pierreqi/r_code_50k · Datasets at Hugging Face

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/funding_ratio_v2.R

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bmpriya389/Pension_Simulator

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funding_ratio_v2.R

library(BayesBridge) rm(list=ls()) source("functions.R") ###################### Load winklevoss and mortality tables ################################# load('E://SUNYALBANY/Pension_Simulation_Project/Code/Data/winklevossdata.RData') load('E://SUNYALBANY/Pension_Simulation_Project/Code/Data/Mortality.RData') ######################### Parameters constant but likely to change ########################## ea<-30 a_sgr<-0.05 sal=60000 sgr<-0.0568 mort<-2 afc<-5 bf<-0.02 i<-0.08 cola<-0.01 amortization<-30 vesting<-5 cm<-'EAN' median_p<-0.854 median_dr<-0.08 median_sgr<-0.0568 pop<-rep(1,71) afc<-5 retire<-65 get_qxt(ea,retire) pop_type='Uniform' pop_size=100000 ca=37 median=45 inflation=2 pop<-rep(1,71) active<-seq(30,65) pgr<-sgr-inflation retirees<-seq(66,100) ea<-30 sum(get_ARC(pop,ea,retire,median_p,median_dr,median_sgr,i,a_sgr,sgr,pgr,cola,afc,bf,cm,mort,vesting,amortization)) get_FR(pop,ea,retire,median_p,median_dr,median_sgr,i,a_sgr,sgr,cola,afc,bf,cm,mort,vesting) get_median_asset(ea,retire,median_p,median_dr,median_sgr,a_sgr,cola,afc,bf,cm,mort,vesting) get_nc_pop(pop,ea,retire,i,a_sgr,sgr,cola,afc,bf,cm,mort,vesting) get_NC(ea,retire,i,a_sgr,sgr,cola,afc,bf,cm,mort,vesting) active<-rep(1,36) retirees<-rep(1,71) pgr<-3.68 get_stat(ea,retire,active,retirees,i,a_sgr,sgr,cola,afc,bf,cm,mort,vesting) get_aal_pop(pop,ea,retire,i,a_sgr,sgr,cola,afc,bf,cm,mort,vesting) get_AAL(ea,retire,i,a_sgr,sgr,cola,afc,bf,cm,mort,vesting) get_PVTC_t(ea,retire,i,a_sgr,sgr,cola,afc,bf,mort,vesting) get_PVTC(ea,retire,i,a_sgr,sgr,cola,afc,bf,mort,vesting,a) get_term_cost(ea,retire,i,a_sgr,sgr,cola,afc,bf,mort,vesting) get_vesting_cost(ea,retire,i,a_sgr,cola,afc,bf,mort,vesting) get_gxv(ea,retire,vesting) get_xPVFBr(ea,retire,i,a_sgr,sgr,cola,afc,bf,mort) get_rPVFBx(ea,retire,i,a_sgr,sgr,cola,afc,bf,mort) get_a_after_r(ea,retire,i,cola,mort) get_tla_t(ea,retire,i,sgr,mort) get_tla(ea,retire,i,sgr,mort,a) get_am(ea,retire,i,amortization) get_ar(ea,retire,i,cola,mort) get_replacement_rate(ea,retire,a_sgr,sgr,afc,bf) get_sal_ca(ea,retire,ca,sal,inflation,sgr) get_sal_vector_ea(ea,retire,sal,inflation,sgr) get_rxpxT(ea,retire,mort) get_xpxT(ea,retire,mort) get_vxr(ea,retire,i) get_vrx(ea,retire,i) get_acc_benefit_65(ea,retire,a_sgr,sgr,afc,bf) afc<-10 get_acc_benefit_r(ea,retire,a_sgr,sgr,afc,bf) get_acc_benefits(ea,retire,a_sgr,afc,bf) get_xpmr(ea,retire,mort) get_rpmx(ea,retire,mort) get_xpmx(ea,retire,mort) get_mort(ea,retire,mort) get_exp_sal_r(ea,retire,a_sgr,sgr) get_acc_sal(ea,retire,a_sgr) get_exp_sal(ea,retire,sgr) get_act_sal(ea,retire,a_sgr) 3.5105/5.516 get_a_after_r(30,65,0.08,0.02,2) get_act_sal(30,65,0.056)[length(get_act_sal(30,65,0.056))] population_retirees(ea,retire,mort,population) population=generate_pop(ea,retire,pop_type,pop_size,median) age_grp_labels(ea,retire,age_limit) age_limit=70 age_information(ea,retire)

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/frailty_20160823.R

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ElisabethDahlqwist/Frailty

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refs/heads/master

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frailty_20160823.R

rm(list=ls()) library(data.table) #library(tictoc) library(numDeriv) library(survival) #---DATA GENERATION--- n=20 m=2 eta.true=1 alpha0.true=1.8 theta.true=0.4 beta1.true=1.3 beta2.true=2 beta3.true=0.5 par.true=c(alpha0.true,eta.true,theta.true,beta1.true, beta2.true, beta3.true) id=rep(1:n,each=m) u=rep(rgamma(n,shape=1/theta.true,scale=theta.true),each=m) x=rnorm(n*m) z=rnorm(n*m) alpha=alpha0.true/(u*exp(beta1.true*x + beta2.true*z + beta3.true*z*x))^(1/eta.true) t=rweibull(n*m,shape=eta.true,scale=alpha) #right-censoring #c <- rep(Inf,n*m) c <- runif(n*m,0,10) delta <- as.numeric(t<c) t <- pmin(t,c) #strong left-truncation #tstar <- rep(0,n*m) tstar <- runif(n*m,0,2) incl <- t>tstar incl <- ave(x=incl,id,FUN=sum)==m id <- id[incl] x <- x[incl] z <- z[incl] t <- t[incl] delta <- delta[incl] tstar <- tstar[incl] data <- data.frame(t, tstar, delta, x, z, id) d <- as.vector(tapply(delta,id,FUN=sum)) D <- max(d) j <- 0:(D-1) #---LIKELIHOOD FUNCTION--- logp <- c(log(alpha0.true),log(eta.true),log(theta.true),beta1.true, beta2.true, beta3.true) formula=Surv(tstar, t, delta) ~ x + z + z*x clusterid<-"id" frailty <- function(formula, data, logp, clusterid){ call <- match.call() ## Defining input arguments X <- as.matrix(model.matrix(formula, data)[, -1]) clusterid <- data[, clusterid] n <- nrow(X) ncluster <- length(unique(clusterid)) nbeta <- length(attr(terms(formula), "term.labels")) npar <- 3 + length(attr(terms(formula), "term.labels")) if(missing(logp)) logp <- c(rep(0, npar)) tstar <- data[, as.character(terms(formula)[[2]][2])] t <- data[, as.character(terms(formula)[[2]][3])] delta <- data[, as.character(terms(formula)[[2]][4])] d <- as.vector(tapply(delta,clusterid,FUN=sum)) #### Likelihood #### like <- function(logp){ # Defining parameter names alpha <- exp(logp[1]) eta <- exp(logp[2]) theta <- exp(logp[3]) beta <- as.matrix(logp[4:npar]) # Constructing elements in the log-likelihood B <- as.vector(X%*%beta) h <- delta*log(eta*t^(eta-1)/alpha^eta*exp(B)) H <- as.vector((t/alpha)^eta*exp(B)) Hstar <- (tstar/alpha)^eta*exp(B) temp <- data.table(h,H,Hstar) temp <- temp[, j = lapply(.SD, sum), by = clusterid] h <- temp$h H <- temp$H Hstar <- temp$Hstar G <- d*log(theta)+cumsum(c(0,log(1/theta+j)))[d+1] # Log-likelihood ll <- -mean(G+h+1/theta*log(1+theta*Hstar)-(1/theta+d)*log(1+theta*H)) return(ll) } # The function "temp" aggregate the data table x by cluster id temp <- function(x){ temp <- data.table(x) temp <- as.matrix(temp[, j = lapply(.SD, sum), by = clusterid])[, -1] } #### Gradient #### gradientfunc <- function(logp, score=F){ if(missing(score)) score = FALSE alpha <- exp(logp[1]) eta <- exp(logp[2]) theta <- exp(logp[3]) beta <- as.matrix(logp[4:npar]) # Constructing elements for gradient B <- as.vector(X%*%beta) h.eta <- delta*(1+eta*(log(t)-log(alpha))) h.beta <- X*delta H <- (t/alpha)^eta*exp(B) Hstar <- (tstar/alpha)^eta*exp(B) H.eta <- eta*log(t/alpha)*H Hstar.eta <- eta*log(tstar/alpha)*Hstar Hstar.eta[tstar==0] <- 0 H.beta <- X * H Hstar.beta <- X* Hstar # Aggregate all elements that are sums over cluster id h.alpha <- -d*eta h.eta <- temp(h.eta) h.beta <- temp(h.beta) H <- temp(H) Hstar <- temp(Hstar) H.alpha <- -eta*H H.eta <- temp(H.eta) Hstar.eta <- temp(Hstar.eta) H.beta <- temp(H.beta) Hstar.beta <- temp(Hstar.beta) Hstar.alpha <- -eta*Hstar K <- H/(1+theta*H) Kstar <- Hstar/(1+theta*Hstar) G.theta <- d-cumsum(c(0,1/(1+theta*j)))[d+1] # Second derivatives of the log-likelihood dl.dalpha <- h.alpha+Hstar.alpha/(1+theta*Hstar)-(1+theta*d)*H.alpha/(1+theta*H) dl.deta <- h.eta+Hstar.eta/(1+theta*Hstar)-(1+theta*d)*H.eta/(1+theta*H) dl.dtheta <- G.theta+1/theta*(log(1+theta*H)-log(1+theta*Hstar))+Kstar-(1+d*theta)*K dl.dbeta <- as.matrix(h.beta+Hstar.beta/(1+theta*Hstar)-(1+theta*d)*H.beta/(1+theta*H)) # Gradient for all parameters gradient <- -c(mean(dl.dalpha), mean(dl.deta), mean(dl.dtheta), colMeans(dl.dbeta)) # Score function for all parameters and individuals if(score == TRUE){ score <- -cbind(dl.dalpha, dl.deta, dl.dtheta, dl.dbeta) return(score=score) } else {return(gradient)} #names(gradient) <- c("logalpha","logeta","logtheta","beta") } #### Hessian #### hessianfunc <- function(logp){ alpha <- exp(logp[1]) eta <- exp(logp[2]) theta <- exp(logp[3]) beta <- as.matrix(logp[4:npar]) B <- as.vector(X%*%beta) XX <- c(X)*X[rep(1:nrow(X), nbeta), ] h.eta <- delta*(1+eta*(log(t)-log(alpha))) H <- (t/alpha)^eta*exp(B) Hstar <- (tstar/alpha)^eta*exp(B) H.eta <- eta*log(t/alpha)*H Hstar.eta <- eta*log(tstar/alpha)*Hstar Hstar.eta[tstar==0] <- 0 H.eta.eta <- H.eta+eta^2*(log(t/alpha))^2*H Hstar.eta.eta <- Hstar.eta+eta^2*(log(tstar/alpha))^2*Hstar Hstar.eta.eta[tstar==0] <- 0 H.eta.beta <- eta*log(t/alpha)*(H*X) Hstar.eta.beta <- eta*log(tstar/alpha)*(Hstar*X) Hstar.eta.beta[tstar==0] <- 0 H.beta <- cbind(H[rep(1:length(H), nbeta)]*XX, clusterid) Hstar.beta <- cbind(Hstar[rep(1:length(H), nbeta)]*XX, clusterid) # Aggregate h.eta <- temp(h.eta) H <- temp(H) Hstar <- temp(Hstar) H.eta <- temp(H.eta) Hstar.eta <- temp(Hstar.eta) H.eta.eta <- temp(H.eta.eta) Hstar.eta.eta <- temp(Hstar.eta.eta) H.eta.beta <- temp(H.eta.beta) Hstar.eta.beta <- temp(Hstar.eta.beta) h.alpha.alpha <- 0 h.alpha.eta <- -d*eta h.eta.eta <- h.eta-d H.alpha <- -eta*H Hstar.alpha <- -eta*Hstar H.alpha.alpha <- eta^2*H Hstar.alpha.alpha <- eta^2*Hstar H.alpha.eta <- -eta*(H+H.eta) Hstar.alpha.eta <- -eta*(Hstar+Hstar.eta) K <- H/(1+theta*H) Kstar <- Hstar/(1+theta*Hstar) G.theta.theta <- cumsum(c(0,theta*j/(1+theta*j)^2))[d+1] # Hessian, derivative of gradient of alpha with respect to all parameters except beta dl.dalpha.dalpha <- -mean(h.alpha.alpha+Hstar.alpha.alpha/(1+theta*Hstar)- theta*(Hstar.alpha/(1+theta*Hstar))^2- (1+theta*d)*(H.alpha.alpha/(1+theta*H)- theta*(H.alpha/(1+theta*H))^2)) dl.dalpha.deta <- -mean(h.alpha.eta+Hstar.alpha.eta/(1+theta*Hstar)- theta*Hstar.alpha*Hstar.eta/(1+theta*Hstar)^2- (1+theta*d)*(H.alpha.eta/(1+theta*H)- theta*H.alpha*H.eta/(1+theta*H)^2)) dl.dalpha.dtheta <- -mean(theta*(-Hstar.alpha*Hstar/(1+theta*Hstar)^2+ H.alpha*H/(1+theta*H)^2-d*(H.alpha/(1+theta*H)-theta*H.alpha*H/(1+theta*H)^2))) dl.dalpha <- cbind(dl.dalpha.dalpha, dl.dalpha.deta, dl.dalpha.dtheta) # Hessian, derivative of gradient of eta with respect to all parameters except beta dl.deta.deta <- -mean(h.eta.eta+Hstar.eta.eta/(1+theta*Hstar)- theta*(Hstar.eta/(1+theta*Hstar))^2-(1+theta*d)* (H.eta.eta/(1+theta*H)-theta*(H.eta/(1+theta*H))^2)) dl.deta.dtheta <- -mean(theta*(-Hstar.eta*Hstar/(1+theta*Hstar)^2+ H.eta*H/(1+theta*H)^2-d*(H.eta/(1+theta*H)- theta*H.eta*H/(1+theta*H)^2))) dl.deta <- cbind(dl.dalpha.deta, dl.deta.deta, dl.deta.dtheta) # Hessian, derivative of gradient of theta with respect to all parameters except beta dl.dtheta.dtheta <- -mean(G.theta.theta+1/theta*(log(1+theta*Hstar)-log(1+theta*H))+ K-Kstar+theta*(K^2-Kstar^2)+d*theta*K*(theta*K-1)) dl.dtheta <- cbind(dl.dalpha.dtheta, dl.deta.dtheta, dl.dtheta.dtheta) # Hessian, derivative of gradient of beta with respect to all parameters H <- (t/alpha)^eta*exp(B) Hstar <- (tstar/alpha)^eta*exp(B) XX <- c(X)*X[rep(1:nrow(X), nbeta), ] nbeta_rep <- rep(1:nbeta, each = nrow(X)) ### Creating squared cluster sums of H.beta and Hstar.beta H.beta <- as.matrix(temp(H * X)) H.beta2 <- H.beta[rep(1:nrow(H.beta), nbeta), ] * c(H.beta) Hstar.beta <- as.matrix(temp(Hstar * X)) Hstar.beta2 <- Hstar.beta[rep(1:nrow(Hstar.beta), nbeta), ] * c(Hstar.beta) ### Creating Cross products of covariates multiplied with H and Hstar Hstar.beta.beta <- data.table(nbeta_rep, clusterid, Hstar * XX) H.beta.beta <- data.table(nbeta_rep, clusterid, H * XX) ### Aggregate H and Hstar over clusters H <- temp(H) Hstar <- temp(Hstar) ### Calculating Hstar2 <- theta*(sum(H*X))^2/(1+theta*sum(H))^2 and H2 <- (1+d*theta)*(sum(H*X))^2/(1+theta*H)^2 Hstar2 <- theta * Hstar.beta2 / (1 + theta * Hstar)^2 H2 <- theta * (1+d*theta) * H.beta2 / (1 + theta * H)^2 ### Aggregate Hstar.beta.beta and H.beta.beta over cluster Hstar.beta.beta <- data.table(clusterid, nbeta_rep, Hstar.beta.beta) Hstar.beta.beta <- as.matrix(Hstar.beta.beta[, j = lapply(.SD, sum), by = .(nbeta_rep, clusterid)])[, -1:-2, drop=FALSE] # because columns are droped it is no longer a matrix H.beta.beta <- data.table(clusterid, nbeta_rep, H.beta.beta) H.beta.beta <- as.matrix(H.beta.beta[, j = lapply(.SD, sum), by = .(nbeta_rep, clusterid)])[, -1:-2, drop=FALSE] ### Calculate Hstar1 <- Hstar.beta.beta/(1+theta*Hstar) and H1 <- theta * (1 + d * theta)*H.beta.beta/(1+theta*H) Hstar1 <- Hstar.beta.beta / (1 + theta * Hstar) H1 <- (1 + d * theta) * H.beta.beta / (1 + theta * H) dl.dbeta.dbeta <- -(Hstar.beta.beta/(1+theta*Hstar)- theta*Hstar.beta2/(1+theta*Hstar)^2-(1+theta*d)* (H.beta.beta/(1+theta*H)-theta*H.beta2/(1+theta*H)^2)) ## Aggregate over clusters nbeta_rep2 <- rep(1:nbeta, each = length(H)) dl.dbeta.dbeta <- data.table(nbeta_rep2, dl.dbeta.dbeta) dl.dbeta.dbeta <- as.matrix(dl.dbeta.dbeta[, j = lapply(.SD, mean), by = .(nbeta_rep2)])[, -1] # Derivative of gradient of alpha with respect to beta H.alpha.beta <- -eta*H.beta Hstar.alpha.beta <- -eta*Hstar.beta dl.dalpha.dbeta <- -(colMeans(as.matrix(Hstar.alpha.beta/(1+theta*Hstar)- theta*Hstar.alpha*Hstar.beta/(1+theta*Hstar)^2- (1+theta*d)*(H.alpha.beta/(1+theta*H)- theta*H.alpha*H.beta/(1+theta*H)^2)))) dl.dalpha <- cbind(dl.dalpha, t(dl.dalpha.dbeta)) # Derivative of gradient of eta with respect to beta dl.deta.dbeta <- -t(colMeans(as.matrix(Hstar.eta.beta/(1+theta*Hstar)- theta*Hstar.eta*Hstar.beta/(1+theta*Hstar)^2- (1+theta*d)*(H.eta.beta/(1+theta*H)- theta*H.eta*H.beta/(1+theta*H)^2)))) dl.deta <- cbind(dl.deta, dl.deta.dbeta) # Derivative of gradient of theta with respect to beta dl.dtheta.dbeta <- -t(colMeans(as.matrix(theta*(-Hstar.beta*Hstar/(1+theta*Hstar)^2+ H.beta*H/(1+theta*H)^2 -d*(H.beta/(1+theta*H)-theta*H.beta*H/(1+theta*H)^2))))) dl.dtheta <- cbind(dl.dtheta, dl.dtheta.dbeta) ### Derivative of gradient of beta with respect to all parameters dl.dbeta <- rbind(dl.dalpha.dbeta, dl.deta.dbeta, dl.dtheta.dbeta, dl.dbeta.dbeta) hessian <- cbind(t(dl.dalpha), t(dl.deta), t(dl.dtheta), dl.dbeta) #colnames(hessian) <- c("logalpha","logeta","logtheta","beta") #rownames(hessian) <- colnames(hessian) return(hessian) } ######################################## ### Optimize the likelihood function fit <- optim(par=logp, fn=like, gr=gradientfunc, method="BFGS", hessian=FALSE) par <- fit$par ### Get the score function score <- gradientfunc(par, score=TRUE) ### Get the hessian hessian <- hessianfunc(par) #### Output #### out <- c(list(X=X, fit = fit, par = par, score = score, hessian = hessian, call = call, formula = formula, data = data, logp = logp, clusterid =clusterid, ncluster = ncluster, n = n)) class(out) <- "frailty" return(out) } print("true") print(logp) estimates<-frailty(formula, data, logp, clusterid="id") estimates print.frailty<-function(x, ...){ cat("Call:", "\n") print.default(x$call) cat("\nEstimated parameters in the Gamma-Weibull frailty model:", "\n") cat("\n") table.est <- t(as.matrix(x$par)) colnames(table.est) <- c("log(\U003B1)", "log(\U003B7)", "log(\U003B8)", names(as.data.frame(x$X))) r <- rep("", , 1) rownames(table.est) <- c(r) print.default(table.est) cat("\n") cat("Number of observations:", x$n, "\n") cat("Number of clusters:", x$ncluster) } summary.frailty <- function(object, digits = max(3L, getOption("digits") - 3L), ...){ ans <- list(AF = AF, CI.transform = CI.transform, confidence.level = confidence.level, confidence.interval = confidence.interval, n.obs = object$n, n.cases = object$n.cases, n.cluster = object$n.cluster, modelcall = modelcall, call = object$call, method = method, formula = object$formula, exposure = object$exposure, outcome = object$outcome, fit = fit, sandwich = object$sandwich) } class(ans) <- "summary.AF" return(ans) }

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plot2.R

##Note: set working directory to the directory with the R files # and ensure data is in same directory ## read rawdata from file rawdata<-read.table("household_power_consumption.txt", header=TRUE,sep=";",na.strings=c("?"), stringsAsFactors=F) ## select days from specified dates, create a datetime column from date and time columns data <- rawdata[(rawdata$Date == "1/2/2007") | (rawdata$Date == "2/2/2007"),] data$DateTime <- strptime(paste(data$Date, data$Time), "%d/%m/%Y %H:%M:%S") ## open png file device, make plots, and close device png(filename="plot2.png",width = 480, height = 480, units = "px") plot(data$DateTime, data$Global_active_power, type="l", xlab="", ylab="Global Active Power (kilowatts)") dev.off()

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cran/BBcor

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/methods.R \name{summary.bbcor} \alias{summary.bbcor} \title{Summarize posterior samples from bbcor object} \usage{ \method{summary}{bbcor}(object, ci = 0.9, decimals = 2, ...) } \arguments{ \item{object}{An object of class \code{bbcor}} \item{ci}{The desired credible interval} \item{decimals}{The number of decimals points to which estimates should be rounded} \item{...}{Currently ignored} } \value{ A \code{data.frame} summarizing the relations } \description{ Summarize posterior samples from bbcor object } \examples{ Y <- mtcars[, 1:5] bb <- bbcor(Y, method = "spearman") summary(bb) }

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danieljwilson/CMMC-2018

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stevens_law.R

# Steven's law function stevens_law <- function(stimuli, a, b){ sub_int = a * stimuli^b return(sub_int) } stimuli <- c(2,4,6,8,10,12) predictions <- stevens_law(stimuli, 1, 0.7) # Simpler way to write the function stevens_law2 <- function(x,a,b) # Example observations observations = c(1.1, 1.5, 2.1, 2.5, 2.7, 3.2) # Plot observations against predictions plot(stimuli, predictions, type = 'l', las=1, ylim=c(0, 7)) points(stimuli, observations, type='p', las=1, ylab="") # Function to calculate the RMSD rmsd = function (params, stimuli, observations){ a = params[1] b = params[2] predictions = stevens_law(stimuli, a, b) rmsd_error = sqrt(mean((observations-predictions)^2)) return(rmsd_error) } # Test out the above function rmsd(c(0.5,.9), stimuli, observations) # Minimize the rmsd using optim results <- optim(c(2,1), rmsd, stimuli=stimuli, observations=observations) # stimuli = stimuli refers the stimuli variable in the rmsd function, and on the right is what we are passing # in to that variable from our local environment best_predictions <- stevens_law(stimuli, results$par[1], results$par[2]) # Plot observations against predictions plot(stimuli, best_predictions, type = 'l', las=1, ylim=c(0, 4), col='red') points(stimuli, observations, type='p', las=1, ylab="")

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# # This is the user-interface definition of a Shiny web application. You can # run the application by clicking 'Run App' above. # # Find out more about building applications with Shiny here: # # http://shiny.rstudio.com/ # library(shiny) library(wordcloud) # Define the app #### ui <- shinyUI(navbarPage("Next-Word Prediction App", tabPanel( "Prediction", fluidRow( column(12,align = "center", h3("Instructions",style = "color:darkred"), HTML(" Dated: 13 Aug, 2020; Author: Satindra Kathania,Ph.D, mail_to: <a href='mailto:satindrak@gmail.com'>satindrak@gmail.com</a>"), "The app need 5-10 sec to initialize, then start by entering your text in the input box below. Once you click submit, the App will display a barplot with Top 10 predicted words according to backoff model score. This App also display the wordcloud for most predicted words. Click on 'Summary' for more details. This App also display the wordcloud for most predicted words. Note, currently this app only supports 'English' language.", br(), # Link to report a('More information on the project', href='https://rpubs.com/SatKat_2020/648814', target = '_blank'), br(), # Link to repo a('Link to the GitHub Repository', href='https://github.com/SatK-ds2020/DS-capstone-project-2020', target = '_blank'), br(), ) ), fluidRow( column(12,align = "center", # Text input tags$style(type="text/css", "textarea {width:80%}") , tags$textarea(id="text", rows=3, placeholder = "Please enter your text...", ""), tags$head(tags$style("#text{color: red; font-size: 20px; font-style: bold; }")), # Submit button for making the prediction submitButton("Submit") ) ), fluidRow( column(12, fluidRow( column(6, h3("Phrases to try",style = "color:darkred"), "[1] loved nine lives last night you cannot cancel ............. (the)", br(), " [2] fun i often wonder why..............(i) ", br(), "[3]time chitchat continued commentators i suppose i................. (should) ", br(), "[4] card for you today made with digital image............... (from)" , br(), "[5] to use flowers woohoo i am so...............(excited) ", br(), "[6] winner will be..........(announced) ", br(), "[7] grueling hour boot camps that cost per head has.............. (begun) ", br(), "[8] the front page philip orlando the new.............(york) " , br(), "[9] miles from downtown cleveland but look how................(far) ", br(), "[10] developed fever while the..................(rest) " ), column(6, h3("Top suggested Next Word:" ), textOutput('text1'), tags$head(tags$style("#text1{color: red; font-size: 30px; font-style: italic; }")) )) )), fluidRow( column(12, fluidRow( column(6, h3("Top 10 Predicted Words",alignment="center"), plotOutput("barchart")), column(6, h3("Wordcloud for the Predicted Words"), plotOutput('wordcloud')) )) ) ), #tabpanel tabPanel("Summary", h3("Information about the App", style = 'color:darkred'), HTML("<footer> Author: Satindra Kathania,Ph.D, mail_to: <a href='mailto:satindrak@gmail.com'>satindrak@gmail.com</a> </footer> 1. This is a NextWord Prediction App and is a part of Coursera Data Science Specialization-Capstone Project offered by John Hopkins University. This web app is inspired by Swiftkey, a predictive keyboard on smart phones. 2. This app uses a text-prediction model based on N-gram frequency with 'stupid backoff algorithm'. The text you entered in the inputbox, it's last four words will be used to predict the next word, for example, if the sentence is 'it happened for the first time' the words 'for the first time' will be used to predict next words. 3. This prediction model uses 1-5 ngrams as dictionary to search,with a precalculated back-off score from highest to lowest frequency. The probability of next word is depends on the backoff score which is calculated by dividing the counts of matches found in ngram/n-1 gram multiplied by **backoff factor= 0.4** with each droping n-grams - First the search started with 5-grams model to match last 4 words of User Input and the most likely next word is predicted with highest score. - If no suitable match is found in the 5-gram, the model search 'back's off' to a 4-gram, i.e. using the last three words, from the user provided phrase, to predict the next best match. - If no 4-gram match is found, the model 'back's off' to a 3-gram, i.e. using the last two words from the user provided phrase, to predict a suitable match. - If no bigram word is found, the model default 'back's off' to the highest frequency unigram word available. 4. The general strategy for Building this predicitive language model is as followed:- - Creating the large corpus with sampling and pooling the blogs, news, and twitter data with stop words. - Cleaning the corpus and Building n-grams frequency tables, up to 5-grams. These frequency tables serve as frequency dictionaries for the predictive model to search for the match. - Probability of a term is modeled based on the Markov chain assumption that the occurrence of a term is dependent on the preceding terms. - Remove sparse terms, threshold is determined. - Use a('backoff strategy', href='https://en.wikipedia.org/wiki/Katz%27s_back-off_model') to predict so that if the probability a quad-gram is very low, use tri-gram to predict, and so on. 5. For more information about the project App and its code, please click on the hyperlinks available on previous page.However, for more details about NLP, prediction algorithms and its techniques, follow the below references: ") ), tabPanel("References", h3("More information on linguistic models", style = 'color:darkred'), HTML(" <h3> References </h3> 1.Large language models in machine translation, http://www.aclweb.org/anthology/D07-1090.pdf' 2.Real Time Word Prediction Using N-Grams Model, IJITEE,ISSN: 2278-3075, Vol.8 Issue-5, 2019' 3.Tokenizer: Introduction to the tokenizers R Package, https://cran.r-project.org/web/packages/tokenizers/' 4.Smoothing and Backoff,'http://www.cs.cornell.edu/courses/cs4740/2014sp/lectures/smoothing+backoff.pdf' 5.N-gram Language Models,'https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf' 6.Speech and Language Processing,'https://web.stanford.edu/class/cs124/lec/languagemodeling.pdf' 7.Katz’s back-off model, Wikipedia,https://en.wikipedia.org/wiki/Katz%27s_back-off_model' 8.Good-Turing frequency estimation, Wikipedia,'https://en.wikipedia.org/wiki/Good%E2%80%93Turing_frequency_estimation' 9.NLP Lunch Tutorial: Smoothing,'https://nlp.stanford.edu/~wcmac/papers/20050421-smoothing-tutorial.pdf' 10.Word Prediction Using Stupid Backoff With a 5-gram Language Model,' https://rpubs.com/pferriere/dscapreport' 11.Katz’s Backoff Model Implementation in R,'https://thachtranerc.wordpress.com/2016/04/12/katzs-backoff-model-implementation-in-r' 12.NLP: Language Models,'https://www.csd.uwo.ca/courses/CS4442b/L9-NLP-LangModels.pdf' ") ) ) )

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/filterByLift.R \name{filterByLift} \alias{filterByLift} \title{Filtering edges by lift} \usage{ filterByLift(g, lift.threshold = 2) } \arguments{ \item{g}{igraph object} \item{lift.threshold}{lift.threshold} } \value{ } \description{ Filtering edges by lift }

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treatEffect<-3 for(){ for(q in 1:100){ treatData <- matrix(rnorm(400), nrow = 20, ncol = 20) contData <- matrix(rnorm(400), nrow = 20, ncol = 20) # we have filled in our 2 20x20 arrays with N(0,1) obs. for(b in 1:20){ treatData[b,b] <-'+'(treatData[b,b],treatEffect) } # added treatment effect to diagonal tSubjMeans<-seq(0,0,length.out=20) tItemMeans<-seq(0,0,length.out=20) cSubjMeans<-seq(0,0,length.out=20) cItemMeans<-seq(0,0,length.out=20) for(w in 1:20){ tSubjMeans[w] <- mean(treatData[w,(1:20)]) cSubjMeans[w] <- mean(contData[w,(1:20)]) tItemMeans[w] <- mean(treatData[(1:20),w]) cItemMeans[w] <- mean(contData[(1:20),w]) } subjDiff<-seq(0,0,length.out=20) itemDiff<-seq(0,0,length.out=20) for(s in 1:20){ subjDiff[s]<-tSubjMeans[s]-cSubjMeans[s] itemDiff[s]<-tItemMeans[s]-cItemMeans[s] } SubjDiffSD<-sd(subjDiff) ItemDiffSD<-sd(itemDiff) Treat<-c(seq(0,0,length.out=20),seq(1,1,length.out=20)) # VVV calculates subject ANOVA VVV subjData<-c(cSubjMeans,tSubjMeans) subjLM<-lm(subjData ~ Treat) subjAnova<-aov(subjLM) subjAnovaPVal<-summary(subjAnova)[[1]][["Pr(>F)"]][[1]] # VVV calculates item ANOVA VVV itemData<-c(cItemMeans,tItemMeans) itemLM <-lm(itemData ~ Treat) itemAnova<-aov(itemLM) subjAnovaPVal<-summary(itemAnova)[[1]][["Pr(>F)"]][[1]] ******code for effect sizes... PLUG IN subject std error term****************** SubjEffectSize <- (mean(tSubjMeans)-mean(cSubjMeans)/.2289 ) ItemEffectSize <- (mean(tItemMeans)-mean(cItemMeans)/.2289 ) ItemEffectSize SubjEffectSize # VVV begins code for quasi-F and min-F calculations VVV # VVV generates coding for treatment tTreat<- matrix(1, nrow = 20, ncol = 20) cTreat<- matrix(0, nrow = 20, ncol = 20) # VVV generate coding for subject factor tSubj<- matrix(0, nrow = 20, ncol = 20) cSubj<- matrix(0, nrow = 20, ncol = 20) for(a in 1:20){ tSubj[a,]<-a cSubj[a,]<-a } # VVV generate coding for item factor tWord<- matrix(0, nrow = 20, ncol = 20) cWord<- matrix(0, nrow = 20, ncol = 20) for(b in 1:20){ for(d in 1:20){ tWord[b,d]<-d cWord[b,d]<-d } } Data<-c(cData,tData) trt<-c(cTreat,tTreat) sub<-c(cSubj,tSubj) wrd<-c(cWord,tWord) Treat<- factor(trt) Subj<- factor(sub) Word <- factor(wrd) aov(Data ~ Treat + Treat:Subj + Treat:Word + Subj:Treat:Word) #Mean Squares for Quasi F can be calculated ********CALCULATING OF F CRIT VALUE USING FORMULAs*************************** quasiF<-(MSt + MStsw)/(MSts + MStw) minF<-MSt/(MSts + MStw) i<-(MSt + MStsw)^2/(MSt^2 + (MStsw^2/722)) j<-(MSts + MStw)^2/(MSts^2/38 + MStw^2/38) Fcrit <-qf(.95, df1 = i , df2 = j) Fcrit quasiF minF i j } }

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inject.Rd

\name{inject} \alias{inject} \docType{data} \title{Single array for injection molding experiment} \description{ Data from the single array for injection molding experiment in chapter 12 of Design and Analysis of Experiments with R } \usage{data(inject)} \format{ A data frame with 20 observations on the following 8 variables. \describe{ \item{\code{A}}{a numeric vector } \item{\code{B}}{a numeric vector } \item{\code{C}}{a numeric vector } \item{\code{D}}{a numeric vector } \item{\code{E}}{a numeric vector } \item{\code{F}}{a numeric vector } \item{\code{G}}{a numeric vector } \item{\code{shrinkage}}{a numeric vector } } } \source{ Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall } \examples{ data(inject) } \keyword{datasets}

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kappalambda.R

gkLambda <- function (tab, weights=c("None","Linear","Quadratic"), W = diag(nrow(tab))) { K <- nrow(tab) if (nrow(tab)!=ncol(tab)) stop("Expected a square matrix.") if (missing(W)) { if (is.character(weights)) weights <- match.arg(weights) W <- switch(weights, None=W, Linear= W - abs(outer(1:K,1:K,"-"))/(K-1), Quadratic= W - (outer(1:K,1:K,"-")/(K-1))^2) } N <- sum(tab) prow <- rowSums(tab)/N pmax <- max(colSums(sweep(W,1,prow,"*"))) lambda <- (sum(W*tab)/N - pmax)/(1-pmax) return(lambda) } fcKappa <- function (tab, weights=c("None","Linear","Quadratic"), W = diag(nrow(tab))) { K <- nrow(tab) if (nrow(tab)!=ncol(tab)) stop("Expected a square matrix.") if (missing(W)) { if (is.character(weights)) weights <- match.arg(weights) W <- switch(weights, None=W, Linear= W - abs(outer(1:K,1:K,"-"))/(K-1), Quadratic= W - (outer(1:K,1:K,"-")/(K-1))^2) } N <- sum(tab) agree <- sum(W*tab)/N prow <- rowSums(tab)/N pcol <- colSums(tab)/N expagree <-sum(W*outer(prow,pcol)) (agree - expagree)/(1-expagree) } gkGamma <- function (tab) { tab <- as.matrix(tab) N <- sum(tab) rtab <- tab/N PIs <- PId <- 0 for (a in 1:(nrow(rtab)-1)) { for (aa in (a+1):nrow(rtab)) { for (b in 1:ncol(rtab)) { if (ncol(rtab) > b) { for (bb in (b+1):ncol(rtab)) { PIs <- PIs + rtab[a,b]*rtab[aa,bb] } } if (b > 1) { for (bb in 1:(b-1)) { PId <- PId + rtab[a,b]*rtab[aa,bb] } } } } } PIs <- 2*PIs PId <- 2*PId PIt <- 1-PIs-PId (PIs-PId)/(1-PIt) }

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output_output_html.R

make_view_output_html <- function(path) { file_path <- file.path(path, "output.html") print_line("<!DOCTYPE html PUBLIC \"-//W3C//DTD XHTML 1.0 Strict//EN\" \"http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd\"> <html xmlns=\"http://www.w3.org/1999/xhtml\"> <head> <title><%= header %></title> </head> <body> <%= output %> </body> </html> ", file_path = file_path, append = FALSE) }

