`X.pv`) X2 <- colnames(object2$`X.pv`) if (length(X1)==0L) { REML1 <- NULL ## compatible with both ML or REML tests } else REML1 <- (object$APHLs$p_v != object$APHLs$p_bv) if (length(X2)==0L) { REML2 <- NULL ## idem } else REML2 <- (object2$APHLs$p_v != object2$APHLs$p_bv) REML <- unique(c(REML1,REML2)) meth1 <- object$HL meth2 <- object2$HL if (! identical(object$family[c("family","link")],object2$family[c("family","link")] ) ) { stop("Models may not be nested (distinct families)") ## but COMpoisson vs poisson ? } if (! identical(meth1,meth2) || length(REML)>1 ) { stop("object fitted by different methods cannot be compared") } if ( ! is.null(X1)) X1 <- sapply(strsplit(X1,':'), function(x) paste(sort(x),collapse=':')) ## JBF 2015/02/23: sort variables in interaction terms before comparison if ( ! is.null(X2)) X2 <- sapply(strsplit(X2,':'), function(x) paste(sort(x),collapse=':')) dX12 <- setdiff(X1,X2) dX21 <- setdiff(X2,X1) if (length(dX12)>0 && length(dX21)>0) { warning("Fixed-effect models may not be nested") # F I X M E : correct detection of non-nested models } else if (length(dX12)>0) { Xnest <- "2in1" } else if (length(dX21)>0) { Xnest <- "1in2" } else Xnest <- NULL if (object$spaMM.version < "2.2.116") { ranterms1 <- attr(object$ZAlist,"ranefs") } else ranterms1 <- attr(object$ZAlist,"exp_ranef_strings") if (object2$spaMM.version < "2.2.116") { ranterms2 <- attr(object2$ZAlist,"ranefs") } else ranterms2 <- attr(object2$ZAlist,"exp_ranef_strings") randist1 <- lapply(object$rand.families, function(v) paste(paste(v)[1:2],collapse="")) ## makes a string from each $family and $link randist2 <- lapply(object2$rand.families, function(v) paste(paste(v)[1:2],collapse="")) ## makes a string from each $family and $link ranterms1 <- paste(ranterms1,randist1) ## joins each term and its distrib ranterms2 <- paste(ranterms2,randist2) ## joins each term and its distrib dR12 <- setdiff(ranterms1,ranterms2) dR21 <- setdiff(ranterms2,ranterms1) if (length(dR12)>0 && length(dR21)>0) { stop("Non-nested random-effect models") } else if (length(dR12)>0) { Rnest <- "2in1" } else if (length(dR21)>0) { Rnest <- "1in2" } else Rnest <- NULL nest <- c(Xnest,Rnest) unest <- unique(nest) if (length(nest)==0L) { ## NULL,NULL stop("Fixed-effect specifications do not appear different from each other.") } else if (length(unest)==2) { stop("Models not nested (opposite nestings for fixed and random terms). ") } else { df1 <- length(X1) df2 <- length(X2) if (!is.null(Rnest)) { lambda.object <- object$lambda.object if (!is.null(lambda.object)) df1 <- df1+length(unlist(lambda.object$coefficients_lambdaS)) cov.mats <- .get_compact_cov_mats(object$strucList) if (length(cov.mats)) { nrows <- unlist(lapply(cov.mats,NROW)) df1 <- df1+sum(nrows*(nrows-1)/2) } lambda.object <- object2$lambda.object if (!is.null(lambda.object)) df2 <- df2+length(unlist(lambda.object$coefficients_lambdaS)) cov.mats <- .get_compact_cov_mats(object2$strucList) if ( length(cov.mats)) { nrows <- unlist(lapply(cov.mats,NROW)) df2 <- df2+sum(nrows*(nrows-1)/2) } } if (unest=="1in2") { fullm <- object2 nullm <- object df <- df2-df1 } else { fullm <- object nullm <- object2 df <- df1-df2 } if (length(nest)==2) { message("Nested models differing both by in their fixed and in their random terms. ") message("Tentatively using marginal likelihood to compare them... ") testlik <- "p_v" } else { if (is.null(Rnest)) { ## fixed effect test if (REML) { ## checking the comparability of REML fits if ( ! is.null(fullm$distinctX.Re) ) { df.f.Re <- ncol(fullm$distinctX.Re) } else df.f.Re <- ncol(fullm$`X.pv`) if ( ! is.null(nullm$distinctX.Re) ) { df.n.Re <- ncol(nullm$distinctX.Re) } else df.n.Re <- ncol(nullm$`X.pv`) if ( df.f.Re != df.n.Re ) { warning("LRT comparing REML fits with different designs is highly suspect") } } testlik <- "p_v" } else { ## random effect test if ( ! REML) warning("ML fits used to compare different random-effects models...") testlik <- "p_bv" ## used in both case, identical to p_v in the non-standard case stop("The two models have identical fixed-effect formulas\n and cannot yet be compared properly by this function.") ## need to take into account correlations in random slope models for example } } } return(list(fullfit=fullm,nullfit=nullm,test_obj=testlik,df=df)) } # (fixme?) : create as.lm method for HLfit object? LRT <- function(object,object2,boot.repl=0,nb_cores=NULL,...) { ## compare two HM objects if (nrow(object$data)!=nrow(object2$data)) { stop("models were not both fitted to the same size of dataset.") } info <- .compare_model_structures(object,object2) nullfit <- info$nullfit fullfit <- info$fullfit test_obj <- info$test_obj df <- info$df LRTori <- 2*(logLik(fullfit,which=test_obj)-logLik(nullfit,which=test_obj)) pvalue <- 1-pchisq(LRTori,df=df) ## but not valid for testing null components of variance resu <- list(nullfit=nullfit,fullfit=fullfit,basicLRT = data.frame(chi2_LR=LRTori,df=df,p_value=pvalue)) ## format appropriate for more tests if (boot.repl>0) { if (boot.repl<100) message("It is recommended to set boot.repl>=100 for Bartlett correction") nb_cores <- .check_nb_cores(nb_cores=nb_cores) aslistfull <- as.list(getCall(fullfit)) aslistfull$processed <- NULL ## may capture bugs if (nb_cores>1) for(st in names(aslistfull)[-1]) aslistfull[[st]] <- eval(aslistfull[[st]]) ## force evaluation before running in another R session aslistnull <- as.list(getCall(nullfit)) aslistnull$processed <- NULL ## may capture bugs if (nb_cores>1) for(st in names(aslistnull)[-1]) aslistnull[[st]] <- eval(aslistnull[[st]]) simbData <- nullfit$data if (tolower(nullfit$family$family)=="binomial") { cbf <- .check_binomial_formula(nullfit=nullfit, data=fullfit$data, fullfit=fullfit) cbindTest <- cbf$cbindTest if (cbindTest) { nnegname <- cbf$nnegname nposname <- cbf$nposname aslistfull$formula <- cbf$full_formula aslistnull$formula <- cbf$null_formula } } else cbindTest <- FALSE eval_replicate <- function(newy,only_vector=TRUE) { ## only_vector controls how to handle errors if (cbindTest) { simbData[[nposname]] <- newy simbData[[nnegname]] <- .get_BinomialDen(nullfit) - newy } else {simbData[[as.character(nullfit$predictor[[2L]])]] <- newy} ## allows y~x syntax for binary response ## analyze under both models aslistfull$data <- simbData aslistnull$data <- simbData fullfit <- (eval(as.call(aslistfull))) if (inherits(fullfit,"try-error")) { if (only_vector) { return(c(NA,NA)) } else return(fullfit) } nullfit <- try(eval(as.call(aslistnull))) if (inherits(nullfit,"try-error")) { if (only_vector) { return(c(NA,NA)) } else return(nullfit) } ## return pair of likelihoods return(c(logLik(fullfit,which=test_obj),logLik(nullfit,which=test_obj))) } bootblob <- .eval_boot_replicates(eval_replicate=eval_replicate,boot.repl=boot.repl,nullfit=nullfit,nb_cores=nb_cores, aslistfull=aslistfull, aslistnull=aslistnull,simbData=simbData) bootreps <- bootblob$bootreps colnames(bootreps) <- paste(c("full.","null."),test_obj,sep="") bootdL <- bootreps[,1]-bootreps[,2] meanbootLRT <- 2*mean(bootdL) resu <- c(resu,list(rawBootLRT = data.frame(chi2_LR=LRTori,df=df,p_value=(1+sum(bootdL>=LRTori/2))/(boot.repl+1)))) ## format appropriate for more tests LRTcorr <- LRTori*df/meanbootLRT resu <- c(resu,list(BartBootLRT = data.frame(chi2_LR=LRTcorr,df=df,p_value=1-pchisq(LRTcorr,df=df)))) ## format appropriate for more tests bootInfo <- list(meanbootLRT = meanbootLRT,bootreps = bootreps, RNGstates=bootblob$RNGstates) resu <- c(resu,list(bootInfo=bootInfo)) ## keeps the sublist structure, which is not compatible with hglmjob.R... } class(resu) <- c("fixedLRT",class(resu)) return(resu) } ## anova treated as alias for LRT anova.HLfit <- function(object, object2=NULL, ..., method="") { # if (method=="anova.lm" && is.null(object2)) { # #identical(fullm$models[c("eta","lambda","phi")],list(eta="etaGLM",lambda="",phi="phiScal")) # .anova_HLfit_lm(object, ...) ## may now handle factors but not continuosu variance => source in 'ignored' directory # } else LRT(object,object2, ...) }

9e198fcff35c70b65a2ba1d77c7cc4a177cc9000

16cbcd4b55e9df1e91f2d69702790023c9cf6780

/252286816.r

4dbf7be97b366dc44af745c1094aec27dec09548

[]

no_license

erex/MT3607-peer-review

3f65c9a168f34e947fe0e531e773029384c19314

bc0750e9a7fb5f2d0a7c7e35b34b3a80213d9fde

refs/heads/master

2020-06-03T06:12:34.093705

2014-10-10T09:49:51

null

0

null

UTF-8

R

false

6,156

r

252286816.r

#I confirm that the attached is my own work, except where clearly indicated in the text. # Normal RV generator # purpose: my.rnorm is a random number generator to simulate random normal variables. # Simulated by the uniform RV command "runif" then transformed by an algorithm into Normal random variables. # inputs: n - number of values to return. positive numeric integer. default = 1 # mean - mean of returned values. numeric. default = 0 # sd - standard deviation of returned values. positive numeric. default = 1. # outputs: n random normal variables with mean "mean" and standard deviation "sd" # "invalid arguments" if n, mean or sd arguments are not acceptable forms. my.rnorm <- function(n,mean=0,sd=1){ if (sd >= 0 && n==as.integer(n) && n >=0 && is.numeric(mean)==T && is.numeric(sd)==T && is.numeric(n)==T){ #check if entered arguments are valid c <- vector(length = n) #c stores the n random normal variables for (i in 1:n){ #loop for each normal RV to be simulated c[i]<-(((sum(runif(16))-8)*((12/16)^0.5))*sd)+mean #performs the algorithmic transformation from uniform RV to Normal RV } return(c) #returns n normal RVs } else { # if the entered arguments were not valid stop("invalid arguments") # an error message stating invalid arguments is returned } } # Chi-Squared RV generator # Purpose: my.rchisq function simulates random chi-squared variables. # Chi-squared random variables are simulated by summing squared my.rnorm generated standard normal variables. # Inputs: n - number of values to return. Must be positive numeric integer. No Default # df - degrees of freedom ie: how many squared normal variables are summed. Positive numeric integer. Default = 1 # Outputs: n chi-squared random variables with df degrees of freedom # "invalid arguments" if n or df are not acceptable forms. my.rchisq <- function( n, df=1){ if (n==as.integer(n) && df==as.integer(df) && n >=0 && df >=0 && is.numeric(n)==T && is.numeric(df)==T){ # checks if entered arguments are valid c <- vector(length = n) #create a vector in which to store the n random chi-squared variables for (i in 1:n){ #loop for each Chi-squared RV to be simulated c[i] <- sum((my.rnorm(n = df, sd = 1, mean = 0))^2) #sums the squared normal RVs } return(c) #returns n chi-squared RVs } else { # if the entered arguments were not valid stop("invalid arguments") # an error message stating invalid arguments is returned } } #F-Distribution RV generator # Purpose: my.rf function simulates random F-Distribution variables. # F-Distribution RVs simulated by simulating 2 chi-squared RVs, dividing by their degrees of freedom, then dividing one by the other # Inputs: n - number of values to return. Must be positive numeric integer. No Default # df1 - numerator degrees of freedom ie: no of squared normal RVs are summed in the chi-squared RV. Positive numeric integer. Default = 1 # df2 - denominator degrees of freedom ie: no of squared normal RVs are summed in the Chi-Squared RV. Positive numeric integer. Default = 1 # Outputs: n F-Distribution RVs with df1 and df2 degrees of freedom # "invalid arguments" if n, df1 or df2 are not acceptable forms. my.rf <- function(n, df1=1, df2=1){ if (n==as.integer(n) && df1==as.integer(df1) && df2==as.integer(df2) && n >= 0 && df1 >= 0 && df2 >= 0 && is.numeric(n)==T && is.numeric(df1)==T && is.numeric(df2)==T){ #checks if entered arguments are valid c <- vector(length=n) #c stores the n random F-Distribution variables for (i in 1:length(c)){ #loop for n F-Distribution RVs c[i] <- (((my.rchisq(1,df1))/df1)/((my.rchisq(1,df2))/df2)) #Transforms by the algorithm the Chi-Squared RVs into F-Distribution RVs } return(c) #returns n F-Distribution RVs } else { # if the entered arguments were not valid stop("invalid arguments") # an error message stating invalid arguments is returned } } #Test 1 #purpose: Ask the functions for a random number of RVs then check that number of RVs are returned and if they are numeric. #inputs: none #output: "pass test" if the correct number of numeric values are returned # "fail test" if the incorrect number of values are returned or values are not numeric test.1 <- function(){ a <- sample(1:100,1) #assigns a a random integer between 1 and 100 b <- sample(1:100,1) #assigns b a random integer between 1 and 100 c <- sample(1:100,1) #assigns c a random integer between 1 and 100 t <- c(my.rnorm(a),my.rchisq(b),my.rf(c)) #concatenates RVs from each function into a vector t. if (length(t)==(a+b+c) && t==as.numeric(t)){ # verifies that t contains the correct number of RVs and they are numeric return("pass test") # returns a success } else { #if not numeric or incorrect numer of RVs return("fail test") #return a failure } } #Test 2 # purpose: Creates a distribution of 5000 random variables from the my.rnorm function. Should be the Normal Distibution N(0.1) # Uses a shapiro-wilks test to perform a hypothesis test on the distibution being Normal. # Null hypothesis H0:if the distribution is normal. Alternative hypothesis H1: the distribution is not normal #inputs: none #outputs: "Accept H0: Distribution is Normal" if p-value > 0.05 # "Reject H0 in favour of H1:Distribution is not Normal" if p-value < 0.05 test.2 <- function(){ reject.H0 <- shapiro.test(my.rnorm(5000)) #implimentation note - reject.H0 is a list of 4 results from shapiro-wilk test, the 2nd of which is the p-value if ((reject.H0[2]<=0.05)==F){ return("Accept H0: Distribution is Normal") } else { return("Reject H0 in favour of H1:Distribution is not Normal") } }

dd575c47e9c9df6c6a6773e2e8cd7680ea96485e

8858836e281c2624d097fe62f73fa4a648f72f72

/empirical.R

a8abc7efa2aef44030712effce7a7d28ad94f5e8

[]

no_license

aripurwantosp/psoks_fit_lognormal_wafm

19e2032fcc739fcb388bd25b3baee16254eb0644

e6412dfa8aef7129b28e2e8e0e7b7527460f6bdf

refs/heads/master

2021-07-08T18:28:23.657143

2020-07-24T13:11:52

169,533,768

0

null

UTF-8

R

false

17,478

r

empirical.R

#PSO-KS Empirical Study Fromm IDHS 2017 Data #Fitting Lognormal Distribution to Women Age at First Marriage Data # #Ari Purwanto Sarwo Prasojo & Puguh Prasetyoputra (2019) #Research Center for Population, Indonesian Institute of Sciences #________________________________________________________________ #Load package, source, data ---- source("macro/stat.R") source("macro/pso.fitdist.R") library(fitdistrplus) library(readstata13) library(dplyr) library(ggplot2) #/function to calculate lognormal properties (mode, mean, variance) lnorm_prop <- function(theta){ mode <- exp(theta[1]-theta[2]^2) mean <- exp(theta[1]+.5*theta[2]^2) var <- (exp(theta[2]^2)-1)*exp(2*theta[1]+theta[2]^2) centr <- c(mode,mean,var) names(centr) <- c("mode","mean","var") return(centr) } #/read data form stata format evwomen <- read.dta13("sdki17-evwomen.dta") # View(evwomen) # str(evwomen) # National Aggregate ------------------------------------------------------ #/data sample ---- all_afm <- filter(evwomen, V501 != "Never in union") all_afm <- all_afm$V511 #/fit distribution ---- psoks_all_afm <- pso.fitdist(dt = all_afm, dist = "lognormal", stat = "ks.stat", limit = c(0,100), max.iter = 200, n.swarm = 20) #//pso behaviour pso_beh <- data.frame(iter = 0:200, ks = psoks_all_afm$stat.trace) ggplot(pso_beh, aes(iter, ks)) + geom_line(color = "brown1") + geom_point(color = "brown1") + labs(title = "PSO-KS's Behaviour, Fitting Lognormal Distribution", subtitle = "Women's age at first marriage, national aggregation", x = "Iteration", y = "KS Distance") + theme_bw() mle_all_afm <- fitdist(all_afm, distr = "lnorm", method = "mle") mme_all_afm <- fitdist(all_afm, distr = "lnorm", method = "mme") est_all_afm <- rbind(psoks_all_afm$solution, mle_all_afm$estimate, mme_all_afm$estimate) colnames(est_all_afm) <- c("meanlog","sdlog") rownames(est_all_afm) <- c("PSOKS","MLE","MME") cat("Parameter estimation, national\n") cat("---------------------------------------------\n") print(est_all_afm) #/properties ---- all_prop <- rbind(lnorm_prop(psoks_all_afm$solution), lnorm_prop(mle_all_afm$estimate), lnorm_prop(mme_all_afm$estimate)) rownames(all_prop) <- c("PSOKS","MLE","MME") cat("Properties, national\n") cat("---------------------------------------------\n") print(all_prop) #/goodness of fit statistcs ---- #//mse of distribution fitting mse_psoks_all_afm <- mse.stat(all_afm,"lognormal", meanlog = psoks_all_afm$solution[1], sdlog = psoks_all_afm$solution[2]) mse_mle_all_afm <- mse.stat(all_afm,"lognormal", meanlog = mle_all_afm$estimate[1], sdlog = mle_all_afm$estimate[2]) mse_mme_all_afm <- mse.stat(all_afm,"lognormal", meanlog = mme_all_afm$estimate[1], sdlog = mme_all_afm$estimate[2]) #//ks distance ks_psoks_all_afm <- ks.stat(all_afm,"lognormal", meanlog = psoks_all_afm$solution[1], sdlog = psoks_all_afm$solution[2]) ks_mle_all_afm <- ks.stat(all_afm,"lognormal", meanlog = mle_all_afm$estimate[1], sdlog = mle_all_afm$estimate[2]) ks_mme_all_afm <- ks.stat(all_afm,"lognormal", meanlog = mme_all_afm$estimate[1], sdlog = mme_all_afm$estimate[2]) #//log-likelihood loglik_psoks_all_afm <- loglik(all_afm, "lognormal", psoks_all_afm$solution) loglik_mle_all_afm <- loglik(all_afm, "lognormal", mle_all_afm$estimate) loglik_mme_all_afm <- loglik(all_afm, "lognormal", mme_all_afm$estimate) stat_all_afm <- rbind(c(mse_psoks_all_afm, mse_mle_all_afm, mse_mme_all_afm), #mse c(ks_psoks_all_afm, ks_mle_all_afm, ks_mme_all_afm), #ks c(loglik_psoks_all_afm, loglik_mle_all_afm, loglik_mme_all_afm)) #loglik colnames(stat_all_afm) <- c("PSOKS","MLE","MME") rownames(stat_all_afm) <- c("mse dist","ks distance","loglik") cat("Goodness of fit measure, national aggregation\n") cat("---------------------------------------------\n") print(stat_all_afm) #/graph fitting ---- #//histogram & pdf len_all_afm <- length(all_afm) den_all_psoks <- dlnorm(all_afm, meanlog = psoks_all_afm$solution[1], sdlog = psoks_all_afm$solution[2]) den_all_mle <- dlnorm(all_afm, meanlog = mle_all_afm$estimate[1], sdlog = mle_all_afm$estimate[2]) den_all_mme <- dlnorm(all_afm, meanlog = mme_all_afm$estimate[1], sdlog = mme_all_afm$estimate[2]) den_all_afm <- data.frame(afm = rep(all_afm,3), Fit = c(rep("PSO-KS", len_all_afm), rep("MLE", len_all_afm), rep("MME", len_all_afm)), Density = c(den_all_psoks, den_all_mle, den_all_mme)) all_hist <- hist(all_afm, freq = FALSE) ggplot(den_all_afm, aes(x = afm)) + geom_histogram(aes(y = ..density..), breaks = all_hist$breaks, fill = "gray70", color = "white") + xlim(min(all_afm),max(all_afm)) + geom_line(aes(x = afm, y = Density, color = Fit), size = .8) + labs(title = "Histogram & Lognormal PDF", subtitle = "Women's age at first marriage, national", x = "Age", y = "Density") + theme_bw() #//ecdf & cdf cumden_all_psoks <- plnorm(all_afm, meanlog = psoks_all_afm$solution[1], sdlog = psoks_all_afm$solution[2]) cumden_all_mle <- plnorm(all_afm, meanlog = mle_all_afm$estimate[1], sdlog = mle_all_afm$estimate[2]) cumden_all_mme <- plnorm(all_afm, meanlog = mme_all_afm$estimate[1], sdlog = mme_all_afm$estimate[2]) cumden_all_afm <- data.frame(afm = rep(all_afm,3), Fit = c(rep("PSO-KS", len_all_afm), rep("MLE", len_all_afm), rep("MME", len_all_afm)), Cumden = c(cumden_all_psoks, cumden_all_mle, cumden_all_mme)) ggplot(data = cumden_all_afm, aes(afm))+ stat_ecdf(geom = "step") + geom_line(aes(x = afm, y = Cumden, color = Fit), size = .7) + labs(title = "Empirical & Lognormal CDF", subtitle = "Women's age at first marriage, national", x = "Age", y = "Cumulative Density") + theme_bw() # Diassagregation by Residence (Rural/Urban) --------------------------------- #/data sample ---- rural_afm <- filter(evwomen, V501 != "Never in union" & V025 == "Rural")$V511 urban_afm <- filter(evwomen, V501 != "Never in union" & V025 == "Urban")$V511 #/fit distribution ---- psoks_rural_afm <- pso.fitdist(dt = rural_afm, dist = "lognormal", stat = "ks.stat", limit = c(0,100), max.iter = 200, n.swarm = 20) psoks_urban_afm <- pso.fitdist(dt = urban_afm, dist = "lognormal", stat = "ks.stat", limit = c(0,100), max.iter = 200, n.swarm = 20) #//pso behaviour pso_beh_reg <- data.frame(iter = rep(0:200,2), Residence = c(rep("Rural", 201),rep("Urban", 201)), ks = c(psoks_rural_afm$stat.trace, psoks_urban_afm$stat.trace)) ggplot(pso_beh_reg, aes(iter, ks, group = Residence)) + geom_line(aes(color = Residence)) + geom_point(aes(color = Residence)) + labs(title = "PSO-KS's Behaviour, Fitting Lognormal Distribution", subtitle = "Women's age at first marriage, national by residence", x = "Iteration", y = "KS Distance") + theme_bw() mle_rural_afm <- fitdist(rural_afm, distr = "lnorm", method = "mle") mle_urban_afm <- fitdist(urban_afm, distr = "lnorm", method = "mle") mme_rural_afm <- fitdist(rural_afm, distr = "lnorm", method = "mme") mme_urban_afm <- fitdist(urban_afm, distr = "lnorm", method = "mme") est_rural_afm <- rbind(psoks_rural_afm$solution, mle_rural_afm$estimate, mme_rural_afm$estimate) colnames(est_rural_afm) <- c("meanlog","sdlog") rownames(est_rural_afm) <- c("PSOKS","MLE","MME") cat("Parameter estimation, national-rural region\n") cat("---------------------------------------------\n") print(est_rural_afm) est_urban_afm <- rbind(psoks_urban_afm$solution, mle_urban_afm$estimate, mme_urban_afm$estimate) colnames(est_urban_afm) <- c("meanlog","sdlog") rownames(est_urban_afm) <- c("PSOKS","MLE","MME") cat("Parameter estimation, national-urban region\n") cat("---------------------------------------------\n") print(est_urban_afm) #/properties ---- rural_prop <- rbind(lnorm_prop(psoks_rural_afm$solution), lnorm_prop(mle_rural_afm$estimate), lnorm_prop(mme_rural_afm$estimate)) rownames(rural_prop) <- c("PSOKS","MLE","MME") cat("Properties, national-rural region\n") cat("---------------------------------------------\n") print(rural_prop) urban_prop <- rbind(lnorm_prop(psoks_urban_afm$solution), lnorm_prop(mle_urban_afm$estimate), lnorm_prop(mme_urban_afm$estimate)) rownames(urban_prop) <- c("PSOKS","MLE","MME") cat("Properties, national-urban region\n") cat("---------------------------------------------\n") print(urban_prop) #/goodness of fit statistcs ---- #//mse of distribution fitting mse_psoks_rural_afm <- mse.stat(rural_afm,"lognormal", meanlog = psoks_rural_afm$solution[1], sdlog = psoks_rural_afm$solution[2]) mse_mle_rural_afm <- mse.stat(rural_afm,"lognormal", meanlog = mle_rural_afm$estimate[1], sdlog = mle_rural_afm$estimate[2]) mse_mme_rural_afm <- mse.stat(rural_afm,"lognormal", meanlog = mme_rural_afm$estimate[1], sdlog = mme_rural_afm$estimate[2]) mse_psoks_urban_afm <- mse.stat(urban_afm,"lognormal", meanlog = psoks_urban_afm$solution[1], sdlog = psoks_urban_afm$solution[2]) mse_mle_urban_afm <- mse.stat(urban_afm,"lognormal", meanlog = mle_urban_afm$estimate[1], sdlog = mle_urban_afm$estimate[2]) mse_mme_urban_afm <- mse.stat(urban_afm,"lognormal", meanlog = mme_urban_afm$estimate[1], sdlog = mme_urban_afm$estimate[2]) #//ks distance ks_psoks_rural_afm <- ks.stat(rural_afm,"lognormal", meanlog = psoks_rural_afm$solution[1], sdlog = psoks_rural_afm$solution[2]) ks_mle_rural_afm <- ks.stat(rural_afm,"lognormal", meanlog = mle_rural_afm$estimate[1], sdlog = mle_rural_afm$estimate[2]) ks_mme_rural_afm <- ks.stat(rural_afm,"lognormal", meanlog = mme_rural_afm$estimate[1], sdlog = mme_rural_afm$estimate[2]) ks_psoks_urban_afm <- ks.stat(urban_afm,"lognormal", meanlog = psoks_urban_afm$solution[1], sdlog = psoks_urban_afm$solution[2]) ks_mle_urban_afm <- ks.stat(urban_afm,"lognormal", meanlog = mle_urban_afm$estimate[1], sdlog = mle_urban_afm$estimate[2]) ks_mme_urban_afm <- ks.stat(urban_afm,"lognormal", meanlog = mme_urban_afm$estimate[1], sdlog = mme_urban_afm$estimate[2]) #//log-likelihood loglik_psoks_rural_afm <- loglik(rural_afm, "lognormal", psoks_rural_afm$solution) loglik_mle_rural_afm <- loglik(rural_afm, "lognormal", mle_rural_afm$estimate) loglik_mme_rural_afm <- loglik(rural_afm, "lognormal", mme_rural_afm$estimate) loglik_psoks_urban_afm <- loglik(urban_afm, "lognormal", psoks_urban_afm$solution) loglik_mle_urban_afm <- loglik(urban_afm, "lognormal", mle_urban_afm$estimate) loglik_mme_urban_afm <- loglik(urban_afm, "lognormal", mme_urban_afm$estimate) stat_rural_afm <- rbind(c(mse_psoks_rural_afm, mse_mle_rural_afm, mse_mme_rural_afm), #mse c(ks_psoks_rural_afm, ks_mle_rural_afm, ks_mme_rural_afm), #ks c(loglik_psoks_rural_afm, loglik_mle_rural_afm, loglik_mme_rural_afm)) #loglik stat_urban_afm <- rbind(c(mse_psoks_urban_afm, mse_mle_urban_afm, mse_mme_urban_afm), #mse c(ks_psoks_urban_afm, ks_mle_urban_afm, ks_mme_urban_afm), #ks c(loglik_psoks_urban_afm, loglik_mle_urban_afm, loglik_mme_urban_afm)) #loglik colnames(stat_rural_afm) <- colnames(stat_urban_afm) <- c("PSOKS","MLE","MME") rownames(stat_rural_afm) <- rownames(stat_urban_afm) <- c("mse dist","ks distance","loglik") cat("Goodness of fit measure, national-rural\n") cat("---------------------------------------------\n") print(stat_rural_afm) cat("Goodness of fit measure, national-urban\n") cat("---------------------------------------------\n") print(stat_urban_afm) #/graph fitting ---- #//histogram & pdf reg_hist <- hist(rural_afm) len_rural_afm <- length(rural_afm) len_urban_afm <- length(urban_afm) den_rural_psoks <- dlnorm(rural_afm, meanlog = psoks_rural_afm$solution[1], sdlog = psoks_rural_afm$solution[2]) den_rural_mle <- dlnorm(rural_afm, meanlog = mle_rural_afm$estimate[1], sdlog = mle_rural_afm$estimate[2]) den_rural_mme <- dlnorm(rural_afm, meanlog = mme_rural_afm$estimate[1], sdlog = mme_rural_afm$estimate[2]) den_urban_psoks <- dlnorm(urban_afm, meanlog = psoks_urban_afm$solution[1], sdlog = psoks_urban_afm$solution[2]) den_urban_mle <- dlnorm(urban_afm, meanlog = mle_urban_afm$estimate[1], sdlog = mle_urban_afm$estimate[2]) den_urban_mme <- dlnorm(urban_afm, meanlog = mme_urban_afm$estimate[1], sdlog = mme_urban_afm$estimate[2]) reg_df <- data.frame(afm = c(rep(rural_afm, 3), rep(urban_afm, 3)), Residence = c(rep("Rural", 3*len_rural_afm), rep("Urban", 3*len_urban_afm)), Fit = c(rep("PSO-KS",len_rural_afm), rep("MLE",len_rural_afm), rep("MME",len_rural_afm), rep("PSO-KS",len_urban_afm), rep("MLE",len_urban_afm), rep("MME",len_urban_afm)), Density = c(den_rural_psoks, den_rural_mle, den_rural_mme, den_urban_psoks, den_urban_mle, den_urban_mme)) ggplot(data = reg_df)+ geom_histogram(aes(x = afm, y = ..density..), fill = "gray70", color = "white", breaks = reg_hist$breaks) + xlim(min(all_afm),max(all_afm)) + geom_line(aes(x = afm, y = Density, color = Fit), size = .8) + labs(title = "Histogram & Lognormal PDF", subtitle = "Women's age at first marriage, national by residence", x = "Age", y = "Density") + theme_bw() + facet_grid(Residence ~.) #//ecdf & cdf cum_rural_psoks <- plnorm(rural_afm, meanlog = psoks_rural_afm$solution[1], sdlog = psoks_rural_afm$solution[2]) cum_rural_mle <- plnorm(rural_afm, meanlog = mle_rural_afm$estimate[1], sdlog = mle_rural_afm$estimate[2]) cum_rural_mme <- plnorm(rural_afm, meanlog = mme_rural_afm$estimate[1], sdlog = mme_rural_afm$estimate[2]) cum_urban_psoks <- plnorm(urban_afm, meanlog = psoks_urban_afm$solution[1], sdlog = psoks_urban_afm$solution[2]) cum_urban_mle <- plnorm(urban_afm, meanlog = mle_urban_afm$estimate[1], sdlog = mle_urban_afm$estimate[2]) cum_urban_mme <- plnorm(urban_afm, meanlog = mme_urban_afm$estimate[1], sdlog = mme_urban_afm$estimate[2]) reg_cumd <- data.frame(afm = c(rep(rural_afm, 3), rep(urban_afm, 3)), Residence = c(rep("Rural", 3*len_rural_afm), rep("Urban", 3*len_urban_afm)), Fit = c(rep("PSO-KS",len_rural_afm), rep("MLE",len_rural_afm), rep("MME",len_rural_afm), rep("PSO-KS",len_urban_afm), rep("MLE",len_urban_afm), rep("MME",len_urban_afm)), Cumden = c(cum_rural_psoks, cum_rural_mle, cum_rural_mme, cum_urban_psoks, cum_urban_mle, cum_urban_mme)) ggplot(data = reg_cumd, aes(afm))+ stat_ecdf(geom = "step") + geom_line(aes(x = afm, y = Cumden, color = Fit), size = .7) + labs(title = "Empirical & Lognormal CDF", subtitle = "Women's age at first marriage, national by residence", x = "Age", y = "Cumulative Density") + theme_bw() + facet_grid(Residence ~.) # Properties -------------------------------------------------------------- prop <- function(theta){ meanlog <- theta[1] sdlog <- theta[2] mode <- exp(meanlog-sdlog^2) mean <- exp(meanlog + .5*sdlog^2) vari <- (exp(sdlog^2)-1)*exp(2*meanlog + sdlog^2) proper <- c(mode, mean, vari) names(proper) <- c("mode", "mean", "variance") return(proper) } # t <- rlnorm(100, meanlog = 1, sdlog = .5) # Mode(t) # mean(t) # var(t) # fit <- fitdist(t,"lnorm","mle") # prop(fit$estimate) #National nat_psoks <- c(2.994, 0.213) prop(nat_psoks) nat_mle <- c(2.997, 0.221) prop(nat_mle) nat_mme <- c(2.997, 0.223) prop(nat_mme) #Reside #/rural rur_psoks <- c(2.949, 0.201) prop(rur_psoks) rur_mle <- c(2.956, 0.220) prop(rur_mle) rur_mme <- c(2.955, 0.224) prop(rur_mme) #/urban ur_psoks <- c(3.037, 0.209) prop(ur_psoks) ur_mle <- c(3.038, 0.215) prop(ur_mle) ur_mme <- c(3.038, 0.215) prop(ur_mme)

c91ba9d797599fc6d98d80b5862707a0d206a8f7

992b927610cfd0528def7a7a2a440058f22b6828

/man/ovun.sample.Rd

12247e2991912e2fab92a8df1339d00b7a0738d6

[]

no_license

ramnathv/ROSE

1a15478e7a846fdca459e970595997efe311caa4

6e6e32cd79515887920d65857dfb929254500ee6

refs/heads/master

2020-04-01T21:52:57.067164

2018-10-18T19:57:54

153,679,959

1

0

null

2018-10-18T19:57:06

2018-10-18T19:57:05

null

UTF-8

R

false

3,321

rd

ovun.sample.Rd

\name{ovun.sample} \alias{ovun.sample} \title{ Over-sampling, under-sampling, combination of over- and under-sampling. } \description{ Creates possibly balanced samples by random over-sampling minority examples, under-sampling majority examples or combination of over- and under-sampling. } \usage{ ovun.sample(formula, data, method="both", N, p=0.5, subset=options("subset")$subset, na.action=options("na.action")$na.action, seed) } \arguments{ \item{formula}{ An object of class \code{\link{formula}} (or one that can be coerced to that class). See \code{\link{ROSE}} for information about interaction among predictors or their transformations. } \item{data}{ An optional data frame, list or environment (or object coercible to a data frame by \code{as.data.frame}) in which to preferentially interpret ``formula''. If not specified, the variables are taken from ``environment(formula)''. } \item{method}{ One among \code{c("over", "under", "both")} to perform over-sampling minority examples, under-sampling majority examples or combination of over- and under-sampling, respectively. } \item{N}{ The desired sample size of the resulting data set. If missing and \code{method} is either \code{"over"} or \code{"under"} the sample size is determined by oversampling or, respectively, undersampling examples so that the minority class occurs approximately in proportion \code{p}. When \code{method = "both"} the default value is given by the length of vectors specified in \code{formula}. } \item{p}{ The probability of resampling from the rare class. If missing and \code{method} is either \code{"over"} or \code{"under"} this proportion is determined by oversampling or, respectively, undersampling examples so that the sample size is equal to \code{N}. When \code{method ="both"} the default value given by 0.5. } \item{subset}{ An optional vector specifying a subset of observations to be used in the sampling process. The default is set by the \code{\link{subset}} setting of \code{\link{options}}. } \item{na.action}{ A function which indicates what should happen when the data contain 'NA's. The default is set by the \code{\link{na.action}} setting of \code{\link{options}}. } \item{seed}{ A single value, interpreted as an integer, recommended to specify seeds and keep trace of the sample. } } \value{ The value is an object of class \code{ovun.sample} which has components \item{Call}{The matched call.} \item{method}{The method used to balance the sample. Possible choices are \cr \code{c("over", "under", "both")}.} \item{data}{ The resulting new data set.} } \seealso{ \code{\link{ROSE}}. } \examples{ # 2-dimensional example # loading data data(hacide) # imbalance on training set table(hacide.train$cls) # balanced data set with both over and under sampling data.balanced.ou <- ovun.sample(cls~., data=hacide.train, N=nrow(hacide.train), p=0.5, seed=1, method="both")$data table(data.balanced.ou$cls) # balanced data set with over-sampling data.balanced.over <- ovun.sample(cls~., data=hacide.train, p=0.5, seed=1, method="over")$data table(data.balanced.over$cls) }

c8ad64e143f549d7b5ec1bcc63025cedf7ecbf67

be3adc68f32d8ab896996a7236325dea700ff416

/run_analysi.R

a4d148385fcb38034419f136652b354c768fcbc2

[]

no_license

santiagoferriere/Coursera-Getting-and-Cleaning-Data-Course-Project

9e790ad7ae40b47c6eb0f07f0692bf94e73349af

68548c60c99f0f69c33de3eb470662c66d6d0f74

refs/heads/master

2022-11-25T23:20:31.615560

2020-07-27T03:58:06

282,745,855

0

null

UTF-8

R

false

2,247

r

run_analysi.R

#reading features and activity data #Acordarse que la carpeta esta un nivel abajo del wd library(dplyr) features <- read.table("./UCI HAR Dataset/features.txt") activities <- read.table("./UCI HAR Dataset/activity_labels.txt") test <- read.table("./UCI HAR Dataset/test/X_test.txt") train <- read.table("./UCI HAR Dataset/train/X_train.txt") ytrain <- read.table("./UCI HAR Dataset/train/y_train.txt") ytest <- read.table("./UCI HAR Dataset/test/y_test.txt") subjecttrain <- read.table("./UCI HAR Dataset/train/subject_train.txt") subjecttest <- read.table("./UCI HAR Dataset/test/subject_test.txt") activities <- read.table("./UCI HAR Dataset/activity_labels.txt") #################### feat <- as.character(features[, 2]) colnames(test) <- feat colnames(train) <- feat colnames(ytrain) <- "activity" colnames(ytest) <- "activity" colnames(subjecttest) <- "subject" colnames(subjecttrain) <- "subject" # Cambiar nombres a las columnas de acuerdo a un vector test <- cbind(test, ytest) train <- cbind(train, ytrain) test <- cbind(test, subjecttest) train <- cbind(train, subjecttrain) # uno columnas a los data sets existentes tate <- rbind(test, train) # uno los data sets mn_sd <- tate[, grep("mean()|std()|subject|activity", colnames(tate)) ] #selecciono las columnas que contengan las palabras "mean()", "std()", "subject" o "activity" description <- factor(mn_sd$activity, levels = activities[, 1], labels = activities[, 2]) #remplazo las actividades (que eran numericas) por su valor en palabras mn_sd <- mutate(mn_sd, description ) # añado la col description al data.frame existente ##################################### for_act <- mn_sd for_act <- split(for_act, for_act$activity) # separar el data.frame por activity prev <- for_act[[1]] prev <- sapply(prev, mean) prev <- as.data.frame(prev) prev <- rotate_df(prev) # crear un data.frame llamado prev for(i in 2:6) { fr <- for_subj[[i]] mn <- sapply(fr, mean) dmn <- as.data.frame(mn) dmn <- rotate_df(dmn) #esta saliendo volteado el data.frame prev <- rbind( prev, dmn) } mean_act <- setattr(prev, "row.names", activities[ , 2]) ## cambiar nombre de las rows!

cb774fe280c5c635be084e9ec693557cf6207f8d

a8f98c245c02fa7741cbdba001f5e71a0069e7a8

/total_jobs/plot.R

847308ae3053f79f1ae082ceedc51cce943ccd31

[]

no_license

schelcj/biostat-cluster-reports

b4d6aa711921c8989ae7a0c9f7ac4880becae75f

0f6a03e4058df0f9380f13450acca3f65a96d241

refs/heads/master

2021-01-22T11:38:21.289928

2015-01-20T14:35:22

2,601,256

0

null

UTF-8

R

false

420

r

plot.R

data <- read.table('report.dat', header=T, sep=",") u_range <- range(0, data$total_jobs) pdf('report.pdf') plot(data$total_jobs, type="o", lwd=2, lty=1, pch=NA, col="blue", ann=F, axes=F, ylim=u_range) axis(1, 1:52, lab=T) labels <-seq(0, length(axTicks(2))^2, length(axTicks(2))) axis(2, at=axTicks(2), labels=labels[0:length(axTicks(2)-1)], cex=0.8, tick=TRUE, line=NA, pos=NA) title(ylab="Jobs") box() dev.off()

6c81baf712f977181d4e7ce98b3021ea03f9a605

aa06ef73c7eabdeb93b93bbb02d1f5e05758b728

/Multivariate_Analysis/Vegan_Demo3_20190208.R

83388a7a58d77a6b4f0288a2401e5a86a5b6671e

[ "BSD-3-Clause" ]

permissive

lehostert/Stream_Ecology_Lab_Stats_Workshop

a0b82e6aa91c3a2e982b1136064261f2b3ed6c60

f1767f7096573f719b3722193b72bdbf2fe86bde

refs/heads/master

2021-07-21T14:57:43.822246

2020-05-12T04:08:18

161,689,145

1

BSD-3-Clause

2020-05-12T03:59:26

2018-12-13T20:04:27

R

UTF-8

R

false

2,048

r

Vegan_Demo3_20190208.R

library(tidyverse) library(vegan) ### Ordination Techniques Continued fish <- read.csv("~/Documents/Github/Stats_Workshop_SEL/Multivariate_Analysis/five_assemblages.csv", header = T, na = ".", row.names = 1) fish.evi <- read.delim2("~/Documents/Github/Stats_Workshop_SEL/Multivariate_Analysis/TV.txt", sep = ",", header = T, na = ".", row.names = 1) LTEF <- read.csv("~/Documents/Github/Stats_Workshop_SEL/Multivariate_Analysis/LTEF_fish_data.csv", header = T, na = ".", row.names = 1) Indic_Species <- read.csv("~/Documents/Github/Stats_Workshop_SEL/Multivariate_Analysis/indicator_species_data.csv", header = T, na = ".", row.names = 1) indic_spp.type <- Indic_Species %>% select(Type) indic_spp.data <- Indic_Species %>% select(-c(Type)) indic_spp.dist2 <- vegdist(indic_spp.data) indic_spp.anov2 <- with(indic_spp.type, anosim(indic_spp.dist, Type)) summary(indic_spp.anov2) plot(indic_spp.anov2) indic_spp.dist <- vegdist(Indic_Species) indic_spp.ano <- anosim(indic_spp.dist, Indic_Species$Type) summary(indic_spp.ano) plot(indic_spp.ano) ##Example data(dune) data(dune.env) dune.dist <- vegdist(dune) dune.ano <- with(dune.env, anosim(dune.dist, Management)) summary(dune.ano) plot(dune.ano) #### MRPP Multi- Reponse Permutation Procedure data(dune) data(dune.env) dune.mrpp <- with(dune.env, mrpp(dune, Management)) dune.mrpp indic.mrpp <- with(indic_spp.type, mrpp(indic_spp.data, Type)) indic.mrpp # Save and change plotting parameters def.par <- par(no.readonly = TRUE) layout(matrix(1:2,nr=1)) plot(dune.ord <- metaMDS(dune), type="text", display="sites" ) with(dune.env, ordihull(dune.ord, Management)) with(dune.mrpp, { fig.dist <- hist(boot.deltas, xlim=range(c(delta,boot.deltas)), main="Test of Differences Among Groups") abline(v=delta); text(delta, 2*mean(fig.dist$counts), adj = -0.5, expression(bold(delta)), cex=1.5 ) } ) par(def.par) ## meandist dune.md <- with(dune.env, meandist(vegdist(dune), Management)) dune.md summary(dune.md) plot(dune.md) plot(dune.md, kind="histogram")

2698bb3022c9cf5f8e244cbc1af5480293591a9f

f25acfa44ded4602f844422ef93c8233ea400039

/plot2.R

9ce8d737f7ddbb9ded3cd76f43c40f00f5da859f

[]

no_license

irving/ExData_Plotting1

d6f2d16c9f0d3384e9c1b01285d335a852483569

0095f0ac46b0b429dce2365e56afed835e818c72

refs/heads/master

2021-01-16T12:19:50.002003

2015-06-07T01:13:38

36,740,862

0

null

2015-06-02T15:00:56

null

UTF-8

R

false

1,291

r

plot2.R

# Project 1 for Coursera Exploratory Data Analysis course # June 2015 section. # # This program reads a data file, selects the small portion of it to be used # And generates an exploratory plot from it. library(data.table) library(dplyr) library(lubridate) library(sqldf) # Check for file before reading. It should be in the same folder if (!file.exists("household_power_consumption.txt")){ stop("Unable to find data file. Please place \"household_power_consumption.txt\" in the same folder as this script to run.") } # open the output channel png(file="plot2.png", width=480, height=480) # read the data # NOTE: Assumes data are in same folder. # NOTE: I found info about a method to read just some rows of a file # using sqldf here: http://r.789695.n4.nabble.com/How-to-set-a-filter-during-reading-tables-td893857.html # So my reading code is adapted from that. pow <- file("household_power_consumption.txt") attr(pow, "file.format") <- list(sep = ";", header = TRUE) p2 <- data.table(sqldf("select * from pow where Date == \"1/2/2007\" OR Date == \"2/2/2007\"")) # never forget to close a file when you're done with it close(pow) p2[,When:= dmy_hms(paste(Date, Time))] plot(p2$When, p2$Global_active_power, type="l", ylab="Global Active Power (kilowatts)", xlab="") dev.off()

8bd55f3977ba86aba894a75787b3b93a6c5ac44a

03f2c9c3e7d87c11400511a2994536efa8eba6de

/R/submissions/glm_0.58566_june_14_2014/avs_models_submission.R

7842f0bf037d940baeb02db188d5c5e3083ebcb6

[]

no_license

lorenzol/kaggle_avs

43dc9b16ad8be1a2726ecae7a5ebdddf8031247e

c61d2d43562dad2411bc22994f84b968f68e4ab5

refs/heads/master

2021-01-10T22:09:55.147687

2014-09-18T00:46:23

null

0

null

UTF-8

R

false

1,141

r

avs_models_submission.R

load(file="test.RData") # Create Submission File on Test Data (must have test data ready) ######################################################## # name of test file = test # use the model to generate predictions repeater_glm <- predict(glm.tune, newdata = test, type="prob") repeater_gbm <- predict(gbm.tune, newdata = test, type="prob") repeater_glmnet <- predict(glmnet.tune, newdata = test, type="prob") # Create dataset for export ######################################################### repeater_glm <- data.frame(cbind("id" = test$id, "repeatProbability" = repeater_glm$t)) repeater_gbm <- data.frame(cbind("id" = test$id, "repeatProbability" = repeater_gbm$t)) repeater_glmnet <- data.frame(cbind("id" = test$id, "repeatProbability" = repeater_glmnet$t)) # write predictions to csv file for submission to Kaggle ######################################################### write.csv(repeater_glm, file="glm_tune_model1.csv", row.names=FALSE, quote=FALSE) write.csv(repeater_gbm, file="gbm_tune_model1.csv", row.names=FALSE, quote=FALSE) write.csv(repeater_glmnet, file="glmnet_tune_model1.csv", row.names=FALSE, quote=FALSE)