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dynamodb_transact_write_items.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dynamodb_operations.R \name{dynamodb_transact_write_items} \alias{dynamodb_transact_write_items} \title{TransactWriteItems is a synchronous write operation that groups up to 100 action requests} \usage{ dynamodb_transact_write_items( TransactItems, ReturnConsumedCapacity = NULL, ReturnItemCollectionMetrics = NULL, ClientRequestToken = NULL ) } \arguments{ \item{TransactItems}{[required] An ordered array of up to 100 \code{TransactWriteItem} objects, each of which contains a \code{ConditionCheck}, \code{Put}, \code{Update}, or \code{Delete} object. These can operate on items in different tables, but the tables must reside in the same Amazon Web Services account and Region, and no two of them can operate on the same item.} \item{ReturnConsumedCapacity}{} \item{ReturnItemCollectionMetrics}{Determines whether item collection metrics are returned. If set to \code{SIZE}, the response includes statistics about item collections (if any), that were modified during the operation and are returned in the response. If set to \code{NONE} (the default), no statistics are returned.} \item{ClientRequestToken}{Providing a \code{ClientRequestToken} makes the call to \code{\link[=dynamodb_transact_write_items]{transact_write_items}} idempotent, meaning that multiple identical calls have the same effect as one single call. Although multiple identical calls using the same client request token produce the same result on the server (no side effects), the responses to the calls might not be the same. If the \code{ReturnConsumedCapacity} parameter is set, then the initial \code{\link[=dynamodb_transact_write_items]{transact_write_items}} call returns the amount of write capacity units consumed in making the changes. Subsequent \code{\link[=dynamodb_transact_write_items]{transact_write_items}} calls with the same client token return the number of read capacity units consumed in reading the item. A client request token is valid for 10 minutes after the first request that uses it is completed. After 10 minutes, any request with the same client token is treated as a new request. Do not resubmit the same request with the same client token for more than 10 minutes, or the result might not be idempotent. If you submit a request with the same client token but a change in other parameters within the 10-minute idempotency window, DynamoDB returns an \code{IdempotentParameterMismatch} exception.} } \description{ \code{\link[=dynamodb_transact_write_items]{transact_write_items}} is a synchronous write operation that groups up to 100 action requests. These actions can target items in different tables, but not in different Amazon Web Services accounts or Regions, and no two actions can target the same item. For example, you cannot both \code{ConditionCheck} and \code{Update} the same item. The aggregate size of the items in the transaction cannot exceed 4 MB. See \url{https://www.paws-r-sdk.com/docs/dynamodb_transact_write_items/} for full documentation. } \keyword{internal}

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\name{Lxyz2ll} \Rdversion{1.1} \alias{Lxyz2ll} \title{Cartesian to Lat-Lon } \description{Cartesian vector to Lat-Lon List } \usage{ Lxyz2ll(X) } \arguments{ \item{X}{list, x,y,z} } \value{list of lat and lon } \author{ Jonathan M. Lees<jonathan.lees@unc.edu> } \seealso{xyz2ll} \examples{ Lll2xyz(23, 157) } \keyword{misc}

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\name{capitalize_first_letter} \alias{capitalize_first_letter} \title{Capitalization of First Letter} \description{ Capitalizes the first letter of the word. } \usage{ capitalize_first_letter(x) } \arguments{ \item{x}{A character vector.} } \value{It ouputs a character vector of the same length.} \examples{ capitalize_first_letter(word) }

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function (x, pi, mulog, sdlog, max_iter = 500L, tol = 1e-06) { e <- get("data.env", .GlobalEnv) e[["lnorm_C"]][[length(e[["lnorm_C"]]) + 1]] <- list(x = x, pi = pi, mulog = mulog, sdlog = sdlog, max_iter = max_iter, tol = tol) .Call("_mixR_lnorm_C", x, pi, mulog, sdlog, max_iter, tol) }

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zihua/DESeq2

c5ae93c4263f0e3c414233c32bfa832311cf07e2

eefb97f4a8cd3fa7ecfbb8969edb4d304d3746b8

refs/heads/master

2020-12-03T05:17:28.377255

2015-10-15T20:58:48

45,465,791

3

0

null

2015-11-03T12:43:52

null

UTF-8

R

false

699

r

test_vst.R

dds <- makeExampleDESeqDataSet(n=100, m=4) design(dds) <- ~ 1 dds <- estimateSizeFactors(dds) dds <- estimateDispersionsGeneEst(dds) dds <- estimateDispersionsFit(dds, fitType="parametric") vsd <- varianceStabilizingTransformation(dds, blind=FALSE) dds <- estimateDispersionsFit(dds, fitType="local") vsd <- varianceStabilizingTransformation(dds, blind=FALSE) dds <- estimateDispersionsFit(dds, fitType="mean") vsd <- varianceStabilizingTransformation(dds, blind=FALSE) # test VST basics/errors dds <- makeExampleDESeqDataSet(n=20, m=4) colnames(dds) <- NULL varianceStabilizingTransformation(dds) head(varianceStabilizingTransformation(assay(dds))) expect_error(getVarianceStabilizedData(dds))

6044e2e79e747fd2d523686e8ff7c81edbd12d8d

2ae02c5ab707a50623436d73946444a89673c687

/R/SS_executivesummaryMK.R

097306cb3e5b63b6c182736ab9ab3dbbd70caf5b

[]

no_license

mkapur/kaputils

2e1b075fb5641fbbd2c122b6c26a254add6bb6b0

7d734acf9697af9c153b5228fe99f03dfd017238

refs/heads/master

2021-11-24T23:31:28.820396

2021-11-10T20:43:51

125,903,739

2

1

null

UTF-8

R

false

34,243

r

SS_executivesummaryMK.R

#' A function to create a executive summary tables from an SS Report.sso file #' #' Reads the Report.sso within the directory and creates executive summary #' tables as required by the current Terms of Reference for West Coast #' groundfish. Works with Stock Synthesis versions 3.24U and later. #' Additionally, historical catch and numbers at ages tables are created. #' #' @param dir Locates the directory of the files to be read in, double #' backslashes (or forwardslashes) and quotes necessary. #' @param plotdir Directory where the table will be saved. The default #' saves the table to the dir location where the Report.sso file is located. #' @param quant to calculate confidence intervals, default is set at 0.95 #' @param es.only = only the executive summary tables will be produced, default is false which #' will return all executive summary tables, historical catches, and numbers-at-ages #' @param nsex This will allow the user to calculate single sex values based on the new sex #' specification (-1) in SS for single sex models. Default value is FALSE. TRUE will not divide by 2. #' @return A csv files containing executive summary tables. #' @author Chantel Wetzel #' @export SS_executivesummaryMK <- function(dir, plotdir = 'default', quant = 0.95, es.only = FALSE, nsex = FALSE){ basemod1 <- SS_output(dir, covar = TRUE) # Check to make sure dir is a dir if(!is.character(dir) || !file.info(dir)$isdir){ stop("Input 'dir' should be a directory") } # Make sure dir contains the report file repfile <- file.path(dir, "Report.sso") if(is.na(file.info(repfile)$size)){ stop("Report.sso not found in 'dir': ", dir) } #Read in the base model using r4ss wd <- paste(dir, "/Report.sso", sep="") base <- readLines(wd) rawrep <- read.table(file= wd , col.names = 1:400, fill = TRUE, quote = "", colClasses = "character", nrows = -1, comment.char = "") if (plotdir == 'default') { csv.dir = paste0(dir,"/tables/") } if (plotdir != 'default') { csv.dir = paste0(plotdir,"/tables/")} dir.create(csv.dir, showWarnings = FALSE) SS_versionCode <- base[grep("#V",base)] SS_version <- base[grep("Stock_Synthesis",base)] SS_version <- SS_version[substring(SS_version,1,2)!="#C"] # remove any version numbering in the comments SS_versionshort <- toupper(substr(SS_version,1,8)) SS_versionNumeric <- as.numeric(substring(SS_versionshort,5)) #================================================================================================= # Function Sections #================================================================================================= # Function to force R to print correct decimal place print.numeric <- function(x, digits) { formatC(x, digits = digits, format = "f") } comma <- function(x, digits=0) { formatC(x, big.mark=",", digits, format = "f") } emptytest <- function(x){ sum(!is.na(x) & x=="")/length(x) } # Funtion to calculate confidence intervals getDerivedQuant.fn <- function(dat, label, yrs, quant, divisor=1) { # modify old header to new value names(dat)[names(dat)=="LABEL"] <- "Label" allYrs <- suppressWarnings(as.numeric(substring(dat$Label[substring(dat$Label,1,3)=="SSB"],5,8))) allYrs <- allYrs[!is.na(allYrs)] finalYr <- as.numeric(substring(dat$Label[substring(dat$Label,1,8)=="OFLCatch"],10,13))[1] if(is.null(yrs)) { yrs <- allYrs } if(yrs[1]<0) { yrs <- (finalYr+yrs):finalYr } out <- dat[dat$Label%in%paste(label,yrs,sep="_"),] out.value <- out$Value <- out$Value/divisor out$StdDev <- out$StdDev/divisor if(label=="Recr") { #use lognormal out.lower <- exp(log(out.value)-qnorm(1-(1-quant)/2)*sqrt(log((out$StdDev/out.value)^2+1))) out.upper <- exp(log(out.value)+qnorm(1-(1-quant)/2)*sqrt(log((out$StdDev/out.value)^2+1))) } else { out.lower <- out.value-qnorm(1-(1-quant)/2)*out$StdDev out.upper <- out.value+qnorm(1-(1-quant)/2)*out$StdDev } return(data.frame(Year=yrs,Value=out.value,LowerCI=out.lower,UpperCI=out.upper)) } # Function to pull values from the read in report file and calculate the confidence intervals Get.Values <- function(dat, label, yrs, quant, single = FALSE){ if (label == "Recr") { label = " Recr"} if(!single){ dq = mapply(function(x) out = as.numeric(strsplit(dat[grep(paste(label,"_",x,sep=""),dat)]," ")[[1]][3]), x = yrs) sd = mapply(function(x) out = as.numeric(strsplit(dat[grep(paste(label,"_",x,sep=""),dat)]," ")[[1]][4]), x = yrs) if (label == "Main_RecrDev" || label == "Late_RecrDev" || label == "ForeRecr") { zz = ifelse(SS_versionNumeric < 3.3, 10, 11) #sd = mapply(function(x) out = as.numeric(strsplit(dat[grep(paste(label,"_",x,sep=""),dat)]," ")[[1]][zz]), x = yrs) sd = mapply(function(x) out = strsplit(dat[grep(paste(label,"_",x,sep=""),dat)]," ")[[1]][zz], x = yrs) if (sd[1] == "NA") { sd = rep(0, length(dq)) } sd = as.numeric(sd) } } if(single){ dq = as.numeric(strsplit(dat[grep(label,dat)]," ")[[1]][3]) sd = as.numeric(strsplit(dat[grep(label,dat)]," ")[[1]][4]) } if(label == " Recr" || label == "Recr_virgin"){ low = exp(log(dq) - qnorm(1-(1-quant)/2) * sqrt(log(1 + (sd/dq) * (sd/dq)))) high= exp(log(dq) + qnorm(1-(1-quant)/2) * sqrt(log(1 + (sd/dq) * (sd/dq)))) } if(label != " Recr" && label != "Recr_virgin"){ low = dq - qnorm(1-(1-quant)/2)*sd high= dq + qnorm(1-(1-quant)/2)*sd } if (!single) { return(data.frame(yrs, dq, low, high)) } if (single) { return(data.frame(dq, low, high)) } } matchfun <- function(string, obj=rawrep[,1], substr1=TRUE) { # return a line number from the report file (or other file) # sstr controls whether to compare subsets or the whole line match(string, if(substr1){substring(obj,1,nchar(string))}else{obj} ) } matchfun2 <- function(string1,adjust1,string2,adjust2,cols="nonblank",matchcol1=1,matchcol2=1, objmatch=rawrep,objsubset=rawrep,substr1=TRUE,substr2=TRUE,header=FALSE) { # return a subset of values from the report file (or other file) # subset is defined by character strings at the start and end, with integer # adjustments of the number of lines to above/below the two strings line1 <- match(string1, if(substr1){ substring(objmatch[,matchcol1],1,nchar(string1)) }else{ objmatch[,matchcol1] }) line2 <- match(string2, if(substr2){ substring(objmatch[,matchcol2],1,nchar(string2)) }else{ objmatch[,matchcol2] }) if(is.na(line1) | is.na(line2)) return("absent") if(is.numeric(cols)) out <- objsubset[(line1+adjust1):(line2+adjust2),cols] if(cols[1]=="all") out <- objsubset[(line1+adjust1):(line2+adjust2),] if(cols[1]=="nonblank"){ # returns only columns that contain at least one non-empty value out <- objsubset[(line1+adjust1):(line2+adjust2),] out <- out[,apply(out,2,emptytest) < 1] } if(header && nrow(out)>0){ out[1,out[1,]==""] <- "NoName" names(out) <- out[1,] out <- out[-1,] } return(out) } #================================================================================================= # Determine the model version and dimensions of the model #================================================================================================= rawdefs <- matchfun2("DEFINITIONS",1,"LIKELIHOOD",-1) # Determine the number of fishing fleets if (SS_versionNumeric >= 3.3){ # version 3.30 defs <- rawdefs[-(1:3),apply(rawdefs[-(1:3),],2,emptytest)<1] defs[defs==""] <- NA FleetNames <- as.character(defs[grep("Fleet_name",defs$X1),-1]) FleetNames <- FleetNames[!is.na(FleetNames)] fleet_ID <- 1: length(FleetNames) fleet_type <- as.numeric(defs[grep("Fleet_type",defs$X1),-1])# as.numeric(defs[4:(3+length(fleet_ID)),1]) nfleets <- sum(fleet_type[!is.na(fleet_type)] <= 2 ) } if (SS_versionNumeric < 3.3){ # version 3.20 - 3.24 defs <- rawdefs[-(1:3),apply(rawdefs[-(1:3),],2,emptytest)<1] defs[defs==""] <- NA lab <- defs$X1 catch_units <- as.numeric(defs[grep("Catch_units",lab),-1]) IsFishFleet <- !is.na(catch_units) nfleets <- sum(IsFishFleet) } begin <- matchfun(string = "TIME_SERIES", obj = rawrep[,1])+2 end <- matchfun(string = "SPR_series", obj = rawrep[,1])-1 temptime <- rawrep[begin:end,2:3] endyr <- max(as.numeric(temptime[temptime[,2]=="TIME",1])) startyr <- min(as.numeric(rawrep[begin:end,2]))+2 foreyr <- max(as.numeric(temptime[temptime[,2]=="FORE",1])) hist <- (endyr - 11):(endyr + 1) fore <- (endyr+1):foreyr all <- startyr:foreyr #====================================================================== # Determine number of areas in the model #====================================================================== nareas <- max(as.numeric(rawrep[begin:end,1])) #====================================================================== # Determine the fleet name and number for fisherie with catch #====================================================================== begin <- matchfun(string = "CATCH", obj = rawrep[,1])+2 end <- matchfun(string = "TIME_SERIES", obj = rawrep[,1])-1 temp <- rawrep[begin:end, 1:18] names <- unique(temp[,2]) # This is a list of fishery names with catch associated with them fleet.num <- unique(temp[,1]) #====================================================================== # Find summary age #====================================================================== ts <- matchfun2("TIME_SERIES", -1,"Area", -1) smry.age <- as.numeric(toupper(substr(ts[2,2],14,15))) #====================================================================== # Two-sex or Singl-sex model #====================================================================== selex <- matchfun2("LEN_SELEX",6,"AGE_SELEX",-1,header=TRUE) nsexes <- ifelse(SS_versionNumeric < 3.3, length(unique(as.numeric(selex$gender))), length(unique(as.numeric(selex$Sex)))) sexfactor = 2 if (nsex) {sexfactor = 1} #====================================================================== # Determine the number of growth patterns #====================================================================== find.morph <- matchfun2("MORPH_INDEXING", 1, "MOVEMENT", -2, header=TRUE) nmorphs <- dim(find.morph)[1] / nsexes #====================================================================== #ES Table a Catches from the fisheries #====================================================================== xx = ifelse(SS_versionNumeric < 3.3, 12, 15) catch = NULL; total.catch = total.dead = 0 ind = hist[1:(length(hist)-1)] for(a in 1:nareas){ for (i in 1:nfleets){ killed = mapply(function(x) killed = as.numeric(strsplit(base[grep(paste(fleet.num[i], names[i], x, sep=" "),base)]," ")[[1]][xx]), x = ind) # killed = mapply(function(x) killed = as.numeric(strsplit(base[grep(paste(fleet.num[i], names[i], nareas[a], x, sep=" "),base)]," ")[[1]][xx]), x = ind) input.catch = mapply(function(x) input.catch = as.numeric(strsplit(base[grep(paste(fleet.num[i], names[i], x, sep=" "),base)]," ")[[1]][xx+1]), x = ind) # input.catch = mapply(function(x) input.catch = as.numeric(strsplit(base[grep(paste(fleet.num[i], names[i], nareas[a], x, sep=" "),base)]," ")[[1]][xx+1]), x = ind) total.dead = total.dead + killed total.catch = total.catch + input.catch catch = cbind(catch, input.catch) } es.a = data.frame(ind, comma(catch, digits = 2), comma(total.catch, digits = 2), comma(total.dead, digits = 2)) colnames(es.a) = c("Years", names, "Total Catch", "Total Dead") write.csv(es.a, paste0(csv.dir, "/a_Catches_Area", nareas[a], "_ExecutiveSummary.csv"), row.names = F) } # SS_readforecastMK(paste0(dir,"/forecast.ss")) fore <- readLines(paste0(dir,"/forecast.ss"))[44:(length( readLines(paste0(dir,"/forecast.ss")))-2)] %>% strsplit(.," ") df <- data.frame() for(e in 1:length(fore)){ df[e,"Years"] <- as.numeric(fore[[e]][2]) df[e,"Fleet"] <- as.numeric(fore[[e]][4]) df[e,"Total_Catch"] <- as.numeric(fore[[e]][9]) # df[e,"Total_Dead"] <- NA } df %>% select(Years, Fleet, Total_Catch) %>% spread(Fleet,Total_Catch) # fore <- df2 <- read.csv(paste0(dir,"/tempForeCatch.csv")) %>% select(X.Year, Fleet, dead.B.) %>% spread(Fleet, dead.B.) #====================================================================== #ES Table b Spawning Biomass and Depletion #====================================================================== hist <- (endyr - 11):(endyr + 5) ssb = Get.Values(dat = base, label = "SPB", hist, quant ) # ssb = Get.Values(dat = base, label = "SSB", hist, quant ) if (nsexes == 1) { ssb$dq = ssb$dq / sexfactor ; ssb$low = ssb$low / sexfactor ; ssb$high = ssb$high / sexfactor } depl = Get.Values(dat = base, label = "Bratio" , hist, quant ) for (i in 1:length(hist)){ dig = ifelse(ssb[i,2] < 100, 1, 0)} es.b = data.frame(hist, comma(ssb$dq,digits = dig), paste0(comma(ssb$low,digits = dig), "\u2013", comma(ssb$high,digits = dig)), print(100*depl$dq, digits = 1), paste0(print(100*depl$low,digits = 1), "\u2013", print(100*depl$high,digits = 1))) colnames(es.b) = c("Years", "Spawning Output", "95% Asymptotic Interval", "Estimated Depletion (%)", "95% Asymptotic Interval") write.csv(es.b, paste0(csv.dir, "/b_SSB_ExecutiveSummary.csv"), row.names = F) hist <- (endyr - 11):(endyr + 1) #====================================================================== #ES Table c Recruitment #====================================================================== parameters <- matchfun2("PARAMETERS",1,"DERIVED_QUANTITIES", -1, header=TRUE) recdevMain <- parameters[substring(parameters$Label,1,12)=="Main_RecrDev",] recdevLate <- parameters[substring(parameters$Label,1,12)=="Late_RecrDev",] temp <- toupper(substr(recdevLate$Label,14,17)) late.yrs <- as.numeric(temp) recdevFore <- parameters[substring(parameters$Label,1, 8)=="ForeRecr",] temp <- toupper(substr(recdevFore$Label,10,13)) fore.yrs <- as.numeric(temp) ind <- fore.yrs <= max(hist) fore.yrs <- 2015:2019 #fore.yrs[ind] end <- ifelse(length(late.yrs) == 0, fore.yrs - 1, late.yrs - 1) # recruits = Get.Values(dat = base, label = "Recr" , (endyr - 11):(endyr + 5), quant ) if (dim(recdevMain)[1] != 0){ recdevs = Get.Values(dat = base, label = "Main_RecrDev", yrs = 1971:end, quant ) # recdevs = Get.Values(dat = base, label = "Main_RecrDev", yrs = hist[1]:end, quant ) devs = cbind(recdevs$dq, recdevs$low, recdevs$high) if (length(late.yrs) > 0 ){ late.recdevs = Get.Values(dat = base, label = "Late_RecrDev", yrs = late.yrs, quant ) devs = cbind(c(recdevs$dq, late.recdevs$dq), c(recdevs$low, late.recdevs$low), c(recdevs$high, late.recdevs$high)) } if(length(fore.yrs) > 0){ fore.recdevs = Get.Values(dat = base, label = "ForeRecr", yrs = fore.yrs, quant ) if (length(late.yrs) > 0){ devs = cbind(c(recdevs$dq, late.recdevs$dq, fore.recdevs$dq), c(recdevs$low, late.recdevs$low, fore.recdevs$low), c(recdevs$high, late.recdevs$high, fore.recdevs$high)) } if (length(late.yrs) == 0){ devs = cbind(c(recdevs$dq, fore.recdevs$dq), c(recdevs$low, fore.recdevs$low), c(recdevs$high, fore.recdevs$high)) } } # Zero out the sd for years where devs were not estimated devs.out = data.frame(print(devs[,1], digits = 3), paste0(print(devs[,2],digits = 3), "\u2013", print(devs[,3], digits = 3))) } if (dim(recdevMain)[1] == 0) { devs.out = data.frame(rep(0, length(hist)), rep(0, length(hist))) } for (i in 1:length(hist)){ dig = ifelse(recruits[i,2] < 100, 1, 0)} es.c = data.frame(recruits$yrs, comma(recruits$dq, dig), paste0(comma(recruits$low, dig), "\u2013", comma(recruits$high, dig)))#, #devs.out ) colnames(es.c) = c("Years", "Recruitment", "95% Asymptotic Interval") #, "Recruitment Deviations", "95% Asymptotic Interval") write.csv(es.c, paste0(csv.dir, "/c_Recr_ExecutiveSummary.csv"), row.names = F) #====================================================================== #ES Table d 1-SPR (%) #====================================================================== hist <- (endyr - 11):(endyr + 5) spr.name = ifelse(SS_versionNumeric >= 3.30, "SPR_report_basis", "SPR_ratio_basis") spr_type = strsplit(base[grep(spr.name,base)]," ")[[1]][3] #if (spr_type != "1-SPR") { # print(":::::::::::::::::::::::::::::::::::WARNING:::::::::::::::::::::::::::::::::::::::") # print(paste("The SPR is being reported as", spr_type, ".")) # print("West coast groundfish assessments typically report 1-SPR in the executive summary") # print(":::::::::::::::::::::::::::::::::::WARNING:::::::::::::::::::::::::::::::::::::::") } adj.spr = Get.Values(dat = base, label = "SPRratio" , hist, quant) f.value = Get.Values(dat = base, label = "F" , hist, quant) es.d = data.frame(hist, print(adj.spr$dq*100,2), paste0(print(adj.spr$low*100,2), "\u2013", print(adj.spr$high*100,2)), print(f.value$dq,3), paste0(print(f.value$low,3), "\u2013", print(f.value$high,3))) colnames(es.d) = c("Years", paste0("Estimated ", spr_type, " (%)"), "95% Asymptotic Interval", "Harvest Rate (proportion)", "95% Asymptotic Interval") write.csv(es.d, paste0(csv.dir, "/d_SPR_ExecutiveSummary.csv"), row.names = F) hist <- (endyr - 11):(endyr + 1) #====================================================================== #ES Table e Reference Point Table #====================================================================== # Find the values within the forecast file rawforecast <- readLines(paste0(dir, "/forecast.ss")) rawstarter <- readLines(paste0(dir, "/starter.ss")) spr <- as.numeric(strsplit(rawforecast[grep("SPRtarget",rawforecast)]," ")[[1]][1]) ssb.virgin = Get.Values(dat = base, label = "SPB_Virgin", hist, quant, single = TRUE) # ssb.virgin = Get.Values(dat = base, label = "SSB_unfished", hist, quant, single = TRUE) smry.virgin = data.frame("dq" = basemod1$derived_quants[grep("SmryBio_Unfished", basemod1$derived_quants$Label),"Value"], "low" = basemod1$derived_quants[grep("SmryBio_Unfished", basemod1$derived_quants$Label),"Value"] - 1.96*basemod1$derived_quants[grep("SmryBio_Unfished", basemod1$derived_quants$Label),"StdDev"], "high" = basemod1$derived_quants[grep("SmryBio_Unfished", basemod1$derived_quants$Label),"Value"] + 1.96*basemod1$derived_quants[grep("SmryBio_Unfished", basemod1$derived_quants$Label),"StdDev"]) rec.virgin = Get.Values(dat = base, label = "Recr_Virgin", hist, quant, single = TRUE) # rec.virgin = Get.Values(dat = base, label = "Recr_unfished", hist, quant, single = TRUE) final.depl = 100*depl[dim(depl)[1],2:4] b.target = Get.Values(dat = base, label = "SSB_Btgt", hist, quant, single = TRUE) spr.btarg = Get.Values(dat = base, label = "SPR_Btgt", hist, quant, single = TRUE) f.btarg = Get.Values(dat = base, label = "Fstd_Btgt", hist, quant, single = TRUE) yield.btarg= Get.Values(dat = base, label = "TotYield_Btgt", hist, quant, single = TRUE) b.spr = Get.Values(dat = base, label = "SSB_SPR", hist, quant, single = TRUE) f.spr = Get.Values(dat = base, label = "Fstd_SPR", hist, quant, single = TRUE) yield.spr = Get.Values(dat = base, label = "TotYield_SPRtgt", hist, quant, single = TRUE) b.msy = Get.Values(dat = base, label = "SSB_MSY", hist, quant, single = TRUE) spr.msy = Get.Values(dat = base, label = "SPR_MSY", hist, quant, single = TRUE) f.msy = Get.Values(dat = base, label = "Fstd_MSY", hist, quant, single = TRUE) msy = Get.Values(dat = base, label = "TotYield_MSY", hist, quant, single = TRUE) # Convert spawning quantities for single-sex models if (nsexes == 1){ ssb.virgin = ssb.virgin / sexfactor b.target = b.target / sexfactor b.spr = b.spr / sexfactor b.msy = b.msy / sexfactor } es.e = matrix(c( comma(ssb.virgin$dq, dig), paste0(comma(ssb.virgin$low, dig), "\u2013", comma(ssb.virgin$high, dig)), comma(smry.virgin$dq, dig), paste0(comma(smry.virgin$low, dig), "\u2013", comma(smry.virgin$high, dig)), comma(ssb$dq[dim(ssb)[1]], dig), paste0(comma(ssb$low[dim(ssb)[1]],dig), "\u2013", comma(ssb$high[dim(ssb)[1]],dig)), comma(rec.virgin$dq, dig), paste0(comma(rec.virgin$low, dig), "\u2013", comma(rec.virgin$high, dig)), print(final.depl$dq, 2), paste0(print(final.depl$low, 2), "\u2013", print(final.depl$high, 2)), "", "", comma(b.target$dq, dig), paste0(comma(b.target$low, dig), "\u2013", comma(b.target$high, dig)), print(spr.btarg$dq, 3), paste0(print(spr.btarg$low, 3), "\u2013", print(spr.btarg$high, 3)), print(f.btarg$dq, 3), paste0(print(f.btarg$low, 3), "\u2013", print(f.btarg$high, 3)), comma(yield.btarg$dq, dig), paste0(comma(yield.btarg$low, dig), "\u2013", comma(yield.btarg$high, dig)), "", "", comma(b.spr$dq, dig), paste0(comma(b.spr$low, dig), "\u2013", comma(b.spr$high, dig)), print(spr, 3), " NA ", print(f.spr$dq, 3), paste0(print(f.spr$low, 3), "\u2013", print(f.spr$high, 3)), comma(yield.spr$dq, dig), paste0(comma(yield.spr$low, dig), "\u2013", comma(yield.spr$high, dig)), "", "", comma(b.msy$dq, dig), paste0(comma(b.msy$low, dig), "\u2013", comma(b.msy$high, dig)), print(spr.msy$dq, 3), paste0(print(spr.msy$low, 3), "\u2013", print(spr.msy$high, 3)), print(f.msy$dq, 3), paste0(print(f.msy$low, 3), "\u2013", print(f.msy$high, 3)), comma(msy$dq, dig), paste0(comma(msy$low, dig), "\u2013", comma(msy$high, dig)) ), ncol=2, byrow=T ) es.e = noquote(es.e) colnames(es.e) = c("Estimate", "95% Asymptotic Interval") rownames(es.e) = c("Unfished Spawning Biomass (mt)", paste0("Unfished Age ", smry.age, "+ Biomass (mt)"), paste0("Spawning Biomass", " (", hist[length(hist)], ")"), "Unfished Recruitment (R0)", paste0("Depletion ", "(", hist[length(hist)], ")"), "Reference Points Based SB40%", "Proxy Spawning Biomass (SB40%)", "SPR resulting in SB40%", "Exploitation Rate Resulting in SB40%", "Yield with SPR Based On SB40% (mt)", "Reference Points based on SPR proxy for MSY", "Proxy spawning biomass (SPR50)", "SPR50", "Exploitation rate corresponding to SPR50", "Yield with SPR50 at SBSPR (mt)", "Reference points based on estimated MSY values", "Spawning biomass at MSY (SBMSY)", "SPRMSY", "Exploitation rate corresponding to SPRMSY", "MSY (mt)") write.csv(es.e, paste0(csv.dir, "/e_ReferencePoints_ExecutiveSummary.csv")) # #====================================================================== # # ES Table f is the historical harvest # #====================================================================== ind = hist ofl = rep("fill_in", length(ind)) abc = rep("fill_in", length(ind)) acl = rep("fill_in", length(ind)) catch = c(comma(total.catch, digits = 2), "NA") dead = c(comma(total.dead, digits = 2), "NA") es.f = data.frame(ind, ofl, abc, acl, catch, dead) colnames(es.f) = c("Years", "OFL", "ABC", "ACL", "Landings", "Total Dead") write.csv(es.f, paste0(csv.dir, "/f_Manage_ExecutiveSummary.csv"), row.names = F) # # #====================================================================== # #ES Table g Predicted Quantities # #====================================================================== ofl.fore = Get.Values(dat = base, label = "OFLCatch" , yrs = fore, quant) abc.fore = Get.Values(dat = base, label = "ForeCatch" , yrs = fore, quant) ## ssb for all forecast yrs ssb.fore = data.frame("yrs" = fore, "dq" = basemod1$derived_quants[grep(paste0("SSB_",fore,collapse = "|"), basemod1$derived_quants$Label),"Value"], "low" = basemod1$derived_quants[grep(paste0("SSB_",fore,collapse = "|"), basemod1$derived_quants$Label),"Value"] - 1.96*basemod1$derived_quants[grep("SmryBio_Unfished", basemod1$derived_quants$Label),"StdDev"], "high" = basemod1$derived_quants[grep(paste0("SSB_",fore,collapse = "|"), basemod1$derived_quants$Label),"Value"] + 1.96*basemod1$derived_quants[grep("SmryBio_Unfished", basemod1$derived_quants$Label),"StdDev"]) #Get.Values(dat = base, label = "SSB" , yrs = fore, quant) depl.fore = Get.Values(dat = base, label = "Bratio", yrs = fore, quant) if (nsexes == 1) { ssb.fore$dq = ssb.fore$dq / sexfactor; ssb.fore$low = ssb.fore$low / sexfactor; ssb.fore$high = ssb.fore$high / sexfactor} smry.fore = 0 for(a in 1:nareas){ temp = mapply(function(x) temp = as.numeric(strsplit(base[grep(paste(a, x,"FORE",sep=" "),base)]," ")[[1]][6]), x = fore) smry.fore = smry.fore + temp } es.g = data.frame(fore, comma(ofl.fore$dq, 2), comma(abc.fore$dq, 2), comma(smry.fore, 2), comma(ssb.fore$dq, 2), print(depl.fore$dq*100,2)) colnames(es.g) = c("Year", "Predicted OFL (mt)", "ABC Catch (mt)", paste0("Age ", smry.age, "+ Biomass (mt)"), "Spawning Biomass (mt)", "Depletion (%)") write.csv(es.g, paste0(csv.dir, "/g_Projections_ExecutiveSummary.csv"), row.names = F) # # #====================================================================== # #ES Table h decision table # #====================================================================== # # To be done later # # # #====================================================================== # #ES Table i the summary table # #====================================================================== # hist <- (endyr - 11):(endyr + 5) # ind = length(hist)-1 # smry = 0 # for(a in 1:nareas){ # temp = mapply(function(x) temp = as.numeric(strsplit(base[grep(paste(a, x,"TIME",sep=" "),base)]," ")[[1]][6]), x = hist[1:ind]) # smry = smry + temp # } # # smry = c(smry, smry.fore[1]) # # es.i = matrix(c(hist, # c(print(adj.spr$dq[1:(length(hist)-1)],2), "NA"), # c(print(f.value$dq[1:(length(hist)-1)],2), "NA"), # comma(smry, dig), # comma(ssb$dq[1:length(hist)], dig), # paste0(comma(ssb$low[1:length(hist)], dig), "\u2013", comma(ssb$high[1:length(hist)], dig)), # comma(recruits$dq, dig), # paste0(comma(recruits$low, dig), "\u2013", comma(recruits$high, dig)), # print(depl$dq[1:length(hist)]*100, 1), # paste0(print(depl$low[1:length(hist)]*100,1), "\u2013", print(depl$high[1:length(hist)]*100,1))), # ncol = length(hist), byrow = T) # # es.i = noquote(es.i) # # rownames(es.i) = c(" Years", # "1-SPR", # "Exploitation_Rate", # paste0("Age ", smry.age, "+ Biomass (mt)"), # "Spawning Biomass (mt)", # "95% Confidence Interval", # "Recruitment", # "95% Confidence Interval", # "Depletion (%)", # "95% Confidence Interval") # # write.csv(es.i, paste0(csv.dir, "/i_Summary_ExecutiveSummary.csv")) # hist <- (endyr - 11):(endyr +1) # # if (es.only == FALSE){ # #====================================================================== # # Total Catch when discards are estimated # #====================================================================== # xx = ifelse(SS_versionNumeric < 3.3, 12, 15) # total.dead = total.catch = 0 # catch = NULL # ind = startyr:endyr # for(a in 1:nareas){ # for (i in 1:nfleets){ # killed = mapply(function(x) killed = as.numeric(strsplit(base[grep(paste(fleet.num[i], names[i], nareas[a], x, sep=" "),base)]," ")[[1]][xx]), x = ind) # input.catch = mapply(function(x) input.catch = as.numeric(strsplit(base[grep(paste(fleet.num[i], names[i], nareas[a], x, sep=" "),base)]," ")[[1]][xx+1]), x = ind) # total.dead = total.dead + killed # total.catch = total.catch + input.catch # catch = cbind(catch, input.catch) # } # mortality = data.frame(ind, comma(catch, 2), comma(total.catch,2), comma(total.dead,2)) # colnames(mortality) = c("Year",names, "Total Catch", "Total Dead") # # write.csv(mortality, paste0(csv.dir, "/_CatchesAllYrs_Area", nareas[a], ".csv"), row.names = F) # } # # #====================================================================== # #Numbers at age # #====================================================================== # if ( nareas > 1) { print(paste0("Patience: There are ", nareas, " areas that are being pulled and combined to create the numbers-at-age tables.")) } # # if(SS_versionNumeric < 3.30) { # maxAge = length(strsplit(base[grep(paste("1 1 1 1 1 1", startyr,sep=" "),base)]," ")[[1]]) - 14 # # if (nsexes == 1) { # natage.f = natage.m = 0 # for(a in 1:nareas){ # temp = mapply(function(x) temp = as.numeric(strsplit(base[grep(paste(a,"1 1 1 1", x,sep=" "),base)]," ")[[1]][14:(14+maxAge)]), x = startyr:endyr) # natage.f = natage.f + t(temp) # } # # colnames(natage.f) = 0:maxAge # rownames(natage.f) <- startyr:endyr # # write.csv(natage.f, paste0(csv.dir, "/_natage.csv")) # } # # if (nsexes == 2) { # natage.f = natage.m = 0 # for(a in 1:nareas){ # for (b in 1:nmorphs){ # n = b # temp = mapply(function(x) temp = as.numeric(strsplit(base[grep(paste(a, b, "1 1 1", n, x,sep=" "),base)]," ")[[1]][14:(14+maxAge)]), x = startyr:endyr) # natage.f = natage.f + t(temp) # n = ifelse(nmorphs ==1, nsexes, b + nsexes) # temp = mapply(function(x) temp = as.numeric(strsplit(base[grep(paste(a, b, "2 1 1", n, x,sep=" "),base)]," ")[[1]][14:(14+maxAge)]), x = startyr:endyr) # natage.m = natage.m + t(temp) # } # } # # colnames(natage.f) = 0:maxAge; colnames(natage.m) = 0:maxAge # rownames(natage.f) <- startyr:endyr ; rownames(natage.m) <- startyr:endyr # # write.csv(natage.f, paste0(csv.dir, "/_natage_f.csv")) # write.csv(natage.m, paste0(csv.dir, "/_natage_m.csv")) # } # } # SS v3.24 verions loop # # # Check to see if numbers-at-age is calculated # if(SS_versionNumeric >= 3.30) { # check = as.numeric(strsplit(rawstarter[grep("detailed output", rawstarter)]," ")[[1]][1]) # if (check == 2) { "Detailed age-structure set in starter file set = 2 which does not create numbers-at-age table."} # # if (check != 2){ # maxAge = length(strsplit(base[grep(paste("1 1 1 1 1 1 1", startyr,sep=" "),base)]," ")[[1]]) - 14 # # if (nsexes == 1) { # natage.f = natage.m = 0 # for(a in 1:nareas){ # temp = mapply(function(x) temp = as.numeric(strsplit(base[grep(paste(a,"1 1 1 1 1 1", x,sep=" "),base)]," ")[[1]][14:(14+maxAge)]), x = startyr:endyr) # natage.f = natage.f + t(temp) # } # # colnames(natage.f) = 0:maxAge # rownames(natage.f) <- startyr:endyr # # write.csv(natage.f, paste0(csv.dir, "/_natage.csv")) # } # # if (nsexes == 2) { # natage.f = natage.m = 0 # for(a in 1:nareas){ # for (b in 1:nmorphs){ # n = b # temp = mapply(function(x) temp = as.numeric(strsplit(base[grep(paste(a, b, "1 1 1 1", n, x,sep=" "),base)]," ")[[1]][14:(14+maxAge)]), x = startyr:endyr) # natage.f = natage.f + t(temp) # n = ifelse(nmorphs ==1, nsexes, b + nsexes) # temp = mapply(function(x) temp = as.numeric(strsplit(base[grep(paste(a, b, "2 1 1 1", n, x,sep=" "),base)]," ")[[1]][14:(14+maxAge)]), x = startyr:endyr) # natage.m = natage.m + t(temp) # } # } # # colnames(natage.f) = 0:maxAge; colnames(natage.m) = 0:maxAge # rownames(natage.f) <- startyr:endyr ; rownames(natage.m) <- startyr:endyr # # write.csv(natage.f, paste0(csv.dir, "/_natage_f.csv")) # write.csv(natage.m, paste0(csv.dir, "/_natage_m.csv")) # } # } #check loop # } # SS version 3.30 # # } #nareas } ## not run:: # SS_executivesummaryMK(dir = "C:/Users/Maia Kapur/Dropbox/UW/assessments/china_2019_update/chinarock-update-2019/crCen/base2015")

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/script/old/20191210-GLMM.R

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wetinhsu/Macaca-population-trend

fe14e5dfa6290b4a982175feae290053c60446b9

a0e738ec817ae70b8ea042eeb7b64df4c9b5cb10

refs/heads/master

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20191210-GLMM.R

# data analysis library(data.table) library(lme4) library(car) library(magrittr) library(multcomp) library(ggplot2) library(psych) library(readxl) library(MuMIn) #------------------------------------------------ #Original data---- M.data.o <- read_excel("./data/clean/for analysis.xlsx", sheet=1) %>% setDT %>% .[, DATE := as.IDate(paste(Year, Month, Day, sep = "/"))] %>% .[TypeName %like% "混", TypeName.n := "mixed"] %>% .[TypeName %like% "竹林", TypeName.n := "Bamboo"] %>% .[TypeName %like% "闊葉", TypeName.n := "broad-leaved"] %>% .[TypeName %like% "針葉", TypeName.n := "coniferous"] %>% .[, TypeName.1 := ifelse(Distance>20, "Not forest", TypeName.n)] %>% .[, County := ordered(County, c("宜蘭縣","基隆市","台北市","臺北市", "新北市","台北縣","臺北縣", "桃園縣","桃園市","新竹市", "新竹縣","苗栗縣", "台中市","臺中市", "台中縣","臺中縣", "彰化縣","南投縣","南投市", "雲林縣","嘉義縣","嘉義市", "台南市","臺南市", "台南縣","臺南縣", "高雄縣","高雄市", "屏東縣", "花蓮縣", "台東縣","臺東縣"))] %>% .[County %in% list("宜蘭縣","基隆市","台北市","臺北市", "新北市","台北縣","臺北縣", "桃園縣","桃園市","新竹市", "新竹縣","苗栗縣"), Region := "North"] %>% .[County %in% list("台中市","臺中市", "台中縣","臺中縣", "彰化縣","南投縣","南投市", "雲林縣","嘉義縣","嘉義市"), Region := "Center"] %>% .[County %in% list("台南市","臺南市", "台南縣","臺南縣", "高雄縣","高雄市", "屏東縣"), Region := "South"]%>% .[County %in% list("花蓮縣", "台東縣","臺東縣"), Region := "East"] %>% .[, julian.D := yday(DATE)] %>% .[, Altitude_c := substr(Site_N,1,1)] %>% setDT M.data.o$Year %<>% as.numeric M.data.o$Survey %<>% as.numeric M.data.o$Point %<>% as.numeric M.data.o$Macaca_sur %<>% as.numeric M.data.o$Month %<>% as.numeric M.data.o$Day %<>% as.numeric M.data.o$Distance %<>% as.numeric M.data.o$julian.D %<>% as.numeric M.data.o$Region %<>% as.factor M.data.o$TypeName.1 %<>% as.factor M.data.o$Site_N %<>% as.factor #Remove duplicate data------------------------------------------- M.data <- M.data.o %>% copy(.) %>% .[Year %in% 2015 & Survey %in% 2 & Site_N %in% "A29-17" & Point %in% 7, Macaca_sur := NA] %>% .[Year %in% 2015 & Survey %in% 2 & Site_N %in% "A33-28" & Point %in% 7, Macaca_sur := NA] %>% .[Year %in% 2016 & Survey %in% 1 & Site_N %in% "B33-01" & Point %in% 4, Macaca_sur := NA] %>% .[Year %in% 2017 & Survey %in% 1 & Site_N %in% "B38-07" & Point %in% 7, Macaca_sur := NA] %>% .[Year %in% 2018 & Survey %in% 1 & Site_N %in% "A35-15" & Point %in% 5, Macaca_sur := NA] %>% .[Year %in% 2018 & Survey %in% 2 & Site_N %in% "A28-10" & Point %in% 6, Macaca_sur := NA] %>% .[Year %in% 2019 & Survey %in% 1 & Site_N %in% "B14-02" & Point %in% 6, Macaca_sur := NA] %>% .[Year %in% 2019 & Survey %in% 1 & Site_N %in% "B38-08" & Point %in% 5, Macaca_sur := NA] %>% .[Year %in% 2019 & Survey %in% 2 & Site_N %in% "A20-02" & Point %in% 3, Macaca_sur := NA] %>% .[Year %in% 2019 & Survey %in% 2 & Site_N %in% "A33-32" & Point %in% 6, Macaca_sur := NA] #--------------------------------------------------------------------- M.data <- M.data %>% .[is.na(Macaca_sur), Macaca_sur := 0] %>% .[, Year := as.numeric(Year)] %>% .[, Year.re := Year - min(Year) + 1] M.data %>% .[Macaca_sur %in% 1, .N, list(TypeName.1, Macaca_dist)] %>% dcast(.,Macaca_dist ~TypeName.1, value.var="N") #============================================== df <- M.data %>% .[Year < 2019,] %>% .[!(TypeName.1 %in% "Not forest"), ] #------------------------------------------- allFit(glmer(Macaca_sur ~ TypeName.1 + Year.re + Altitude + julian.D + Region + (1|Site_N), family = binomial, data = df)) #嘗試使用一系列優化程序重新擬合glmer模型 df$Altitude.1 <- scale(df$Altitude,scale =T) df$julian.D.1 <- scale(df$julian.D,scale =T) m1 <- glmer(Macaca_sur ~ Year.re + TypeName.1 + Altitude.1 + julian.D.1 + Region + (1|Site_N), family = binomial, data = df, control = glmerControl(optimizer = "bobyqa")) summary(m1) #anova table============================================== Anova(m1) summary(glht(m1, linfct = mcp(TypeName.1 = "Tukey"))) summary(glht(m1, linfct = mcp(Survey = "Tukey"))) summary(glht(m1, linfct = mcp(Altitude.1 = "Tukey"))) summary(glht(m1, linfct = mcp(Region = "Tukey"))) #AICc============================================== options(na.action = "na.fail") d1<- dredge( glmer(Macaca_sur ~ TypeName.1 + Year.re + Altitude.1 + julian.D.1 + Region + (1|Site_N), family = binomial, data = df, control = glmerControl(optimizer = "bobyqa")), trace = T) summary(model.avg(d1)) summary(model.avg(d1, subset = delta < 2)) importance(d1) sw(model.avg(d1, subset = delta < 2)) #Estimate2============================================== ##<25 aa<- df %>% setDT %>% .[, A := ifelse(Macaca_dist %in% "A", Macaca_sur,0)] %>% .[, AB := ifelse(Macaca_dist %in% c("A","B"), Macaca_sur,0)] %>% .[, .(Mean = mean(A, na.rm=T), SD = sd(A, na.rm=T)/sqrt(length(A)), n = .N), by = list(TypeName.1,Survey, Region)] %>% .[, N:= sum(n)] sum(aa$N*(aa$N-aa$n)*(aa$SD)^2/aa$n, na.rm=T)/(unique(aa$N)^2) mean(aa$Mean) ##<100 bb<- df %>% setDT %>% .[, A := ifelse(Macaca_dist %in% "A", Macaca_sur,0)] %>% .[, AB := ifelse(Macaca_dist %in% c("A","B"), Macaca_sur,0)] %>% .[, .(Mean = mean(AB, na.rm=T), SD = sd(AB, na.rm=T)/sqrt(length(AB)), n = .N), by = list(TypeName.1,Survey, Region)] %>% .[, N:= sum(n)] sum(bb$N*(bb$N-bb$n)*(bb$SD)^2/bb$n, na.rm=T)/(unique(bb$N)^2) mean(bb$Mean) #Estimate3============================================== ##<25 aa<- df %>% setDT %>% .[, A := ifelse(Macaca_dist %in% "A", Macaca_sur,0)] %>% .[, AB := ifelse(Macaca_dist %in% c("A","B"), Macaca_sur,0)] %>% .[, julian.D_f := cut(julian.D, breaks = c(seq(0,210,15)), include.lowest = T)] %>% .[, .(Mean = mean(A, na.rm=T), SD = sd(A, na.rm=T)/sqrt(length(A)), n = .N), by = list(TypeName.1,julian.D_f, Region)] %>% .[, N:= sum(n)] (se.25 <- sum(aa$N*(aa$N-aa$n)*(aa$SD)^2/aa$n, na.rm=T)/(unique(aa$N)^2)) mean(aa$Mean) se.25^0.5*1.28 #80%CI=se*1.28 ##<100 bb<- df %>% setDT %>% .[, A := ifelse(Macaca_dist %in% "A", Macaca_sur,0)] %>% .[, AB := ifelse(Macaca_dist %in% c("A","B"), Macaca_sur,0)] %>% .[, julian.D_F := cut(julian.D, breaks = c(seq(0,210,15)), include.lowest = T)] %>% .[, .(Mean = mean(AB, na.rm=T), SD = sd(AB, na.rm=T)/sqrt(length(AB)), n = .N), by = list(TypeName.1,julian.D_f, Region)] %>% .[, N:= sum(n)] (se.100 <- sum(bb$N*(bb$N-bb$n)*(bb$SD)^2/bb$n, na.rm=T)/(unique(bb$N)^2)) mean(bb$Mean) se.100^0.5*1.28

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/R/other_plots.R

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[]

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laresbernardo/lares

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8883d6ef3c3f41d092599ffbdd4c9c352a9becef

refs/heads/main

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other_plots.R

#################################################################### #' Plot Result with Nothing to Plot #' #' This function lets the user print a plot without plot, with a #' customizable message. It is quite useful for Shiny renderPlot when #' using filters and no data is returned. #' #' @family Visualization #' @param message Character. What message do you wish to show? #' @param size Numeric. Font size for \code{message} input. #' @param ... Additional parameters passed to \code{theme_lares()}. #' @return Empty ggplot2 object (with a \code{message} if set). #' @examples #' Sys.unsetenv("LARES_FONT") # Temporal #' noPlot(message = "No plot to show!") #' noPlot(background = "#FF5500", size = 7) #' @export noPlot <- function(message = "Nothing to show here!", size = 4.5, ...) { ggplot(data.frame(), aes(x = 0, y = 0, label = message)) + theme_lares(clean = TRUE, ...) + geom_text(size = size) } #################################################################### #' Export ggplot2, gridExtra, or any plot object into rendered file #' #' Export any \code{ggplot2}, \code{gridExtra}, or any plot object #' created with R into rendered \code{png} or \code{jpg} file. #' #' @family Tools #' @param p Plot object. Plot to render and export. #' @param name Character. File's name or suffix if vars is not \code{NA}. No need #' to include file format on file name. #' @param vars Vector. Variable names to identify by filename. #' @param sep Character. Separator for \code{vars}. #' @param format Character. One of: \code{png} or \code{jpeg}. #' @param width,height,res Numeric. Plot's width, height, and res (for grids). #' @param dir,subdir Character. In which directory/subdirectory do you #' wish to save the plot? Working directory as default \code{dir}. #' @param quiet Boolean. Display successful message with filename when saved? #' @return No return value, called for side effects. #' @examples #' p <- noPlot() #' export_plot(p, name = "noplot", width = 10, height = 8, res = 300, dir = tempdir()) #' export_plot(p, name = "noplot2", subdir = "newplots", dir = tempdir()) #' @export export_plot <- function(p, name = "plot", vars = NA, sep = ".vs.", width = 8, height = 6, format = "png", res = 300, dir = getwd(), subdir = NA, quiet = FALSE) { check_opts(format, c("png", "jpeg")) # File name name <- sub("\\..[^\\.]*$", "", name) end <- paste0(".", format) if (!is.na(vars)) { names <- v2t(cleanText(as.character(vars), spaces = FALSE), sep = sep, quotes = FALSE) file_name <- paste0(name, "_", names, end) } else { file_name <- paste0(name, end) } # Create directory if needed if (!is.na(subdir)) { dir <- file.path(dir, subdir) if (!dir.exists(dir)) dir.create(dir) } file_name <- paste(dir, file_name, sep = "/") export_fx <- base::get(format) export_fx(file_name, height = height * res, width = width * res, res = res) plot(p) dev.off() if (!quiet) message(paste("Plot saved as", file_name)) } #################################################################### #' Plot timeline as Gantt Plot #' #' This function plots groups of observartions with timelines in a #' Gantt Plot way. Only works if start and end are date format values. #' #' @family Visualization #' @param event Vector. Event, role, label, or row. #' @param start Vector. Start date. #' @param end Vector. End date. Only one day be default if not defined #' @param label Vector. Place, institution, or label. #' @param group Vector. Academic, Work, Extracurricular... Pass as factor #' to keep a specific order #' @param title Character. Title for the plot #' @param subtitle Character. Subtitle for the plot #' @param interactive Boolean. Run with plotly? #' @param save Boolean. Save the output plot in our working directory #' @param subdir Character. Into which subdirectory do you wish to save the plot to? #' @return ggplot2 object #' @examples #' Sys.unsetenv("LARES_FONT") # Temporal #' cols <- c("Role", "Place", "Type", "Start", "End") #' today <- as.character(Sys.Date()) #' cv <- data.frame(rbind( #' c("Marketing Science Partner", "Facebook", "Work Experience", "2019-12-09", today), #' c("Data Scientist Consultant", "MatrixDS", "Work Experience", "2018-09-01", today), #' c("R Community Contributor", "lares library", "Extra", "2018-07-18", today), #' c("Lead Data Scientist", "MEG", "Work Experience", "2019-01-15", "2019-12-09"), #' c("Head of Analytics", "Comparamejor/R5", "Work Experience", "2016-08-01", "2019-01-15"), #' c("Big Data & Data Science Programme", "UdC", "Academic", "2017-09-01", "2018-02-28"), #' c("Project Engineer", "Polytex", "Work Experience", "2016-05-15", "2016-09-01"), #' c("Big Data Analyst", "MEG", "Work Experience", "2016-01-01", "2016-04-30"), #' c("Advanced Excel Instructor", "ARTS", "Work Experience", "2015-11-01", "2016-04-30"), #' c("Continuous Improvement Intern", "PAVCO", "Work Experience", "2015-04-01", "2015-08-30"), #' c("Mechanical Design Intern", "SIGALCA", "Work Experience", "2013-07-01", "2013-09-30"), #' c("DJs Online Community Owner", "LaresDJ.com / SoloParaDJs", "Extra", "2010-01-05", "2020-05-20"), #' c("Mechanical Engineer Degree", "USB", "Academic", "2009-09-15", "2015-11-20"), #' c("DJ and Composer/Producer", "Legacy Discplay", "Extra", "2009-05-01", "2015-04-30") #' )) #' colnames(cv) <- cols #' plot_timeline( #' event = cv$Role, #' start = cv$Start, #' end = cv$End, #' label = cv$Place, #' # Simple trick to re-arrange the grids #' group = factor(cv$Type, levels = c("Work Experience", "Academic", "Extra")) #' ) #' @export plot_timeline <- function(event, start, end = start + 1, label = NA, group = NA, title = "Curriculum Vitae Timeline", subtitle = "Bernardo Lares", interactive = FALSE, save = FALSE, subdir = NA) { # Let's gather all the data df <- data.frame( Role = as.character(event), Place = as.character(label), Start = as.Date(as.character(start)), End = as.Date(as.character(end)), Type = group ) # Duplicate data for ggplot's geom_lines cvlong <- data.frame( pos = rep(as.numeric(rownames(df)), 2), name = rep(as.character(df$Role), 2), type = rep(factor(df$Type, ordered = TRUE), 2), where = rep(as.character(df$Place), 2), value = c(df$Start, df$End), label_pos = rep(df$Start + floor((df$End - df$Start) / 2), 2) ) # Plot timeline maxdate <- max(df$End) p <- ggplot(cvlong, aes( x = .data$value, y = reorder(.data$name, -.data$pos), label = .data$where, group = .data$pos )) + geom_vline(xintercept = maxdate, alpha = 0.2) + labs(title = title, subtitle = subtitle, x = NULL, y = NULL, colour = NULL) + theme( panel.background = element_rect(fill = "white", colour = NA), axis.ticks = element_blank(), panel.grid.major.x = element_line(size = 0.25, colour = "grey80") ) # scale_x_date(expand = c(0, 0)) if (!is.na(cvlong$type)[1] || length(unique(cvlong$type)) > 1) { p <- p + geom_line(aes(colour = .data$type), size = 7) + facet_grid(.data$type ~ ., scales = "free", space = "free") + guides(colour = "none") } p <- p + geom_label(aes(x = .data$label_pos), colour = "black", size = 2, alpha = 0.7) + theme_lares(pal = 2, legend = "none") # Export file name and folder for plot if (save) { file_name <- "cv_timeline.png" if (!is.na(subdir)) { # dir.create(file.path(getwd(), subdir), recursive = T) file_name <- paste(subdir, file_name, sep = "/") } p <- p + ggsave(file_name, width = 8, height = 6) message(paste("Saved plot as", file_name)) } if (interactive) { try_require("plotly") p <- ggplotly(p) } return(p) } #################################################################### #' Chords Plot #' #' This auxiliary function plots discrete and continuous values results #' #' @family Visualization #' @param origin,dest Vectors. Origin and destination vectors #' @param weight Vector. Weight for each chord. #' @param mg Numeric. Margin adjust for plot in case of need #' @param title Character. Title for the plot #' @param subtitle Character. Subtitle for the plot #' @param pal Vector. Colour pallete. Order matters. #' @return chordDiagram object #' @examples #' # You must have "circlize" library to use this auxiliary function: #' \dontrun{ #' df <- data.frame(from = c(1, 1, 2, 3, 4, 1, 6), to = c(4, 4, 4, 2, 2, NA, NA)) #' plot_chord(df$from, df$to) #' } #' @export plot_chord <- function(origin, dest, weight = 1, mg = 3, title = "Chord Diagram", subtitle = "", pal = NA) { try_require("circlize") if (length(origin) != length(dest)) { stop("The origin and dest vectors should have the same length!") } df <- data.frame(origin, dest, weight) %>% mutate( origin = ifelse(.data$origin == "", " ", as.character(.data$origin)), dest = ifelse(.data$dest == "", " ", as.character(.data$dest)) ) %>% replaceall(NA, "NA") colnames(df) <- c("orig_reg", "dest_reg", "flow") uniq <- unique(c(as.character(df$orig_reg), as.character(df$dest_reg))) if (is.na(pal)) pal <- names(lares_pal()$palette) if (length(unique(origin)) > length(pal)) { stop("Too many chords to plot and not enough colours :(") } col <- c( pal[seq_along(unique(origin))], rep("darkgrey", length(unique(uniq)) - length(unique(origin))) ) chordDiagram( x = df, grid.col = col, transparency = 0.2, directional = 1, preAllocateTracks = list( track.height = uh(mg, "mm"), track.margin = c(uh(mg, "mm"), 0) ), direction.type = c("arrows", "diffHeight"), diffHeight = -0.04, annotationTrack = c("grid", "axis"), annotationTrackHeight = c(0.05, 0.1), link.arr.type = "big.arrow", link.sort = TRUE, link.largest.ontop = TRUE ) title(main = title, line = -1, sub = subtitle, font.sub = 3, family = "Arial Narrow") legend("bottomright", pch = 15, col = col, legend = unique(origin), bg = "transparent", box.lty = 0, cex = 0.8 ) }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/hs_resource.R \name{hs_folder} \alias{hs_folder} \title{Get Resource Folder} \usage{ hs_folder(id, pathname, ...) } \arguments{ \item{id}{Resource ID} \item{pathname}{Path to folder in resource's contents} \item{...}{Unused} } \value{ A \link[tibble:tibble-package]{tibble}. } \description{ Get Resource Folder }

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plot4 <- function(){ # if global variable d1 not exist... # call common function to load data # from the dates 2007-02-01 and 2007-02-02 # and save data as global variable d1 if(!exists("d1")){ source("loadData.R") d1 <<- loadData() } # extract Global_active_power vector for easier reference ap <- d1$Global_active_power #remove NA values ap <- ap[!is.na(ap)] # extract Voltage vector for easier reference v1 <- d1$Voltage #remove NA values v1 <- v1[!is.na(v1)] # extract Sub_metering_1 vector for easier reference m1 <- d1$Sub_metering_1 #remove NA values m1 <- m1[!is.na(m1)] # extract Sub_metering_2 vector for easier reference m2 <- d1$Sub_metering_2 #remove NA values m2 <- m2[!is.na(m2)] # extract Sub_metering_3 vector for easier reference m3 <- d1$Sub_metering_3 #remove NA values m3 <- m3[!is.na(m3)] # extract Global_reactive_power vector for easier reference rp <- d1$Global_reactive_power #remove NA values rp <- rp[!is.na(rp)] # extract Time vector for easier reference dow <- d1$Time # open png device png(filename="plot4.png", width=480, height=480) # par assign plots in 2x2 grid par(mfrow=c(2,2)) # generate the topleft plot ("Global_active_power" vs "Time") plot(dow, ap, type="n", xlab="", ylab="Global Active Power (kilowatts)") lines(dow, ap) # generate the topright plot ("Voltage" vs "Time) plot(dow, v1, type="n", xlab="datetime", ylab="Voltage") lines(dow, v1) # generate the bottomleft plot ("Sub_metering_1/2/3" vs "Time) plot(dow, m1, type="n", xlab="", ylab="Energy sub metering") lines(dow, m1) lines(dow, m2, col="red") lines(dow, m3, col="blue") legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), col=c("black", "red", "blue"), lty=c(1,1,1)) # generate the bottomright plot ("Global_reactive_power" vs "Time) plot(dow, rp, type="n", xlab="datetime", ylab="Global_reactive_power") lines(dow, rp) # close png device dev.off() }

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generateOutput.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/core-functions.R, R/core-functions-batch.R, % R/core-streaming-functions.R \docType{methods} \name{generateOutput} \alias{generateOutput} \alias{generateOutput,AnalysisPipeline-method} \alias{generateOutput,StreamingAnalysisPipeline-method} \title{Generate a list of outputs from Pipeline objects} \usage{ generateOutput(object) \S4method{generateOutput}{AnalysisPipeline}(object) \S4method{generateOutput}{StreamingAnalysisPipeline}(object) } \arguments{ \item{object}{object that contains input, pipeline, registry and output} } \value{ Updated Pipeline object with the outputs at each step stored in the \code{output} slot. Specific outputs can be obtained by using the \link{getOutputById} function } \description{ Generate a list of outputs from Pipeline objects } \details{ \code{generateOutput} is a generic function that is implemented for various types of pipeline objects such as \code{AnalysisPipeline} and \code{StreamingAnalysisPipeline} The sequence of operations stored in the pipeline object are run and outputs generated, stored in a list } \seealso{ Other Package core functions: \code{\link{BaseAnalysisPipeline-class}}, \code{\link{MetaAnalysisPipeline-class}}, \code{\link{assessEngineSetUp}}, \code{\link{checkSchemaMatch}}, \code{\link{createPipelineInstance}}, \code{\link{exportAsMetaPipeline}}, \code{\link{genericPipelineException}}, \code{\link{getInput}}, \code{\link{getLoggerDetails}}, \code{\link{getOutputById}}, \code{\link{getPipelinePrototype}}, \code{\link{getPipeline}}, \code{\link{getRegistry}}, \code{\link{initDfBasedOnType}}, \code{\link{initialize,BaseAnalysisPipeline-method}}, \code{\link{loadMetaPipeline}}, \code{\link{loadPipeline}}, \code{\link{loadPredefinedFunctionRegistry}}, \code{\link{loadRegistry}}, \code{\link{prepExecution}}, \code{\link{registerFunction}}, \code{\link{savePipeline}}, \code{\link{saveRegistry}}, \code{\link{setInput}}, \code{\link{setLoggerDetails}}, \code{\link{updateObject}}, \code{\link{visualizePipeline}} } \concept{Package core functions}

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library(devFunc) ### Name: checkValues ### Title: Checking if the value of vectors (of length 1) is authorized. ### Aliases: checkValues ### ** Examples lossType <- 'absolute' checkValues(list(lossType), list(c('absolute', 'quadratic'))) ## No test: checkValues(list(lossType), list(c('absolute', 'quadratic'), c('test', 'test2'))) ## End(No test) #The next error message is weird, since it does not return the real name of the listObject #that found to be wrong. lossType <- 'absolute55' listObjects <- list(lossType) listValues <- list(c('absolute', 'quadratic')) ## No test: checkValues(listObjects, listValues) #Now it is ok... checkValues(list(lossType), list(c('absolute', 'quadratic'))) ## End(No test)

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estimate_r5<-function(X.arrayA2,X.arrayB2,X.arrayC2,Z,ini,lower=rep(c(-5,-16,-5),c(5,5,1)),upper=rep(8,length(ini))){ drt<-apply(Z,2,mean) Zr<-Z-matrix(rep(drt,dim(Z)[1]),ncol=length(drt),byrow=TRUE) SIG<-var(Zr) EIG<-eigen(x=SIG,symmetric=TRUE) UUU<-EIG$vectors LAM<-EIG$values CCC<-UUU%*%diag(1/sqrt(LAM)) Zr<-Zr%*%CCC n_MO<-dim(Z)[1] lut<-dim(X.arrayC2)[1] m_MO<-1 kk<-dim(Z)[2] ddd<-dim(X.arrayA2)[1] eee<-dim(X.arrayB2)[1] marg.post<-function(paras){ #fff<<-rbind(fff,paras) r<-exp(paras) aaA<-exp(makeA5.cpp(D1=X.arrayA2[,1,],D2=X.arrayA2[,2,],D3=X.arrayA2[,3,],D4=X.arrayA2[,4,],D5=X.arrayA2[,5,],R=r[1:5])) aaB<-exp(makeA5.cpp(D1=X.arrayB2[,1,],D2=X.arrayB2[,2,],D3=X.arrayB2[,3,],D4=X.arrayB2[,4,],D5=X.arrayB2[,5,],R=r[6:10])) aaC<-exp(-r[11]*(X.arrayC2)) # iaaA<-solve.cpp(aaA) # iaaB<-solve.cpp(aaB) # iaaC<-solve.cpp(aaC) iaaA<-solve(aaA) iaaB<-solve(aaB) iaaC<-solve(aaC) iaaZ<-cbind(kron.prod(y=Zr[,1],matrices=list(iaaC,iaaB,iaaA)),kron.prod(y=Zr[,2],matrices=list(iaaC,iaaB,iaaA)),kron.prod(y=Zr[,3],matrices=list(iaaC,iaaB,iaaA))) detaa<-n_MO*((1/ddd)*determinant(aaA)$modulus[1]+(1/eee)*determinant(aaB)$modulus[1]+(1/lut)*determinant(aaC)$modulus[1]) ret<--0.5*kk*detaa-0.5*sum(iaaZ*Zr) ret} dmarg.post<-function(paras){ #ggg<<-rbind(ggg,paras) r<-exp(paras) aaA<-exp(makeA5.cpp(D1=X.arrayA2[,1,],D2=X.arrayA2[,2,],D3=X.arrayA2[,3,],D4=X.arrayA2[,4,],D5=X.arrayA2[,5,],R=r[1:5])) aaB<-exp(makeA5.cpp(D1=X.arrayB2[,1,],D2=X.arrayB2[,2,],D3=X.arrayB2[,3,],D4=X.arrayB2[,4,],D5=X.arrayB2[,5,],R=r[6:10])) aaC<-exp(-r[11]*(X.arrayC2)) # iaaA<-solve.cpp(aaA) # iaaB<-solve.cpp(aaB) # iaaC<-solve.cpp(aaC) iaaA<-solve(aaA) iaaB<-solve(aaB) iaaC<-solve(aaC) der<-c() for(i in 1:5){ dA<--r[i]*X.arrayA2[,i,]*aaA iAdA<-iaaA%*%dA iAdAiA<-iAdA%*%iaaA iaaZ<-cbind(kron.prod(y=Zr[,1],matrices=list(iaaC,iaaB,iAdAiA)),kron.prod(y=Zr[,2],matrices=list(iaaC,iaaB,iAdAiA)),kron.prod(y=Zr[,3],matrices=list(iaaC,iaaB,iAdAiA))) der[i]<-0.5*sum(iaaZ*Zr)-0.5*kk*lut*eee*sum(diag(iAdA))} for(i in 1:5){ dB<--r[i+5]*X.arrayB2[,i,]*aaB iBdB<-iaaB%*%dB iBdBiB<-iBdB%*%iaaB iaaZ<-cbind(kron.prod(y=Zr[,1],matrices=list(iaaC,iBdBiB,iaaA)),kron.prod(y=Zr[,2],matrices=list(iaaC,iBdBiB,iaaA)),kron.prod(y=Zr[,3],matrices=list(iaaC,iBdBiB,iaaA))) der[i+5]<-0.5*sum(iaaZ*Zr)-0.5*kk*lut*ddd*sum(diag(iBdB))} dC<--r[11]*X.arrayC2*aaC iCdC<-iaaC%*%dC iCdCiC<-iCdC%*%iaaC iaaZ<-cbind(kron.prod(y=Zr[,1],matrices=list(iCdCiC,iaaB,iaaA)),kron.prod(y=Zr[,2],matrices=list(iCdCiC,iaaB,iaaA)),kron.prod(y=Zr[,3],matrices=list(iCdCiC,iaaB,iaaA))) der[11]<-0.5*sum(iaaZ*Zr)-0.5*kk*ddd*eee*sum(diag(iCdC)) der} opt<-optim(fn=marg.post,gr=dmarg.post,par=ini,control=list(fnscale=-1,maxit=200),method="L-BFGS-B",lower=lower,upper=upper) return(opt)} GP_functions5<-function(X.arrayA2,X.arrayB2,X.arrayC2,Z,opt.logr,DESIGN,design,uni.times){ drt<-apply(Z,2,mean) Zr<-Z-matrix(rep(drt,dim(Z)[1]),ncol=length(drt),byrow=TRUE) n_MO<-dim(Z)[1] lut<-dim(X.arrayC2)[1] m_MO<-1 kk<-dim(Z)[2] ddd<-dim(X.arrayA2)[1] eee<-dim(X.arrayB2)[1] r<-exp(opt.logr) aaA<-exp(makeA5.cpp(D1=X.arrayA2[,1,],D2=X.arrayA2[,2,],D3=X.arrayA2[,3,],D4=X.arrayA2[,4,],D5=X.arrayA2[,5,],R=r[1:5])) aaB<-exp(makeA5.cpp(D1=X.arrayB2[,1,],D2=X.arrayB2[,2,],D3=X.arrayB2[,3,],D4=X.arrayB2[,4,],D5=X.arrayB2[,5,],R=r[6:10])) aaC<-exp(-r[11]*(X.arrayC2)) iaaA<-solve.cpp(aaA) iaaB<-solve.cpp(aaB) iaaC<-solve.cpp(aaC) iaaZ<-cbind(kron.prod(y=Zr[,1],matrices=list(iaaC,iaaB,iaaA)),kron.prod(y=Zr[,2],matrices=list(iaaC,iaaB,iaaA)),kron.prod(y=Zr[,3],matrices=list(iaaC,iaaB,iaaA))) residualv<-as.vector(Zr) s.hat<-t(Zr)%*%iaaZ srt1<-c();srt2<-c();srt3<-c();srt4<-c();srt5<-c() for(bob in times[what.times==1]){ srt1<-c(srt1,(1:length(uni.times))[uni.times==bob])} for(bob in times[what.times==2]){ srt2<-c(srt2,(1:length(uni.times))[uni.times==bob])} for(bob in times[what.times==3]){ srt3<-c(srt3,(1:length(uni.times))[uni.times==bob])} for(bob in times[what.times==4]){ srt4<-c(srt4,(1:length(uni.times))[uni.times==bob])} for(bob in times[what.times==5]){ srt5<-c(srt5,(1:length(uni.times))[uni.times==bob])} srt<-list(srt1,srt2,srt3,srt4,srt5) SRT<-c(srt[[1]],31+srt[[2]],62+srt[[3]],93+srt[[4]],124+srt[[5]]) SRT<-c(SRT,155+SRT,2*155+SRT) ####################################################################################################### sigtilde<-function(THETA,INI_X,TIM){ aA<-makeaa.cpp(DESIGN=DESIGN,THETA=THETA,R=r[1:5]) aB<-makeaa.cpp(DESIGN=design,THETA=INI_X,R=r[6:10]) aC<-makeaa.cpp(DESIGN=matrix(uni.times,ncol=1),THETA=matrix(TIM,ncol=1),R=r[11]) ata<-aA%*%iaaA btb<-aB%*%iaaB ctc<-aC%*%iaaC tata<-t(ata) 1-diags.cpp(X=aA,Y=tata)*as.vector(aB%*%t(btb))*as.vector(aC%*%t(ctc))} mu2hat<-function(thet,x,tim){ aA<-matrix(exp(-rowSums.cpp(matrix(rep(r[1:5],ddd),ncol=5,byrow=TRUE)*(matrix(rep(thet,ddd),ncol=5,byrow=TRUE)-DESIGN)^2)),nrow=1) aB<-matrix(exp(-rowSums.cpp(matrix(rep(r[6:10],eee),ncol=5,byrow=TRUE)*(matrix(rep(x,eee),ncol=5,byrow=TRUE)-design)^2)),nrow=1) aC<-matrix(exp(-r[11]*(rep(tim,each=length(uni.times))-rep(uni.times,length(tim)))^2),nrow=length(tim),byrow=TRUE) ata<-aA%*%iaaA btb<-aB%*%iaaB ctc<-aC%*%iaaC out<-cbind(kron3cpp(aa0=ata,aa1=btb,aa2=ctc,yy=Zr[,1]),kron3cpp(aa0=ata,aa1=btb,aa2=ctc,yy=Zr[,2]),kron3cpp(aa0=ata,aa1=btb,aa2=ctc,yy=Zr[,3])) matrix(rep(drt,length(tim)),ncol=kk,byrow=TRUE)+out} mu2hatB<-function(THETA,INI_X,TIM){ aA<-makeaa.cpp(DESIGN=DESIGN,THETA=THETA,R=r[1:5]) aB<-makeaa.cpp(DESIGN=design,THETA=INI_X,R=r[6:10]) aC<-makeaa.cpp(DESIGN=matrix(uni.times,ncol=1),THETA=matrix(TIM,ncol=1),R=r[11]) ata<-aA%*%iaaA btb<-aB%*%iaaB ctc<-aC%*%iaaC out<-cbind(kron3cpp(aa0=ata,aa1=btb,aa2=ctc,yy=Zr[,1]),kron3cpp(aa0=ata,aa1=btb,aa2=ctc,yy=Zr[,2]),kron3cpp(aa0=ata,aa1=btb,aa2=ctc,yy=Zr[,3])) matrix(rep(drt,dim(out)[1]),ncol=kk,byrow=TRUE)+out} Mhat<-function(theta){ nnn<-length(uni.times) ddd<-dim(DESIGN) diffs<-matrix(rep(theta,ddd[1]),ncol=ddd[2],byrow=TRUE)-DESIGN AAA<-Mhat5.cpp(RESIDUAL=Zr,D2=diffs^2,R=r[1:5],iaaA=iaaA) matrix(rep(drt,mm),ncol=kk,byrow=TRUE)+AAA[SRT[1:mm],]} mhat<-function(theta){ as.vector(Mhat(theta))} ######################################################################################################### dMhat<-function(theta){ nnn<-length(uni.times) ddd<-dim(DESIGN) diffs<-matrix(rep(theta,dim(DESIGN)[1]),ncol=dim(DESIGN)[2],byrow=TRUE)-DESIGN out<-dMhat5.cpp(RESIDUAL=Zr,iaaA=iaaA,D=diffs,D2=diffs^2,R=r[1:5]) out2<-array(0,dim=c(nnn*5,5,kk)) for(j in 1:5){ out2[,j,]<-out[((j-1)*nnn*5+1):(j*nnn*5),]} out2[SRT[1:mm],,]} dmhat<-function(theta){ out<-dMhat(theta) out2<-cbind(as.vector(out[,1,]),as.vector(out[,2,]),as.vector(out[,3,]),as.vector(out[,4,]),as.vector(out[,5,])) out2} ############################################################################################################ d2Mhat<-function(theta){ nnn<-length(uni.times) ddd<-dim(DESIGN) diffs<-matrix(rep(theta,ddd[1]),ncol=ddd[2],byrow=TRUE)-DESIGN out<-d2Mhat5.cpp(RESIDUAL=Zr,iaaA=iaaA,D=diffs,D2=diffs^2,R=r[1:5]) output<-array(0,dim=c(nnn*5,5,5,kk)) for(ii in 1:5){ for(jj in 1:5){ output[,ii,jj,]<-out[((ii-1)*5+jj-1)*155+(1:155),]}} output<-output[SRT[1:mm],,,] output} d2mhat<-function(theta){ out<-d2Mhat(theta) out2<-array(0,dim=c(5,mm*kk,5)) for(j in 1:5){ out2[j,,]<-cbind(as.vector(out[,j,1,]),as.vector(out[,j,2,]),as.vector(out[,j,3,]),as.vector(out[,j,4,]),as.vector(out[,j,5,]))} out2} ############################################################################################################### list(s.hat=s.hat,sigtilde=sigtilde,Mhat=Mhat,mhat=mhat,dMhat=dMhat,dmhat=dmhat,d2Mhat=d2Mhat,d2mhat=d2mhat,mu2hat=mu2hat,mu2hatB=mu2hatB) }