2b917081095186cf2998a923a34e54c6028a68d9

ca88f343307390ee7854779d5a6d3d7797eefbf2

/nomear_paises.R

681dea8c2515c0e1291e87a052a3141c4271392b

[]

no_license

FlavioMarchi/Mapeamento-Tecnologico

570f84e34aaa210184ca650702ea1ee0f599d8c9

072d3356fc8ef663742b20ea946f1dd78a2493d2

refs/heads/master

2021-09-01T21:17:24.669599

2017-12-28T17:04:27

115,642,464

0

null

UTF-8

R

false

1,022

r

nomear_paises.R

nomear_paises <- function(country, flag = TRUE){ if(flag == TRUE){ custom_match01 <- c(WO = "World Office", EP = "European Union", SU = "Soviet Union", AP = "NA", EA = "NA", OA = "NA", UK = "UK") country <- countrycode(country, "iso2c", "country.name.en", custom_match = custom_match01)} for (i in 1:length(country)) { if (country[i] == "United Kingdom of Great Britain and Northern Ireland" ) { country[i] <- "UK" } else if(country[i] == "United States of America"){ country[i] <- "USA" } else if(country[i] == "Republic of Korea"){ country[i] <- "South Korea" } else if (country[i] == "Russian Federation"){ country[i] <- "Russia" }else if (country[i] =="Taiwan, Province of China"){ country[i] <- "Taiwan" }else if (country[i] =="United Kingdom"){ country[i] <- "UK" } } return(country) }

ffb723f4f8f283e0657ca4d226147e463d4f3a35

22e937ad20cc8b4609d7e4b883c2c1dc5c701a27

/man/plot.shotperformance.Rd

57c579617504e65351bc045f00b3569db0782378

[]

no_license

sndmrc/BasketballAnalyzeR

becf8ac9f302b6fb02a6ee901e5656d90d2568f3

9e8a01f0bece63442c30348d24205244a1c6aa76

refs/heads/master

2023-06-10T14:13:01.684480

2023-05-31T20:17:42

142,006,460

34

7

null

UTF-8

R

false

true

1,645

rd

plot.shotperformance.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.shotperformance.R \name{plot.shotperformance} \alias{plot.shotperformance} \title{Plots a bubbleplot representing the data contained in the dataframe produced by the function 'shotperformance'} \usage{ \method{plot}{shotperformance}(x, title = "Shooting performance", ...) } \arguments{ \item{x}{an object of class \code{ashotperformance} obtained using the shotperformance function} \item{title}{character, plot title.} \item{...}{other graphical parameters.} } \value{ A \code{ggplot2} object } \description{ Plots a bubbleplot representing the data contained in the dataframe produced by the function 'shotperformance' } \examples{ # Draw the plot for the performances on 2 point shots, when the high pressure situation is # the one regarding shots taken when \code{shotclock} is between 0 and 2 PbP <- PbPmanipulation(PbP.BDB) PbP <- scoredifference(PbP, team_name = "GSW", player_data=Pbox, team_data = Tadd) PbP <- shotclock(PbP, sec_14_after_oreb = FALSE, team_data = Tadd) players_perf <- shotperformance(PbP, shotclock_interval = c(0, 2), player_data=Pbox, team_data = Tadd, shot_type = "2P", teams = "GSW") plot(players_perf) } \references{ P. Zuccolotto and M. Manisera (2020) Basketball Data Science: With Applications in R. CRC Press. P. Zuccolotto, M. Manisera and M. Sandri (2018) Big data analytics for modeling scoring probability in basketball: The effect of shooting under high pressure conditions. International Journal of Sports Science & Coaching. } \author{ Andrea Fox }

d8c0e8fa512249a0f1acc04d6b867ac782e5475d

63c5a1fcadd2d4a94fb81678bdc5f3e45df01855

/R/segment.R

44747c0d3253e2100826b6d6998637acbe6bd113

[]

no_license

aoles/EBImage

884709162b60b93b75a0fdf6ec937d535a8ab7ef

d0f7973ac05a96914c63570e450730abd326aef5

refs/heads/devel

2023-06-23T11:26:44.695456

2023-04-09T20:52:13

22,993,924

67

35

null

2023-06-15T13:36:39

2014-08-15T15:25:54

R

UTF-8

R

false

2,071

r

segment.R

## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - watershed = function (x, tolerance=1, ext=1) { validImage(x) tolerance = as.numeric(tolerance) if (tolerance<0) stop( "'tolerance' must be non-negative" ) ext = as.integer(ext) if (ext<1) stop( "'ext' must be a positive integer" ) .Call(C_watershed, castImage(x), tolerance, ext) } ## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - propagate = function (x, seeds, mask=NULL, lambda=1e-4) { validImage(x) checkCompatibleImages(x, seeds) if (!is.integer(seeds)) storage.mode(seeds) = 'integer' if (!is.null(mask)) { checkCompatibleImages(x, mask) if (!is.integer(mask)) storage.mode(mask) = 'integer' } lambda = as.numeric(lambda) if (lambda<0.0) stop("'lambda' must be positive" ) return(.Call(C_propagate, castImage(x), seeds, mask, lambda)) } ## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ocontour = function(x) { validImage(x) if(!is.integer(x)) storage.mode(x) = 'integer' y = .Call(C_ocontour, x) names(y) = seq_along(y) y = y[sapply(y, length)>0] # remove missing objects } ## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - bwlabel = function(x) { validImage(x) .Call(C_bwlabel, x) } ## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - colorLabels = function(x, normalize = TRUE){ len = length( (d = dim(x)) ) res <- x # linearize image data for convenient processing dim(res) = c(prod(d[1:2]), if(len>2) prod(d[3:len]) else 1) f = function(y, m) { idx <- y > 0 y[idx] <- sample(m)[y[idx]] y } tmp = apply(res, 2, function(y) { m = max(y) replicate(3, f(y, m)) }) # restore proper dimensions dim(tmp) = c(d[1:2], 3, if(len>2) d[3:len] else NULL) if ( is.Image(x) ) { imageData(res) <- tmp colorMode(res) <- Color } else { res = new("Image", .Data = tmp, colormode = Color) } if (normalize) normalize(res) else res }

dfd77d4126b2c4029e1c297e9aaccb86e7f3ffb8

29585dff702209dd446c0ab52ceea046c58e384e

/RSDA/R/sym.cor.R

511176b9eea190ac6461d8efb00f5e694ad63112

[]

no_license

ingted/R-Examples

825440ce468ce608c4d73e2af4c0a0213b81c0fe

d0917dbaf698cb8bc0789db0c3ab07453016eab9

refs/heads/master

2020-04-14T12:29:22.336088

2016-07-21T14:01:14

null

0

null

UTF-8

R

false

878

r

sym.cor.R

sym.cor <- function(sym.var.x,sym.var.y,method=c('centers','interval','billard','histogram'),na.rm=FALSE, ...) { method<-match.arg(method) if(method=='centers') { if((sym.var.x$var.type=='$C')&&(sym.var.y$var.type=='$C')) return(cor(sym.var.x$var.data.vector,sym.var.y$var.data.vector)) if((sym.var.x$var.type=='$I')&&(sym.var.y$var.type=='$I')) return(cor((sym.var.x$var.data.vector[,1]+sym.var.x$var.data.vector[,2])/2, (sym.var.y$var.data.vector[,1]+sym.var.y$var.data.vector[,2])/2)) else stop("Impossible to compute the Standard Deviation for this type of variable with this method") } if(method=='billard') { if((sym.var.x$var.type=='$I')&&(sym.var.y$var.type=='$I')) return(sym.cov(sym.var.x,sym.var.y,method='billard')/(sym.sd(sym.var.x,method='billard')*sym.sd(sym.var.y,method='billard'))) } }

b3ab6dfd31e5abaa64db58fcbed40e1c0656c742

c5138fb9dacb509d778881d2ad97ab056dbc16a3

/R/neural-nets.R

82fffa27321278bb5dff0aeecc289975cf431d2c

[]

no_license

cran/deep

8f5c142e5e2aef91a14ded9b9b2f80af0eabd067

1a3c7676464430b245b9f092649c87202c606aad

refs/heads/master

2020-12-21T22:24:49.733218

2019-12-20T10:50:03

236,581,866

0

null

UTF-8

R

false

5,740

r

neural-nets.R

#' deep: A Neural Networks Framework #' #' The deep package provides classes for layers, types of neurons and the #' neural network as a whole. #' #' @docType package #' @name deep NULL #' The main NeuralNetwork class, that holds the layers. #' #' @field eta The learning tax, representes the size of the weight adjustment #' between each epoch of training. #' #' @field layers This field is a list of the layers of the network, you can use #' subsetting to inspect them. #' #' @examples #' # Create a dataset #' dataset <- iris #' dataset$Petal.Length <- NULL #' dataset$Petal.Width <- NULL #' dataset <- dataset[dataset$Species != "versicolor",] #' dataset$Code <- as.integer(dataset$Species == "virginica") #' dataset <- dataset[sample(20),] #' #' # Create the network #' net <- neuralNet(2, perceptronLayer(1)) #' #' # Train the network, takes a while #' net$train(dataset[,c(1,2), drop=FALSE], dataset[,'Code', drop=FALSE], epochs = 10) #' #' # Check the output #' net$compute(c(1,2)) #' #' # See accuracy #' net$validationScore(dataset[,c(1,2), drop=FALSE], dataset[,'Code', drop=FALSE]) #' neuralNet <- setRefClass( "NeuralNetwork", fields = list(eta = "numeric", layers = "vector") ) #' @method initialize This runs when you instantiate the class, either with #' \code{neuralNet$new()} or \code{neuralNet()}, the intent is to set up the #' internal fields and layers passed in ... . #' #' @method compute Gives the output of passing data inside the network. #' @param input The actual data to be fed into the network, this input's #' dimentions vary with the first layer in your network. #' @return Vector of the outputs of the last layer in your network. #' #' @method train Runs the backpropagation algorithm to train all layers of #' the network #' @param ins The list of vectors of inputs to the first layer in the network #' @param outs The list of vectors of outputs of the last layer in the network #' @param epochs How many rounds of training to run #' @param tax This is the learning rate, aka eta #' @param maxErr A contition to early stop the training process #' @return Vector of computed values of the same size of the last layer #' neuralNet$methods( initialize = function(input, ...) { # Initialize each layer layers <<- vector("list", ...length()) for (i in 1:...length()) { l <- ...elt(i) switch (class(l), "PerceptronLayer" = { layers[[i]] <<- perceptronLayer(l$n, input) input <- l$n }, "McCullochPittsLayer" = { layers[[i]] <<- mcCullochPittsLayer(l$n, input) input <- l$n }, stop("argument in ... is not a layer!") ) } }, compute = function(input) { for (layer in layers) { input <- layer$output(input) } input }, train = function (ins, outs, epochs = 1, tax = .01, maxErr = 0) { nLayers <- length(layers) r <- nrow(ins) for (epoch in 1:epochs) { # Initialize changes vector ch <- vector("list", nLayers) ch[[1]] <- vector("list", layers[[1]]$n) for (neu in 1:layers[[1]]$n) { ch[[1]][[neu]] <- list( "ws" = vector("numeric", length(ins[1,])), "b" = vector("numeric", 1) ) } if (nLayers > 1) { for (l in 2:nLayers) { ch[[l]] <- vector("list", layers[[l]]$n) for (ne in 1:layers[[l]]$n) { ch[[l]][[ne]] <- list( "ws" = vector("numeric", layers[[l-1]]$n), "b" = vector("numeric", 1) ) } } } for (i in 1:r) { inputs <- vector("list", nLayers + 1) inputs[[1]] <- ins[i,] # Record each output for (l in 1:nLayers) { inputs[[l+1]] <- layers[[l]]$output(inputs[[l]]) } cost <- sum(outs[i,] - inputs[[nLayers+1]]) # Calculate weight changes li <- nLayers newErr <- cost for (l in rev(layers)) { err <- newErr newErr <- vector("numeric", length(inputs[[li]])) ni <- 1 for (neu in l$neurons) { d <- neu$ws*inputs[[li]]*err[[ni]]*tax db <- err[[ni]]*tax ch[[li]][[ni]][["ws"]] <- ch[[li]][[ni]][["ws"]] + d ch[[li]][[ni]][["b"]] <- ch[[li]][[ni]][["b"]] + db newErr <- newErr + err[[ni]]*neu$ws ni <- ni + 1 } li <- li - 1 } } # Average changes and apply li <- 1 for (l in layers) { ni <- 1 for (neu in l$neurons) { wsChange <- ch[[li]][[ni]]$ws/r bChange <- ch[[li]][[ni]]$b/r neu$ws <- neu$ws + unlist(wsChange, use.names = F) neu$bias <- neu$bias - unlist(bChange, use.names = F) ni <- ni + 1 } li <- li + 1 } } }, validationScore = function(ins, outs) { corrects <- 0 for (i in 1:nrow(ins)) { corrects <- corrects + as.integer(compute(ins[i,]) == outs[i,]) } corrects/nrow(ins) } )

6ec6971b11622ea65ed7f6fa14c32498360358c4

350f369998282044eeff0794540189c89ad8710c

/inst/doc/qle_with_R.R

413f60b26c7cfc7262c89e4fe04c68db935220fd

[]

no_license

cran/qle

26b2edf6e372d4a966aa85754ba4c88377036290

857a96cfcf8dbbf116c944c23924f6cedb37abd8

refs/heads/master

2021-09-24T10:39:46.030022

2018-10-08T11:00:03

110,973,979

1

0

null

UTF-8

R

false

24,022

r

qle_with_R.R

### R code from vignette source 'qle_with_R.Rnw' ### Encoding: UTF-8 ################################################### ### code chunk number 1: qle_with_R.Rnw:1083-1085 ################################################### options(useFancyQuotes="UTF-8") options(digits=4, prompt="R> ") ################################################### ### code chunk number 2: qle_with_R.Rnw:1168-1170 ################################################### library(qle) data(mm1q) ################################################### ### code chunk number 3: qle_with_R.Rnw:1173-1177 (eval = FALSE) ################################################### ## options(mc.cores=8L) ## options(qle.multicore="mclapply") ## RNGkind("L'Ecuyer-CMRG") ## set.seed(1326) ################################################### ### code chunk number 4: qle_with_R.Rnw:1184-1188 ################################################### cond <- list("n"=100) simfn <- function(tet,cond){ mean(rgeom(cond$n,prob=1-tet[1])) } ################################################### ### code chunk number 5: qle_with_R.Rnw:1192-1194 ################################################### lb <- c("rho"=0.05) ub <- c("rho"=0.95) ################################################### ### code chunk number 6: qle_with_R.Rnw:1200-1204 (eval = FALSE) ################################################### ## nsim <- 10 ## X <- multiDimLHS(N=9,lb=lb,ub=ub, ## method="maximinLHS",type="matrix") ## sim <- simQLdata(sim=simfn,cond=cond,nsim=nsim,X=X) ################################################### ### code chunk number 7: qle_with_R.Rnw:1206-1209 ################################################### sim <- mm1q$sim X <- attr(sim,"X") nsim <- attr(sim,"nsim") ################################################### ### code chunk number 8: qle_with_R.Rnw:1215-1217 (eval = FALSE) ################################################### ## qsd <- getQLmodel(sim, lb, ub, obs=c("N"=1), ## var.type="wlogMean",verbose=TRUE) ################################################### ### code chunk number 9: qle_with_R.Rnw:1221-1222 (eval = FALSE) ################################################### ## S0 <- qscoring(qsd,x0=c("rho"=0.8)) ################################################### ### code chunk number 10: qle_with_R.Rnw:1224-1227 ################################################### OPT <- mm1q$OPT qsd <- mm1q$qsd S0 <- mm1q$S0 ################################################### ### code chunk number 11: qle_with_R.Rnw:1229-1230 ################################################### print(S0) ################################################### ### code chunk number 12: qle_with_R.Rnw:1244-1249 (eval = FALSE) ################################################### ## OPT <- qle(qsd,simfn,cond=cond, ## global.opts = list("maxeval"=5, "NmaxLam"=5), ## local.opts = list("nextSample"="score","weights"=0.5, ## "ftol_abs"=1e-4, "lam_max"=1e-5), ## method = c("qscoring","bobyqa","direct"), iseed=1326) ################################################### ### code chunk number 13: qle_with_R.Rnw:1254-1273 ################################################### library(graphics) # statistics op <- par(xaxs='i', yaxs='i') rho <- as.matrix(seq(0.1,0.9,by=0.001)) y <- as.numeric(unlist(simQLdata(sim=simfn,cond=cond,nsim=nsim,X=rho,mode="mean"))) T <- qsd$qldata[grep("mean.",names(qsd$qldata))] Y <- predictKM(qsd$covT,rho,X,T,krig.type="var") # steady state values y0 <- rho/(1-rho) plot(NULL, type="n", xlab=expression(rho), ylab="y",xlim=c(0,1), ylim=c(0,10)) lines(as.numeric(rho),y,col="black",lt=2,lwd=0.3) lines(as.numeric(rho),Y,col="blue",lwd=0.3) lines(as.numeric(rho),y0,col="red",lwd=0.3) legend("topleft", c("Number of customers in the system", "Expected number at steady state","Kriging approximation"), lty=c(2,1,1),col=c("black","red","blue"), xpd=TRUE,pt.cex=1,cex=1) par(op) ################################################### ### code chunk number 14: qle_with_R.Rnw:1275-1297 ################################################### op <- par(xaxs='i', yaxs='i') p <- seq(lb,ub,by=0.0001) QD <- quasiDeviance(X,qsd,value.only=TRUE) qd <- quasiDeviance(as.matrix(p),qsd) y <- sapply(qd,"[[","value") score <- sapply(qd,"[[","score") ## plot quasi-deviance and quasi-score function plot(NULL, type="n", xlab=expression(rho), ylab="",xlim=c(0,1), ylim=c(-10,50)) abline(h=0,col="gray") points(X,QD,pch=3,cex=1) lines(p,score, type='l',col="blue",lwd=1.5) lines(p,y,col="black",lwd=0.8) legend("topright", c("quasi-deviance","quasi-score","sample points", "approximate root","additional samples"), lty=c(1,1),lwd=c(1.5,1.5,NA,NA,NA),pch=c(NA,NA,3,5,8), col=c("black","blue","black","magenta","green"),pt.cex=1,cex=1) points(S0$par,S0$val,col="magenta",pch=5,cex=1) nmax <- OPT$ctls["maxeval","val"] X <- as.matrix(qsd$qldata[,1]) Xnew <- OPT$qsd$qldata[(nrow(X)+1):(nrow(X)+nmax),1] points(cbind(Xnew,0),pch=8,cex=2,col="green") par(op) ################################################### ### code chunk number 15: qle_with_R.Rnw:1305-1306 ################################################### OPT ################################################### ### code chunk number 16: qle_with_R.Rnw:1311-1330 ################################################### op <-par(xaxs='i', yaxs='i') qd <- quasiDeviance(as.matrix(p),OPT$qsd) y <- sapply(qd,"[[","value") score <- sapply(qd,"[[","score") ## plot quasi-deviance and quasi-score function plot(NULL, type="n", xlab=expression(rho), ylab="",xlim=c(0,1), ylim=c(-10,50)) abline(h=0,col="gray") lines(p,score, type='l',col="blue",lwd=1.5) lines(p,y,col="black",lwd=0.8) legend("topright", c("quasi-deviance","quasi-score","sample points", "QL estimate"), lty=c(1,1),lwd=c(1,1,NA,NA,NA),pch=c(NA,NA,3,5,8), col=c("black","blue","black","magenta","green"),pt.cex=1,cex=1) X <- as.matrix(OPT$qsd$qldata[,1]) QD <- quasiDeviance(X,OPT$qsd,value.only=TRUE) points(X,QD,pch=3,cex=1) points(OPT$par,OPT$val,col="magenta",pch=5) par(op) ################################################### ### code chunk number 17: qle_with_R.Rnw:1340-1341 ################################################### checkMultRoot(OPT,verbose = TRUE) ################################################### ### code chunk number 18: qle_with_R.Rnw:1351-1354 ################################################### X <- as.matrix(OPT$qsd$qldata[,1]) Tstat <- OPT$qsd$qldata[grep("mean.",names(qsd$qldata))] predictKM(OPT$qsd$covT,c("rho"=0.5),X,Tstat) ################################################### ### code chunk number 19: qle_with_R.Rnw:1393-1395 (eval = FALSE) ################################################### ## tet0 <- c("rho"=0.5) ## obs0 <- simQLdata(sim=simfn,cond=cond,nsim=100,X=tet0) ################################################### ### code chunk number 20: qle_with_R.Rnw:1397-1399 ################################################### tet0 <- mm1q$tet0 obs0 <- mm1q$obs0 ################################################### ### code chunk number 21: qle_with_R.Rnw:1404-1411 ################################################### mle <- do.call(rbind, lapply(obs0[[1]],function(y,n){ tet <- 1-1/(1+y[[1]]) c("mle.rho"=tet,"mle.var"=(tet*(1-tet)^2)/n) }, n=cond$n)) x <- mle[,1]-tet0 mle.var <- c(sum(x^2)/length(x),mean(mle[,2])) ################################################### ### code chunk number 22: qle_with_R.Rnw:1415-1427 (eval = FALSE) ################################################### ## OPTS <- parLapply(cl,obs0[[1]], ## function(obs,...) { ## qle(...,obs=obs) ## }, ## qsd=qsd, ## sim=simfn, ## cond=cond, ## global.opts=list("maxeval"=10,"NmaxLam"=10), ## local.opts=list("nextSample"="score","weights"=0.5, ## "ftol_abs"=1e-4,"lam_max"=1e-5, ## "useWeights"=TRUE), ## method=c("qscoring","bobyqa","direct")) ################################################### ### code chunk number 23: qle_with_R.Rnw:1429-1430 ################################################### OPTS <- mm1q$OPTS ################################################### ### code chunk number 24: qle_with_R.Rnw:1432-1441 ################################################### # get results QLE <- do.call(rbind, lapply(OPTS, function(x) { c("qle"=x$par,"qle.var"=1/as.numeric(x$final$I)) })) y <- QLE[,1]-tet0 # MSE and average estimated variance of the parameters qle.var <- c(sum(y^2)/length(y),mean(QLE[,2])) ################################################### ### code chunk number 25: qle_with_R.Rnw:1443-1444 ################################################### Stest0 <- mm1q$Stest0 ################################################### ### code chunk number 26: qle_with_R.Rnw:1473-1474 (eval = FALSE) ################################################### ## Stest0 <- qleTest(OPT,sim=simfn,cond=cond,obs=obs0,cl=cl) ################################################### ### code chunk number 27: qle_with_R.Rnw:1476-1477 ################################################### print(Stest0) ################################################### ### code chunk number 28: qle_with_R.Rnw:1506-1510 ################################################### data(normal) OPT <- qsd$OPT QS <- qsd$QS simfunc <- qsd$simfn ################################################### ### code chunk number 29: qle_with_R.Rnw:1512-1520 (eval = FALSE) ################################################### ## # use a local cluster ## cl <- makeCluster(8L) ## clusterSetRNGStream(cl,1234) ## # simulation function ## simfunc <- function(pars) { ## x <- rnorm(10,mean=pars["mu"],sd=pars["sigma"]) ## c("T1"=median(x),"T2"=mad(x)) ## } ################################################### ### code chunk number 30: qle_with_R.Rnw:1525-1527 (eval = FALSE) ################################################### ## lb <- c("mu"=0.5,"sigma"=0.1) ## ub <- c("mu"=8.0,"sigma"=5.0) ################################################### ### code chunk number 31: qle_with_R.Rnw:1533-1537 (eval = FALSE) ################################################### ## sim <- simQLdata(sim=simfunc, ## nsim=10,N=8,lb=lb,ub=ub,method="maximinLHS") ## # reset number of simulations (10 x 10) ## attr(sim,"nsim") <- 100 ################################################### ### code chunk number 32: qle_with_R.Rnw:1543-1544 (eval = FALSE) ################################################### ## obs <- structure(c("T1"=2,"T2"=1),class="simQL") ################################################### ### code chunk number 33: qle_with_R.Rnw:1547-1548 (eval = FALSE) ################################################### ## qsd <- getQLmodel(sim,lb,ub,obs,var.type="wcholMean") ################################################### ### code chunk number 34: qle_with_R.Rnw:1552-1553 (eval = FALSE) ################################################### ## QS <- qscoring(qsd, x0=c("mu"=5,"sigma"=3.0)) ################################################### ### code chunk number 35: qle_with_R.Rnw:1555-1556 ################################################### print(QS) ################################################### ### code chunk number 36: qle_with_R.Rnw:1565-1571 (eval = FALSE) ################################################### ## OPT <- qle(qsd, ## simfunc, ## nsim=20, ## global.opts=list("maxeval"=50), ## local.opts=list("lam_max"=1e-3,"weights"=0.5, ## "useWeights"=FALSE,"test"=TRUE),cl=cl) ################################################### ### code chunk number 37: qle_with_R.Rnw:1573-1574 ################################################### OPT ################################################### ### code chunk number 38: qle_with_R.Rnw:1578-1620 ################################################### op <- par(mfrow=c(1, 2), mar=c(5.1, 4.1, 1.1, 1.1), oma=c(5,4,1,1),xaxs='i', yaxs='i', cex=2.2, cex.axis=2.2, cex.lab=2.2) # get points for plotting theta0 <- c("T1"=2,"T2"=1) x <- seq(qsd$lower[1],qsd$upper[1],by=0.05) y <- seq(qsd$lower[2],qsd$upper[2],by=0.05) p <- as.matrix(expand.grid(x,y)) X <- as.matrix(qsd$qldata[,1:2]) Tstat <- qsd$qldata[grep("mean.",names(qsd$qldata))] Xp <- quasiDeviance(X,qsd,value.only=TRUE) D <- quasiDeviance(p,qsd,value.only=TRUE) z <- matrix(D,ncol=length(y)) Xnext <- as.matrix(OPT$qsd$qldata[,1:2]) Dnext <- quasiDeviance(p,OPT$qsd,value.only=TRUE) znext <- matrix(Dnext,ncol=length(y)) nmax <- OPT$ctls["maxeval","val"] Xnew <- OPT$qsd$qldata[(nrow(X)+1):(nrow(X)+nmax),c(1,2)] # left plot(x=0,y=0,type="n", xlim=range(x),ylim=range(y), xlab=expression(mu),ylab=expression(sigma)) contour(x,y,z,col="black",lty="solid",nlevels=50,add=TRUE) # points(X,pch=23,cex=2,bg="black") points(Xnew,pch=8,cex=2,col="green") # right plot(x=0,y=0,type="n", xlim=range(x),ylim=range(y), xlab=expression(mu),ylab=expression(sigma)) contour(x,y,znext,col="black",lty="solid",nlevels=50,add=TRUE) points(Xnext,pch=23,cex=2,bg="black") points(rbind(OPT$par),pch=18,cex=2.5,col="magenta") points(rbind(unlist(theta0)),pch=17,cex=2.5,col="red") # legend par(fig = c(0, 1, 0, 1), oma = c(0, 0, 0, 0), mar = c(0, 0, 0, 0), new = TRUE) plot(0, 0, type = "n", bty = "n", xaxt = "n", yaxt = "n") cols <- c("black","green","magenta","red") legend("bottomleft",text.width=c(0.45,0.45,0.45,0.45), legend=c("initial design points", "new sample points", "estimated parameter","true parameter"), pch=c(23,8,18,17),col=cols,pt.bg=cols,bty='n', horiz=TRUE,xpd=TRUE,pt.cex=2.5,cex=2.5) par(op) ################################################### ### code chunk number 39: qle_with_R.Rnw:1629-1630 ################################################### checkMultRoot(OPT,verbose=TRUE) ################################################### ### code chunk number 40: qle_with_R.Rnw:1638-1641 ################################################### obs0 <- simQLdata(simfunc,X=OPT$par,nsim=1000,mode="matrix")[[1]] var(obs0) attr(OPT$final,"Sigma") ################################################### ### code chunk number 41: qle_with_R.Rnw:1643-1644 (eval = FALSE) ################################################### ## stopCluster(cl) ################################################### ### code chunk number 42: qle_with_R.Rnw:1674-1680 ################################################### data(matclust) OPT <- matclust$OPT qsd <- matclust$qsd cvm <- matclust$cvm Stest <- matclust$Stest library(spatstat) ################################################### ### code chunk number 43: qle_with_R.Rnw:1684-1686 ################################################### data(redwood) fitMat <- kppm(redwood, ~1, "MatClust") ################################################### ### code chunk number 44: qle_with_R.Rnw:1689-1690 ################################################### fitMat$modelpar ################################################### ### code chunk number 45: qle_with_R.Rnw:1692-1694 (eval = FALSE) ################################################### ## RNGkind("L'Ecuyer-CMRG") ## set.seed(297) ################################################### ### code chunk number 46: qle_with_R.Rnw:1698-1707 ################################################### simStat <- function(X,cond){ x <- Kest(X,r=cond$rr,correction="best") x <- x[[attr(x,"valu")]] x <- x[x>0] if(anyNA(x) || any(!is.finite(x))) { warning(.makeMessage("`NA`, `NaN` or `Inf` detected.","\n")) x <- x[!is.nan(x) & is.finite(x)]} return(c(intensity(X),x)) } ################################################### ### code chunk number 47: qle_with_R.Rnw:1711-1715 ################################################### simClust <- function(theta,cond){ X <- rMatClust(theta["kappa"],theta["R"],theta["mu"],win=cond$win) simStat(X,cond) } ################################################### ### code chunk number 48: qle_with_R.Rnw:1720-1724 ################################################### nsim <- 50 Nsample <- 12 cond <- list(win=owin(c(0, 2),c(0, 2)), rr=seq(0,0.3,by=0.05)) ################################################### ### code chunk number 49: qle_with_R.Rnw:1727-1729 ################################################### lb <- c("kappa"=20,"R"=0.01,"mu"=1) ub <- c("kappa"=30,"R"=0.25,"mu"=5) ################################################### ### code chunk number 50: qle_with_R.Rnw:1733-1737 (eval = FALSE) ################################################### ## cl <- makeCluster(8L) ## clusterSetRNGStream(cl) ## clusterCall(cl,fun=function(x) library("spatstat", character.only=TRUE)) ## clusterExport(cl=cl,varlist=c("simStat"), envir=environment()) ################################################### ### code chunk number 51: qle_with_R.Rnw:1741-1742 (eval = FALSE) ################################################### ## obs0 <- simStat(redwood,cond) ################################################### ### code chunk number 52: qle_with_R.Rnw:1745-1747 (eval = FALSE) ################################################### ## sim <- simQLdata(sim=simClust,cond=cond,nsim=nsim, ## method="randomLHS",lb=lb,ub=ub,N=Nsample,cl=cl) ################################################### ### code chunk number 53: qle_with_R.Rnw:1750-1752 (eval = FALSE) ################################################### ## qsd <- getQLmodel(sim,lb,ub,obs0,criterion="qle", ## var.type="kriging",verbose=TRUE) ################################################### ### code chunk number 54: qle_with_R.Rnw:1762-1763 (eval = FALSE) ################################################### ## cvm <- prefitCV(qsd, reduce=FALSE, verbose=TRUE) ################################################### ### code chunk number 55: qle_with_R.Rnw:1769-1770 (eval = FALSE) ################################################### ## crossValTx(qsd, cvm, type = "acve") ################################################### ### code chunk number 56: qle_with_R.Rnw:1772-1773 ################################################### matclust$ACVE ################################################### ### code chunk number 57: qle_with_R.Rnw:1778-1779 (eval = FALSE) ################################################### ## crossValTx(qsd, cvm, type = "mse") ################################################### ### code chunk number 58: qle_with_R.Rnw:1781-1782 ################################################### matclust$MSE ################################################### ### code chunk number 59: qle_with_R.Rnw:1793-1794 (eval = FALSE) ################################################### ## crossValTx(qsd, cvm, type = "ascve") ################################################### ### code chunk number 60: qle_with_R.Rnw:1796-1797 ################################################### matclust$ASCVE ################################################### ### code chunk number 61: qle_with_R.Rnw:1807-1808 ################################################### attr(cvm,"type") <- "max" ################################################### ### code chunk number 62: qle_with_R.Rnw:1812-1815 ################################################### x0 <- c("kappa"=24,"R"=0.08,"mu"=2.5) searchMinimizer(x0,qsd,info=TRUE, method="direct",cvm=cvm,verbose=TRUE) ################################################### ### code chunk number 63: qle_with_R.Rnw:1818-1821 ################################################### qscoring(qsd,x0, opts=list("ftol_rel"=1e-6,"slope_tol"=1e-4), cvm=cvm) ################################################### ### code chunk number 64: qle_with_R.Rnw:1837-1844 (eval = FALSE) ################################################### ## qs.opts <- ## list("xscale"=c(10,0.1,1), ## "xtol_rel"=1e-10, ## "ftol_stop"=1e-8, ## "ftol_rel"=1e-6, ## "ftol_abs"=1e-4, ## "score_tol"=1e-4) ################################################### ### code chunk number 65: qle_with_R.Rnw:1847-1863 (eval = FALSE) ################################################### ## OPT <- qle(qsd, simClust, cond=cond, ## qscore.opts = qs.opts, ## global.opts = list("maxiter"=10,"maxeval" = 20, ## "weights"=c(50,10,5,1,0.1), ## "NmaxQI"=5,"nstart"=100, ## "xscale"=c(10,0.1,1)), ## local.opts = list("lam_max"=1e-2, ## "nobs"=200, # number of (bootstrap) observations for testing ## "nextSample"="score", # sampling criterion ## "ftol_abs"=1e-2, # lower bound on criterion value, triggers testing ## "weights"=c(0.55), # constant weight factor ## "eta"=c(0.025,0.075), # ignored, automatic adjustment of weights ## "test"=TRUE), # testing approximate root is enabled ## method = c("qscoring","bobyqa","direct"), # restart methods ## errType="max", # use max of kriging and CV error ## iseed=297, cl=cl) # store seed and use given cluster object ################################################### ### code chunk number 66: qle_with_R.Rnw:1866-1867 ################################################### print(OPT) ################################################### ### code chunk number 67: qle_with_R.Rnw:1870-1871 ################################################### attr(OPT,"optInfo") ################################################### ### code chunk number 68: qle_with_R.Rnw:1876-1877 ################################################### OPT$final ################################################### ### code chunk number 69: qle_with_R.Rnw:1881-1883 ################################################### S0 <- searchMinimizer(OPT$par,OPT$qsd, method="bobyqa",cvm=OPT$cvm,verbose=TRUE) ################################################### ### code chunk number 70: qle_with_R.Rnw:1888-1891 ################################################### QS <- qscoring(OPT$qsd,OPT$par, opts=list("slope_tol"=1e-4,"score_tol"=1e-3), cvm=OPT$cvm) ################################################### ### code chunk number 71: qle_with_R.Rnw:1895-1897 ################################################### par <- rbind("QS"=QS$par,"S0"=S0$par) checkMultRoot(OPT,par=par) ################################################### ### code chunk number 72: qle_with_R.Rnw:1900-1901 ################################################### QS$par ################################################### ### code chunk number 73: qle_with_R.Rnw:1906-1917 (eval = FALSE) ################################################### ## par0 <- OPT$par ## obs0 <- OPT$qsd$obs ## # testing `par0` with observed statistics `obs0` ## # which can be replaced by the user and are obsolete below ## Stest <- qleTest(OPT, # estimation results ## par0=par0, # parameter to test ## obs0=obs0, # alternative observed statistics ## sim=simClust,cond=cond,nsim=100, ## method=c("qscoring","bobyqa","direct"), # restart methods ## opts=qs.opts,control=list("ftol_abs"=1e-8), # minimization options ## multi.start=1L,cl=cl,verbose=TRUE) # multi-start and parallel options ################################################### ### code chunk number 74: qle_with_R.Rnw:1919-1920 ################################################### print(Stest) ################################################### ### code chunk number 75: qle_with_R.Rnw:1923-1924 (eval = FALSE) ################################################### ## stopCluster(cl) ################################################### ### code chunk number 76: qle_with_R.Rnw:1933-1934 ################################################### diag(attr(Stest,"qi"))^0.5 ################################################### ### code chunk number 77: qle_with_R.Rnw:1937-1938 ################################################### sqrt(diag(attr(Stest,"msem"))) ################################################### ### code chunk number 78: qle_with_R.Rnw:1942-1943 ################################################### attr(Stest,"msem")

25d32961e0235ee365e936d70142d4bf007c4798

b7b87a80d60ae83d4789e9e2df1be3cb87676ba5

/other/crosstalk.R

8e4ea1518c0572e455abbc714be448c2145ba42f

[]

no_license

malvikarajeev/misc_code

ff169c87586ba3583c38893cb7c5eccce03318eb

6e603a015cc5cdb0343f7a0518912bd7e8fe0239

refs/heads/master

2023-04-21T13:58:40.716294

2021-04-20T07:08:49

270,853,898

1

0

null

UTF-8

R

false

994

r

crosstalk.R

#####experimenting with crosstalk #devtools::install_github("rstudio/crosstalk") #devtools::install_github("jcheng5/d3scatter") #devtools::install_github("rstudio/leaflet") library(d3scatter) library(crosstalk) library(leaflet) library(sf) library(sp) library(rgdal) library(rgeos) library(ggplot2) library(ggthemes) library(plotly) shared_dat <- merge(q85, q65, by = c('city', 'state', 'lat','long','state','zip')) shared_dat <- SharedData$new(shared_dat) #####linked brushing using bootstrap shared_quakes <- SharedData$new(sample_n(q85_long, 100)) bscols( leaflet(shared_quakes, width = "100%", height = 300) %>% addTiles() %>% addMarkers(), d3scatter(shared_quakes, ~option, ~no_of_ppl, ~state, width = "100%", height = 300) ) ##ADDING FILTERS: what to filter by? library(dplyr) ######### library(d3scatter) library(crosstalk) library(leaflet) library(sf) library(sp) library(rgdal) library(rgeos) library(ggplot2) library(ggthemes) library(plotly)

62c3e4f7047553806eb015ad18d81890fe8c64fd

6b987d7b5663bd70e2cd902a99deb3feb5741c5d

/R/param.R

6dd84c4619b1a5d289938d707d01778f8cd99dde

[ "Apache-2.0" ]

permissive

NicolasDurrande/gpflowr

081e67d52f5c863c04340ef4330177a2f428a6d0

c82aead1a82883af49c619962147a8e83c809a39

refs/heads/master

2021-01-12T18:16:27.999422

2016-11-22T16:10:57

71,359,140

1

0

null

2016-10-19T13:17:46

null

UTF-8

R

false

25,295

r

param.R

# define R6 classes: Parentable, Param, DataHolder, Parameterized and ParamList # port of GPflow/GPflow/param.py # ' @title Parentable class # ' # ' @description A very simple class for objects in a tree, where each node contains a # ' reference to '_parent'. # ' # ' @details This class can figure out its own name (by seeing what it's called by the # ' _parent's __dict__) and also recurse up to the highest_parent. # ' Parentable <- R6Class('Parentable', public = list( # enclosing env of parent .parent_env = NULL, # own hex in parent's .hex_list .hex = NULL, # named list of shas of children .hex_list = list(), # set the environment of the parent .set_parent_env = function (parent) self$.parent_env <- parent$.__enclos_env__, # bespoke assignment, to build tree structure `$<-` = function (x, i, value) { # when setting values, if the new value is a parentable, # link the child (value) and parent (self) if (inherits(value, 'Parentable')){ # generate unique hex to index the child in the parent hex <- get_hex() value[['.hex']] <- hex self[['.hex_list']][[i]] <- hex # and give the child the environment of the parent value[['.set_parent_env']](self) } # either way, assign the value self[[i]] <- value self }, # get the index to a child which_child = function (child) { if (!inherits(child, 'Parentable')) stop ('Parentables can only have children that are also Parentables') # return the index, retaining its name in the hex list idx <- match(child$.hex, self$.hex_list) names(idx) <- names(self$.hex_list)[idx] idx }, print = function (...) { # find the classes to which this object belongs and print them classes <- class(self$clone()) main_class <- classes[1] other_classes <- classes[-1] other_classes <- other_classes[other_classes != 'R6'] if (length(other_classes) > 0) { inheritance_msg <- sprintf('(inheriting from %s)\n', paste(other_classes, collapse = ' < ')) } else { inheritance_msg <- '' } msg <- sprintf('%s object\n%s', main_class, inheritance_msg) cat (msg) } # used for pickling, so ignore for now # would need to write a dict class # .getstate <- function () { # # get list of elements, remove parent, return list? # d <- self$.dict # d$pop('_parent') # return (d) # }, # .setstate = function (d) { # # replace list of elements with new list of elements, then remove parent? # self$.dict$update(d) # self$parent <- NULL # } ), active = list( # make name and long_name properties name = function (value) { # let the user know they can't assign names in this way if (!missing(value)) warning ('name assignment ignored') # An automatically generated name, given by the # reference of the _parent to this instance if (is.null(self$parent)) return ('unnamed') # get the index idx <- self$parent$which_child(self) if (inherits(self$parent, 'ParamList')) return (sprintf('item%i', self$parent$.list$index(self))) if (length(idx) == 0) stop("mis-specified parent. This Param's .parent does not contain a reference to it.") if (length(idx) > 1) stop("This Param appears to be doubly referenced by a parent") names(idx) }, long_name = function (value) { # This is a unique identifier for a param object # within a structure, made by concatenating the names # through the tree. # let the user know they can't assign names in this way if (!missing(value)) warning ('name assignment ignored') if (is.null(self$parent)) return (self$name) paste(self$parent$long_name, self$name, sep = '$') }, parent = function (value) { # get the parent object from its environment if (missing(value)) self$.parent_env$self else self$.parent_env$self <- value }, highest_parent = function (value) { # A reference to the top of the tree, usually a Model # instance if (missing(value)) { if (is.null(self$parent)) self else self$parent$highest_parent } else { if (is.null(self$parent)) self <- value else self$parent$highest_parent <- value } } )) Param <- R6Class('Param', inherit = Parentable, public = list( .array = NULL, .tf_array = NULL, .log_jacobian = NULL, prior = NULL, transform = NULL, fixed = FALSE, initialize = function (array, transform = transforms$Identity) { self$value <- as.array(array) self$transform <- transform }, # get_parameter_dict = function (d) # d[[self$long_name]] <- self$value, # # set_parameter_dict = function (d) # self$value <- d[[self$long_name]], # get_samples_df = function (samples) { # # Given a numpy array where each row is a valid free-state # # vector, return a pandas.DataFrame which contains the # # parameter name and associated samples in the correct form # # (e.g. with positive constraints applied). # # if (self$fixed) # # return (pd.Series([self.value for _ in range(samples.shape[0])], name=self.long_name)) # start <- self$highest_parent()$get_param_index(self)[1] # end <- start + self$size - 1 # samples <- samples[, start:end] # # samples <- samples.reshape((samples.shape[0],) + self.shape) # samples <- self$transform$forward(samples) # # return (pd.Series([v for v in samples], name=self.long_name)) # }, make_tf_array = function (free_array) { # free_array is a tensorflow vector which will be the optimisation # target, i.e. it will be free to take any value. # Here we take that array, and transform and reshape it so that it can be # used to represent this parameter # Then we return the number of elements that we've used to construct the # array, so that it can be sliced for the next Param. # fixed parameters are treated by tf.placeholder if (self$fixed) return (0) free_size <- self$size x_free <- free_array[1:free_size] mapped_array <- self$transform$tf_forward(x_free) self$.tf_array <- tf$reshape(mapped_array, self$shape) self$.log_jacobian <- self$transform$tf_log_jacobian(x_free) return (free_size) }, get_free_state = function () { # Take the current state of this variable, as stored in # self.value, and transform it to the 'free' state. This is # a numpy method. if (self$fixed) return (0) return (self$transform$backward(self$value)) }, # get_feed_dict = function() { # # Return a dictionary matching up any fixed-placeholders to their values # d <- list() # if (self$fixed) # d[[self$.tf_array]] <- self$value # return (d) # }, set_state = function (x) { # Given a vector x representing the 'free' state of this Param, transform # it 'forwards' and store the result in self._array. The values in # self._array can be accessed using self.value # This is a numpy method. if (self$fixed) return (0) new_x <- self$transform$forward(x) new_array <- array(new_x, dim = self$shape) stopifnot(all(dim(new_array) == dim(self$.array))) self$.array <- new_array return (self$size) }, build_prior = function () { # Build a tensorflow representation of the prior density. # The log Jacobian is included. if (is.null(self$prior)) return (tf$constant(0.0, float_type)) else if (is.null(self$.tf_array)) # pragma: no cover stop ("tensorflow array has not been initialized") else return (self$prior$logp(self$.tf_array) + self$.log_jacobian) }#, # # `$<-` = function (x, i, value) { # # When some attributes are set, we need to recompile the tf model before # # evaluation. # self[[i]] <- value # if (i %in% recompile_keys) # self$highest_parent$.needs_recompile <- TRUE # # # when setting the fixed attribute, make or remove a placeholder appropraitely # if (i == 'fixed') { # if (value) # self$.tf_array <- tf$placeholder(dtype = float_type, # shape = self$.array$shape, # name = self$name) # else # self$.tf_array = NULL # } # }, # def __str__(self, prepend=''): # return prepend + \ # '\033[1m' + self.name + '\033[0m' + \ # ' transform:' + str(self.transform) + \ # ' prior:' + str(self.prior) + \ # (' [FIXED]' if self.fixed else '') + \ # '\n' + str(self.value) # getstate = function (self) { # d <- super$getstate() # d$pop('_tf_array') # d$pop('_log_jacobian') # return (d) # }, # # setstate = function (self) { # super$setstate(d) # self$.log_jacobian <- NULL # self$fixed <- self$fixed # } ), # point 'value' at the array active = list( value = property('.array'), shape = function (value) dim(self$.array), size = function (value) prod(self$shape) ) ) # DataHolder <- R6Class('DataHolder', # inherit = Parentable, # public = list( # # )) Parameterized <- R6Class('Parameterized', inherit = Parentable, public = list( x = NULL, .tf_mode = FALSE, .tf_mode_storage = list(), initialize = function () { self$.tf_mode <- FALSE }, get_parameter_dict = function (d = NULL) { if (is.null(d)) d <- list() for (p in self$sorted_params) p$get_parameter_dict(d) d }, set_parameter_dict = function (d) { for (p in self$sorted_params) p$set_parameter_dict(d) }, `$` = function (x, i) { # return a tensorflow array if `x` is in tf_mode, # and the object containing that array otherwise # equivalent to python __getattribute__ method o <- x[[i]] if (has(x, '.tf_mode') && x[['.tf_mode']] && has(o, '.tf_array')) o <- o[['.tf_array']] o }, # `$<-` = function (x, i, value) { # # }, .kill_autoflow = function () { # remove all AutoFlow storage dicts recursively self$.tf_mode_storage <- list() for (i in seq_len(self$sorted_params)) { if (inherits(self$sorted_params[[i]])) self$sorted_params[[i]]$.kill_autoflow() } }, make_tf_array = function (X) { # X is a tf placeholder. It gets passed to all the # children of this class (that are Parameterized or # Param objects), which then construct their # tf_array variables from consecutive sections. nrow <- X$get_shape()$as_list()[1] count <- 0 for (i in seq_along(self$sorted_params)) count <- count + self$sorted_params[[i]]$make_tf_array(X[count:nrow]) count }, get_free_state = function () { # recurse get_free_state on all child parameters, and hstack them. free_states <- lapply(self$sorted_params, function(x) x$get_free_state()) array(do.call(c, free_states)) }, get_feed_dict = function () { # Recursively fetch a dictionary matching up fixed-placeholders to # associated values lapply(c(self$sorted_params, self$data_holders), function(x) x$get_feed_dict()) }, set_state = function (x) { # Set the values of all the parameters by recursion nrow <- x$get_shape()$as_list()[1] count <- 0 for (name in names(self$sorted_params)) count <- count + self$sorted_params[[name]]$set_state(x[count:nrow]) count }, tf_mode = function () { on.exit(self$.end_tf_mode()) self$.begin_tf_mode() return (self$clone()) }, .begin_tf_mode = function () { self$.tf_mode <- TRUE }, .end_tf_mode = function () { self$.tf_mode <- FALSE }, build_prior = function () { # Build a tf expression for the prior by summing all child-node priors. nparam <- length(self$sorted_params) pri <- self$sorted_params[[1]]$build_prior() if (nparam > 0) { for (i in 2:nparam) pri <- pri + self$sorted_params[[i]]$build_prior() } }#, # # str = function (object, prepend = '') { # # }, # # .html_table_rows = function (name_prefix = '') { # # }, # # .repr_html_ = function () { # # }, # # .__setstate__ = function (d) { # # } ), active = list( sorted_params = function (value) { # Return a list of all the child parameters, sorted by id. This makes # sure they're always in the same order. if (!missing(value)) warning ('assignment ignored') # find names of elements names <- names(self) names <- names[names != 'parent'] # pull out those that are Param-esque params <- list() for (name in names) { if (inherits(self[[name]], c('Param', 'Parameterized'))) params[[name]] <- self[[name]] } # order them params <- params[order(names(params))] }, data_holders = function (value) { # Return a list of all the child DataHolders if (!missing(value)) warning ('assignment ignored') params <- list() for (name in names(self)) { if (inherits(self[[name]], 'DataHolder')) params[[name]] <- self[[name]] } params }, fixed = function (value) { if (!missing(value)) { for (name in names(self)) self[[name]]$fixed <- value } else { ans <- vapply(self$sorted_params, function(x) x$fixed, FALSE) return (ans) } } )) ParamList <- R6Class('ParamList', inherit = Parameterized, public = list( ))