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/UMD_DMD_plot.R

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leklab/DMD

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UMD_DMD_plot.R

library(rvest) library(tidyverse) #functions getexons <- function(start, end, n) { rep(as.integer(start):as.integer(end), times = n) } ##Deletions #get the data dels <- read_html("http://www.umd.be/DMD/4DACTION/W_DMDT1/3") %>% html_nodes('tr') %>% html_text() %>% gsub("\\r", ";", .) %>% gsub("\\s+;", ";", .) %>% gsub(";\\s+", ";", .) %>% gsub("\\#", "n", .) %>% gsub(";$", "", .) %>% trimws() %>% .[grepl("^p|^Protein",.)] %>% read_delim(., delim = ";", trim_ws = T) %>% rename(Records = `n records`) dels$Records <- as.integer(dels$Records) dels$exons <- gsub("Large rearrangementDeletion from exon ", "", dels$Rearrangement) dels <- dels %>% separate(exons, c('start','end'), sep = ' to ') dels <- dels %>% mutate(type = 'Deletion') n_patients_dels <- sum(dels$Records) allexons_dels <- mapply(getexons, dels$start, dels$end, dels$Records) exoncounts_dels <- tibble(exon = 1:79, count = as.integer(table(unlist(allexons_dels))), pct_patients = as.integer(table(unlist(allexons_dels))) / n_patients_dels * 100, type = 'Deletion') #add frameshift info fr_del <- dels %>% filter(`Mutation type` == 'Fr.') allexons_dels_fr <- mapply(getexons, fr_del$start, fr_del$end, fr_del$Records) fr.table <- table(unlist(allexons_dels_fr)) fr_del_counts <- tibble(exon = names(fr.table), count = fr.table, frame = 'Frameshift', type = 'Deletion', type_frame = 'Frameshift Deletion') infr_del <- dels %>% filter(`Mutation type` == 'InF') allexons_dels_infr <- mapply(getexons, infr_del$start, infr_del$end, infr_del$Records) infr.table <- table(unlist(allexons_dels_infr)) infr_del_counts <- tibble(exon = names(infr.table), count = infr.table, frame = 'In-frame', type = 'Deletion', type_frame = 'In-Frame Deletion') ##Duplications dups <- read_html("http://www.umd.be/DMD/4DACTION/W_DMDT1/4") %>% html_nodes('tr') %>% html_text() %>% gsub("\\r", ";", .) %>% gsub("\\s+;", ";", .) %>% gsub(";\\s+", ";", .) %>% gsub("\\#", "n", .) %>% gsub(";$", "", .) %>% trimws() %>% .[grepl("^p|^Protein",.)] %>% read_delim(., delim = ";", trim_ws = T) %>% rename(Records = `n records`) dups$Records <- as.integer(dups$Records) dups$exons <- gsub("Large .* exon ", "", dups$Rearrangement) dups <- dups %>% separate(exons, c('start','end'), sep = ' to ') dups <- dups %>% filter(str_detect(Rearrangement,'Duplication')) dups <- dups %>% mutate(end = if_else(str_detect(Rearrangement,'5\''), 1L, as.integer(end))) dups <- dups %>% mutate(type = 'Duplication') n_patients_dups <- sum(dups$Records) allexons_dups <- mapply(getexons, dups$start, dups$end, dups$Records) exoncounts_dups <- tibble(exon = 1:79, count = as.integer(table(unlist(allexons_dups))), pct_patients = as.integer(table(unlist(allexons_dups))) / n_patients_dups * 100, type = 'Duplication') #add frameshift info fr_dup <- dups %>% filter(`Mutation type` == 'Fr.') allexons_dups_fr <- mapply(getexons, fr_dup$start, fr_dup$end, fr_dup$Records) fr_dup.table <- table(unlist(allexons_dups_fr)) fr_dup_counts <- tibble(exon = names(fr_dup.table), count = fr_dup.table, frame = 'Frameshift', type = 'Duplication', type_frame = 'Frameshift Duplication') infr_dup <- dups %>% filter(`Mutation type` == 'InF') allexons_dups_infr <- mapply(getexons, infr_dup$start, infr_dup$end, infr_dup$Records) infr_dup.table <- table(unlist(allexons_dups_infr)) infr_dup_counts <- tibble(exon = names(infr_dup.table), count = infr_dup.table, frame = 'In-frame', type = 'Duplication', type_frame = 'In-Frame Duplication') ## merge deldup <- bind_rows(exoncounts_dels, exoncounts_dups) n_total <- n_patients_dels + n_patients_dups deldup <- deldup %>% mutate(pct_from_total = count / n_total * 100) deldup_frame <- bind_rows(fr_del_counts, infr_del_counts, fr_dup_counts, infr_dup_counts) deldup_frame$exon <- as.integer(deldup_frame$exon) n_total_fr <- sum(fr_del$Records) + sum(fr_dup$Records) + sum(infr_del$Records) + sum(infr_dup$Records) deldup_frame <- deldup_frame %>% mutate(pct_from_total = count / n_total * 100) deldup_frame$type_frame <- factor(deldup_frame$type_frame, levels = c("Frameshift Deletion", "In-Frame Deletion", "Frameshift Duplication", "In-Frame Duplication")) #plot ggplot(deldup, aes(exon, pct_from_total, col = type, fill = type)) + geom_bar(stat = 'identity') + theme_bw() + ylab("Pct of patients with\ndeleted/duplicated exon") + theme(legend.position = c(0.2,0.83), legend.title = element_blank()) ggsave('UMD_DMD_dels_dups_stacked.png', width = unit(4, 'in'), height = unit(3, 'in')) ggplot(deldup_frame, aes(exon, pct_from_total, col = type_frame, fill = type_frame)) + geom_bar(stat = 'identity') + theme_bw() + ylab("Pct of patients with\ndeleted/duplicated exon") + theme(legend.position = c(0.2,0.8), legend.text = element_text(size = 4), legend.key.size = unit(0.4, 'cm'), legend.title = element_blank()) + scale_fill_manual(values=c("brown2", "coral", "navy", 'cyan2')) + scale_color_manual(values=c("brown2", "coral", "navy", 'cyan2')) ggsave('UMD_DMD_dels_dups_frames_stacked.png', width = unit(4, 'in'), height = unit(3, 'in'))

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/R/vc_email.R

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trinker/valiData

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#' Validates If Email #' #' Validates If Email #' #' @param data A data frame. #' @param x Column name from \code{data} (character string). #' @param \dots ignored. #' @export #' @examples #' dat <- data.frame( #' email =c('cookie@cookiemonster.com', 'joe@hometown.uni' #' , 'meet@seven', '@Jim', 'joe@gmail.com', NA), #' stringsAsFactors = FALSE #' ) #' vc_email(dat, 'email') vc_email <- function(data, x, ...){ ## select the column & replace missing with NA col <- sub_out_missing(data[[x]]) ## record missing (NA) is_na <- is.na(col) ## expression to validate against (elementwise) RFC 5321 compliant #regex <- "^[a-z0-9.!#$%&'*+/=?^_`{|}~-]+@[a-z0-9.-]+\\.[a-z]{2,}$" ## updated to be less strict via: http://stackoverflow.com/a/38137215/1000343 regex <- "^(([^<>()\\[\\]\\.,;:\\s@\"]+(\\.[^<>()\\[\\]\\.,;:\\s@\"]+)*)|(\".+\"))@(([^<>()\\.,;\\s@\"]+\\.{0,1})+[^<>()\\.,;:\\s@\"]{2,})$" is_valid <- grepl(regex, col, ignore.case = TRUE, perl = TRUE) is_valid[is_na] <- NA ## valid columnwise: Are all elelemnts either valid or NA? are_valid <- all(is_valid|is_na) ## generate the comment if (!are_valid){ message <- sprintf( "The following rows of %s do not follow the format of allowable emails:\n\n%s\n\n\n\n", sQuote(x), output_truncate(which(!(is_valid|is_na))) ) } else { message <- NULL } ## construct vc list & class vc_output <- list( column_name = x, valid = are_valid, message = message, passing = is_valid, missing = is_na, call = 'vc_email' ) class(vc_output) <- 'vc' vc_output } # regex <- "^(([^<>()\\[\\]\\.,;:\\s@\"]+(\\.[^<>()\\[\\]\\.,;:\\s@\"]+)*)|(\".+\"))@(([^<>()\\.,;\\s@\"]+\\.{0,1})+[^<>()\\.,;:\\s@\"]{2,})$" # email_test_data$is_valid <- grepl(regex, email_test_data$email, ignore.case = TRUE, perl = TRUE) ## email_test_data <- structure(list(email = c("Sean.O'Conner@anyuniv.edu", "prettyandsimple@example.com", "very.common@example.com", ## "disposable.style.email.with+symbol@example.com", "other.email-with-dash@example.com", ## "x@example.com", "\"much.more unusual\"@example.com", "\"very.unusual.@.unusual.com\"@example.com", ## "\"very.(),:;<>[]\\\".VERY.\\\"very@\\\\ \\\"very\\\".unusual\"@strange.example.com", ## "example-indeed@strange-example.com", "admin@mailserver1", "#!$%&'*+-/=?^_`{}|~@example.org", ## "\"()<>[]:,;@\\\\\\\"!#$%&'-/=?^_`{}| ~.a\"@example.org", "\" \"@example.org", ## "example@localhost", "example@s.solutions", "user@com", "user@localserver", ## "user@[IPv6:2001:db8::1]", "user@[1:2:3:4:5::6]", "Abc.example.com", ## "A@b@c@example.com", "a\"b(c)d,e:f;g<h>i[j\\k]l@example.com", ## "just\"not\"right@example.com", "this is\"not\\allowed@example.com", ## "this\\ still\\\"not\\\\allowed@example.com", "john..doe@example.com", ## "john.doe@example..com"), valid = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, ## TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, ## TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, ## FALSE), is_valid = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ## NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA ## )), .Names = c("email", "valid", "is_valid"), row.names = c(NA, ## -28L), class = "data.frame")

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/kernelModel.R

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brittzinator/PhDRes

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kernelModel.R

library(SuppDists) #for the rinvGauss (Wald) function logprofile <- function(z,K,z0,d) log((z-d)/z0)/K rWALD <- function(n,m,Umeanlog,Usdlog,H,Fmeanlog,Fsdlog,h) { # n: number of random numbers # m: wind measurement height # Umeanlog: mean log wind speed (m/s) at m # Usdlog: sd log wind speed (m/s) at m # H: mean plant heigh (m) # Fmeanlog: mean log terminal velocity (m/s) # Fsdlog: sd log terminal velocity (m/s) # h: mean vegetation height (m) K <- 0.4 # von Karman constant C0 <- 3.125 # Kolmogorov constant Aw <- 1.3 # ratio of sigmaw to ustar d <- 0.7*h # zero-plane displacement z0 <- 0.1*h # roughness length Um <- rlnorm(n,meanlog=Umeanlog,sdlog=Usdlog) # simulate n wind events assuming lognormal distribution ustar <- K*Um/log((2-d)/z0) U <- (ustar/H)*integrate(logprofile,lower=z0+d,upper=H,K=K,z0=z0,d=d)$value # compute average wind speed between H and z0+d sigma <- sqrt( (4*((Aw)^4)*K*(H-d)/C0) * ustar/U ) f <- rlnorm(n,meanlog=Fmeanlog,sdlog=Fsdlog) # draw n terminal velocities assuming lognormal distribution nu <- H*U/f lambda <- (H/sigma)^2 return(rinvGauss(n,nu=nu,lambda=lambda)) } distances<-rWALD(n=1000,m=1,Umeanlog=2,Usdlog=.2,H=1.1,Fmeanlog=2,Fsdlog=.5,h=.5) plot(density(distances), xlim=c(0,10))

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/Plot1.R

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TXu8/ExData_Plotting1

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Plot1.R

##Program for 1st assignment - Plot 1 setwd("filepath/") powerdata<-read.csv("filepath/household_power_consumption.txt",header = TRUE, sep = ";") powerdata1<-powerdata[which(powerdata$Date == "1/2/2007"|powerdata$Date == "2/2/2007"),] powerdata1$Global_active_power<-as.numeric(as.character(powerdata1$Global_active_power)) hist(powerdata1$Global_active_power,col="red",xlab = "Global Active Power (Kilowatts)",ylab="Frequency",main="Global Active Power") dev.copy(png,file="Plot1.png") dev.off()

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vlyubchich/funtimes

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DR.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DR.R \name{DR} \alias{DR} \title{Downhill Riding (DR) Procedure} \usage{ DR(X, method, minPts = 3, theta = 0.9, B = 500, lb = -30, ub = 10) } \arguments{ \item{X}{an \eqn{n\times k} matrix where columns are \eqn{k} objects to be clustered, and each object contains n observations (objects could be a set of time series).} \item{method}{the clustering method to be used -- currently either \dQuote{TRUST} \insertCite{Ciampi_etal_2010}{funtimes} or \dQuote{DBSCAN} \insertCite{Ester_etal_1996}{funtimes}. If the method is \code{DBSCAN}, then set \code{MinPts} and optimal \eqn{\epsilon} is selected using DR. If the method is \code{TRUST}, then set \code{theta}, and optimal \eqn{\delta} is selected using DR.} \item{minPts}{the minimum number of samples in an \eqn{\epsilon}-neighborhood of a point to be considered as a core point. The \code{minPts} is to be used only with the \code{DBSCAN} method. The default value is 3.} \item{theta}{connectivity parameter \eqn{\theta \in (0,1)}, which is to be used only with the \code{TRUST} method. The default value is 0.9.} \item{B}{number of random splits in calculating the Average Cluster Deviation (ACD). The default value is 500.} \item{lb, ub}{endpoints for a range of search for the optimal parameter.} } \value{ A list containing the following components: \item{P_opt}{the value of the optimal parameter. If the method is \code{DBSCAN}, then \code{P_opt} is optimal \eqn{\epsilon}. If the method is \code{TRUST}, then \code{P_opt} is optimal \eqn{\delta}.} \item{ACD_matrix}{a matrix that returns \code{ACD} for different values of a tuning parameter. If the method is \code{DBSCAN}, then the tuning parameter is \eqn{\epsilon}. If the method is \code{TRUST}, then the tuning parameter is \eqn{\delta}.} } \description{ Downhill riding procedure for selecting optimal tuning parameters in clustering algorithms, using an (in)stability probe. } \details{ Parameters \code{lb,ub} are endpoints for the search for the optimal parameter. The parameter candidates are calculated in a way such that \eqn{P:= 1.1^x , x \in {lb,lb+0.5,lb+1.0,...,ub}}. Although the default range of search is sufficiently wide, in some cases \code{lb,ub} can be further extended if a warning message is given. For more discussion on properties of the considered clustering algorithms and the DR procedure see \insertCite{Huang_etal_2016;textual}{funtimes} and \insertCite{Huang_etal_2018_riding;textual}{funtimes}. } \examples{ \dontrun{ ## example 1 ## use iris data to test DR procedure data(iris) require(clue) # calculate NMI to compare the clustering result with the ground truth require(scatterplot3d) Data <- scale(iris[,-5]) ground_truth_label <- iris[,5] # perform DR procedure to select optimal eps for DBSCAN # and save it in variable eps_opt eps_opt <- DR(t(Data), method="DBSCAN", minPts = 5)$P_opt # apply DBSCAN with the optimal eps on iris data # and save the clustering result in variable res res <- dbscan(Data, eps = eps_opt, minPts =5)$cluster # calculate NMI to compare the clustering result with the ground truth label clue::cl_agreement(as.cl_partition(ground_truth_label), as.cl_partition(as.numeric(res)), method = "NMI") # visualize the clustering result and compare it with the ground truth result # 3D visualization of clustering result using variables Sepal.Width, Sepal.Length, # and Petal.Length scatterplot3d(Data[,-4],color = res) # 3D visualization of ground truth result using variables Sepal.Width, Sepal.Length, # and Petal.Length scatterplot3d(Data[,-4],color = as.numeric(ground_truth_label)) ## example 2 ## use synthetic time series data to test DR procedure require(funtimes) require(clue) require(zoo) # simulate 16 time series for 4 clusters, each cluster contains 4 time series set.seed(114) samp_Ind <- sample(12,replace=F) time_points <- 30 X <- matrix(0,nrow=time_points,ncol = 12) cluster1 <- sapply(1:4,function(x) arima.sim(list(order = c(1, 0, 0), ar = c(0.2)), n = time_points, mean = 0, sd = 1)) cluster2 <- sapply(1:4,function(x) arima.sim(list(order = c(2 ,0, 0), ar = c(0.1, -0.2)), n = time_points, mean = 2, sd = 1)) cluster3 <- sapply(1:4,function(x) arima.sim(list(order = c(1, 0, 1), ar = c(0.3), ma = c(0.1)), n = time_points, mean = 6, sd = 1)) X[,samp_Ind[1:4]] <- t(round(cluster1, 4)) X[,samp_Ind[5:8]] <- t(round(cluster2, 4)) X[,samp_Ind[9:12]] <- t(round(cluster3, 4)) # create ground truth label of the synthetic data ground_truth_label = matrix(1, nrow = 12, ncol = 1) for(k in 1:3){ ground_truth_label[samp_Ind[(4*k - 4 + 1):(4*k)]] = k } # perform DR procedure to select optimal delta for TRUST # and save it in variable delta_opt delta_opt <- DR(X, method = "TRUST")$P_opt # apply TRUST with the optimal delta on the synthetic data # and save the clustering result in variable res res <- CSlideCluster(X, Delta = delta_opt, Theta = 0.9) # calculate NMI to compare the clustering result with the ground truth label clue::cl_agreement(as.cl_partition(as.numeric(ground_truth_label)), as.cl_partition(as.numeric(res)), method = "NMI") # visualize the clustering result and compare it with the ground truth result # visualization of the clustering result obtained by TRUST plot.zoo(X, type = "l", plot.type = "single", col = res, xlab = "Time index", ylab = "") # visualization of the ground truth result plot.zoo(X, type = "l", plot.type = "single", col = ground_truth_label, xlab = "Time index", ylab = "") } } \references{ \insertAllCited{} } \seealso{ \code{\link{BICC}}, \code{\link[dbscan]{dbscan}} } \author{ Xin Huang, Yulia R. Gel } \keyword{trend} \keyword{ts}

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library(wordcloud) library(Matrix) words = colnames(DTM_df) frequencies = colSums(DTM_df) wordcloud(words, freq = frequencies, max.words = 100, colors = brewer.pal(name = "Accent", n = 8))

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library(R2D2) mat <- matrix( rep(1:10, 2), ncol = 2 ) print(mat) mat2 <- matrix( rep(1:15, 2), ncol = 2 ) print(mat2) r2d2_bc = r2d2(mat, mat, 1, 0, 0) stopifnot( all.equal(mat, r2d2_bc$r2d2_bc) ) stopifnot( all.equal(r2d2_bc$visited_time, rep(1, nrow(mat))) ) r2d2_bc = r2d2(mat, mat, 2, 0, 0) stopifnot( all.equal(mat, r2d2_bc$r2d2_bc) ) stopifnot( all.equal(r2d2_bc$visited_time, rep(1, nrow(mat))) ) r2d2_bc = r2d2(mat, mat2, 1, 0, 0) stopifnot( all.equal( matrix(rep(c(1, 3, 3, 4, 6, 6, 8, 8, 9, 11, 11, 12, 14, 14, 15), 2), ncol = 2), r2d2_bc$r2d2_bc ) ) r2d2_bc = r2d2(mat2, mat, 1, 0, 0) stopifnot( all.equal(mat, r2d2_bc$r2d2_bc) )

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## Exploratory Data Analysis / Coursera / Aug 2014 ## Project 1 / Plot 1 ## load data filePath <- "../../data/household_power_consumption.txt" if (file.exists(filePath)) { print("Data File Found") #fullData <- read.table(filePath, sep=";", head = TRUE, na.strings = c("?")) ## subdata dates 2007-02-01 and 2007-02-02 data <- read.table(filePath, sep=";", head = TRUE, na.strings = c("?"), skip= 66636, nrow = 2880) ## plot and save graphic png("plot1.png", width = 480, height = 480, units = 'px') hist(data[,3], col="red", ### define your color main="Global Active Power", ### main label xlab="Global Active Power (kilowatts)", ### x-label ylab="Frequency") ### y-label ## export image dev.off() } else { print("Data File Not Found") }

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source('sensitivity_testing_reduced_B_001.R') source('sensitivity_testing_reduced_B_05.R') source('sensitivity_testing_reduced_B_099.R') WAIC_0.1 <- read.csv("output_tables_reduced/ waics Boundness_social_models .csv") %>% mutate(prior = "0.1") WAIC_0.01 <- read.csv("output_tables_reduced/ waics Boundness_social_models prior_0.01 .csv") %>% mutate(prior = "0.01") WAIC_0.5 <- read.csv("output_tables_reduced/ waics Boundness_social_models prior_0.5 .csv") %>% mutate(prior = "0.5") WAIC_0.99 <- read.csv("output_tables_reduced/ waics Boundness_social_models prior_0.99 .csv") %>% mutate(prior = "0.99") sensitivity_B <- as.data.frame(rbind(WAIC_0.1, WAIC_0.01, WAIC_0.5, WAIC_0.99)) %>% mutate(response="fusion") source('sensitivity_testing_reduced_I_001.R') source('sensitivity_testing_reduced_I_05.R') source('sensitivity_testing_reduced_I_099.R') WAIC_0.1 <- read.csv("output_tables_reduced/ waics Informativity_social_models .csv") %>% mutate(prior = "0.1") WAIC_0.01 <- read.csv("output_tables_reduced/ waics Informativity_social_models prior_0.01 .csv") %>% mutate(prior = "0.01") WAIC_0.5 <- read.csv("output_tables_reduced/ waics Informativity_social_models prior_0.5 .csv") %>% mutate(prior = "0.5") WAIC_0.99 <- read.csv("output_tables_reduced/ waics Informativity_social_models prior_0.99 .csv") %>% mutate(prior = "0.99") sensitivity_I <- as.data.frame(rbind(WAIC_0.1, WAIC_0.01, WAIC_0.5, WAIC_0.99)) %>% mutate(response="informativity") sensitivity_all <- as.data.frame(rbind(sensitivity_B, sensitivity_I)) write.csv(sensitivity_all, "output_tables_reduced/Table_sensitivity.csv") sensitivity_all <- sensitivity_all %>% flextable() %>% autofit() %>% merge_v(j=c("response", "prior")) %>% fix_border_issues() save_as_docx( "Summary of sensitivity" = sensitivity_all, path = "output_tables_reduced/Table_sensitivity.docx")

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\name{plot.parentimage} \alias{plot.parentimage} \title{ plot.parentimage - Graphical representation highlighting possible parents of individuals} \description{ Graphical representation highlighting possible parents of individuals } \usage{ plot.parentimage(x, start = 1, num = (ncol(x)-start), cutoffsib = 3, cutoffpar = 5,\dots) } \arguments{ \item{x}{ Result from the function \link{scoreKinship} } \item{start}{ Start at individual } \item{num}{ Show this many individuals (DEFAULT: End of the resultset) } \item{cutoffsib}{ Sibling cutoff (DEFAULT: value > 3*std-dev) } \item{cutoffpar}{ Parental cutoff (DEFAULT: value > 5*std-dev) } \item{\dots}{ Additional arguments to plotting function } } \details{ } \value{ plotting routine, no return value } \references{ } \author{ Danny Arends \email{Danny.Arends@gmail.com} Maintainer: Danny Arends \email{Danny.Arends@gmail.com} } \note{ num parameter should be larger than 2 } \seealso{ \itemize{ \item \code{\link{scoreKinship}} - Calculates kinshipscores based on SNP markerdata \item \code{\link{parents}} - Finds tripplets of individuals with parents from SNP markerdata \item \code{\link{kinshipNetwork}} - Created a .SIF network with kinship relations between individuals } } \examples{ #Create a population at H/W equilibrium population <- CreatePopulationHW(100,300) #Breed randomly a generation (parents) population <- BreedRandom(population,25) #Score kinship in our breeding example result <- scoreKinship(population$data,plot=FALSE) #plot the suspected parents plot.parentimage(result) } \keyword{ ~kwd1 } \keyword{ ~kwd2 }

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library(testthat) test_check("tricky")

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checkPlotTest = function(plotTest,isTrain) { if(is.null(plotTest)) { #if plotTest is unspecified/NULL #if all obseravtions of isTrain are TRUE plot train else plot test observations plotThese = if(all(isTrain)) isTrain else !isTrain } else { #match with options matchArg = pmatch(plotTest,c(T,F,"andTrain")) #possible arguments are TRUE FALSE and "andTrain" if(length(matchArg)<1 || is.na(matchArg)) { stop(paste("plotTest= '",plotTest,"' is an incorrect argument")) } #if plotTest is T, plot test set if there is one, else raise error if(matchArg==1) plotThese = if(!all(isTrain)) !isTrain else { stop("no test set found, did you pass a Xtest when computing forestFloor?") } #if not to plot test set, then plot train, train should also be there if(matchArg==2) plotThese = isTrain #if plotTest is "andTrain" blot both train and test set if(matchArg==3) { plotThese = rep(T,length(isTrain)) if(all(isTrain)) warning("no test found, only plotting train") } } return(plotThese) }

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################################### LOAD DATA ############################################################ # If you'd like to read in data from a database install.packages("RMySQL") # to read and write Micorsoft Excel files from R install.packages("XLConnect") install.packages("xlsx") # to read SAS or SPSS install.packages("foreign") ################################### MANAGE DATA ############################################################ # Essential shortcuts for subsetting, summarizing, rearranging, and joining together data sets. # dplyr is our go to package for fast data manipulation. install.packages("dplyr") # Tools for changing the layout of your data sets. install.packages("tidyr") # regular expressions and character strings install.packages("stringr") # Tools that make working with dates and times easier install.packages("lubridate") ################################### VISUALIZE DATA ############################################################ # beautiful graphics :) install.packages("ggplot2") # 3D visualizations install.packages("rgl") # use Google Chart tools to visualize data in R (Gapminder) install.packages("googleVis") # maps install.packages("leafleteVis") # time series install.packages("dygraphs") # tables install.packages("dt") # diagrams install.packages("diagrammeR") ################################### MODEL DATA ############################################################ # Generalized Additive Models install.packages("mgcv") # Linear mixed effects models install.packages("lme4") # Non-Linear mixed effects models install.packages("nlme") # random forest install.packages("randomForest") # multiple comparison testing install.packages("multcomp") # Visualization tools and tests for categorical data install.packages("vcd") # Lasso and elastic-net regression methods with cross validation install.packages("glmnet") # survival analysis install.packages("survival") # training regression and classification models install.packages("caret") install.packages("gmodels") # decision tree install.packages("rpart") # ROC curve install.packages("ROCR") ################################### REPORT RESULTS ############################################################ install.packages("shiny") # Reporting install.packages("R Markdown") ################################### SPATIAL DATA ############################################################ # loading and using spatial data including shapefiles install.packages("sp") install.packages("maptools") # use map polygons for plots install.packages("maps") # Download street maps straight from Google maps and use them as a background in your ggplots. install.packages("ggmap") ######################### TIME SERIES AND FINANCIAL DATA ############################################################ # format for saving time series objects in R install.packages("zoo") # manipulating time series data sets install.packages("xts") # Tools for downloading financial data, plotting common charts, and doing technical analysis install.packages("quantmod") # To write high performance R code install.packages("quantmod") # Big Data (An alternative way to organize data sets for very, very fast operations) install.packages("data.table") # Use parallel processing in R to speed up your code or to crunch large data sets install.packages("parallel")

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#Prepare a model for strength of concrete data using Neural Networks concrete <- read.csv(file.choose()) str(concrete) class(concrete) names(concrete) attach(concrete) summary(concrete) library(neuralnet) library(nnet) install.packages("NeuralNetTools") library(NeuralNetTools) hist(cement, prob = T, breaks = 30) lines(density(cement)) summary(cement) hist(slag, prob = T, breaks = 30) lines(density(slag)) summary(slag) hist(ash, prob = T, breaks = 30) lines(density(ash)) summary(ash) hist(water, prob = T, breaks = 30) lines(density(water)) summary(water) hist(superplastic, prob = T, breaks = 30) lines(density(superplastic)) summary(superplastic) hist(coarseagg, prob = T, breaks = 30) lines(density(coarseagg)) summary(coarseagg) hist(fineagg, prob = T, breaks = 30) lines(density(fineagg)) summary(fineagg) hist(strength, prob = T, breaks = 30) lines(density(strength)) summary(strength) names(concrete) sd(cement) sd(slag) sd(ash) sd(water) sd(superplastic) sd(coarseagg) sd(fineagg) sd(age) sd(strength) var(cement) var(slag) var(ash) var(water) var(superplastic) var(coarseagg) var(fineagg) var(age) var(strength) library(moments) skewness(cement) skewness(slag) skewness(ash) skewness(water) skewness(superplastic) skewness(coarseagg) skewness(fineagg) skewness(age) skewness(strength) kurtosis(cement) kurtosis(slag) kurtosis(ash) kurtosis(water) kurtosis(superplastic) kurtosis(coarseagg) kurtosis(fineagg) kurtosis(age) kurtosis(strength) # Apply Normalization normalize<-function(x){ return ( (x-min(x))/(max(x)-min(x))) } concrete_norm<-as.data.frame(lapply(concrete,FUN=normalize)) summary(concrete_norm$strength) summary(strength) # Data Partition set.seed(123) ind <- sample(2, nrow(concrete_norm), replace = TRUE, prob = c(0.7,0.3)) concrete_train <- concrete_norm[ind==1,] concrete_test <- concrete_norm[ind==2,] # Creating a neural network model on training data concrete_model <- neuralnet(strength~cement+slag+ash+water+superplastic+coarseagg+fineagg+age,data = concrete_train) str(concrete_model) plot(concrete_model, rep = "best") summary(concrete_model) par(mar = numeric(4), family = 'serif') plotnet(concrete_model, alpha = 0.6) # Evaluating model performance set.seed(12323) model_results <- compute(concrete_model,concrete_test[1:8]) predicted_strength <- model_results$net.result cor(predicted_strength,concrete_test$strength) str_max <- max(concrete$strength) str_min <- min(concrete$strength) unnormalize <- function(x, min, max) { return( (max - min)*x + min ) } ActualStrength_pred <- unnormalize(predicted_strength,str_min,str_max) head(ActualStrength_pred) # Improve the model performance : set.seed(12345) concrete_model2 <- neuralnet(strength~cement+slag+ash+water+superplastic+coarseagg+fineagg+age,data= concrete_train,hidden = 5) plot(concrete_model2, rep = "best") summary(concrete_model2) model_results2<-compute(concrete_model2,concrete_test[1:8]) predicted_strength2<-model_results2$net.result cor(predicted_strength2,concrete_test$strength) plot(predicted_strength,concrete_test$strength) par(mar = numeric(4), family = 'serif') plotnet(concrete_model2, alpha = 0.6) concrete_model3 <- neuralnet(strength~.,data= concrete_train,hidden = 7) plot(concrete_model3, rep = "best") summary(concrete_model3) model_results3<-compute(concrete_model3,concrete_test[1:8]) predicted_strength3<-model_results3$net.result cor(predicted_strength3,concrete_test$strength) plot(predicted_strength,concrete_test$strength) par(mar = numeric(4), family = 'serif') plotnet(concrete_model3, alpha = 0.6)

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03_assess_meanSil.R

library(mclust) library(cluster) # allmtd = list.files('/home-4/whou10@jhu.edu/scratch/Wenpin/rna_imputation/clustering/result/') # allmtd = setdiff(allmtd,c('deepimpute','scimpute')) mtd = as.character(commandArgs(trailingOnly = T)[1]) print(mtd) allf = c("sc_10x","sc_celseq2","sc_dropseq","sc_10x_5cl", "sc_celseq2_5cl_p1", "sc_celseq2_5cl_p2","sc_celseq2_5cl_p3") df = data.frame(allf = allf, k = c(rep(3,3), rep(5,4))) existf = sub('.rds','',list.files(paste0('/home-4/whou10@jhu.edu/scratch/Wenpin/rna_imputation/clustering/result/',mtd,'/'))) if (length(existf)>0){ ACC <- PUR <- ARI <- meanSil <- NULL for (f in intersect(allf, existf)){ print(f) clu = readRDS(paste0('/home-4/whou10@jhu.edu/scratch/Wenpin/rna_imputation/clustering/result/',mtd,'/',f,'.rds')) ct = sub('.*:','',names(clu)) acc <- -mean(sapply(unique(clu),function(i){ p = table(ct[clu==i])/ sum(clu==i) sum(p * log(p)) })) pur <- -mean(sapply(unique(ct),function(sct){ p = table(clu[ct==sct])/ sum(ct == sct) sum(p * log(p)) })) ACC = c(ACC, acc) PUR = c(PUR, pur) ## adjusted rank index suppressMessages(library(mclust)) ARI <- c(ARI,adjustedRandIndex(ct,clu)) ## sum of silhouette mat = readRDS(paste0('/home-4/whou10@jhu.edu/scratch/Wenpin/rna_imputation/result/procimpute/cellbench/',mtd,'/',f,'.rds')) d = dist(t(mat)) ## use cell by gene for dist() s = silhouette(clu, dist=d) meanSil = c(meanSil,mean(s[,3])) } df = data.frame(method=mtd, data = intersect(allf, existf), Hacc =ACC, Hpur=PUR, ARI = ARI, meanSil = meanSil) } saveRDS(df,paste0('/home-4/whou10@jhu.edu/scratch/Wenpin/rna_imputation/clustering/perf/meanSil/',mtd,'.rds'))