97f25911f28a71d375aae3a23b8ccbbf64d7d73c

a15bafa2b845fff694d5428c27796ae02fc04b17

/man/distanceheatmaps.Rd

22e7257351207978fa6ad68a265d8ddb32150f14

[]

no_license

cran/IntClust

741db502112c3407aefa5eb5d08b92e2d2a75f7a

235fe4a67855c96b48247aa2f02895604caf507e

refs/heads/master

2018-10-30T23:12:34.173124

2018-07-30T11:10:15

55,162,571

0

null

UTF-8

R

false

true

610

rd

distanceheatmaps.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Functions.R \name{distanceheatmaps} \alias{distanceheatmaps} \title{Determine the distance in a heatmap} \usage{ distanceheatmaps(Data1, Data2, names = NULL, nrclusters = 7) } \arguments{ \item{Data1}{The resulting clustering of method 1.} \item{Data2}{The resulting clustering of method 2.} \item{names}{The names of the objects in the data sets. Default is NULL.} \item{nrclusters}{The number of clusters to cut the dendrogram in. Default is NULL.} } \description{ Internal function of \code{HeatmapPlot} }

cb320cd5423587ea5c995ccc87df4664c417ef34

86061b2a5b230a1f30f8bf361b4a22b5e73623c4

/Green_Values.R

b6b9242a161a232717e0df0b15b865e9328f69f0

[]

no_license

C-WWU/Seminar

f1e0f0ee10bdfbbe8f185bcc2239de63cd663c83

30d0f3f545a3b7117abf80f3e7b8dfd401d9f9a0

refs/heads/master

2023-03-06T15:42:34.040389

2021-02-17T15:00:42

324,803,807

0

null

UTF-8

R

false

18,065

r

Green_Values.R

#----------------------FINAL CODE IN SOCIALLY IRRESPONSIBLE ALGORITHMS------------------------------ #install and load relevant packages install.packages("cowplot") install.packages("randomForest") install.packages("pROC") install.packages("readr") install.packages("caret") install.packages("e1071") install.packages("stepPlr") install.packages("mlbench") install.packages("readxl") install.packages("DMwR") install.packages("ROSE") install.packages("ranger") install.packages("MASS") install.packages("pdp") install.packages("elasticnet") install.packages("glmnet") install.packages("Matrix") install.packages("Hmisc") library(ggplot2) library(cowplot) library(randomForest) library(pROC) library(readr) library(caret) library(e1071) library(plyr) library(dplyr) library(stepPlr) library(mlbench) library(readxl) library(DMwR) library(ROSE) library(ranger) library(tidyverse) library(MASS) library(pdp) library(elasticnet) library(glmnet) library(Matrix) library(Hmisc) options(max.print = 100000) ####################### #Green Values: numeric ###################### #--------------------------------------DATA PRE-PROCESSING------------------------------------------ # load data load("data_for_analysis.RData") #data <- full #oder ändern zu data <- reduced_set cols_names <- names(data) cols_names #define data for analysis data_Green1 <- data[,c(305, 27:255)] #Gibt es NAs in der DV? sum(is.na(data_Green1$Green_Values)) #keine NAs #ist die Variable unbalanced? table(data_Green1$Green_Values) #Überhang zu höheren Werten, aber nicht zu stark (mean: 4,97) max(table(data_Green1$Green_Values)/sum(table(data_Green1$Green_Values))) #no information rate 7,65% #----------------------------------------DATA PARTITIONING------------------------------------ #Training und Test Dataset set.seed(1997) # Partitioning of the data: Create index matrix of selected values index <- createDataPartition(data_Green1$Green_Values, p=.8, list= FALSE, times= 1) # Create train_dfGeschlecht & test_dfGeschlecht train_dfGreen1 <- data_Green1[index,] test_dfGreen1 <- data_Green1[-index,] #---------------------------------------------------RANDOM FOREST---------------------------------------------------- #--------------------------------------------BUILDING AND TRAINING THE MODEL--------------------------------------------- # Specify the type of training method used & number of folds --> bei uns 10-fold Cross-Validation myControl = trainControl( method = "cv", number = 10, verboseIter = TRUE, allowParallel=TRUE, search = "grid", ) #set tuning grid set.seed(1997) myGrid = expand.grid(mtry = c(10:20), splitrule = "extratrees", min.node.size = c(5,10,15)) ####-------tree 1: mtry, splitrule and min.node.size tunen -------------------------------------------------- # test of the ideal mtry, splitrule and min-node.size for 500 trees set.seed(1997) RFGreen1_1 <- train(Green_Values ~ ., data=train_dfGreen1, tuneGrid = myGrid, method="ranger", metric= "RMSE", num.tree = 500, trControl = myControl, na.action = na.omit, importance = 'impurity') # Print model to console RFGreen1_1 summary(RFGreen1_1) plot(RFGreen1_1) #best mtry:10 #splitrule: extratrees #min.node.size used: 10 # predict outcome using model from train_df applied to the test_df predictions <- predict(RFGreen1_1, newdata=test_dfGreen1) MAE(predictions, test_dfGreen1$Green_Values) RMSE(predictions, test_dfGreen1$Green_Values) R2(predictions, test_dfGreen1$Green_Values) #calculate Pearson coefficient for predictions and actual values # Correlations with significance levels pearsonGreen1_1 <- cor.test(predictions, test_dfGreen1$Green_Values, method = "pearson") pearsonGreen1_1 spearmanGreen1_1 <- cor.test(predictions, test_dfGreen1$Green_Values, method = "spearman") spearmanGreen1_1 ####-------tree 2: num.tree prüfen -------------------------------------------------- #1000 num.tree ausprobieren --> ist mehr besser? set.seed(1997) RFGreen1_2 <- train(Green_Values ~ ., data=train_dfGreen1, tuneGrid = myGrid, method="ranger", metric= "RMSE", num.tree = 1000, trControl = myControl, na.action = na.omit, importance = 'impurity') # Print model to console RFGreen1_2 summary(RFGreen1_2) plot(RFGreen1_2) #mtry = 10, extratrees, min.node.size = 10 # predict outcome using model from train_df applied to the test_df predictions <- predict(RFGreen1_2, newdata=test_dfGreen1) MAE(predictions, test_dfGreen1$Green_Values) RMSE(predictions, test_dfGreen1$Green_Values) R2(predictions, test_dfGreen1$Green_Values) #calculate Pearson coefficient for predictions and actual values # Correlations with significance levels pearsonGreen1_2 <- cor.test(predictions, test_dfGreen1$Green_Values, method = "pearson") pearsonGreen1_2 spearmanGreen1_2 <- cor.test(predictions, test_dfGreen1$Green_Values, method = "spearman") spearmanGreen1_2 #num.trees 1000 performs slightly better ####-------tree 3: Final -------------------------------------------------- #final getunte Werte einsetzen set.seed(1997) RFGreen1_fin <- RFGreen1_2 # Print model RFGreen1_fin summary(RFGreen1_fin) #evaluate variable importance # Mean Decrease Gini - Measure of variable importance based on the Gini impurity index used for the calculation of splits in trees. varImp(RFGreen1_fin) plot(varImp(RFGreen1_fin), 20, main = "Green_Values") # predict outcome using model from train_df applied to the test_df predictions <- predict(RFGreen1_fin, newdata=test_dfGreen1) MAE(predictions, test_dfGreen1$Green_Values) RMSE(predictions, test_dfGreen1$Green_Values) R2(predictions, test_dfGreen1$Green_Values) #calculate Pearson coefficient for predictions and actual values # Correlations with significance levels pearsonGreen1_fin <- cor.test(predictions, test_dfGreen1$Green_Values, method = "pearson") pearsonGreen1_fin spearmanGreen1_fin <- cor.test(predictions, test_dfGreen1$Green_Values, method = "spearman") spearmanGreen1_fin #--------------Variable Direction: Partial Plots----------------------------------------- #checking direction of the 10 most important variables imp <- importance(RFGreen1_fin$finalModel) imp <- as.data.frame(imp) impvar <- rownames(imp)[order(imp[1], decreasing=TRUE)] impvar <- impvar[1:20] PartialPlots <- RFGreen1_fin PartialPlots %>% partial(pred.var = impvar[1]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[2]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[3]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[4]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[5]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[6]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[7]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[8]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[9]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[10]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[11]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[12]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[13]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[14]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[15]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[16]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[17]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[18]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[19]) %>%plotPartial PartialPlots %>% partial(pred.var = impvar[20]) %>%plotPartial #------------------------------------------------WHEN BEST MODEL IS FOUND----------------------------------------------------- #save model to disk besttree_Green1 <- RFGreen1_fin saveRDS(besttree_Green1, "./tree_Green1.rds") #load the model super_model <- readRDS("./tree_Green1.rds") print(super_model) ####################### #Green Values 2: binary ####################### #--------------------------------------DATA PRE-PROCESSING------------------------------------------ # load data load("data_for_analysis.RData") cols_names <- names(data) cols_names #define data for analysis data_Green2 <- data[,c(306, 27:255)] #Gibt es NAs in der DV? sum(is.na(data_Green2$Green2)) #keine NAs #ist die Variable unbalanced? table(data_Green2$Green2) #Verteilung ca 1:6 --> in Tests mit beachten! max(table(data_Green2$Green2)/sum(table(data_Green2$Green2))) #no information rate 84% #----------------------------------------DATA PARTITIONING------------------------------------ #Training und Test Dataset set.seed(1997) # Partitioning of the data: Create index matrix of selected values index <- createDataPartition(data_Green2$Green2, p=.8, list= FALSE, times= 1) # Create train_dfGeschlecht & test_dfGeschlecht train_dfGreen2 <- data_Green2[index,] test_dfGreen2 <- data_Green2[-index,] #---------------------------------------------------RANDOM FOREST---------------------------------------------------- #--------------------------------------------BUILDING AND TRAINING THE MODEL--------------------------------------------- # Specify the type of training method used & number of folds --> bei uns 10-fold Cross-Validation set.seed(1997) myControl1 = trainControl( method = "cv", number = 10, verboseIter = TRUE, summaryFunction = twoClassSummary, classProbs = TRUE, allowParallel=TRUE, sampling = "smote", search = "grid" ) #set tuning grid set.seed(1997) myGrid = expand.grid(mtry = c(10:20), splitrule = "extratrees", min.node.size = c(5,10,15)) ####-------tree 1: mtry, splitrule and min.node.size tunen -------------------------------------------------- # test of the ideal mtry, splitrule and min-node.size for 500 trees set.seed(1997) RFGreen2_1 <- train(Green2 ~ ., data=train_dfGreen2, tuneGrid = myGrid, method="ranger", metric= "ROC", num.tree = 500, na.action = na.omit, trControl = myControl1, importance = 'impurity') # Print models to console RFGreen2_1 summary(RFGreen2_1) plot(RFGreen2_1) #mtry = 13, extratrees, min.node.size = 10 # predict outcome using model from train_df applied to the test_df predictions1 <- predict(RFGreen2_1, newdata=test_dfGreen2) # Create confusion matrix confusionMatrix(data=as.factor(predictions1), as.factor(test_dfGreen2$Green2)) #check for auc test_roc <- function(model, data) { roc(test_dfGreen2$Green2, predict(model, data, type = "prob")[, "Ja"]) } #model auc: 0,6346 RFGreen2_1 %>% test_roc(data = test_dfGreen2) %>% auc() #check ROC plot model_list <- list(Model1 = RFGreen2_1) model_list_roc <- model_list %>% map(test_roc, data = test_dfGreen2) model_list_roc %>% map(auc) results_list_roc <- list(NA) num_mod <- 1 for(the_roc in model_list_roc){ results_list_roc[[num_mod]] <- tibble(tpr = the_roc$sensitivities, fpr = 1 - the_roc$specificities, model = names(model_list)[num_mod]) num_mod <- num_mod + 1 } results_df_roc <- bind_rows(results_list_roc) # Plot ROC curve custom_col <- c("#000000", "#009E73", "#0072B2", "#D55E00", "#CC79A7") ggplot(aes(x = fpr, y = tpr, group = model), data = results_df_roc) + geom_line(aes(color = model), size = 1) + scale_color_manual(values = custom_col) + geom_abline(intercept = 0, slope = 1, color = "gray", size = 1) + theme_bw(base_size = 18) ####-------tree 2: num.tree prüfen -------------------------------------------------- #1000 für num.tree ausprobieren --> ist mehr besser? #set random seed again set.seed(1997) RFGreen2_2 <- train(Green2 ~ ., data=train_dfGreen2, tuneGrid = myGrid, method="ranger", metric= "ROC", num.tree = 1000, na.action = na.omit, trControl = myControl1, importance = 'impurity') # Print models to console RFGreen2_2 summary(RFGreen2_2) plot(RFGreen2_2) #mtry = 13, extratrees, min.node.size = 10 # predict outcome using model from train_df applied to the test_df predictions2 <- predict(RFGreen2_2, newdata=test_dfGreen2) # Create confusion matrix confusionMatrix(data=as.factor(predictions2), as.factor(test_dfGreen2$Green2)) #check for auc test_roc <- function(model, data) { roc(test_dfGreen2$Green2, predict(model, data, type = "prob")[, "Ja"]) } #model auc: 0,6341 RFGreen2_2 %>% test_roc(data = test_dfGreen2) %>% auc() #ROC plot model_list <- list(Model1 = RFGreen2_2) model_list_roc <- model_list %>% map(test_roc, data = test_dfGreen2) model_list_roc %>% map(auc) results_list_roc <- list(NA) num_mod <- 1 for(the_roc in model_list_roc){ results_list_roc[[num_mod]] <- tibble(tpr = the_roc$sensitivities, fpr = 1 - the_roc$specificities, model = names(model_list)[num_mod]) num_mod <- num_mod + 1 } results_df_roc <- bind_rows(results_list_roc) # Plot ROC curve custom_col <- c("#000000", "#009E73", "#0072B2", "#D55E00", "#CC79A7") ggplot(aes(x = fpr, y = tpr, group = model), data = results_df_roc) + geom_line(aes(color = model), size = 1) + scale_color_manual(values = custom_col) + geom_abline(intercept = 0, slope = 1, color = "gray", size = 1) + theme_bw(base_size = 18) #better num.trees: 500 trees sorts 1 person more correctly ####-------tree 3: Final -------------------------------------------------- #final getunte Werte einsetzen set.seed(1997) RFGreen2_fin <- RFGreen2_1 # Print models RFGreen2_fin summary(RFGreen2_fin) #evaluate variable importance # Mean Decrease Gini - Measure of variable importance based on the Gini impurity index used for the calculation of splits in trees. varImp(RFGreen2_fin) plot(varImp(RFGreen2_fin), 20, main = "Green_Values") # predict outcome using model from train_df applied to the test_df predictions3 <- predict(RFGreen2_fin, newdata=test_dfGreen2) # Create confusion matrix confusionMatrix(data=as.factor(predictions3), as.factor(test_dfGreen2$Green2)) #check for auc test_roc <- function(model, data) { roc(test_dfGreen2$Green2, predict(model, data, type = "prob")[, "Ja"]) } #model auc: 0,6346 RFGreen2_fin %>% test_roc(data = test_dfGreen2) %>% auc() #compare different ROC plots model_list <- list(Model1 = RFGreen2_1, Model2 = RFGreen2_2) model_list_roc <- model_list %>% map(test_roc, data = test_dfGreen2) model_list_roc %>% map(auc) results_list_roc <- list(NA) num_mod <- 1 for(the_roc in model_list_roc){ results_list_roc[[num_mod]] <- tibble(tpr = the_roc$sensitivities, fpr = 1 - the_roc$specificities, model = names(model_list)[num_mod]) num_mod <- num_mod + 1 } results_df_roc <- bind_rows(results_list_roc) # Plot ROC curve custom_col <- c("#000000", "#009E73", "#0072B2", "#D55E00", "#CC79A7") ggplot(aes(x = fpr, y = tpr, group = model), data = results_df_roc) + geom_line(aes(color = model), size = 1) + scale_color_manual(values = custom_col) + geom_abline(intercept = 0, slope = 1, color = "gray", size = 1) + theme_bw(base_size = 18) #--------------Variable Direction: Partial Plots----------------------------------------- #checking direction of the 10 most important variables imp <- importance(RFGreen2_fin$finalModel) imp <- as.data.frame(imp) impvar <- rownames(imp)[order(imp[1], decreasing=TRUE)] impvar <- impvar[1:20] PartialPlots <- RFGreen2_fin PartialPlots %>% partial(pred.var = impvar[1], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[2], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[3], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[4], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[5], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[6], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[7], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[8], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[9], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[10], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[11], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[12], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[13], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[14], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[15], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[16], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[17], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[18], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[19], which.class = "Ja") %>%plotPartial PartialPlots %>% partial(pred.var = impvar[20], which.class = "Ja") %>%plotPartial #------------------------------------------------WHEN BEST MODEL IS FOUND----------------------------------------------------- #save model to disk besttree_Green2 <- RFGreen2_fin saveRDS(besttree_Green2, "./tree_Green2.rds") #load the model besttree_Green2 <- readRDS("./tree_Green2.rds") print(besttree_Green2)

f330389081bab83b63905ad911a2776e81e39112

92e597e4ffc9b52cfb6b512734fb10c255543d26

/man/firstElement.Rd

96af7264d5b2ce4ccdc15e8f00e52b3394770cb8

[ "MIT" ]

permissive

KWB-R/kwb.utils

3b978dba2a86a01d3c11fee1fbcb965dd15a710d

0930eaeb9303cd9359892c1403226a73060eed5b

refs/heads/master

2023-05-12T15:26:14.529039

2023-04-21T04:28:29

60,531,844

9

1

MIT

2023-04-21T04:28:30

2016-06-06T13:52:43

R

UTF-8

R

false

true

303

rd

firstElement.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vector.R \name{firstElement} \alias{firstElement} \title{First Element} \usage{ firstElement(x) } \arguments{ \item{x}{object} } \value{ first element: x[1] } \description{ Returns the first element using the function head }

3e2c04218020fefdf364e94a0105206f480c3dc9

c871ac402d8b98b7cfba55092fe7b0ae1480e74e

/Anc_char_state/ANS.R

8f24e84cdd0cc66f1950e5912fbc0a7022c04c1f

[]

no_license

sergeitarasov/Course_Helsinki_March2019

6a7849119336ce0d371dd6f63a8b7541ddd26b9d

46b4b03e4841c0271c680b425ffda6d0ac8cf607

refs/heads/master

2023-01-12T01:30:45.583752

2023-01-03T17:42:54

177,663,343

0

null

UTF-8

R

false

2,291

r

ANS.R

library("corHMM") library("phytools") library("plyr") ######### # Simulating data ######### # simulate tree tree<-pbtree(n=200, scale=100) plot(tree) # simulate character history # make Q matrix Q <- matrix( c( -0.03, 0.01, 0.01, 0.01, 0.03, -0.09, 0.03, 0.03, 0.01, 0.01, -0.03, 0.01, 0.03, 0.03, 0.03, -0.09 ), 4,4, byrow = T ) # simulate character history on tree hist <- sim.history(tree, Q, nsim=1) plot(hist) ######### # Inference using ML and corHMM package (Traditional model) ######### # make 2-rate Q matrix for inference Qinf <- matrix( c( 0, 1, 1, 1, 2, 0, 2, 2, 1, 1, 0, 1, 2, 2, 2, 0 ), 4,4, byrow = T ) diag(Qinf) <- NA # Inference using 2 rates taxa <- cbind(hist$tip.label, hist$states) rate2 <- rayDISC(hist, taxa, rate.mat=Qinf, node.states="marginal", model="ARD", root.p="maddfitz") # plot plotRECON(tree, rate2$states,title="2-rate Matrix") # make 1-rate Q matrix for inference Qinf1 <- matrix( c( 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0 ), 4,4, byrow = T ) diag(Qinf1) <- NA # Inference using 1 rate taxa <- cbind(hist$tip.label, hist$states) rate1 <- rayDISC(hist, taxa, rate.mat=Qinf1, node.states="marginal", model="ARD", root.p="maddfitz") # plot plotRECON(tree, rate1$states,title="1-rate Matrix") # Compare AICs rate1 rate2 # make ARD Q matrix for inference QinfARD <- matrix( c( 0, 1, 2, 3, 4, 0, 5, 6, 7, 8, 0, 9, 10, 11, 12, 0 ), 4,4, byrow = T ) diag(QinfARD) <- NA taxa <- cbind(hist$tip.label, hist$states) rate1 <- rayDISC(hist, taxa, rate.mat=QinfARD, node.states="marginal", model="ARD", root.p="maddfitz") ######### # Inference using ML and corHMM package (HMM) ######### char.recode<-mapvalues(hist$states, from = c(1, 2, 3,4), to = c('1&2', '1&2', '3&4','3&4') ) # Inference using 2 rate HMM taxa.hmm <- cbind(hist$tip.label, char.recode) rate2.hmm <- rayDISC(hist, taxa.hmm, rate.mat=Qinf, node.states="marginal", model="ARD", root.p="maddfitz") # Inference using 1 rate HMM rate1.hmm <- rayDISC(hist, taxa.hmm, rate.mat=Qinf1, node.states="marginal", model="ARD", root.p="maddfitz")

c0c3dec3eca78a641c79be5b7ab946cd7c4b2b2a

ccbdf07cc6a176b3337dd3e650cb8904f7ab80bc

/global.R

83011b3139efffd3ca1e381f417fb142803b29a0

[ "MIT" ]

permissive

keegangeorge/shiny_app_dev

29f32a045d26376ea40e75c5318a4aa32eab9cb5

db7408070e15b66825d8ec32bd379753e3981038

refs/heads/main

2023-06-26T04:01:07.554448

2021-07-28T23:58:54

390,544,542

0

MIT

2021-07-29T00:07:07

null

UTF-8

R

false

703

r

global.R

# Load the libraries and import function files here. # Global.R is run one time at app initiallization. library(bs4Dash) library(shiny) library(fresh) # other imports packages1<-c("oligo","GEOquery","affy","limma","arrayQualityMetrics","sva","Biobase","affyPLM", "simpleaffy")#, "AffyBatch") for(x in packages1){ library(x,character.only=TRUE) } packages2<-c("ggplot2", "pheatmap") for(y in packages2){ library(y,character.only=TRUE) } packages12<-c("stringr","R.utils", "EnhancedVolcano", "shinyWidgets", "bs4Dash", "shiny", "fresh") for(y in packages12){ library(y,character.only=TRUE) } #Import Functions from External R scripts. source("functions/testFunction.R", local = TRUE)$value

233712715d5bad4b256911efa980f27e7795beab

c5cdb6ddfad911d21273feddd05b082c75c29f27

/R/sim.index.R

67e9e2ade3e3e2b34eda6864f415f4562150a1be

[]

no_license

cran/ONETr

6d313681e1a124c521fdb737e469f2af8867f310

d5f427e9f697bab27e634b120fb140de14fef9f2

refs/heads/master

2020-05-18T06:26:09.646327

2015-08-23T00:00:00

21,559,611

0

1

null

UTF-8

R

false

1,154

r

sim.index.R

sim.index <- function(list1, list2, FUN, index=c("sd","ji","all")){ job1 <- FUN(list1) job2 <- FUN(list2) if(".attrs.id" %in% names(job1) & ".attrs.id" %in% names(job2)){ if(index == "sd"){ sd <- 2*length(intersect(job1$.attrs.id,job2$.attrs.id))/(length(unique(job1$.attrs.id))+length(unique(job2$.attrs.id))) # Sorensen-Dice index list("Sorensen-Dice index"=round(sd,2)) } else if(index == "ji"){ ji <- length(intersect(job1$.attrs.id,job2$.attrs.id))/length(union(job1$.attrs.id,job2$.attrs.id)) # Jaccard index list("Jaccard index"=round(ji,2)) } else{ sd <- 2*length(intersect(job1$.attrs.id,job2$.attrs.id))/(length(unique(job1$.attrs.id))+length(unique(job2$.attrs.id))) # Sorensen-Dice index ji <- length(intersect(job1$.attrs.id,job2$.attrs.id))/length(union(job1$.attrs.id,job2$.attrs.id)) # Jaccard index print(list("Sorensen-Dice index"=round(sd,2),"Jaccard index"=round(ji,2))) # print both in a list } } else{ message("This function cannot yet handle this job data type. Please try another.") } }

c44293d5ccef7774b28b4510afd5e12ecd16ac5c

63ef3937256d030123bcb2aac6c08d73c7a372bb

/tests/testthat/test-mcmc-nnls.R

e6d9dc7d8762e02e8b5e1633381e6143601e4f27

[]

no_license

shackett/simmer

740c3c8caa2b6c0c6244ed9e073b6f5eab322183

7138a45fb9653ce50d8556676a9fd1cc00bf0395

refs/heads/master

2020-03-30T15:56:51.864765

2016-09-16T15:38:04

58,556,604

7

2

null

UTF-8

R

false

1,443

r

test-mcmc-nnls.R

test_that("fit_reaction_equations applies the MCMC-NNLS algorithm to one reaction equation", { options(stringsAsFactors = F) rMech_summary_table <- suppressMessages(readr::read_tsv(system.file("extdata", "reactionEqn_fitting", "rMech_summary_table.tsv", package = "simmer"))) reactionForms <- suppressMessages(readr::read_rds(system.file("extdata", "reactionEqn_fitting", "reactionForms.Rds", package = "simmer"))) expect_equal(colnames(rMech_summary_table), c("reaction", "rMech", "modelType")) expect_equal(rMech_summary_table$rMech, names(reactionForms)) markov_pars <- list() markov_pars$sample_freq <- 20 #what fraction of markov samples are reported (this thinning of samples decreases sample autocorrelation) markov_pars$n_samples <- 200 #how many total markov samples are desired markov_pars$burn_in <- 0 #how many initial samples should be skipped fitted_reaction_equations <- fit_reaction_equations(reactionForms[[1]]) expect_equal(names(fitted_reaction_equations), names(reactionForms[1])) expect_equal(names(fitted_reaction_equations[[1]]), c("kineticPars", "all_species", "kineticParPrior", "markovChain", "logLikelihood", "occupancyEq", "metabolites", "enzymes", "flux", "specSD", "specCorr")) expect_equal(nrow(fitted_reaction_equations[[1]]$markovChain), markov_pars$n_samples) })

6a7e42b1f364febd39937c369f87b078cc440d30

dda9309eb6ab192ad4e955a46e5620791a9f9d05

/inst/doc/FishPhyloMaker_vignette.R

b8aa09c989405ffef3e813bfaaec577e8c9109aa

[ "MIT" ]

permissive

cran/FishPhyloMaker

a9fd0aa90f4c9433b80508fbead9885418b92ba6

e2e74e2bdaa9be715907b4f148cbdb8fad1c8686

refs/heads/master

2023-08-21T19:20:02.438998

2021-09-15T07:00:10

406,826,654

0

null

UTF-8

R

false

2,734

r

FishPhyloMaker_vignette.R

## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----install_pkg, echo=TRUE, eval=FALSE--------------------------------------- # devtools::install_github("GabrielNakamura/FishPhyloMaker", ref = "main") ## ----read_data, eval=FALSE, echo=TRUE----------------------------------------- # library(FishPhyloMaker) # data(neotropical_comm) # data_comm <- neotropical_comm[, -c(1, 2)] # removing Latitude and Longitude ## ----taxon_data, echo=TRUE, eval=FALSE---------------------------------------- # taxon_data <- FishTaxaMaker(data_comm, allow.manual.insert = TRUE) # Characidae # Characiformes # Characidae # Characiformes # Characidae # Characiformes # Loricariidae # Siluriformes # Characidae # Characiformes # Cichlidae # Cichliformes # Crenuchidae # Characiformes # Gymnotidae # Gymnotiformes # Loricariidae # Siluriformes # Loricariidae # Siluriformes # Loricariidae # Siluriformes # Loricariidae # Siluriformes # Heptapteridae # Siluriformes # Characidae # Characiformes # Loricariidae # Siluriformes # Characidae # Characiformes ## ----phylo_make, eval=FALSE, echo=TRUE---------------------------------------- # phylo_fish_streams <- FishPhyloMaker(data = taxon_data, # return.insertions = TRUE, # insert.base.node = TRUE, # progress.bar = TRUE) ## ----plot_phylo, eval=FALSE, echo=TRUE---------------------------------------- # library(ggtree) # tree.PR<- phylo_fish_streams$Phylogeny # # tree.PR <- ape::makeNodeLabel(tree.PR) # phylo <- tree.PR # # rm.famNames <- which(table(taxon_dataPR$f) == 1) # monotipic families # names.fam <- setdiff(unique(taxon_dataPR$f), names(rm.famNames)) # removing monotipic families from the names # # for (i in 1:length(names.fam)) { # set <- subset(taxon_dataPR, f == names.fam[i]) # phylo <- ape::makeNodeLabel(phylo, "u", nodeList = list(Fam_name = set$s)) # # phylo$node.label[which(phylo$node.label == # "Fam_name") ] <- paste(set$f[1]) # } # # pos.node <- unlist(lapply(names.fam, function(x){ # which(phylo$node.label == x) + length(phylo$tip.label) # })) # # df.phylo <- data.frame(Fam.names = names.fam, # node.number = pos.node) # # plot.base <- ggtree(phylo) + theme_tree2() # plot1 <- revts(plot.base) + scale_x_continuous(labels=abs) # # # PR.PG <- plot1 + geom_hilight(data = df.phylo, aes(node = node.number, fill = Fam.names), # alpha = .6) + # scale_fill_viridis(discrete = T, name = "Family names")

b5bac5850f03ddab200e990ba1d64b3e374c5272

abb02adb369d8edf6499d048d903db1f30a3cedc

/run_analysis.R

d04428b93e6686f881086620120582961e665683

[]

no_license

WajihaSaid/run_analysis

63bcd948e1b56761aea65b88bfe9f63cf8f653b4

583df5ccbf442409172efcefcd9ea680851d3fd7

refs/heads/master

2021-01-19T07:53:40.044636

2014-08-24T19:10:13

null

0

null

UTF-8

R

false

2,119

r

run_analysis.R

#create folder for storing, reading and writing data if(!file.exists("./run_analysis")) {dir.create("./run_analysis")} #get wd to restore to at the end of the procedure wd <- getwd() #set wd to the directory where data is saved setwd("./run_analysis") #download and unzip data. Store paths to files in filehandle download.file("https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip","./temp",method="curl",quiet=TRUE) filehandle <- unzip("./temp", exdir="./") #load relevant files from filehandle features <- read.table(filehandle[2],sep=" ", colClasses="character") activity_labels <-read.table(filehandle[1],col.names=c("index","labels")) testsubject <- read.table(filehandle[14],col.names='subject_name') testx <- read.table(filehandle[15]) testy <- read.table(filehandle[16],col.names='activity_name') trainsubject <- read.table(filehandle[26],col.names='subject_name') trainx <- read.table(filehandle[27]) trainy <- read.table(filehandle[28],col.names='activity_name') #renaming column names on test x and test y data with feature names, this is Step 4 library(data.table) colnames(testx) <- features$V2 colnames(trainx) <- features$V2 #Consolidating & merging data, writing it out to file. This is Step 1 totaltest <- cbind(testsubject, testy, testx) totaltrain <- cbind(trainsubject, trainy, trainx) totaldata <- rbind(totaltest,totaltrain) write.table(totaldata,file="totaldata.txt",sep=" ",row.names=FALSE) #Extracting only the measures with mean and stdev, this is Step 2 extracteddata <-totaldata[,c(grepl("subject|activity|mean\$\$|std\$\$",colnames(totaldata),perl=TRUE))] #Replacing activity numbers with descriptive activity names, Step 3 extracteddata$activity_name<-factor(extracteddata$activity_name,activity_labels$index,activity_labels$labels) #Creating tidy data set and saving to file, Step 5 tidydata <- aggregate(extracteddata[,3:ncol(extracteddata)],by=list(subject_name=extracteddata$subject_name,activity_name=extracteddata$activity_name),mean) write.table(tidydata,file="tidydata.txt",sep=" ",row.names=FALSE) #reset working directory setwd(wd)

65fd9229e446c897625a190493865bd285777a97

3646e6edc7b38488a562a99d34c4f764c7163b23

/WTCTools/man/leaderboard15.Rd

eb125144652fe62e3d5fd4cf090b298b8ebdbdeb

[]

no_license

CSJCampbell/WTCTools

2d98f0659ffc79edaa10a0a949799e3a577a0d64

e97b1af05ad2003a5a7811f7f7acc72dfddb7773

refs/heads/master

2021-06-02T17:17:12.413625

2017-09-21T01:18:12

41,179,428

1

0

null

UTF-8

R

false

true

705

rd

leaderboard15.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/scoreSequence.R \docType{data} \name{leaderboard15} \alias{leaderboard15} \title{World Team Championships 2015 Team Leaderboard} \format{Data frame with columns:\itemize{ \item Rank integer noting team position \item Team character name of team \item Rounds.won integer number of rounds won \item Team.SoS integer strength of schedule \item Games.won integer number of games won \item Total.CP integer control points scored \item Total.VP integer army points scored }} \description{ Team results for WTC 2015. } \examples{ hist(leaderboard15$Total.CP, col = "#99334455") } \keyword{datasets}

3bf7803e0aac09f4053ed8f0b95ded01f82944b1

93def2f4add7bbb43889435eb42fb6dda5eec619

/hw2/hw2_Adam.R

bcccdf48b1d11a0bfd96ccfd922979e0954eaad2

[]

no_license

mikeasilva/data-621-group-projects

9e13c9df18956e05d6c47427ae8073204ba47cae

0a0c33e59322e382de3d0191703b157c48d4f072

refs/heads/master

2020-07-23T19:47:43.130227

2019-12-19T17:48:26

207,687,536

1

6

null

2019-12-19T17:48:28

2019-09-11T00:24:24

HTML

UTF-8

R

false

1,988

r

hw2_Adam.R

################################################## # Functions to compute classification measures. ################################################## # I know you have these already, but I thought # it may be good practice to do my own. # accuracy <- function(df, pred, act){ sum(pred == act)/nrow(df) } errors <- function(df, pred,act){ sum(pred != act)/nrow(df) } precision <- function(df, pred, act){ sum(pred == 1 & act == 1) / (sum(pred == 1 & act == 1) + sum(pred == 1 & act == 0)) } sensitivity <- function(df, pred, act){ sum(pred == 1 & act == 1) / (sum(act == 1)) } specificity <- function(df, pred, act){ sum(pred == 0 & act == 0) / (sum(pred == 0 & act == 0) + sum(pred == 1 & act == 0)) } false_positive <- function(df, pred, act){ sum(pred == 1 & act == 0) / (sum(act == 0)) } f1 <- function(df, pred, act){ p <- precision(df, pred, act) s <- sensitivity(df, pred, act) 2 * (p * s)/ (p + s) } ######################## # ROC Curve generation ######################## # I think this will also work with your functions, Mike. # ROC <- function(df, prob, act){ require(dplyr) outcome <- data.frame(matrix(ncol = 4, nrow = 0)) colnames(outcome) <- c("prob","TPR","FPR") for (thresh in seq(0,1,0.01)){ outcome <- rbind(outcome, data.frame( prob=thresh, TPR = sensitivity(df,ifelse(prob >= thresh,1,0),act), FPR = false_positive(df,ifelse(prob >= thresh,1,0),act), area = 0 ) ) } # Get AUC outcome$area <- (dplyr::lag(outcome$FPR) - outcome$FPR) * outcome$TPR plot.new() plot(x=outcome$FPR, y=outcome$TPR, type="l",main="ROC Curve", xlab="False Positive Rate (FPR)", ylab="True Positive Rate (TPR)") abline(a=0,b=1,lty=2) text(0.6,0.1,paste("AUC = ",round(sum(outcome$area, na.rm = T),3))) } ROC(df,df$scored.probability,df$class)

2cf916aeec968f4a498df2aced90c6645ca1253c

adc2fd58b4326ecfaf0e743698246da8846c981d

/Data_Summarization/lab/Data_Summarization_Lab.R

fb6c892ef2e77b5b2ef89dca9ca9fd60031beb55

[ "MIT" ]

permissive

andrewejaffe/summerR_2016

c26d8e709c26100e1e40df3d9c1ad213fa34d94a

8f47a6735f039499eba66e30e0b40c59a36c1c6b

refs/heads/gh-pages

2020-04-06T03:36:03.439029

2016-06-24T19:25:02

60,783,896

0

4

null

2016-06-14T02:00:58

2016-06-09T15:08:11

HTML

UTF-8

R

false

1,199

r

Data_Summarization_Lab.R

#################### # Data Summarization - Lab # 6/15/2016 #################### # Bike Lanes Dataset: BikeBaltimore is the Department of Transportation's bike program. # https://data.baltimorecity.gov/Transportation/Bike-Lanes/xzfj-gyms # Download as a CSV (like the Monuments dataset) in your current working directory # Note its also available at: # http://www.aejaffe.com/summerR_2016/data/Bike_Lanes.csv # 1. How many bike "lanes" are currently in Baltimore? # You can assume each observation/row is a different bike "lane" # 2. How many (a) feet and (b) miles of bike "lanes" are currently in Baltimore? # 3. How many types of bike lanes are there? Which type has # (a) the most number of and (b) longest average bike lane length? # 4. How many different projects do the "bike" lanes fall into? # Which project category has the longest average bike lane? # 5. What was the average bike lane length per year that they were installed? # 6. (a) Numerically [hint: `quantile()`] and # (b) graphically [hint: `hist()` or `plot(density())`] # describe the distribution of bike "lane" lengths. # 7. Then describe as above, after stratifying by i) type then ii) number of lanes

b612042dd5348ffb5dda8479b9610d11c87734c5

1ef6c2a474a51bb9cf01a8c523bae221e0c6665b

/Codes/1_GetData.R

a5afb9c9875396cc9fb6f7eda137a58487221a7c

[]

no_license

acszczep/f-curve

0b72347f71fd19cea792417cd557b95925a478d4

70d72ef26dd69b627f64b9e0049b520128eb45c2

refs/heads/master

2022-06-17T22:02:07.532757

2020-05-16T10:16:28

null

0

null

UTF-8

R

false

12,960

r

1_GetData.R

#------------------------------------------------------------------------------------- # A daily fever curve for the Swiss economy #------------------------------------------------------------------------------------- # Feel free to copy, adapt, and use this code for your own purposes at # your own risk. # # Please cite: # Burri, Marc and Daniel Kaufmann (2020): "A daily fever curve for the # Swiss economy", IRENE Working Paper No., University of Neuchâtel, # https://github.com/dankaufmann/f-curve # #------------------------------------------------------------------------------------- # V 1.0 #------------------------------------------------------------------------------------- # Packages and settings #rm(list = ls()) source("AllPackages.R") endDate <- Sys.Date() updateNews <- FALSE # Choose whether you want to update news (takes up to 20 min) #------------------------------------------------------------------------------------- # Download the data #------------------------------------------------------------------------------------- # Macro data (only GDP, rest loaded in separate file) and trendecon download.file(url = "https://www.seco.admin.ch/dam/seco/en/dokumente/Wirtschaft/Wirtschaftslage/VIP%20Quartalssch%C3%A4tzungen/qna_p_csa.xls.download.xls/qna_p_csa.xls", destfile = "../Data/PIBSuisse.xls", mode="wb") download.file(url = "https://raw.githubusercontent.com/trendecon/data/master/daily/trendecon_sa.csv", destfile = "../Data/TrendEcon.csv", mode="wb") # Get news indicators if (updateNews) { # Scrape News from Web and save in all.RData file # Note: # - can take up to 20 min. # - Depends on Python (including working selenium) and cURL updateNewsIndicator() } load(file="../Data/News/all.RData") load(file="../Data/News/nzz.RData") load(file="../Data/News/fuw.RData") load(file="../Data/News/ta.RData") News.CH <- df_all_ch %>% select("time", "mean") %>% ts_xts() %>% ts_span(start = "2000-01-01", end = endDate) News.FOR <- df_all_int %>% select("time", "mean") %>% ts_xts() %>% ts_span(start = "2000-01-01", end = endDate) News.NZZ.CH <- df_nzz_ch %>% select("time", "mean") %>% ts_xts() %>% ts_span(start = "2000-01-01", end = endDate) News.NZZ.FOR <- df_nzz_int %>% select("time", "mean") %>% ts_xts() %>% ts_span(start = "2000-01-01", end = endDate) News.FUW.CH <- df_fuw_ch %>% select("time", "mean") %>% ts_xts() %>% ts_span(start = "2000-01-01", end = endDate) News.FUW.FOR <- df_fuw_int %>% select("time", "mean") %>% ts_xts() %>% ts_span(start = "2000-01-01", end = endDate) News.TA.CH <- df_ta_ch %>% select("time", "mean") %>% ts_xts() %>% ts_span(start = "2000-01-01", end = endDate) News.TA.FOR <- df_ta_int %>% select("time", "mean") %>% ts_xts() %>% ts_span(start = "2000-01-01", end = endDate) GDP <- read.xlsx("../Data/PIBSuisse.xls", sheetName = "real_q", as.data.frame = TRUE, startRow = 11) GDP <- (xts(GDP[,3], order.by = as.Date(paste(GDP[,1], GDP[,2]*3-2, "01", sep = "-")))) GDPDefl <- read.xlsx("../Data/PIBSuisse.xls", sheetName = "defl_q", as.data.frame = TRUE, startRow = 11) GDPDefl <- (xts(GDPDefl[,3], order.by = as.Date(paste(GDPDefl[,1], GDPDefl[,2]*3-2, "01", sep = "-")))) NGDP <- GDP*GDPDefl Tecon <- read.csv("../Data/TrendEcon.csv") Tecon <- xts(Tecon[,2], order.by = as.Date(Tecon[,1])) # Financial market variables download.file(url = "https://www.six-group.com/exchanges/downloads/indexdata/hsb_maturity_gov_y.csv", destfile = "../Data/ObligationsConf.csv", mode="wb") download.file(url = "https://www.six-group.com/exchanges/downloads/indexdata/hsb_maturity_dom_non_gov_rating_sbi_y.csv", destfile = "../Data/ObligationsEnt.csv", mode="wb") download.file(url = "https://www.six-group.com/exchanges/downloads/indexdata/h_vsmi_30.csv", destfile = "../Data/VIX.csv", mode="wb") download.file(url = "https://www.six-group.com/exchanges/downloads/indexdata/hsb_foreign_gov_y.csv", destfile = "../Data/ForGov.csv", mode="wb") download.file(url = "https://www.six-group.com/exchanges/downloads/indexdata/hsb_foreign_cor_y.csv", destfile = "../Data/ForCorp.csv", mode="wb") download.file(url = "https://www.six-group.com/exchanges/downloads/indexdata/hsb_maturity_for_rating_sbi_y.csv", destfile = "../Data/ForShort.csv", mode="wb") download.file(url = "https://www.bundesbank.de/statistic-rmi/StatisticDownload?tsId=BBK01.WT1010&its_csvFormat=de&its_fileFormat=csv&mode=its", destfile = "../Data/GermanBondYield.csv", mode="wb") download.file(url = "http://sdw.ecb.europa.eu/quickviewexport.do;jsessionid=62044D6532AB70D16C184E5A8FFADEEC?SERIES_KEY=165.YC.B.U2.EUR.4F.G_N_A.SV_C_YM.SR_1Y&type=csv", destfile = "../Data/EuroShortRate.csv", mode="wb") # German government bond yields Gov10.DEU <- read.csv("../Data/GermanBondYield.csv", sep = ";", skip = 5, na.strings = "Kein Wert vorhanden", stringsAsFactors = FALSE, ) Gov10.DEU <- Gov10.DEU[,1:2] Gov10.DEU[,2] <- as.numeric(gsub(",", ".", gsub("\\.", "", Gov10.DEU[,2]))) Gov10.DEU <- Gov10.DEU[!is.na(Gov10.DEU[,2]),] Gov10.DEU <- xts(Gov10.DEU[,2], order.by = as.Date(Gov10.DEU[,1])) Gov1.EUR <- read.csv("../Data/EuroShortRate.csv", sep = ",", skip = 4, stringsAsFactors = FALSE, ) Gov1.EUR <- xts(Gov1.EUR[,2], order.by = as.Date(Gov1.EUR[,1])) LIB1.EUR <- ts_ts(ts_fred('EUR12MD156N')) # Volatility CH VIX.CH <- read.csv("../Data/VIX.csv", sep = ";", skip = 1) VIX.CH <- xts(VIX.CH[,3], order.by = dmy(VIX.CH[,1])) # Bond yields history SNB BondHistory <- read.xlsx("../Data/BondsHistory/SNBBondHistory.xlsx", startRow = 17, sheetName = "Report") Gov1.CH.h <- xts(BondHistory$EID1, order.by = as.Date(BondHistory$Date)) Gov2.CH.h <- xts(BondHistory$EID2, order.by = as.Date(BondHistory$Date)) Gov8.CH.h <- xts(BondHistory$EID8, order.by = as.Date(BondHistory$Date)) Gov9.CH.h <- xts(BondHistory$EID8, order.by = as.Date(BondHistory$Date)) Gov10.CH.h <- xts(BondHistory$EID10, order.by = as.Date(BondHistory$Date)) Bank8.CH.h <- xts(BondHistory$BANK8, order.by = as.Date(BondHistory$Date)) Ind8.CH.h <- xts(BondHistory$IND8, order.by = as.Date(BondHistory$Date)) Gov10.DEU.h <- xts(BondHistory$DEU10, order.by = as.Date(BondHistory$Date)) ForA8.h <- xts(BondHistory$FORA, order.by = as.Date(BondHistory$Date)) ForAA8.h <- xts(BondHistory$FORAA, order.by = as.Date(BondHistory$Date)) ForAAA8.h <- xts(BondHistory$FORAAA, order.by = as.Date(BondHistory$Date)) # Bonds raw data SIX Gov <- read.csv("../Data/ObligationsConf.csv", sep = ";", skip = 4) GovLab <- read.csv("../Data/ObligationsConf.csv", sep = ";", nrows = 3) NonGov <- read.csv("../Data/ObligationsEnt.csv", sep = ";", skip = 4) NonGovLab <- read.csv("../Data/ObligationsEnt.csv", sep = ";", nrows = 3) ForGov <- read.csv("../Data/ForGov.csv", sep = ";", skip = 4) ForGovLab <- read.csv("../Data/ForGov.csv", sep = ";", nrows = 3) ForCorp <- read.csv("../Data/ForCorp.csv", sep = ";", skip = 4) ForCorpLab <- read.csv("../Data/ForCorp.csv", sep = ";", nrows = 3) ForShort <- read.csv("../Data/ForShort.csv", sep = ";", skip = 4) ForShortLab <- read.csv("../Data/ForShort.csv", sep = ";", nrows = 3) # More recent data from SIX Gov2.CH <- xts(Gov[,GovLab[2,] == "SBI Dom Gov 1-3 Y"], order.by = dmy(Gov[,1])) Gov8.CH <- xts(Gov[,GovLab[2,] == "SBI Dom Gov 7-10 Y"], order.by = dmy(Gov[,1])) AAA_BBB2.CH <- xts(NonGov[,NonGovLab[2,] == "SBI Dom Non-Gov AAA-BBB 1-3 Y"], order.by = dmy(NonGov[,1])) AAA_BBB8.CH <- xts(NonGov[,NonGovLab[2,] == "SBI Dom Non-Gov AAA-BBB 7-10 Y"], order.by = dmy(NonGov[,1])) AAA_AA2.CH <- xts(NonGov[,NonGovLab[2,] == "SBI Dom Non-Gov AAA-AA 1-3 Y"], order.by = dmy(NonGov[,1])) AAA_AA8.CH <- xts(NonGov[,NonGovLab[2,] == "SBI Dom Non-Gov AAA-AA 7-10 Y"], order.by = dmy(NonGov[,1])) AAA_A8.CH <- xts(NonGov[,NonGovLab[2,] == "SBI Dom Non-Gov AAA-AA 7-10 Y"], order.by = dmy(NonGov[,1])) AAA_A2.CH <- xts(NonGov[,NonGovLab[2,] == "SBI Dom Non-Gov AAA-A 1-3 Y"], order.by = dmy(NonGov[,1])) AAA_A10.CH <- xts(NonGov[,NonGovLab[2,] == "SBI Dom Non-Gov AAA-A 7-10 Y"], order.by = dmy(NonGov[,1])) ForAAA_BBB <- xts(ForCorp[,ForCorpLab[2,] == "SBI For Corp AAA-BBB Y"], order.by = dmy(ForCorp[,1])) ForAAA_A <- xts(ForCorp[,ForCorpLab[2,] == "SBI For Corp AAA-A Y"], order.by = dmy(ForCorp[,1])) ForAAA_AA <- xts(ForCorp[,ForCorpLab[2,] == "SBI For Corp AAA-AA Y"], order.by = dmy(ForCorp[,1])) ForAAA_BBB2 <- xts(ForShort[,ForShortLab[2,] == "SBI For AAA-BBB 1-3 Y"], order.by = dmy(ForShort[,1])) # Link 2Y history with 1-3 year current data Gov1.EUR.l <- ts_bind(ts_span(LIB1.EUR, ts_summary(LIB1.EUR)$start, ts_summary(Gov1.EUR)$start), Gov1.EUR) p <- ts_ggplot( `Linked data` = Gov1.EUR.l, `ECB data` = Gov1.EUR, `LIBOR data` = LIB1.EUR, title = "Short-term euro bond yields" ) p <- ggLayout(p) p ggsave(filename = "../Results/LinkedData/Short.EUR.pdf", width = figwidth, height = figheight) # Link 2Y history with 1-3 year current data Gov2.CH.l <- ts_bind(ts_span(Gov2.CH.h, ts_summary(Gov2.CH.h)$start, ts_summary(Gov2.CH)$start), Gov2.CH) p <- ts_ggplot( `Linked data` = Gov2.CH.l, `SIX Data (1-3Y)` = Gov2.CH, `SNB Data (2Y)` = Gov2.CH.h, title = "Short-term confederation bond yields" ) p <- ggLayout(p) ggsave(filename = "../Results/LinkedData/GovShort.CH.pdf", width = figwidth, height = figheight) # Link 8Y history with 7-10 year current data Gov8.CH.l <- ts_bind(ts_span(Gov8.CH.h, ts_summary(Gov8.CH.h)$start, ts_summary(Gov8.CH)$start), Gov8.CH) p <- ts_ggplot( `Linked data` = Gov8.CH.l, `SIX Data (7-10Y)` = Gov8.CH, `SNB Data (8Y)` = Gov8.CH.h, title = "Long-term confederation bond yields" ) p <- ggLayout(p) ggsave(filename = "../Results/LinkedData/GovLong.CH.pdf", width = figwidth, height = figheight) # Link 8Y history banks with average between AA-AAA and A-AAA corporate bonds AAA8.CH.l <- ts_bind(ts_span(Bank8.CH.h, ts_summary(Bank8.CH.h)$start, ts_summary(AAA_AA8.CH)$start), (AAA_AA8.CH+AAA_A8.CH)/2) p <- ts_ggplot( `Linked data` = AAA8.CH.l, `SIX Data (incl. A 7-10Y)` = AAA_A8.CH, `SIX Data (incl. AA 7-10Y)` = AAA_AA8.CH, `SNB Data Banks (8Y)` = Bank8.CH.h, title = "Long-term corporate bond yields" ) p <- ggLayout(p) ggsave(filename = "../Results/LinkedData/CorpLong1.CH.pdf", width = figwidth, height = figheight) # Link 8Y history manufacturing with adjusted AAA-BBB yield index (use same mean and volatility Ind8.CH.h.adj <- (Ind8.CH.h-mean(ts_span(Ind8.CH.h, ts_summary(AAA_BBB8.CH)$start), na.rm = TRUE)[1])/sqrt(var(ts_span(Ind8.CH.h, ts_summary(AAA_BBB8.CH)$start), na.rm=TRUE))[1] Ind8.CH.h.adj <- Ind8.CH.h.adj*sqrt(var(AAA_BBB8.CH, na.rm=TRUE))[1] + mean(AAA_BBB8.CH, na.rm = TRUE)[1] BBB8.CH.l <- ts_bind(ts_span(Ind8.CH.h.adj, ts_summary(Ind8.CH.h.adj)$start, ts_summary(AAA_BBB8.CH)$start), AAA_BBB8.CH) p <- ts_ggplot( `Linked data` = BBB8.CH.l, `SIX Data (incl. BBB 7-10Y)` = AAA_BBB8.CH, `SNB Data Manufact (adj. 8Y)` = Ind8.CH.h.adj , title = "Long-term corporate bond yields" ) p <- ggLayout(p) ggsave(filename = "../Results/LinkedData/CorpLong2.CH.pdf", width = figwidth, height = figheight) # Link 8Y bonds of foreign corporations # Take the average from SIX data and link with data of SNB # Not clear that same credit rating and/or maturity ForCorp.l <- ts_bind(ts_span(ForAAA8.h, ts_summary(ForAAA8.h)$start, ts_summary(ForAAA_A)$start), (ForAAA_A+ForAAA_AA+ForAAA_BBB)/3) p <- ts_ggplot( `Linked data` = ForCorp.l, `SIX Data (incl. A 7-10Y)` = ForAAA_A, `SIX Data (incl. AA 7-10Y)` = ForAAA_AA, `SIX Data (incl. BBB 7-10Y)` = ForAAA_BBB, `SNB Data AAA (8Y)` = ForAAA8.h, title = "Long-term foreign corporate bond yields" ) p <- ggLayout(p) ggsave(filename = "../Results/LinkedData/ForCorpLong.CH.pdf", width = figwidth, height = figheight) # Construct final data set IRDIFF.CH <- Gov2.CH.l - Gov1.EUR.l TS.CH <- Gov8.CH.l - Gov2.CH.l RP.CH <- AAA8.CH.l - Gov8.CH.l #RP2.CH <- BBB8.CH.l - Gov8.CH.l RPShort.CH <- AAA_BBB2.CH - Gov2.CH.l RPShort.FOR <- ForAAA_AA - Gov2.CH.l RP.FOR <- ForCorp.l - Gov8.CH.l TS.EUR <- Gov10.DEU - Gov1.EUR.l TS.US <- ts_xts(ts_fred("T10Y2Y")) VIX.US <- ts_xts(ts_fred("VIXCLS")) # Check start and end dates ts_summary(IRDIFF.CH) ts_summary(TS.CH) ts_summary(RP.CH) ts_summary(RPShort.CH) ts_summary(RPShort.FOR) ts_summary(RP.FOR) ts_summary(TS.EUR) ts_summary(TS.US) ts_summary(VIX.US) ts_summary(VIX.CH) # Export the data (only those series that work well!) Indicators <- ts_c(TS.CH, RP.CH, RPShort.CH, VIX.CH, IRDIFF.CH, News.CH, News.FOR, RPShort.FOR, RP.FOR, TS.US, VIX.US, TS.EUR, Tecon, News.NZZ.CH, News.FUW.CH, News.TA.CH, News.NZZ.FOR, News.FUW.FOR, News.TA.FOR) # Save indicators for f-curve save(list = c("GDP", "NGDP", "GDPDefl", "Indicators"), file = "../Data/IndicatorData.RData")

a8feb13b62e8e32bcc548ace2849c6e68061df6c

d97e908015d9fafe3adf3183738a3e9fc89ceef3

/Integrative_analysis/Integrative-FimoMotif_presence_in_Iri_MarkerGene_nearest_dynamicDMAR.r