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/Week03_script.R

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jakeane/qss17_assignments

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2020-12-04T19:40:01

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Week03_script.R

## Data Visualization (GOVT16-QSS17) Fall 2020 ## Ggplot2, Part I (Week 3) ## ## Name: John Keane ## Date: 10/5/20 # Load libraries library(tidyverse) # 1. First, let's work with the IMDB dataset # (a) movie <- read_csv("hw2data/movie_metadata.csv") print("movie head:") head(movie) print("movie tail:") tail(movie) # (b) ggplot(movie[!is.na(movie$color), ], aes(x = color, y = imdb_score)) + geom_point(position = "jitter") + labs( title = "IMDB Scores of Movies by Color", x = "Color", y = "IMDB Score" ) + theme_minimal() # (c) ggplot(movie[!is.na(movie$color), ], aes(x = imdb_score, fill = color)) + geom_histogram() + labs( title = "Distribution of IMDB Scores by Color", x = "IMDB Score", y = "Count", fill = "Color" ) + theme_minimal() # (d) ggplot( movie[!is.na(movie$color), ], aes( x = imdb_score, y = ..density.., fill = color ) ) + geom_histogram(position = "identity", alpha = 0.6) + labs( title = "Distribution of IMDB Scores by Color", x = "IMDB Score", y = "Density", fill = "Color" ) + theme_minimal() # 2. For this problem, load up the approval dataset approve <- read_csv("hw2data/approval_data.csv") print("approve head:") head(approve) print("approve tail:") tail(approve) # (a) approval_types <- approve %>% mutate(yearQrt = year + (0.25 * (qrt - 1))) %>% select(yearQrt, econapp, fpapp) %>% pivot_longer(names_to = "type", values_to = "value", c(econapp, fpapp)) approval_types # (b) ggplot(approval_types, aes(x = yearQrt, y = value, color = type)) + geom_line() + labs( title = "Economic and Foreign Policy Approval of Executive Admin", x = "Year", y = "Approval Rating", color = "Approval Type" ) + scale_color_discrete(labels = c("Economic", "Foreign")) + theme_minimal() # (c) ggplot(approval_types, aes(x = yearQrt, y = value, color = type)) + geom_line(alpha = 0.4) + geom_smooth() + labs( title = "Economic and Foreign Policy Approval of Executive Admin", x = "Year", y = "Approval Rating", color = "Approval Type" ) + scale_color_discrete(labels = c("Economic", "Foreign")) + theme_minimal() # (d) approve %>% mutate(yearQrt = year + (0.25 * (qrt - 1))) %>% select(yearQrt, qrtinfl, qrtunem) %>% pivot_longer( names_to = "type", values_to = "value", c(qrtinfl, qrtunem) ) %>% ggplot(aes(x = yearQrt, y = value, color = type)) + geom_line(alpha = 0.4) + geom_smooth() + labs( title = "Inflation and Employment Rates Over Time", x = "Year", y = "Rate", color = "Type" ) + scale_color_discrete(labels = c("Inflation", "Unemployment")) + theme_minimal() # WIID dataset wiid <- read_csv("hw2data/WIID_Dec2018.csv") print("WIID head:") head(wiid) print("WIID tail:") tail(wiid) # (a) wiid %>% filter( year == 2000, country %in% c("Germany", "France", "Italy", "Spain", "Norway") ) %>% group_by(country) %>% summarize(avgGini = mean(gini_reported, na.rm = TRUE)) %>% ggplot(aes(x = country, y = avgGini, label = country)) + geom_point() + geom_text(vjust = -1.0) + theme_minimal() + labs( title = "Average Gini Coefficient by Country", x = "Country", y = "Average Gini Coefficient" ) # (b) wiid %>% filter(region_un == "Asia") %>% ggplot(aes(x = gini_reported, y = ..density.., fill = region_un_sub)) + geom_density(alpha = 0.3) + theme_minimal() + labs( title = "Income Inequality in Asia", y = "Density", fill = "UN Sub-Region", x = "Gini Coefficient" ) # (c) wiid %>% filter(region_un == "Europe") %>% group_by(country) %>% summarize(meanGini = mean(gini_reported, na.rm = TRUE)) %>% ggplot(aes( y = fct_reorder(country, meanGini, min), x = meanGini, label = country )) + geom_point() + theme_minimal() + theme( axis.title.y = element_blank(), axis.text.y = element_text(size = 6) ) + labs(x = "Gini Coefficient", title = "Income Inequality in Europe") # (d) color_pallete <- colorRampPalette(c("orange", "red", "purple", "blue", "green")) wiid %>% filter(region_un == "Africa") %>% mutate(avgGini = mean(gini_reported, na.rm = TRUE)) %>% group_by(country) %>% summarize(avgGiniDiff = mean(gini_reported - avgGini, na.rm = TRUE)) %>% ggplot(aes( x = fct_reorder(country, avgGiniDiff, min), y = avgGiniDiff, fill = country )) + geom_bar(stat = "identity", show.legend = FALSE) + scale_fill_manual( values = color_pallete( length(unique(wiid$country[wiid$region_un == "Africa"])) ) ) + coord_flip() + theme_minimal() + labs( title = "Income Inequality in Africa", y = "Gini Coefficient", x = "Country" ) # (e) ggplot(wiid, aes(x = gini_reported, y = ..density.., fill = region_un)) + geom_histogram(alpha = 0.5) + scale_fill_manual( values = color_pallete(length(unique(wiid$region_un))) ) + theme_minimal() + labs( title = "Global Income Inequality", x = "Gini Coefficient", fill = "UN Region", y = "Density" ) # (f) wiid %>% filter(region_un == "Americas") %>% group_by(region_un_sub, year) %>% summarize(medianGini = median(gini_reported, na.rm = TRUE)) %>% ggplot(aes(x = year, y = medianGini, color = region_un_sub)) + geom_point(alpha = 0.4) + geom_smooth() + scale_color_manual( values = color_pallete( length(unique(wiid$region_un_sub[wiid$region_un == "Americas"])) ) ) + theme_minimal() + labs( title = "Income Inequality in the Americas", x = "Year", y = "Gini Coefficient", color = "UN Sub-Region" ) # (g) wiid %>% filter(region_un_sub == "Western Asia") %>% ggplot( aes( x = factor( incomegroup, levels = c( "Low income", "Lower middle income", "Upper middle income", "High income" ) ), y = gini_reported ) ) + geom_dotplot( alpha = 0.6, stackdir = "center", binaxis = "y", binwidth = 0.8 ) + stat_summary(fun = "median", color = "red") + theme_minimal() + labs( title = "Income Inequality in Western Asia by Income Group", x = "Income Group", y = "Gini Coefficient" )

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/feature_sel/shrunken_features.R

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shrunken_features.R

library(pamr) library(pROC) library(mccr) library(foreach) library(doParallel) source('feature_sel/pamr.listgenes.R') source('get_results.R') source('helper_func.R') #' Extracts genes from the shrunken object #' #' @param shrunken.genes.df.list list containing data frames of output pamr.listgenes() w.r.t each group #' @return List containing gene names for each group get.genes.shrunken <- function(shrunken.genes.df.list) { genes = list() for(i in seq_along(shrunken.genes.df.list)) { genes[[i]] = shrunken.genes.df.list[[i]][['genes.list']][,2] } return(genes) } #' Gives the shrunken gene object for given data w.r.t each group #' #' @param data normalised data containing samples as rows and columns as genes #' @param train.ind.list List containing indexes of the folds #' @param stages Stage of every sample in data #' @param cores Number of CPU cores to be used #' @param type Indicates whether there are 1 or multiple(type = 1) folds #' @return List containing gene object for all groups and AFs get.shrunken.object <- function(data, train.ind.list, stages, cores, type = 1, min.genes = 1, min.range = 0.02) { registerDoParallel(cores = cores) pamr.genes.list <- foreach(i = 1:length(train.ind.list)) %dopar% { if(type == 1) train.ind <- sort(unlist(train.ind.list[-i])) else train.ind <- sort(unlist(train.ind.list[[i]])) train.model <- pamr.train(list(x = as.matrix(t(data[train.ind,])), y = stages[train.ind])) cv.model <- pamr.cv(train.model, data = list(x = t(as.matrix(data[train.ind,])), y = stages[train.ind]),nfold = 5) type <- as.factor(stages[train.ind])[1] mccs <- sapply(seq_along(cv.model$threshold), function(x) { mccr(get.order(stages[train.ind], type), get.order(cv.model$yhat[,x], type)) }) thr.ind = sort(which(mccs == max(mccs)), decreasing = T)[1] if(min.genes != 1) thr.ind <- max(which(mccs > (mccs[thr.ind] - min.range))) genes.list <- pamr.listgene(train.model, data = list(x=as.matrix(t(data[train.ind,])), y=stages[train.ind]), threshold = cv.model$threshold[thr.ind], fitcv = cv.model, genenames = T) return(list(genes.list = genes.list, aucs = aucs)) } return(pamr.genes.list) }

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/rankall.R

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rankall.R

rankall <- function(outcome, num = "best") { ## Read outcome data ## get directory from environment; ## if it's not there, use default directory<-Sys.getenv("directory") if (is.null(directory)){ directory="C:\\Users\\grier006\\Documents\\R\\R-week4" } setwd(directory) ## Read outcome data data<- read.csv("outcome-of-care-measures.csv", colClasses="character") ## Check that outcome is valid if (!(outcome %in% c("heart attack", "heart failure", "pneumonia"))){ stop("Invalid Outcome") } ## per Hospital_Revised_Flatfiles.pdf ## Heart Attack mortality rate: col. 11 ## Heart Failure mortaility rate: col. 17 ## Pneumonia mortality rate: col. 23 outcomeCol = NULL if (outcome == "heart attack"){ outcomeCol<-11 } else if (outcome == "heart failure"){ outcomeCol<-17 } else{ outcomeCol<-23 } # We only need 3 of the 40+ columns: # State (7), Hospital (2), outcomeCol myData<-data[,c(2, 7, outcomeCol)] # remove missing data goodCases<-complete.cases(myData) myData=myData[goodCases,] # Make the output more readable names(myData)[3]<-"Rate" # Rate should be numeric for sort myData[,3]<-suppressWarnings(as.numeric(myData[,3])) # Split by state StateData<-split(myData, myData$State) ## For each state, find the hospital of the given rank ans <- lapply(StateData, function (s, num){ s<-s[order(s$Rate, s$Hospital.Name),] if (num == "best"){ return(s[1]) } else if (num == "worst"){ # return(s[nrow(s)]) return (s[which.max(s$Rate),]) } else if (is.numeric(num)){ if (num > nrow(s)){ result<-"NA" } else{ result<-s[num,] } } }, num) ## Return a data frame with the hospital names and the ## (abbreviated) state name return(ans) } result<-rankall("heart attack", 20) head(result) result<-rankall("pneumonia", "worst") tail(result)

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/R/print.pval.R

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2020-12-24T08:41:02.243028

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print.pval.R

print.pval <- function(x, digits = max(3, getOption("digits") - 3),...){ statistic2<-c("X2","deviance","Freeman-Tukey")[x$statnum==(1:3)] cat("Under the",statistic2,"statistic \n") cat("\n") cat("Summary statistics for T_pred \n") print(round(summary(x$Tpred),digits=digits)) cat("\n") cat("Summary statistics for T_obs \n") print(round(summary(x$Tobs),digits=digits)) cat("\n") cat("Bayesian p-value = ",round(x$pval,digits=digits),"\n")}

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/part_2-regression/random-forest-regression/random_forest_regression.R

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mdrijwan123/Machine-Learning-Udemy-A-to-Z

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random_forest_regression.R

# Importing the dataset # --------------------- dataset = read.csv('../../data_files/Position_Salaries.csv') dataset = dataset[2:3] # Fitting Regression to the dataset # install.packages('randomForest') library(randomForest) set.seed(1234) regressor = randomForest(x=dataset[1], y=dataset$Salary, ntree=500) # Predicting a new result with Polynomial Regression y_pred = predict(regressor, data.frame(Level=6.5)) library(ggplot2) # Build dataset to smoothly visualize pedicted graph in higher resolution x_grid = seq(min(dataset$Level), max(dataset$Level), 0.1) # Visualizing the Polynomial Regression results ggplot() + geom_point(aes(x=dataset$Level, y=dataset$Salary), color='red') + geom_line(aes(x=x_grid, y=predict(regressor, newdata=data.frame(Level=x_grid))), color='blue') + ggtitle('Truth and Bluff (Random Forest Regression Model)') + xlab('Level') + ylab('Salary')

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/R/RcppExports.R

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bruce1995/BAGEL

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2022-11-14T15:14:25.649593

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RcppExports.R

# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 compute_mu <- function(H, e, alpha, gamma, R, beta, Z, data, D, Q, S, B) { .Call('_BAGEL_compute_mu', PACKAGE = 'BAGEL', H, e, alpha, gamma, R, beta, Z, data, D, Q, S, B) } compute_muk2 <- function(alphakt, gammakt, Rk, betak, Zkt, datak, D, Q, S, B) { .Call('_BAGEL_compute_muk2', PACKAGE = 'BAGEL', alphakt, gammakt, Rk, betak, Zkt, datak, D, Q, S, B) } update_alpha <- function(H, e, alpha, gamma, R, beta, Z, data, D, Q, S, B, mu, Y, sigma_square, sigma_square_alpha = 1) { .Call('_BAGEL_update_alpha', PACKAGE = 'BAGEL', H, e, alpha, gamma, R, beta, Z, data, D, Q, S, B, mu, Y, sigma_square, sigma_square_alpha) } update_gamma <- function(H, e, alpha, gamma, R, beta, Z, data, D, Q, S, B, mu, Y, sigma_square, sigma_square_gamma = 1) { .Call('_BAGEL_update_gamma', PACKAGE = 'BAGEL', H, e, alpha, gamma, R, beta, Z, data, D, Q, S, B, mu, Y, sigma_square, sigma_square_gamma) } update_beta <- function(H, e, alpha, gamma, R, beta, Z, data, D, Q, S, B, Y, sigma_square, sigma_square_beta = 1) { .Call('_BAGEL_update_beta', PACKAGE = 'BAGEL', H, e, alpha, gamma, R, beta, Z, data, D, Q, S, B, Y, sigma_square, sigma_square_beta) } compute_likelihood <- function(a, mu, U, sigma_square) { .Call('_BAGEL_compute_likelihood', PACKAGE = 'BAGEL', a, mu, U, sigma_square) } update_R <- function(H, e, alpha, gamma, R, beta, Z, data, D, Q, S, B, a, U, mu, Y, rho = 0.5, sigma_square = 1) { .Call('_BAGEL_update_R', PACKAGE = 'BAGEL', H, e, alpha, gamma, R, beta, Z, data, D, Q, S, B, a, U, mu, Y, rho, sigma_square) } update_e <- function(H, e, alpha, gamma, R, beta, Z, data, D, Q, S, B, a, U, mu, Y, N, sigma_square = 1, m0 = 1, rho = 0.5, H_max = 50L) { .Call('_BAGEL_update_e', PACKAGE = 'BAGEL', H, e, alpha, gamma, R, beta, Z, data, D, Q, S, B, a, U, mu, Y, N, sigma_square, m0, rho, H_max) } update_sig_square <- function(omega, a = 1, b = 1) { .Call('_BAGEL_update_sig_square', PACKAGE = 'BAGEL', omega, a, b) } update_sig_square_cub <- function(Y, a = 1, b = 1) { .Call('_BAGEL_update_sig_square_cub', PACKAGE = 'BAGEL', Y, a, b) } update_omega <- function(Y, mu, Comega, sigma_square, Q) { .Call('_BAGEL_update_omega', PACKAGE = 'BAGEL', Y, mu, Comega, sigma_square, Q) } update_gamma_sigma_square <- function(gamma, B, a = 1, b = 1) { .Call('_BAGEL_update_gamma_sigma_square', PACKAGE = 'BAGEL', gamma, B, a, b) } update_Y <- function(U, a, mu, Q, sigma_square = 1) { .Call('_BAGEL_update_Y', PACKAGE = 'BAGEL', U, a, mu, Q, sigma_square) } update_a <- function(U, mu, a, sigma, step = 0.1) { .Call('_BAGEL_update_a', PACKAGE = 'BAGEL', U, mu, a, sigma, step) } update_Comega <- function(omega, Comega, Q, sigma_square, step = 0.05, lower = -0.999, upper = 0.999) { .Call('_BAGEL_update_Comega', PACKAGE = 'BAGEL', omega, Comega, Q, sigma_square, step, lower, upper) }

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/plot2.R

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plot2.R

## Before running this script, check that the datafile "household_power_consumption.txt" ## is unzipped and in your working directory. if (!file.exists("household_power_consumption.txt")){ print("Download and unzip household_power_consumption.txt in your working directory") } ## Read the datafile. Since we will only be using data from the dates 2007-02-01 ## and 2007-02-02, only this data is read. (Makes the script run much faster also.) housepower<-read.table("household_power_consumption.txt", header = TRUE, sep = ";", skip = 66636, nrows = 2880) names(housepower)<-c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3") ## Combine the Date and Time into 1 object, so this can be used as the x axis of the plot. ## Also convert this to class POSIXlt housepower$DateTime<-as.POSIXlt(paste(housepower$Date,housepower$Time),format = "%d/%m/%Y %H:%M:%S",) ## Open a PNG graphics device for the plot file to be written in. ## Create plot ## Close graphics device png(filename = "plot2.png", width = 480, height = 480, units = "px") plot(housepower$DateTime, housepower$Global_active_power, type = "n", xlab = "", ylab = "Global Active Power (kilowatts)") lines(housepower$DateTime, housepower$Global_active_power) dev.off()

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/data/genthat_extracted_code/GUILDS/examples/maxLikelihood.ESF.Rd.R

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surayaaramli/typeRrh

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2023-05-05T04:05:31.617869

2019-04-25T22:10:06

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maxLikelihood.ESF.Rd.R

library(GUILDS) ### Name: maxLikelihood.ESF ### Title: Maximization of the loglikelihood given the standard Neutral ### Model, using the Etienne Sampling Formula ### Aliases: maxLikelihood.ESF ### ** Examples A <- c(1,1,1,3,5,8) maxLikelihood.ESF( c(7,0.1), abund = A)

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/data/genthat_extracted_code/powerSurvEpi/examples/ssizeCT.default.Rd.R

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surayaaramli/typeRrh

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library(powerSurvEpi) ### Name: ssizeCT.default ### Title: Sample Size Calculation in the Analysis of Survival Data for ### Clinical Trials ### Aliases: ssizeCT.default ### Keywords: survival design ### ** Examples # Example 14.42 in Rosner B. Fundamentals of Biostatistics. # (6-th edition). (2006) page 809 ssizeCT.default(power = 0.8, k = 1, pE = 0.3707, pC = 0.4890, RR = 0.7, alpha = 0.05)

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Ch16_airbnb_random_forest.R

############################################################ # # DATA ANALYSIS TEXTBOOK # RANDOM FOREST # ILLUSTRATION STUDY # Airbnb London 2017 march 05 data # v2.1 2019-04-24 # v2.2 2019-07-25 added todo-s # v2.3 2019-08-12 some small changes by BG # v2.4 2019-19-18 major update by Zsuzsi # v2.5 2019-19-19 small fixes by BG, some minor todos # v2.6 small fixes by Zs, better graphs # v2.7 new model definitions, new train-holdout split # v2.8 2019-10-04 many small changes by GB, some minor todos # v2.9 2020-01-11 data cleaning taken out to prep code + minor graph changes + minor model changes # v3.0 2020-01-12 table edits, revert varimp graphs to pct # ############################################################ # # WHAT THIS CODES DOES: # # Define models # Run random forest, GBM # Shows tuning # Evaluate the performance # Compares models # Does diagnostics ########################################################### # CLEAR MEMORY # best to start new session rm(list=ls()) library(rattle) library(tidyverse) library(caret) library(ranger) library(Hmisc) library(knitr) library(kableExtra) library(xtable) # location folders data_in <- "lab2/data/" data_out <- "lab2/data/" output <- "lab2/output/" # load ggplot theme function source("helper_functions/theme_bg.R") source("helper_functions/da_helper_functions.R") source("lab2/code/Ch14_airbnb_prediction_functions.R") ######################################################################################### # # PART I # Loading and preparing data ---------------------------------------------- # ######################################################################################### # Used area area <- "london" data <- read_csv(paste0(data_in, "airbnb_", area, "_workfile_adj.csv")) %>% mutate_if(is.character, factor) %>% filter(!is.na(price)) count_missing_values <- function(data) { num_missing_values <- map_int(data, function(x) sum(is.na(x))) num_missing_values[num_missing_values > 0] } count_missing_values(data) # Sample definition and preparation --------------------------------------- # We focus on normal apartments, n<8 data <- data %>% filter(n_accommodates < 8) # copy a variable - purpose later, see at variable importance data <- data %>% mutate(n_accommodates_copy = n_accommodates) # basic descr stat ------------------------------------------- skimr::skim(data) summary(data$price) Hmisc::describe(data$price) describe(data$f_room_type) describe(data$f_property_type) table(data$f_number_of_reviews) # create train and holdout samples ------------------------------------------- # train is where we do it all, incl CV set.seed(2801) # First pick a smaller than usual training set so that models run faster and check if works # If works, start anew without these two lines # try <- createDataPartition(data$price, p = 0.2, list = FALSE) #data <- data[try, ] # CUSTOM NEIGHBORHOOD FILTER FOR SMALLER RUNTIMES selected_boroughs <- c("Hackney", "Camden") data <- data %>% filter( f_neighbourhood_cleansed %in% selected_boroughs) data <- data %>% mutate( f_neighbourhood_cleansed = factor( f_neighbourhood_cleansed, levels = selected_boroughs)) train_indices <- createDataPartition(data$price, p = 0.7, list = FALSE) data_train <- data[train_indices, ] data_holdout <- data[-train_indices, ] dim(data_train) dim(data_holdout) # Define models: simpler, extended ----------------------------------------------------------- # Basic Variables inc neighnourhood basic_vars <- c( "n_accommodates", "n_beds", "n_days_since", "f_property_type","f_room_type", "f_bathroom", "f_cancellation_policy", "f_bed_type", "f_neighbourhood_cleansed") # reviews reviews <- c("n_number_of_reviews", "flag_n_number_of_reviews" ,"n_review_scores_rating", "flag_review_scores_rating") # Dummy variables amenities <- grep("^d_.*", names(data), value = TRUE) #interactions for the LASSO # from ch14 X1 <- c("n_accommodates*f_property_type", "f_room_type*f_property_type", "f_room_type*d_familykidfriendly", "d_airconditioning*f_property_type", "d_cats*f_property_type", "d_dogs*f_property_type") # with boroughs X2 <- c("f_property_type*f_neighbourhood_cleansed", "f_room_type*f_neighbourhood_cleansed", "n_accommodates*f_neighbourhood_cleansed" ) predictors_1 <- c(basic_vars) predictors_2 <- c(basic_vars, reviews, amenities) predictors_E <- c(basic_vars, reviews, amenities, X1,X2) ######################################################################################### # # PART II # RANDOM FORESTS ------------------------------------------------------- # ######################################################################################### # do 5-fold CV train_control <- trainControl(method = "cv", number = 5, verboseIter = FALSE) # set tuning tune_grid <- expand.grid( .mtry = c(7, 9), .splitrule = "variance", .min.node.size = c(5, 10) ) # simpler model for model A (1) set.seed(1234) system.time({ rf_model_1 <- train( formula(paste0("price ~", paste0(predictors_1, collapse = " + "))), data = data_train, method = "ranger", trControl = train_control, tuneGrid = tune_grid, importance = "impurity" ) }) rf_model_1 # set tuning for benchamrk model (2) tune_grid <- expand.grid( .mtry = c(10, 12), .splitrule = "variance", .min.node.size = c(5, 10) ) set.seed(1234) system.time({ rf_model_2 <- train( formula(paste0("price ~", paste0(predictors_2, collapse = " + "))), data = data_train, method = "ranger", trControl = train_control, tuneGrid = tune_grid, importance = "impurity" ) }) rf_model_2 # # auto tuning first # set.seed(1234) # system.time({ # rf_model_2auto <- train( # formula(paste0("price ~", paste0(predictors_2, collapse = " + "))), # data = data_train, # method = "ranger", # trControl = train_control, # tuneLength = 2, # importance = "impurity" # ) # }) # rf_model_2auto # evaluate random forests ------------------------------------------------- results <- resamples( list( model_1 = rf_model_1, model_2 = rf_model_2 # model_2b = rf_model_2b ) ) summary(results) # Save outputs ------------------------------------------------------- # Show Model B rmse shown with all the combinations rf_tuning_modelB <- rf_model_2$results %>% select(mtry, min.node.size, RMSE) %>% rename(nodes = min.node.size) %>% spread(key = mtry, value = RMSE) kable(x = rf_tuning_modelB, format = "latex", digits = 2, caption = "CV RMSE") %>% add_header_above(c(" ", "vars" = 3)) %>% cat(.,file= paste0(output,"rf_tuning_modelB.tex")) # Turning parameter choice 1 result_1 <- matrix(c( rf_model_1$finalModel$mtry, rf_model_2$finalModel$mtry, rf_model_1$finalModel$min.node.size, rf_model_2$finalModel$min.node.size ), nrow=2, ncol=2, dimnames = list(c("Model A", "Model B"), c("Min vars","Min nodes")) ) kable(x = result_1, format = "latex", digits = 3) %>% cat(.,file= paste0(output,"rf_models_turning_choices.tex")) # Turning parameter choice 2 result_2 <- matrix(c(mean(results$values$`model_1~RMSE`), mean(results$values$`model_2~RMSE`) ), nrow=2, ncol=1, dimnames = list(c("Model A", "Model B"), c(results$metrics[2])) ) kable(x = result_2, format = "latex", digits = 3) %>% cat(.,file= paste0(output,"rf_models_rmse.tex")) ######################################################################################### # # PART III # MODEL DIAGNOSTICS ------------------------------------------------------- # ######################################################################################### ######################################################################################### # Variable Importance Plots ------------------------------------------------------- ######################################################################################### # first need a function to calculate grouped varimp group.importance <- function(rf.obj, groups) { var.imp <- as.matrix(sapply(groups, function(g) { sum(importance(rf.obj)[g], na.rm = TRUE) })) colnames(var.imp) <- "MeanDecreaseGini" return(var.imp) } # variable importance plot # 1) full varimp plot, full # 2) varimp plot grouped # 3) varimp plot , top 10 # 4) varimp plot w copy, top 10 rf_model_2_var_imp <- importance(rf_model_2$finalModel)/1000 rf_model_2_var_imp_df <- data.frame(varname = names(rf_model_2_var_imp),imp = rf_model_2_var_imp) %>% mutate(varname = gsub("f_neighbourhood_cleansed", "Borough:", varname) ) %>% mutate(varname = gsub("f_room_type", "Room type:", varname) ) %>% arrange(desc(imp)) %>% mutate(imp_percentage = imp/sum(imp)) ############################## # 1) full varimp plot, above a cutoff ############################## # to have a quick look plot(varImp(rf_model_2)) cutoff = 600 rf_model_2_var_imp_plot <- ggplot(rf_model_2_var_imp_df[rf_model_2_var_imp_df$imp>cutoff,], aes(x=reorder(varname, imp), y=imp_percentage)) + geom_point(color=color[3], size=1) + geom_segment(aes(x=varname,xend=varname,y=0,yend=imp_percentage), color=color[3], size=0.75) + ylab("Importance") + xlab("Variable Name") + coord_flip() + scale_y_continuous(labels = scales::percent) + theme_bg() + theme(axis.text.x = element_text(size=4), axis.text.y = element_text(size=4), axis.title.x = element_text(size=4), axis.title.y = element_text(size=4)) rf_model_2_var_imp_plot ggsave(paste0(output, "rf_varimp1.png"), width=mywidth_small, height=myheight_small, unit="cm", dpi=1200) cairo_ps(filename = paste0(output, "rf_varimp1.eps"), width = mywidth_small, height = myheight_small, pointsize = 8, fallback_resolution = 1200) print(rf_model_2_var_imp_plot) dev.off() ############################## # 2) full varimp plot, top 10 only ############################## # have a version with top 10 vars only rf_model_2_var_imp_plot_b <- ggplot(rf_model_2_var_imp_df[1:10,], aes(x=reorder(varname, imp), y=imp_percentage)) + geom_point(color=color[3], size=2) + geom_segment(aes(x=varname,xend=varname,y=0,yend=imp_percentage), color=color[3], size=1.5) + ylab("Importance") + xlab("Variable Name") + coord_flip() + scale_y_continuous(labels = scales::percent) + theme_bg() + theme(axis.text.x = element_text(size=4), axis.text.y = element_text(size=4), axis.title.x = element_text(size=4), axis.title.y = element_text(size=4)) rf_model_2_var_imp_plot_b ggsave(paste0(output, "rf_varimp1_b.png"), width=mywidth_small, height=myheight_small, unit="cm", dpi=1200) cairo_ps(filename = paste0(output, "rf_varimp1_b.eps"), width = mywidth_small, height = myheight_small, pointsize = 8, fallback_resolution = 1200) print(rf_model_2_var_imp_plot_b) dev.off() ############################## # 2) varimp plot grouped ############################## # grouped variable importance - keep binaries created off factors together varnames <- rf_model_2$finalModel$xNames f_neighbourhood_cleansed_varnames <- grep("f_neighbourhood_cleansed",varnames, value = TRUE) f_cancellation_policy_varnames <- grep("f_cancellation_policy",varnames, value = TRUE) f_bed_type_varnames <- grep("f_bed_type",varnames, value = TRUE) f_property_type_varnames <- grep("f_property_type",varnames, value = TRUE) f_room_type_varnames <- grep("f_room_type",varnames, value = TRUE) groups <- list(f_neighbourhood_cleansed=f_neighbourhood_cleansed_varnames, f_cancellation_policy = f_cancellation_policy_varnames, f_bed_type = f_bed_type_varnames, f_property_type = f_property_type_varnames, f_room_type = f_room_type_varnames, f_bathroom = "f_bathroom", n_days_since = "n_days_since", n_accommodates = "n_accommodates", n_beds = "n_beds") rf_model_2_var_imp_grouped <- group.importance(rf_model_2$finalModel, groups) rf_model_2_var_imp_grouped_df <- data.frame(varname = rownames(rf_model_2_var_imp_grouped), imp = rf_model_2_var_imp_grouped[,1]) %>% mutate(imp_percentage = imp/sum(imp)) rf_model_2_var_imp_grouped_plot <- ggplot(rf_model_2_var_imp_grouped_df, aes(x=reorder(varname, imp), y=imp_percentage)) + geom_point(color=color[3], size=2) + geom_segment(aes(x=varname,xend=varname,y=0,yend=imp_percentage), color=color[3], size=1.5) + ylab("Importance") + xlab("Variable Name") + coord_flip() + scale_y_continuous(labels = scales::percent) + theme_bg() + theme(axis.text.x = element_text(size=8), axis.text.y = element_text(size=8), axis.title.x = element_text(size=8), axis.title.y = element_text(size=8)) rf_model_2_var_imp_grouped_plot ggsave(paste0(output, "rf_varimp_grouped1.png"), width=mywidth_large, height=myheight_large, unit="cm", dpi=1200) cairo_ps(filename = paste0(output, "rf_varimp_grouped1.eps"), width = mywidth_large, height = myheight_large, pointsize = 12, fallback_resolution = 1200) print(rf_model_2_var_imp_grouped_plot) dev.off() # a side investigation ------------------------------------------------------- # with an important variable duplicated: how does it change? ############################## # 4) varimp plot w copy, top 10 ############################## # repeat model B # done smartly: we take mtry min.node.size from the final model set.seed(1234) rf_model_2_with_copy <- train( formula(paste0("price ~", paste0(c(predictors_2, "n_accommodates_copy"), collapse = " + "))), data = data_train, method = "ranger", trControl = train_control, tuneGrid = data.frame( .mtry = rf_model_2$finalModel$mtry, .splitrule = "variance", .min.node.size = rf_model_2$finalModel$min.node.size ), importance = "impurity" ) rf_model_2_with_copy_var_imp <- importance(rf_model_2_with_copy$finalModel) rf_model_2_with_copy_var_imp_df <- data.frame(varname = names(rf_model_2_with_copy_var_imp), imp = rf_model_2_with_copy_var_imp) %>% arrange(desc(imp)) %>% mutate(imp_percentage = imp/sum(imp)) # only keep top 10 as well rf_model_2_with_copy_var_imp_plot <- ggplot(rf_model_2_with_copy_var_imp_df[1:10,], aes(x=reorder(varname, imp), y=imp_percentage)) + geom_point(color=color[3], size=2) + geom_segment(aes(x=varname,xend=varname,y=0,yend=imp_percentage), color=color[3], size=1.5) + ylab("Importance") + xlab("Variable Name") + coord_flip() + scale_y_continuous(labels = scales::percent) + theme_bg() + theme(axis.text.x = element_text(size=4), axis.text.y = element_text(size=4), axis.title.x = element_text(size=4), axis.title.y = element_text(size=4)) rf_model_2_with_copy_var_imp_plot ggsave(paste0(output, "rf_varimp_withcopy.png"), width=mywidth_small, height=myheight_small, unit="cm", dpi=1200) cairo_ps(filename = paste0(output, "rf_varimp_withcopy.eps"), width = mywidth_small, height = myheight_small, pointsize = 8, fallback_resolution = 1200) print(rf_model_2_with_copy_var_imp_plot) dev.off() ######################################################################################### # Partial Dependence Plots ------------------------------------------------------- ######################################################################################### pltnames_pdp <- list("n_accommodates" = "rf_pdp_n_accom", "f_room_type" = "rf_pdp_roomtype") pdp::partial(rf_model_2, pred.var = "n_accommodates", pred.grid = distinct_(data_train, "n_accommodates"), train = data_train) %>% autoplot( ) + theme_bg() + theme(axis.text.x = element_text(size=16), axis.text.y = element_text(size=16), axis.title.x = element_text(size=16), axis.title.y = element_text(size=16)) pdp::partial(rf_model_2, pred.var = "f_room_type", pred.grid = distinct_(data_train, "f_room_type"), train = data_train) %>% autoplot( ) + theme_bg() + theme(axis.text.x = element_text(size=16), axis.text.y = element_text(size=16), axis.title.x = element_text(size=16), axis.title.y = element_text(size=16)) # Subsample performance: RMSE / mean(y) --------------------------------------- # NOTE we do this on the holdout set. # ---- cheaper or more expensive flats - not used in book data_holdout_w_prediction <- data_holdout %>% mutate(predicted_price = predict(rf_model_2, newdata = data_holdout)) ggplot(data_holdout_w_prediction, aes(x = price, y = price - predicted_price)) + geom_point(alpha = 0.01, color = color[3]) + geom_vline(xintercept = median(data_holdout_w_prediction[["price"]]), linetype = "dashed") + theme_bw() describe(data_holdout_w_prediction$n_accommodates) ######### create nice summary table of heterogeneity a <- data_holdout_w_prediction %>% mutate(is_low_size = ifelse(n_accommodates <= 3, "small apt", "large apt")) %>% group_by(is_low_size) %>% summarise( rmse = RMSE(predicted_price, price), mean_price = mean(price), rmse_norm = RMSE(predicted_price, price) / mean(price) ) b <- data_holdout_w_prediction %>% filter(f_neighbourhood_cleansed %in% c("Westminster", "Camden", "Kensington and Chelsea", "Tower Hamlets", "Hackney", "Newham")) %>% group_by(f_neighbourhood_cleansed) %>% summarise( rmse = RMSE(predicted_price, price), mean_price = mean(price), rmse_norm = rmse / mean_price ) c <- data_holdout_w_prediction %>% filter(f_property_type %in% c("Apartment", "House")) %>% group_by(f_property_type) %>% summarise( rmse = RMSE(predicted_price, price), mean_price = mean(price), rmse_norm = rmse / mean_price ) d <- data_holdout_w_prediction %>% summarise( rmse = RMSE(predicted_price, price), mean_price = mean(price), rmse_norm = RMSE(predicted_price, price) / mean(price) ) # Save output colnames(a) <- c("", "RMSE", "Mean price", "RMSE/price") colnames(b) <- c("", "RMSE", "Mean price", "RMSE/price") colnames(c) <- c("", "RMSE", "Mean price", "RMSE/price") d<- cbind("All", d) colnames(d) <- c("", "RMSE", "Mean price", "RMSE/price") line1 <- c("Type", "", "", "") line2 <- c("Apartment size", "", "", "") line3 <- c("Borough", "", "", "") result_3 <- rbind(line2, a, line1, c, line3, b, d) %>% transform(RMSE = as.numeric(RMSE), `Mean price` = as.numeric(`Mean price`), `RMSE/price` = as.numeric(`RMSE/price`)) options(knitr.kable.NA = '') kable(x = result_3, format = "latex", booktabs=TRUE, linesep = "",digits = c(0,2,1,2), col.names = c("","RMSE","Mean price","RMSE/price")) %>% cat(.,file= paste0(output, "performance_across_subsamples.tex")) options(knitr.kable.NA = NULL) ########################################## ######################################################################################### # # PART IV # HORSERACE: compare with other models ----------------------------------------------- # ######################################################################################### # OLS with dummies for area # using model B set.seed(1234) system.time({ ols_model <- train( formula(paste0("price ~", paste0(predictors_2, collapse = " + "))), data = data_train, method = "lm", trControl = train_control ) }) ols_model_coeffs <- ols_model$finalModel$coefficients ols_model_coeffs_df <- data.frame( "variable" = names(ols_model_coeffs), "ols_coefficient" = ols_model_coeffs ) %>% mutate(variable = gsub("`","",variable)) # * LASSO # using extended model w interactions set.seed(1234) system.time({ lasso_model <- train( formula(paste0("price ~", paste0(predictors_E, collapse = " + "))), data = data_train, method = "glmnet", preProcess = c("center", "scale"), tuneGrid = expand.grid("alpha" = 1, "lambda" = seq(0.01, 0.25, by = 0.01)), trControl = train_control ) }) lasso_coeffs <- coef( lasso_model$finalModel, lasso_model$bestTune$lambda) %>% as.matrix() %>% as.data.frame() %>% rownames_to_column(var = "variable") %>% rename(lasso_coefficient = `1`) # the column has a name "1", to be renamed lasso_coeffs_non_null <- lasso_coeffs[!lasso_coeffs$lasso_coefficient == 0,] regression_coeffs <- merge(ols_model_coeffs_df, lasso_coeffs_non_null, by = "variable", all=TRUE) regression_coeffs %>% write.csv(file = paste0(output, "regression_coeffs.csv")) # CART set.seed(1234) system.time({ cart_model <- train( formula(paste0("price ~", paste0(predictors_2, collapse = " + "))), data = data_train, method = "rpart", tuneLength = 10, trControl = train_control ) }) fancyRpartPlot(cart_model$finalModel, sub = "") # GBM ------------------------------------------------------- gbm_grid <- expand.grid(interaction.depth = c(1, 5, 10), # complexity of the tree n.trees = 250, # number of iterations, i.e. trees shrinkage = c(0.05, 0.1), # learning rate: how quickly the algorithm adapts n.minobsinnode = 20 # the minimum number of training set samples in a node to commence splitting ) set.seed(1234) system.time({ gbm_model <- train(formula(paste0("price ~", paste0(predictors_2, collapse = " + "))), data = data_train, method = "gbm", trControl = train_control, verbose = FALSE, tuneGrid = gbm_grid) }) gbm_model # much more tuning # faster, for testing #gbm_grid2 <- expand.grid(interaction.depth = c( 5, 7, 9, 11), # complexity of the tree # n.trees = (1:10)*50, # number of iterations, i.e. trees # shrinkage = c(0.05, 0.1), # learning rate: how quickly the algorithm adapts # n.minobsinnode = c(10,20) # the minimum number of training set samples in a node to commence splitting #) # # the next will be in final model, loads of tuning # gbm_grid2 <- expand.grid(interaction.depth = c(1, 3, 5, 7, 9, 11), # complexity of the tree # n.trees = (1:10)*50, # number of iterations, i.e. trees # shrinkage = c(0.02, 0.05, 0.1, 0.15, 0.2), # learning rate: how quickly the algorithm adapts # n.minobsinnode = c(5,10,20,30) # the minimum number of training set samples in a node to commence splitting # ) # set.seed(1234) # system.time({ # gbm_model2 <- train(formula(paste0("price ~", paste0(predictors_2, collapse = " + "))), # data = data_train, # method = "gbm", # trControl = train_control, # verbose = FALSE, # tuneGrid = gbm_grid2) # }) # gbm_model2 # and get prediction rmse and add to next summary table # ---- compare these models final_models <- list("OLS" = ols_model, "LASSO (model w/ interactions)" = lasso_model, "CART" = cart_model, "Random forest (smaller model)" = rf_model_1, "Random forest" = rf_model_2, # "Random forest (auto tuned)" = rf_model_2auto, "GBM (basic tuning)" = gbm_model # "GBM (broad tuning)" = gbm_model2 ) results <- resamples(final_models) %>% summary() # Save output -------------------------------------------------------- # Model selection is carried out on this CV RMSE result_4 <- imap(final_models, ~{ mean(results$values[[paste0(.y,"~RMSE")]]) }) %>% unlist() %>% as.data.frame() %>% rename("CV RMSE" = ".") kable(x = result_4, format = "latex", digits = 3, booktabs=TRUE, linesep = "") %>% cat(.,file= paste0(output,"horse_race_of_models_cv_rmse.tex")) # evaluate preferred model on the holdout set ----------------------------- RMSE(predict(rf_model_2, newdata = data_holdout), data_holdout[["price"]])

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#' Bayesian global model, on encapsule un modèle global générique dans la fonction #' @param tab_input mcmc list #' @param n_proj nombre d'annee de projection #' @examples #' #' #Les données d'entrée #' data(tableau_pa) #' #' resultats_pa<-delta (tab=tableau_pa, esp="BOBO",list_param=c("mois","zone","type_pirogue","motorisation","zone2","saison","engin_peche2"), type_donnee="commercial", effort="auto", titre="PA", param_test=c("mois","zone","type_pirogue","motorisation","zone2","saison","engin_peche2"), espece_id_list='espece', var_eff_list= c("nb_jour_peche", "nb_sorties"), ope_id=c("presence","code_pays","annee", "mois", "zone", "zone2" , "saison", "type_pirogue" , "motorisation" , "engin_peche", "engin_peche2"), col_capture='captures', logtrans="auto", interactions="auto", facteur_flotille="engin_peche2", seuil=0.05) #' pre_global_model_pa<-sqldf('select annee as Year,sum(i_ab) as C,avg("E.dens") as I from resultats_pa group by annee',drv='SQLite') #' bgm(pre_global_model_pa) #' @export bgm<- function (tab_input,min_c,max_c,Er,CVr,CV_Cobs,CV_Iobs,n_proj=0){ # Load the model # ---------------------------------------- # Load the model, written in a .txt file model_file<-paste0(path.package("demerstem"), "/model_BiomProd_WithScenario_JAGS.txt") source(model_file) # Write the model specification into a virtual text file with name "model", to be found by JAGS # The file "model" will be used by functions "jags.model" textConnection model <- textConnection(model_JAGS) # Load and format data # ----------------------------------------- n_obs <- length(tab_input$Year) Year <- tab_input$Year #Il manque 2014 à voir pourquoi ? (Dans les captures ?) # Needed to build the equilibrium curve B_e <- seq(from = 0, to = 1500, by = 50) n_equi <- length(B_e) # Format data as a list to be read in JAGS data <- list("I_obs" = tab_input$I, "C_obs" = tab_input$C, "n_obs" = n_obs, "n_proj" = n_proj, "B_e" = B_e, "n_equi" = n_equi, "Year"=Year,"min_c"=min_c,"max_c"=max_c,"Er"=Er,"CVr"=CVr,"CV_Cobs"=CV_Cobs,"CV_Iobs"=CV_Iobs) # MCMC options # ---------------------------------------------------------------------- n.chains = 3 # Adapt the MCMC samplers with n.adapt iterations n.adapt = 10000 # Iteration after adapting n.burnin <- 1000 n.stored <- 1000 n.thin <- 10 n.iter <- n.stored*n.thin # MANAGING NUMBER of ITERATIONS # (works for both para = T and para = F) # total number of REALIZED iterations = n.iter # total number of STORED iterations = n.iter/n.thin # Run the model to save MCMC results # --------------------------------------------------------------------- # Variable to store monitor <- c( "B", "h", "D", "C", "r", "r_p", "K", "K_p", "q", "sigma2p", "C_MSY", "C_MSY_p", "B_MSY", "h_MSY", "risk", "Over_C","Over_h", "C_e", "I_pred", "C_pred") # Compile the model, create a model object with the name "model.compiled.jags" and adapt the MCMC samplers # with n.adapt iterations print("adapting phase") model.compiled <- jags.model(file = model, data=data, n.chain=n.chains, n.adapt=n.adapt) # Iteration after adapting # Start to compute CPU time ptm <- proc.time() # Burnin period (not stored) print("burn-in") update(model.compiled, n.iter=n.burnin) # Store mcmc samples print("mcmc stored for results") mcmc <- coda.samples(model=model.compiled,variable.names=monitor,n.iter=n.iter,thin=n.thin) time.to.run.mcmc <- proc.time() - ptm print(time.to.run.mcmc) # ---------------------------------------------------------------------------------------------- # Run the model to compute DIC # --------------------------------------------------------------------- # Start from a compiled model that has already been updated dic.pD <- dic.samples(model.compiled, n.iter, "pD") dic.pD # Deviance Information Criterion # Alternative penalization of the Deviance # dic.popt <- dic.samples(model.compiled.jags, n.iter, "popt") # dic.popt # ----------------------------------------------------------------------- # -------------------------------------------- # Work with mcmc.list # -------------------------------------------- # "mcmc" is an object of the class "mcmc.list" (see package library(coda) # to explore, plot ... mcmc objects is(mcmc) # Names of the variables stored in the mcmc list varnames(mcmc) return(mcmc) }

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blblm.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/blblm.R \name{blblm} \alias{blblm} \title{main function to run linear regression with mini bag bootstrap} \usage{ blblm(formula, data, m = 10, B = 5000, cluster_size = 1) } \arguments{ \item{formula}{an object with class "formula"} \item{data}{a data frame or a vector of filenames} \item{m}{how many times we split the data} \item{B}{number of bootstrap performed} \item{cluster_size}{number of clusters} } \value{ blblm object } \description{ main function to run linear regression with mini bag bootstrap }

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error.HGgaussian.r

error.HGgaussian <- function (object, X, y, weights, fv, ...) { if (missing(fv)) r <- (y - object$fv) else r <- (y - fv) mean(r * r * weights) }

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sagarpa.R

library(tidyverse) library(lubridate) library(stringr) set.wd('../data/') municipal <- read_csv('../data/maiz_municipal_2004_2015.csv') municipal_1 <- municipal %>% gather(key = anio, value = precio, -Nomestado, -Idmunicipio, -Nommunicipio) %>% mutate(cve_mun = str_pad(Idmunicipio,width=3,pad='0',side='left'), precio_kg = precio/1000) %>% select(nom_ent_corto=Nomestado,cve_mun,nom_mun=Nommunicipio,anio,precio_kg) catalogo_entidad <- read_csv('../data/catalogo_entidades.csv') municipal_2 <- municipal_1 %>% left_join(catalogo_entidad, by = 'nom_ent_corto') %>% select(cve_ent, nom_ent, cve_mun, nom_mun, anio, precio_kg) %>% arrange(cve_ent, cve_mun) %>% mutate(precio_kg = ifelse(precio_kg < 0.30, NA, precio_kg), id = paste0(cve_ent,cve_mun), precio_kg_log = log(precio_kg)) write_csv(x = municipal_2, path = '../data/municipal_2.csv')

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library(ggplot2) library(dplyr) mtcars_scatter_abline <- function(xvar, yvar, xlab = xvar, ylab = yvar, ...){ plot(mtcars[[xvar]], mtcars[[yvar]], pch = 19, xlab = xlab, ylab = ylab, ...) abline(lm( mtcars[[yvar]] ~ mtcars[[xvar]])) } mtcars_scatter_abline("wt", "disp", ylab = "Displacement", main = "Weight vs. Displacement") scatter_mtcars <- function(xvar, yvar, data){ ggplot(data, aes(x = !!sym(xvar), y = !!sym(yvar))) + geom_point() } scatter_mtcars("disp", "wt", data = mtcars)

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globals.R

FTPROOT_REFPRICE <- "ftp://pubftp.spp.org/TCR/ReferencePrices" FTPROOT_DAPRICE <- "ftp://pubftp.spp.org/Markets/DA/LMP_By_SETTLEMENT_LOC" TEAColors <- c( "#0BC1AC", "#919191", "#87E8E7", "#D0272A", "#5FCE5B", "#535210", "#F55165", "#277DCE", "#531033", "#101153", "#FFAD5C", "#533110", "#0BC1AD", "#FF3D3D", "#D86F4B", "#0B7BC1", "#7BC10B", "#535210" ) getDfPathsFromPathList <- function(lstPaths) { dfPaths <- tibble::tibble(Source = purrr::map_chr(lstPaths, function(entry) entry[['Source']]), Sink = purrr::map_chr(lstPaths, function(entry) entry[['Sink']])) }

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NH_rt_mod12.R

setwd("/Users/michael/Data_Analysis/PD_Inhibition_DDM/Code/mlm/gng") if (!require(pacman)) { install.packages("pacman"); library(pacman) } p_load(car, brms, nlme, lme4, loo, readr, tidyverse, emmeans, cowplot, glmmTMB, bbmle, broom, MplusAutomation) # this is an admixture of Alison and Nate's code. # Load data # Data has already been cleaned and initial analyses completed by NH. # For RT analyses, use variable rt_log_trim_grp # For outcome analyses, use response -> this indicates whether they correctly Go vs No Go'd (can figure out whether they spressed space or not based on the stim) # Cond refers to number of prior go trials # Stim refers whether go or no go trial # Use trial_z rather than trial (for scaling purposes) # Will likely add in prev_rt in more complex analyses #basedir <- "~/github_repos/PD_Inhibition_DDM" basedir <- "~/Data_Analysis/PD_Inhibition_DDM" #NH add from GNG RT analyses gng <- read.csv(file.path(basedir, "Data/preprocessed/go_nogo_full_accCode.csv")) %>% mutate(bad_subj = if_else(subj_idx == 15, 1, 0)) #flagged 15 as bad as this was the one person who needed to be according to NH's preprocessing; will test how inclusion of 15 alters results gng <- mutate(gng, cond= factor(cond, levels = c("OneGo","ThreeGo", "FiveGo", "SevenGo"))) gng <- gng %>% group_by(subj_idx) %>% mutate( prev_rt = ifelse(lag(rt_log_trim_grp) == 0, NA, lag(rt_log_trim_grp)), prev_rt_z = scale(prev_rt), prev_error = ifelse(lag(response) == 1, 0, 1), id = as.character(subj_idx)) # gng_rt <- dplyr::filter(gng, stim == "Go", response == 1) #only analyze GO RTs! gng$cond_id <- with(gng, interaction(cond,id)) gng <- mutate(gng, block_trial_z = as.vector(scale(block_trial))) #%>% filter(response == 1, stim == "Go") # gng <- mutate(gng, block_trial_z = as.vector(scale(block_trial))) #pull self reps # fscores <- read.csv(paste0(basedir,"/Outputs/factor_structure/PDDDM_factor_scores_5-18-20.csv")) %>% rename(subj_idx = subject) fscores_update <- read.csv(file.path(basedir, "Code/factor_structure/AC_bsem_cfa_parcel_mod_savedata.csv")) %>% select(SUBJECT, DISINHIB.Median, ANTAG.Median) %>% rename(subj_idx = SUBJECT, DISINH = DISINHIB.Median, ANTAG = ANTAG.Median) #%>% mutate(DISINH = -1*DISINH) #reverse code Constraint to set in maladaptive direction. cor(fscores_update) #make sure scores positively correlate gng <- gng %>% left_join(fscores_update, by = "subj_idx") mpq <- get(load(file.path(basedir, "Data/preprocessed/MPQ_all_scored_final.RData"))) %>% filter(exclude_MPQ == 0) %>% select(subject, MPS_wbR:MPS_abR) snap <- get(load(file.path(basedir, "Data/preprocessed/SNAP_all_scored_final.RData"))) %>% filter(exclude_SNAP == 0) %>% select(subject, NEGTEMP:HDWK, DISINHP) k10 <- sas7bdat::read.sas7bdat(file.path(basedir, "Data/SAS Originals/k10.sas7bdat")) %>% dplyr::select(subject, k10Total) %>% dplyr::rename(K10=k10Total) stai <- sas7bdat::read.sas7bdat(file.path(basedir, "Data/SAS Originals/stai.sas7bdat")) %>% dplyr::select(subject, staitotal) %>% dplyr::rename(STAI=staitotal) self_reps <- inner_join(mpq, snap, by = "subject") self_reps <- left_join(self_reps, k10, by = "subject") self_reps <- left_join(self_reps, stai, by = "subject") #mpluscmd <- "/Users/natehall/Desktop/Mplus/mplus" mpluscmd <- "/Applications/Mplus/mplus" # RT analyses # block trial*trial and prev_rtXblock_trial effects on RT. homogonous covariance structure estimated per subject and variance estimated per cond. Random intercept and slope of trial and block_trial estmimated for each subject. Random slope of block trial estimated per condition within subject. # Winning from NH updates: Drop nesting of condition for simplicity, as in flanker_acc_traits # minfo[["m12"]] <- c(fixed="Trials from no-go, Trial number, Trials from no-go x Trial number", l2="Condition within subject: Trials from no-go\nSubject: Intercept, Trials from no-go, Trial") # # m12 <- lmer(rt_log_trim_grp ~ block_trial_z*trial_z + (1 + block_trial_z + trial_z| id) + (1 + block_trial_z | id:cond), # na.action = na.exclude, # data = gng_rt, control=lmerControl(optCtrl=list(maxfun=2e4)), REML = FALSE) #bump up iterations) # summary(m12) tofactor <- self_reps %>% select(subject, MPS_agR, AGG, MISTRUST, MPS_alR, MANIP, PROPER, MPS_clR, IMPUL, MPS_tdR, MPS_acR, HDWK, K10, STAI) %>% # mutate(IMPUL =-1*IMPUL) %>% #score toward constraint to make loadings upright. Update 6/25: reverse coding to scale positively with high externalizing mutate(PROPER = -1*PROPER, MPS_tdR = -1*MPS_tdR, #P2 MPS_clR = -1*MPS_clR, #P3 HDWK = -1*HDWK, MPS_acR = -1*MPS_acR ) %>% mutate_at(vars(-subject), list(~as.vector(scale(.)))) %>% dplyr::rename(id=subject) ggcorrplot::ggcorrplot(cor(tofactor %>% select(-id), use="pairwise")) #looks right for_msem <- gng %>% mutate(id = subj_idx) %>% inner_join(tofactor, by="id") %>% ungroup() %>% select(id, response, cond, stim, trial_z, rt_log_trim_grp, block_trial_z, prev_error, MPS_agR, AGG, MISTRUST, MPS_alR, MANIP, PROPER, MPS_clR, IMPUL, MPS_tdR, MPS_acR, HDWK, K10, STAI) %>% mutate(cond = ifelse(cond == "OneGo", 0, ifelse(cond == "ThreeGo", 1, ifelse(cond == "FiveGo", 2,3))), stim = ifelse(stim == "Go", 0,1)) head(for_msem) for_msem_rt <- for_msem %>% dplyr::filter(stim == 0 & response == 1) #only analyze GO RTs! lattice::histogram(~rt_log_trim_grp, for_msem_rt) #looks normal to me xtabs(~id, for_msem_rt) #all similar lattice::histogram(~block_trial_z, for_msem_rt) #okay lattice::histogram(~trial_z, for_msem_rt) #relatively uniform given that trial unfolds linearly # m12 <- lmer(rt_log_trim_grp ~ block_trial_z*trial_z + (1 + block_trial_z + trial_z| id) + (1 + block_trial_z | id:cond), ## MNH: Translate m12 to Mplus (no traits) rt_with_mod_gng <- mplusObject( TITLE = "GnG M12 Mplus", DEFINE = " t_ixn = block_trial_z*trial_z; !ixn of trials from no-go and overall trial; ", VARIABLE = " WITHIN = trial_z block_trial_z t_ixn; USEVARIABLES = id rt_log_trim_grp block_trial_z trial_z t_ixn; CLUSTER = id; ", ANALYSIS = " TYPE=TWOLEVEL RANDOM; ESTIMATOR=BAYES; BITERATIONS=(15000); CHAINS=4; PROCESSORS=4; ", MODEL = " %WITHIN% b_trial | rt_log_trim_grp ON trial_z; b_block_trial | rt_log_trim_grp ON block_trial_z; rt_log_trim_grp ON t_ixn; %BETWEEN% [b_trial b_block_trial]; !slope means b_trial b_block_trial; !slope variances !slope correlations b_trial b_block_trial WITH b_trial b_block_trial; [rt_log_trim_grp] (b0); !mean average log RT ", PLOT = "TYPE = PLOT2;", OUTPUT = "TECH1 TECH8 STANDARDIZED CINTERVAL;", rdata = for_msem_rt ) mout <- mplusModeler(rt_with_mod_gng, modelout="gng_m12_mplus.inp", run=TRUE, Mplus_command = mpluscmd, hashfilename = FALSE) # attempt 1, not looking too great. --------------------------------------- # rt_with_mod_gng <- mplusObject( # TITLE = "Antagonism, Disinhibition moderates gng random slopes and fixed effects interaction", # DEFINE = " # t_ixn = block_trial_z*trial_z; !ixn of trials from no-go and overall trial; # p1=MEAN(MISTRUST MPS_alR); # p2=MEAN(PROPER MPS_tdR); # p3=MEAN(MPS_clR IMPUL); # p4=MEAN(MPS_acR HDWK); # p5=MEAN(MPS_agR AGG); # ", # # VARIABLE = " # WITHIN = trial_z block_trial_z; # USEVARIABLES = id rt_log_trim_grp block_trial_z trial_z # MANIP p1 p2 p3 p4 p5 # t_ixn; # BETWEEN = MANIP p1 p2 p3 p4 p5; # # CLUSTER = id; # ", # ANALYSIS = " # TYPE=TWOLEVEL RANDOM; # ESTIMATOR=BAYES; # BITERATIONS=(15000); # BCONVERGENCE=.02; # CHAINS=2; # PROCESSORS=2; # ", # # # MODEL = " # %WITHIN% # # b_trial | rt_log_trim_grp ON trial_z; # b_block_trial | rt_log_trim_grp ON block_trial_z; # b_trial_ixn | rt_log_trim_grp ON t_ixn; # # # %BETWEEN% # [b_trial b_block_trial b_trial_ixn]; !slope means # b_trial b_block_trial b_trial_ixn; !slope variances # # !slope correlations # b_trial b_block_trial b_trial_ixn WITH # b_trial b_block_trial b_trial_ixn; # # [rt_log_trim_grp] (b0); !mean average log RT # ![t_ixn] (bixn); !mean ixn term at between subjects level # # # !TRAIT MODEL # # antag BY # p5* ! MPS_agR AGG # p1 ! MISTRUST MPS_alR # MANIP; # antag@1; # # disinhib BY # p2* !PROPER MPS_tdR # p3 !MPS_clR IMPUL # p4 !MPS_acR HDWK; # p1; # disinhib@1; # # antag WITH disinhib; # # !disinhib BY p1; !modification index cross-loading for fit # # !TRAIT MODERATION # # ! trait moderates flanker performance # b_block_trial ON antag (b_bA) # disinhib (b_bD); # # b_trial ON antag (t_bA) # disinhib (t_bD); # # b_trial_ixn ON antag (ixn_bA) # disinhib (ixn_bD); # # !N.B. Leaving out the association of antag with rt_inv # !omits a hugely important relationship. # !Thus, allow antag as a predictor of average (person) RT # # rt_log_trim_grp ON antag (bA) # disinhib (bD); # # ", # PLOT = "TYPE = PLOT2;", # OUTPUT = "TECH1 TECH8 STANDARDIZED CINTERVAL;", # rdata = for_msem_rt # ) # mout <- mplusModeler(rt_with_mod_gng, # modelout="rt_with_mod_gng.inp", run=TRUE, Mplus_command = mpluscmd, hashfilename = FALSE) # # # flextable(mout$results$parameters$stdyx.standardized %>% # filter(!paramHeader %in% c("Variances")) %>% # select(-sig, -posterior_sd) %>% # mutate(pval=2*pval, #convert to 2-tailed # pval=if_else(pval < .05, as.character(paste0(pval, "*")), as.character(pval))) # ) %>% autofit()# %>% save_as_docx(path = "~/Desktop/flanker_rt_traits.docx") # # wrong to fit ixn as random ---------------------------------------------- rt_with_mod_gng2 <- mplusObject( TITLE = "Antagonism, Disinhibition moderates gng random slopes and fixed effects interaction", DEFINE = " t_ixn = block_trial_z*trial_z; !ixn of trials from no-go and overall trial; p1=MEAN(MISTRUST MPS_alR); p2=MEAN(PROPER MPS_tdR); p3=MEAN(MPS_clR IMPUL); p4=MEAN(MPS_acR HDWK); p5=MEAN(MPS_agR AGG); CENTER trial_z block_trial_z t_ixn (GRANDMEAN); !make intercepts easy to understand ", VARIABLE = " WITHIN = trial_z block_trial_z t_ixn; USEVARIABLES = id rt_log_trim_grp block_trial_z trial_z MANIP p1 p2 p3 p4 p5 t_ixn; BETWEEN = MANIP p1 p2 p3 p4 p5; CLUSTER = id; ", ANALYSIS = " TYPE=TWOLEVEL RANDOM; ESTIMATOR=BAYES; BITERATIONS=(30000); CHAINS=4; PROCESSORS=4; ", MODEL = " %WITHIN% b_trial | rt_log_trim_grp ON trial_z; b_block_trial | rt_log_trim_grp ON block_trial_z; rt_log_trim_grp ON t_ixn; %BETWEEN% [b_trial b_block_trial]; !slope means b_trial b_block_trial; !slope variances !slope correlations b_trial b_block_trial WITH b_trial b_block_trial; [rt_log_trim_grp] (b0); !mean average log RT !TRAIT MODEL antag BY p5* ! MPS_agR AGG p1 ! MISTRUST MPS_alR MANIP; antag@1; disinhib BY p2* !PROPER MPS_tdR p3 !MPS_clR IMPUL p4 !MPS_acR HDWK p1; !cross-load disinhib@1; antag WITH disinhib; !TRAIT MODERATION ! trait moderates flanker performance b_block_trial ON antag (b_bA) disinhib (b_bD); b_trial ON antag (t_bA) disinhib (t_bD); !this would only make sense if we had meaningful between-person variation in !the interaction and were decomposing person emans of the interaction for analysis. !or, we could add a random slope of the interaction and model that here b_ixn ON ... !but as written, this doesn't make sense. !t_ixn ON antag (ixn_bA) ! disinhib (ixn_bD); !average RT on traits rt_log_trim_grp ON antag (bA) disinhib (bD); ", PLOT = "TYPE = PLOT2;", OUTPUT = "TECH1 TECH8 STANDARDIZED CINTERVAL;", rdata = for_msem_rt ) mout <- mplusModeler(rt_with_mod_gng2, modelout="rt_with_mod_gng2.inp", run=TRUE, Mplus_command = mpluscmd, hashfilename = FALSE) flextable(mout$results$parameters$stdyx.standardized %>% filter(!paramHeader %in% c("Variances")) %>% select(-sig, -posterior_sd) %>% mutate(pval=2*pval, #convert to 2-tailed pval=if_else(pval < .05, as.character(paste0(pval, "*")), as.character(pval))) ) %>% autofit()# %>% save_as_docx(path = "~/Desktop/flanker_rt_traits.docx") # try dropping ixn? ------------------------------------------------------- rt_with_mod_gng3 <- mplusObject( TITLE = "Antagonism, Disinhibition moderates gng random slopes and fixed effects interaction", DEFINE = " !t_ixn = block_trial_z*trial_z; !ixn of trials from no-go and overall trial; p1=MEAN(MISTRUST MPS_alR); p2=MEAN(PROPER MPS_tdR); p3=MEAN(MPS_clR IMPUL); p4=MEAN(MPS_acR HDWK); p5=MEAN(MPS_agR AGG); ", VARIABLE = " WITHIN = trial_z block_trial_z; USEVARIABLES = id rt_log_trim_grp block_trial_z trial_z MANIP p1 p2 p3 p4 p5; !t_ixn; BETWEEN = MANIP p1 p2 p3 p4 p5; CLUSTER = id; ", ANALYSIS = " TYPE=TWOLEVEL RANDOM; ESTIMATOR=BAYES; BITERATIONS=(15000); BCONVERGENCE=.02; CHAINS=2; PROCESSORS=2; ", MODEL = " %WITHIN% b_trial | rt_log_trim_grp ON trial_z; b_block_trial | rt_log_trim_grp ON block_trial_z; %BETWEEN% [b_trial b_block_trial]; !slope means b_trial b_block_trial; !slope variances !slope correlations b_trial b_block_trial WITH b_trial b_block_trial; [rt_log_trim_grp] (b0); !mean average log RT !TRAIT MODEL antag BY p5* ! MPS_agR AGG p1 ! MISTRUST MPS_alR MANIP; antag@1; disinhib BY p2* !PROPER MPS_tdR p3 !MPS_clR IMPUL p4 !MPS_acR HDWK; p1; disinhib@1; antag WITH disinhib; !disinhib BY p1; !modification index cross-loading for fit !TRAIT MODERATION ! trait moderates flanker performance b_block_trial ON antag (b_bA) disinhib (b_bD); b_trial ON antag (t_bA) disinhib (t_bD); !t_ixn ON antag (ixn_bA) ! disinhib (ixn_bD); !N.B. Leaving out the association of antag with rt_inv !omits a hugely important relationship. !Thus, allow antag as a predictor of average (person) RT rt_log_trim_grp ON antag (bA) disinhib (bD); ", PLOT = "TYPE = PLOT2;", OUTPUT = "TECH1 TECH8 STANDARDIZED CINTERVAL;", rdata = for_msem ) mout <- mplusModeler(rt_with_mod_gng3, modelout="rt_with_mod_gng3.inp", run=TRUE, Mplus_command = mpluscmd, hashfilename = FALSE) flextable(mout$results$parameters$stdyx.standardized %>% filter(!paramHeader %in% c("Variances")) %>% select(-sig, -posterior_sd) %>% mutate(pval=2*pval, #convert to 2-tailed pval=if_else(pval < .05, as.character(paste0(pval, "*")), as.character(pval))) ) %>% autofit()# %>% save_as_docx(path = "~/Desktop/flanker_rt_traits.docx") # ACCURACY ANALYSES ------------------------------------------------------- #winning model: m19 # fixed: stimulus, condition, trial, trialxstimulus # random: stimulus acc_with_mod_gng <- mplusObject( TITLE = "Antagonism, Disinhibition GNG ACC", DEFINE = " ts_ixn = stim*trial_z; !ixn of overall trial and stimulus; p1=MEAN(MISTRUST MPS_alR); p2=MEAN(PROPER MPS_tdR); p3=MEAN(MPS_clR IMPUL); p4=MEAN(MPS_acR HDWK); p5=MEAN(MPS_agR AGG); ", VARIABLE = " ! WITHIN = stim; !if my understanding is correct, this will only specify an effect at the within-level USEVARIABLES = id response stim cond trial_z MANIP p1 p2 p3 p4 p5 ts_ixn; BETWEEN = MANIP p1 p2 p3 p4 p5 cond trial_z ts_ixn; CLUSTER = id; ", ANALYSIS = " TYPE=TWOLEVEL RANDOM; ESTIMATOR=BAYES; BITERATIONS=(15000); BCONVERGENCE=.02; CHAINS=2; PROCESSORS=2; ", MODEL = " %WITHIN% b_stim | response ON stim; b_cond | response ON cond; b_trial | response ON trial_z b_ts_ixn | response ON ts_ixn %BETWEEN% [b_stim b_cond b_trial b_ts_ixn]; !slope means b_stim b_cond b_trial b_ts_ixn; !slope variances [response] (b0); !mean average ACC !TRAIT MODEL antag BY p5* ! MPS_agR AGG p1 ! MISTRUST MPS_alR MANIP; antag@1; disinhib BY p2* !PROPER MPS_tdR p3 !MPS_clR IMPUL p4 !MPS_acR HDWK; p1; disinhib@1; antag WITH disinhib; !disinhib BY p1; !modification index cross-loading for fit. Included above. !TRAIT MODERATION ! trait moderates flanker performance b_stim ON antag (b_bA) disinhib (b_bD); ts_ixn ON antag (ixn_bA) disinhib (ixn_bD); cond ON antag disinhib; !N.B. Leaving out the association of antag with rt_inv !omits a hugely important relationship. !Thus, allow antag as a predictor of average (person) RT response ON antag (bA) disinhib (bD); ", PLOT = "TYPE = PLOT2;", OUTPUT = "TECH1 TECH8 STANDARDIZED CINTERVAL;", rdata = for_msem ) mout <- mplusModeler(acc_with_mod_gng, modelout="acc_with_mod_gng.inp", run=TRUE, Mplus_command = mpluscmd, hashfilename = FALSE) flextable(mout$results$parameters$stdyx.standardized %>% filter(!paramHeader %in% c("Variances")) %>% select(-sig, -posterior_sd) %>% mutate(pval=2*pval, #convert to 2-tailed pval=if_else(pval < .05, as.character(paste0(pval, "*")), as.character(pval))) ) %>% autofit()# %>% save_as_docx(path = "~/Desktop/flanker_rt_traits.docx")

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/R/nyc311.R

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thisisdaryn/workshop

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nyc311.R

#' NYC 311 Service Requests for Jan 1-14 2020 #' #' #' #' @source <https://nycopendata.socrata.com/Social-Services/311-Service-Requests-from-2010-to-Present/erm2-nwe9> #' @format Data frame with columns #' \describe{ #' \item{Key}{Case identifier} #' \item{Created}{Date Created} #' \item{Closed}{Date Closed} #' \item{Agency}{NYC Agency Acronym} #' \item{Type}{Type of complaint} #' \item{LocType}{Type of location} #' \item{Zip}{ZIP code of complaint} #' \item{Status}{Complaint status: Pending, Assigned, Started, In Progress, Open or Closed } #' \item{Borough}{Borough of NYC} #' \item{Latitude}{Latitude} #' \item{Longitude}{Longitude} #' } "nyc311"

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/Lab1.R

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nguyench95/R-Labs

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Lab1.R

########################################################################## # CSC-315, Fall 2019 # Lab 1: R programming # Name: ########################################################################## ########################################################################## # Add R code to the script below in order to explicitly output the # answers to the following questions. Turn in this assignment # by creating a Notebook and turning in a physical copy. ########################################################################## #1) What is 34+29*12 34+29*12 #2) What is the sum of all integers between (and including) -100 and +100. index <- -100:100 sum(index) #3) Create a vector that contains the numbers 1-10. vector <- 1:10 #4) Change the 3rd value of the above vector to 99 vector[3] <- 99 #5) Create a vector that contains the numbers 1-10, 17, and 25. vector2 <- c(1:10, 17, 25) #6) Create a vector that contains two elements, your first name and last name name <- c("Christopher", "Nguyen") #7) Create a matrix that contains the numbers 87, 89, and 91 in the 1st row and # 76, 88, and 83 in the second row. Set or change the column names to # "ExamI", "ExamII", and "ExamIII", and set or change the row names to # "Joseph Smith" and "Amy Davis" m <- matrix(c(87,89,91,76,88,83),byrow= TRUE, nrow=2, dimnames = list(c("Joseph Smith", "Amy Davis"), c("ExamI", "ExamII", "ExamIII"))) #8) Calculate the average grade for Amy Davis, using the 'mean' function. avggrade <- mean(m[2,]) #9) "Joseph" prefers to be called "Joe". Change the name of the 1st row of the matrix # to "Joe Smith". You should do this in a statement that only changes the name of # the first row. Note that R allows you to directly assign a value to any element # (or elements) of rownames(m). rownames(m)[rownames(m)=="Joseph Smith"] <- "Joe Smith" #10) Create a list that includes the following objects: # (1) a vector that contains two elements, your first name and last name; # (2) your major person <- list(name <- c("Christopher", "Nguyen"), major = "Computer Science") #11) Read in the survey.txt file as was done in class (and put the code for this in your script!) d <- read.csv("https://gdancik.github.io/CSC-315/data/datasets/survey.txt") #12) How many individuals surveyed did not use FB (i.e., spent 0 hours / week on FB) colnames(d) # get names of columns summary(d) # summarizes each column; results depend on column type noFB <- d[d$FB == 0,] noFBcount <- length(noFB) print(noFBcount) #13) What are the GPAs of the three students with the lowest College GPAs (you # should only display these GPAs)? Hint: use the 'sort' function. sort <- d[order(College GPA),] threelowest <- (sort[length(sort)], sort[length(sort)-1, sort[length(sort)-2]] #14) What are the GPAs of the three students with the highest college GPAs? # Hint: use the sort function with decreasing = TRUE #15) Use the 'filter' function from 'dplyr' to create a data frame (tibble) # called surveyA that includes the students with a 3.5 college GPA or higher surveyA <- filter(d, College GPA >= 3.5) print(surveyA) #16) Display the 'Gender' column from surveyA and comment on the number of # individuals who are male, and the number who are female. females <- select(filter (surveyA, Gender == "Female")) males <- select(filter (surveyA, Gender == "Male"))

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/Laboratori di Esercitazione/Training Lezione/Training 1 - ML1.R

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AngelusGi/MachineLearningDivino

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Training 1 - ML1.R

#Simulazione di modello di Regressione lineare # MODELLO : Y=(B0+B1X)+E # n : dimensione training set # m : dimensione test set # X : reddito ( tra 1000 e 2000 ) # Y : consumi # A : B0<-250 #Costi fissi B1<-0.7 #70% del reddito va in consumi n<-80 #Su 100 dati 80 verranno usati per il training m<-20 #Il resto verrano usati per il test set.seed(1) #Fissiamo un seed comune per la generazione della stessa randomizzazione X<-rnorm(100, 1500, 100) # #Y<-B0+B1*x E<-rnorm(100,0,50) Y<-B0+B1*X+E #Modello A<-matrix(c(X,Y),100,2, byrow = F) #Immetto i dati pseudo-casuali in una matrice A<-as.data.frame(A) #ne faccio un data set colnames(A)<-c("reddito","consumi") #rinomino le colonne #plot(X,Y) A.training<-A[1:80,] #definisco le prime 80 come training #la virgola non � sbagliata, per sintassi sto definendo le righe #che ho scelto e non avendo messo parametri dopo la virgola #indico che sto prendendo tutte le colonne A.test<-A[81:100,] #le restanti come tes output.lm<-lm(consumi~reddito, data = A.training) #metto in output il risultato del linearmodel(lm) summary(output.lm) #resoconto output plot(A.training) abline(output.lm) #aggiungo al plot del grafico la linea lm che rappresenta la mia #regressione lineare names(output.lm) is.list(output.lm) #chiedo se output.lm � una lista output.lm$coefficients #stampa dei coefficienti res<-output.lm$residuals #metto i residui dentro res plot(res) #plot dei residui abline(h=0) hist(res) #istogramma #per valutare se un training � buono, si pu� fare l'analisi dei residui #pi� i residui sono simmetrici pi� il modello � buono #------------------------Parte di test----------------------# A.test plot(A.test) abline(output.lm) #mi calcolo i valori predetti dalla retta #utilizzo output.lm per dargli il modello calcolato con il training #ed i nuovi dati su cui testare la predizzione Y<-predict.lm(output.lm, A.test) #valori predetti Z<-A.test$consumi #valori osservati dei consumi X<-A.test$reddito plot(X,Y) abline(output.lm) sum((Z-Y)^2) #Valore utile a stabilire se una procedura � migliore di un'altra plot(Z,Y) abline(coef = c(0,1)) #---------------Capitolo 3 del libro--------------------# #Andare nei package e spuntare la libreria MASS data() #visualizzo tutti i dataset disponibili attach(Boston) #scelgo il dataset Boston #force("Boston") #se non lo ha caricatoBos #scrivere solo Boston per controllare se viusalizza tutti i dati #lm(formula = medv~. , data =Boston) output.lm<-lm(formula = medv ~ . , data = Boston ) summary(output.lm) #----------------Esercizio--------------------------------#0 #Dataset Boston #Primi 400 records come training #106 records per il test #utilizzando al massimo 5 variabili, trovare il miglior modello #per la scelta delle variabili, prestare attenzion alle ultime due colonne #prendo il #togliere i valori con il Pr(>|t|) pi� altro e senza stelline accanto output.lm<-lm(formula = medv ~ . -age-indus-chas-zn, data = Boston) summary(output.lm) #funzione di correlazione per sapere se due variabili sono dipendenti tra loro cor(Boston) #genera una matrice la quale mostra la correlazone tra le variabili della mtrice

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/R/calc_congress.R

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Hershberger/partycalls

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calc_congress.R

#' Calculate congress corresponding to a year #' #' Simple conversion between year and congress #' @param year integer year #' @return integer congress #' @export calc_congress <- function(year) { (year - 1788) / 2 }

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/source/Practice_set.1/step2.SCRAN_normalization_featureSelection.r

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mgood2/BIML-2019-SingleCellRNAseq-

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step2.SCRAN_normalization_featureSelection.r