7680180d0bec6ca32ae1f8aec8e5eab67f6eaa2e

[ "MIT" ]

permissive

yjchen1201/zebrafish_pigment_cell_dev

ca7393fab7c6942c260c39fd9b7c2bc167d71e86

f4c0e3f81619778cf4e13955b23209c108ba9b94

refs/heads/main

2023-04-17T10:06:59.855315

2021-09-08T22:31:54

399,949,907

0

2

MIT

2021-09-07T04:56:48

2021-08-25T20:31:54

R

UTF-8

R

false

10,552

r

Integrative-FimoMotif_presence_in_Iri_MarkerGene_nearest_dynamicDMAR.r

####### USE FIMO TO scan FOR IRI-SPECIFIC TF motifs ########### # Read DMAR list generated from "Integrative-generate_DMAR_master_list_02.r" DMAR <- read.table("All_DMAR_Combined_wINFO.bed",sep = "\t",header =T, stringsAsFactors =F) DMAR_Iri_accessible <- DMAR[DMAR_new$DMRs24vIri >0 | DMAR_new$DARs24vIri >0 | DMAR_new$DMRMelvIri >0 | DMAR_new$DARMelvIri <0,] write.table(DMAR_Iri_accessible[,c(1,2,3,4)], "DMAR_Iri_Accesible_for_FIMO.bed", sep = "\t", quote = F, col.names = F, row.names =F) #DMAR_Mel_accessible <- DMAR_new[DMAR_new$DMRs24vMel >0 | DMAR_new$DARs24vMel >0 | DMAR_new$DMRMelvIri <0 | DMAR_new$DARMelvIri >0,] #write.table(DMAR_Mel_accessible[,c(1,2,3,4)], "DMAR_Mel_Accesible_for_FIMO.bed", sep = "\t", quote = F, col.names = F, row.names =F) # Use UCSC twoBitToFa to convert regions in bed file to fasta system("module load kentUCSC") system("twoBitToFa -bed=DMAR_Iri_Accesible_for_FIMO.bed danRer10.2bit DMAR_Iri_Accesible_for_FIMO.fasta") # Use fimo to scan for Iri TFs system("module meme") system("fimo Iri_TFs_FIMO.meme DMAR_Iri_Accesible_for_FIMO.fasta") #system("mast Iri_TFs_FIMO.meme DMAR_Iri_Accesible_for_FIMO.fasta") #system("mcast Iri_TFs_FIMO.meme DMAR_Iri_Accesible_for_FIMO.fasta") # Use Parse_fimo.py code to parse out motif presence in bash system("module load python3") system("python3 Parse_fimo.py fimo.txt") #fimo.txt is the output from fimo step # Read the output generated using Parsed_fimo.py FIMO <- read.table("fimo_parsed.txt",sep = "\t", header = T, quote = "", stringsAsFactors =F) # merge DMAR_Iri_accessible table with FIMO table by chrompos column DMAR_Iri_FIMO <- merge(DMAR_Iri_accessible[,c(1:32)],FIMO, by = "chrompos", all.x = T) DMAR_Iri_FIMO[is.na(DMAR_Iri_FIMO)] <- 0 # Save Iri accessibleDMAR with FIMO scanned motifs #write.table(DMAR_Iri_FIMO[,c(2:4,1,5:52)], "accessibleDMAR_Iri_wFIMOmotif.txt", sep = "\t", col.names = T, row.names = F, quote =F) write.table(DMAR_Iri_FIMO[,c(2:4,1,5:52)], "accessibleDMAR_Iri_wFIMOmotif_noheader.txt", sep = "\t", col.names = F, row.names = F, quote =F) # Input file "Danio_rerio.GRCz10.85.GENE.PROMOTER.bed" is generated from "Integrative-promoter-centric.r" # import prmoter information prom <- read.table("Danio_rerio.GRCz10.85.GENE.PROMOTER.bed", sep = "\t", stringsAsFactors =F, header = F, quote ="") # extract Iri marker genee promoter info out Iri_MarkerGenes <- prom[prom$gene == "ENSDARG00000003732"|prom$gene =="ENSDARG00000002933"|prom$gene == "ENSDARG00000016706" |prom$gene == "ENSDARG00000077467" | prom$gene == "ENSDARG00000098745" |prom$gene == "ENSDARG00000003732" |prom$gene == "ENSDARG00000059279" |prom$gene == "ENSDARG00000008861" | prom$gene == "ENSDARG00000021032"| prom$gene == "ENSDARG00000024431",] # Read DEG info ## "DEG_p0.01_ENSEMBL_to_Gene_Name.txt" and "DEGs_combined_samples_p0.01_wTFinfo.txt" are from RNA/RNA_02_DEGanalysis.r DEG2_genenames<- read.table("DEG_p0.01_ENSEMBL_to_Gene_Name.txt",header = T, ,quote = "", sep = "\t",stringsAsFactors = F) DEG2 <- read.table("DEGs_combined_samples_p0.01_wTFinfo.txt",header = T, ,quote = "", sep = "\t",stringsAsFactors = F) colnames(DEG2_genenames) <- c("gene","genename") DEG3 <- merge(DEG2,DEG2_genenames, by = "gene", all.x = T) # Add DEG info to Iri Marker gene table Iri_MarkerGenes2 <- merge(Iri_MarkerGenes,DEG3[,c(1:15)],by = "gene", all.x = T) Iri_MarkerGenes2[is.na(Iri_MarkerGenes2)] <- 0 Iri_MarkerGenes2[Iri_MarkerGenes2$gene == "ENSDARG00000021032",]$genename.x <- "foxd3" # Extend promoter region to 50kb on both side Iri_MarkerGenes2$start50kb <- Iri_MarkerGenes2$start-50000 Iri_MarkerGenes2$end50kb <- Iri_MarkerGenes2$end+50000 # Correct the start to 0 if it is <0 Iri_MarkerGenes2[Iri_MarkerGenes2$start50kb < 0,]$start50kb <-0 # Save Iri marker gene promoter extended information table write.table(Iri_MarkerGenes2[,c(2,22,23,1,14)],"Iri_MarkerGenes_promoter_50kb_extended.bed", sep = "\t", col.names = F, row.names = F, quote =F) # Use bedtools to intersect Iri markergene promoter extended regions with Iri accessible DMARs with FIMO motifs system("module load bedtools") system("bedtools intersect -wao -a Iri_MarkerGenes_promoter_50kb_extended.bed -b accessibleDMAR_Iri_wFIMOmotif_noheader.txt > Iri_MarkerGenes_promoter_50kb_extended_wDMAR_FIMO.txt") # Import the intersected file Iri_Marker_50kb_DMAR_FIMO <- read.table("Iri_MarkerGenes_promoter_50kb_extended_wDMAR_FIMOMotif.txt", header = F, stringsAsFactors = F, sep = "\t", quote = "") colnames(Iri_Marker_50kb_DMAR_FIMO) <- c("chr","start50kb","end50kb","gene","genename",colnames(DMAR_Iri_FIMO),"overlap") colnames(Iri_Marker_50kb_DMAR_FIMO)[6:9] <- c("chr","start","end","chrompos") Iri_Marker_50kb_DMAR_FIMO[Iri_Marker_50kb_DMAR_FIMO == "."]<-0 Iri_Marker_50kb_DMAR_FIMO[,c(38:45)] <- sapply(Iri_Marker_50kb_DMAR_FIMO[,c(38:45)],as.numeric) Iri_DEG_50kb_DMAR_FIMO[Iri_DEG_50kb_DMAR_FIMO$genename == "alx1" | Iri_DEG_50kb_DMAR_FIMO$genename == "alx3"| Iri_DEG_50kb_DMAR_FIMO$genename == "alx4a" | Iri_DEG_50kb_DMAR_FIMO$genename == "alx4b" | Iri_DEG_50kb_DMAR_FIMO$genename == "ltk"| Iri_DEG_50kb_DMAR_FIMO$genename == "ednrba" | Iri_DEG_50kb_DMAR_FIMO$genename == "pnp4a",] Iri_Marker_50kb_DMAR_FIMO_ALL <- rbind(Iri_Marker_50kb_DMAR_FIMO,Iri_DEG_50kb_DMAR_FIMO[Iri_DEG_50kb_DMAR_FIMO$genename == "gbx2" | Iri_DEG_50kb_DMAR_FIMO$genename == "alx1" | Iri_DEG_50kb_DMAR_FIMO$genename == "alx3"| Iri_DEG_50kb_DMAR_FIMO$genename == "alx4a" | Iri_DEG_50kb_DMAR_FIMO$genename == "alx4b" | Iri_DEG_50kb_DMAR_FIMO$genename == "ltk"| Iri_DEG_50kb_DMAR_FIMO$genename == "ednrba"| Iri_DEG_50kb_DMAR_FIMO$genename == "pnp4a",]) Iri_Marker_50kb_DMAR_FIMO_ALL <- merge(Iri_Marker_50kb_DMAR_FIMO_ALL[Iri_Marker_50kb_DMAR_FIMO_ALL$genename == "tfap2a" | Iri_Marker_50kb_DMAR_FIMO_ALL$GBX2 > 0 |Iri_Marker_50kb_DMAR_FIMO_ALL$ALX1 > 0 | Iri_Marker_50kb_DMAR_FIMO_ALL$ALX3 > 0 |Iri_Marker_50kb_DMAR_FIMO_ALL$ALX4 > 0 |Iri_Marker_50kb_DMAR_FIMO_ALL$SOX10 > 0 |Iri_Marker_50kb_DMAR_FIMO_ALL$ETS1 > 0 |Iri_Marker_50kb_DMAR_FIMO_ALL$TFEC > 0,],DEG3[,c(0:15)], by = "gene", all.x = T) Iri_Marker_50kb_DMAR_FIMO_ALL[is.na(Iri_Marker_50kb_DMAR_FIMO_ALL)] <- 0 #Iri TF specific# Iri_Marker_50kb_DMAR_FIMO_TF <- Iri_Marker_50kb_DMAR_FIMO_ALL[Iri_Marker_50kb_DMAR_FIMO_ALL$genename != "ltk" & Iri_Marker_50kb_DMAR_FIMO_ALL$genename != "atic" & Iri_Marker_50kb_DMAR_FIMO_ALL$genename != "ednrba",] Iri_Marker_50kb_DMAR_FIMO_TF<- Iri_Marker_50kb_DMAR_FIMO_TF[order(Iri_Marker_50kb_DMAR_FIMO_TF$genename),] Iri_Marker_50kb_DMAR_FIMO_TF <- Iri_Marker_50kb_DMAR_FIMO_TF[Iri_Marker_50kb_DMAR_FIMO_TF$ALX1 >0 |Iri_Marker_50kb_DMAR_FIMO_TF$ALX3 >0 |Iri_Marker_50kb_DMAR_FIMO_TF$ALX4 >0 |Iri_Marker_50kb_DMAR_FIMO_TF$GBX2>0 |Iri_Marker_50kb_DMAR_FIMO_TF$SOX10 >0 |Iri_Marker_50kb_DMAR_FIMO_TF$ETS1 >0 |Iri_Marker_50kb_DMAR_FIMO_TF$TFEC >0,] ######## Generate supFig7c heatmap plot ########### name <- make.unique(Iri_Marker_50kb_DMAR_FIMO_TF[,9]) df.OG2 <- Iri_Marker_50kb_DMAR_FIMO_TF[,c(38:44)] row.names(df.OG2) <- name colnames(df.OG2) <- colnames(Iri_Marker_50kb_DMAR_FIMO_TF[,c(38:45)]) ht2 = Heatmap(df.OG2, column_title = "Iri TFs DMARs\n(50kb from DEG promoter)",name= "Motif\nOccurrence",col = colorRamp2(c(0, 2, 4), c("#FFFEF2","#17C0F2","#0084A1")),row_names_side = "left",row_names_max_width = unit(20, "cm"), cluster_rows = F, cluster_columns = F,show_row_names = T,clustering_method_rows= "ward.D",row_dend_side = c("right"),) ht2 name <- make.unique(Iri_Marker_50kb_DMAR_FIMO_TF[,5]) df.OG2 <- (Iri_Marker_50kb_DMAR_FIMO_TF[,c(35)]) row.names(df.OG2) <- name colnames(df.OG2) <- colnames(Iri_Marker_50kb_DMAR_FIMO_TF[,c(35)]) ht = Heatmap(df.OG2,name= "Annotation",col = c("#FFD4E5","#FFB3BA","#FFDFBA","#FFFFBA","#BAFFC9"), cluster_rows = F, cluster_columns = F) ht name <- make.unique(Iri_Marker_50kb_DMAR_FIMO_TF[,5]) df.OG2 <- Iri_Marker_50kb_DMAR_FIMO_TF[,c(37)] row.names(df.OG2) <- name colnames(df.OG2) <- colnames(Iri_Marker_50kb_DMAR_FIMO_TF[,c(37)]) ht1 = Heatmap(df.OG2,name= "Type",col = c("#D2A8DD","#A6FDFC","#FFE3D6"), cluster_rows = F, cluster_columns = F) ht1 name <- make.unique(Iri_Marker_50kb_DMAR_FIMO_TF[,5]) df.OG2 <- Iri_Marker_50kb_DMAR_FIMO_TF[,c(64)] df.OG2 <- df.OG2*-1 row.names(df.OG2) <- name colnames(df.OG2) <- colnames(Iri_Marker_50kb_DMAR_FIMO_TF[,c(64)]) ht3= Heatmap(df.OG2,name= "Fold Change",col = colorRamp2(c(-5, 0, 5), c("#BAFFC9", "#FFFEF2","#C9C9FF")), cluster_rows = F, cluster_columns = FALSE)+scale_x_discrete(labels=c("s24vIri_log2_change" = "24hpf vs Iri")) ht3 name <- make.unique(Iri_Marker_50kb_DMAR_FIMO_TF[,5]) df.OG2 <- Iri_Marker_50kb_DMAR_FIMO_TF[,c(70,72)] #Motif occurrence row.names(df.OG2) <- name colnames(df.OG2) <- colnames(Iri_Marker_50kb_DMAR_FIMO_TF[,c(70,72)]) ht4= Heatmap(df.OG2,name= "RPKM",col = colorRamp2(c(0,50,200,300,400), c("#BAE1FF","#FFFFBA","#FFB3BA","#E0301E","#800000")), cluster_rows = F, cluster_columns = FALSE) ht4 ht2+ht+ht1+ht3+ht4 ######## Generate supFig8 stacked bar plot ########### #Import table (can be found in "Input_files" folder) data<-read.table(file="supFig8_table_Motif_presence_in_IriDMAR_nearest_IriDEGs.txt", header=TRUE,sep="\t") #Reorder the genes, and motif combination data$genename <- reorder(data$genename, data$Order) data$Motif_presence <- factor(data$Motif_presence, levels=c("ALX only","SOX10 only","TFEC only", "ALX & SOX10","ALX & TFEC","SOX10 & TFEC","ALX & SOX10 & TFEC","None")) #choose color mypalette <-c(brewer.pal(8,"Set3")) #plot p3<-ggplot(data, aes(fill=Motif_presence, y=DMAR_count, x=genename))+ geom_bar(position="stack", stat="identity")+ labs(y= "DM/AR counts", x="Gene name")+ coord_flip()+ theme_bw() + theme( panel.border = element_rect(colour = "black",size = 0.5), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black",size = 0.25), axis.title=element_text(size=12,face = "bold"), axis.text.x = element_text(face = "bold",size = 12, color="black"), axis.text.y = element_text(face = "bold.italic",size = 8, color="black") )+ theme(panel.background = element_rect(fill = 'white',colour = 'black'))+ theme(legend.title=element_text(size=12,face = "bold"),legend.text=element_text(size=10,face = "bold"))+ scale_fill_discrete(name = "Motif presence", breaks=c("ALX only","SOX10 only","TFEC only", "ALX & SOX10","ALX & TFEC","SOX10 & TFEC","ALX & SOX10 & TFEC","None"))+ theme(legend.justification=c(0.95,0.95), legend.position=c(0.95,0.95))+ scale_fill_manual(values = mypalette)+ scale_color_manual(values=mypalette) p3

9b2969c238f5749e4643266450e8f885f45b99b2

a79da6845229920cc17f3b25cb0b667262715b88

/man/analyse_trscr_cnv.Rd

91847e11d64e8c53a9111faafbd9bd96d3d4d4ad

[]

no_license

fchuffar/methmybeachup

58d55ec37d711b7fe3a6a8cdc9022105e7c6c514

f74cb52edac31eaf76efe174a2ae7d1c38a09750

refs/heads/master

2021-01-12T02:53:01.934598

2017-12-05T20:27:25

78,121,581

0

2

null

UTF-8

R

false

true

875

rd

analyse_trscr_cnv.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/analyse_meth.R \name{analyse_trscr_cnv} \alias{analyse_trscr_cnv} \title{A Function Analyses CNV and Transcriptome data} \usage{ analyse_trscr_cnv(gene, trscr_res, cnv_res, meth_idx, ctrl_idx, cols, idxes, PLOT = TRUE) } \arguments{ \item{gene}{A vector describing the gene (line of a bed file).} \item{trscr_res}{A vector of expression values.} \item{cnv_res}{A vector of CN values.} \item{meth_idx}{A vector of sample for which methylom analysis is available.} \item{ctrl_idx}{A vector of sample used to define threshold} \item{cols}{A color vectors indexed by by samples names.} \item{idxes}{A list of index defining sample groups.} \item{PLOT}{A boolean defining if graphical output must be dispayed on the graphical output.} } \description{ This function analyses methylome data. }

6b75fd2e159cc608b5556b1dc887ede4efb8b627

cd32d300faed890d520e27399e93662b47388926

/Projekt1/trzejMuszkieterowie_Projekt1/wykresMiecze.r

85878b565306d33658f7ca8918232583496db2e0

[]

no_license

Siemashko/TechnikiWizualizacjiDanych2018

f01a7b3b513cf07d31261d176e56fb7b99583df6

c6072805f3a9b3c1f0d7db34c2728cb4e20d562f

refs/heads/master

2020-03-30T16:08:56.922922

2019-01-16T11:08:26

151,395,331

0

1

null

2019-01-09T11:39:22

2018-10-03T10:24:06

HTML

UTF-8

R

false

4,216

r

wykresMiecze.r

library(ggplot2) library(ggimage) library(extrafont) # DO STWORZENIA WYKRESU TRZEBA POBRAĆ I WGRAĆ CZCIONKĘ DO CZCIONEK NA KOMPUTERZE windowsFonts(k = windowsFont("starjout")) loadfonts() windowsFonts(sans="starjout") loadfonts(device="win") loadfonts(device="postscript") colors <- c("Blue", "Red", "Green", "Yellow", "Purple", "Violet", "White", "orange", "others") numbers <- c(40,38,27,7,6,4,1,1,4) numbers <- numbers +5 data <- data.frame(colors,numbers) order <- c(1,6,2,9,5,6,8,3,4) data$colors <- factor(data$colors, levels = rev(data$colors)) p <- ggplot(data, aes(data,x=colors,y=numbers)) + geom_segment(aes(x=data$colors, xend = data$colors, y=rep(5, 9), yend = 0), color = "gray", size = 15) + geom_segment(aes(x=data$colors, xend = data$colors, y=data$numbers+0.5, yend = 5), color = c("blue", "red", "green", "yellow", "purple", "violet", "white","orange", "gray"), size = 20, alpha = 0.1) + geom_segment(aes(x=data$colors, xend = data$colors, y=data$numbers, yend = 0), color = c("blue", "red", "green", "yellow", "purple", "violet", "white","orange", "gray"), size = 11, alpha = 0.65) + geom_segment(aes(x=data$colors, xend = data$colors, y=data$numbers-0.5, yend = 0), color = "white", size = 8, alpha = 0.9) + geom_segment(aes(x=data$colors, xend = data$colors, y=data$numbers+0.2, yend = 0), color = c("blue", "red", "green", "yellow", "purple", "violet", "white","orange", "gray"), size = 15, alpha = 0.2) + geom_segment(aes(x=data$colors, xend = data$colors, y=data$numbers+0.6, yend = 5), color = c("blue", "red", "green", "yellow", "purple", "violet", "white","orange", "gray"), size = 13, alpha = 0.1) + geom_segment(aes(x=data$colors, xend = data$colors, y=data$numbers-0.5, yend = 0), color = "white", size = 6, alpha = 0.8) + coord_flip()+xlab("") + ylab("") + scale_y_continuous(breaks = seq(5,45,5),labels = c("0","5","10","15","20","25","30","35","40")) + geom_segment(aes(x=data$colors, xend = data$colors, y=rep(5, 9), yend = 0), color = "gray17", size = 14) + geom_segment(aes(x=data$colors, xend = data$colors, y=rep(5, 9), yend = 1), color = "gray", size = 5) + geom_segment(aes(x=data$colors, xend = data$colors, y=rep(5, 9), yend = 4.5), color = "gray", size = 16) + geom_segment(aes(x=data$colors, xend = data$colors, y=rep(-2, 9), yend = 0), color = "gray", size = 12) + geom_segment(aes(x=data$colors, xend = data$colors, y=rep(-1.5, 9), yend = -1), color = "black", size = 12) + geom_segment(aes(x=data$colors, xend = data$colors, y=data$numbers+0.15, yend = 5), color = c("blue", "red", "green", "yellow", "purple", "violet", "white","orange", "gray"), size = 20, alpha = 0.1) + theme_dark() + theme(panel.grid.minor = element_line(colour="grey20", size=0.5), panel.grid = element_line(colour = "darkgray", size = 0.4), text = element_text(size = rel(5.5), colour = "yellow",family="Star Jedi Outline"), axis.text.y = element_text(colour = "white", family = "Star Jedi Outline", size = rel(4.5)), axis.text.x = element_text(colour = "white",family = "Star Jedi Outline",size = rel(4.5))) img = "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a7/Flag_of_Afghanistan_%281880%E2%80%931901%29.svg/900px-Flag_of_Afghanistan_%281880%E2%80%931901%29.svg.png" p <- ggbackground(p, img) p # panel.background = element_rect(fill = "transparent") # bg of the panel # , plot.background = element_rect(fill = "transparent", color = NA), # ggsave("plot.png",p) # ggsave(p, filename = "tr_tst2.png", bg = "transparent")

3b1956909c340cd068aa0f783cb6e75ca22e6561

91b06d684a3c638d6053241f5599be3f0abcd1e4

/Part3_R기초/sec 6_1 (Vector, Factor).R

6ac60844fe4fd2fe31d04919cb267bbefad6db02

[]

no_license

limwonki0619/R_Data_Analysis

2680b07e68e5f44afd5c167a7338487c4205da62

8efd42061be81996b0f3eb4ee6e2e1bd3ff3d8b0

refs/heads/master

2020-05-29T13:19:54.578617

2019-08-26T11:33:42

189,156,993

2

1

null

UTF-8

R

false

6,999

r

sec 6_1 (Vector, Factor).R

#--------------------------------------------------------------# # 코딩에서 가장 중요한 것 두 가지 # # # # 1. Indentation # # # # 2. Comment # #--------------------------------------------------------------# #--------------------------------------------------------------# #------------- section 6 : R Vector 형태 다루기 ---------------# #--------------------------------------------------------------# # 6.1 Vector : 단일값(스칼라)들이 한 군데 모여 있는 자료구조 ----------------------------------- c(1:5) c(1,2,3,4,"5") # 숫자가 문자로 자동변환 # Vector는 한가지 타입의 데이터 형태만 가질 수 있다. # vector 생성 연산자 : # vector 생성 함수 : c(), seq(), rep c(1:5) c(1,2,3,4,"5") # 숫자가 문자로 자동변환 seq(1,5,2) # 위치 argument seq(by=2, from=2, to=6) # 키워드 argument seq(-3,3, length.out = 61) # length.out : from ~ to 사이에 생성할 vector의 개수 지정 seq(-3,3, 0.1) # 간격 설정 rep(1:3,each=2) # 각각 반복 rep(1:3,time=2) # 전체 반복 # 어떤 컴퓨터 언어에서든지 자료에 access 할 수 있다고 할 때, CRUD를 알고 있어야 한다 # C : creat # R : retrieve # U : update # D : delete # 6.1.1 벡터 내의 원소에 접근하기 - 슬리이싱(Slicing)과 필터링(Filtering) ---------------------- vec1 <- c(1:5); vec1 vec1[3] # 세 번째 요소만 보여준다. R에서는 1에서 시작, 타 언어는 0 vec1[-3] # 세 번째 요소만 빼고 보여준다. vec1[2:4] # 2-4번쨰 요소를 보여준다. length(vec1) # length : 벡터의 원소개수 파악 vec1[1:(length(vec1)-2)] vec1[1:length(vec1)] vec1[-(1:3)] # 1-3번째 요소를 뺴고 보여준다. vec1[2] <- 6; vec1 # vec1의 두 번째 값을 6으로 변경 vec1 <- c(vec1,7); vec1 # 벡터에 새로운 내용을 추가할 수 있다. vec1[7] <- 8; vec1 vec1[9] <- 9; vec1 # 요소사이에 빈 공간은 NA로 처리된다. append(vec1,10,after=3) # vec1의 3 번쨰 값 뒤에 10을 넣고자할 때 사용할 수 있다. append(vec1,(-7:-1),after=7) # 다양한 값 설정이 가능하다. append(vec1,100,after=0) # 0은 가장 앞자리를 의미한다. ex <- c(1,3,7,NA,12) ex < 10 ex[ex %% 2 == 0] # ex에서 2로 나누어 나머지가 0인 수, 즉 짝수를 추출 ex[is.na(ex)] # ex에서 값이 NA인 값 추출 ex[ex %% 2 == 0 & is.na(ex)] # ex에서 짝수이며, NA가 아닌 값 추출 # 6.1.2 벡터로 연산하기 ------------------------------------------------------------------------ c(1,2,3)+c(4,5,6) c(1,2,3)+1 var1 <- c(1,2,3) var2 <- c(4,5,6) var1 + var2 var3 <- c('3','4',5) var1 + var3 # 다른 형태의 변수는 연산을 할 수 없다. union(var1, var3) # 데이터의 형태가 다를 경우 union을 사용해야 한다. # union = 합집합(var1 ∪ var2) var4 <- c(1,2,3,4,5) var1; var4 # 1,2,3,(1),(2) 객체의 길이가 서로다른 경우 순환 원리가 적용됨 var1+var4 # + 1,2,3, 4 , 5 # = 2,4,6, 5 , 7 var2 <- c(3,4,5) var1 - var2 intersect(var1,var2) # intersect = 교집합 (var1 ∩ var2) setdiff(var1,var2) # setdiff = 차집합 (var1 - var2) setdiff(var2,var1) # 6.1.3 벡터의 각 컬럼에 이름 지정하기 -------------------------------------------------------- fruits <- c(10,20,30); fruits names(fruits) <- c('apple','banana','peach'); fruits # names(변수명)는 벡터의 이름 설정 함수다. # 6.1.4 벡터에 연속적인 데이터 할당하기 ------------------------------------------------------- var5 <- seq(1,6); var5 var6 <- seq(2,-2); var6 even <- seq(2,10,by=2); even # seq(from, to, by), by는 증가분 즉, 간격을 의미한다. odd <- seq(1,10,2); odd var8 <- rep(1:3, times=2); var8 # rep(x, times) x값을 전체 반복 var9 <- rep(1:3, each=2); var9 # rep(x, each) x값을 각각 반복 # 6.1.5 벡터의 길이 찾기 ---------------------------------------------------------------------- length(var1) nrow(var1) # nrow는 number of row로 벡터에서는 사용이 불가하고 배열에서 사용 # 6.1.6 벡터에 특정 문자의 포함 여부 찾기 ----------------------------------------------------- 5 %in% even # even 변수에 5의 포함여부 묻기 4 %in% even # 6.1 연습문제 p 323 -------------------------------------------------------------------------- # 1. seq() 함수를 사용해 date4 변수에 2015-01-01 부터 2015-01-31까지 1씩 증가하는 날짜를 입력하는 방법은? date4 <- seq(as.Date("2015-01-01"),as.Date("2015-01-31"),by=1); date4 # 2. vec1의 3번째 요소값을 뺀 vec1의 값을 출력하는 방법은? vec1 <- c('사과','배','감','버섯','고구마'); vec1 vec1[-3] # 3. vec1과 vec2의 합집합, 차집합, 교집합을 구하는 방법은? vec1 <- c('봄','여름','가을','겨울') vec2 <- c('봄','여름','늦여름','초가을') union(vec1,vec2) setdiff(vec1,vec2) intersect(vec1,vec2) rm(list=ls()) # *** Factor : 질적 자료를 저장하는 자료구조 ------------------------------------------------------------------------------ # factor(x = character(), levels, labels = levels, ordered=FALSE) # x : factor로 만들 데이터 # levels : 주어진 데이터 중 factor의 각 값(수준)으로 할 값을 벡터 형태로 지정(여기서 빠진 값은 NA로 처리) # labels : 실제 값 외에 사용할 각 수준의 이름, 예를 들어 데이터에서 1이 남자를 가리킬 경우 labels를 통해 "남" 혹은 "M"으로 변경 # ordered : 순위형 자료 여부(T/F)로, levels에 입력한 순서를 가짐. 순서형 자료에 사용 x <- 1:5 factor(x, levels = c(1:4)) factor(x, levels = c(1:4), labels = c('a','b','c','d')) # 레이블 변경 factor(x, levels = c(1:4), ordered = T) # 순서 자동 지정 (ordered = T 옵션 : 순서형 자료) factor(x, levels = c(4,2,3,1,5)) # 순서 직접 지정 weekends <- c("일","월","화","수","목","금","토") factor(weekends, levels = c("월","화","수","목","금","토","일")) # 순서 변경 factor(1:7, levels = 1:7, labels = c("일","월","화","수","목","금","토"), ordered = T)

01a939bfb765c924f31f02a288a6d6b4005fe94c

2730ee0c85e25f0ec675f6b6060c801ca3f9642a

/tools/rpkg/man/rel_from_df.Rd

1a00698704a632ea9daf2f1cc91f10e5dbf30b56

[ "MIT" ]

permissive

tiagokepe/duckdb

ff725a6771b940498cedd7a32981e6a3df54b9f5

5b2859f520fa5bd130bf5bdd14d7113d1db0e140

refs/heads/master

2022-07-21T23:54:19.442338

2022-07-12T16:21:06

241,940,233

1

MIT

2020-02-20T16:59:50

2020-02-20T16:59:49

null

UTF-8

R

false

true

524

rd

rel_from_df.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/relational.R \name{rel_from_df} \alias{rel_from_df} \title{Convert a R data.frame to a DuckDB relation object} \usage{ rel_from_df(con, df) } \arguments{ \item{con}{a DuckDB DBI connection object} \item{df}{the data.frame} } \value{ the \code{duckdb_relation} object wrapping the data.frame } \description{ Convert a R data.frame to a DuckDB relation object } \examples{ con <- DBI::dbConnect(duckdb::duckdb()) rel <- rel_from_df(con, mtcars) }

7f8f4b58b0f5ca11853810b5346baa04ea1c1719

ade30db1a78b731ef3fe4720597390cddaa9c3c1

/Boston2.R

42a513d7173d4a9c6e2c160259188068ab7843d0

[]

no_license

tiinasip/IODS-project

98ecafc0d8c7e957fc7a16fb925ad57862c149d3

c89019acd51ed3d1564505381dcd1d2d656214c6

refs/heads/master

2020-08-31T01:51:56.500785

2019-12-05T19:10:18

218,550,450

0

null

2019-10-30T14:46:40

2019-10-30T14:46:34

null

UTF-8

R

false

1,073

r

Boston2.R

##reload data, different name is given library(MASS) library(ggplot2) library(tidyr) library(cluster) # load the data data("Boston") summary(Boston) boston_scaled2 <- scale(Boston) summary(boston_scaled2) # class of the boston_scaled object class(boston_scaled2) # change the object to data frame boston_scaled2<-as.data.frame(boston_scaled2) #distance calculation dist_eu<- dist(boston_scaled2) summary(dist_eu) #kmeans clusterin, first try with 4 clusters km4 <-kmeans(boston_scaled2, centers = 4) # plot the Boston dataset with clusters pairs(boston_scaled2, col = km4$cluster) #Find the optimal number for k set.seed(123) # determine the number of clusters k_max <- 10 # calculate the total within sum of squares twcss <- sapply(1:k_max, function(k){kmeans(boston_scaled2, k)$tot.withinss}) # visualize the results qplot(x = 1:k_max, y = twcss, geom = 'line',main="WGSS and groups in K-means solution") #optimal number of groups is 2 km <-kmeans(boston_scaled2, centers = 2) str(km) # plot the Boston dataset with clusters pairs(boston_scaled2, col = km$cluster)

dfddbec54ab99a214d1143bd21000feb5295b74e

5555c8f3f24a2e571ed10d2c59e90f8477b5a1a4

/network_analysis.R

17abbb31cb884fbbd6c96d361c0da34350d12a1f

[]

no_license

c2huc2hu/mcviz

409866e19ad7aaf9b5360783f897b9cfc5be720d

a41c00a45eef36c7df17a84855e34059b31c0b24

refs/heads/master

2020-05-18T13:46:43.205721

2015-02-28T05:01:01

31,170,494

0

null

UTF-8

R

false

381

r

network_analysis.R

library(rPython) library(igraph) python_file <-file("fb.py", "r") python.load(python_file) post <-python.get("tree") net_matrix <-comment_network(post) net <-graph.edgelist(net_matrix) net.sp <- shortest.paths(net) net.hc <- hclust(dist(net.sp)) plot(net.hc, labels=FALSE) net <-network_cluster(net, net.hc, 7) plot(net, vertex.label=NA, vertex.size=3, edge.arrow.size=0.05)

c33d747f2aea1e8b7035f3c67864baaf5e5ef9d4

268c1997d0fb8a1e068288fe921023f548002ad3

/man/summary.Rd

c72fa59d68a90fca3fe6f59ff9bc366d696835f5

[]

no_license

tom-gu/biosurvey

686269b345f87c57ca6f2ef9179ae524badbf23f

4f75a60de2ed42441be72ad258d54c8a3ccf3267

refs/heads/master

2022-12-26T03:19:35.372797

2020-10-03T21:43:46

271,777,062

0

null

2020-06-12T11:00:13

2020-06-12T11:00:12

null

UTF-8

R

false

true

1,067

rd

summary.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Methods.R \name{summary} \alias{summary} \alias{summary.master_matrix} \alias{summary,master_matrix-method} \alias{summary,master_selection-method} \alias{summary,base_PAM-method} \alias{summary,PAM_subset-method} \alias{summary.master_selection} \alias{summary.base_PAM} \alias{summary.PAM_subset} \title{Summary of attributes and results} \usage{ \method{summary}{master_matrix}(object, ...) \method{summary}{master_selection}(object, nrow = 6, ncol = 2, ...) \method{summary}{base_PAM}(object, ...) \method{summary}{PAM_subset}(object, ...) } \arguments{ \item{object}{object of class master_matrix or master_selection.} \item{...}{additional arguments affecting the summary produced. Ignored in these functions.} \item{nrow}{number of rows to be printed for selected_sites in a master_selection object.} \item{ncol}{number of columns to be printed for selected_sites in a master_selection object.} } \value{ A printed summary. } \description{ Summary of attributes and results }

f19eb12f18e643bddd047544edc7cc62ae33f96d

18bd07f679785ef493611720aebe702cfa9a28f5

/chapter2.R

63c134d8ec7f8e61a9de39880c74339afaf39d4a

[]

no_license

Tomajo/StatisticalRethinkingObsolet

0aa82c4fcdf935250447dad379174ca6d2d1f970

2469cd0f922086e3fc33745bb04e14bb41acae91

refs/heads/master

2021-09-07T21:46:56.131398

2018-03-01T13:19:29

null

0

null

UTF-8

R

false

572

r

chapter2.R

##### Pagina 40 Statistics Rethinking #define grid p_grid<-seq(from=0, to=1,length.out = 20) #define prior prior<-rep(1,20) #compute likelihood at each value in grid likelihood<-dbinom(6,size = 9,prob = p_grid) #compute product of likelihood and prior unstd.posterior<-likelihood*prior #standarize the posterior, so it sums to 1 posterior <- unstd.posterior/sum(unstd.posterior) plot(p_grid,posterior, type = "b", xlab = "probability of water", ylab = "posterior probability") mtext("20 points") # prior<-ifelse(p_grid<0.5,0,1) # # prior<-exp(-5*abs(p_grid-0.5))

f6fa1406d1e60c2a4415257856c149a82776ea69

4b290a0098c237b5c8f2737f70a406eaf78c6b2b

/Problems/control_Structures-1.R

287670eb7e767f6c8b191f289bd06adda22affbc

[]

no_license

Philip-Abraham/JohnsHopkins_R_Programming

71849251f51a7f9736f8abd86438c988aa98d1ae

1c1278890530beb378fd0707ef4c4c3c7ba8689d

refs/heads/master

2021-07-04T15:51:44.200953

2017-09-28T02:30:07

105,092,754

0

null

UTF-8

R

false

2,122

r

control_Structures-1.R

#This is a valid if/else structure. x<-100 if(x > 3) { y <- 10 } else { y <- 0 } #So is this one. x<-100 y <- if(x > 3) { 10 } else { 0 } # for loops take an interator variable and assign it successive values from a sequence or vector. For # loops are most commonly used for iterating over the elements of an object (list, vector, etc.) # This loop takes the i variable and in each iteration of the loop gives it values 1, 2, 3, ..., 10, and then # exits. for(i in 1:10) { print(i) } #These four loops have the same behavior. x <- c("a", "b", "c", "d") for(i in 1:4) { print(x[i]) } for(i in seq_along(x)) { print(x[i]) } for(letter in x) { print(letter) } for(i in 1:4) print(x[i]) #if u only have one statement in the "for loop", then curly braces not needed. #for loops can be nested. #Be careful with nesting though. Nesting beyond 2-3 levels is often very difficult to read/understand. x <- matrix(1:6, 2, 3) for(i in seq_len(nrow(x))) { for(j in seq_len(ncol(x))) { print(x[i, j]) } } #While loops begin by testing a condition. If it is true, then they execute the loop body. Once the loop # body is executed, the condition is tested again, and so forth. # While loops can potentially result in infinite loops if not written properly. Use with care! count <- 0 while(count < 10) { print(count) count <- count + 1} #Sometimes there will be more than one condition in the test. #Conditions are always evaluated from left to right. z <- 5 while(z >= 3 && z <= 10) { print(z) coin <- rbinom(1, 1, 0.5) print("coin=") print(coin) if(coin == 1) { ## random walk z <- z + 1 } else { z <- z - 1 } } #Repeat initiates an infinite loop;The only way to exit a repeat loop is to call break. x0 <- 1 tol <- 1e-8 repeat { x1 <- x0/2.22 if(abs(x1 - x0) < tol) { break } else { x0 <- x1 } } # next is used to skip an iteration of a loop x2<-1.5 for(i in 1:100) { if(i <= 20) { ## Skip the first 20 iterations next } ## Do something here x3<-(x2)^.3 }

23fcdaa0dbc2b75bcf107ee0069118a5cd0d2743

2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0

/fuzzedpackages/gRain/R/loadSaveHuginNet.R

2823d9f3d0df51f6c62f887c0124a01d8619372e

[]

no_license

akhikolla/testpackages

62ccaeed866e2194652b65e7360987b3b20df7e7

01259c3543febc89955ea5b79f3a08d3afe57e95

refs/heads/master

2023-02-18T03:50:28.288006

2021-01-18T13:23:32

329,981,898

7

1

null

UTF-8

R

false

12,680

r

loadSaveHuginNet.R

## ## Reading / writing Bayesian networks from / to HUGIN net files ## #' @title Load and save Hugin net files #' #' @description These functions can load a net file saved in the #' 'Hugin format' into R and save a network in R as a file in the #' 'Hugin format'. #' #' @name load-save-hugin #' #' @aliases loadHuginNet saveHuginNet #' @param gin An independence network #' @param file Name of HUGIN net file. Convenient to give the file the #' extension '.net' #' @param description A text describing the network, defaults to #' \code{file} #' @param details Debugging information #' @return An object of class `grain`. #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @seealso \code{\link{grain}} #' @references Søren Højsgaard (2012). Graphical Independence #' Networks with the gRain Package for R. Journal of Statistical #' Software, 46(10), 1-26. #' \url{http://www.jstatsoft.org/v46/i10/}. #' @keywords utilities #' @examples #' #' #' ## Load HUGIN net file #' tf <- system.file("huginex", "chest_clinic.net", package = "gRain") #' chest <- loadHuginNet(tf, details=1) #' chest #' #' ## Save a copy #' td <- tempdir() #' saveHuginNet(chest, paste(td,"/chest.net",sep='')) #' #' ## Load the copy #' chest2 <- loadHuginNet(paste(td,"/chest.net",sep='')) #' #' tf <- system.file("huginex", "golf.net", package = "gRain") #' golf <- loadHuginNet(tf, details=1) #' #' saveHuginNet(golf, paste(td,"/golf.net",sep='')) #' golf2 <- loadHuginNet(paste(td,"/golf.net",sep='')) #' #' @export loadHuginNet loadHuginNet <- function(file, description=NULL, details=0){ if (is.null(description)) description <- rev(unlist(strsplit(file, "/")))[1] xxx <-.readHuginNet(file,details) yyy <-.transformHuginNet2internal(xxx) universe <- .asUniverse(yyy) plist <- lapply(yyy$potentialList, .hpot2cptable, universe) ##return(plist) value <- grain(compileCPT(plist)) return(value) } #' @export loadHuginNet loadHuginNet2 <- function(file, description=NULL, details=0){ if (is.null(description)) description <- rev(unlist(strsplit(file, "/")))[1] xxx <-.readHuginNet(file,details) yyy <-.transformHuginNet2internal(xxx) universe <- .asUniverse(yyy) plist <- lapply(yyy$potentialList, .hpot2cptable, universe) plist } .transformHuginNet2internal <- function(x){ nodeList2 <- lapply(x$nodeList, .getNodeSpec) potentialList2 <- lapply(x$potentialList, .getPotentialSpec) nl <- .makeNodeNamesUnique(nodeList2) repeat{ if (length(nl$nonunique)==0) break() nl <- .makeNodeNamesUnique(nl$nodeList) } nodeList2 <- nl$nodeList value <- structure(list(nodeList=nodeList2, potentialList=potentialList2)) class(value)<- "huginnet" return(value) } .readHuginNet <- function(file, details=0){ .infoPrint(details, 1, cat(".HUGIN netfile:", file,"\n")) nodeCount <- 0 con <- file(file, "rb") repeat{ cline <- .getLine(con); #print(cline) if (!length(cline)) break() if (.hasToken("node", cline)) ## Fragile if 'node' is the name of a variable... nodeCount <- nodeCount + 1 } close(con) .infoPrint(details, 3, cat("...there are around", nodeCount, "nodes \n")) ## Data structure for holding specification (possibly too long) ## nodeList <- potentialList <- as.list(rep(NA, nodeCount)) con <- file(file, "rb") currNode <- currPotential <- 1 state<-"start" repeat{ cline <- .getLine(con); #print(cline) if (!length(cline)) break() switch(state, "start"={ if (.hasToken("net",cline)){ state="net" .infoPrint(details, 2, cat("..NET action\n")) wline <- cline } }, "net"={ wline <- c(wline, cline) if (.hasToken("}",cline)){ state="run1" .infoPrint(details,2,cat("..end NET action\n")) } }, "run1"={ if (.hasToken("node", cline)){ state="node" .infoPrint(details, 2, cat("..NODE action\n")) } else { if (.hasToken("potential", cline)){ state="potential"; .infoPrint(details,2, cat("..POTENTIAL action\n")) } } wline <- cline }, "node"={ wline <- c(wline, cline) if (.hasToken("}",cline)){ state="run1"; .infoPrint(details,2,cat("..end NODE action\n")) nodeList[[currNode]] <- wline; currNode <- currNode + 1 } }, "potential"={ wline <- c(wline, cline) if (.hasToken("}",cline)){ state="run1"; .infoPrint(details,2, cat("..end POTENTIAL action\n")) potentialList[[currPotential]] <- wline; currPotential <- currPotential + 1 } } ) } close(con) nodeList <- nodeList[!sapply(lapply(nodeList, is.na),all)] potentialList <- potentialList[!sapply(lapply(potentialList, is.na),all)] value <- structure(list(nodeList=nodeList, potentialList=potentialList)) return(value) } .asUniverse <- function(from){ ccshort <-sapply(from$nodeList, function(x)x$nodeVar) ccnames <-sapply(from$nodeList, function(x)x$nodeLabel) cclabels <-lapply(from$nodeList, function(x)x$nodeStates) names(cclabels) <- ccnames di <- c(lapply(cclabels, length),recursive=TRUE) list(nodes=ccnames, short=ccshort, levels=cclabels, nlev=di) } .hpot2cptable <- function(cpot, universe){ idx <- match(c(cpot[c("nodeVar","parentVar")],recursive=TRUE), universe$short) vpa <- universe$nodes[idx] v <- vpa[1] cptable(vpa, values=cpot$potential, levels=universe$levels[[v]]) } .getLine <- function(con) { readLines(con, n=1) } .hasToken <- function(token, cline) { ##print(cline) cline <- gsub("^ +","",cline) a <- unlist(strsplit(cline," "))[1] if (!is.na(a)) a==token else FALSE } .tokenIdx <- function(token, x){ idx <- which(as.logical(lapply(x, function(d) grep(token,d)))) idx } .capWords <- function(s, strict = FALSE) { cap <- function(s) paste(toupper(substring(s,1,1)), {s <- substring(s,2); if(strict) tolower(s) else s}, sep = "", collapse = " " ) sapply(strsplit(s, split = " "), cap, USE.NAMES = !is.null(names(s))) } ## .toCamel <- function(s){ ## s<-gsub(" +"," ",s) ## s<-unlist(strsplit(s, " ")) ## paste(sapply(s, .capWords),collapse='') ## } .toCamel <- function(s){ s<-gsub(" +"," ",s) s<-unlist(strsplit(s, " ")) paste(c(s[1],sapply(s[-1], .capWords)),collapse='') } .getNodeSpec <- function(nodeSpec){ tmp <- nodeSpec[.tokenIdx("node", nodeSpec)] nodeVar <- gsub("node +","",tmp)[1] nodeVar <- gsub(" +","",nodeVar) tmp <- nodeSpec[.tokenIdx("label", nodeSpec)] nodeLabel <- gsub(" +label += +","",tmp); nodeLabel <- gsub(";", "", nodeLabel) nodeLabel <- gsub('"',"", nodeLabel) nodeLabel <- gsub(" +"," ",nodeLabel) if (length(nodeLabel) && nchar(nodeLabel)>0){ nodeLabel <- .toCamel(nodeLabel) nl <- gsub("[^[:alnum:]]","",nodeLabel) nodeLabel <- gsub("[^[:alnum:]|\\.]","",nodeLabel) base<-as.character(0:9) if(subsetof(unlist(strsplit(nl,"")), base)){ nodeLabel <- paste("X",nodeLabel,sep='') } } else { ##if (nchar(nodeLabel)==0) nodeLabel <- nodeVar } tmp <- nodeSpec[.tokenIdx("states", nodeSpec)] nodeStates <- gsub(" +states += +","",tmp); nodeStates <- gsub("[\$,\$;]","",nodeStates); nodeStates <- unlist(strsplit(nodeStates, '\\"')) nodeStates <- sapply(nodeStates, function(d) gsub("^ +","",d)) nodeStates <- nodeStates[sapply(nodeStates, nchar)>0] nodeStates <- sapply(nodeStates, .toCamel) nodeStates <- gsub(" +",".", nodeStates) names(nodeStates)<-NULL value <- list(nodeVar=nodeVar, nodeLabel=nodeLabel, nodeStates=nodeStates) value } .getPotentialSpec <- function(potSpec){ tmp <- potSpec[.tokenIdx("potential", potSpec)] tmp <- gsub("potential +","", tmp) tmp <- gsub("[\$,\$,|]","", tmp) tmp <- gsub(" +"," ", tmp) tmp <- unlist(strsplit(tmp," ")) tmp <- tmp[sapply(tmp, nchar)>0] nodeVar <- tmp[1] parentVar <- tmp[-1] sss <- paste(potSpec,collapse="") ##; ss <<- sss sss2 <- gsub("^.*data[[:space:]]*=([^;]*);(.*)", "\\1", sss) ##; ss2<<-sss2 ##sss3: ((( 0.5 1.2E-5 ) ( 3E3 0.5 )) ( 0.5 0.5 ) ( 0.5 0.5 ))) sss3 <- gsub("\\)[^\\)]*\$", ") (", sss2) ##; ss3<<-sss3 ## sss4: " 0.5 1.2E-5 3E3 0.5 0.5 0.5 0.5 0.5 "s sss4 <- gsub("[\\(,\$,\\}]","", sss3) ## sss5: remove leading white space: "0.5 1.2E-5 3E3 0.5 0.5 0.5 0.5 0.5 " sss5 <- gsub("^[[:space:]]*","",sss4) ## sss6: remove trailing white space: "0.5 1.2E-5 3E3 0.5 0.5 0.5 0.5 0.5" sss6 <- gsub("[[:space:]]$*","",sss5) ## sss7: split to atoms sss7 <- strsplit(sss6, " +")[[1]] ###: Now create numerical values pot <- as.numeric( sss7 ) value <- list(nodeVar=nodeVar, parentVar=rev(parentVar), potential=pot) value } .makeNodeNamesUnique <- function(nodeList2){ nl<-t(sapply(nodeList2, function(d)unlist(d[1:2]))) nonunique <- names(which(table(nl[,2])>1)) if (length(nonunique)){ cat ("Label(s): {", nonunique, "} appears mode than once in NET file\n") for (i in 1:length(nonunique)){ cnu <- nonunique[i] idx<-which(cnu ==nl[,2]) for (j in idx){ a <- nodeList2[[j]]$nodeVar cat(" Replacing label", cnu, " with node name", a, "\n") nodeList2[[j]]$nodeLabel <- a } } } return(list(nodeList=nodeList2, nonunique=nonunique)) } #' @export #' @rdname load-save-hugin saveHuginNet <- function(gin, file, details=0){ if (!inherits( gin, "grain")) stop("Not a grain object") if (is.null(gmd <- getgin(gin, "universe"))) stop("Strange error: no universe in network") if (is.null(cptlist <- getgin(gin, "cptlist"))){ cat("Object does not have 'cptlist' component; creating one for you...\n") cptlist <- make_cptlist(gin) } vlab <- gmd$levels vnam <- gmd$nodes nn <- length(vlab) th <- cumsum(c(0,rep(2*pi/nn, nn-1))) r <- 100 coords <- lapply(th, function(d) round(r+r*c(cos(d), sin(d)))) con <- file(file, "wb") ## Write (trivial) net specification ## writeLines("net\n{", con) writeLines(" node_size = (100 30);", con) writeLines("\n}\n\n", con) ## Write node specification ## for (ii in 1:length(vlab)){ st <-paste("node ", vnam[ii],"\n","{","\n",sep='') writeLines(st, con, sep="") ## cat(st) st <- paste(" label = \"\";","\n") writeLines(st, con, sep="") ## cat(st) st <- paste(" position = (", paste(coords[[ii]], collapse=' '), ");\n") writeLines(st, con, sep="") ## cat(st) st2 <- sapply(vlab[[ii]], function(d) paste('"',d,'"',sep='')) st <- paste(" states = (", paste(st2, collapse=' '), ");\n") writeLines(st, con, sep="") ## cat(st) st <- paste("}\n") writeLines(st, con, sep="") ## cat(st) } for (ii in 1:length(cptlist)){ cpot <- cptlist[[ii]] nam <- varNames(cpot) ## BRIS lev <- valueLabels(cpot) ## BRIS val <- cpot ## BRIS v <- nam[1] pa <- nam[-1] lev <- rev(lev[-1]) wval <- val if (length(lev)>0){ for (kk in 1:length(lev)){ ##print("splitVec:"); print(wval); print(class(wval)) wval<-splitVec(wval,length(lev[[kk]])) } } ##print(wval); print(class(wval)) plx <- printlist(wval) if (length(pa)){ st <- paste("potential (",v, "|", paste(rev(pa), collapse=' '),")\n") writeLines(st,con,sep="") ## cat(st) st <- "{\n"; writeLines(st,con,sep="") ## cat(st) st <- paste(" data = \n") writeLines(st,con,sep="") ## cat(st) ##a<-lapply(plx, cat, "\n") a<-lapply(plx, writeLines, con, sep="\n") st <- paste(";\n}\n") writeLines(st,con,sep="") ## cat(st) } else { st <- paste("potential (", v, ")\n") writeLines(st,con,sep="") ## cat(st) st <- "{\n"; writeLines(st,con,sep="") ## cat(st) st <- paste(" data = ", plx, ";\n") writeLines(st,con,sep="") ## cat(st) st <- "}\n\n"; writeLines(st,con,sep="") ## cat(st) } } close(con) }

a76c4dc435b4ea57b70703f854d6b212a6260494

72d9009d19e92b721d5cc0e8f8045e1145921130

/GGMncv/R/predict.R

9aa5ace38a5b984503eb102a57c1e2cfa618b643

[]

no_license

akhikolla/TestedPackages-NoIssues

be46c49c0836b3f0cf60e247087089868adf7a62

eb8d498cc132def615c090941bc172e17fdce267

refs/heads/master

2023-03-01T09:10:17.227119

2021-01-25T19:44:44

332,027,727

1

0

null

UTF-8

R

false

1,219

r

predict.R

#' Predict method for \code{ggmncv} Objects #' #' @description Predicted values based on a \code{ggmncv} object #' #' @param object An object of class \code{ggmncv} #' #' @param train_data Data used for model fitting. #' #' @param newdata An optional data frame in which to look for variables with which to predict. #' If omitted, the fitted values are used. #' #' @param ... Currently ignored #' #' @return A matrix of predicted values #' #' @examples #' # data #' Y <- scale(Sachs) #' #' # test data #' Ytest <- Y[1:100,] #' #' # training data #' Ytrain <- Y[101:nrow(Y),] #' #' fit <- ggmncv(cor(Ytrain), n = nrow(Ytrain)) #' #' pred <- predict(fit, train_data = Y, #' newdata = Ytest) #' #' round(apply((pred - Ytest)^2, 2, mean), 2) #' @export predict.ggmncv <- function(object, train_data, newdata = NULL,...){ object$x <- train_data if(isSymmetric(as.matrix(object$x))){ stop("data matrix not found") } if(!is.null(newdata)){ x_scale <- as.matrix(newdata) } else { x_scale <- as.matrix(scale(object$x)) } coefs <- as.matrix(unclass(coef(object))) p <- ncol(x_scale) y_hat <- sapply(1:p, function(x) x_scale[,-x] %*% coefs[x,]) return(y_hat) }

2a0a185e33779f7c2ec2d521bbcf77df79d1e60a

51e54e1ce824a1953d69650b56dfb31ba4b1aa69

/ggplot_demo.R

6aec16ecba2f27ddfd1a2a6a2368b583a6c4c9b3

[]

no_license

DHS-OEDA/spatial_workshop

4ba3a2aeae4da782ba61688f42f90fed029e1e41

7b0e686dde13c12df5f3c429d1c2fa9ff9759c3e

refs/heads/master

2020-03-12T11:14:44.700781

2018-04-24T20:25:19

130,591,810

0

null

UTF-8

R

false

1,275

r

ggplot_demo.R

# static map demo in R # this is just a quick demo of the ggplot2 development version's simple features compatibility # To run this script you will need a US Census API key available here: # https://api.census.gov/data/key_signup.html # Then call this function to store your key. # tidycensus::census_api_key("MY_KEY_HERE", install = TRUE, overwrite = TRUE) library(tidyverse) library(tidycensus) library(sf) library(viridis) devtools::install_github("tidyverse/ggplot2") require(ggplot2) racevars <- c(White = "P0050003", Black = "P0050004", Asian = "P0050006", Hispanic = "P0040003") multnomah <- get_decennial(geography="tract", variables = racevars, state="OR", county = "Multnomah County", geometry=TRUE, summary_var = "P0010001") head(multnomah) labels <- c("P0050003" = "White", "P0050004" = "Black", "P0050006" = "Asian", "P0040003" = "Any Hispanic") multnomah %>% mutate(pct = 100 * (value / summary_value)) %>% ggplot(aes(fill = pct, color = pct)) + facet_wrap(~variable, labeller=labeller(variable = labels)) + geom_sf() + coord_sf(crs = 26910) + scale_fill_viridis(option = "C") + scale_color_viridis(option = "C") + ggtitle('Select racial and ethnic demographics, Multnomah County')

960fff18b6ca1625802ca989387f1313321b7984

6490ecf3d1374ac7f4fc146cc4d467307e3870ed

/src/ndvi_over_time.R

718370632a444836dfcd5cf1df2dda75e5408620

[]

no_license

danyellshub/Hayman_Fire_NDVI_Trace

1a0ca73962f09c93758efe583fef3c32ed141d71

5373ffc47cc09a7db74ba57e0469b63d8c05099d

refs/heads/master

2020-07-18T00:07:13.759696

2019-09-13T20:47:13

206,130,645

0

null

2019-09-03T17:10:15

2019-09-03T17:10:14

null

UTF-8

R

false

1,962

r

ndvi_over_time.R

library(tidyverse) library(tidyr) library(ggthemes) library(lubridate) #Reading in the data and removing na ndvi <- read_csv('data/hayman_ndvi.csv') %>% rename(burned=2,unburned=3) %>% filter(!is.na(burned), !is.na(unburned)) # Converting from wide to long data ndvi_long <- gather(ndvi, key='site', value='NDVI', -DateTime) # Plotting all the data ggplot(ndvi_long,aes(x=DateTime,y=NDVI,color=site)) + geom_point(shape=1) + geom_line() + theme_few() + scale_color_few() + theme(legend.position=c(0.3,0.3)) # Summarizing the data by year ndvi_annual <- ndvi_long %>% mutate(year=year(DateTime)) %>% mutate(month=month(DateTime)) %>% filter(month %in% c(5,6,7,8,9)) %>% group_by(site,year) %>% summarize(mean_NDVI=mean(NDVI)) #Here making an annual plot ggplot(ndvi_annual,aes(x=year,y=mean_NDVI,color=site)) + geom_point(shape=1) + geom_line() + theme_few() + scale_color_few() + theme(legend.position=c(0.3,0.3)) # Summarizing the data by month ndvi_month <- ndvi_long %>% mutate(year=year(DateTime)) %>% mutate(month=month(DateTime)) %>% group_by(site,month)%>% summarize(mean_NDVI=mean(NDVI)) #Here making a month plot ggplot(ndvi_month,aes(x=month,y=mean_NDVI,color=site)) + geom_point(shape=1) + geom_line() + theme_few() + scale_color_few() + theme(legend.position=c(0.6,0.2)) #pre post analysis ndvi_month_pre_post <- ndvi_long %>% mutate(year = year(DateTime), month = month(DateTime), treatment = cut(year, breaks = c(0,2003,2020), labels = c('pre-burn', 'post-burn'))) %>% group_by(month, site, treatment) %>% summarize(mean_ndvi = mean(NDVI)) ggplot(ndvi_month_pre_post,aes(x=month,y=mean_ndvi,color=treatment)) + geom_point(shape=1) + geom_line() + theme_few() + scale_color_few() + theme(legend.position=c(0.6,0.2)) + facet_wrap(~site)

1671e1af79fee58f725069915a39ff4c37b0f64b

bc90e649b58c73662bc993b49daebf780ad68f7f

/beer_pca.r

c753835e06f82c267a5bea1a4dd9863b4528a760

[]

no_license

YangJongWon/study

cd71d5423ce97ca49206f0b9573d5de57059d2b4

35170f8ffa3c95d765cffa35595ba53b543ba28b

refs/heads/master

2020-03-31T15:49:38.947966

2018-12-14T00:41:34

152,352,994

0

null

UTF-8

R

false

507

r

beer_pca.r

install.packages("readxl") install.packages("HSAUR") install.packages("corrplot") library(readxl) library(HSAUR) library(corrplot) setwd("F:/BigData3/R/beer") beer<-read_xls("beer_pca.xls") beer_cost<-beer beer_cost$cost<-max(beer_cost$cost)-beer_cost$cost beer_cost.pca<-princomp(beer_cost,scale=T) beer_cost.pca summary(beer_cost.pca) biplot(beer_cost.pca) screeplot(beer_cost.pca,type = 'l',pch=19) ?predict() str(beer_cost) #pred_beer<-predict(beer_cost) bc<-cor(beer_cost) corrplot(bc,method='number')

75b925769b933596e5267516ce2cfce880ae3671

5c5915807ea728324875a615a1b9c5b919f2962f

/modules/amAnalysisMergeLandCover/amServer.R

7cfd944d3c62d61edff8803bc39b5b21f256e8fa

[]

no_license

demel/AccessMod_shiny

3d969228ff6ca8a9076a30a75fbf94ed60a87d55

70ffe0ba8ea6c558466689fdb419e3061afb971e

refs/heads/master

2021-01-17T06:44:53.433833

2015-11-02T18:27:09

null

0

null

UTF-8

R

false

29,059

r

amServer.R

# ___ __ ___ __ ______ # / | _____ _____ ___ _____ _____ / |/ /____ ____/ / / ____/ # / /| | / ___// ___// _ \ / ___// ___// /|_/ // __ \ / __ / /___ \ # / ___ |/ /__ / /__ / __/(__ )(__ )/ / / // /_/ // /_/ / ____/ / # /_/ |_|\___/ \___/ \___//____//____//_/ /_/ \____/ \__,_/ /_____/ # # Module 1 : Add road and barrier to an existing land cover in a given order. # # input : road, barrier, land cover # output : merged land cover observe({ amModEnabled<-listen$tabControl_module_selector if(isTRUE(!is.null(amModEnabled) && amModEnabled)){ #------------------------------------------------------------------------------# # MERGING NEW LANDCOVER #------------------------------------------------------------------------------# # populate selectize input observe({ mapStack<-dataList$raster[grep('^stack_',dataList$raster)] updateSelectInput(session,'mapStack',choices=mapStack,selected=mapStack) }) # button to hide stack items observe({ rmMerge<-input$btnRmMerge if(!is.null(rmMerge)&& rmMerge>0){ updateSelectInput(session = session, inputId = "mapStack",selected='') } }) observe({ addMerge<-input$btnAddMerge if(!is.null(addMerge)&& addMerge>0){ mapStack<-dataList$raster[grep('^stack_',dataList$raster)] updateSelectInput(session = session, inputId = "mapStack",choices=mapStack,selected=mapStack) } }) observeEvent(input$btnDeleteStack,{ sel<-amNameCheck(dataList,input$mapStack,'raster') if(length(sel)>0){ for(m in sel){ rmRastIfExists(m) } amUpdateDataList(listen) } }) stackConflictTable<-reactive({ #TODO: use only stack present in mapStack amErrorAction(title='stack conflict table',{ #sel<-amNameCheck(dataList,dataList$raster[grep('^stack*',dataList$raster)],'raster') sel<-amNameCheck(dataList,input$mapStack,'raster') btnStack<-input$btnAddStackRoad btnStack<-input$btnAddStackLcv listen$updatedConflictTable tbl<-data.frame(map="-",class="-",label="-") if(!is.null(sel)){ tbl=data.frame() sel <- sel[! sel %in% grep('^stack_barrier*',sel,value=T)] for(m in sel){ t<-read.table(text=execGRASS('r.category',map=m,intern=T), sep="\t", stringsAsFactors=F ) t$map=m names(t)<-c('class','label','map') tbl=rbind(t,tbl) } dupClass <- tbl$class[duplicated(tbl$class)] tbl <- tbl[tbl$class %in% dupClass,] tbl <- tbl[order(tbl$class),] } return(tbl) }) }) # validation observe({ tbl<-stackConflictTable() stackList <- amNameCheck(dataList,input$mapStack,'raster') stackTags <- input$stackTag rmArtefact<-input$cleanArtefact amErrorAction(title='stack merge validation',{ # conflict table update if(!is.null(tbl)){ isolate({ nRowConflict <- nrow(tbl) # test if nrow >0 and send a message to UI in case of conflict if(nRowConflict>1){ tbl$newClass=tbl$class }else{ tbl<-data.frame(map=as.character(NA),class=as.integer(NA),label=as.character(NA),newClass=as.integer(NA)) } # render hotable with a possibly empty table output$stackConflict<-renderHotable({tbl}, stretched='last',readOnly=c(1,2,3) ) }) } # validation process if(TRUE){ err = character(0) stackItemMissing <- isTRUE(any(sapply(stackList,is.null))) hasConflict <- isTRUE(!is.null(tbl) && nrow(tbl) > 1) hasTag <- isTRUE(!any(stackTags=='', is.null(stackTags), nchar(stackTags)<1)) stackLcvName <- config$dataClass[config$dataClass$id=="amStackLcv","class"] stackNotOneLcv <- !isTRUE(length(grep(stackLcvName,stackList))==1) if(stackItemMissing){ err <- c(err,"Stack listed not found, relaunch the application.") }else{ if(hasConflict){ nConf <- duplicated(tbl$class) nConf <- length(nConf[nConf]) confPlur <- ifelse(nConf>1,"conflicts","conflict") err <- c(err,paste(nConf,confPlur,"of class found. See under \"Conflicting classes among items in the stack\"")) }else{ if(stackNotOneLcv){ err <- c(err,"At least one land cover stack item is required to proceed.") }else{ if(!hasTag){ err <- c(err,"Please enter a least one tag") } } } } if(length(err)>0){ err <- HTML(paste("<div>",icon('exclamation-triangle'),err,'</div>',collapse="")) msgList <- tagList(tags$b('Validation issues:'),err) disBtn <- TRUE }else{ msgList <- tagList(p('')) disBtn <- FALSE } } # set outputname if no validation problem if(!is.null(rmArtefact) && hasTag && !disBtn){ stackTag <- amGetUniqueTags(stackTags) addTag<-function(base,tag=stackTag,sepT=config$sepTagFile,sepC=config$sepClass){ paste(c(base,paste(tag,collapse=sepT)),collapse=sepC) } # existing dataset (use local scoping) outTxt <- function(x,condition=TRUE){ if(isTRUE(condition)){ e <- x %in% dataList$df$origName y <- paste(amGetClass(x,config$sepClass),'[',paste(stackTag,collapse=" "),']') if(e){ return(sprintf(" %s <b style=\"color:#FF9900\"> (overwrite warning)</b> ",y)) }else{ return(sprintf("%s <b style=\"color:#00CC00\">(ok)</b>",y)) } }else{ NULL } } # set names merged <- addTag(amClassInfo('amLcvM')$class) bridges <- addTag(amClassInfo('amLcvMB')$class) # output lines out <- c(outTxt(merged),outTxt(bridges)) # take ony merged name if not rm artefect if(!rmArtefact)out=out[1] outMap <- tagList( tags$b('Output dataset:'), HTML(paste("<div>",icon('sign-out'),out,"<div/>",collapse="")) ) }else{ outMap="" } output$stackNameInfo<-renderUI(outMap) output$stackWarning<-renderUI({msgList}) amActionButtonToggle(id='btnMerge',session,disable=disBtn) }) }) # if btn correct stack is pressed # reclassify raster. # NOTE: we can't do a simple update using r.mapcalc or r.category : we need to keep cat label. observeEvent(input$btnCorrectStack,{ # get input table with modified column cTable<-hot.to.df(input$stackConflict) nCtbl<-nrow(cTable) # if number of row is greater than one if(nCtbl>1){ # for each map in table for(m in cTable$map){ # get tables orig and new classes oClass = cTable[cTable$map==m,'class'] nClass = cTable[cTable$map==m,'newClass'] # if texts in classes are different if(!identical(paste(oClass),paste(nClass))){ # read table from raster category tbl<-read.csv( text=execGRASS('r.category', map=m, intern=T), sep='\t', header=F, stringsAsFactors=F ) tbl[is.na(tbl$V2),'V2']<-"no label" rulesFile<-tempfile() # extract all old classes clOrig=tbl$V1 clNew=tbl$V1 clNew[clNew==oClass]<-nClass # compose a new rules file and rules=paste(clOrig,"=",clNew," ",tbl$V2,collapse="\n") write(x=rules,file=rulesFile) execGRASS('g.copy',raster=c(m,'tmp_reclass'),flags='overwrite') execGRASS('r.reclass', input='tmp_reclass',output='tmp_reclass_2', rules=rulesFile,flags='overwrite') execGRASS('r.resample',input='tmp_reclass_2',output=m,flags='overwrite') # signal change to reactive stack conflict table using a listener. listen$updatedConflictTable<-runif(1) } } } }) # merge action observeEvent(input$btnMerge,{ timeCheck<-system.time({ amActionButtonToggle(session=session,id='btnMerge',disable=TRUE) stackTag<-input$stackTag sel<-amNameCheck(dataList,input$mapStack,'raster') if(!is.null(sel) && isTRUE(nchar(stackTag)>0)){ amErrorAction(title='Module 1: merge process',{ updateTextInput(session,'stackTag',value="") selL<-length(sel) cleanBridge<-input$cleanArtefact inc<-1/(selL+1)*100 message('Merging land cover map requested.') stackTag<-amSubPunct(stackTag,config$sepTagFile,rmTrailingSep=T,rmLeadingSep=T,rmDuplicateSep=T) addTag<-function(base,tag=stackTag,sepT=config$sepTagFile,sepC=config$sepClass){ paste(c(base,paste(tag,collapse=sepT)),collapse=sepC) } # set names merged<-addTag(amClassInfo('amLcvM')$class) bridges<-addTag(amClassInfo('amLcvMB')$class) mapPosition=1 tempBase<-'tmp__' isFirstMap=TRUE rmRastIfExists('tmp_*') if(amRastExists('MASK'))execGRASS('r.mask',flags='r') message(paste('stack will be merged in this order:',paste(sel,collapse=', '))) amUpdateProgressBar(session,"stackProgress",1) # Use barrier as mask for each stack element # keep order in tempMap name. eg. tmp__12_stack_road_test for(i in 1:length(sel)){ map<-sel[i] message(paste('Proceding map',map,'MASK is',amRastExists('MASK'))) if(length(grep('stack_barrier__', map))>0){ if(amRastExists('MASK')){ execGRASS('r.mapcalc',expression=paste("MASK=isnull(",map,")?MASK:null()"),flags="overwrite") }else{ execGRASS('r.mask',raster=map,flags=c('i')) } }else{ tempMap=paste0(tempBase,mapPosition,'_',map) execGRASS('r.mapcalc',expression=paste(tempMap,"=",map),flags='overwrite') } mapPosition=mapPosition+1 amUpdateProgressBar(session,'stackProgress',i*inc) } #removing temp mask and active mask rmRastIfExists('tmp_mask__*') if(amRastExists('MASK'))execGRASS('r.mask',flags='r') # get list of tmp__stack... maps. tempMapList<-execGRASS('g.list',type='raster',pattern=paste0(tempBase,'*'),intern=TRUE) if(length(tempMapList)>1){ execGRASS('r.patch',input=paste(tempMapList,collapse=','),output=merged,flags=c('overwrite')) }else{ execGRASS('g.copy',raster=paste0(tempMapList,',',merged),flags='overwrite') } # In accessmod accessibility analysis, a null cell is a barrier, e.g. a river, mountain, militarized zone. # When we patch road maps to land cover maps, small overlaps can appear on top of predefined barrier. # Those overlaps act as briges when used in travel time analyis, thus, create shortcuts and wrong calculation. # If we used densified lines during rasterization process of roads, we can safely set the "one cell diagonal # bridge" as barrier without breaking road continuity. # # Example: # X=non-null cell in <road_map>; N=null in <merged_map>; A=non-null cell in <merged_map> # X will be set as null in fallowing cases: # # X N # N A # # N X # A N if(cleanBridge){ message('Cleaning artefact/bridges of one sel') fromRoad<-sel[grep('stack_road',sel)] amBridgeFinder(fromRoad,merged,bridges) amBridgeRemover(bridges,removeFromMap=merged) } execGRASS('r.colors',map=merged,color='random') rmRastIfExists(paste0(tempBase,'*')) message(paste(merged,'created')) amUpdateProgressBar(session,'stackProgress',100) amUpdateDataList(listen) }) } amActionButtonToggle(session=session,id='btnMerge',disable=FALSE) }) print(timeCheck) }) #------------------------------------------------------------------------------# # ADD TO STACK LANDCOVER #------------------------------------------------------------------------------# observe({ lcv<-dataList$raster[grep('^land_cover__',dataList$raster)] lcvTable<-dataList$table[grep('^table_land_cover__',dataList$table)] if(length(lcv)<1)lcv="" if(length(lcvTable)<1)lcvTable="" updateSelectInput(session,'landCoverSelect',choices=lcv) updateSelectInput(session,'landCoverSelectTable',choices=lcvTable) }) # toggle buttons to merge lcv table and add to stack observe({ lS<-amNameCheck(dataList,input$landCoverSelect,'raster') # lT<-amNameCheck(dataList,input$landCoverSelectTable,'table',dbCon=isolate(grassSession$dbCon)) tbl <- hot.to.df(input$landCoverRasterTable) if(TRUE){ err = character(0) uTable = tolower(gsub("\\s","",unlist(tbl))) hasEmptyCells <- isTRUE("-" %in% uTable || "" %in% uTable || NA %in% uTable) hasDuplicate <- isTRUE(any(duplicated(uTable))) lcvNotFound <- isTRUE(is.null(lS)) if(lcvNotFound){ err <- c(err,"Land cover layer not found") }else{ if(hasEmptyCells){ err <- c(err,"The table has empty values") }else{ if(hasDuplicate) err <- c(err,"The table has duplicated values") } } if(length(err)>0){ err <- HTML(paste("<div>",icon('exclamation-triangle'),err,'</div>',collapse="")) disBtn <- TRUE }else{ disBtn <- FALSE } } # send result to ui if(length(err)>0){ msgList <- tagList(tags$b('Validation issues:'),err) }else{ msgList <- tagList(p('Save labels and add land cover data to the stack:')) } output$stackLandcoverValidation <- renderUI(msgList) # lab<-hot.to.df(input$landCoverRasterTable)$label # disableMerge=any(is.null(lS),lS=='',is.null(lT),lT=="") # disableStack=any(is.null(lS),lS=='',is.null(lab),"" %in% lab,NA %in% lab) amActionButtonToggle(id='btnAddStackLcv',session,disable=disBtn) #amActionButtonToggle(id='mergeLcvUndo',session,disable=!allow) # amActionButtonToggle(id='mergeLcv',session,disable=disableMerge) },label='observeBtnsLcv') observe({ tblUpdated<-hot.to.df(input$landCoverRasterTable) isolate({ tblOriginal<-isolate(landCoverRasterTable()) testNrow<-nrow(tblUpdated)==nrow(tblOriginal) # rule 1 : if nrow doesnt match, return original if(!is.null(tblUpdated) && !is.null(tblOriginal) && testNrow){ # rule 2: do not allow changing class tblValidated<-data.frame(class=tblOriginal$class,label=amSubPunct(tblUpdated$label,'_')) }else{ tblValidated<-tblOriginal } output$landCoverRasterTable<- renderHotable({tblValidated}, readOnly = FALSE, fixed=1, stretched='last') }) }) # Get reactive land cover cat table from raster. landCoverRasterTable<-reactive({ sel<-amNameCheck(dataList,input$landCoverSelect,'raster') if(!is.null(sel)){ tbl<-read.csv( text=execGRASS('r.category', map=sel, intern=T), sep='\t', header=F, stringsAsFactors=F ) names(tbl)<-config$tableColNames[['table_land_cover']] tbl[,1]<-as.integer(tbl[,1]) tbl[,2]<-as.character(amSubPunct(tbl[,2],'_')) }else{ tbl<-data.frame(as.integer(NA),as.character(NA)) names(tbl)<-config$tableColNames[['table_land_cover']] } tbl }) landCoverSqliteTable<-reactive({ sel<-amNameCheck(dataList,input$landCoverSelectTable,'table',dbCon=isolate(grassSession$dbCon)) if(!is.null(sel)){ tbl<-dbGetQuery(isolate(grassSession$dbCon),paste('select * from',sel)) tbl[,1]<-as.integer(tbl[,1]) tbl[,2]<-amSubPunct(tbl[,2],'_') }else{ tbl<-data.frame(as.integer(NA),as.character(NA)) names(tbl)<-config$tableColNames[['table_land_cover']] } tbl }) # Save change in the lcv map. landCoverRasterSave<-function(selLcv,tblLcv){ if(!is.null(selLcv) && !is.null(tblLcv)){ tblOut<-tempfile() stackName<-paste0('stack_',selLcv) amMsg(session,type="log",text=paste('Add to stack requested for: ',selLcv,'. Stack name is',stackName)) write.table(tblLcv,file=tblOut,row.names=F,col.names=F,sep='\t',quote=F) execGRASS('r.category', map=selLcv, rules=tblOut) execGRASS('g.copy',raster=paste0(selLcv,',',stackName),flags='overwrite') colorSetting<-unlist(strsplit(config$dataClass[config$dataClass$class=='stack_land_cover','colors'],'&')) execGRASS('r.colors',map=stackName,color=colorSetting[1]) } } # if select lcv map change or undo btn is pressed, update hotable with value from raster. observe({ input$mergeLcvUndo # re evaluate if undo is pressed tblSqlite<-landCoverSqliteTable() tblRaster<-landCoverRasterTable() output$landCoverRasterTable<-renderHotable(tblRaster,readOnly=F,fixedCols=1,stretched='last') output$landCoverSqliteTable<-renderHotable(tblSqlite,readOnly=T,fixedCols=1,stretched='last') }) # if merge button is pressed, merge external and raster table observeEvent(input$mergeLcv,{ print('btn merge lcv pressed') #if(!is.null(btn) && btn > 0){ tbl<-hot.to.df(isolate(input$landCoverRasterTable)) tbl[tbl==""]<-NA #tblOrig$label<-NULL tblExt<-hot.to.df(isolate(input$landCoverSqliteTable)) tblExt[tblExt==""]<-NA # merging. we have to preserve Y classes and manual edits ! # so, replacing only NA with corresponding value from ext table. # It doesn't seems to be doable with merge(y,x,all.x=T,by='class') # so, do it manually. naClass<-tbl[is.na(tbl$label),]$class # class of missing label tblExtSub<-na.omit(tblExt[tblExt$class %in% naClass,]) # find corresponding value in ext tbl[tbl$class %in% tblExtSub$class,]$label<-tblExtSub$label #replace value with non-na remplacement tbl[,1]<-as.integer(tbl[,1]) tbl[,2]<-amSubPunct(tbl[,2],'_') output$landCoverRasterTable<- renderHotable({tbl}, readOnly = FALSE, fixedCols=1, stretched='last') #} # }) }) # if stack btn is pressed, save in GRASS. observe({ btn<-input$btnAddStackLcv amErrorAction(title='Add to stack: lcv',{ isolate({ sel<-amNameCheck(dataList,input$landCoverSelect,'raster') tbl<-hot.to.df(input$landCoverRasterTable) if(!is.null(btn) && btn>0 && !is.null(sel)){ amUpdateProgressBar(session,"lcvStackProgress",1) landCoverRasterSave(sel,tbl) amUpdateDataList(listen) amUpdateProgressBar(session,"lcvStackProgress",100) } }) }) }) #------------------------------------------------------------------------------# # ADD TO STACK ROAD #------------------------------------------------------------------------------# # populate selectize input observe({ roadList<-amListData('amRoad',dataList) if(length(roadList)==0)hfList=character(1) amDebugMsg('Road 1. update input. roadList=',roadList) updateSelectInput(session,'roadSelect',choices=roadList,selected=roadList[1]) }) # get road table columns observe({ sel<-amNameCheck(dataList,input$roadSelect,'vector') amErrorAction(title='get road table columns',{ if(!is.null(sel)){ cla<-grassDbColType(sel,'INTEGER') cla<-cla[!cla %in% c('cat')] lab<-grassDbColType(sel,'CHARACTER') }else{ cla="" lab="" } updateSelectInput(session,'roadSelectClass',choices=cla,selected=cla[1]) updateSelectInput(session,'roadSelectLabel',choices=lab,selected=lab[1]) }) }) # create raod preview table observe({ cla<-input$roadSelectClass lab<-input$roadSelectLabel amErrorAction(title='create road preview table',{ isolate({ sel<-amNameCheck(dataList,input$roadSelect,'vector') if(!is.null(sel) && !is.null(cla) && !cla=="" && !is.null(lab) && !lab==""){ q=paste('SELECT DISTINCT',cla,',',lab,' FROM',sel,'LIMIT',config$maxRowPreview) tbl<-dbGetQuery(grassSession$dbCon,q) names(tbl)<-config$tableColNames[['table_stack_road']] tbl[,2]<-amSubPunct(tbl[,2],'_') }else{ tbl<-data.frame("-","-") names(tbl)<-config$tableColNames[['table_stack_road']] tbl } output$roadPreviewTable<-renderHotable({tbl},readOnly=T,stretched='all',fixedCols=2) if(TRUE){ err = character(0) uTable = tolower(gsub("\\s","",unlist(tbl))) hasEmptyCells <- isTRUE("-" %in% uTable || "" %in% uTable || NA %in% uTable) hasDuplicate <- isTRUE(any(duplicated(uTable))) roadLayerNotFound <- isTRUE(is.null(sel)) if(roadLayerNotFound){ err <- c(err,"Road layer not found") }else{ if(hasEmptyCells){ err <- c(err,"The table has empty values") }else{ if(hasDuplicate) err <- c(err,"The table has duplicated values") } } if(length(err)>0){ err <- HTML(paste("<div>",icon('exclamation-triangle'),err,'</div>',collapse="")) disBtn <- TRUE }else{ disBtn <- FALSE } } # send result to ui if(length(err)>0){ msgList <- tagList(tags$b('Validation issues:'),err) }else{ msgList <- ""# tagList(tags$b('Ready to compute.')) } output$stackRoadValidation <- renderUI(msgList) amActionButtonToggle(session=session,id='btnAddStackRoad',disable=disBtn) }) }) }) # Add vector road to raster road stack observeEvent(input$btnAddStackRoad,{ amErrorAction(title='Module 1: add stack road',{ amActionButtonToggle(session=session,id='btnAddStackRoad',disable=TRUE) sel<-amNameCheck(dataList,input$roadSelect,'vector') cla<-input$roadSelectClass lab<-input$roadSelectLabel if(!is.null(sel) && !is.null(cla) && !is.null(lab)){ tbl<-hot.to.df(input$roadPreviewTable) message('Module 1: Spliting',sel) tblN <- nrow(tbl) amUpdateProgressBar(session,'roadStackProgress',1) #increment inc <- 1/tblN*100 for(i in 1:tblN){ class <- tbl[i,'class'] label <- tolower(amSubPunct(vect = tbl[i,'label'],sep='_')) #labelRule <- amSubPunct(vect = tbl[i,'label'],sep=' ') labelRule <- amSubPunct(label,sep=' ') tmpFile<-tempfile() tmpRules<-paste0(class,'\t',labelRule) write(tmpRules,file=tmpFile) outNameTmp<-paste0('tmp__',sel) outNameStack<-paste0('stack_',sel,'_',label) message(paste('Vector add to stack : extract class',class,' from ',sel)) execGRASS('v.extract', input=sel, output=outNameTmp, where=paste0(cla,"=",class), flags='overwrite' ) message(paste('Vector add to stack : Vector to raster, class',class,' from',outNameTmp)) execGRASS('v.to.rast', use='val', type='line', input=outNameTmp, output=outNameStack, value=class, flags=c('d','overwrite') ) colorSetting<-unlist(strsplit(config$dataClass[config$dataClass$class=='stack_road','colors'],'&')) execGRASS('r.colors',map=outNameStack,color=colorSetting[1]) message(paste('Vector add to stack : setting categories. class',class,' for',outNameStack)) execGRASS('r.category', map=outNameStack, rules=tmpFile ) rmVectIfExists(outNameTmp) amUpdateProgressBar(session,'roadStackProgress',i*inc) } amUpdateDataList(listen) } amActionButtonToggle(session=session,id='btnAddStackRoad',disable=FALSE) }) }) #------------------------------------------------------------------------------# # ADD TO STACK BARRIER #------------------------------------------------------------------------------# # populate select input observe({ barrierList<-dataList$vector[grep('barrier__',dataList$vector)] if(length(barrierList)<1)barrierList="" updateSelectInput(session,'barrierSelect',choices=barrierList,selected=barrierList[1]) }) # toggle add to stack barrier btn observe({ bT<-input$barrierType bS<-amNameCheck(dataList,input$barrierSelect,'vector') disableBtn<-any(is.null(bT), bT=="", is.null(bS) , bS=="") amActionButtonToggle(id="btnAddStackBarrier",session,disable=disableBtn) }) # preview table of barrier features barrierPreview<-reactive({ sel<-amNameCheck(dataList,input$barrierSelect,'vector') amErrorAction(title='Module 1: barrier preview',{ if(length(sel)>0 && !sel==""){ tbl<-read.table(text = execGRASS('v.info',map=sel,flags='t',intern=T),sep="=") names(tbl)<-c('type','count') tbl$type <- as.character(tbl$type) tbl<-tbl[tbl$type %in% c('areas','lines','points'),] tbl[tbl$type == "areas","type"] <- "polygons" return(tbl) }else{ tbl<-data.frame(as.character(NA),as.integer(NA)) names(tbl)<-c('type','count') return(tbl) } }) }) # render table observe({ tbl<-barrierPreview() output$barrierPreviewTable <- renderHotable({tbl},readOnly=T,fixedCols=2,stretched='all') }) # pre select feature based on max count by type observe({ tbl<-barrierPreview() if(isTRUE(!any(is.na(tbl)))){ sel <- tbl[which.max(tbl$count),'type'] if(sel=="polygons"){ sel == "areas" } updateRadioButtons(session,'barrierType',selected=gsub('s$','',sel)) } }) # add to stack process observeEvent(input$btnAddStackBarrier,{ amErrorAction(title='Add to stack : barrier',{ amActionButtonToggle(session=session,id='btnAddStackBarrier',disable=TRUE) sel<-amNameCheck(dataList,input$barrierSelect,'vector') type <- input$barrierType if(!is.null(sel) && !sel==''){ cl=1 la='barrier' tmpFile<-tempfile() write(paste0(cl,'\t',la),tmpFile) inc=1/length(sel)*100 amUpdateProgressBar(session,'barrierProgress',1) for(i in 1:length(sel)){ s<-sel[i] outNameStack<-paste0('stack_',s) message('Barrier add to stack : Vector to raster, class',cl,' from',outNameStack) execGRASS('v.to.rast',use='val', input=s, output=outNameStack, type=type, value=cl, flags=c('overwrite',if(type=='line')'d')# bug densified lines with area: not working. ) execGRASS('r.category',map=outNameStack,rules=tmpFile) rmVectIfExists('tmp__') amUpdateProgressBar(session,'barrierProgress',1*inc) } amUpdateDataList(listen) } amActionButtonToggle(session=session,id='btnAddStackBarrier',disable=FALSE) }) }) } })

44edb52e9d52b2194a3f4701de6998e5e382f3b9

4459eb5432916b4ad6c5c5d911b50c9d2fec1ad5

/man/Selectivity.Rd

27683b995b099c779ce26b042895265c3ad6f94f

[]

no_license

braverock/PerformanceAnalytics

057af55b0a4ddeb4befcc02e36a85f582406b95c

49a93f1ed6e2e159b63bf346672575f3634ed370

refs/heads/master

2023-08-03T10:18:27.115592

2023-03-29T09:23:17

58,736,268

209

113

null

2023-05-23T17:46:08

2016-05-13T12:02:42

R

UTF-8

R

false

true

1,188

rd

Selectivity.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Selectivity.R \name{Selectivity} \alias{Selectivity} \title{Selectivity of the return distribution} \usage{ Selectivity(Ra, Rb, Rf = 0, ...) } \arguments{ \item{Ra}{an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns} \item{Rb}{return vector of the benchmark asset} \item{Rf}{risk free rate, in same period as your returns} \item{\dots}{any other passthru parameters} } \description{ Selectivity is the same as Jensen's alpha } \details{ \deqn{Selectivity = r_p - r_f - \beta_p * (b - r_f)}{Selectivity = r_p - r_f - beta_p * (b - r_f)} where \eqn{r_f} is the risk free rate, \eqn{\beta_r} is the regression beta, \eqn{r_p} is the portfolio return and b is the benchmark return } \examples{ data(portfolio_bacon) print(Selectivity(portfolio_bacon[,1], portfolio_bacon[,2])) #expected -0.0141 data(managers) print(Selectivity(managers['2002',1], managers['2002',8])) print(Selectivity(managers['2002',1:5], managers['2002',8])) } \references{ Carl Bacon, \emph{Practical portfolio performance measurement and attribution}, second edition 2008 p.78 } \author{ Matthieu Lestel }

e52e4adc3b7f7306ce151d45b78e1de256b266a9

866de7bdc48ab2f44126a44b7790b41d6b11951b

/tests/testthat/test-superdiv.R

ddd767b4b6594d1c202af0fe01e8e9258874ff88

[ "BSD-2-Clause" ]

permissive

claireh93/rdiversity

1ccc589b1b82125309fcfef776d9dff097c58c30

2826e891704b305ded35b1865d8dfd0f60a9ff76

refs/heads/master

2020-12-11T07:41:58.693995

2016-05-25T09:09:48

59,580,987

0

null

2016-05-24T14:35:59

2016-05-24T14:35:58

null

UTF-8

R

false

827

r

test-superdiv.R

context('Testing the superdiv() function') pop <- data.frame(a=c(1,1,0),b=c(2,0,0),c=c(3,1,0)) pop <- pop / sum(pop) test_that("Supercommunity diversity across multiple populations", { expect_equivalent(as.matrix(superdiv(alpha(supercommunity(pop)), 0)$diversity), t(5)) expect_equivalent(as.matrix(superdiv(alphabar(supercommunity(pop)), 0)$diversity), t(7/4)) expect_equivalent(as.matrix(superdiv(beta(supercommunity(pop)), 0)$diversity), t(19/48)) expect_equivalent(as.matrix(superdiv(betabar(supercommunity(pop)), 0)$diversity), t(13/12)) expect_equivalent(as.matrix(superdiv(rho(supercommunity(pop)), 0)$diversity), t(11/4)) expect_equivalent(as.matrix(superdiv(rhobar(supercommunity(pop)), 0)$diversity), t(15/16)) expect_equivalent(as.matrix(superdiv(gamma(supercommunity(pop)), 0)$diversity), t(2)) })

bb2947642a6885294b004b27019dc595fe1a718b

a18c2a7cf79b96fd50d45dab7493a482d37eddb0

/data/cellrangerRkit/man/write_cluster_specific_genes.Rd

c3962a83e4fc4c664c180d572889c06d087b2f06

[ "MIT" ]

permissive

buenrostrolab/10x_scRNAseq

b6514c07873ae2a9c8959498234958fb833db568

8e65ceffd8a7186d0c81b159e6b316bc2bfdc6bf

refs/heads/master

2021-01-11T01:53:54.856901

2016-11-21T03:41:37

70,646,869

2

0

null

UTF-8

R

false

true

884

rd

write_cluster_specific_genes.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/util.r \name{write_cluster_specific_genes} \alias{write_cluster_specific_genes} \title{Write cluster-specific genes to file} \usage{ write_cluster_specific_genes(gene_clusters, gene_folder_path, n_genes = 10, output_type = "symbol") } \arguments{ \item{gene_clusters}{A list of genes that are prioritized (output from function: prioritize_genes)} \item{gene_folder_path}{Path to folder to store the cluster-specific genes} \item{n_genes}{Number of genes to include for each cluster} \item{output_type}{The format of the output gene list "ensembl" or "symbol} } \value{ matrix that includes all the genes written to a folder } \description{ Write cluster-specific genes to file } \examples{ \dontrun{ write_cluster_specific_genes(genes_to_plot,GENE_RES_FOLDER, n_genes=10,output_type='ensembl') } }

dad6c5cad2cda560e02cd7c253cc02ea8c2f84fc

12acaeb0d68e55bb2eab8c508fdd637b461f2717

/cachematrix.R

e004e13dbc59ec529009505d9f135fd87003e82c

[]

no_license

Martin77PP/ProgrammingAssignment2

a0a838c1fc991e74b98cce6eaecf978a27b1398f

997969bbdccf815f49c81a68489dedb8ee8318b7

refs/heads/master

2021-01-17T20:20:06.451022

2014-08-24T12:44:57

null

0

null

UTF-8

R

false

2,089

r

cachematrix.R

# makeCacheMatrix is a function that take as argument a square matrix, in this # function: # set is a function that stores in the cache the matrix (argument of makeCacheMatrix) # and initialize in the cache the value s of solve(matrix); # get get the matrix; # setSolve apply the function solve (inverse of the square matrix) and stores # in the cache the value s of solve(matrix); # getSolve get the value s of solve(matrix); # finally the MakeCachematrix create a list with 4 elements containing the # functions: set, get, setSolve and getSolve. makeCacheMatrix <- function(x = matrix()) { s <- NULL set <- function(y) { x <<- y s <<- NULL } get <- function() x setSolve <- function(solve) s <<- solve getSolve <- function() s list(set = set, get = get, setSolve = setSolve, getSolve = getSolve) } ## cacheSolve is a function that take as argument the value of makeCacheMatrix, ## to s is assigned the value stored in getSolve, ## the if loop check if the value is null, if not it print the message ## "getting cached data" and return the value s stored inth cache by getSolve ## and end the caheSolve function; ## if s is null to data is passed the matrix makeCacheMatrix$get, to s is ## assigned the value of solve(matrix) and then is passed the value s to setSolve ## that will store it in the cache, finally s is returned ## cacheSolve <- function(x, ...) { s <- x$getSolve() if(!is.null(s)) { message("getting cached data") return(s) } data <- x$get() s <- solve(data, ...) x$setSolve(s) s ## Return a matrix that is the inverse of 'x' } mtrx1 <- matrix(1:4, 2) # make a square matrix mCMx1 <- makeCacheMatrix(mtrx1) # assign the makeCaheMatrix value inv1 <- cacheSolve(mCMx1) # assign to inv1 the cahesolve valu test1 <- mtrx1 %*% inv1 # check if the value of inv1 is the inverse of mtrx1 mtrx2 <- matrix(8:11, 2) mCMx2 <- makeCacheMatrix(mtrx2) inv2 <- cacheSolve(mCMx2) test2 <- mtrx2 %*% inv2

04658da1242caa089cef450c46a7d1e38462a639

6d340495ef5b1858b957b1e34ee7f53eb1b12bc7

/kpca-sim.R

6b5816f5cf0e0ed3e62b1dbf4cbf001e8c5fb867

[]

no_license

chblw/stt7330-projet1

ba52ffa7c13be1c5b271cdb00140176b59991223

387dea97f8784b273cb56472e94ddff239df0815

refs/heads/master

2021-09-11T09:54:52.770364

2018-04-06T15:47:40

125,749,392

0

null

UTF-8

R

false

2,608

r

kpca-sim.R

xlibrary(kernlab) library(readr) library(dplyr) library(plotly) angles <- runif(200, 0, 2 * pi) radius <- runif(200, c(0, 7), c(2, 9)) circle_class <- rep(c(2,4), 100) x <- radius * cos(angles) y <- radius * sin(angles) circle_data <- cbind(x, y) plot(circle_data, col = circle_class, asp = 1) phi_data = function(x,y) { return(c(x^2,sqrt(2)*x*y,y^2)) } projected_data =numeric(600) for(index_to_project in 1:200){ point_to_project = circle_data[index_to_project,] projected_point = phi_data(point_to_project[1],point_to_project[2]) projected_data[(3*(index_to_project-1)+1):(3*(index_to_project))] = projected_point } projected_data = matrix(projected_data, nrow=200, ncol=3, byrow = TRUE) projected_data_as_dataframe = as.data.frame(projected_data) colnames(projected_data_as_dataframe) = c("x", "y", "z") pca <- princomp(projected_data_as_dataframe) plot(pca$score[,1],pca$scores[,2], col=circle_class,main="Données transformées selon les composantes principales", xlab="Première composante principale", ylab="Deuxième composante principale") plotly::plot_ly(projected_data_as_dataframe, x=~x, y=~y, z=~z, color=circle_class) %>% add_markers() %>% layout(scene = list(xaxis = list(title = 'x'), yaxis = list(title = 'y'), zaxis = list(title = 'x*y'))) kerneled_data =(circle_data[,1] * circle_data[,2])^2 plot(cbind(kerneled_data, rep(1,200)), col=circle_class, xlab="Log-valeur du noyau", yaxt="n" , main="Valeur de la fonction noyau polynomial", log="x") kpc_rbf <- kpca(circle_data, kernel = "rbfdot", kpar = list(sigma = 0.05), features = 2) kpc_bessel <- kpca(circle_data, kernel = "besseldot", kpar = list(sigma = 0.6, order = 3), features = 2) par(mfrow = c(1, 1)) plot(rotated(kpc_rbf), col = circle_class, pch = 19, main = "kpca rbf", asp = 1, xlab = "Première composante", ylab = "Deuxième composante") plot(rotated(kpc_bessel), col = circle_class, pch = 19, main = "kpca bessel", asp = 1, xlab = "Première composante", ylab = "Deuxième composante") pc <- prcomp(circle_data, rank. = 1) kpc_rbf <- kpca(circle_data, kernel = "rbfdot", kpar = list(sigma = 0.05), features = 1) kpc_bessel <- kpca(circle_data, kernel = "besseldot", kpar = list(sigma = 0.6, order = 3), features = 1) plot(pc$x, rep(0, 300), col = circle_class, pch = 19, main = "pca", xlab = "Première composante") plot(rotated(kpc_rbf), rep(0, 300), col = circle_class, pch = 19, main = "kpca rbf", xlab = "Première composante") plot(rotated(kpc_bessel), rep(0, 300), col = circle_class, pch = 19, main = "kpca bessel", xlab = "Première composante")

fbb1a460bc6e111f8cde546ddf4d540afa392234

6796f2593432f8ab8bc3f3db317f22e2022184ce

/Coronavirus.R

04d7ddb46d4f4413862328d4c843a25cf57d4fb9

[]

no_license

aj-vy/coronavirus

bdd18e81c1938653d75286a83f22e9af63c4b27a

447165e60c6bf475a20854228a6a6dab3e195ecb

refs/heads/master

2022-04-11T06:31:40.340308

2020-03-25T14:13:18

null

0

null

UTF-8

R

false

1,725

r

Coronavirus.R

library(tidyverse) library(rvest) rm(list=ls()) # Page: https://www.worldometers.info/coronavirus/ url=read_html('https://www.worldometers.info/coronavirus/') df=url%>%html_nodes('.main_table_countries_div') df=url%>%html_nodes('.main_table_countries_div tbody') df_t=df[[1]] df_t_c=df[[1]] %>% html_nodes('tr td')%>% html_text() df_t_c=length(df_t_c)/10 # Vectors Country=c() TotalCases=c() NewCases=c() TotalDeaths=c() NewDeaths=c() TotalRecovered=c() ActiveCases=c() Serious_Critical=c() TotCases1Mpop=c() TotDeaths1Mpop=c() # Extract Nodes for(i in 1:df_t_c){ Nodos=xml_child(df_t, i) Country=c(Country,xml_child(Nodos, 1)%>%html_text()) TotalCases=c(TotalCases,xml_child(Nodos, 2)%>%html_text()) NewCases=c(NewCases,xml_child(Nodos, 3)%>%html_text()) TotalDeaths=c(TotalDeaths,xml_child(Nodos, 4)%>%html_text()) NewDeaths=c(NewDeaths,xml_child(Nodos, 5)%>%html_text()) TotalRecovered=c(TotalRecovered,xml_child(Nodos, 6)%>%html_text()) ActiveCases=c(ActiveCases,xml_child(Nodos, 7)%>%html_text()) Serious_Critical=c(Serious_Critical,xml_child(Nodos, 8)%>%html_text()) TotCases1Mpop=c(TotCases1Mpop,xml_child(Nodos, 9)%>%html_text()) TotDeaths1Mpop=c(TotDeaths1Mpop,xml_child(Nodos, 10)%>%html_text()) print(i) } # Create dataframe casos=data.frame( Country, TotalCases, NewCases, TotalDeaths, NewDeaths, TotalRecovered, ActiveCases, Serious_Critical, TotCases1Mpop, TotDeaths1Mpop, stringsAsFactors = F ) # Delete files rm(df,df_t,Nodos,url,Country, TotalCases, NewCases, TotalDeaths, NewDeaths, TotalRecovered, ActiveCases, Serious_Critical, TotCases1Mpop, TotDeaths1Mpop,df_t_c,i) # Write file csv write_csv(casos,'casos.csv')

26e60a1b1564808f4f9a63ae6a617b4e6ad9e7e4

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/ArDec/examples/ardec.lm.Rd.R

f2f0755dbb152158fc6ff37823032fb0ad17b188

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

196

r

ardec.lm.Rd.R

library(ArDec) ### Name: ardec.lm ### Title: Fit an autoregressive model as a linear regression ### Aliases: ardec.lm ### Keywords: ts ### ** Examples data(tempEng) model=ardec.lm(tempEng)

9d1b0a9496b191bc785c21a74e8888f0e48d2312

bdef750a2d626c9d70ddcad057dfbd83dda66245

/tests/testthat/tests_calc_summaryStats.R

09a55695aaf9f80e65af74e8b5120b71c3b8c6c9

[ "LicenseRef-scancode-warranty-disclaimer" ]

no_license

cpenn-usgs/sedReview

4f6cabd39f7d73f86efb0fb1c097c6f728c08526

505906643f56f4aec2e244e64900dba0a0729c7e

refs/heads/master

2021-01-11T04:33:16.981342

2020-08-24T19:09:50

71,158,151

0

null

2016-10-17T16:22:56

null

UTF-8

R

false

450

r

tests_calc_summaryStats.R

context("calc_summaryStats") test_that("Return values check", { data("exampleData", package = "sedReview") # calc_summaryStatsOut <- calc_summaryStats(exampleData) # saveRDS(calc_summaryStatsOut,"tests/testthat/data/calc_summaryStatsOut.rds") calc_summaryStatsOut <- readRDS("data/calc_summaryStatsOut.rds") calc_summaryStatsOutTest <- calc_summaryStats(exampleData) expect_equal(calc_summaryStatsOut, calc_summaryStatsOutTest) })

f049ae3b0657ecdce048877661d8686f7512e40a

51fdd67d355df9ed4378bc86e432386edd31d4ca

/man/tremmel_shape_pal.Rd

1f6d389f67f6be99f9dac500188c88dd901bf8e3

[]

no_license

bobbbbbi/Data-Visualisation-with-ggplot2-ggthemes

f434c6dede486849236ddc26d6a31a3ee093ffe9

6ff7f1589b6199bf4c11ffde12b5fed9ceee4fce

refs/heads/master

2020-06-19T08:08:40.380067

2017-05-01T00:02:28

94,182,518

3

0

null

UTF-8

R

false

true

1,499

rd

tremmel_shape_pal.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/shapes.R \name{tremmel_shape_pal} \alias{tremmel_shape_pal} \title{Shape palette from Tremmel (1995) (discrete)} \usage{ tremmel_shape_pal(overlap = FALSE, n3alt = TRUE) } \arguments{ \item{overlap}{use an empty circle instead of a solid circle when \code{n == 2}.} \item{n3alt}{If \code{TRUE} then use a solid circle, plus sign and empty triangle, else use a solid circle, empty circle, and empty triangle.} } \description{ Based on experiments Tremmel (1995) suggests the following shape palettes: } \details{ If two symbols, then use a solid circle and plus sign. If three symbols, then use a solid circle, empty circle, and an empty triangle. However, that set of symbols does not satisfy the requirement that each symbol should differ from the other symbols in the same feature dimension. A set of three symbols that satisfies this is a circle (curvature), plus sign (number of terminators), triangle (line orientation). If more than three groups of data, then separate the groups into different plots. } \references{ Tremmel, Lothar, (1995) "The Visual Separability of Plotting Symbols in Scatterplots" Journal of Computational and Graphical Statistics, \url{http://www.jstor.org/stable/1390760} } \seealso{ Other shapes: \code{\link{circlefill_shape_pal}}, \code{\link{cleveland_shape_pal}}, \code{\link{scale_shape_circlefill}}, \code{\link{scale_shape_cleveland}}, \code{\link{scale_shape_tremmel}} }

df935f73d3c3301ca8086ac740067180a8135a0d

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/minimaxdesign/examples/CtoB.Rd.R

8d389e88a9cae832d170c6a62d2bde72f3c90996

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

465

r

CtoB.Rd.R

library(minimaxdesign) ### Name: CtoB ### Title: Inverse Rosenblatt transformation from the unit hypercube to the ### unit ball. ### Aliases: CtoB ### ** Examples ## Not run: ##D # Map the first 100 points of the Sobol' sequence in 3D ##D # onto the unit ball in 3D ##D library(randtoolbox) ##D des <- sobol(100,3) ##D des_ball <- CtoB(des) ##D ##D pairs(des_ball,xlim=c(-1,1),ylim=c(-1,1),pch=16) ##D ##D ## End(Not run)