# filteredSCEset <- readRDS(file = paste(PATH_PBMC_dataset, "filteredSCEset.rds", sep = "/")) filteredSCEset <- sce[, which(sce$filtering==1)] library(scran) clusters <- quickCluster(filteredSCEset, method="igraph") filteredSCEset <- computeSumFactors(filteredSCEset, cluster=clusters) filteredSCEset <- normalize(filteredSCEset) rownames(filteredSCEset@assays$data$logcounts) <- rownames(filteredSCEset) colnames(filteredSCEset@assays$data$logcounts) <- colnames(filteredSCEset) # plot(sizeFactors(filteredSCEset), (filteredSCEset$total_counts)/1000, log="xy", # ylab="Library size (kilo)", xlab = "Size factor") ### filtering genes library(Matrix) # keep_feature <- rowMeans(filteredSCEset@assays$data$logcounts)!=0 keep_feature <- rowSums((filteredSCEset@assays$data$logcounts) != 0) > 3 filteredSCEset <- filteredSCEset[keep_feature, ] ### Select Highly variable genes (feature selection) var.fit <- trendVar(filteredSCEset, parametric=TRUE, use.spikes=FALSE)#, design = batchDesign) var.out <- decomposeVar(filteredSCEset, var.fit) hvg <- var.out[which(var.out$FDR < 0.05 & var.out$bio > .01),] dim(hvg) saveRDS(filteredSCEset, file = "filteredSCEset.rds") saveRDS(hvg, file = "hvg.rds") plot(y= var.out$total, x=var.out$mean, pch=16, cex=0.3, ylab="Variance of log-expression", xlab="Mean log-expression") o <- order(var.out$mean) lines(y=var.out$tech[o], x=var.out$mean[o], col="dodgerblue", lwd=2) points(y=var.out$total[var.out$FDR <=0.05 & var.out$bio > 0.1], x=var.out$mean[var.out$FDR <=0.05 & var.out$bio > 0.1], pch=16, cex=0.3, col="red")

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/R/skill_scores.R

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laubblatt/cleaRskyQuantileRegression

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skill_scores.R

#' Collection of skill scores #' #' #' @filename skill_scores.R #' @export MSE_SkillScore #' @export rmse #' NULL MSE_SkillScore = function(o,p,ref) { #' Calc the Skill Score with the Mean Squared Error as skill #' #' Can be interpreted as reduction of variance #' Skill of 1 is perfect; Skill of 0 no improvement #' #' @version 0.1 2018-12 new simple fun used in BSRN clear sky study #' @references http://www.cawcr.gov.au/projects/verification/ #' @param o vector of observation #' @param p vector of prediction whose skill is evaluated #' @param ref the reference prediction (naive, prior etc) #' @value Skill score of MSE #' @details rows with missing values are removed # Skill score - Equation for equation for skill score # Answers the question: What is the relative improvement of the forecast over some reference forecast? # Range: Lower bound depends on what score is being used to compute skill and what reference forecast is used, # but upper bound is always 1; 0 indicates no improvement over the reference forecast. Perfect score: 1. # Characteristics: Implies information about the value or worth of a forecast relative to an alternative (reference) forecast. # In meteorology the reference forecast is usually persistence (no change from most recent observation) or climatology. # The skill score can be unstable for small sample sizes. # When MSE is the score used in the above expression then the resulting statistic is called the reduction of variance. dtopr = data.table(o,p,ref) dtnonan = na.omit(dtopr) dtnonan (MSEp = dtnonan[ , 1/.N * sum( (p -o)^2 )]) (MSEref = dtnonan[ , 1/.N * sum( (ref -o)^2 )]) (MSEp - MSEref) / ( 0 - MSEref) } rmse <- function(obs, pred) sqrt(mean((obs-pred)^2,na.rm=TRUE))

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/man/broad_zhenghe.Rd

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cran/SPMS

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broad_zhenghe.Rd

\name{broad_zhenghe} \alias{broad_zhenghe} %- Also NEED an '\alias' for EACH other topic documented here. \title{ To redundancy the text result. } \description{ Function according to the rule that we formulate to redundancy the result. } \usage{ broad_zhenghe(paths, dORb, inG) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{paths}{ The paths is fixed, which is set by setPath function at first. } \item{dORb}{ The parameter is convenient to users to choose to run depth or broad funtions. } \item{inG}{ According to the interest genes number(inG) in sub-pathway to limit the result. } } \value{ It returns the text result } \references{ None } \author{ Xiaomeng Ni } \examples{ ##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. #--broad_zhenghe(paths,"broad",3) ## The function is currently defined as function (paths, dORb, inG) { w <- sub(" ", "", paste(paths, "/temp")) setwd(w) if (length(which("resulttemp" == dir())) > 0) { n <- sub(" ", "", paste(w, "/resulttemp")) setwd(n) unlink(dir()) } else { dir.create("resulttemp") } setwd(paths) if (length(which("txtResult" == dir())) > 0) { n <- sub(" ", "", paste(paths, "/txtResult")) setwd(n) unlink(dir()) } else { dir.create("txtResult") } num <- 1 a <- sub(" ", "", paste(paths, "/temp/unUse")) setwd(a) for (z in 1:length(dir(a))) { if (length(dir(a)) == 0) break name <- substr(dir(a)[1], 1, 8) pathway <- dir(a)[1] a <- sub(" ", "", paste(paths, "/temp/unUse1")) setwd(a) result11 <- read.delim(pathway, sep = "\t", header = FALSE, quote = "", na.strings = "NA", stringsAsFactors = FALSE) unlink(pathway) b <- sub(" ", "", paste(paths, "/temp/unUse")) setwd(b) result10 <- read.table(pathway, sep = "\t", header = FALSE, na.strings = "NA", stringsAsFactors = FALSE) unlink(pathway) result1 <- result11 result <- result10 if (dim(result)[1] > 1) { for (i in 1:(dim(result)[1] - 1)) { for (j in (i + 1):dim(result)[1]) { if (result[i, 4] == result[j, 4] && result[i, 5] == result[j, 5]) { n <- c() m <- c() for (l in 4:(4 + result[i, 5] - 1)) { n <- c(n, as.character(result1[i, l])) } for (p in 4:(4 + result[j, 5] - 1)) { m <- c(m, as.character(result1[j, p])) } if (length(pmatch(n, m)) == result[i, 5] && length(pmatch(n, m)) == result[j, 5] && (is.na(sum(pmatch(n, m))) == FALSE)) { result[j, ] = 0 result1[j, ] = 0 } } } } } result <- unique(result) result1 <- unique(result1) if (dim(result)[1] > 1) { for (i in 1:(dim(result)[1] - 1)) { for (j in (i + 1):dim(result)[1]) { if (result[i, 4] == result[j, 4] && result[i, 5] <= result[j, 5]) { n <- c() m <- c() for (l in 10:(10 + result[i, 4] - 1)) { n <- c(n, as.character(result[i, l])) } for (p in 10:(10 + result[j, 4] - 1)) { m <- c(m, as.character(result[j, p])) } if (length(pmatch(n, m)) == result[i, 4] && (is.na(sum(pmatch(n, m))) == FALSE)) { result[j, ] = 0 result1[j, ] = 0 } } if (result[i, 4] == result[j, 4] && result[i, 5] >= result[j, 5]) { n <- c() m <- c() for (l in 10:(10 + result[i, 4] - 1)) { n <- c(n, as.character(result[i, l])) } for (p in 10:(10 + result[j, 4] - 1)) { m <- c(m, as.character(result[j, p])) } if (length(pmatch(m, n)) == result[j, 4] && (is.na(sum(pmatch(m, n))) == FALSE)) { result[i, ] = 0 result1[i, ] = 0 } } } } } result <- unique(result) result1 <- unique(result1) if (dim(result)[1] > 1) { for (i in 1:(dim(result)[1] - 1)) { for (j in (i + 1):dim(result)[1]) { if (result[i, 4] < result[j, 4] && ((result[i, 5] - result[i, 4]) == (result[j, 5] - result[j, 4]))) { n <- c() m <- c() for (l in 4:(4 + result[i, 5] - 1)) { n <- c(n, as.character(result1[i, l])) } for (p in 4:(4 + result[j, 5] - 1)) { m <- c(m, as.character(result1[j, p])) } if (length(pmatch(n, m)) == result[i, 5]) { result[i, ] = 0 result1[i, ] = 0 } } if (result[i, 4] > result[j, 4] && ((result[i, 5] - result[i, 4]) == (result[j, 5] - result[j, 4]))) { n <- c() m <- c() for (l in 4:(4 + result[i, 5] - 1)) { n <- c(n, as.character(result1[i, l])) } for (p in 4:(4 + result[j, 5] - 1)) { m <- c(m, as.character(result1[j, p])) } if (length(pmatch(m, n)) == result[j, 5]) { result[j, ] = 0 result1[j, ] = 0 } } } } } result <- unique(result) result1 <- unique(result1) for (i in 1:dim(result)[1]) { if (result[i, 4] < inG) { result[i, ] = 0 result1[i, ] = 0 } } result <- unique(result) result1 <- unique(result1) if (max(result[, 4]) == 0) next for (i in 1:dim(result)[1]) { mark1 <- c() for (j in 1:result[i, 4]) { mark1 <- c(mark1, result[i, (9 + j)]) } if (length(unique(mark1)) == 1) { result[i, ] = 0 result1[i, ] = 0 } else { mark2 <- c() mark2 <- unique(mark1) for (j in 1:length(mark2)) { k <- 0 k = which(mark1 == mark2[j]) if (length(k)/length(mark1) >= 0.5) { result[i, ] = 0 result1[i, ] = 0 break } } } } result <- unique(result) result1 <- unique(result1) for (i in 1:dim(result)[1]) { if (result[i, 1] == 0) { result <- result[-i, ] result1 <- result1[-i, ] break } } if (dim(result)[1] == 0) next for (i in 1:dim(result)[1]) { result[i, 2] = num result[i, 9] <- as.character(gsub(" ", "", paste("broad_subPathwayGraph(", "'", paths, "'", ",", i, ",", "'", name, "'", ")"))) num <- num + 1 } c <- sub(" ", "", paste(paths, "/txtResult")) setwd(c) write.table(result, "txtResult.txt", sep = "\t", col.names = FALSE, append = TRUE, row.names = FALSE, quote = TRUE) d <- sub(" ", "", paste(paths, "/temp/resulttemp")) setwd(d) write.table(result1, pathway, sep = "\t", col.names = FALSE, append = TRUE, row.names = FALSE, na = "NA", quote = FALSE) } n1 <- sub(" ", "", paste(paths, "/txtResult")) setwd(n1) if (length(dir(n1)) > 0) { gR <- scan("txtResult.txt", what = character(), sep = "\t", na.strings = "NA") unlink("txtResult.txt") k <- c() gResult1 <- data.frame() nn <- paste0(substr(gR[1], 1, (nchar(gR[1]) - 4))) k <- c(k, grep(nn, gR)) if (length(k) > 2) { for (i in 1:(length(k)/2 - 1)) { l <- 1 gResult2 <- matrix("0", 1, 50) for (j in k[2 * i - 1]:(k[2 * i + 1] - 1)) { gResult2[1, l] <- gR[j] l = l + 1 } gResult1 <- rbind(gResult1, gResult2) } l <- 1 gResult2 <- matrix("0", 1, 50) for (i in k[2 * (length(k)/2 - 1) + 1]:length(gR)) { gResult2[1, l] <- gR[i] l <- l + 1 } gResult1 <- rbind(gResult1, gResult2) } else { l <- 1 gResult2 <- matrix("0", 1, 50) for (i in k[2 * (length(k)/2 - 1) + 1]:length(gR)) { gResult2[1, l] <- gR[i] l <- l + 1 } gResult1 <- rbind(gResult1, gResult2) } gResult <- gResult1 gResult[, 1] = as.character(gResult[, 1]) list1 <- list() for (i in 1:dim(gResult)[1]) { k <- c() k <- which(gResult[i, 1] == gResult[, 1]) list1 <- c(list1, list(k = k)) } data <- unique(list1) for (i in 1:length(data)) { if (length(data[[i]]) == 1) { gResult[data[[i]], 1] = paste0(gResult[data[[i]], 1], "_", "1") } else { for (j in 1:length(data[[i]])) { gResult[data[[i]][j], 1] = paste0(gResult[data[[i]][j], 1], "_", (data[[i]][j] - data[[i]][1] + 1)) } } } gResult2 <- gResult m <- c() k <- 0 for (i in 10:dim(gResult2)[2]) { if (length(which(gResult2[, i] == 0)) == dim(gResult2)[1]) { m <- c(m, i) } } for (i in 1:length(m)) { gResult2 <- gResult2[, -(m[i] - k)] k = k + 1 } write.table(gResult2, "txtResult.txt", sep = "\t", col.names = FALSE, append = TRUE, row.names = FALSE, quote = TRUE) if (exists("gResult2") && dim(gResult2)[1] > 0) { return(gResult2) } else { print("Cannot find subpathway!") } } } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ pathway } \keyword{ zhenghe }% __ONLY ONE__ keyword per line

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factCheck 02 - get pages data.R

library(tidyverse) library(xml2) library(rvest) dataRAW <- read_rds(path = "data/factCheck/pagesScraped.rds") exclude <- read_rds(path = "data/factCheck/pageDetails.rds") %>% select(link) %>% distinct() dataRAW <- dataRAW %>% anti_join(exclude) for(i in 1:nrow(dataRAW)) { page <- read_html(dataRAW$link[i]) details <- page %>% html_nodes(".entry-content__text--smaller , .entry-content__button--smaller , .entry-content__text--explanation , .entry-title , strong , .entry-content__text--org") details %>% html_text() %>% enframe(value = "text") %>% left_join(details %>% html_attr("href") %>% enframe(value = "fullArticleUrl")) %>% mutate(link = dataRAW$link[i]) %>% bind_rows(read_rds("data/factCheck/pageDetails.rds")) %>% distinct() %>% write_rds(path = "data/factCheck/pageDetails.rds") print(paste(i,"###", dataRAW$link[i])) Sys.sleep(runif(n = 1, min = 0.3333, max = 3)) }

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ftpUpload = # # what is the name of the file or the contents of the file # function(what, to, asText = inherits(what, "AsIs") || is.raw(what), ..., curl = getCurlHandle()) { if(!asText && !inherits(what, "connection")) { file = file(what, "rb") on.exit(close(file)) } else file = what curlPerform(url = to, upload = TRUE, readfunction = uploadFunctionHandler(file, asText), ..., curl = curl) } uploadFunctionHandler = # # returns the function that is called as the READFUNCTION callback. # # This handles raw, character and file contents. # function(file, asText = inherits(file, "AsIs") || is.raw(file)) { if(asText) { pos = 1 isRaw = is.raw(file) len = if(isRaw) length(file) else nchar(file) function(size) { if(pos > len) return(if(isRaw) raw(0) else character(0)) ans = if(isRaw) file[seq(pos, length = size)] else substring(file, pos, pos + size) pos <<- pos + len ans } } else function(size) { readBin(file, raw(), size) } } CFILE = function(filename, mode = "r") { filename = path.expand(filename) .Call("R_openFile", filename, as.character(mode)) } setMethod("close", "CFILE", function(con, ...) { .Call("R_closeCFILE", con@ref, PACKAGE = "RCurl") con })

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analysis.R

#! /usr/bin/env Rscript #load libraries library(DBI) library(RSQLite) #set up database connection drv<-dbDriver("SQLite") database_location<-"../flamingo_database.db" db<-dbConnect(drv,database_location) #loadin data dbListTables(db) dbListFields(db,"allocator") allocator_sql<-"select 'ID' from 'allocator'" allocator_rs<-dbSendQuery(db,allocator_sql) allocator_rs #dbGetRowsAffected(allocator_rs) #dbColumnInfo(allocator_rs) #manipulate data #display data #close connection dbDisconnect(db)

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test-extend.R

context('extend') test_that('extend works', { expected_first_line <- '.message, .success, .error, .warning ' css <- compile_sass('test-extend.scss') expect_equal( strsplit(css, '\\{')[[1]][1], expected_first_line ) })

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h_file_proc.R

##@S This file contains function to aid with repeating file processing tasks ## (file-name processing, etc.) #' Find all files with appropriate file extensions and extract code #' #' @param FD [\code{\link{FilesDescription}}] :: A specification of codefiles #' #' @return [\code{\link{Codebase}}] :: Specification and container for codefiles #' and filenames #' #' @export #' extract_Codebase = function(FD) { files = find_files(FD = FD) code = extract_code(files) return(Codebase(files = files, code = code)) } #' Find all files #' #' See \code{\link{FilesDescription}} to see how to describe a file. #' #' @param FD [\code{\link{FilesDescription}}] :: A specification of codefiles #' #' @return [vector-char] :: A vector of filenames that match the given #' \code{\link{FilesDescription}} #' #' @export #' find_files = function(FD) { if (!inherits(x = FD, "FilesDescription")) { stop("Input class is not of class FilesDescription") } ## Find appropriate filename extension if (FD$mode == "R") { ext_regex = "[.]R$" } else if (FD$mode == "C") { ext_regex = "[.](c|cc|cpp|h|hh)$" } else if (FD$mode == "all") { ext_regex = "." } ## Start with exact files if any allfiles = FD$files ## Check file directories if any for(j in seq_along(FD$dirlist)) { temp = list.files(path = FD$dirlist[[j]]$dir, recursive = TRUE, full.names = TRUE) ## Find files with correct filename extension temp = temp[grep(ext_regex, temp)] ## Apply file_regex as appropriate if (!is.null(FD$dirlist[[j]]$file_regex)) { temp = temp[grep(file_regex, temp)] } allfiles = c(allfiles, temp) } ## Add files from file-listing allfiles = c(allfiles, c(FD$filelist, recursive = TRUE)) return(allfiles) } #' Extracts code for each file input #' #' @param files [vector-char] :: Filenames to extract code from #' #' @return [list-vector-char] :: A list of code read from each given input file #' #' @export #' extract_code = function(files) { res = list() for(j in seq_along(files)) { res[[j]] = readLines(files[j]) } return(res) }

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generate_sample.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/icdgenerator.R \name{generate_sample} \alias{generate_sample} \title{Generate Random ICD Data} \usage{ generate_sample(version = 10, nrows = 1, dcols = 1, pcols = 1, gcols = 0, pct_empty = 0.2, quiet = TRUE) } \arguments{ \item{version}{ICD version 9 or 10. Defaults to 10.} \item{nrows}{number of rows to generate} \item{dcols}{number of diagnostic codes to generate for each row} \item{pcols}{number of procedure codes to generate for each row} \item{gcols}{number of "other" columns to generate} \item{pct_empty}{the percentage of diagnostic and procedure codes to be "missing" in the resulting data frame.} \item{quiet}{If \code{FALSE}, then report incremental timing. To suppress incremental timing set \code{quiet = TRUE}.} } \description{ Create a data frame using randomly selected ICD diagnosis and procedure codes } \examples{ eg <- generate_sample( version = 9, nrows = 340, dcols = 29, pcols = 15, gcols = 114 ) head(eg) }