3dce69cd7ee476ec0b459e1679a2268d9d30fe24

767ef79477c8c9cd67906280ff11553e84386cfc

/server.r

df5e76942b26790dabf8efc06892770f861be0c2

[]

no_license

RichardKen1/Health-insurance-comparer

6d8c6afa05db9bcfd54b4e1d6e69f8c0e34234d0

b454debb684e0b1787987a20994f90476bd1de95

refs/heads/master

2016-09-13T03:08:13.316164

2016-04-22T19:23:24

56,879,789

1

0

null

UTF-8

R

false

7,478

r

server.r

library(shiny) library(Rcpp) library(ggplot2) cost<-read.csv("age.and.gender/age and gender.csv", header=FALSE) fillout<- read.csv("fillout.data/Copy of fillout dataset.csv", header=TRUE) pop<-read.csv("Polished.population/Polished Population Data.csv") percentile<- read.csv("Polished.percentile/Polished Percentile Data.csv") p_fillout<- read.csv("probability.fillout/probability_ fillout dataset.csv") cppFunction('double find_max(DataFrame udf){ Rcpp::DoubleVector value = udf["Cost"]; double max=0; for(int i=0; i<5; i++){ if(value[i]>=max) max=value[i]; } return max; }') cppFunction('DataFrame find_percentile(DataFrame percentile, DataFrame p_fillout, int age){ Rcpp::CharacterVector xplot = p_fillout["Medical.Procedure"]; Rcpp::DoubleVector yplot = p_fillout["Probability"]; Rcpp:: DoubleVector min_age = percentile["Age.Min"]; Rcpp:: DoubleVector max_age = percentile["Age.Max"]; Rcpp:: DoubleVector data_pop = percentile["Medical.Condtions.POP"]; double true_cost[5]; double true_per[5]; for(int i=0; i<5; i++){ true_cost[i]=0; true_per[i]=0; } int m=0; for(int n=0; n<18; n++) { if(age>=min_age[n] && age<=max_age[n]) true_per[m]=data_pop[n]; if(n==3) m=1; if(n==7) m=2; if(n==9) m=3; if(n==13) m=4; } for(int i=0; i<5; i++){ yplot[i]=true_per[i]; } return DataFrame::create(_["Medical.Procedure"]=xplot,_["Probability"]=yplot ); }') cppFunction('DataFrame uninsured_munge(DataFrame fillout, DataFrame cost, DataFrame pop, int age){ Rcpp::CharacterVector xplot = fillout["Medical.Procedure"]; Rcpp::DoubleVector yplot = fillout["Cost"]; Rcpp:: DoubleVector data_cost = cost["V9"]; Rcpp:: DoubleVector data_pop = pop["Medical.Condtions.POP"]; Rcpp:: DoubleVector min_age = pop["Age.Min"]; Rcpp:: DoubleVector max_age = pop["Age.Max"]; double true_cost[5]; double true_pop[5]; for(int i=0; i<5; i++){ true_cost[i]=0; true_pop[i]=0; } int med_start[5]; med_start[0]=1028; med_start[1]=92; med_start[2]=524; med_start[3]=416; med_start[4]=920; for(int j=0; j<5; j++) { if(age>=0 && age<=18) true_cost[j]=data_cost[med_start[j]]; else if(age>= 19 && age<= 44){ med_start[j]=med_start[j]+1; true_cost[j]=data_cost[med_start[j]]; } else if(age>=45 && age<=64){ med_start[j]=med_start[j]+2; true_cost[j]=data_cost[med_start[j]]; } else if(age>=65 && age<=84){ med_start[j]=med_start[j]+3; true_cost[j]=data_cost[med_start[j]]; } else if(age>=85 && age<=100){ med_start[j]=med_start[j]+4; true_cost[j]=data_cost[med_start[j]]; } else true_cost[j]=0; } int m=0; for(int n=0; n<18; n++) { if(age>=min_age[n] && age<=max_age[n]) true_pop[m]=data_pop[n]; if(n==3) m=1; if(n==7) m=2; if(n==9) m=3; if(n==13) m=4; } for( int z=0; z<5; z++){ if(true_pop[z]==0) yplot[z]=0; else yplot[z]=((true_cost[z])/true_pop[z])*1000000; } return DataFrame::create(_["Medical.Procedure"]=xplot,_["Cost"]=yplot ); }') cppFunction('DataFrame insured_munge(DataFrame fillout, DataFrame cost, DataFrame pop, int age){ Rcpp::CharacterVector xplot = fillout["Medical.Procedure"]; Rcpp::DoubleVector yplot = fillout["Cost"]; Rcpp:: DoubleVector data_cost = cost["V9"]; Rcpp:: DoubleVector data_pop = pop["Medical.Condtions.POP"]; Rcpp:: DoubleVector min_age = pop["Age.Min"]; Rcpp:: DoubleVector max_age = pop["Age.Max"]; double true_cost[5]; double true_pop[5]; for(int i=0; i<5; i++){ true_cost[i]=0; true_pop[i]=0; } int med_start[5]; med_start[0]=992; med_start[1]=56; med_start[2]=488; med_start[3]=380; med_start[4]=884; for(int j=0; j<5; j++) { if(age>=0 && age<=18) true_cost[j]=data_cost[med_start[j]]; else if(age>= 19 && age<= 44){ med_start[j]=med_start[j]+1; true_cost[j]=data_cost[med_start[j]]; } else if(age>=45 && age<=64){ med_start[j]=med_start[j]+2; true_cost[j]=data_cost[med_start[j]]; } else if(age>=65 && age<=84){ med_start[j]=med_start[j]+3; true_cost[j]=data_cost[med_start[j]]; } else if(age>=85 && age<=100){ med_start[j]=med_start[j]+4; true_cost[j]=data_cost[med_start[j]]; } else true_cost[j]=0; } int m=0; for(int n=0; n<18; n++) { if(age>=min_age[n] && age<=max_age[n]) true_pop[m]=data_pop[n]; if(n==3) m=1; if(n==7) m=2; if(n==9) m=3; if(n==13) m=4; } for( int z=0; z<5; z++){ if(true_pop[z]==0) yplot[z]=0; else yplot[z]=((true_cost[z])/true_pop[z])*1000000; } return DataFrame::create(_["Medical.Procedure"]=xplot,_["Cost"]=yplot ); }') shinyServer( function(input, output){ output$percent_table <-renderTable({find_percentile(percentile, p_fillout, input$age)}) output$uninsured_plot <-renderPlot({ udf<-uninsured_munge(fillout, cost, pop, input$age) max<-find_max(udf) ggplot(data=udf, aes(x=Medical.Procedure, y=Cost, fill=Cost)) + geom_bar(colour="black", stat="identity") + ylim(0, max+50)+ ggtitle("Cost Without Insurance")+theme(plot.title=element_text(face="bold")) }) output$insured_plot <-renderPlot({ udf<-uninsured_munge(fillout, cost, pop, input$age) idf<-insured_munge(fillout, cost, pop, input$age) max<-find_max(udf) ggplot(data=idf, aes(x=Medical.Procedure, y=Cost, fill=Cost))+ geom_bar(colour="black", stat="identity") + ylim(0, max+50)+ ggtitle("Cost With Insurance")+theme(plot.title=element_text(face="bold")) }) } )

38d03891985c79c2bd10edae22543c3118ca3064

903da089f3ac659f7295a2b1d351981394e8bcdc

/man/mc.bootstrap.Rd

08887bbe3a68986c1c7d177bfa7cd6446e25503d

[]

no_license

cran/mcr

1f27b59cda2a87be199a8f6534bec6882154b042

069b879be631491ed07a54a0f348b1adbebf7867

refs/heads/master

2023-02-06T22:38:44.942104

2023-01-26T21:00:19

17,697,375

1

0

null

UTF-8

R

false

true

4,856

rd

mc.bootstrap.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mcBootstrap.r \name{mc.bootstrap} \alias{mc.bootstrap} \title{Resampling estimation of regression parameters and standard errors.} \usage{ mc.bootstrap( method.reg = c("LinReg", "WLinReg", "Deming", "WDeming", "PaBa", "PaBaLarge", "TS", "PBequi"), jackknife = TRUE, bootstrap = c("none", "bootstrap", "nestedbootstrap"), X, Y, error.ratio, nsamples = 1000, nnested = 25, iter.max = 30, threshold = 1e-08, NBins = 1e+06, slope.measure = c("radian", "tangent") ) } \arguments{ \item{method.reg}{Regression method. It is possible to choose between five regression types: \code{"LinReg"} - ordinary least square regression, \code{"WLinReg"} - weighted ordinary least square regression,\code{"Deming"} - Deming regression, \code{"WDeming"} - weighted Deming regression, \code{"PaBa"} - Passing-Bablok regression.} \item{jackknife}{Logical value. If TRUE - Jackknife based confidence interval estimation method.} \item{bootstrap}{Bootstrap based confidence interval estimation method.} \item{X}{Measurement values of reference method} \item{Y}{Measurement values of test method} \item{error.ratio}{Ratio between squared measurement errors of reference- and test method, necessary for Deming regression. Default 1.} \item{nsamples}{Number of bootstrap samples.} \item{nnested}{Number of nested bootstrap samples.} \item{iter.max}{maximum number of iterations for weighted Deming iterative algorithm.} \item{threshold}{Numerical tolerance for weighted Deming iterative algorithm convergence.} \item{NBins}{number of bins used when 'reg.method="PaBaLarge"' to classify each slope in one of 'NBins' bins of constant slope angle covering the range of all slopes.} \item{slope.measure}{angular measure of pairwise slopes used for exact PaBa regression (see \code{\link{mcreg}} for details).\cr \code{"radian"} - for data sets with even sample numbers median slope is calculated as average of two central slope angles.\cr \code{"tangent"} - for data sets with even sample numbers median slope is calculated as average of two central slopes (tan(angle)).\cr} } \value{ a list consisting of \item{glob.coef}{Numeric vector of length two with global point estimations of intercept and slope.} \item{glob.sigma}{Numeric vector of length two with global estimations of standard errors of intercept and slope.} \item{xmean}{Global (weighted-)average of reference method values.} \item{B0jack}{Numeric vector with point estimations of intercept for jackknife samples. The i-th element contains point estimation for data set without i-th observation} \item{B1jack}{Numeric vector with point estimations of slope for jackknife samples. The i-th element contains point estimation for data set without i-th observation} \item{B0}{Numeric vector with point estimations of intercept for each bootstrap sample. The i-th element contains point estimation for i-th bootstrap sample.} \item{B1}{Numeric vector with point estimations of slope for each bootstrap sample. The i-th element contains point estimation for i-th bootstrap sample.} \item{MX}{Numeric vector with point estimations of (weighted-)average of reference method values for each bootstrap sample. The i-th element contains point estimation for i-th bootstrap sample.} \item{sigmaB0}{Numeric vector with estimation of standard error of intercept for each bootstrap sample. The i-th element contains point estimation for i-th bootstrap sample.} \item{sigmaB1}{Numeric vector with estimation of standard error of slope for each bootstrap sample. The i-th element contains point estimation for i-th bootstrap sample.} \item{nsamples}{Number of bootstrap samples.} \item{nnested}{Number of nested bootstrap samples.} \item{cimeth}{Method of confidence interval calculation (bootstrap).} \item{npoints}{Number of observations.} } \description{ Generate jackknife or (nested-) bootstrap replicates of a statistic applied to data. Only a nonparametric ballanced design is possible. For each sample calculate point estimations and standard errors for regression coefficients. } \references{ Efron, B., Tibshirani, R.J. (1993) \emph{An Introduction to the Bootstrap}. Chapman and Hall. Carpenter, J., Bithell, J. (2000) Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. \emph{Stat Med}, \bold{19 (9)}, 1141--1164. } \author{ Ekaterina Manuilova \email{ekaterina.manuilova@roche.com}, Fabian Model \email{fabian.model@roche.com}, Sergej Potapov \email{sergej.potapov@roche.com} }

81c2f92d7dbee97d2f4be12a03c0e47b80fff821

ff7141cb3fe8fe044dc3381a5672726d3733496b

/Chunk.R

12007162e01fe6184e518f61ff31aab88036e783

[]

no_license

kenjimyzk/knitr-template

f5cfd87f69d891cd96151bb989ab021d7f373e92

184e75e43d812f44467638e2d189447abbe2ec58

refs/heads/master

2021-01-16T18:43:21.655575

2014-01-19T08:24:37

15,764,462

0

null

UTF-8

R

false

244

r

Chunk.R

## ---- Chunk01 ---- x <- runif(100) y <- 10 + 2 * x + rnorm(100) ## ---- Chunk02 ---- result <- lm(y~x) ## ---- Chunk03 ---- library(xtable) print(xtable(result, digits=3), floating=FALSE) ## ---- Chunk04 ---- plot(x,y) abline(result)

6d3f9e7daeb2dd19566147de6da047fa26601ffd

3d084d7aba8db3cbcd9acbbcb9d7684d56645ed1

/Tournament.R

f4925c2f546729511b9229fba330a42183916613

[]

no_license

profnote/MLB_Playoffs_Prediction

3d46b32e9be580978fc27727475cd15447bdc587

8dfe769e0d2f650dddd3f53b9974d8b4354616f4

refs/heads/master

2022-12-03T23:24:06.784802

2020-08-21T15:25:13

226,808,965

0

null

UTF-8

R

false

2,376

r

Tournament.R

# Tournament.R # # Predicts the outcome of the entire tounament of 8 MLB teams # The logistic regression model predictors selected by lasso: A.b.K and B.b.K (strikeouts per plate) library(caret) # import and filter data team_stats <- read.csv("dataPPS.csv") teams <- team_stats[team_stats$Season==2019,-c(1,21:27)] columns <- colnames(teams)[3:19] # train CV logistic model (based on 2001-2018 data) AUBdata <- read.csv("AvsB.csv") AUBdata <- subset(AUBdata, select = c(A.b.K, B.b.K, result)) # train a logistic regression model via 10 fold cross-validation ctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 10) #AUB_las_LR <- train(result ~ A.b.K + B.b.K, data = AUBdata, method = 'glm', family = binomial(), trControl = ctrl) # predict 2019 matches results <- read.csv("AvsB2019.csv") res2019 <- results[results$Year==2019,] res2019 <- subset(res2019, select = c(win, Lose, A.b.K, B.b.K, result)) res2019$win_p <- 0 res2019$pred <- "lose" pred.matches <- function(res2019){ for (i in 1:nrow(res2019)) { win_prob <- predict(AUB_las_LR, newdata = res2019[i,], type = "prob") res2019$win_p[i] = win_prob[,2] if (res2019$win_p[i] > 0.5){ res2019$pred[i] <- "win" } print(res2019[i,]) } } # Assign teams by data row according to real brackets team1 <- teams[1,] #Astros team2 <- teams[6,] #Rays team3 <- teams[8,] #Yankees team4 <- teams[7,] #Twins team5 <- teams[5,] #Nationals team6 <- teams[4,] #Dodgers team7 <- teams[3,] #Cardinals team8 <- teams[2,] #Braves bracket <- rbind(team1, team2, team3, team4, team5, team6, team7, team8) # predict the outcome of the whole tournament winning_team <- "" round_name <- c("Quarterfinals", "Semifinals", "Grand finals") playoff <- function(){ for (rnd in 1:3) { for (match in 2^(3-rnd):1) { temp_stats <- data.frame(bracket[2*match-1, "b.K"], bracket[2*match, "b.K"]) colnames(temp_stats) <- c("A.b.K", "B.b.K") win_prob <- predict(AUB_las_LR, newdata = temp_stats, type = "prob") teamA <- bracket[2*match-1, 2] teamB <- bracket[2*match, 2] if (win_prob["win"]>0.5){ winning_team = teamA bracket <- bracket[-2*match,] } else { winning_team = teamB bracket <- bracket[-(2*match-1),] } print(paste(round_name[rnd], ": ", teamA, "vs", teamB, ", winner =", winning_team)) } } }

769e48edc76c9954fccd90f7bcf1cf344d8a62ff

06f0689d9b960bf8a86e96cea4a0308e24e1413b

/man/PERFect_sim.Rd

6958791c436ec51c49930e7038f2694c86ed568a

[]

no_license

cxquy91/PERFect

4746fc51dcf45dd86b4765759ea08601b65b79b6

199ad444e10c81c472deefe5dff5e32dff977b41

refs/heads/master

2020-07-24T08:07:50.134566

2020-04-26T03:33:03

207,858,306

3

0

null

2019-09-16T22:58:11

2019-09-11T16:31:48

R

UTF-8

R

false

true

5,881

rd

PERFect_sim.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PERFect_sim.R \name{PERFect_sim} \alias{PERFect_sim} \title{Simulation PERFect filtering for microbiome data} \usage{ PERFect_sim(X,infocol = NULL, Order = "NP", Order.user = NULL, normalize = "counts", center = FALSE, quant = c(0.1, 0.25, 0.5), distr = "sn", alpha = 0.1, rollmean = TRUE, direction = "left", pvals_sim = NULL, nbins = 30, col = "red", fill = "green", hist_fill = 0.2, linecol = "blue") } \arguments{ \item{X}{OTU table, where taxa are columns and samples are rows of the table. It should be a in data frame format with columns corresponding to taxa names. It could contains columns of metadata.} \item{infocol}{Index vector of the metadata. We assume user only gives a taxa table, but if the metadata of the samples are included in the columns of the input, this option needs to be specified.} \item{Order}{Taxa ordering. The default ordering is the number of occurrences (NP) of the taxa in all samples. Other types of order are p-value ordering, number of connected taxa and weighted number of connected taxa, denoted as \code{"pvals"}, \code{"NC"}, \code{"NCw"} respectively. More details about taxa ordering are described in Smirnova et al. User can also specify their preference order with Order.user.} \item{Order.user}{User's taxa ordering. This argument takes a character vector of ordered taxa names.} \item{normalize}{Normalizing taxa count. The default option does not normalize taxa count, but user can convert the OTU table into a proportion table using the option \code{"prop"} or convert it into a presence/absence table using \code{"pres"}.} \item{center}{Centering OTU table. The default option does not center the OTU table.} \item{quant}{Quantile values used to fit the distribution to log DFL values. The number of quantile values corresponds to the number of parameters in the distribution the data is fitted to. Assuming that at least 50\% of taxa are not informative, we suggest fitting the log Skew-Normal distribution by matching the 10\%, 25\% and 50\% percentiles of the log-transformed samples to the Skew-Normal distribution.} \item{distr}{The type of distribution to fit log DFL values to. While we suggest using Skew-Normal distribution, and set as the default distribution, other choices are available. \describe{ \item{\code{"sn"}}{Skew-Normal distribution with 3 parameters: location xi, scale omega^2 and shape alpha} \item{\code{"norm"}}{Normal distribution with 2 parameters: mean and standard deviation sd} \item{\code{"t"}}{Student t-distribution with 2 parameters: n degrees of freedom and noncentrality ncp} \item{\code{"cauchy"}}{Cauchy distribution with 2 parameters: location and scale} }} \item{alpha}{Test level alpha, set to 0.1 by default.} \item{rollmean}{Binary TRUE/FALSE value. If TRUE, rolling average (moving mean) of p-values will be calculated, with the lag window set to 3 by default.} \item{direction}{Character specifying whether the index of the result should be left- or right-aligned or centered compared to the rolling window of observations, set to "left" by default.} \item{pvals_sim}{Object resulting from simultaneous PERFect with taxa abundance ordering, allowing user to perform Simultaneous PERFect with p-values ordering. Be aware that the choice of distribution for both methods must be the same.} \item{nbins}{Number of bins used to visualize the histogram of log DFL values, set to 30 by default.} \item{col}{Graphical parameter for color of histogram bars border, set to "red" by default.} \item{fill}{Graphical parameter for color of histogram fill, set to "green" by default.} \item{hist_fill}{Graphical parameter for intensity of histogram fill, set to 0.2 by default.} \item{linecol}{Graphical parameter for the color of the fitted distribution density, set to "blue" by default.} } \value{ A list is returned containing: \item{filtX}{Filtered OTU table.} \item{info}{The metadata information.} \item{pvals}{P-values of the test.} \item{DFL}{Differences in filtering loss values.} \item{fit}{Fitted values and further goodness of fit details passed from the \code{fitdistr()} function.} \item{hist}{Histogram of log differences in filtering loss.} \item{est}{Estimated distribution parameters.} \item{pDFL}{Plot of differences in filtering loss values.} } \description{ Simultaneous filtering of the provided OTU table X at a test level alpha. One distribution is fit to taxa simultaneously. } \details{ Filtering is the process of identifying and removing a subset of taxa according to a particular criterion. Function \code{PERFect_sim()} filters the provided OTU table X and outputs a filtered table that contains signal taxa. \code{PERFect_sim()} calculates differences in filtering loss DFL for each taxon according to the given taxa order. By default, the function fits Skew-Normal distribution to the log-differences in filtering loss but Normal, t, or Cauchy distributions can be also used. This is implementation of Algorithm 1 described in Smirnova et al. } \examples{ data(mock2) # Proportion data matrix Prop <- mock2$Prop # Counts data matrix Counts <- mock2$Counts dim(Counts) # 240x46 # Perform simultaenous filtering of the data res_sim <- PERFect_sim(X=Counts) dim(res_sim$filtX) # 240x10, removing 36 taxa colnames(res_sim$filtX) # signal taxa #permutation perfect colored by FLu values pvals_Plots(PERFect = res_sim, X = Counts, quantiles = c(0.25, 0.5, 0.8, 0.9), alpha=0.05) } \references{ Azzalini, A. (2005). The skew-normal distribution and related multivariate families. Scandinavian Journal of Statistics, 32(2), 159-188. Smirnova, E., Huzurbazar, H., Jafari, F. ``PERFect: permutationfiltration of microbiome data", to be submitted. } \seealso{ \code{\link{PERFect_perm}} } \author{ Ekaterina Smirnova }

3ea434768bf909fb4c83f156c9f5433c376652d4

9aafde089eb3d8bba05aec912e61fbd9fb84bd49

/codeml_files/newick_trees_processed/9491_0/rinput.R

7c6cc7bf3a9ab123fa17672882185e3c04c776a4

[]

no_license

DaniBoo/cyanobacteria_project

6a816bb0ccf285842b61bfd3612c176f5877a1fb

be08ff723284b0c38f9c758d3e250c664bbfbf3b

refs/heads/master

2021-01-25T05:28:00.686474

2013-03-23T15:09:39

null

0

null

UTF-8

R

false

135

r

rinput.R

library(ape) testtree <- read.tree("9491_0.txt") unrooted_tr <- unroot(testtree) write.tree(unrooted_tr, file="9491_0_unrooted.txt")

5596ecff3b491ec02fd217f92e00ddef0ed8f09a

f5ecf7e7692092ddb3d77242a239d43f49d446f2

/man/FlowLiteral-class.Rd

4502b074515927ac0c32906e165daaa88ce677d8

[]

no_license

andref1989/Flow

5c3aee2f206f2918d47d535b65132325f2047bd9

7526166ff94d6ae3fa9ee9627f830c4e0a25a0a7

refs/heads/new_master

2020-06-06T15:31:39.702865

2019-07-15T20:17:04

192,778,870

0

null

2019-07-11T20:15:40

2019-06-19T17:45:07

R

UTF-8

R

false

true

633

rd

FlowLiteral-class.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Flow.R \docType{class} \name{FlowLiteral-class} \alias{FlowLiteral-class} \title{Class to represent a literal argument to a task (ie a static path or value), used for wiring modules into tasks} \description{ S4 class for \code{FlowLiteral} Class \code{FlowLiteral} is a simple container for storing arguments to Task that are to be interpreted literally used when initializing a task config. } \section{Slots}{ \describe{ \item{arg}{scalar character argument that will be interpreted literally} } } \author{ Marcin Imielinski }

d0a6a43778c9f4b70fc24a3208e58ef12007ffcc

cebf0d6fbb54696c4a9209734cdc466bb623b46f

/Exercise_7.R

f647c550deb19ae6f76e2d9aa93558d711dc09c9

[]

no_license

slynch5/ICB2019_Exercise07

d0649fbd13dee6954e51c16c6b3fd364099c6a66

6fed1d2992ecd3e30d12651f25a403cae910c513

refs/heads/master

2020-09-06T17:32:37.838291

2019-11-12T01:38:17

220,495,645

0

null

2019-11-08T15:32:12

2019-11-08T15:32:11

null

UTF-8

R

false

1,824

r

Exercise_7.R

## Exercise_7: Custom Functions in R ## Write a function that returns the odd (1,3,5,etc.) rows of any ## dataframe passed as an argument iris = read.csv("iris.csv", header = TRUE, sep = ",", quote = "") # Read in .csv file (Test file) oddFunc <- function(dataFrame){ oddOut <- dataFrame[(seq(from=1,to=nrow(iris),by=2)),] return(oddOut) } oddFunc(iris) # Odd rows of the dataframe passed to the oddFunc function; example uses 'iris' dataframe ## Repeat a subset of last week's exercise, but write functions ## to accomplish these tasks. # Return the number of observations for a given species included # in the data set speciesFunc <- function(species){ output <- paste(species,nrow(iris[grep(species,iris$X.Species.),])) return(output) } speciesFunc("setosa") # Input of species name needs quotes around it for function to work speciesFunc("versicolor") speciesFunc("virginica") # Return a dataframe for flowers with Sepal.Width greater than # a value specified by the function user greaterWidth <- function(value){ iris[which(iris$X.Sepal.Width. >= value, arr.ind = TRUE),] } greaterWidth(3.5) # Example of calling function and returning desired data for value = 3.5 # Write the data for a given species to a comma-delimited file # with the given species name as the file name; Hint: look at # paste() to add the .csv extension to your file. createCSV <- function(species){ species2 <- iris[grep(species,iris$X.Species.),1:5] file_name <- paste(species, '.csv', sep="") write.table(species2, file = file_name, sep = " ", quote = FALSE) } createCSV("setosa") # Calls function to create a .csv file for "setosa" data createCSV("versicolor") # Calls function to create a .csv file for "versicolor" data createCSV("virginica") # Calls function to create a .csv file for "virginica" data

90fe2ebb511cd385cc6f4a3d76e0dea91f49bc13

50959bf31e007657fe5f5dcf65f86b276e6fb4c1

/man/strr_process_property.Rd

da0454f5ef74ddb9e3ac8e94621e06e4caf77a02

[]

no_license

UPGo-McGill/strr

dc1aebc2f1d9eed9733058215b4cc75975ce8491

d960ca300d6efc8b865beeaa267819bf3f0d9717

refs/heads/master

2023-07-26T14:01:09.518232

2023-07-06T19:07:38

183,255,784

1

0

null

2020-07-29T02:30:48

2019-04-24T15:22:28

R

UTF-8

R

false

true

2,666

rd

strr_process_property.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/strr_process_property.R \name{strr_process_property} \alias{strr_process_property} \title{Function to process raw property STR tables into UPGo format} \usage{ strr_process_property(property, keep_cols = FALSE, quiet = FALSE) } \arguments{ \item{property}{An unprocessed property data frame in the raw AirDNA format, with either 37 or 56 fields.} \item{keep_cols}{A logical scalar. If the `property` table has 56 fields, should the superfluous 19 fields be kept, or should the table be trimmed to the 37 fields which UPGo uses (default)?} \item{quiet}{A logical scalar. Should the function execute quietly, or should it return status updates throughout the function (default)?} } \value{ A list with three elements: 1) the processed `property` table; 2) an `error` table identifying corrupt or otherwise invalid row entries; 3) a `missing_geography` table identifying property_IDs with missing latitude/longitude coordinates. } \description{ \code{strr_process_property} takes raw property tables from AirDNA and cleans them for analysis or for upload in the UPGo database storage format. } \details{ A function for cleaning raw property tables from AirDNA and preparing them for subsequent analysis or upload in the UPGo format. The function also produces error files which identify possible corrupt or missing lines in the input file. The function expects the input property file to have either 56 fields (the default for a raw table from AirDNA) or 36 fields (the default for UPGo, after the `Zipcode`, `Average Daily Rate (USD)`, `Average Daily Rate (Native)`, `Annual Revenue LTM (USD)`, `Annual Revenue LTM (Native)`, `Occupancy Rate LTM`, `Number of Bookings LTM`, `Count Reservation Days LTM`, `Count Available Days LTM`, `Count Blocked Days LTM`, `Calendar Last Updated`, `Security Deposit (Native)`, `Cleaning Fee (Native)`, `Extra People Fee (Native)`, `Published Nightly Rate (USD)`, `Published Monthly Rate (USD)`, `Published Weekly Rate (USD)`, `Airbnb Listing URL`, and `HomeAway Listing URL` fields are removed on import). Eventually the function will be updated to support inputs from Inside Airbnb as well. Because the expectation is that the input files will be very large, the function uses updating by reference on the property input table. This saves a considerable amount of memory by avoiding making an unnecessary copy of the input table, but has the side effect of changing the initial input file even if the output is being assigned to a new object. An `update_by_reference` argument may be added to the function in the future to override this behaviour. }

09922be36faef4ff2b3282c081af17bf12c9b308

771bcf8c606a6b9313b1583e4dceaa15d0d44ad4

/inst/examples/sales/man/print-methods.Rd

a7ead2d559e8a4120801e401010a6b0b6cf27c43

[]

no_license

MangoTheCat/testCoverage

5aa438f202636f9b464817a367f7d6d55911ddfa

339ffef39d851c79a0011fc3444e1128b687308c

refs/heads/master

2020-12-24T05:17:25.157657

2018-11-19T10:01:29

23,995,468

18

4

null

2014-10-02T09:49:37

2014-09-13T13:53:25

R

UTF-8

R

false

true

570

rd

print-methods.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/unique.R \docType{methods} \name{print,SalesData-method} \alias{print,SalesData} \alias{print,SalesData-method} \title{Print Sales Data} \usage{ \S4method{print}{SalesData}(x, ...) } \arguments{ \item{x}{SalesData object.} \item{\dots}{further arguments passed to or from other methods.} } \description{ A method to print an object. } \examples{ x <- new(Class = "SalesData", Date = as.Date(x = 1:3, origin = "2015-12-18"), Daily.Total = 1:3, Outlet = "CHIPPENHAM") print(x) }

957752c8fd4e1074f86036422b81c9d0056227f8

a8751ed8f4113510037204fb0f03964235fa2250

/man/plot.MSLT.S.Rd

ce62943f074ee5790031bd8b3eafc869bcbc7e08

[]

no_license

al00014/Biograph

83ed141195adfec30634576e6a99c4ed54cbf0a3

15b46e3416f83964aab1baeaa58d1d92fa4e7e2b

refs/heads/master

2023-04-23T19:58:23.499789

2016-03-31T17:50:43

null

0

null

UTF-8

R

false

1,647

rd

plot.MSLT.S.Rd

\name{plot.MSLT.S} \alias{plot.MSLT.S} \title{Plots survival function. } \description{Plot method for object of class 'MSLT.S' using the ggplot2 package. It plots the multistate survival function by as estimated by \code{\link{MSLT.S}} } \usage{ \method{plot}{MSLT.S}(x, e0, order,colours,title,area,xmin,xmax,...) } \arguments{ \item{x}{The multistate survival function. An object of class \code{MSLT.S} produced by the \code{MSLT.S} function. It is one of two components of the object produced by the \code{MSLT.S} function.} \item{e0}{Life expectancy at reference age (e.g. at birth)} \item{order}{Desired sequence of states in plot} \item{colours}{Colours} \item{title}{Title for multistate survival plot} \item{area}{Graph type: area (area=TRUE) or bar (area=FALSE)} \item{xmin}{Minimum value on x-axis} \item{xmax}{Maximum value on x-axis} \item{\dots}{Further arguments to plot} } \value{The function plot.MSLT.S returns the multistate survival function (S) and the plot. It returns an object with the following components: \item{S }{The multistate survival function (values used in the plot) } \item{plot }{The plot produced by the ggplot2 package. } } \author{Frans Willekens} \seealso{MSLT.S} \examples{ # The multistate life table based on occurrence-exposure rates data (GLHS) param <- Parameters (GLHS) cr <- Cumrates (irate=3,Bdata=GLHS) S <- MSLT.S(cr$oe) radix <- c(10000,0) mslt <- MSLT.e (S,radix) # Plot the multistate survival function (object of class 'MSLT.S' ) z<- plot (x=S$S,e0=mslt$e0,title="Multistate survival function",area=TRUE,order=c("N","J")) }

17383a1bc7f7d4925c65ee0958224584675f7f29

030d72630276cb91cc7be3647c25dda7942d8842

/6_3_Mtry_OOBerror_RF(spam)/code/Question_6_3.R

6602ee0fe7d6bf242a98ab0027b7158f56bf6d1d

[]

no_license

WL0118/Supervised_Learning

a2ba36c84ad5aa41a2bb5a8264173c6e5c51fb54

9b0d3aefb2baf3fb8b257c2b9549048969fd89b0

refs/heads/master

2023-03-18T09:18:30.842612

2021-03-12T16:46:25

327,725,976

0

null

UTF-8

R

false

1,966

r

Question_6_3.R

rm(list = ls()) set.seed(777) library(randomForest) library(trainControl) library(caret) SPAM = read.csv("Data/SPAM.csv",header = FALSE,sep=' ') train_ind = sample(1:nrow(SPAM), nrow(SPAM)*0.60) train = SPAM[train_ind,] test = SPAM[-train_ind,] error_store_test <- c() error_store <- c() for(i in 4:8){ model = randomForest(x=train[1:57], y=as.factor(train[,58]), ntree=500, mtry = i, na.action = na.omit ) y_hat <- predict(model, newdata = test[1:57], type = "response") predicted_y<-y_hat yy <- as.factor(test$V58) conf_mat<-confusionMatrix(y_hat, yy) acc_RF<-conf_mat$overall[1] error_store_test <- c(error_store_test, unname(acc_RF)) print(model$err.rate[,1]) error_store <- c(error_store, sum(model$err.rate[,1])/500) } model <- randomForest(x=train[1:57], y=as.factor(train[,58]), ntree=500, mtry = 30, importance=TRUE ) print(sum(model$err.rate[,1])/500) metric <- "Accuracy" mtry<- sqrt(57) control <- trainControl(method="boot", search="random") bestmtry<-tuneRF(train[1:57],as.factor(train[,58]), stepFactor = 1.3,mtryStart =mtry,improve = 1e-5, ntree=500) mtry_num<- 4:8 plot(x=mtry_num,y=error_store_test, main = "test error by mtry", ylab = "Accuracy") plot(y=error_store,x= mtry_num, main = "train error by mtry", ylab = "Accuracy") error_store_test_ntree <- c() error_store_ntree <- c() tree_num<- 1:10*100 for(i in 1:10){ model = randomForest(x=train[1:57], y=as.factor(train[,58]), ntree=i*100, mtry = 5, na.action = na.omit ) y_hat <- predict(model, newdata = test[1:57], type = "response") predicted_y<-y_hat yy <- as.factor(test$V58) conf_mat<-confusionMatrix(y_hat, yy) acc_RF<-conf_mat$overall[1] error_store_test_ntree <- c(error_store_test, unname(acc_RF)) print(model$err.rate[,1]) error_store_ntree <- c(error_store, sum(model$err.rate[,1])) } plot(error_store_test_ntree, main = "test error by mtry", ylab = "Accuracy")

cefbdbf7b2cf2006cf4b1df8da181e0b53ab4da6

144b6d59fc15074f37713eff96eb0aefab6537a6

/client2UseCaseTest.R

05ad41701b7377d7337275218bca30d46d56e99b

[]

no_license

dkdupuis/R

0aaab4d0183ebc7ff96f3261e326268c4cc8445a

95cf3dc4efd7048de7a33d1df19d2411066db89d

refs/heads/master

2021-01-20T18:20:13.436051

2020-02-04T15:09:34

60,870,121

0

null

UTF-8

R

false

1,500

r

client2UseCaseTest.R

parDirLoc <- paste(getwd(),"/c1/", sep = "") parDir <- dir(parDirLoc) for(i in 1:length(parDir)) { subDirLoc <- paste(parDirLoc, parDir[i], "/", sep = "") subDir <- dir(subDirLoc) for(r in 1:length(subDir)) { file <- paste(subDirLoc, subDir[r], sep = "") watchData <- read.timeseries(file) anchorDate <- paste("X",gsub("-",".",as.character(watchData[watchData$category=="anchor_date",]$label)),sep = "") dates <- detectTopics(file, daysToUse = 115, mainCatagoryLabel = "watchlist_score", useDC = FALSE) #+ 1 print(paste("Topic - ", as.character(watchData[watchData$category=="watchlist_score",]$label),sep = "")) topicID <- watchData[watchData$category=="watchlist_topic",]$uuid burstScores <- burstOutliers(file,filterToUse = edgeFilter(), daysToWeigh = 1, daysToUse = 116, burstParam = .5) for(t in 1:length(dates)) { colDate <- paste("X", gsub("-", ".", dates[t]), sep = "") rowOfMax <- which.max(burstScores[,colnames(burstScores)==colDate]) infID <- as.character(burstScores$uuid[rowOfMax]) print(dates[t]) print(paste("Influencer - ",as.character(burstScores$label[rowOfMax]),sep = "")) bashCom <- paste("bash explaininf.sh",topicID,dates[t],infID, sep = " ") print(system(bashCom, intern = TRUE)) } } }

ecf5f89701ad2bab29c14bd5eac971bb7db19842

845a71cc97ffeef053d0ca142649d4f231126e5d

/R/shiny_2/server.R

d7b4407586ee6572dc0d3a687997004228633556

[]

no_license

ewkerez/data_science

94b086c20c0ea30f89f0a6c33537c904a0e488ae

6095188091af2dc6e90983c7f28101b13fe40878

refs/heads/master

2021-04-27T03:41:24.735415

2018-06-13T15:06:56

122,718,381

0

null

UTF-8

R

false

1,583

r

server.R

################################################################################ # A file including backend code (server) ################################################################################ # global r otwiera się tylko raz i dopeiro potem patrzymy na ui i server # dobre to wciagania paczek, możemy trzymać tutaj stałe zmiennne # zdefiniowac połącznien z bazą danych shinyServer(function(input, output, session) { out$delayRangeUI <- renderUI({ sliderInput( inputId = "delayRange", label = "Okresl przedzial opóżnienia:", min = 0, max = 1000, value = c(100,1000) ) }) agreg_data <- reactive({ aggDelayFlights <- modFlights %>% filter(name %in% input$carrierName) %>% group_by(name, hour) %>% summarise(delayed_flight_perc = sum( dep_delay > input$delayRange[1] & dep_delay < input$delayRange[2] & distance >= input$distance_valuse/ n())) }) output$delay_plot <- renderPlot({ # Aggregate data # Create a plot ggplot(agreg_data(), aes(hour, delayed_flight_perc, fill= name)) + geom_col(position = 'dodge') + theme_hc(base_size = 18) + scale_fill_hc() + xlab("Hour") + ylab("Percentage of delayed flights") + scale_y_continuous(labels = scales::percent) + scale_x_continuous(limits = c(0,24), breaks = seq(0, 24, 2)) }) output$table_plot <- renderTable({ agreg_data() %>% mutate( delayed_flight_perc = scales::percent(delayed_flight_perc) ) }) })

c150b6470c04d58be091343b3c6ab5a7c724fe82

e205d4542b2f7d13bc3c1a3bba2eae4c16cfc743

/man/Qconduction_substrate.Rd

372c4ddb61319398766763c18b231ddd6ffb6142

[ "MIT" ]

permissive

trenchproject/TrenchR

03afe917e19b5149eae8a76d4a8e12979c2b752f

7164ca324b67949044827b743c58196483e90360

refs/heads/main

2023-08-20T11:54:26.054952

2023-08-04T03:52:42

78,060,371

8

NOASSERTION

2022-09-15T21:36:08

2017-01-04T23:09:28

R

UTF-8

R

false

true

2,934

rd

Qconduction_substrate.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/energybalance_functions.R \name{Qconduction_substrate} \alias{Qconduction_substrate} \title{Conductance Assuming Substrate Thermal Conductivity is Rate Limiting} \usage{ Qconduction_substrate(T_g, T_b, D, K_g = 0.5, A, proportion) } \arguments{ \item{T_g}{\code{numeric} surface temperature (K).} \item{T_b}{\code{numeric} body temperature (K).} \item{D}{\code{numeric} characteristic dimension of the animal (m).} \item{K_g}{\code{numeric} thermal conductivity of substrate (\ifelse{html}{\out{W K<sup>-1</sup> m<sup>-1</sup>}}{\eqn{W K^-1 m^-1}{ASCII}}).} \item{A}{\code{numeric} surface area (\ifelse{html}{\out{m<sup>2</sup>}}{\eqn{m^2}{ASCII}}).} \item{proportion}{\code{numeric} proportion in contact to the surface.} } \value{ \code{numeric} conductance (W). } \description{ The function calculates conductance (W) of an ectothermic animal to its substrate. The method assumes the major resistance to conduction is the substrate and that the interior of the animal is equal in temperature to its surface (thermally well mixed) \insertCite{Spotila1992}{TrenchR}. } \examples{ Qconduction_substrate(T_g = 293, T_b = 303, D = 0.01, K_g = 0.3, A = 10^-2, proportion = 0.2) } \references{ \insertAllCited{} } \seealso{ Other biophysical models: \code{\link{Grashof_number_Gates}()}, \code{\link{Grashof_number}()}, \code{\link{Nusselt_from_Grashof}()}, \code{\link{Nusselt_from_Reynolds}()}, \code{\link{Nusselt_number}()}, \code{\link{Prandtl_number}()}, \code{\link{Qconduction_animal}()}, \code{\link{Qconvection}()}, \code{\link{Qemitted_thermal_radiation}()}, \code{\link{Qevaporation}()}, \code{\link{Qmetabolism_from_mass_temp}()}, \code{\link{Qmetabolism_from_mass}()}, \code{\link{Qnet_Gates}()}, \code{\link{Qradiation_absorbed}()}, \code{\link{Qthermal_radiation_absorbed}()}, \code{\link{Reynolds_number}()}, \code{\link{Tb_CampbellNorman}()}, \code{\link{Tb_Gates2}()}, \code{\link{Tb_Gates}()}, \code{\link{Tb_butterfly}()}, \code{\link{Tb_grasshopper}()}, \code{\link{Tb_limpetBH}()}, \code{\link{Tb_limpet}()}, \code{\link{Tb_lizard_Fei}()}, \code{\link{Tb_lizard}()}, \code{\link{Tb_mussel}()}, \code{\link{Tb_salamander_humid}()}, \code{\link{Tb_snail}()}, \code{\link{Tbed_mussel}()}, \code{\link{Tsoil}()}, \code{\link{actual_vapor_pressure}()}, \code{\link{boundary_layer_resistance}()}, \code{\link{external_resistance_to_water_vapor_transfer}()}, \code{\link{free_or_forced_convection}()}, \code{\link{heat_transfer_coefficient_approximation}()}, \code{\link{heat_transfer_coefficient_simple}()}, \code{\link{heat_transfer_coefficient}()}, \code{\link{saturation_vapor_pressure}()}, \code{\link{saturation_water_vapor_pressure}()} } \concept{biophysical models}

d2d1050e1ac734deacdcbfd214df6d838f1caa90

c8fd30b59b962df19adff091bfa28fb766520d13

/R/archived_20181010/gp_archive_20180504/gp_univariate/stan_gp_test.R

0b40af4bc3cfec293e3ecd290233c7a6c9c1af61

[]

no_license

tkmckenzie/Iterative_ML

bdde722ce8ffb90561e155339105ade24ee2f9b8

ee274ef242d4e73b0138cb39fdb323382654b2bf

refs/heads/master

2021-06-20T10:55:42.637141

2019-06-18T22:12:59

136,051,159

0

null

UTF-8

R

false

614

r

stan_gp_test.R

library(rstan) setwd("~/docs/Iterative_ML_paper/R/gp_univariate") rm(list = ls()) load("data.RData") N = length(x) N.pred = 100 x.pred = seq(min(x), max(x), length.out = N.pred) alpha = 1 length.scale = 0.15 sigma = 0.32 stan.data = list(N = N, N_pred = N.pred, x_pred = x.pred, y = y, x = x, alpha = alpha, length_scale = length.scale, sigma = sigma) stan.fit = stan("gp_univariate_genfit.stan", data = stan.data, algorithm = "Fixed_param", chains = 1, iter = 2, warmup = 1) stan.extract = extract(stan.fit) fit = t(stan.extract$f_pred[1,,])

f936824bf619ca4d8a617ad6bf457b800a0ab67b

785085d938a1178aac085aa1bc220c355c82692b

/plot4.R

867d5ca062c897c25734ddd97715ffbecade1d70

[]

no_license

mandeldm/ExData_Plotting1

ee2a9af830d361c63f12713000fb33a8c63fdb26

4b9507c64063bf40409b3a7b332fc477baf87f9b

refs/heads/master

2020-12-25T10:14:01.102344

2015-04-12T22:45:31

31,859,562

0

null

2015-03-08T18:02:42

null

UTF-8

R

false

1,835

r

plot4.R

powdata2 <- read.table("household_power_consumption.txt", sep=";", dec=".", header=TRUE, stringsAsFactors=FALSE, na.strings="?", colClasses=c(rep("character",2), rep("numeric",7))) powdata2 <- within(powdata2, datetime <- paste(Date, Time)) powdata2$datetime2 <- strptime(powdata2$datetime, format="%d/%m/%Y %H:%M:%S") powdata3 <- subset(powdata2, datetime2>=strptime("2007-02-01", format="%Y-%m-%d") & datetime2<strptime("2007-02-03", format="%Y-%m-%d")) par_default <- par(no.readonly = TRUE) par(mfrow = c(2, 2), mar = c(4, 5, 0.5, 0.5), oma = c(0, 0, 0, 0)) with(powdata3, { plot(datetime2, Global_active_power, type="l", ylab="Global Active Power", xlab="") plot(datetime2, Voltage, type="l", ylab="Voltage", xlab="datetime") plot(powdata3$datetime2, powdata3$Sub_metering_1, type="l", ylab="Energy sub metering", xlab="") points(powdata3$datetime2, powdata3$Sub_metering_2, type="l", ylab="Energy sub metering", xlab="", col="red") points(powdata3$datetime2, powdata3$Sub_metering_3, type="l", ylab="Energy sub metering", xlab="", col="blue") legend("topright", legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), col=c("black", "red", "blue"), lty=1, inset=0.15, bty="n") plot(datetime2, Global_reactive_power, type="l", ylab="Global_reactive_power", xlab="datetime") }) dev.copy(png, file="plot4.png") dev.off() par(par_default) # Restoring previous parameters so that subsequent graphs are not placed in 2x2 grid etc

7490c661b57932e70ca98056a8bb13d43199c21c

20bb25b978b163ac11161d7c455d4ef42233d520

/basicR.R

7c6687a642e392a18eb97a798cc0774d463e0a4e

[]

no_license

monicamurugesan/DS-Basics

4fecbcda0e811b240f6d6ae9ab31b7a1caf98ebe

50308cde07b08bdc58924af3b17c76be5a4377af

refs/heads/master

2022-12-03T12:24:25.051826

2020-08-27T15:17:23

290,532,225

0

null

UTF-8

R

false

488

r

basicR.R

1+1 x<- 1+1 x a <-50 b <-20 a*b+x a+b*x (6*3)+5/2 exp(1) db <-c(1,2,4.5) db d <-(1,2,4) rep(1,10) rep(2,3) vect_x <-c(2,0,0,4) vect <-c(1,2,3,4) vec1 <-vect_x+vect vec1 vec1*4 vec1[1:4] vec1[2]=3 vect_char <- c('R','python','java',10,100.5) vect2<-c('R','python','java',1) c <-c("a","b","c") c df <-data.frame(x=1:3,y=c("a","b","c")) df[1,1] df[1,] df1 <-data.frame(height=c(102,120),width=c(50,50)) df1 df1[1,1] df[c(1,3),2] df[c(1,3),1] df[c(1,3),1:2]

ab4af11f38dd5251af26ebb3229c1bc5e9b320aa

78132aee39db22e146b6fd42c525cc74d2004e26

/01GenerateData.R

96de13920bcfae74160d822df28d8692167b9ad1

[ "MIT" ]

permissive

jamespaul007/GeospatialLineGraphs

fa2878cfc63738beae2e63926b82061debd6eda8

717569c9e70cf90e503242f6dc4f269b66655a4d

refs/heads/master

2021-01-18T06:14:09.689996

2014-08-04T15:16:48

null

0

null

UTF-8

R

false

3,567

r

01GenerateData.R

library(foreach) library(doParallel) all.data <- read.csv("./DataSets/WorldPopulation.csv", header=TRUE, stringsAsFactors=FALSE) # The following are used to manipulate various data sets # colnames(all.data) <- c("Name", "Mass", "Latitude", "Longitude") # Meteorites # all.data$X <- as.numeric(all.data$X) # all.data$Y <- as.numeric(all.data$Y) # all.data$Mass <- as.numeric(all.data$Mass) # Time the code start <- proc.time() startEnd <- function(lats, lngs) { # Find the "upper left" (NW) and "bottom right" (SE) coordinates of a set of data. # # Args: # lats: A list of latitude coordinates # lngs: A list of longitude coordinates # # Returns: # A list of values corresponding to the northwest-most and southeast-most coordinates # Convert to real number and remove NA values lats <- na.omit(as.numeric(lats)) lngs <- na.omit(as.numeric(lngs)) topLat <- max(lats) topLng <- min(lngs) botLat <- min(lats) botLng <- max(lngs) return(c(topLat, topLng, botLat, botLng)) } startEndVals <- startEnd(all.data$Y, all.data$X) remove(startEnd) startLat <- startEndVals[1] endLat <- startEndVals[3] startLng <- startEndVals[2] endLng <- startEndVals[4] remove(startEndVals) num_intervals = 200.0 interval <- (startEndVals[1] - startEndVals[3]) / num_intervals remove(num_intervals) lat.list <- seq(startLat, endLat + interval, -1*interval) # testLng <- -66.6462379307115 # testLat <- 45.9581234392 # Prepare the data to be sent in # If you have a value you want to sum, use this data <- all.data[,c("Y", "X", "DN")] # If you want to perform a count, use this # data <- all.data[,c("Longitude", "Latitude")] # data["Value"] <- 1 sumInsideSquare <- function(pointLat, pointLng, interval, data) { # Sum all the values that fall within a square on a map given a point, # an interval of the map, and data that contains lat, lng and the values # of interest colnames(data) <- c("lat", "lng", "value") # Data inside boundaries data <- na.omit(data[data$lng >= pointLng & data$lng < pointLng + interval & data$lat >= pointLat - interval & data$lat < pointLat,]) return(sum(data$value)) } # Debugging # squareSumTemp <- sumInsideSquare(testLat, testLng, interval, data) # Given a start longitude and an end longitude, calculate an array of values # corresponding to the sums for that latitude calcSumLat <- function(startLng, endLng, lat, interval, data) { row <- c() lng <- startLng while (lng < endLng) { row <- c(row, sumInsideSquare(lat, lng, interval, data)) lng <- lng + interval } return(row) } # Debugging # rowTemp <- calcSumLat(startLng, endLng, testLat, interval, data) # write.csv(rowTemp, file = "Temp.csv", row.names = FALSE) # Set up parallel computing with the number of cores you have cl <- makeCluster(detectCores(), outfile = "./Progress.txt") registerDoParallel(cl) all.sums <- foreach(lat=lat.list) %dopar% { lat.data <- calcSumLat(startLng, endLng, lat, interval, data) # Progress indicator that works on Mac/Windows print((startLat - lat)/(startLat - endLat)*100) # Prints to Progress.txt lat.data } stopCluster(cl = NULL) # Convert to data frame all.sums.frame <- data.frame(all.sums) # Save to disk so I don't have to run it again write.csv(all.sums.frame, file = "./GeneratedData/WorldPopulation.csv", row.names = FALSE) # End timer totalTime <- proc.time() - start print(totalTime) remove(all.sums, data, cl, endLat, endLng, startLat, startLng, lat.list, start, startEndVals, totalTime, calcSumLat, sumInsideSquare, interval)

561223d8f72b369a61264c3d9aa72d889ca7de9e

da725622bc962b639e1eb6df535b433e4366bcc5

/skillPathways/skillsData.R

96d387f0fa1b59fff3019ea5cf5dbd2a61f24f31

[]

no_license

bekahdevore/rKW

5649a24e803b88aa51a3e64020b232a23bd459fa

970dcf8dc93d4ec0e5e6a79552e27ddc0f850b91

refs/heads/master

2020-04-15T12:41:49.567456

2017-07-25T16:29:31

63,880,311

0

1

null

UTF-8

R

false

363

r

skillsData.R

library(dplyr) library(stringr) skills <- read.csv("skills.csv") wagesEducation <- read.csv("skillsOccupationsWagesAndEducation.csv") #Remove - from SOC in EMSI data wagesEducation$SOC <- str_replace_all(wagesEducation$SOC, '-','') skillsData <- merge(wagesEducation, skills, by="SOC") write.csv(skillsData, file = "skillsData.csv")

361d95eaefb124b1940917e68e0ce577e758a982

55f449a5496f173f62fc037574c4d7fc5a1c72b3

/man/mbes.Rd

da337cd727cbcbe8e66df1d6762d14d2d1862bd9

[]

no_license

davan690/samplingbook

3db105da3416cd48f824c9df37127c5d0d0683af

62cf8cc97d27be5bb9ef02bdedd82f21a1e69e4c

refs/heads/master

2023-04-06T16:24:40.942282

2021-04-02T21:13:07

null

0

null

UTF-8

R

false

5,937

rd

mbes.Rd

\name{mbes} \alias{mbes} \title{Model Based Estimation} \description{ mbes is used for model based estimation of population means using auxiliary variables. Difference, ratio and regression estimates are available. } \usage{ mbes(formula, data, aux, N = Inf, method = 'all', level = 0.95, ...) } \arguments{ \item{formula}{object of class \code{formula} (or one that can be coerced to that class): symbolic description for connection between primary and secondary information} \item{data}{data frame containing variables in the model} \item{aux}{known mean of auxiliary variable, which provides secondary information} \item{N}{positive integer for population size. Default is \code{N=Inf}, which means that calculations are carried out without finite population correction.} \item{method}{estimation method. Options are \code{'simple','diff','ratio','regr','all'}. Default is \code{method='all'}.} \item{level}{coverage probability for confidence intervals. Default is \code{level=0.95}.} \item{\dots}{further options for linear regression model} } \details{ The option \code{method='simple'} calculates the simple sample estimation without using the auxiliary variable. The option \code{method='diff'} calculates the difference estimate, \code{method='ratio'} the ratio estimate, and \code{method='regr'} the regression estimate which is based on the selected model. The option \code{method='all'} calculates the simple and all model based estimates. For methods \code{'diff'}, \code{'ratio'} and \code{'all'} the formula has to be \code{y~x} with \code{y} primary and \code{x} secondary information. For method \code{'regr'}, it is the symbolic description of the linear regression model. In this case, it can be used more than one auxiliary variable. Thus, \code{aux} has to be a vector of the same length as the number of auxiliary variables in order as specified in the formula. } \value{ The function \code{mbes} returns an object, which is a list consisting of the components \item{call}{is a list of call components: \code{formula} formula, \code{data} data frame, \code{aux} given value for mean of auxiliary variable, \code{N} population size, \code{type} type of model based estimation and \code{level} coverage probability for confidence intervals} \item{info}{is a list of further information components: \code{N} population size, \code{n} sample size, \code{p} number of auxiliary variables, \code{aux} true mean of auxiliary variables in population and \code{x.mean} sample means of auxiliary variables} \item{simple}{is a list of result components, if \code{method='simple'} or \code{method='all'} is selected: \code{mean} mean estimate of population mean for primary information, \code{se} standard error of the mean estimate, and \code{ci} vector of confidence interval boundaries} \item{diff}{is a list of result components, if \code{method='diff'} or \code{method='all'} is selected: \code{mean} mean estimate of population mean for primary information, \code{se} standard error of the mean estimate, and \code{ci} vector of confidence interval boundaries} \item{ratio}{is a list of result components, if \code{method='ratio'} or \code{method='all'} is selected: \code{mean} mean estimate of population mean for primary information, \code{se} standard error of the mean estimate, and \code{ci} vector of confidence interval boundaries} \item{regr}{is a list of result components, if \code{type='regr'} or \code{type='all'} is selected: \code{mean} mean estimate of population mean for primary information, \code{se} standard error of mean estimate, \code{ci} vector of confidence interval boundaries, and \code{model} underlying linear regression model} } \references{ Kauermann, Goeran/Kuechenhoff, Helmut (2010): Stichproben. Methoden und praktische Umsetzung mit R. Springer. } \author{Juliane Manitz} \seealso{\code{\link{Smean}}, \code{\link{Sprop}}} \examples{ ## 1) simple suppositious example data(pop) # Draw a random sample of size=3 set.seed(802016) data <- pop[sample(1:5, size=3),] names(data) <- c('id','x','y') # difference estimator mbes(formula=y~x, data=data, aux=15, N=5, method='diff', level=0.95) # ratio estimator mbes(formula=y~x, data=data, aux=15, N=5, method='ratio', level=0.95) # regression estimator mbes(formula=y~x, data=data, aux=15, N=5, method='regr', level=0.95) ## 2) Bundestag election data(election) # draw sample of size n = 20 N <- nrow(election) set.seed(67396) sample <- election[sort(sample(1:N, size=20)),] # secondary information SPD in 2002 X.mean <- mean(election$SPD_02) # forecast proportion of SPD in election of 2005 mbes(SPD_05 ~ SPD_02, data=sample, aux=X.mean, N=N, method='all') # true value Y.mean <- mean(election$SPD_05) Y.mean # Use a second predictor variable X.mean2 <- c(mean(election$SPD_02),mean(election$GREEN_02)) # forecast proportion of SPD in election of 2005 with two predictors mbes(SPD_05 ~ SPD_02+GREEN_02, data=sample, aux=X.mean2, N=N, method= 'regr') ## 3) money sample data(money) mu.X <- mean(money$X) x <- money$X[which(!is.na(money$y))] y <- na.omit(money$y) # estimation mbes(y~x, aux=mu.X, N=13, method='all') ## 4) model based two-phase sampling with mbes() id <- 1:1000 x <- rep(c(1,0,1,0),times=c(10,90,70,830)) y <- rep(c(1,0,NA),times=c(15,85,900)) phase <- rep(c(2,1), times=c(100,900)) data <- data.frame(id,x,y,phase) # mean of x out of first phase mean.x <- mean(data$x) mean.x N1 <- length(data$x) # calculation of estimation for y est.y <- mbes(y~x, data=data, aux=mean.x, N=N1, method='ratio') est.y # correction of standard error with uncertaincy in first phase v.y <- var(data$y, na.rm=TRUE) se.y <- sqrt(est.y$ratio$se^2 + v.y/N1) se.y # corrected confidence interval lower <- est.y$ratio$mean - qnorm(0.975)*se.y upper <- est.y$ratio$mean + qnorm(0.975)*se.y c(lower, upper) }

d7d49e705160cfcca4dbd9e282dd93c4c8c0079c

95e509eb99116d4311c0b71c39a0863e15c2da86

/plot4.R

cc5356f6b22632cf99c03021eb35864e5d46477b

[]

no_license

weiweigithub/ExData_Plotting1

8f5edb839f67131865e7a5ea4feaa53afa8ce554

764c72852a61fe9a201437ac84adeaa988661452

refs/heads/master

2021-01-17T16:22:18.121627

2015-03-08T22:40:47

31,867,993

0

null

2015-03-08T22:18:25

null

UTF-8

R

false

1,516

r

plot4.R

## Reading the downloaded data file from local folder rawdata<-read.table("household_power_consumption.txt", header = TRUE, colClasses = "character", sep = ";") ## Extract the portion of the data within the specified time period data<-filter(rawdata, Date=="1/2/2007" | Date=='2/2/2007') ## Extract the data for plotting datetime<- strptime(paste(data$Date, data$Time), format = "%d/%m/%Y %H:%M:%S") gap<-as.numeric(data$Global_active_power) grp<-as.numeric(data$Global_reactive_power) vol<-as.numeric(data$Voltage) sm1<-as.numeric(data$Sub_metering_1) sm2<-as.numeric(data$Sub_metering_2) sm3<-as.numeric(data$Sub_metering_3) ## Plot all plots in one window par(mfrow=c(2,2)) ## The first plot, row 1 column 1 with(data, plot(datetime, gap, type="l", xlab = "", ylab="Global Active Power")) ## The second plot, row 1 column 2 with(data, plot(datetime, vol, type ="l", ylab = "Voltage")) ## The third plot, row 2 column 1 (3 lines in one plot) with(data, plot(datetime, sm1, type = "n", xlab = "", ylab="Energy sub metering")) with(data, lines(datetime, sm1, col="black")) with(data, lines(datetime, sm2, col="red")) with(data, lines(datetime, sm3, col="blue")) legend("topright", lty = c(1,1,1), col = c("black", "red", "blue"), legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3")) ## The fourth plot, row 2 column 2 with(data, plot(datetime, grp, type="l")) ## Copy the plot to specified format and close the PNG device dev.copy(png, file = "plot4.png", width =480, height=480) dev.off()

f75729e56d623ea273cf1329d87623f72edcbbf2

42f4fea527642694954067192d4c881f387f1264

/R/RcppExports.R

3a739d2838f62d4b6b0b37abb57b649123fd68f9

[]

no_license

jonbramble/RScattnlay

34e520c75e58e1562d56655d846bde8e3ef2294b

f0e2e2e8d74f37084249aaa41c4e26cf98f300a9

refs/heads/master

2022-11-22T15:23:15.009484

2018-04-28T16:57:01

30,366,821

1

null

2020-01-29T20:25:49

2015-02-05T16:54:04

C

UTF-8

R

false

345

r

RcppExports.R

# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 S4_AMPL <- function(fullstack) { .Call('_Rscattnlay_S4_AMPL', PACKAGE = 'Rscattnlay', fullstack) } S4_SCATTNLAY <- function(fullstack) { .Call('_Rscattnlay_S4_SCATTNLAY', PACKAGE = 'Rscattnlay', fullstack) }

c1774094b8aadb4e398687da45f40fd07ab5b17d

bb226480c35979bb7ec0ef70c53fa0a8c4fec5ed

/R/hello.R

0533166e7e367f75bfc191652fd08f80c3a3bba5

[]

no_license

hulinhui-code/hulinhui

78b078bcb2eff2ab0622aa8dab3610cb01bb63ca

50226bd2a2fa410628d5967ddaf5ccb38163ef3e

refs/heads/main

2023-03-24T20:28:00.620345

2021-03-22T03:57:32

332,137,455

0

null

UTF-8

R

false

8,210

r

hello.R

# Hello, world! #' @export hello <- function() { print('Good luck') } updateR <- function() { lib <- (.packages()) # 已加载包列表 if ('mypkg' %in% lib) {detach(package:mypkg)} #如果mypkg已加载，则卸载 library(devtools) install_github('hulinhui-code/hulinhui', lib = c('D:/R pkg lib', 'C:/Users/Jack/Documents/R/win-library/4.0')) # 同时放在C盘（默认安装盘） # library(mypkg) # 安装完成加载 ## 没用，更新完后还是要重新载入 } mygithub_install <- function(package_name, github_name){ lib <- (.packages()) # 已加载包列表 if (package_name %in% lib) {remove(package_name, lib="D:/R pkg lib")} #如果已加载，则卸载 library(devtools) install_github(github_name, lib = c('D:/R pkg lib')) } myInstall <- function(package_name) { install.packages(package_name, lib="D:/R pkg lib") } myLibrary <- function(package_name) { if (length(package_name) ==1) { library(package_name, lib="D:/R pkg lib", character.only=TRUE) } else for (pkg in package_name){ library(pkg, lib="D:/R pkg lib", character.only=TRUE) } } display_file <- function(pdf_path){ pdf_html <- paste('<iframe src="',pdf_path, '" align="center" width="1111" height="900" frameBorder="0"></iframe>', sep="") IRdisplay::display_html(pdf_html) # return(pdf_html) } display_panel <- function(address1, address2){ pdf_html <- paste('<iframe src="',address1, '" align="center" width="650" height="900" frameBorder="0"></iframe><iframe src="', address2, '" align="center" width="650" height="900" frameBorder="0"></iframe>', sep="") IRdisplay::display_html(pdf_html)} display_missing_data <- function(df){df[which(rowSums(is.na(df))>=1),]} yt <- function(short_code_url){ embed_html <- paste('<iframe src="https://www.youtube.com/embed/',short_code_url, '" align="center" width="900" height="506" frameBorder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>', sep="") IRdisplay::display_html(embed_html) } open_wd <- function(dir = getwd()){ if (.Platform['OS.type'] == "windows"){ shell.exec(dir) } else { system(paste(Sys.getenv("R_BROWSER"), dir)) } } open_r <- function(){ shell.exec("D:/HonorFiles/备份文件夹/代码集/Jupyter/胡林辉/R/display_file.R") } 蓝灯<- function(){ shell.exec("C:/Users/Jack/AppData/Roaming/Lantern/lantern.exe") } OCR_app<- function(){ shell.exec("D:/Python App/图形化界面应用_OCR.exe") } cal_metrics <- function(label, pred){ # 根据金标准和预测值的列表计算最佳cutoff及对应的sens和spec # label: 金标准，0 1 变量 # pred: 模型预测值，连续变量 roc.p=pROC::roc(label, pred) if (roc.p$auc>0.5){ cutoff=roc.p$thresholds[which.max(roc.p$sensitivities+roc.p$specificities)] sensitivity=roc.p$sensitivities[which.max(roc.p$sensitivities+roc.p$specificities)] specificity=roc.p$specificities[which.max(roc.p$sensitivities+roc.p$specificities)] df=data.frame(type='positive classification', auc=round(roc.p$auc,3),cutoff=cutoff, sensitivity=sensitivity,specificity=specificity) return(df) } else{ cutoff=roc.p$thresholds[which.min(roc.p$sensitivities+roc.p$specificities)] sensitivity=roc.p$sensitivities[which.min(roc.p$sensitivities+roc.p$specificities)] specificity=roc.p$specificities[which.min(roc.p$sensitivities+roc.p$specificities)] df=data.frame(type='negative classification', auc=1-round(roc.p$auc,3),cutoff=cutoff, sensitivity=1-sensitivity,specificity=1-specificity) return(df) } } cal_statistics_sspn<-function(fp, tp, tn, fn, decimal_digit=3){ z=1.95996 zsq = z**2 a=fp b=tp c=tn d=fn ab=fp+tp cd=tn+fn ac=fp+tn bd=tp+fn bc=tp+tn nn=fp+tp+tn+fn # 计算accuracy accu = bc/nn accu_l95b = binom.test(bc, nn)[['conf.int']][1] accu_u95b = binom.test(bc, nn)[['conf.int']][2] # 计算prevanlence prev = bd/nn ## lower limit of 95%CI of prev # p=prev n = nn q = 1 - p num = (2*n*p)+zsq-1-(z*sqrt(zsq-2-(1/n)+4*p*((n*q)+1))) denom = 2*(n+zsq) prev_l95b = num/denom if (p==0) {prev_l95b=0} else{p=prev} ## upper limit of 95%CI of prev # num = (2*n*p)+zsq+1+(z*sqrt(zsq+2-(1/n)+4*p*((n*q)-1))) denom = 2*(n+zsq) prev_u95b = num/denom if (p==1) {prev_u95b=1} else{p=prev} # 计算senstivity sens = tp/bd n = bd p = sens ## begin l95b # q = 1-p num = (2*n*p)+zsq-1-(z*sqrt(zsq-2-(1/n)+4*p*((n*q)+1))) denom = 2*(n+zsq) sens_l95b = num/denom if (p==0) {sens_l95b = 0} else{p = sens} ## begin u95b # num = (2*n*p)+zsq+1+(z*sqrt(zsq+2-(1/n)+4*p*((n*q)-1))); denom = 2*(n+zsq); sens_u95b = num/denom; if (p==1) {sens_u95b = 1} else{p = sens} #计算Specificity spec = tn/ac n = ac p = spec ## begin l95b # q = 1-p; num = (2*n*p)+zsq-1-(z*sqrt(zsq-2-(1/n)+4*p*((n*q)+1))) denom = 2*(n+zsq) spec_l95b = num/denom if (p==0) {spec_l95b = 0} else{p = spec} ## begin u95b # num = (2*n*p)+zsq+1+(z*sqrt(zsq+2-(1/n)+4*p*((n*q)-1))) denom = 2*(n+zsq) spec_u95b = num/denom if (p==1) {spec_u95b = 1} else{p = spec} #计算阳性率ppos ppos = ab/nn n = nn p = ppos ## begin l95b # q = 1-p num = (2*n*p)+zsq-1-(z*sqrt(zsq-2-(1/n)+4*p*((n*q)+1))) denom = 2*(n+zsq) ppos_l95b = num/denom if (p==0) { ppos_l95b = 0} else(p = ppos) ## begin u95b # num = (2*n*p)+zsq+1+(z*sqrt(zsq+2-(1/n)+4*p*((n*q)-1))) denom = 2*(n+zsq) ppos_u95b = num/denom if (p==1) {ppos_u95b = 1} else(p = ppos) #计算阴性率pneg pneg = cd/nn n = nn p = pneg ## begin l95b # q = 1-p num = (2*n*p)+zsq-1-(z*sqrt(zsq-2-(1/n)+4*p*((n*q)+1))) denom = 2*(n+zsq) pneg_l95b = num/denom if (p==0) {pneg_l95b = 0} else{p = pneg} ## begin u95b # num = (2*n*p)+zsq+1+(z*sqrt(zsq+2-(1/n)+4*p*((n*q)-1))) denom = 2*(n+zsq) pneg_u95b = num/denom if (p==1) {pneg_u95b = 1} else{p = pneg} #计算阳性预测值PPV ppv = b/ab n = ab p = ppv ##begin l95b q = 1-p num = (2*n*p)+zsq-1-(z*sqrt(zsq-2-(1/n)+4*p*((n*q)+1))) denom = 2*(n+zsq) ppv_l95b = num/denom if (p==0) {ppv_l95b = 0} else{p = ppv} ##begin u95b# num = (2*n*p)+zsq+1+(z*sqrt(zsq+2-(1/n)+4*p*((n*q)-1))) denom = 2*(n+zsq) ppv_u95b = num/denom if (p==1) {ppv_u95b = 1} else{p = ppv} #计算阴性预测值NPV npv = c/cd n = cd p = npv ##begin l95b q = 1-p num = (2*n*p)+zsq-1-(z*sqrt(zsq-2-(1/n)+4*p*((n*q)+1))); denom = 2*(n+zsq); npv_l95b = num/denom; if (p==0) {npv_l95b = 0} else{p = npv} ## begin u95b num = (2*n*p)+zsq+1+(z*sqrt(zsq+2-(1/n)+4*p*((n*q)-1))); denom = 2*(n+zsq); npv_u95b = num/denom; if (p==1) {npv_u95b = 1} else{p = npv} # 计算阳性似然比 #计算阴性似然比 pl = sens/(1-spec); nl = (1-sens)/spec; xp = sqrt(((1-sens)/b)+(spec/a)) xn = sqrt((sens)/d)+((1-spec)/c) lgpl = log(pl) lgnl = log(nl) ## 95%CI pl_l95b = exp(lgpl-(1.95996*xp)); pl_u95b = exp(lgpl+(1.95996*xp)); ## 阴性似然比95%CI nl_l95b = exp(lgnl-(1.95996*xn)); nl_u95b = exp(lgnl+(1.95996*xn)); statistics_df = data.frame(Metric=c('Prevalance','Accuracy', 'Senstivity','Specificity','Positive','Negative', 'Positive Predictive Value','Negative Predictive Value', 'Positive likelihood Ratios','Negative likelihood Ratios'), Estimated.value = c(prev,accu, sens,spec,ppos,pneg,ppv,npv,pl,nl), Lower.95CI = c(prev_l95b,accu_l95b, sens_l95b,spec_l95b,ppos_l95b, pneg_l95b,ppv_l95b,npv_l95b,pl_l95b,nl_l95b), Upper.95CI=c(prev_u95b,accu_u95b, sens_u95b,spec_u95b,ppos_u95b, pneg_u95b,ppv_u95b,npv_u95b,pl_u95b,nl_u95b) ) options(digits=decimal_digit) return(statistics_df)}

e25c4c77acc0c58017a8c261bc750c837f05b6f4

6ee5627b7d048d87dd4527badf7ca0443358382a

/mammals_simulated_bootstrap/templates/plot_bk.R

0295f36f3f55761035a8487ef22ca05de799b07a

[]

no_license

evolbioinfo/booster-workflows

0bae01d5b9f41316c555cb985cfac8a1841a6499

341b012a99f706a5e363af83d6b85d8b7c3f089e

refs/heads/master

2021-01-23T12:32:13.905501

2018-04-03T07:52:03

93,164,761

1

0

null

UTF-8

R

false

4,341

r

plot_bk.R

#!/bin/env Rscript library(ggplot2) library(plyr) ## FBP fbp=read.table("!{fbp}",header=T,na.strings = "N/A") colnames(fbp)=c("tree","brid","length","boot","depth","simu") fbp=fbp[fbp$depth>1,] svg("fbp_simu_vs_boot.svg") ggplot(fbp,aes(x=simu,y=boot))+geom_point(size=2,alpha=0.2)+ggtitle("FBP") dev.off() ## TBE tbe=read.table("!{tbe}",header=T,na.strings = "N/A") colnames(tbe)=c("tree","brid","length","boot","depth","simu") tbe=tbe[tbe$depth>1,] svg("tbe_simu_vs_boot.svg") ggplot(tbe,aes(x=simu,y=boot))+geom_point(size=2,alpha=0.2)+ggtitle("TBE") dev.off() sink("!{div}_!{seed}_correlations.txt") print("FBP boot vs. simu Spearman") cor(fbp$boot,fbp$simu,method = "spearman") print("FBP boot vs. simu Pearson") cor(fbp$boot,fbp$simu,method = "pearson") print("TBE boot vs. simu Spearman") cor(tbe$boot,tbe$simu,method = "spearman") print("TBE boot vs. simu Pearson") cor(tbe$boot,tbe$simu,method = "pearson") sink() ## Bias Analysis matbe=unlist(lapply(seq(0,1,0.01),function(v){ mean(tbe$boot[tbe$simu>=v & tbe$simu<v+0.2])-mean(tbe$simu[tbe$simu>=v & tbe$simu<v+0.2]) })) mafbp=unlist(lapply(seq(0,1,0.01),function(v){ mean(fbp$boot[fbp$simu>=v & fbp$simu<v+0.2])-mean(fbp$simu[fbp$simu>=v & fbp$simu<v+0.2]) })) svg("!{div}_!{seed}_bias_tbe.svg") plot(seq(0,1,0.01),matbe,type='l',ylim=c(-0.25,0.25),lwd=2,xaxt='n') abline(h=0,lwd=2) axis(1, at=seq(0,1,0.25)) dev.off() svg("!{div}_!{seed}_bias_fbp.svg") plot(seq(0,1,0.01),mafbp,type='l',ylim=c(-0.25,0.25),lwd=2,xaxt='n') abline(h=0,lwd=2) axis(1, at=seq(0,1,0.25)) dev.off() ## Rogue analysis rogues=read.table(gzfile("!{refrogues}"),stringsAsFactors=F) rogueboots=read.table(text=paste0(head(readLines("!{tbelogboot}"), -1)),stringsAsFactors=F,sep=":",skip = 8,header=F) colnames(rogueboots)=c("name","index") roguesimu=read.table(text=paste0(head(readLines("!{tbelogsimu}"), -1)),stringsAsFactors=F,sep=":",skip = 8,header=F) colnames(roguesimu)=c("name","index") roguesimu$name=gsub(" ","",roguesimu$name) rogueboots$name=gsub(" ","",rogueboots$name) svg("!{div}_!{seed}_rogues_simu_vs_boot.svg") plot(roguesimu$index/10,rogueboots$index/10, pch=20, col=ifelse(rogueboots$name%in%rogues$V1,"orange","blue"), cex=ifelse(rogueboots$name%in%rogues$V1,2,0.5), xlab="Instablity score / Simulated", ylab="Instability score / Bootstrap") legend("topleft", legend=c("Rogues", "Others"),col=c("orange", "blue"), pch=c(20,20),pt.cex=c(2,0.5)) abline(v=arrange(roguesimu,desc(index))[100,"index"]/10) abline(h=arrange(rogueboots,desc(index))[100,"index"]/10) dev.off() sink("!{div}_!{seed}_correlations.txt",append=T) print("Number of rogues in the first 100 instable taxa (simu)") table(head(arrange(roguesimu,desc(index)), n = 100)[,"name"]%in%rogues$V1) print("Number of rogues in the first 100 instable taxa (boot)") table(head(arrange(rogueboots,desc(index)), n = 100)[,"name"]%in%rogues$V1) sink() # ROC curves nrogues=length(rogues$V1) totaltaxa=length(rogueboots$name) simucumu=unlist(lapply(1:1449,function(c){sum(head(arrange(roguesimu,desc(index)), n = c)[,"name"]%in%rogues$V1)}))/nrogues bootcumu=unlist(lapply(1:1449,function(c){sum(head(arrange(rogueboots,desc(index)), n = c)[,"name"]%in%rogues$V1)}))/nrogues svg("!{div}_!{seed}_rogues_cumu.svg") plot(1:1449,simucumu,type='l',ylab ="% of total rogues found in first x instable taxa",xlab="First x instable taxa",lwd=2) lines(1:1449,bootcumu,col="blue",lwd=2) abline(v=100) legend("bottomright", legend=c("Bootstrap trees", "Simulation-based trees"),col=c("blue", "black"),lty=1,lwd=2) dev.off() # Or in instability index simucumuvp=unlist(lapply(seq(0,34,0.01),function(c){sum(roguesimu$name[roguesimu$index>c]%in%rogues$V1)/nrogues})) simucumufp=unlist(lapply(seq(0,34,0.01),function(c){sum(!(roguesimu$name[roguesimu$index>c]%in%rogues$V1))/(totaltaxa-nrogues)})) bootcumuvp=unlist(lapply(seq(0,34,0.01),function(c){sum(rogueboots$name[rogueboots$index>c]%in%rogues$V1)/nrogues})) bootcumufp=unlist(lapply(seq(0,34,0.01),function(c){sum(!(rogueboots$name[rogueboots$index>c]%in%rogues$V1))/(totaltaxa-nrogues)})) svg("!{div}_!{seed}_rogues_roc.svg") plot(simucumufp,simucumuvp,type='l',ylab ="VP",xlab="FP",lwd=2) lines(bootcumufp,bootcumuvp,col="blue",lwd=2) legend("bottomright", legend=c("Boot", "Simu"),col=c("blue", "black"),lty=1,lwd=2) dev.off()

d75f0ac02cf216d3f4b98407c83b008c5f2090b5

a02204c9d3c1c5fac13696196431d64baaf4f835

/R/new_samples.R

27aa61d311be1fc7abe7c8261bffc8039296d5d2

[]

no_license

haihaba/gcms

2e5de03ae8ca734bc972b79d116b6a36a6c77b17

8f9c7ec06f7ab27a3a51f7f4b4398d3cc9c02853

refs/heads/master

2020-04-15T06:04:38.335020

2018-05-26T14:31:01

null

0

null

UTF-8

R

false

32,599

r

new_samples.R

##' Function new_samples ##' ##' Function new_samples ##' @export ##' @param projectpath new_samples<-function(projectpath){ require(tcltk) ## Skapa prediktionskatalog startup <- 1 while(startup){ cat("\n\n===============================\n") cat("===== Process new samples =====\n") cat("===============================\n\n") startup <- menu(c("Set path","Back"),title="") if(startup == 1){ temp <- tk_choose.dir(caption="Select directory.") if(!is.na(temp)){ predpath <- temp if(length(list.files(projectpath))){ cat("¤¤¤¤\n¤¤¤¤\n") cat("Warning! ",predpath," is not empty! Some files might be overwritten!\n") cat("¤¤¤¤\n¤¤¤¤\n") } a <- 1 startup <- 0 file.copy(file.path(projectpath,"SETTINGS.Rdata"),file.path(predpath)) #file.copy(file.path(projectpath,"sampleinfo.Rdata"),file.path(predpath)) file.copy(file.path(projectpath,"maxMZ.Rdata"),file.path(predpath)) dir.create(file.path(predpath,"Edges"),showWarnings=FALSE) #dir.create(file.path(predpath,"ALKANE_SERIE"),showWarnings=FALSE) dir.create(file.path(predpath,"HMCR"),showWarnings=FALSE) if(file.exists(file.path(projectpath,"HMCR","MCR.Rdata"))) file.copy(file.path(projectpath,"HMCR","MCR.Rdata"),file.path(predpath,"HMCR")) #if(file.exists(file.path(projectpath,"ALKANE_SERIE","RT_INFO.Rdata"))) # file.copy(file.path(projectpath,"ALKANE_SERIE","RT_INFO.Rdata"),file.path(predpath,"ALKANE_SERIE")) file.copy(file.path(projectpath,"Edges","edges.Rdata"),file.path(predpath,"Edges")) if(file.exists(file.path(predpath,"newsamples.Rdata"))) load(file.path(predpath,"newsamples.Rdata")) }else{ cat("\n\n Error! You must specify where to store prediction files to continue!\n") a <- 0 } }else if(startup == 2 | !startup){ a <- 0 startup <- 0 } } ### Menu while(a){ cat("\n\n===============================\n") cat("===== Process new samples =====\n") cat("===============================\n\n") cat("Path:\t",predpath,"\n\n") predmenu <- c("Import and smooth data","Select samples","Align samples","Check for outliers","Compress data","Resolve data","Export to NIST","Quit") if(exists("newsamples")){ if(length(newsamples)){ predvector <- c(TRUE,TRUE,TRUE,TRUE,FALSE,TRUE,TRUE,TRUE) cat("Number of prediction files selected: ",length(newsamples),"\n") }else predvector <- c(TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE) }else predvector <- c(TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE) a <- menu(predmenu[predvector],title="") if(!a | a == sum(predvector)){ a <- 0 }else if(a == 1){ #Import data importmenu <- 1 while(importmenu){ importmenu <- menu(c("Leco CSV","Leco CSV (special)","Andi NetCDF","Done"),title = "Import files") if(importmenu == 4 | !importmenu) importmenu <- 0 if(importmenu == 1) cat("\nSorry, not implemented yet\n") else if(importmenu == 2) cat("\nSorry, not implemented yet\n") else if(importmenu == 3) read_cdf(predpath) } }else if(a == 2){ b <- 1 while(b){ b <- menu(c("Select samples", "Remove files","List files","Done"),title="Select samples") if(b == 1){ temp <- tk_choose.files(caption="Select .Rdata files.", multi = TRUE,filters = matrix(c("Rdata files (*.Rdata", "*.Rdata"),1,2,byrow=T)) if(length(temp)){ if(exists("newsamples")) newsamples <- unique(c(newsamples,temp)) else newsamples <- temp predvector <- c(TRUE,TRUE,TRUE,TRUE,TRUE,TRUE) more <- 1 while(more == 1){ more <- menu(c("Yes","No"),title="Choose files from another directory?") if(more == 1){ temp <- tk_choose.files(,caption="Select .Rdata files.", multi = TRUE,filters = matrix(c("Rdata files (*.Rdata", "*.Rdata"),1,2,byrow=T)) newsamples <- unique(c(newsamples,temp)) } } } }else if(b == 2){ remfiles <- select.list(basename(newsamples),multiple=TRUE,title="Remove samples from prediction set") newsamples <- newsamples[!(basename(newsamples) %in% remfiles)] }else if(b == 3){ if(exists("newsamples")) select.list(basename(newsamples)) else cat("No samples selected!\n") }else if(b == 4 | !b){ b <- 0 save(newsamples,file=file.path(predpath,"newsamples.Rdata")) } } }else if(a == 3){ if(file.exists(file.path(projectpath,"Aligned","target.Rdata"))){ load(file.path(projectpath,"Aligned","target.Rdata")) cat("Aligning samples..\n") aligndatapred(projectpath,predpath,newsamples,target,minshift,datasource,ion) }else cat("Error! No original target file found!") }else if(a == 4){ check_data(predpath) }else if(a == 5){ DATA <- sigma_unfold(predpath) scoresAR(predpath,DATA) }else if(a == 6){ cat("Resolving data.. \n") results <- find_spectrum2(predpath,projectpath) if(length(results)){ read_win2(predpath,results$type,results$windowlist) cat("Exporting spectrum to NIST...\n") spec2NIST2(predpath,results$type,all=TRUE) } }else if(a == 7){ #Export to NIST spec2NIST2(predpath,results$type) }else if(a == 8 | !a) a <- 0 } } ##' Function scoresAR ##' ##' Function scoresAR ##' @param predpath ##' @param DATA ##' @return DATA, SPECTRUM, VARID1, OBSID1, VARID2 scoresAR <- function(predpath,DATA){ if(missing(DATA)) load(file.path(predpath,"HMCR","REG","MVA_DATA.Rdata")) load(file.path(predpath,"info.Rdata")) load(file.path(predpath,"Aligned","SCAN_RANGE.Rdata")) D <- numeric() VARID1 <- character() spectrum <- numeric() for(i in 1:max(ROWID1[,1])) { textstr <- paste("Checking window number:", i) cat(textstr,"\n") #do_log(predpath,textstr) int <- which(ROWID1[,1]==i) XX <- DATA[,int] # DATA = X SSX <- sum(XX^2) if(min(dim(XX))>2) { CS <- ARmethod1(XX,predpath) comp <- ncol(CS$C) D <- cbind(D,CS$C) S_temp <- matrix(0,nrow=comp,ncol=max(SCAN_RANGE)) MZ <- ROWID1[int,2] S_temp[,MZ] <- t(CS$S) spectrum <- rbind(spectrum,S_temp) for(j in 1:comp) { if(i > 99) { if(j < 10) { VARID1 <- rbind(VARID1,paste("W",i,"_C0",j)) }else VARID1 <- rbind(VARID1,paste("W",i,"_C",j)) }else { if(i < 10) { if(j < 10) { VARID1 <- rbind(VARID1,paste("W00",i,"_C0",j)) }else VARID1 <- rbind(VARID1,paste("W00",i,"_C",j)) }else if(j < 10) { VARID1 <- rbind(VARID1,paste("W0",i,"_C0",j)) }else VARID1 <- rbind(VARID1,paste("W0",i,"_C",j)) } } } } #output <- list(D=D,VARID1=VARID1,spectrum=spectrum) DATA <- D load(file.path(predpath,"Aligned","COLUMNID1.Rdata")) OBSID1 <- COLUMNID1 save(DATA,OBSID1,VARID1,file=file.path(predpath,"HMCR","REG","MVA_DATA.Rdata")) write.table(data.frame(ID=OBSID1,DATA),file=file.path(predpath,"HMCR","REG","MVA_DATA.txt"),row.names=FALSE,col.names=c("PrimaryID",VARID1),sep="\t",quote=FALSE) save(spectrum,VARID1,file=file.path(predpath,"HMCR","REG","SPECTRUM.Rdata")) load(files[which.min(shift)]) EDGES_TIME <- SCAN_INFO[,2] save(EDGES_TIME,file=file.path(predpath,"EDGES_TIME.Rdata")) } ##' Function ARmethod1 ##' ##' Function ARmethod1 ##' @param X ##' @param projectpath ##' @return C, S ARmethod1 <- function(X,projectpath){ require(MASS) X[X<0] <- 0 XX <- sweep(X,2,colMeans(X)) vec <- pca(XX,min(c((dim(X)-1),15)))$vec SSX <- sum(XX^2) load(file.path(projectpath,"SETTINGS.Rdata")) limit <- SETTINGS$R2Xc R <- sum(cumsum(diag(t(vec)%*%vec)/SSX)<limit)+1 # R f?r ej vara 0 p <- pca(X,R)$p S <- abs(p) Sstart <- S diff <- 1 R2 <- 0 z <- 0 zz <- 100 C <- X%*%S%*%ginv(t(S)%*%S) C[C<0] <- 0 Cstart <- C while(diff > 1e-6) { z <- z+1 S <- t(X)%*%C%*%ginv(t(C)%*%C) S[S<=0] <- 0 if(R == 1) # Applyfunktionen nedan fungerar endast f?r R>1 { S[,1]/sqrt(sum(S[,1]^2)) }else S[,1:R] <- apply(S[,1:R],2,function(S) S/sqrt(sum(S^2))) CC <- t(S)%*%S if(sum(CC>0.95) > R) { zz <- z z <- 100 } C <- X%*%S%*%ginv(t(S)%*%S) C[C<0] <- 0 R2new <- 1-ss(X-C%*%t(S))/ss(X) diff <- abs(R2-R2new) R2 <- R2new if(any(is.na(R2))) z <- 100 if(z == 100) { cat("Rank reduced after: ",zz, " iterations.\n") z <- 0 R <- R-1 C <- Cstart[,1:R] diff <- 1 R2 <- 0 zz <- 100 } } cat("Rank: ",R,"\n") cat("R2X: ",R2,"\n") cat("Iterations: ",z,"\n") output <- list(C=C,S=S) } ##' Function ss ##' ##' Function ss ##' @param X ##' @return SSX ss<-function(X) SSX <- sum(X^2) ##' Function pca ##' ##' Function pca ##' @export ##' @param X ##' @param comp ##' @return vec, p pca<-function(X,comp){ # Calculates Principal componets of X by calc eigvectors of X'*X or X*X' # Depending on whats easiest to calculate.... if(nrow(as.matrix(X)) < ncol(as.matrix(X))){ tX <- t(X) p <- eigen(X%*%tX)$vectors[,1:comp] if(comp>1){ p <- apply(p,2,function(p) tX%*%p) p <- apply(p,2,function(p) p/sqrt(sum(p^2))) }else{ p <- tX%*%p p <- p/sqrt(sum(p^2)) } }else p <- eigen(t(X)%*%X)$vectors[,1:comp] vec <- X%*%p vecp <- list(vec=vec,p=p) } ##' function aligndatapred ##' ##' Function aligndatapred ##' ##' @param projectpath ##' @param predpath ##' @param newsamples ##' @param target ##' @param minshift ##' @param datasource ##' @param ion aligndatapred <- function(projectpath,predpath,newsamples,target,minshift,datasource,ion){ load(file.path(projectpath,"maxMZ.Rdata")) alignfiles <- newsamples[order(newsamples)] A_DATA <- list() #do_log(predpath,"Aligning data") n <- length(alignfiles) dir.create(file.path(predpath,"Aligned"),showWarnings=FALSE) COLUMNID1 <- cbind(sub("[.][^.]*$", "", basename(alignfiles))) # Filenames without extensions A_DATA$files <- files <- alignfiles A_DATA$fsize <- matrix(0,n,2) for(i in 1:n){ cat("Loading ", paste(basename(alignfiles[i])," (",i,"/",n,")\n",sep="")) load(alignfiles[i]) A_DATA$fsize[i,] <- dim(Xbc) num <- which(SCAN_RANGE == ion) if(i == 1){ NUM_MZ <- ncol(Xbc) NUM_scans <- nrow(Xbc) A_DATA$tic <- matrix(0,n,NUM_scans) A_DATA$bp <- matrix(0,n,NUM_scans) A_DATA$ic <- matrix(0,n,NUM_scans) SUM_MZ <- matrix(0,n,NUM_MZ) } if(nrow(Xbc) > NUM_scans){ A_DATA$tic <- cbind(A_DATA$tic,matrix(0,n,nrow(Xbc)-NUM_scans)) A_DATA$bp <- cbind(A_DATA$bp,matrix(0,n,nrow(Xbc)-NUM_scans)) A_DATA$ic <- cbind(A_DATA$ic,matrix(0,n,nrow(Xbc)-NUM_scans)) NUM_scans <- nrow(Xbc) } if(ncol(Xbc) > NUM_MZ) SUM_MZ <- cbind(SUM_MZ,matrix(0,n,ncol(Xbc)-NUM_MZ)) A_DATA$tic[i,1:nrow(Xbc)] <- rowSums(Xbc) A_DATA$bp[i,1:nrow(Xbc)] <- apply(Xbc,1,max) A_DATA$ic[i,1:nrow(Xbc)] <- t(Xbc[,num]) SUM_MZ[i,1:ncol(Xbc)] <- colSums(Xbc) if(ncol(Xbc) < maxMZ){ Xbc <- cbind(Xbc,matrix(0,nrow = nrow(Xbc),ncol = maxMZ-ncol(Xbc))) SCAN_RANGE <- c(SCAN_RANGE,(max(SCAN_RANGE)+1):maxMZ) save(Xbc,SCAN_INFO,SCAN_RANGE,file = alignfiles[i]) }else if(ncol(Xbc) >= maxMZ){ Xbc <- Xbc[,1:maxMZ] SCAN_RANGE <- SCAN_RANGE[1:maxMZ] save(Xbc,SCAN_INFO,SCAN_RANGE,file = alignfiles[i]) } } A_DATA$ion <- ion A_DATA$ok <- 1 if(file.exists(file.path(predpath,"SETTINGS.Rdata"))){ load(file.path(predpath,"SETTINGS.Rdata")) max_shift <- SETTINGS$MS }else{ cat("No settings for maximum shift found! Using default value (500)") max_shift <- 500 } if(datasource == "TIC"){ DATA <- A_DATA$tic }else if(datasource == "Basepeak"){ DATA <- A_DATA$bp }else if(datasource == "IC") DATA <- A_DATA$ic if(length(target) < ncol(DATA)) target <- c(target,rep(0,ncol(DATA)-length(target))) shift <- numeric() for(i in 1:length(alignfiles)){ CO_VAR <- numeric() A <- target[(max_shift+1):(ncol(DATA)-max_shift)] for(j in -max_shift:max_shift){ B <- DATA[i,(max_shift+1+j):(ncol(DATA)-max_shift+j)] CO_VAR <- c(CO_VAR,sum(A*B)) } shift <- c(shift,(which.max(CO_VAR)-max_shift-1)) } shift <- shift-minshift cat("Min shift: ", min(shift),"\n") cat("Max shift: ", max(shift),"\n") if(min(shift)<0) cat("Adjusting negative shifts...\n") shift <- shift-min(shift) start <- 1+shift[1:nrow(A_DATA$tic)] stop <- ncol(A_DATA$tic)-max(shift)+shift[1:nrow(A_DATA$tic)] BASEPEAK <- matrix(0,nrow(A_DATA$bp),ncol(A_DATA$bp)) TIC <- matrix(0,nrow(A_DATA$tic),ncol(A_DATA$tic)) for(i in 1:nrow(A_DATA$bp)){ TIC[i,1:(stop[i]-start[i]+1)] <- A_DATA$tic[i,start[i]:stop[i]] BASEPEAK[i,1:(stop[i]-start[i]+1)] <- A_DATA$bp[i,start[i]:stop[i]] } TIC_RAW <- A_DATA$tic cat("Saving variables..\n") save(TIC_RAW,file=file.path(predpath,"Aligned","TIC_RAW.Rdata")) save(shift,file=file.path(predpath,"Aligned","shift.Rdata")) save(TIC,file=file.path(predpath,"Aligned","TIC.Rdata")) save(BASEPEAK,file=file.path(predpath,"Aligned","BASEPEAK.Rdata")) save(SUM_MZ,file=file.path(predpath,"Aligned","SUM_MZ.Rdata")) save(COLUMNID1,file=file.path(predpath,"Aligned","COLUMNID1.Rdata")) save(files,file=file.path(predpath,"Aligned","files.Rdata")) save(SCAN_RANGE,file=file.path(predpath,"Aligned","SCAN_RANGE.Rdata")) save(NUM_scans,file=file.path(predpath,"Aligned","NUM_scans.Rdata")) textstr <- paste(files," Shift: ", shift," Scans: ", A_DATA$fsize[,1]," MZ: ",A_DATA$fsize[,2],sep="\n") #do_log(predpath,textstr) } ##' Function read_win2 ##' ##' Function read_win2 ##' @param predpath ##' @param type ##' @param win ##' @return DATA, SPECTRUM, VARID1, OBSID1, VARID2 read_win2<-function(predpath,type,win){ #require(xlsReadWrite) SPECTRUM <- DATA <- VARID1 <- VARID2 <- numeric() load(file.path(predpath,"Edges","edges.Rdata")) num_windows <- length(edges)-1 load(file.path(predpath,"Aligned","files.Rdata")) if(!missing(win)){ if(length(list.files(file.path(predpath,"HMCR",type,"win"))) > length(win)){ cat("\nRecently processed windows: ",win,"\n") cat("Previously processed windows: ", setdiff(as.numeric(substr(basename(list.files(file.path(predpath,"HMCR",type,"win"))),4,6)),win),"\n") if(1 == menu(c("Yes","No"),title="There are some processed windows that are not selected, do you wish to include some of them as well?")) while(!length(win <- sort(as.numeric(substr(basename(tk_choose.files(caption="Select windows to process")),4,6))))) cat("You must select at least one window!\n") } }else{ cat("Select windows.") while(!length(win <- sort(as.numeric(substr(basename(tk_choose.files(caption="Select windows to process")),4,6))))) cat("You must select at least one window!\n") } cat("\n") for(i in win){ if(file.exists(file.path(predpath,"HMCR",type,"win",paste("win",ifelse(i<=99,ifelse(i<=9,paste("00",i,sep=""),paste("0",i,sep="")),i),".Rdata",sep="")))){ load(file.path(predpath,"HMCR",type,"win",paste("win",ifelse(i<=99,ifelse(i<=9,paste("00",i,sep=""),paste("0",i,sep="")),i),".Rdata",sep=""))) if(length(S)){ cat(nrow(as.matrix(C)),"\n") if(nrow(as.matrix(C)) == length(files)){ cat("--------------------------------------------------------\n") SPECTRUM <- cbind(SPECTRUM,S) DATA <- cbind(DATA,C) cat("Window num: ",i,"\nNumber of comps: ",ncol(S),"\nTotal number of comps: ", ncol(SPECTRUM),"\n") VARID2 <- c(VARID2,TIME) for(j in 1:ncol(S)) VARID1 <- rbind(VARID1,ifelse(i>99,ifelse(j<10,paste("Win",i,"_C0",j,sep=""),paste("Win ",i,"_C",j,sep="")),ifelse(i<10,ifelse(j<10,paste("Win00",i,"_C0",j,sep=""),paste("Win00",i,"_C",j,sep="")),ifelse(j<10,paste("Win0",i,"_C0",j,sep=""),paste("Win0",i,"_C",j,sep=""))))) }else cat("Number of files and rows in data matrix does not match!\n") } } } load(file.path(predpath,"Aligned","COLUMNID1.Rdata")) OBSID1 <- COLUMNID1 save(DATA,VARID1,VARID2,OBSID1,SPECTRUM,file=file.path(predpath,"HMCR",type,"MVA_DATA.Rdata")) save(SPECTRUM = SPECTRUM,file=file.path(predpath,"HMCR",type,"SPECTRUM.Rdata")) save(VARID1 = VARID1,file=file.path(predpath,"HMCR",type,"VARID1.Rdata")) save(VARID2 = VARID2,file=file.path(predpath,"HMCR",type,"VARID2.Rdata")) write.table(data.frame(ID = OBSID1,DATA),file=file.path(predpath,"HMCR",type,"MVA_DATA.txt"),sep='\t',row.names = FALSE,col.names = c("Primary ID",VARID1),quote=FALSE) out <- list(DATA = DATA,SPECTRUM = SPECTRUM,VARID1 = VARID1, OBSID1 = OBSID1,VARID2 = VARID2) } ##' Function sigma_unfold ##' ##' Function sigma_unfold ##' @param predpath ##' @return DATA sigma_unfold <- function(predpath){ if(!file.exists(file.path(predpath,"Aligned","shift.Rdata"))){ cat("Error! You need to align data first!\n") return(NULL) }else{ load(file.path(predpath,"Aligned","files.Rdata")) load(file.path(predpath,"Aligned","COLUMNID1.Rdata")) load(file.path(predpath,"Aligned","shift.Rdata")) OBSID1 <- COLUMNID1 load(file.path(predpath,"SETTINGS.Rdata")) DOBL <- SETTINGS$BC2 if(file.exists(file.path(predpath,"Edges","edges.Rdata"))){ load(file.path(predpath,"Edges","edges.Rdata")) load(file.path(predpath,"Aligned","SCAN_RANGE.Rdata")) ROWID1 <- varnamegen(length(edges)-1,length(SCAN_RANGE),min(SCAN_RANGE)) load(file.path(predpath,"Aligned","NUM_scans.Rdata")) for(i in 1:length(files)){ DATA_temp <- numeric() cat("Loading ",basename(files[i]),paste("(",i,"/",length(files),")",sep=""),"\n") load(files[i]) if(shift[i]+tail(edges,1)>nrow(Xbc)) # Avoids "subscript out of bounds" if the shift is too big (outliers) Xbc <- rbind(Xbc,matrix(0,shift[i]+tail(edges,1)-nrow(Xbc),ncol(Xbc))) for(k in 1:(length(edges)-1)){ X_temp <- Xbc[edges[k]:edges[k+1]+shift[i],] if(DOBL){ BL <- apply(X_temp,2,function(X_temp) approx(c(1,length(X_temp)),X_temp[c(1,length(X_temp))],1:length(X_temp))$y) # method = 'linear' by default X_temp <- X_temp-BL X_temp[X_temp<0] <- 0 } DATA_temp <- cbind(DATA_temp, matrix(colSums(X_temp),nrow=1)) } if(i == 1) DATA <- matrix(0,length(files),length(DATA_temp)) if(ncol(DATA) < ncol(DATA_temp)) DATA <- cbind(DATA,matrix(0,nrow=nrow(DATA),ncol=ncol(DATA_temp)-ncol(DATA))) DATA[i,1:ncol(DATA_temp)] <- DATA_temp } dir.create(file.path(predpath,"HMCR","REG"),showWarnings=FALSE) save(DATA,OBSID1,file=file.path(predpath,"HMCR","REG","MVA_DATA.Rdata")) save(edges,shift,ROWID1,files,file=file.path(predpath,"info.Rdata")) output <- DATA }else{ cat("Error! You need to set edges first!\n") return(NULL) } } } ##' Function varnamegen ##' ##' Function varnamegen ##' @param intervals ##' @param peks ##' @param start ##' @return A varnamegen <- function(intervals,peks,start) { #A <- cbind(paste("int ",sort(rep(1:intervals,peks))," M/Z ", rep(start:(start+peks-1),intervals)," ",sep="")) A <- matrix(c(sort(rep(1:intervals,peks)),rep(start:(start+peks-1),intervals)),peks*intervals,2) } ##' Function find_spectrum2 ##' ##' Function find_spectrum2 ##' @param predpath ##' @param projectpath ##' @return type, windwolist find_spectrum2<-function(predpath,projectpath){ require(MASS) datamenu <- character() if(file.exists(file.path(projectpath,"HMCR","REG","MVA_DATA.Rdata"))) datamenu <- "REG H-MCR DATA" if(length(datamenu)){ dataexport <- menu(c(datamenu,"Cancel"),title="Select data") if(dataexport == 0 | dataexport == length(c(datamenu,"Cancel"))) datamenu <- character() } if(!length(datamenu)){ cat("Error! (Aborted by user or no data found)\n\n") return(character()) }else{ type = ifelse(length(datamenu) == 1,strsplit(datamenu," ")[[1]][1],c("REG","CV")[dataexport]) load(file.path(predpath,"Aligned","files.Rdata")) load(file.path(predpath,"Edges","edges.Rdata")) load(file.path(predpath,"Aligned","shift.Rdata")) load(file.path(predpath,"Aligned","SCAN_RANGE.Rdata")) load(file.path(predpath,"maxMZ.Rdata")) load(files[which.min(shift)]) load(file.path(projectpath,"HMCR","MCR.Rdata")) windowlist <- as.numeric(substr(basename(list.files(file.path(projectpath,"HMCR",type,"win"))),4,6)) EDGES_TIME <- SCAN_INFO[,2] load(file.path(projectpath,"SETTINGS.Rdata")) NL <- SETTINGS$NL RT_LIMIT <- SETTINGS$MPS DO_BL <- SETTINGS$BC2 #color <- cbind("red","green","blue","black","purple","grey","yellow4","red","green","blue","black","purple","grey","yellow4","red","green","blue","black","purple","grey","yellow4","red","green","blue","black","purple","grey","yellow4","red","green","blue","black","purple","grey","yellow4") rm(Xbc,SETTINGS) dir.create(file.path(predpath,"Edges","dat"),recursive = TRUE,showWarnings = FALSE) temp <- list.files(file.path(projectpath,"Edges","dat"),full.names=TRUE) dir.create(file.path(predpath,"Edges","dat","Model samples_bg_corr"),showWarnings = FALSE,recursive=TRUE) dir.create(file.path(predpath,"HMCR",type,"win_png"),showWarnings = FALSE,recursive = TRUE) dir.create(file.path(predpath,"HMCR",type,"win"),showWarnings = FALSE,recursive = TRUE) gc() Scores <- numeric() ### attpempt to store the background correction data. bg correction storage in "find_spectrum.R" is done in arrays per window ### scan * m/z * sample. Here processing is sample wise (in "find_spectrum.R", the whole sampleset is in memory at once), ### so the array has to be build step by step ### First array variables for each window has to be defined. Then in the two main loops "sample" and "window" the variables ### are filled sample by sample. After finishing the two loops, the arrays have to be stored in corresponding files #for(win in windowlist){ # scans<-(edges[win+1]-edges[win]+1) # mzs<-length(SCAN_RANGE) # BL<-array(0,dim=c(scans,mzs,length(files))) # #eval(parse(text=paste("BL_",win,"<-NULL",sep=""))) # save(BL,file=file.path(predpath,"Edges","dat","Model samples_bg_corr",paste("bg_win",ifelse(win<=99,ifelse(win<=9,paste("00",win,sep=""),paste("0",win,sep="")),win),".Rdata",sep=""))) # rm(BL) #} if(length(windowlist)){ for(i in 1:length(files)){ load(files[i]) cat("Resolving ",basename(files[i]),paste(" (",i,"/",length(files),")\n",sep="")) for(win in windowlist){ if(edges[win]+shift[i] > 0 & edges[win+1]+shift[i] < nrow(Xbc)){ x <- Xbc[edges[win]:edges[win+1]+shift[i],] if(ncol(x) < maxMZ) x <- cbind(x,matrix(0,nrow=nrow(x),ncol= maxMZ-ncol(x))) if(DO_BL){ # Removes baseline for prediction files BL<-matrix(apply(x,2,function(x) approx(c(1,length(x)),x[c(1,length(x))],1:length(x))$y),nrow=length(edges[win]:edges[win+1]),ncol=maxMZ) # method = 'linear' by default x <- x-BL x[x<0] <- 0 ###test code### #BL_new<-BL #load(file.path(predpath,"Edges","dat","Model\ samples","bg_corr",paste("bg_win",ifelse(win<=99,ifelse(win<=9,paste("00",win,sep=""),paste("0",win,sep="")),win),".Rdata",sep=""))) #BL[,,i]<-BL_new #save(BL,file=file.path(predpath,"Edges","dat","Model samples","bg_corr",paste("bg_win",ifelse(win<=99,ifelse(win<=9,paste("00",win,sep=""),paste("0",win,sep="")),win),".Rdata",sep=""))) #rm(BL_new) rm(BL) } load(file.path(projectpath,"HMCR", "REG","win",paste("win",ifelse(win<=99,ifelse(win<=9,paste("00",win,sep=""),paste("0",win,sep="")),win),".Rdata",sep=""))) # browser() if(!length(S)) c2 <- numeric() else c2 <- do_AR_all_prediction(x,S,CP,RT_LIMIT) if(exists("PCApara")) scores <- (colSums(x) <- PCApara$mean)%*%PCApara$loadings[,1:2] else scores <- numeric() ok <- 1 }else{ load(file.path(projectpath,"HMCR", "REG","win",paste("win",ifelse(win<=99,ifelse(win<=9,paste("00",win,sep=""),paste("0",win,sep="")),win),".Rdata",sep=""))) c2 <- matrix(0,length(edges[win]:edges[win+1]),ncol(as.matrix(C))) ok <- 0 if(exists("PCApara")) scores <- c(0,0) else scores <- numeric() } if(i == 1){ if(ok){ C<-matrix(colSums(as.matrix(c2,ncol=ncol(as.matrix(C)))),nrow=1) CC <- c2 }else{ C<-matrix(colSums(as.matrix(c2,ncol=ncol(as.matrix(C)))),nrow=1)*NA CC<-c2 } Scores <- rbind(Scores,scores) save(C,CC,S,TIME,Scores,file=file.path(predpath,"HMCR",type,"win",paste("win",ifelse(win<=99,ifelse(win<=9,paste("00",win,sep=""),paste("0",win,sep="")),win),".Rdata",sep=""))) }else{ load(file.path(predpath,"HMCR",type,"win",paste("win",ifelse(win<=99,ifelse(win<=9,paste("00",win,sep=""),paste("0",win,sep="")),win),".Rdata",sep=""))) Scores <- rbind(Scores,scores) if(ok){ C<-rbind(C,colSums(as.matrix(c2,ncol=ncol(as.matrix(C))))) CC<-rbind(CC,c2) }else{ C<-rbind(C,colSums(as.matrix(c2,ncol=ncol(as.matrix(C))))*NA) CC<-rbind(CC,c2) } save(C,CC,S,TIME,Scores,file=file.path(predpath,"HMCR",type,"win",paste("win",ifelse(win<=99,ifelse(win<=9,paste("00",win,sep=""),paste("0",win,sep="")),win),".Rdata",sep=""))) } gc() } gc() } # Plot windows # for(win in windowlist){ # if(win %in% as.numeric(substr(basename(list.files(file.path(projectpath,"HMCR",type,"win"))),4,6))){ # rm(Scores) # load(file.path(predpath,"HMCR",type,"win",paste("win",ifelse(win<=99,ifelse(win<=9,paste("00",win,sep=""),paste("0",win,sep="")),win),".Rdata",sep=""))) # if(min(dim(CC)) > 0){ # if(length(Scores)){ # #Plot functions for Cross validation method # } # Hz <- 1/median(diff(SCAN_INFO[,2])) # for(i in 1:ncol(CC)){ # c2 <- matrix(CC[,i],length(edges[win]:edges[win+1]),length(files)) # if(length(EDGES_TIME) < edges[win+1]) # EDGES_TIME <- c(EDGES_TIME,approxExtrap(1:length(EDGES_TIME),EDGES_TIME,xout=(length(EDGES_TIME)+1):edges[win+1])$y) # for(j in 1:ncol(c2)){ # plot(EDGES_TIME[edges[win]:edges[win+1]]+median(shift)/Hz,c2[,j],col=color[i],xlab="Time",ylab="Intensity",type="l",ylim=cbind(0,max(CC)),xlim=range(EDGES_TIME[edges[win]:edges[win+1]]+median(shift)/Hz),main=paste("Window:",win),lwd=1) # par(new=TRUE) # } # } # par(new=FALSE) # #savePlot(file.path(predpath,"HMCR", "REG","win_png",paste("win",ifelse(win<=99,ifelse(win<=9,paste("00",win,sep=""),paste("0",win,sep="")),win),sep="")),type="png") # }else # cat("No data found in window ", win,"!\n") # } # } }else cat("No windows processed.\n") return(list(type=type,windowlist=windowlist)) } } ##' Function do_AR_all_prediction ##' ##' Function do_AR_all_prediction ##' Funciton to do Alternate Regression on a prediction set ##' @param x ##' @param S ##' @param CP ##' @param RT_LIMIT ##' @return Cnew do_AR_all_prediction <- function(x,S,CP,RT_LIMIT){ var <- nrow(x) mz <- ncol(x) C <- x%*%S%*%ginv(t(S)%*%S) C[C<0] <- 0 Cnew <- C*0 for(i in 1:ncol(C)){ c <- as.matrix(C[,i],nrow=var) Cnew[,i] <- unimodal_3(c,1,CP[i],RT_LIMIT) } C <- Cnew } ##' Function unimodal_3 ##' ##' Function unimodal_3 ##' @param C ##' @param obs ##' @param cp ##' @param RT_LIMIT ##' @return C unimodal_3<-function(C,obs,cp,RT_LIMIT){ for(i in 1:obs){ xpeak <- peak_pick(t(C[,i]))$xpeak mp <- which(as.logical(xpeak)) if(!length(mp)){ C[,i] <- C[,i]*0 }else{ if(which.min(abs(mp-cp))) mp <- min(mp[which.min(abs(mp-cp))]) else mp <- 1 if(abs(mp-cp)<RT_LIMIT){ D <- diff(C[1:mp,i]) if(length(which(D<0))) poss <- max(which(D<0)) else poss <- 1 for(j in poss:1) C[j,i] <- min(C[j:mp,i]) D <- diff(C[mp:nrow(C),i]) poss <- if(length(which(D>0))) poss <- min(which(D>0)) else poss <- FALSE if(poss){ for(j in (mp-1+poss):nrow(C)) C[j,i] <- min(C[mp:j,i]) } # Correction of strange peaks knew <- C[(diff(C[,i]) != 0),i] mpnew <- which.max(knew) k <- C[,i]*0 k[1:length(knew)+mp-mpnew] <- knew C[,i] <- k }else C[,i] <- C[,i]*0 } } C } ##' Function peak_pick ##' ##' Function peak_pick ##' Function to find peaks ##' @param x ##' @return xpeak, xout peak_pick<-function(x){ xout <- x <- as.matrix(x) xd1 <- sgolayfilt(xout,3,11,1) NOISE_LIMIT <- median(x) N1 <- which(x>NOISE_LIMIT) N2 <- matrix(0,nrow(x),ncol(x)) for (i in 3:(ncol(x)-2)) if(xd1[i-2] > 0) if(xd1[i-1] > 0) if(xd1[i+1] < 0) if(xd1[i+2] < 0) if(sum(N1 == i) == 1) N2[i] <- TRUE N <- which(as.logical(N2)) if(length(N) > 1){ while(min(diff(N)) < 3){ p1 <- min(which(diff(N) < 3)) p2 <- p1+1 if(xout[N[p1]] < xout[N[p2]]) N <- N[-p1] else N <- N[-p2] if(length(N) == 1) break } } xpeak <- matrix(0,nrow(x),ncol(x)) xpeak[N] <- xout[N] out <- list(xpeak = xpeak, xout = xout) } ##' Function sgolay ##' ##' Function sgolay ##' @param p ##' @param n ##' @param ts ##' @return F sgolay<-function(p, n, m = 0, ts = 1){ library(MASS) if (n %% 2 != 1) stop("sgolay needs an odd filter length n") if (p >= n) stop("sgolay needs filter length n larger than polynomial order p") F = matrix(0., n, n) k = floor(n/2) for (row in 1:(k+1)) { C = ( ((1:n)-row) %*% matrix(1, 1, p+1) ) ^ ( matrix(1, n) %*% (0:p) ) A = ginv(C) F[row,] = A[1+m,] } F[(k+2):n,] = (-1)^m * F[k:1,n:1] if (m > 0) F = F * prod(1:m) / (ts^m) class(F) = "sgolayFilter" F } ##' Function sgolayfilt ##' ##' Function sgolayfilt ##' @param x ##' @param p ##' @param n ##' @param m ##' @param ts sgolayfilt<-function(x, p = 3, n = p + 3 - p%%2, m = 0, ts = 1){ len = length(x) if (class(p) == "sgolayFilter" || (!is.null(dim(p)) && dim(p) > 1)){ F = p n = nrow(F) }else F = sgolay(p, n, m, ts) k = floor(n/2) #z = filter(F[k+1,n:1], 1, x) filt <- F[k+1,n:1] z <- na.omit(stats:::filter(c(rep(0,length(filt) - 1), x), filt, sides = 1)) c(F[1:k,] %*% x[1:n], z[n:len], F[(k+2):n,] %*% x[(len-n+1):len]) }

504754e38750c4fb1d36d5134f3546a00a720a90

a17795f03e21930af5dcbac26d8e19f7500585c4

/lecture9/in_class_exercise.R

70ff9f1476c2564cf3e27687b1a0447f5f0ec30f

[]

no_license

shrebirth1223/Seunghyun_park

7c6764f36404b9a374ca90b4f626041f4099106f

016776b2394f7dec12959a9c94b963e99926419e

refs/heads/master

2020-07-21T20:01:37.639932

2015-07-15T12:31:54

38,341,928

0

null

UTF-8

R

false

4,921

r

in_class_exercise.R

#1. A store keeps track of purchase history. The manager wonders if there is an association between the amount of money a customer spends on their first visit to the store and their second visit. Below is data collected on 10 customers. Each column corresponds to one customer. For example, the first customer spend $20 on the first visit and $23 on the second visit. The second customer spend $32 on first visit and $34 on second, etc. #Money spent on first visit (in dollars): 20,32,35,34,40,51,52,56,57,68 Money spent on second visit (in dollars): 23,34,36,44,42,51,54,57,54,62 first_visit <- c(20,32,35,34,40,51,52,56,57,68) second_visit <- c(23,34,36,44,42,51,54,57,54,62) data=cbind(first_visit,second_visit) #a. Display the relationship between first and second visit dollar amounts? plot(first_visit,second_visit) #b. Describe the pattern in part (a) briefly. Is there a relationship? Is it positive or negative? Is it linear or non-linear? Is it weak or strong? #It shows the positive linear relation ship betweeen first_visit and second_visit, It's quite strong. #c. Calculate the correlation coefficient between the amount of money spent on the first visit and the second visit. corelation <- cor(first_visit,second_visit) standard_error <- sqrt((1-corelation^2)/(length(first_visit)-2)) #d. What does the standard error in part (c) refer to? #e. Calculate an approximate 95% confidence interval for ρ. interval_right <- cor(data)-2*standard_error#first answer interval_left <- cor(data)+2*standard_error#first answer rt<-cor.test(first_visit,second_visit)#Second answer rt$conf.int#Second answer #2. Answer the following question using the data from question (2). #a. Adding $30 to each of the observations for the second visit. How is the correlation coefficient between first and second visits affected? What can you conclude about the effects on the correlation coefficient of adding a constant to one or both of the variables? c1_B <- first_visit + 30 c2_B <- second_visit + 30 cor(first_visit,c2_B) #b. Convert the first visit to cents (i.e., multiply by 100). How does this affect the correlation between the first and second visits? What can you conclude about the effects on the correlation coefficient of multiplying one or both of the variables by a constant? c1_C <- first_visit*100 cor(c1_C,second_visit) #3. Some species seem to thrive in captivity, whereas others are prone to health and behavior difficulties when caged. Maternal care problems in some captive species, for example, lead to high infant mortality. Can these differences be predicted? The following data are measurements of the infant mortality (percentage of births) of 20 carnivore species in captivity along with the log (based-10) of the minimal home range sizes (in km2) of the same species in the wild (Clubb and Mason 2003). For example, -1.3 is the home range and 4 is the captive infant mortality percentage. #Log home range size: -1.3 (4),-0.5 (22),-0.3 (0),0.2 (0),0.1 (11),0.5 (13),1.0 (17),0.3 (25),0.4 (24),0.5 (27),0.1 (29),0.2 (33),0.4 (33),1.3 (42),1.2 (33),1.4 (20),1.6 (19),1.6 (19),1.8 (25),3.1 (65) #a. Draw a scatter plot of these data, with log of home range size as the explanatory variable. Describe the association between the two variables in words. b1 <- c(-1.3,-0.5,-0.3,0.2,0.1,0.5,1.0,0.3,0.4,0.5,0.1,0.2,0.4,1.3,1.2,1.4,1.6,1.6,1.8,3.1) b2 <- c(4,22,0,0,11,13,17,25,24,27,29,33,33,42,33,20,19,19,25,65) plot(b1,b2) #b. Estimate the slope and intercept of the least squares regression line, with the log of home range size as the explanatory variable. Add this line to your plot. m <- lm(b2~b1) abline(m) #c. Does home range size in the wild predict the mortality of captive carnivores? Carry out a formal test. Assume that the species data are independent. # Ho: home range size does not predict infant mortality (beta = 0) # Ha: home range size does predict infant mortality (beta != 0) # answer: b = 9.955, a=16.280, SE=2.766, t=3.6, df=18, P=0.002 # reject null Ho summary(m) a <- m$coefficients[1] b <- m$coefficients[2] a = 16.28047 b = 9.955187 # predicted values vs actual values b3 <- mat.or.vec(20,1) for (i in 1:length(b3)) { b3[i] = b*b1[i] + a } points(b1,b3,col="red") #d. Outliers should be investigated because they might have a substantial effect on the estimate so of the slope and intercept. Recalculate the slope and intercept of the regression line from part (c) after excluding the outlier at large home range size (which correspond to the polar bear). Add the new line to your plot. By how much did it change the slope? b1_p <- c(-1.3,-0.5,-0.3,0.2,0.1,0.5,1.0,0.3,0.4,0.5,0.1,0.2,0.4,1.3,1.2,1.4,1.6,1.6,1.8) b2_p <- c(4,22,0,0,11,13,17,25,24,27,29,33,33,42,33,20,19,25,25) m_p <- lm(b2_p~b1_p) abline(m_p) summary(m_p) # answer: b = 6.600, a=17.510, SE = 3.074, t=2.147, df=17, P = 0.0465 # still reject Ho but not as strongly, the slope changes from 9.955 to 6.600

2c46afbd9847c9d2f34d3c89e1017750b6f8821a

f508935a612e29d265e1501544f94fdaec9d4c47

/plot4.R

76a95b0ed20afc89ba8718284ce343a2952512c6

[]

no_license

Folgs/ExData_Plotting1

54d31e638c9d1d1e4d4a14371fbcfdd0f01e239b

52494f332fba06b6543764ad5754a528877451cc

refs/heads/master

2020-12-28T21:06:42.344077

2016-07-08T10:22:46

62,880,312

0

null

2016-07-08T10:19:03

null

UTF-8

R

false

1,189

r

plot4.R

mydata<-read.table(file="household_power_consumption.txt",sep=";",header=TRUE,stringsAsFactors = FALSE,na.strings = "?") ##We pick the dates we want. mydata<-mydata[(mydata$Date=="2/2/2007")|(mydata$Date=="1/2/2007"),] ##Then we convert the columns we will is into their units mydata$Time<-paste(mydata$Date,mydata$Time,sep=" ") mydata$Time<-strptime(mydata$Time,format="%d/%m/%Y %H:%M:%S") ##We set the space to save the four plots par(mfrow=c(2,2)) ##We open the file and save the four plots, and close it. png(filename = "plot4.png") plot(x=mydata$Time,mydata$Global_active_power,type="l",xlab="Days",ylab="Global Active Power (kilowatts)") plot(mydata$Time,mydata$Sub_metering_1,type="l",xlab="Days",ylab="Energy sub metering") lines(x=mydata$Time,y=mydata$Sub_metering_2,col="red") lines(x=mydata$Time,y=mydata$Sub_metering_3,col="blue") legend(x="topright",legend=c("Sub_meterring_1","Sub_metering_2","Sub_metering_3"),pch=c("l","l","l"),col=c("black","red","blue")) plot(mydata$Time,mydata$Voltage,type="l",xlab="datetime",ylab="Voltage") plot(mydata$Time,mydata$Global_reactive_power,type="l",xlab="datetime",ylab="Global_reactive_power") dev.off()

457d901a108ca00a12f857e5aa6203301bf254d2

95799d6e1ddc4bbbbbbcfb3a8e61d90176fc01a4

/Chapter III/chapter_3_3.R

fa2e236ac1696eb69ca086ae01d1b0ded90dcfa1

[]

no_license

drnmy/mlf

e8e7846a763e5dd89cc965806296b1f9b80142ed

092e3f8db633d4013c6f77022553b3246cb4f401

refs/heads/master

2022-12-04T01:42:18.313331

2020-08-17T15:36:44

284,404,931

1

0

null

UTF-8

R

false

7,591

r

chapter_3_3.R

# Данните са https://archive.ics.uci.edu/ml/datasets/Polish+companies+bankruptcy+data# library(foreign) library(stats) library(caret) readData <- function(bAllData = FALSE) { if (TRUE == bAllData) { d1 <- read.arff("c:/Stanio/Mono_S/Data/1year.arff") d2 <- read.arff("c:/Stanio/Mono_S/Data/2year.arff") d3 <- read.arff("c:/Stanio/Mono_S/Data/3year.arff") d4 <- read.arff("c:/Stanio/Mono_S/Data/4year.arff") d5 <- read.arff("c:/Stanio/Mono_S/Data/5year.arff") data <- rbind(d1,d2,d3, d4, d5) } else { data <- read.arff("c:/Stanio/Mono_S/Data/1year.arff") #data <- read.arff("c:/Stanio/Mono_S/Data/5year.arff") } data } data <- readData(FALSE) #data <- data[, which(names(data) %in% c("class","Attr3", "Attr6", "Attr7", "Attr8", "Attr9"))] split <- createDataPartition(y = data$class,p = 0.85,list = FALSE) train <- data[split,] train <- na.omit(train) train[train == -Inf] <- 0 train[train == Inf] <- 0 test <- data[-split,] test <- na.omit(test) test[test == -Inf] <- 0 test[test == Inf] <- 0 summary(train$class) summary(test$class) cutoffExtremes <- function(data) { #data$Attr3[data$Attr3 < -20] <- -20 #data$Attr6[data$Attr6 < -20] <- -20 #data$Attr7[data$Attr7 < -20] <- -20 #data$Attr7[data$Attr7 > 20] <- 20 #data$Attr8[data$Attr8 < -20] <- -20 #data$Attr8[data$Attr8 > 20] <- 20 #data$Attr9[data$Attr9 < -20] <- -20 #data$Attr9[data$Attr9 > 20] <- 20 data[, !(names(data) %in% c("class"))] <- scale(data[, !(names(data) %in% c("class"))]) data } train <- cutoffExtremes(train) test <- cutoffExtremes(test) trCntl <- trainControl(method = "CV",number = 25) logitModel <- train(class ~ .,data = train,trControl = trCntl,method="glm",family = "binomial") summary(logitModel) confusionMatrix(logitModel) pdata <- predict(logitModel, test) # generate confusion matrix for hold back data confusionMatrix(pdata,reference=test$class) library(ROCR) library(Metrics) logitModel <- glm(class ~ ., family=binomial(link='logit'),data=train) summary(logitModel) pdata <- predict(logitModel, test) prX <- pdata prY <- test$class pr <- prediction( prX, prY) perf <- performance(pr,measure = "tpr",x.measure = "fpr") plot(perf, colorize=TRUE) # As a rule of thumb, a model with good predictive ability should have an AUC closer to 1 (1 is ideal) than to 0.5. auc <- performance(pr, measure = "auc") auc <- auc@y.values[[1]] auc ### 2. MDA library(mda) mdaModel <- mda(class ~ Attr3 + Attr6 + Attr7 + Attr8 + Attr9, train) summary(mdaModel) confusion(mdaModel) plot(mdaModel, data=train) pdata <- predict(mdaModel, test) confusionMatrix(pdata,reference=test$class) pr <- prediction(pdata, test$class) perf <- performance(pr,measure = "tpr",x.measure = "fpr") plot(perf, colorize=TRUE) auc <- performance(pr, measure="auc") auc <- auc@y.values[[1]] auc ### 3. FDA fdaModel <- fda(class ~ Attr3 + Attr6 + Attr7 + Attr8 + Attr9, data=train) summary(fdaModel) confusion(fdaModel) ### 4. LDA library(MASS) ldaModel <- lda(class ~ Attr3 + Attr6 + Attr7 + Attr8 + Attr9, train) summary(ldaModel) confusion(ldaModel) plot(ldaModel) pdata <- predict(ldaModel, test) pr <- prediction(pdata$posterior[,2], test$class) perf <- performance(pr,"tpr","fpr") plot(perf,colorize=TRUE) # As a rule of thumb, a model with good predictive ability should have an AUC closer to 1 (1 is ideal) than to 0.5. auc <- performance(pr, measure = "auc") auc <- auc@y.values[[1]] auc ### 5. Neural network library(neuralnet) #scaledtrain <- scale(train) normalize <- function(x) { return ((x - min(x)) / (max(x) - min(x))) } #normTrain <- as.data.frame(lapply(train, normalize)) #normTest <- as.data.frame(lapply(test, normalize)) normTrain <- train normTrain$Attr3 <- scale(normTrain$Attr3) normTrain$Attr6 <- scale(normTrain$Attr6) normTrain$Attr7 <- scale(normTrain$Attr7) normTrain$Attr8 <- scale(normTrain$Attr8) normTrain$Attr9 <- scale(normTrain$Attr9) normTest <- test normTest$Attr3 <- scale(normTest$Attr3) normTest$Attr6 <- scale(normTest$Attr6) normTest$Attr7 <- scale(normTest$Attr7) normTest$Attr8 <- scale(normTest$Attr8) normTest$Attr9 <- scale(normTest$Attr9) nn <- neuralnet(class ~ ., data=normTrain, hidden=c(0), algorithm="sag", linear.output=FALSE) nn$result.matrix plot(nn) tempTest <- normTest[, !(names(normTest) %in% c("class"))] nn.results <- compute(nn, tempTest) pred.weights <- nn.results$net.result idx <- apply(pred.weights, 1, which.max) - 1 table(idx, test$class) cm <- confusionMatrix(as.factor(idx), normTest$class) cm cm$table / length(normTest$class) prob = compute(nn, tempTest ) prob.result <- prob$net.result train_params <- trainControl(method = "repeatedcv", number = 10, repeats=5) nnet_model <- train(train[,-6], train$class, method = "nnet", trControl= train_params, preProcess=c("scale","center"), na.action = na.omit ) prop.table(table(train$class)) #Baseline Accuracy # Predictions on the training set nnet_predictions_train <-predict(nnet_model, train) table(train$class, nnet_predictions_train) #Predictions on the test set nnet_predictions_test <-predict(nnet_model, test) table(test$class, nnet_predictions_test) ### 6. NNET library(nnet) normTrain <- train normTrain$Attr3 <- scale(normTrain$Attr3) normTrain$Attr6 <- scale(normTrain$Attr6) normTrain$Attr7 <- scale(normTrain$Attr7) normTrain$Attr8 <- scale(normTrain$Attr8) normTrain$Attr9 <- scale(normTrain$Attr9) normTest <- test normTest$Attr3 <- scale(normTest$Attr3) normTest$Attr6 <- scale(normTest$Attr6) normTest$Attr7 <- scale(normTest$Attr7) normTest$Attr8 <- scale(normTest$Attr8) normTest$Attr9 <- scale(normTest$Attr9) nn <- nnet(class ~ ., data=normTrain, size=5, maxit=1000) pdata <- predict(nn, normTest, type="class") pdata <- as.factor(pdata) confusionMatrix(pdata, normTest$class) ### 7. SVM library(e1071) normTrain <- train normTrain$Attr3 <- scale(normTrain$Attr3) normTrain$Attr6 <- scale(normTrain$Attr6) normTrain$Attr7 <- scale(normTrain$Attr7) normTrain$Attr8 <- scale(normTrain$Attr8) normTrain$Attr9 <- scale(normTrain$Attr9) normTest <- test normTest$Attr3 <- scale(normTest$Attr3) normTest$Attr6 <- scale(normTest$Attr6) normTest$Attr7 <- scale(normTest$Attr7) normTest$Attr8 <- scale(normTest$Attr8) normTest$Attr9 <- scale(normTest$Attr9) svmModel <- svm(formula = class ~ ., data = normTrain, type = 'C-classification', kernel = 'sigmoid') tempTest <- normTest[, !(names(normTest) %in% c("class"))] pdata <- predict(svmModel, newdata = tempTest) cm <- confusionMatrix(pdata, normTest$class) cm cm$table / length(normTest$class) graph <- data.frame(matrix(ncol = 5, nrow = 0)) for(i in 1:150) { svmModel <- svm(formula = class ~ ., data = normTrain, type = 'C-classification', kernel = 'sigmoid', class.weights= c("0" = 1, "1" = i)) tempTest <- normTest[, !(names(normTest) %in% c("class"))] pdata <- predict(svmModel, newdata = tempTest) cm <- confusionMatrix(pdata, normTest$class) pct <- (cm$table / length(normTest$class))*100 graph <- rbind(graph, c(i, pct[1,1], pct[1,2], pct[2,1], pct[2,2])) } x <- c("w", "(0,0)", "(0,1)", "(1,0)", "(1,1)") colnames(graph) <- x require(ggplot2) require(reshape2) g <- melt(graph, id.vars="w", variable.name='series') ggplot(g, aes(w,value)) + geom_line(aes(colour = series))

d6728aedd0e7c0964a9b291c42fd34475209f112

e5c43a31a082bbfec5ebbc20b34d373896721579

/R/functions/round.extent.R

9434812c525fc251f314410e60918e70ae00ac10

[]

no_license

geryan/rfst

3dde3a499651f3a1ccc736f8c6597c5972f0e17c

0aac1f0c3b17096af0c5b0b06e1ad80ac6d709ca

refs/heads/master

2023-05-02T12:32:51.743467

2021-04-27T01:26:47

164,573,310

1

null

UTF-8

R

false

104

r

round.extent.R

round.extent <- function(x){ library(raster) extent(x) <- round(extent(x)) return(x) }

8f3c421e6a2985cefeae26cdf716500177b571e9

e26f9101996074842a8bc796aae6280572489aec

/man/copyMatricesToVirtualSdy.Rd

ef8f020b3b6824d6dfc68e9c36f3ee52cada07da

[]

no_license

ehfhcrc/UpdateAnno

5c31ea9aac2ecf0f1b5e256cce3abd93f6c79da8

f8e561750e1d5cdee90d08753a34ff78a9813926

refs/heads/master

2021-01-19T08:03:01.670544

2018-10-10T16:30:29

87,593,499

1

null

2021-01-11T18:32:19

2017-04-07T23:09:30

R

UTF-8

R

false

true

533

rd

copyMatricesToVirtualSdy.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/CopyMatricesToVirtualSdy.R \name{copyMatricesToVirtualSdy} \alias{copyMatricesToVirtualSdy} \title{Function to migrate a copy of EMs from /Studies/ to a virtual study container} \usage{ copyMatricesToVirtualSdy(ISserver, virtualSdy) } \arguments{ \item{ISserver}{immunespace server, either test or prod} \item{virtualSdy}{subdirectory within HIPC container} } \description{ Function to migrate a copy of EMs from /Studies/ to a virtual study container }

3e82818e1a312d77cbd0e0d3e89ad3aeba967f26

473dfd3f5c89fd2bf2087c524c52e484ecc823b6

/man/lineLength.Rd

8949706184a7cf2ac1709685fde996c2176c8908

[]

no_license

cran/SDraw

038ec0a0f2d8a094f89d258d43edb15a003303b2

0b06c5ecbd424a0d9ba59fe5fd4f4bf30a1ce326

refs/heads/master

2021-01-17T19:20:33.351896

2020-07-03T15:20:09

60,879,512

0

null

UTF-8

R

false

true

1,888

rd

lineLength.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/lineLength.r \name{lineLength} \alias{lineLength} \title{Line length} \usage{ lineLength(x, byid = FALSE) } \arguments{ \item{x}{A spatial object inheriting from \code{SpatialLines}, \code{SpatialPolygons}, or \code{SpatialPoints}.} \item{byid}{Whether to return lengths of individual spatial objects (TRUE) or the sum of all length (FALSE).} } \value{ If \code{byid==TRUE}, a vector containing the lengths of individual spatial objects (the points, lines, or polygons) is returned. If \code{byid=FALSE}, the total length of all spatial objects is returned (a single number). If \code{x} inherits from \code{SpatialPoints}, returned value is 0. If \code{x} inherits from \code{SpatialLines}, returned value contains line lengths or the sum of line lengths in \code{x}. If \code{x} inherits from \code{SpatialPolygons}, returned value contains lengths of the perimeter of all polygons, or the sum of perimeters, in \code{x}. When \code{x} contains polygons with holes, the perimeter of the holes is included (i.e., perimeter of holes is positive, not negative). Units of the returned value are same as units of coordinates in \code{x}. E.g., meters if coordinates in \code{x} are UTM meters, decimal degrees if coordinates in \code{x} are lat-long decimal degrees. } \description{ An all-R routine that computes total length of all lines in a \code{SpatialLines*} object. } \details{ Provides the same answer as \code{rgeos::gLength}, but is all-R (does not require rgeos Java library) and does not fire a warning if \code{x} is un-projected (i.e., lat-long). } \examples{ # Length of Hawaii coastline, in kilometers l <- lineLength( HI.coast ) / 1000 } \seealso{ \code{sp::SpatialLines-class} } \author{ Trent McDonald }

f7515a79f77d5c8425a0f1e0eef9bd37d1d57bd6

92427118c4b0dd55689ce8e6cb57555743e54c6b

/tools/dockerfiles/scripts/sc_tools/sc_rna_cluster.R

1fc5c5846e3f4f61b0d73e4aba6b132c21914ee6

[]

no_license

Barski-lab/workflows

c8edc08020a5fedcd4d49cd16ac6ea3bda8b9c15

839de7ba3d04bf73c5884b0e30e0b332fcc07830

refs/heads/master

2023-08-16T19:43:56.821249

2023-08-09T17:28:26

152,797,483

5

11

null

2023-07-05T20:09:52

2018-10-12T19:17:32

Common Workflow Language

UTF-8

R

false

28,119

r

sc_rna_cluster.R

#!/usr/bin/env Rscript options(warn=-1) options("width"=200) options(error=function(){traceback(3); quit(save="no", status=1, runLast=FALSE)}) suppressMessages(library(dplyr)) suppressMessages(library(Seurat)) suppressMessages(library(Signac)) suppressMessages(library(modules)) suppressMessages(library(forcats)) suppressMessages(library(argparse)) suppressMessages(library(tidyselect)) HERE <- (function() {return (dirname(sub("--file=", "", commandArgs(trailingOnly=FALSE)[grep("--file=", commandArgs(trailingOnly=FALSE))])))})() suppressMessages(analyses <- modules::use(file.path(HERE, "modules/analyses.R"))) suppressMessages(debug <- modules::use(file.path(HERE, "modules/debug.R"))) suppressMessages(graphics <- modules::use(file.path(HERE, "modules/graphics.R"))) suppressMessages(io <- modules::use(file.path(HERE, "modules/io.R"))) suppressMessages(prod <- modules::use(file.path(HERE, "modules/prod.R"))) suppressMessages(ucsc <- modules::use(file.path(HERE, "modules/ucsc.R"))) export_all_clustering_plots <- function(seurat_data, args){ Idents(seurat_data) <- "new.ident" # safety measure downsampled_to <- analyses$get_min_ident_size(SplitObject(seurat_data, split.by="new.ident")) # need to split it for consistency print(paste("Downsampling datasets to", downsampled_to, "cells per sample")) downsampled_data <- subset(seurat_data, downsample=downsampled_to) if ("Phase" %in% colnames(seurat_data@meta.data)){ if (length(unique(as.vector(as.character(Idents(seurat_data))))) > 1){ graphics$composition_plot( data=downsampled_data, plot_title=paste( "Composition plot,", "colored by cell cycle phase,", "split by dataset, downsampled" ), legend_title="Phase", group_by="Phase", split_by="new.ident", x_label="Dataset", y_label="Cells percentage", palette_colors=graphics$D40_COLORS, theme=args$theme, rootname=paste(args$output, "cmp_gr_ph_spl_idnt", sep="_"), pdf=args$pdf ) } } for (i in 1:length(args$resolution)){ current_resolution <- args$resolution[i] graphics$dim_plot( data=seurat_data, reduction="rnaumap", plot_title=paste( "UMAP, colored by cluster,", "resolution", current_resolution ), legend_title="Cluster", group_by=paste("rna_res", current_resolution, sep="."), label=TRUE, label_color="black", palette_colors=graphics$D40_COLORS, theme=args$theme, rootname=paste(args$output, "umap_res", current_resolution, sep="_"), pdf=args$pdf ) graphics$silhouette_plot( data=seurat_data, reduction="pca", dims=args$dimensions, downsample=500, plot_title=paste( "Silhouette scores, resolution", current_resolution ), legend_title="Cluster", group_by=paste("rna_res", current_resolution, sep="."), palette_colors=graphics$D40_COLORS, theme=args$theme, rootname=paste(args$output, "slh_res", current_resolution, sep="_"), pdf=args$pdf ) if (length(unique(as.vector(as.character(Idents(seurat_data))))) > 1){ graphics$dim_plot( data=seurat_data, reduction="rnaumap", plot_title=paste( "UMAP, colored by cluster,", "split by dataset,", "resolution", current_resolution ), legend_title="Cluster", group_by=paste("rna_res", current_resolution, sep="."), split_by="new.ident", label=TRUE, label_color="black", palette_colors=graphics$D40_COLORS, theme=args$theme, rootname=paste(args$output, "umap_spl_idnt_res", current_resolution, sep="_"), pdf=args$pdf ) graphics$composition_plot( data=downsampled_data, plot_title=paste( "Composition plot, colored by cluster,", "split by dataset, downsampled,", "resolution", current_resolution ), legend_title="Cluster", group_by=paste("rna_res", current_resolution, sep="."), split_by="new.ident", x_label="Dataset", y_label="Cells percentage", palette_colors=graphics$D40_COLORS, theme=args$theme, rootname=paste(args$output, "cmp_gr_clst_spl_idnt_res", current_resolution, sep="_"), pdf=args$pdf ) graphics$composition_plot( data=downsampled_data, plot_title=paste( "Composition plot, colored by dataset,", "split by cluster, downsampled,", "resolution", current_resolution ), legend_title="Dataset", group_by="new.ident", split_by=paste("rna_res", current_resolution, sep="."), x_label="Cluster", y_label="Cells percentage", palette_colors=graphics$D40_COLORS, theme=args$theme, rootname=paste(args$output, "cmp_gr_idnt_spl_clst_res", current_resolution, sep="_"), pdf=args$pdf ) } if ( all(as.vector(as.character(seurat_data@meta.data$new.ident)) != as.vector(as.character(seurat_data@meta.data$condition))) && length(unique(as.vector(as.character(seurat_data@meta.data$condition)))) > 1 ){ graphics$dim_plot( data=seurat_data, reduction="rnaumap", plot_title=paste( "UMAP, colored by cluster,", "split by grouping condition,", "resolution", current_resolution ), legend_title="Cluster", group_by=paste("rna_res", current_resolution, sep="."), split_by="condition", label=TRUE, label_color="black", palette_colors=graphics$D40_COLORS, theme=args$theme, rootname=paste(args$output, "umap_spl_cnd_res", current_resolution, sep="_"), pdf=args$pdf ) graphics$composition_plot( data=downsampled_data, plot_title=paste( "Composition plot, colored by cluster,", "split by grouping condition, downsampled,", "resolution", current_resolution ), legend_title="Cluster", group_by=paste("rna_res", current_resolution, sep="."), split_by="condition", x_label="Condition", y_label="Cells percentage", palette_colors=graphics$D40_COLORS, theme=args$theme, rootname=paste(args$output, "cmp_gr_clst_spl_cnd_res", current_resolution, sep="_"), pdf=args$pdf ) graphics$composition_plot( data=downsampled_data, plot_title=paste( "Composition plot,", "colored by grouping condition,", "split by cluster, downsampled,", "resolution", current_resolution ), legend_title="Condition", group_by="condition", split_by=paste("rna_res", current_resolution, sep="."), x_label="Cluster", y_label="Cells percentage", palette_colors=graphics$D40_COLORS, theme=args$theme, rootname=paste(args$output, "cmp_gr_cnd_spl_clst_res", current_resolution, sep="_"), pdf=args$pdf ) } if ("Phase" %in% colnames(seurat_data@meta.data)){ graphics$dim_plot( data=seurat_data, reduction="rnaumap", plot_title=paste( "UMAP, colored by cluster,", "split by cell cycle phase,", "resolution", current_resolution ), legend_title="Cluster", group_by=paste("rna_res", current_resolution, sep="."), split_by="Phase", label=TRUE, label_color="black", alpha=0.5, palette_colors=graphics$D40_COLORS, theme=args$theme, rootname=paste(args$output, "umap_spl_ph_res", current_resolution, sep="_"), pdf=args$pdf ) graphics$composition_plot( data=downsampled_data, plot_title=paste( "Composition plot,", "colored by cell cycle phase,", "split by cluster, downsampled,", "resolution", current_resolution ), legend_title="Phase", group_by="Phase", split_by=paste("rna_res", current_resolution, sep="."), x_label="Cluster", y_label="Cells percentage", palette_colors=graphics$D40_COLORS, theme=args$theme, rootname=paste(args$output, "cmp_gr_ph_spl_clst_res", current_resolution, sep="_"), pdf=args$pdf ) } } rm(downsampled_data) gc(verbose=FALSE) } export_all_expression_plots <- function(seurat_data, args) { DefaultAssay(seurat_data) <- "RNA" # safety measure Idents(seurat_data) <- "new.ident" # safety measure if (!is.null(args$genes) && length(args$genes) > 0){ for (i in 1:length(args$genes)){ current_gene <- args$genes[i] graphics$feature_plot( data=seurat_data, features=current_gene, labels=current_gene, reduction="rnaumap", plot_title="UMAP, gene expression", label=FALSE, order=TRUE, max_cutoff="q99", # to prevent cells with overexpressed gene from distoring the color bar combine_guides="keep", width=800, height=800, theme=args$theme, rootname=paste(args$output, "xpr_per_cell", current_gene, sep="_"), pdf=args$pdf ) graphics$expression_density_plot( data=seurat_data, features=current_gene, reduction="rnaumap", plot_title="UMAP, gene expression density", joint=FALSE, width=800, height=800, theme=args$theme, rootname=paste(args$output, "xpr_per_cell_sgnl", current_gene, sep="_"), pdf=args$pdf ) } } for (i in 1:length(args$resolution)) { current_resolution <- args$resolution[i] Idents(seurat_data) <- paste("rna_res", current_resolution, sep=".") graphics$dot_plot( data=seurat_data, features=args$genes, plot_title=paste( "Gene expression dot plot,", "resolution", current_resolution ), x_label="Genes", y_label="Clusters", cluster_idents=FALSE, theme=args$theme, rootname=paste(args$output, "xpr_avg_res", current_resolution, sep="_"), pdf=args$pdf ) if (!is.null(args$genes) && length(args$genes) > 0){ for (i in 1:length(args$genes)){ current_gene <- args$genes[i] graphics$vln_plot( data=seurat_data, features=current_gene, labels=current_gene, plot_title=paste( "Gene expression violin plot,", "resolution", current_resolution ), legend_title="Cluster", log=TRUE, pt_size=0, combine_guides="collect", width=800, height=600, palette_colors=graphics$D40_COLORS, theme=args$theme, rootname=paste(args$output, "xpr_dnst_res", current_resolution, current_gene, sep="_"), pdf=args$pdf ) } } } Idents(seurat_data) <- "new.ident" # safety measure } export_heatmaps <- function(seurat_data, markers, args){ DefaultAssay(seurat_data) <- "RNA" # safety measure Idents(seurat_data) <- "new.ident" # safety measure for (i in 1:length(args$resolution)) { current_resolution <- args$resolution[i] grouped_markers <- markers %>% dplyr::filter(.$resolution==current_resolution) %>% # shouldn't fail even if resolution is not present dplyr::select(-c("resolution")) %>% dplyr::filter(.$p_val_adj <= 0.05) %>% # to have only significant gene markers dplyr::filter(.$pct.1 >= 0.1) %>% # to have at least 10% of cells expressing this gene dplyr::group_by(cluster) %>% dplyr::top_frac(n=-0.25, wt=p_val_adj) %>% # get 25% of the most significant gene markers dplyr::top_n( n=10, # 10 genes per cluster # n=tidyselect::all_of(floor(600/length(unique(markers$cluster)))), wt=avg_log2FC ) if (nrow(grouped_markers) > 0){ # in case we don't have any markers for specific resolution column_annotations <- c(paste("rna_res", current_resolution, sep=".")) if (length(unique(as.vector(as.character(seurat_data@meta.data$new.ident)))) > 1){ column_annotations <- c(column_annotations, "new.ident") # several datasets found } if ( all(as.vector(as.character(seurat_data@meta.data$new.ident)) != as.vector(as.character(seurat_data@meta.data$condition))) && length(unique(as.vector(as.character(seurat_data@meta.data$condition)))) > 1 ){ column_annotations <- c(column_annotations, "condition") # several conditions found } custom_fields <- grep("^custom_", colnames(seurat_data@meta.data), value=TRUE, ignore.case=TRUE) if (length(custom_fields) > 0){ column_annotations <- c(column_annotations, custom_fields) # adding all custom fields } graphics$feature_heatmap( # install.packages("magick") for better rasterization data=seurat_data, assay="RNA", slot="data", features=grouped_markers$feature, split_rows=forcats::fct_inorder(as.character(grouped_markers$cluster)), # fct_inorder fails with numeric show_rownames=TRUE, scale_to_max=TRUE, group_by=unique(column_annotations), # to not show duplicates palette_colors=graphics$D40_COLORS, heatmap_colors=c("black", "yellow"), plot_title=paste( "Gene expression heatmap, resolution", current_resolution ), height=13*length(grouped_markers$feature), rootname=paste(args$output, "xpr_htmp_res", current_resolution, sep="_"), pdf=args$pdf ) } } Idents(seurat_data) <- "new.ident" # safety measure } get_args <- function(){ parser <- ArgumentParser(description="Single-cell RNA-Seq Cluster Analysis") parser$add_argument( "--query", help=paste( "Path to the RDS file to load Seurat object from. This file should include genes", "expression information stored in the RNA assay, as well as 'pca' and 'rnaumap'", "dimensionality reductions applied to that assay." ), type="character", required="True" ) parser$add_argument( "--dimensions", help=paste( "Dimensionality to use when constructing nearest-neighbor graph before clustering", "(from 1 to 50). If single value N is provided, use from 1 to N dimensions. If", "multiple values are provided, subset to only selected dimensions.", "Default: from 1 to 10" ), type="integer", default=10, nargs="*" ) parser$add_argument( "--ametric", help=paste( "Distance metric used when constructing nearest-neighbor graph before clustering.", "Default: euclidean" ), type="character", default="euclidean", choices=c( "euclidean", "cosine", "manhattan", "hamming" ) ) parser$add_argument( "--algorithm", help=paste( "Algorithm for modularity optimization when running clustering.", "Default: louvain" ), type="character", default="louvain", choices=c( "louvain", "mult-louvain", "slm", "leiden" ) ) parser$add_argument( "--resolution", help=paste( "Clustering resolution applied to the constructed nearest-neighbor graph.", "Can be set as an array but only the first item from the list will be used", "for cluster labels and gene markers in the UCSC Cell Browser when running", "with --cbbuild and --diffgenes parameters.", "Default: 0.3, 0.5, 1.0" ), type="double", default=c(0.3, 0.5, 1.0), nargs="*" ) parser$add_argument( "--genes", help=paste( "Genes of interest to build genes expression plots.", "Default: None" ), type="character", nargs="*" ) parser$add_argument( "--diffgenes", help=paste( "Identify differentially expressed genes (putative gene markers) between each", "pair of clusters for all resolutions.", "Default: false" ), action="store_true" ) parser$add_argument( "--logfc", help=paste( "For putative gene markers identification include only those genes that", "on average have log fold change difference in expression between every", "tested pair of clusters not lower than this value. Ignored if '--diffgenes'", "is not set.", "Default: 0.25" ), type="double", default=0.25 ) parser$add_argument( "--minpct", help=paste( "For putative gene markers identification include only those genes that", "are detected in not lower than this fraction of cells in either of the", "two tested clusters. Ignored if '--diffgenes' is not set.", "Default: 0.1" ), type="double", default=0.1 ) parser$add_argument( "--onlypos", help=paste( "For putative gene markers identification return only positive markers.", "Ignored if '--diffgenes' is not set.", "Default: false" ), action="store_true" ) parser$add_argument( "--testuse", help=paste( "Statistical test to use for putative gene markers identification.", "Ignored if '--diffgenes' is not set.", "Default: wilcox" ), type="character", default="wilcox", choices=c("wilcox", "bimod", "roc", "t", "negbinom", "poisson", "LR", "MAST", "DESeq2") ) parser$add_argument( "--pdf", help="Export plots in PDF. Default: false", action="store_true" ) parser$add_argument( "--verbose", help="Print debug information. Default: false", action="store_true" ) parser$add_argument( "--h5seurat", help="Save Seurat data to h5seurat file. Default: false", action="store_true" ) parser$add_argument( "--h5ad", help="Save Seurat data to h5ad file. Default: false", action="store_true" ) parser$add_argument( "--cbbuild", help="Export results to UCSC Cell Browser. Default: false", action="store_true" ) parser$add_argument( "--scope", help=paste( "Save Seurat data to SCope compatible loom file.", "Default: false" ), action="store_true" ) parser$add_argument( "--output", help="Output prefix. Default: ./sc", type="character", default="./sc" ) parser$add_argument( "--theme", help=paste( "Color theme for all generated plots.", "Default: classic" ), type="character", default="classic", choices=c("gray", "bw", "linedraw", "light", "dark", "minimal", "classic", "void") ) parser$add_argument( "--cpus", help="Number of cores/cpus to use. Default: 1", type="integer", default=1 ) parser$add_argument( "--memory", help=paste( "Maximum memory in GB allowed to be shared between the workers", "when using multiple --cpus.", "Default: 32" ), type="integer", default=32 ) args <- parser$parse_args(commandArgs(trailingOnly = TRUE)) return (args) } args <- get_args() print("Input parameters") print(args) if (length(args$dimensions) == 1) { print("Adjusting --dimensions parameter as only a single value was provided") args$dimensions <- c(1:args$dimensions[1]) print(paste("--dimensions was adjusted to", paste(args$dimensions, collapse=", "))) } print( paste( "Setting parallelization to", args$cpus, "cores, and", args$memory, "GB of memory allowed to be shared between the processes" ) ) prod$parallel(args) print(paste("Loading Seurat data from", args$query)) seurat_data <- readRDS(args$query) print("Setting default assay to RNA") DefaultAssay(seurat_data) <- "RNA" debug$print_info(seurat_data, args) if (!all(c("pca", "rnaumap") %in% names(seurat_data@reductions))){ print("Loaded Seurat object doesn't have 'pca' and/or 'rnaumap' reduction(s). Exiting.") quit(save="no", status=1, runLast=FALSE) } print(paste("Clustering RNA data using", paste(args$dimensions, collapse=", "), "principal components")) seurat_data <- analyses$add_clusters( seurat_data=seurat_data, assay="RNA", graph_name="rna", # will be used in all the plot generating functions reduction="pca", args=args ) debug$print_info(seurat_data, args) export_all_clustering_plots( seurat_data=seurat_data, args=args ) if (!is.null(args$genes)){ print("Adjusting genes of interest to include only those that are present in the loaded Seurat object") args$genes <- unique(args$genes) args$genes <- args$genes[args$genes %in% as.vector(as.character(rownames(seurat_data)))] # with RNA assay set as default the rownames should be genes print(args$genes) } all_markers <- NULL if (!is.null(args$genes) || args$diffgenes) { print("Normalizing counts in RNA assay before evaluating genes expression or identifying putative gene markers") DefaultAssay(seurat_data) <- "RNA" seurat_data <- NormalizeData(seurat_data, verbose=FALSE) if (!is.null(args$genes)){ print("Generating genes expression plots") export_all_expression_plots(seurat_data=seurat_data, args=args) } if(args$diffgenes){ print("Identifying differentially expressed genes between each pair of clusters for all resolutions") all_markers <- analyses$get_markers_by_res( # either NULL or definitely not empty seurat_data=seurat_data, assay="RNA", resolution_prefix="rna_res", args=args ) if (!is.null(all_markers)){ io$export_data( all_markers, paste(args$output, "_gene_markers.tsv", sep="") ) export_heatmaps( seurat_data=seurat_data, markers=all_markers, args=args ) } } } if(args$cbbuild){ print("Exporting RNA assay to UCSC Cellbrowser") if(!is.null(all_markers)){ all_markers <- all_markers %>% dplyr::filter(.$resolution==args$resolution[1]) %>% # won't fail even if resolution is not present dplyr::select(-c("resolution")) } print("Reordering reductions to have rnaumap on the first place") # will be shown first in UCSC Cellbrowser reduc_names <- names(seurat_data@reductions) ordered_reduc_names <- c("rnaumap", reduc_names[reduc_names!="rnaumap"]) # we checked before that rnaumap is present seurat_data@reductions <- seurat_data@reductions[ordered_reduc_names] debug$print_info(seurat_data, args) ucsc$export_cellbrowser( seurat_data=seurat_data, assay="RNA", slot="counts", short_label="RNA", markers=all_markers, # can be NULL palette_colors=graphics$D40_COLORS, # to have colors correspond to the plots label_field=paste0("Clustering (rna ", args$resolution[1], ")"), # always use only the first resolution rootname=paste(args$output, "_cellbrowser", sep="") ) } DefaultAssay(seurat_data) <- "RNA" # better to stick to RNA assay by default https://www.biostars.org/p/395951/#395954 print("Exporting results to RDS file") io$export_rds(seurat_data, paste(args$output, "_data.rds", sep="")) if(args$h5seurat){ print("Exporting results to h5seurat file") io$export_h5seurat(seurat_data, paste(args$output, "_data.h5seurat", sep="")) } if(args$h5ad){ print("Exporting results to h5ad file") io$export_h5ad(seurat_data, paste(args$output, "_data.h5ad", sep="")) } if(args$scope){ print("Exporting results to SCope compatible loom file") io$export_scope_loom( # we save only counts slot from the RNA assay seurat_data, paste(args$output, "_data.loom", sep="") ) }

b452902ede75d4c4a9e2968ef63637da4b90adcc

eb62b4e11c2fabee75b4dcfbe5ab2e11a64450b9

/man/get_variant_set.Rd

b87431f87be33f4cfd52a94addf32e05f5ca5817

[]

no_license

cran/Rga4gh

af195a0de5a16298c01d9c0692715ec1e582bf5b

76cd4974e4a1d9fc6dcebe3c46c27f4f164b75e6

refs/heads/master

2020-12-24T10:57:01.533892

2016-11-07T21:07:40

73,116,702

0

1

null

2017-02-08T20:45:22

2016-11-07T20:12:44

R

UTF-8

R

false

true

599

rd

get_variant_set.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get.R \name{get_variant_set} \alias{get_variant_set} \title{GET a Variant Set} \usage{ get_variant_set(client, variant_set_id) } \arguments{ \item{client}{A ga4gh_client object} \item{variant_set_id}{The ID of the Variant Set} } \description{ GET a Variant Set } \examples{ ## Create a client ref_client <- ga4gh_client("http://1kgenomes.ga4gh.org", api_location = "") \dontrun{ library(magrittr) ## Retrieve the object with id 'id' variant_set <- ref_client \%>\% get_variant_set("id") } }

77ef98fdf4c2f87ece177ebc4507cc0aef08b928

67f31a9f56d85ede80920358fe40462c2cb710ed

/man/ctrlP.Rd

d5261e134412bd5a31436e68fb80821abe0485de

[]

no_license

vh-d/VHtools

ff95b01424c210b3451f4ee63d5aaa016e553c2e

a7907e8ba370523ca92985fb73f734a3284896b8

refs/heads/master

2020-04-12T06:25:18.169942

2019-04-09T20:09:34

60,918,606

0

null

UTF-8

R

false

true

526

rd

ctrlP.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/io.R \name{ctrlP} \alias{ctrlP} \title{read from clipboard for easy copy-paste from spreasdsheet (Excel) \code{ctrlP} reads clipboard (copied from excel or other spreasheet) and returns a \code{data.frame}.} \usage{ ctrlP(dec = ",", sep = "\\t", rn = NULL, ...) } \description{ read from clipboard for easy copy-paste from spreasdsheet (Excel) \code{ctrlP} reads clipboard (copied from excel or other spreasheet) and returns a \code{data.frame}. }