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retrieve_spectral_library.R

library(tidyverse) library(data.table) csv_vst = fread("~/Documents/GitHub/neonVegWrangleR/outdir/field_data/neon_vst_data_022021.csv") #%>% filter(height > 3) full_shaded_ids = csv_vst %>% filter(canopyPosition %in% c("Mostly shaded","Full shade") | !str_detect(plantStatus , "Live")) full_shaded_ids = full_shaded_ids %>% filter(!str_detect(plantStatus , "Live")) %>% select(individualID) %>% unique csv_vst = csv_vst %>% filter(canopyPosition %in% c("Full sun", "Open growth", "Partially shaded", NA)) %>% filter(str_detect(plantStatus , "Live")) %>% filter(!str_detect(growthForm,"shrub|sapling")) %>% #filter(!canopyPosition %in% c("Mostly shaded","Full shade")) %>% #filter out trees that have been identified as shaded in a year, and NA in another #filter(!individualID %in% full_shaded_ids) %>% group_by(individualID) %>% slice_max(order_by = height) %>%slice(1) csv_vst = csv_vst %>% filter(!individualID %in% full_shaded_ids$individualID) #csv_vst = csv_vst %>% filter(height > 3) %>% filter(stemDiameter > 5) # filter HSI from shade pixels, and apply BRDF and topographic corrections brick = data.table::fread("./data/indir/brdf_corrected_spectral_library_1920.csv") #brick = brick[,c(3,10:376)] brick = brick[complete.cases(brick),] brick = brick %>% filter(individualID %in% csv_vst$individualID) # brick = brick %>% filter(year == 2018) # brick = brick %>% group_by(site) %>% slice_max(year) # filter out points that are likely too short metadata = clean_typos_taxonID(csv_vst) metadata = remove_too_close_stems(metadata, stem_dist = 2) metadata = inner_join(metadata, brick) metadata$delta_chm = abs(metadata$height - metadata$CHM) metadata = metadata %>% group_by(individualID) %>% mutate(mean_dh = mean(delta_chm), id_dh = min(delta_chm)) metadata = metadata %>% filter(id_dh < 3) metadata$individualID %>% unique %>% length metadata = metadata %>% filter(!is.na(band_50)) ids = metadata %>% select(individualID, siteID) %>% unique # metadata = metadata %>% filter(individualID %in% ids) sites = ids$siteID %>% table %>% data.frame sites #sites = sites %>% filter(Freq >= 40) #metadata = metadata %>% filter(siteID %in% as.character(sites[[1]])) #write_csv(metadata, "./data/indir/metadata_classification.csv") #brick = brick %>% filter(individualID %in% metadata$individualID) #write_csv(brick, "./data/indir/spectra_ben_compare.csv") set.seed(0) noise = apply(metadata[,79:424], 1, function(x)any(x > 1 | x < 0)) metadata[noise,]=NA metadata = metadata %>% filter(band_192 <0.45) %>% filter(band_193 <0.45) %>% filter(band_150 < 0.9) %>% filter(band_199 < 0.4) %>% filter(band_262 < 0.27) %>% filter(band_271 < 0.38) %>% filter(band_272 < 0.38) %>% filter(band_277 < 0.45) %>% filter(band_325 < 0.3) %>% filter(band_358 < 0.25) %>% filter(band_359 < 0.3)%>% filter(band_62 < 0.2) metadata = metadata[complete.cases(metadata[,79:445]),] metadata = metadata[,-c(76:79)] # metadata[metadata$band_192 >0.25,] = NA # metadata = metadata[complete.cases(metadata[,79:424]),] # metadata[metadata$band_200:06 >0.37,] = NA # metadata = metadata[complete.cases(metadata[,79:424]),] # metadata[metadata$band_150 >0.9,] = NA #metadata_ = metadata %>% select(-one_of("band_193")) #metadata = clean_reflectance(metadata) metadata %>%select(contains("band")) %>% plot_spectra # #remove pixels with clear measurement errors # metadata = metadata[complete.cases(metadata[,79:424]),] # noise = apply(metadata[,79:424], 1, function(x)any(x > 0.15)) # metadata[noise,]=NA # metadata = metadata[complete.cases(metadata[,79:424]),] # noise = apply(metadata[,266:422], 1, function(x)any(x > 0.12)) # metadata[noise,]=NA # metadata = metadata[complete.cases(metadata[,79:424]),] # noise = apply(metadata[,146:236], 1, function(x)any(x < 0.28)) # metadata[noise,]=NA # metadata = metadata[complete.cases(metadata[,77:422]),] ids = metadata %>% select(individualID, siteID, taxonID) %>% unique # metadata = metadata %>% filter(individualID %in% ids) species_per_site = ids %>% ungroup %>% select(siteID, taxonID) %>% unique %>% ungroup %>% group_by(siteID) %>% mutate(species = n()) %>% select(-one_of("taxonID")) %>% unique sites = ids$siteID %>% table %>% data.frame sites # # train test split new_df_rfl = list() for(ii in unique(metadata$taxonID)){ taxa_foo = metadata %>% dplyr::filter(taxonID == ii) #%>% unique # refl_foo = taxa_foo %>% select(contains("band")) # if(nrow(taxa_foo)> 30){ # # #check range of spectra # max_ideal = apply(refl_foo[complete.cases(refl_foo),], # MARGIN = 2, function(x)quantile(x, 0.999)) # min_ideal = apply(refl_foo[complete.cases(refl_foo),], # MARGIN = 2, function(x)quantile(x, 0.001)) # # #filter for outliers: too bright # cnd = apply(refl_foo, 1,function(x)(x > max_ideal)) # idx <- (apply(data.frame(cnd), 2, any)) # if(length(idx) !=0){ # idx[is.na(idx)] = T # taxa_foo[idx,] = NA # } # #filter for outliers: too dark # cnd = apply(refl_foo, 1,function(x)(x < min_ideal)) # idx <- (apply(data.frame(cnd), 2, any)) # if(length(idx) !=0){ # idx[is.na(idx)] = T # taxa_foo[idx,] = NA # } # } # new_df_rfl[[ii]] = taxa_foo # #plot spectra of the species plt = taxa_foo %>% select(contains("band")) %>% plot_spectra ggsave(paste("./plots/", ii, ".jpg", sep=""), plot = plt) } new_df_rfl = do.call(rbind.data.frame, new_df_rfl) new_df_rfl = new_df_rfl %>% filter(!is.na(band_54)) new_df_rfl = metadata new_df_rfl = filter_too_rare_out(new_df_rfl, (new_df_rfl$individualID),min_id = 4) species_per_site = new_df_rfl %>% select(siteID, taxonID) %>% unique %>% ungroup %>% group_by(siteID) %>% mutate(species = n()) %>% select(-one_of("taxonID")) %>% unique # previous_split = data.table::fread("./data/metadata_custom_test.csv") # previous_split = previous_split %>% filter(groupID == "test") %>% # select(individualID, siteID,plotID, taxonID)%>% # unique # how to deal with the two years? get 2019 for new_df_rfl %>% ungroup %>% select(year, site) %>% table sites_2018 = new_df_rfl %>% filter(site %in% c("BLAN", "BONA", "GUAN", "GRSM","NIWO", "MLBS", "RMNP"))%>% filter(year == 2018) sites_2019 = new_df_rfl %>% filter(!site %in% c("BLAN", "BONA", "GUAN", "GRSM","NIWO", "MLBS", "RMNP"))%>% filter(year == 2019) new_df_rfl = rbind.data.frame(sites_2018, sites_2019) new_df_rfl = new_df_rfl %>% data.frame %>% filter(delta_chm < 3) set.seed(1987) remove_sites = new_df_rfl %>% select(siteID, taxonID, individualID) %>% unique remove_sites$siteID %>% table new_df_rfl_ = new_df_rfl %>% filter(!siteID %in% c("YELL", "MOAB", "KONZ", "ONAQ", "GRSM", "HEAL")) unique_entries = new_df_rfl_ %>% group_by(individualID)%>% slice(1) train = unique_entries %>% group_by(siteID, taxonID) %>% sample_frac(0.12) train = new_df_rfl_ %>% filter(plotID %in% unique(train$plotID)) test = new_df_rfl_ %>% filter(!individualID %in% unique(train$individualID)) train$groupID = "train" test$groupID = "test" test = test %>% filter(siteID %in% unique(train$siteID)) test = test %>% filter(taxonID %in% unique(train$taxonID)) test$individualID %>% unique %>% length train$individualID %>% unique %>% length train_check = train %>% select(individualID, taxonID) %>% unique %>% ungroup %>% select(taxonID) %>% table %>% sort test_check = test %>% select(individualID, taxonID) %>% unique %>% ungroup %>% select(taxonID) %>% table %>% sort test_check %>% sort train_check %>% sort test$taxonID %>% unique %>% length train$taxonID %>% unique %>% length test$siteID %>% unique %>% length train$siteID %>% unique %>% length new_sp = rbind(train, test) #remove taxa that have less than 4 individuals in the training set #new_sp = new_sp %>% filter(!taxonID %in% c("GYDI", "QUFA")) new_sp = new_sp %>% filter(!taxonID %in% c("PODE3", "QULA3", "AIAL", "QUSH", "ACSA2")) #write_csv(new_sp, "./data/metadata_2021_no_correction.csv") new_sp = new_sp %>% group_by(individualID) %>% slice_min(n=8, order_by = delta_chm) new_sp %>%ungroup %>% filter(groupID == "train") %>% select(taxonID) %>% table %>% sort new_sp[,-c(336:341)] %>% select(contains("band")) %>% plot_spectra #new_sp %>% select(paste("band", 275:279, sep="_")) %>% plot_spectra new_sp = write_csv(new_sp[c(1:78, 425)], "/Volumes/Data/Spectral_library/metadata_1819_feb.csv") write_csv(new_sp[c(14, 79:424)], "/Volumes/Data/Spectral_library/brdf_spectra_1819_feb.csv") # train$taxonID %>% table %>% sort # PICO PIPA2 new_sp %>% filter(taxonID == "ACRU") %>% select(individualID) %>% unique %>% nrow() ACRU = new_sp %>% filter(taxonID == "ACRU") %>% group_by(individualID) %>% slice_sample(n=3) ACSA3 = new_sp %>% filter(taxonID == "ACSA3") %>% group_by(individualID) %>% slice_min(n=5, order_by = delta_chm) PSME = new_sp %>% filter(taxonID == "PSME") %>% group_by(individualID) %>% slice_min(n=4, order_by = delta_chm) PIMA = new_sp %>% filter(taxonID == "PIMA") %>% group_by(individualID) %>% slice_min(n=3, order_by = delta_chm) POTR5 = new_sp %>% filter(taxonID == "POTR5") %>% group_by(individualID) %>% slice_min(n=3, order_by = delta_chm) PIPA2 = new_sp %>% filter(taxonID == "PIPA2") %>% group_by(individualID) %>% slice_min(n=3, order_by = delta_chm) PICO = new_sp %>% filter(taxonID == "PICO") %>% group_by(individualID) %>% slice_min(n=6, order_by = delta_chm) new_sp_ = new_sp %>% filter(!taxonID %in% c("ACSA3", "ACRU", "PSME", "PIMA", "POTR5", "PICO", "PIPA2")) # new_sp_ = rbind.data.frame(new_sp_, ACSA3, ACRU, PSME, PIMA, POTR5,PICO, PIPA2) foo = new_sp_ %>% data.frame %>%filter(groupID == "train") foo$taxonID %>% table %>% sort write_csv(new_sp_[c(1:75, 442)], "/Volumes/Data/Spectral_library/metadata_1819_feb.csv") write_csv(new_sp_[c(14, 79:424)], "/Volumes/Data/Spectral_library/brdf_spectra_1819_feb.csv") # new_sp %>% filter(taxonID == "ACSA3") %>% # select(individualID) %>% unique %>% nrow() # ACSA3 = new_sp %>% filter(taxonID == "ACSA3") %>% # group_by(individualID) %>% slice_min(n=4, order_by = delta_h)%>% # filter(delta_h < 1) # # # new_sp %>% filter(taxonID == "MEPO5") %>% # filter(delta_h < 1)%>% select(individualID) %>% unique %>% nrow() # MEPO5 = new_sp %>% filter(taxonID == "MEPO5") %>% # group_by(individualID) %>% slice_min(n=4, order_by = delta_h) %>% # filter(delta_h < 1) # # # new_sp %>% filter(taxonID == "POTR5") %>% # select(individualID) %>% unique %>% nrow() # POTR5 = new_sp %>% filter(taxonID == "POTR5") %>% # group_by(individualID) %>% slice_min(n=4, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "PSME") %>% # select(individualID) %>% unique %>% nrow() # PSME = new_sp %>% filter(taxonID == "PSME") %>% # group_by(individualID) %>% slice_min(n=4, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "PITA") %>% # select(individualID) %>% unique %>% nrow() # PITA = new_sp %>% filter(taxonID == "PITA") %>% # group_by(individualID) %>% slice_min(n=6, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "PIPA2") %>% # select(individualID) %>% unique %>% nrow() # PIPA2 = new_sp %>% filter(taxonID == "PIPA2") %>% # filter(band_96 > 0.07) %>% # group_by(individualID) %>% slice_min(n=3, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "PIMA") %>% # select(individualID) %>% unique %>% nrow() # PIMA = new_sp %>% filter(taxonID == "PIMA") %>% # #filter(band_96 > 0.07) %>% # group_by(individualID) %>% slice_min(n=3, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "PICO") %>% # select(individualID) %>% unique %>% nrow() # PICO = new_sp %>% filter(taxonID == "PICO") %>% # group_by(individualID) %>% slice_min(n=5, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "PIEN") %>% # select(individualID) %>% unique %>% nrow() # PIEN = new_sp %>% filter(taxonID == "PIEN") %>% # group_by(individualID) %>% slice_min(n=6, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "QUST") %>% # select(individualID) %>% unique %>% nrow() # QUST = new_sp %>% filter(taxonID == "QUST") %>% # group_by(individualID) %>% slice_min(n=6, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "QURU") %>% # select(individualID) %>% unique %>% nrow() # QURU = new_sp %>% filter(taxonID == "QURU") %>% # group_by(individualID) %>% slice_min(n=6, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "TSCA") %>% # select(individualID) %>% unique %>% nrow() # TSCA = new_sp %>% filter(taxonID == "TSCA") %>% # group_by(individualID) %>% slice_min(n=6, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "LITU") %>% # select(individualID) %>% unique %>% nrow() # LITU = new_sp %>% filter(taxonID == "LITU") %>% # group_by(individualID) %>% slice_min(n=6, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp_ = new_sp %>% filter(!taxonID %in% c("ACSA3", "ACRU", "QUST", "PIEN", # "MEPO5", "POTR5", "LITU", "QURU", # "PICO", "PIMA", "PIPA2", "PITA", "PSME", "TSCA")) # # new_sp_ = rbind.data.frame(new_sp_, ACSA3, ACRU, QUST, PIEN,MEPO5, POTR5,PICO, # PIMA, PIPA2, PITA, PSME, QURU, TSCA, LITU) # tmp_ids = max(ids_per_taxa$Freq) #*2 # few_pix_per_id=list() # for(sp in unique(train$taxonID)){ # tmp = new_sp %>% filter(taxonID == sp) # #tmp_ids = max(ids_per_taxa$Freq) # few_pix_per_id[[sp]]=tmp %>% # group_by(individualID) %>% slice_sample(n=ceiling(tmp_ids/nrow(tmp))) # # } # few_pix_per_id = do.call(rbind.data.frame, few_pix_per_id) # few_pix_per_id = rbind.data.frame(few_pix_per_id, test) # # ACRU = new_sp %>% filter(taxonID == "ACRU") %>% # group_by(individualID) %>% slice_sample(n=5) # POTR5 = new_sp %>% filter(taxonID == "POTR5") %>% # group_by(individualID) %>% slice_sample(n=4) # ACSA3 = new_sp %>% filter(taxonID == "ACSA3") %>% # group_by(individualID) %>% slice_sample(n=5) # PIMA = new_sp %>% filter(taxonID == "PIMA") %>% # group_by(individualID) %>% slice_sample(n=3) # PIPA2 = new_sp %>% filter(taxonID == "PIPA2") %>% # group_by(individualID) %>% slice_sample(n=3) # QUST = new_sp %>% filter(taxonID == "QUST") %>% # group_by(individualID) %>% slice_sample(n=10) # PSME = new_sp %>% filter(taxonID == "PSME") %>% # group_by(individualID) %>% slice_sample(n=4) # ABLAL = new_sp %>% filter(taxonID == "ABLAL") %>% # group_by(individualID) %>% slice_sample(n=12) # PICO = new_sp %>% filter(taxonID == "PICO") %>% # group_by(individualID) %>% slice_sample(n=5) # PIEN = new_sp %>% filter(taxonID == "PIEN") %>% # group_by(individualID) %>% slice_sample(n=10) # PITA = new_sp %>% filter(taxonID == "PITA") %>% # group_by(individualID) %>% slice_sample(n=14) # TSCA = new_sp %>% filter(taxonID == "TSCA") %>% # group_by(individualID) %>% slice_sample(n=10) # # replaced_over_sampled = rbind.data.frame(PIMA,ACRU, POTR5, ACSA3, # PIPA2, QUST, PSME, ABLAL, PICO, # PIEN, PITA) # foo = new_sp %>% filter(!taxonID %in% unique(replaced_over_sampled$taxonID)) # foo = rbind.data.frame(replaced_over_sampled, foo) # write_csv(new_sp_, "./data/metadata_1920.csv") # write_csv(new_sp_[c(14, 81:422)], "./data/brdf_spectra_1920.csv") # # species_classification_data = me_ %>% filter(individualID %in% metadata$individualID) # # train = species_classification_data %>% group_by(siteID, taxonID) %>% sample_frac(0.0) # train = species_classification_data %>% filter(plotID %in% unique(train$plotID)) # test = species_classification_data %>% filter(!plotID %in% unique(train$plotID)) # train$groupID = "train" # test$groupID = "test" # test = test %>% filter(siteID %in% unique(train$siteID)) # new_sp = rbind(train, test) # # # write_csv(new_sp, "~/Documents/Data/Field_surveys/VST/data_for_classification.csv") # # f$individualID %>% unique tmp_dat = fread("/Volumes/Data/Spectral_library/metadata_1819.csv") tmp_dat_ = tmp_dat %>% filter(!individualID %in% full_shaded_ids$individualID) tmp_dat.train = tmp_dat_ %>% filter(groupID == "train") tmp_dat.test = tmp_dat_ %>% filter(groupID == "test") tmp_dat.train$taxonID %>% table %>% sort tmp_dat.test$taxonID %>% unique %>% length write_csv(tmp_dat_, "/Volumes/Data/Spectral_library/metadata_1819_feb.csv") tmp_dat = fread("/Volumes/Data/Spectral_library/brdf_spectra_1819.csv") tmp_dat_ = tmp_dat %>% filter(!individualID %in% full_shaded_ids$individualID) write_csv(tmp_dat_, "/Volumes/Data/Spectral_library/brdf_spectra_1819_feb.csv") library(tidyverse) library(data.table) csv_vst = fread("~/Documents/GitHub/neonVegWrangleR/outdir/field_data/neon_vst_data_022021.csv") #%>% filter(height > 3) full_shaded_ids = csv_vst %>% filter(canopyPosition %in% c("Mostly shaded","Full shade") | !str_detect(plantStatus , "Live")) full_shaded_ids = full_shaded_ids %>% filter(!str_detect(plantStatus , "Live")) %>% select(individualID) %>% unique csv_vst = csv_vst %>% filter(canopyPosition %in% c("Full sun", "Open growth", "Partially shaded", NA)) %>% filter(str_detect(plantStatus , "Live")) %>% filter(!str_detect(growthForm,"shrub|sapling")) %>% #filter(!canopyPosition %in% c("Mostly shaded","Full shade")) %>% #filter out trees that have been identified as shaded in a year, and NA in another #filter(!individualID %in% full_shaded_ids) %>% group_by(individualID) %>% slice_max(order_by = height) %>%slice(1) csv_vst = csv_vst %>% filter(!individualID %in% full_shaded_ids$individualID) #csv_vst = csv_vst %>% filter(height > 3) %>% filter(stemDiameter > 5) # filter HSI from shade pixels, and apply BRDF and topographic corrections brick = data.table::fread("./data/indir/brdf_corrected_spectral_library_1920.csv") #brick = brick[,c(3,10:376)] brick = brick[complete.cases(brick),] brick = brick %>% filter(individualID %in% csv_vst$individualID) # brick = brick %>% filter(year == 2018) # brick = brick %>% group_by(site) %>% slice_max(year) # filter out points that are likely too short metadata = clean_typos_taxonID(csv_vst) metadata = remove_too_close_stems(metadata, stem_dist = 2) metadata = inner_join(metadata, brick) metadata$delta_chm = abs(metadata$height - metadata$CHM) metadata = metadata %>% group_by(individualID) %>% mutate(mean_dh = mean(delta_chm), id_dh = min(delta_chm)) metadata = metadata %>% filter(id_dh < 3) metadata$individualID %>% unique %>% length metadata = metadata %>% filter(!is.na(band_50)) ids = metadata %>% select(individualID, siteID) %>% unique # metadata = metadata %>% filter(individualID %in% ids) sites = ids$siteID %>% table %>% data.frame sites #sites = sites %>% filter(Freq >= 40) #metadata = metadata %>% filter(siteID %in% as.character(sites[[1]])) #write_csv(metadata, "./data/indir/metadata_classification.csv") #brick = brick %>% filter(individualID %in% metadata$individualID) #write_csv(brick, "./data/indir/spectra_ben_compare.csv") set.seed(0) noise = apply(metadata[,79:424], 1, function(x)any(x > 1 | x < 0)) metadata[noise,]=NA metadata = metadata[complete.cases(metadata[,79:424]),] metadata = metadata %>% filter(band_192 <0.45) %>% filter(band_193 <0.45) %>% filter(band_150 < 0.9) %>% filter(band_199 < 0.4) %>% filter(band_262 < 0.27) %>% filter(band_271 < 0.38) %>% filter(band_272 < 0.38) %>% filter(band_277 < 0.45) %>% filter(band_325 < 0.3) %>% filter(band_358 < 0.25) %>% filter(band_359 < 0.3) # metadata[metadata$band_192 >0.25,] = NA # metadata = metadata[complete.cases(metadata[,79:424]),] # metadata[metadata$band_200:06 >0.37,] = NA # metadata = metadata[complete.cases(metadata[,79:424]),] # metadata[metadata$band_150 >0.9,] = NA #metadata_ = metadata %>% select(-one_of("band_193")) #metadata = clean_reflectance(metadata) #metadata[,80:120] %>%select(contains("band")) %>% plot_spectra # #remove pixels with clear measurement errors # metadata = metadata[complete.cases(metadata[,79:424]),] # noise = apply(metadata[,79:424], 1, function(x)any(x > 0.15)) # metadata[noise,]=NA # metadata = metadata[complete.cases(metadata[,79:424]),] # noise = apply(metadata[,266:422], 1, function(x)any(x > 0.12)) # metadata[noise,]=NA # metadata = metadata[complete.cases(metadata[,79:424]),] # noise = apply(metadata[,146:236], 1, function(x)any(x < 0.28)) # metadata[noise,]=NA # metadata = metadata[complete.cases(metadata[,77:422]),] ids = metadata %>% select(individualID, siteID, taxonID) %>% unique # metadata = metadata %>% filter(individualID %in% ids) species_per_site = ids %>% ungroup %>% select(siteID, taxonID) %>% unique %>% ungroup %>% group_by(siteID) %>% mutate(species = n()) %>% select(-one_of("taxonID")) %>% unique sites = ids$siteID %>% table %>% data.frame sites # how to deal with the two years? get 2019 for new_df_rfl %>% select(year, site) %>% table sites_2018 = metadata %>% filter(site %in% c("BLAN", "BONA", "GUAN", "GRSM","NIWO", "MLBS", "RMNP"))%>% filter(year == 2018) sites_2019 = metadata %>% filter(!site %in% c("BLAN", "BONA", "GUAN", "GRSM","NIWO", "MLBS", "RMNP"))%>% filter(year == 2019) metadata = rbind.data.frame(sites_2018, sites_2019) # # train test split new_df_rfl = list() for(ii in unique(metadata$taxonID)){ taxa_foo = metadata %>% data.frame %>% dplyr::filter(taxonID == ii) #%>% unique refl_foo = taxa_foo %>% select(contains("band")) if(nrow(taxa_foo)> 4){ pix_ind = apply(refl_foo, 2, function(x){ out = boxplot.stats(x)$out pix_ind = which(x %in% c(out)) pix_ind }) for(bnd in ncol(refl_foo)){ taxa_foo[pix_ind[[bnd]],] = NA } # # #check range of spectra # max_ideal = apply(refl_foo[complete.cases(refl_foo),], MARGIN = 2, function(x)quantile(x, 0.99)) min_ideal = apply(refl_foo[complete.cases(refl_foo),], MARGIN = 2, function(x)quantile(x, 0.01)) #filter for outliers: too bright cnd = apply(refl_foo, 1,function(x)(x > max_ideal)) idx <- (apply(data.frame(cnd), 2, any)) if(length(idx) !=0){ idx[is.na(idx)] = T taxa_foo[idx,] = NA } #filter for outliers: too dark cnd = apply(refl_foo, 1,function(x)(x < min_ideal)) idx <- (apply(data.frame(cnd), 2, any)) if(length(idx) !=0){ idx[is.na(idx)] = T taxa_foo[idx,] = NA } } new_df_rfl[[ii]] = taxa_foo #plot spectra of the species taxa_foo = taxa_foo %>% filter(!is.na(band_54)) plt = taxa_foo %>% select(contains("band")) %>% plot_spectra ggsave(paste("./plots/taxa/", ii, ".jpg", sep=""), plot = plt) } new_df_rfl = do.call(rbind.data.frame, new_df_rfl) new_df_rfl = new_df_rfl %>% filter(!is.na(band_54)) # remove the clear outliers from species new_df_rfl = filter_too_rare_out(new_df_rfl, (new_df_rfl$individualID),min_id = 5) species_per_site = new_df_rfl %>% select(siteID, taxonID) %>% unique %>% ungroup %>% group_by(siteID) %>% mutate(species = n()) %>% select(-one_of("taxonID")) %>% unique # previous_split = data.table::fread("./data/metadata_custom_test.csv") # previous_split = previous_split %>% filter(groupID == "test") %>% # select(individualID, siteID,plotID, taxonID)%>% # unique new_df_rfl = new_df_rfl %>% filter(delta_chm < 3) remove_sites = new_df_rfl %>% select(siteID, taxonID, individualID) %>% unique remove_sites$siteID %>% table new_df_rfl_ = new_df_rfl %>% filter(!siteID %in% c("YELL", "MOAB", "KONZ", "ONAQ", "GRSM", "HEAL")) set.seed(1987) unique_entries = new_df_rfl_ %>% group_by(individualID)%>% slice(1) set.seed(0) train = unique_entries %>% group_by(siteID, taxonID) %>% sample_frac(0.09) train = new_df_rfl_ %>% filter(plotID %in% unique(train$plotID)) test = new_df_rfl_ %>% filter(!individualID %in% unique(train$individualID)) train$groupID = "train" test$groupID = "test" test = test %>% filter(siteID %in% unique(train$siteID)) test = test %>% filter(taxonID %in% unique(train$taxonID)) test$individualID %>% unique %>% length train$individualID %>% unique %>% length train_check = train %>% select(individualID, taxonID) %>% unique %>% ungroup %>% select(taxonID) %>% table %>% sort test_check = test %>% select(individualID, taxonID) %>% unique %>% ungroup %>% select(taxonID) %>% table %>% sort test_check %>% sort train_check %>% sort test$taxonID %>% unique %>% length train$taxonID %>% unique %>% length test$siteID %>% unique %>% length train$siteID %>% unique %>% length new_sp = rbind(train, test) #remove taxa that have less than 4 individuals in the training set new_sp = new_sp %>% filter(!taxonID %in% c("GUOF", "NYBI", "PISA2", "QUMI")) #write_csv(new_sp, "./data/metadata_2021_no_correction.csv") new_sp = new_sp %>% group_by(individualID) %>% slice_min(n=6, order_by = delta_chm) new_sp %>%ungroup %>% filter(groupID == "train") %>% select(taxonID) %>% table %>% sort #new_sp %>% select(paste("band", 275:279, sep="_")) %>% plot_spectra # write_csv(new_sp[c(1:75, 425)], "/Volumes/Data/Spectral_library/metadata_1819_feb.csv") # write_csv(new_sp[c(14, 79:424)], "/Volumes/Data/Spectral_library/brdf_spectra_1819_feb.csv") # train$taxonID %>% table %>% sort # PICO PIPA2 new_sp %>% filter(taxonID == "ACRU") %>% select(individualID) %>% unique %>% nrow() ACRU = new_sp %>% filter(taxonID == "ACRU") %>% group_by(individualID) %>% slice_sample(n=3) ACSA3 = new_sp %>% filter(taxonID == "ACSA3") %>% group_by(individualID) %>% slice_min(n=5, order_by = delta_chm) PSME = new_sp %>% filter(taxonID == "PSME") %>% group_by(individualID) %>% slice_min(n=4, order_by = delta_chm) PIMA = new_sp %>% filter(taxonID == "PIMA") %>% group_by(individualID) %>% slice_min(n=3, order_by = delta_chm) POTR5 = new_sp %>% filter(taxonID == "POTR5") %>% group_by(individualID) %>% slice_min(n=3, order_by = delta_chm) PIPA2 = new_sp %>% filter(taxonID == "PIPA2") %>% group_by(individualID) %>% slice_min(n=3, order_by = delta_chm) PICO = new_sp %>% filter(taxonID == "PICO") %>% group_by(individualID) %>% slice_min(n=6, order_by = delta_chm) new_sp_ = new_sp %>% filter(!taxonID %in% c("ACSA3", "ACRU", "PSME", "PIMA", "POTR5", "PICO", "PIPA2")) # new_sp_ = rbind.data.frame(new_sp_, ACSA3, ACRU, PSME, PIMA, POTR5,PICO, PIPA2) foo = new_sp_ %>% data.frame %>%filter(groupID == "train") foo$taxonID %>% table %>% sort write_csv(new_sp_[c(1:75, 442)], "/Volumes/Data/Spectral_library/metadata_1819_feb.csv") write_csv(new_sp_[c(14, 76:438)], "/Volumes/Data/Spectral_library/brdf_spectra_1819_feb.csv") # new_sp_ = cbind.data.frame(fread("/Volumes/Data/Spectral_library/metadata_1819_feb.csv"), # fread("/Volumes/Data/Spectral_library/brdf_spectra_1819_feb.csv")) for(ii in unique(new_sp_$taxonID)){ taxa_foo = new_sp_ %>% data.frame %>% dplyr::filter(taxonID == ii) #%>% unique plt = taxa_foo %>% select(contains("band")) %>% plot_spectra ggsave(paste("./plots/taxa/clean_", ii, ".jpg", sep=""), plot = plt) } # new_sp %>% filter(taxonID == "ACSA3") %>% # select(individualID) %>% unique %>% nrow() # ACSA3 = new_sp %>% filter(taxonID == "ACSA3") %>% # group_by(individualID) %>% slice_min(n=4, order_by = delta_h)%>% # filter(delta_h < 1) # # # new_sp %>% filter(taxonID == "MEPO5") %>% # filter(delta_h < 1)%>% select(individualID) %>% unique %>% nrow() # MEPO5 = new_sp %>% filter(taxonID == "MEPO5") %>% # group_by(individualID) %>% slice_min(n=4, order_by = delta_h) %>% # filter(delta_h < 1) # # # new_sp %>% filter(taxonID == "POTR5") %>% # select(individualID) %>% unique %>% nrow() # POTR5 = new_sp %>% filter(taxonID == "POTR5") %>% # group_by(individualID) %>% slice_min(n=4, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "PSME") %>% # select(individualID) %>% unique %>% nrow() # PSME = new_sp %>% filter(taxonID == "PSME") %>% # group_by(individualID) %>% slice_min(n=4, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "PITA") %>% # select(individualID) %>% unique %>% nrow() # PITA = new_sp %>% filter(taxonID == "PITA") %>% # group_by(individualID) %>% slice_min(n=6, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "PIPA2") %>% # select(individualID) %>% unique %>% nrow() # PIPA2 = new_sp %>% filter(taxonID == "PIPA2") %>% # filter(band_96 > 0.07) %>% # group_by(individualID) %>% slice_min(n=3, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "PIMA") %>% # select(individualID) %>% unique %>% nrow() # PIMA = new_sp %>% filter(taxonID == "PIMA") %>% # #filter(band_96 > 0.07) %>% # group_by(individualID) %>% slice_min(n=3, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "PICO") %>% # select(individualID) %>% unique %>% nrow() # PICO = new_sp %>% filter(taxonID == "PICO") %>% # group_by(individualID) %>% slice_min(n=5, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "PIEN") %>% # select(individualID) %>% unique %>% nrow() # PIEN = new_sp %>% filter(taxonID == "PIEN") %>% # group_by(individualID) %>% slice_min(n=6, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "QUST") %>% # select(individualID) %>% unique %>% nrow() # QUST = new_sp %>% filter(taxonID == "QUST") %>% # group_by(individualID) %>% slice_min(n=6, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "QURU") %>% # select(individualID) %>% unique %>% nrow() # QURU = new_sp %>% filter(taxonID == "QURU") %>% # group_by(individualID) %>% slice_min(n=6, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "TSCA") %>% # select(individualID) %>% unique %>% nrow() # TSCA = new_sp %>% filter(taxonID == "TSCA") %>% # group_by(individualID) %>% slice_min(n=6, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp %>% filter(taxonID == "LITU") %>% # select(individualID) %>% unique %>% nrow() # LITU = new_sp %>% filter(taxonID == "LITU") %>% # group_by(individualID) %>% slice_min(n=6, order_by = delta_h) %>% # filter(delta_h < 1) # # new_sp_ = new_sp %>% filter(!taxonID %in% c("ACSA3", "ACRU", "QUST", "PIEN", # "MEPO5", "POTR5", "LITU", "QURU", # "PICO", "PIMA", "PIPA2", "PITA", "PSME", "TSCA")) # # new_sp_ = rbind.data.frame(new_sp_, ACSA3, ACRU, QUST, PIEN,MEPO5, POTR5,PICO, # PIMA, PIPA2, PITA, PSME, QURU, TSCA, LITU) # tmp_ids = max(ids_per_taxa$Freq) #*2 # few_pix_per_id=list() # for(sp in unique(train$taxonID)){ # tmp = new_sp %>% filter(taxonID == sp) # #tmp_ids = max(ids_per_taxa$Freq) # few_pix_per_id[[sp]]=tmp %>% # group_by(individualID) %>% slice_sample(n=ceiling(tmp_ids/nrow(tmp))) # # } # few_pix_per_id = do.call(rbind.data.frame, few_pix_per_id) # few_pix_per_id = rbind.data.frame(few_pix_per_id, test) # # ACRU = new_sp %>% filter(taxonID == "ACRU") %>% # group_by(individualID) %>% slice_sample(n=5) # POTR5 = new_sp %>% filter(taxonID == "POTR5") %>% # group_by(individualID) %>% slice_sample(n=4) # ACSA3 = new_sp %>% filter(taxonID == "ACSA3") %>% # group_by(individualID) %>% slice_sample(n=5) # PIMA = new_sp %>% filter(taxonID == "PIMA") %>% # group_by(individualID) %>% slice_sample(n=3) # PIPA2 = new_sp %>% filter(taxonID == "PIPA2") %>% # group_by(individualID) %>% slice_sample(n=3) # QUST = new_sp %>% filter(taxonID == "QUST") %>% # group_by(individualID) %>% slice_sample(n=10) # PSME = new_sp %>% filter(taxonID == "PSME") %>% # group_by(individualID) %>% slice_sample(n=4) # ABLAL = new_sp %>% filter(taxonID == "ABLAL") %>% # group_by(individualID) %>% slice_sample(n=12) # PICO = new_sp %>% filter(taxonID == "PICO") %>% # group_by(individualID) %>% slice_sample(n=5) # PIEN = new_sp %>% filter(taxonID == "PIEN") %>% # group_by(individualID) %>% slice_sample(n=10) # PITA = new_sp %>% filter(taxonID == "PITA") %>% # group_by(individualID) %>% slice_sample(n=14) # TSCA = new_sp %>% filter(taxonID == "TSCA") %>% # group_by(individualID) %>% slice_sample(n=10) # # replaced_over_sampled = rbind.data.frame(PIMA,ACRU, POTR5, ACSA3, # PIPA2, QUST, PSME, ABLAL, PICO, # PIEN, PITA) # foo = new_sp %>% filter(!taxonID %in% unique(replaced_over_sampled$taxonID)) # foo = rbind.data.frame(replaced_over_sampled, foo) # write_csv(new_sp_, "./data/metadata_1920.csv") # write_csv(new_sp_[c(14, 81:422)], "./data/brdf_spectra_1920.csv") # # species_classification_data = me_ %>% filter(individualID %in% metadata$individualID) # # train = species_classification_data %>% group_by(siteID, taxonID) %>% sample_frac(0.0) # train = species_classification_data %>% filter(plotID %in% unique(train$plotID)) # test = species_classification_data %>% filter(!plotID %in% unique(train$plotID)) # train$groupID = "train" # test$groupID = "test" # test = test %>% filter(siteID %in% unique(train$siteID)) # new_sp = rbind(train, test) # # # write_csv(new_sp, "~/Documents/Data/Field_surveys/VST/data_for_classification.csv") # # f$individualID %>% unique tmp_dat = fread("/Volumes/Data/Spectral_library/metadata_1819.csv") tmp_dat_ = tmp_dat %>% filter(!individualID %in% full_shaded_ids$individualID) tmp_dat.train = tmp_dat_ %>% filter(groupID == "train") tmp_dat.test = tmp_dat_ %>% filter(groupID == "test") tmp_dat.train$taxonID %>% table %>% sort tmp_dat.test$taxonID %>% unique %>% length write_csv(tmp_dat_, "/Volumes/Data/Spectral_library/metadata_1819_feb.csv") tmp_dat = fread("/Volumes/Data/Spectral_library/brdf_spectra_1819.csv") tmp_dat_ = tmp_dat %>% filter(!individualID %in% full_shaded_ids$individualID) write_csv(tmp_dat_, "/Volumes/Data/Spectral_library/brdf_spectra_1819_feb.csv")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/functions.R \name{getPatientDataStartEnd} \alias{getPatientDataStartEnd} \title{This function fetches all the patient data (generic) - from a given start date and with a given end date} \usage{ getPatientDataStartEnd(connection, dbms, patient_ids, startDate, endDate, flags, schema, removeDomains = c("")) } \arguments{ \item{connection}{The connection to the database server.} \item{dbms}{The target DBMS for SQL to be rendered in.} \item{patient_ids}{The list of case patient id's to extract data from - NOT a data.frame.} \item{startDate}{The start index date for all patients} \item{endDate}{The end date to fetch data from patients} \item{flags}{The R dataframe that contains all feature/model flags specified in settings.R.} \item{schema}{The database schema being used.} \item{removeDomains=''}{List of domains to not include as features, if any are specified in settings file} } \value{ An object containing the raw feature sets for the patient data. } \description{ This function fetches all the patient data (generic). Returns raw patient data. } \details{ Based on the groups of feature sets determined in the flags variable, this function will fetch patient data within the specified time range the function returns all patient information } \examples{ \dontrun{ patientData <- getPatientDataStartEnd(conn, dbms, patient_ids, start_dates, end_dates, flag , cdmSchema) } }

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\name{EDF_NS.test} \alias{EDF_NS.test} \title{ GoF tests based on the empirical distribution function, the normalized spacings and the probability plots for the Exponential distribution } \description{ Computes the Exponential GoF tests based on the empirical distribution function: the Kolmogorov-Smirnov (KS), Cramer-Von-Mises (CM) and Anderson-Darling (AD) tests, the tests based on the probability plot: Shapiro-Wilk (SW) and Patwardhan (PA) tests and the tests based on the normalized spacings: Gnedenko (Gn) and Gini (G) tests. } \usage{ EDF_NS.test(x, type = "AD", nsim = 200) } \arguments{ \item{x}{a numeric vector of data values.} \item{type}{the type of the test statistic used. "AD" is the default used test of Anderson-Darling,"KS" for Kolmogorov-Smirnov, "CM" for Cramer-Von-Mises, "SW" for Shapiro-Wilk, "PA" for Patwardhan, "Gn" for Gnedenko and "G" for Gini test statistic.} \item{nsim}{an integer specifying the number of replicates used in Monte Carlo.} } \details{ This function computes the GoF test statistics of three different families: the tests based on the empirical distribution function, the tests based on the probability plots and the tests based on the normalized spacings. The p-value of the tests is computed using Monte-Carlo simulations because only the asymptotic distribution of the previous statistics is known. Therefore the tests can be applied to small samples. } \value{An object of class htest.} \references{ \itemize{ \item D'Agostino R.B. and Stephens M.A., \emph{Goodness-of-fit techniques}, Marcel Dekker, 1986. \item Gail M.H. and Gastwirth J.L., A scale-free goodness-of-fit test for the exponential distribution based on the Gini statistic, \emph{Journal of the Royal Statistical Society, Series B}, 40, 350-357, 1978. \item Gnedenko B.V., Belyayev Y.K. and Solovyev A.D., \emph{Mathematical Models of Reliability Theory}, Academic Press, 1969. \item Shapiro S.S. and Wilk M.B., An analysis of variance test for the exponential distribution (complete samples), \emph{Technometrics}, 14, 355-370, 1972. \item Patwardhan G., Tests for exponentiality, \emph{Communications in Statistics, Theory and Methods}, 17, 3705-3722, 1988. } } \author{ Meryam KRIT} \examples{ x1 <- rexp(50,2) #Apply the Kolmogorov-Smirnov test EDF_NS.test(x1,type="KS") x2 <- rlnorm(50,0.2) #Apply the Patwardhan test EDF_NS.test(x2,type="PA") #Apply the Cramer-von Mises test EDF_NS.test(x2,type="CM") #Apply the Gini test EDF_NS.test(x2,type="G") } \keyword{Empirical distribution function} \keyword{Gini} \keyword{Gndenko} \keyword{Shapiro-Wilk} \keyword{Patwardhan}

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library(dplyr) read_power<-read.table("household_power_consumption.txt",sep=";",header=TRUE,stringsAsFactors=FALSE) power<-mutate(read_power,NDate=paste(Date,Time)) power$NDate<-as.POSIXct(power$NDate,format="%d/%m/%Y %H:%M:%S") powerf<-filter(power,NDate<as.POSIXct("2007-02-03",format="%Y-%m-%d") & NDate>=as.POSIXct("2007-02-01",format="%Y-%m-%d")) powerf$Sub_metering_1<-as.numeric(powerf$Sub_metering_1) powerf$Sub_metering_2<-as.numeric(powerf$Sub_metering_2) powerf$Voltage<-as.numeric(powerf$Voltage) powerf$Global_active_power<-as.numeric(powerf$Global_active_power) powerf$Global_reactive_power<-as.numeric(powerf$Global_reactive_power) png(file="plot4.png") par(mfrow=c(2,2),mar=c(4,4,2,1),cex=0.7) with(powerf,plot(NDate,Global_active_power,type="l",xlab="",ylab="Global Active Power")) with(powerf,plot(NDate,Voltage,type="l",xlab="datetime",ylab="Voltage")) with(powerf,plot(NDate,Sub_metering_1,col="green",xlab="",ylab="Energy sub metering",type="l")) with(powerf,lines(NDate,Sub_metering_2,col="red")) with(powerf,lines(NDate,Sub_metering_3,col="blue")) legend("topright",col=c("green","red","blue"),lty=c("solid","solid","solid"),legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3")) with(powerf,plot(NDate,Global_reactive_power,type="l",xlab="datetime",ylab="Global Reactive Power")) dev.off()

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################################################### ### code chunk number 32: Cs22_hood-q3 ################################################### Q.models$hood.independent

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# Write a function called rankhospital that takes three arguments: the 2-character abbreviated name of a # state (state), an outcome (outcome), and the ranking of a hospital in that state for that outcome (num). # The function reads the outcome-of-care-measures.csv file and returns a character vector with the name # of the hospital that has the ranking specified by the num argument. For example, the call ## Function identifies the hospital that matches the mortality rate rank provided for ## a given outcome ## for the defined state rankhospital <- function(state, outcome, num = "best") { ## "state" is a character vector identifying the state scope of hospitals ## "outcome" is a character vector for the outcome scope ## "num" is the character vector identifying the rank that should be returned. ## default is "best" ## Function returns a character vector for the name of the hospital with the ## defined mortality rate ranking ## check if outcome is valid vOutcome <- c("heart attack", "heart failure", "pneumonia") if (!outcome %in% vOutcome){ stop("invalid outcome") } ## end of if ## Read outcome data if (nchar(state)==2) { outData <- read.csv("outcome-of-care-measures.csv", na.strings = "Not Available") } else { stop("invalid state") } ## end of if ## Check that state is valid if (state %in% outData$State){ if (outcome=="heart failure"){ ## mortality rate from heart failure is column 17 ds <- outData[,c(2,7,17)] } else if (outcome=="heart attack") { ## mortality rate from heart attack is column 11 ds <- outData[,c(2,7,11)] } else { ## mortality rate from pneumonia is column 23 ds <- outData[,c(2,7,23)] } ## end of if }else { stop("invalid state") } ## end of if ## order dataframe ds <- na.omit(ds[order(ds$State, ds[ ,3], ds$Hospital.Name), ]) ## split data by state s <- split(ds, ds$State) ## set rank number if(num=="worst"){ n <- nrow(s[[state]]) } else if (num=="best") { n <- 1 } else { n <- as.numeric(num) } ## end of if ## get the list of hospitals h <- lapply(s, function(y) y[n,1]) hName = as.character(h[state]) ## Return hospital name in that state with lowest 30-day death rate hName }

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/37810 assignment 2.R

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37810 assignment 2.R

#37810 Assignment 2_Yi Jin step 6 source('~/GitHub/assignment-2-KumamonYJ/function.set.R') # set coefficient A trueA <- 5 # set coefficient B trueB <- 0 # set standard error trueSd <- 10 # set sample size sampleSize <- 31 # create independent x-values x <- (-(sampleSize-1)/2):((sampleSize-1)/2) # create dependent values according to ax + b + N(0,sd) y <- trueA * x + trueB + rnorm(n=sampleSize,mean=0,sd=trueSd) # plot picture of x and y, with title "Test Data" plot(x,y, main="Test Data") # apply function slopevalues to the sequence slopelikelihoods <- lapply(seq(3, 7, by=.05), slopevalues ) # plot the sequence and the corresponding outcomes of function slopevalues plot (seq(3, 7, by=.05), slopelikelihoods , type="l", xlab = "values of slope parameter a", ylab = "Log likelihood") # assign the startvalue startvalue = c(4,0,10) # apply run_metropolis_MCMC function to the startvalue and iterate for 10000 times chain = run_metropolis_MCMC(startvalue, 10000) # assign burn-in time burnIn = 5000 # delete the first burn-in time rows of chain, then calculate the acceptance rate of the remaining rows of chain. acceptance = 1-mean(duplicated(chain[-(1:burnIn),])) compare_outcomes=function(iteration){ # assign the startvalue m=vector(length=10) std=vector(length=10) for(i in 1:10){ beta1=rnorm(1,5,1) beta0=0 se=rnorm(1,10,1) startvalue = c(beta1,beta0,se) # apply run_metropolis_MCMC function to the startvalue and iterate for 10000 times chain = run_metropolis_MCMC(startvalue, iteration) m[i]=mean(chain[,1]) std[i]=sd(chain[,1]) } return(c(m,std)) } #print("outcome of compare_outcomes(1000)") #compare_outcomes(1000) #print("outcome of compare_outcomes(10000)") #compare_outcomes(10000) #print("outcome of compare_outcomes(100000)") #compare_outcomes(100000) #The first ten numbers are the mean and the last ten are std of the values in the chain for a

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/old_models/discrete_time_models/SEIRS-discrete-time-gamma.R

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SEIRS-discrete-time-gamma.R

### Dynamics of SARS-CoV-2 with waning immunity ### Thomas Crellen thomas.crellen@bdi.ox.ac.uk, April 2020 ### SEIRS discrete time model, gamma (Erlang) distributed waiting times #Clear R environment remove(list = ls()) #libraries require(testthat) #parameters sigma_recip <- 4.5 #Average duration of latent period (days) [From He, X., et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med (2020). https://doi.org/10.1038/s41591-020-0869-5] gamma_recip <- 3.07 #Average duration of infectiousness (days) [From He, X., et al.] R0 <- 2.8 #Basic reproduction number (in the absense of interventions) [From Petra] beta <- R0*(1/gamma_recip) #Transmission parameter omega_recip <- 90 #Average duration of immunity (days) [user specified] m <- 4 #Shape paremeter, latent period n <- 2 #Shape parameter, infectious period o <- 2 #Shape parameter, immune period dt <- 0.25 #time period #vectors pars <- c(beta=beta, sigma=1/sigma_recip, gamma=1/gamma_recip, omega=1/omega_recip, m=m, n=n, o=o) time <- seq(1, 300, by=dt) # State variables yini <- c(S=0.5, E1=0, E2=0, E3=0, E4=0, I1=0.5, I2=0, R1=0, R2=0) #Initial population size S = E1 = E2 = E3 = E4 = I1 = I2 = R1 = R2 = N = numeric(length(time)) #Pars beta = pars["beta"] sigma = pars["sigma"] gamma = pars["gamma"] omega = pars["omega"] m = pars["m"] n = pars["m"] o = pars["o"] S[1] = yini["S"] E1[1] = yini["E1"] E2[1] = yini["E2"] E3[1] = yini["E3"] E4[1] = yini["E4"] I1[1] = yini["I1"] I2[1] = yini["I2"] R1[1] = yini["R1"] R2[1] = yini["R2"] N[1] = S[1] + E1[1] + E2[1] + E3[1] + E4[1] + I1[1] + I2[1] + R1[1] + R2[1] for(i in 1:(length(time)-1)){ t = time[i] S[(i+1)] = S[i] + omega*dt*R2[i]*o - beta*dt*S[i]*(I1[i]+I2[i]) E1[(i+1)] = E1[i] + beta*dt*S[i]*(I1[i]+I2[i]) - E1[i]*sigma*dt*m E2[(i+1)] = E2[i] + sigma*dt*m*(E1[i] - E2[i]) E3[(i+1)] = E3[i] + sigma*dt*m*(E2[i] - E3[i]) E4[(i+1)] = E4[i] + sigma*dt*m*(E3[i] - E4[i]) I1[(i+1)] = I1[i] + E4[i]*sigma*dt*m - I1[i]*gamma*dt*n I2[(i+1)] = I2[i] + gamma*dt*n*(I1[i]-I2[i]) R1[(i+1)] = R1[i] + I2[i]*gamma*dt*n - omega*dt*R1[i]*o R2[(i+1)] = R2[i] + omega*dt*o*(R1[i] - R2[i]) #Check population size (N) = 1 N[(i+1)] = S[(i+1)] + E1[(i+1)] + E2[(i+1)] + E3[(i+1)] + E4[(i+1)] + I1[(i+1)] + I2[(i+1)] + R1[(i+1)] + R2[(i+1)] test_that("Pop size (N) = 1", expect_equal(N[(i+1)], 1)) } #Store as data.frame out <- data.frame(time=time, S=S, E=E1+E2+E3+E4, I=I1+I2, R=R1+R2) #plot with(out, plot(S~time, type="l")) with(out, plot(E~time, type="l")) with(out, plot(I~time, type="l")) with(out, plot(R~time, type="l"))

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/R/reformat_fun.R

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reformat_fun.R

################################################### # regularize_comments <- function(fun) { # env <- environment(fun) # fun <- deparse(fun) # #fun <- gsub("(\\s*`#`\$\")(.*?)\\\"\$$","\\2", fun) # fun <- gsub("(\\s*)`#`\$\"(\\s*)(.*?)\\\"\$$","\\1\\3", fun) # fun <- gsub("\\\\n","\n",fun) # eval(parse(text=paste(fun, collapse = "\n"))[[1]],envir = env) # } # unnest_comments <- function(call) { # if(!is.call(call) || identical(call[[1]], quote(`function`))) { # return(call) # } # # call0 <- lapply(call, function(x) { # call_str <- paste(deparse(x), collapse ="\n") # if(startsWith(call_str, "`#`(")){ # x <- list(extract_comment(x), # clean_call(x)) # } # x # }) # call <- as.call(unlist(call0)) # call[] <- lapply(call, unnest_comments) # call # } # # # # helper for unnest_comments # extract_comment <- function(call){ # if(!is.call(call)) { # return(NULL) # } # if(identical(call[[1]], quote(`#`))){ # return(call[1:2]) # } # unlist(lapply(call, extract_comment))[[1]] # } # # # helper for unnest_comments # clean_call <- function(call){ # if(!is.call(call)) { # return(call) # } # if(identical(call[[1]], quote(`#`))){ # return(call[[3]]) # } # call[] <- lapply(call, clean_call) # call # } # # is_syntactic <- function(x){ # tryCatch({str2lang(x); TRUE}, # error = function(e) FALSE) # } # # nest_comments <- function(fun, prefix){ # src <- deparse(fun, control = "useSource") # # positions of comments # pattern <- paste0("^\\s*", prefix) # commented_lgl <- grepl(pattern, src) # # positions of 1st comments of comment blocks # first_comments_lgl <- diff(c(FALSE, commented_lgl)) == 1 # # ids of comment blocks along the lines # comment_ids <- cumsum(first_comments_lgl) * commented_lgl # # positions of 1st lines after comment blocks # first_lines_lgl <- diff(!c(FALSE, commented_lgl)) == 1 # first_lines_ids <- cumsum(first_lines_lgl) * first_lines_lgl # # # we iterate through these ids, taking max from lines so if code ends with a # # comment it will be ignored # for(i in seq(max(first_lines_ids))){ # comments <- src[comment_ids == i] # line_num <- which(first_lines_ids == i) # line <- src[line_num] # # we move forward character by character until we get a syntactic replacement # # the code replacement starts with "`#`(" and we try all positions of 2nd # # parenthese until something works, then deal with next code block # # j <- 0 # repeat { # break_ <- FALSE # j <- j+1 # line <- src[line_num] # if(j == 1) code <- paste0("`#`('", paste(comments,collapse="\n"),"', ") else code[j] <- "" # for(n_chr in seq(nchar(src[line_num]))){ # code[j] <- paste0(code[j], substr(line, n_chr, n_chr)) # if (n_chr < nchar(line)) # code_last_line <- paste0(code[j],")", substr(line, n_chr+1, nchar(line))) # else # code_last_line <- paste0(code[j],")") # #print(code_last_line) # src_copy <- src # src_copy[(line_num-j+1):line_num] <- c(head(code,-1), code_last_line) # if (is_syntactic(paste(src_copy,collapse="\n"))){ # src <- src_copy # break_ <- TRUE # break} # } # if(break_ || j == 7) break # line_num <- line_num + 1 # } # } # eval(str2lang(paste(src, collapse = "\n")),envir = environment(fun)) # } # # # # repair_call <- function(call){ # if(!is.call(call)) { # return(call) # } # # if # if(call[[1]] == quote(`if`)) { # if(!is.call(call[[3]]) || call[[3]][[1]] != quote(`{`)) # call[[3]] <- as.call(list(quote(`{`), call[[3]])) # if(length(call) == 4 && (!is.call(call[[4]]) || call[[4]][[1]] != quote(`{`))) # call[[4]] <- as.call(list(quote(`{`), call[[4]])) # call[-1] <- lapply(as.list(call[-1]), repair_call) # return(call)} # # for # if(call[[1]] == quote(`for`)) { # if(!is.call(call[[4]]) || call[[4]][[1]] != quote(`{`)) # call[[4]] <- as.call(list(quote(`{`), call[[4]])) # call[-1] <- lapply(as.list(call[-1]), repair_call) # return(call)} # # repeat # if(call[[1]] == quote(`repeat`)) { # if(!is.call(call[[2]]) || call[[2]][[1]] != quote(`{`)) # call[[2]] <- as.call(list(quote(`{`), call[[2]])) # call[-1] <- lapply(as.list(call[-1]), repair_call) # return(call)} # # while # if(call[[1]] == quote(`while`)) { # if(!is.call(call[[3]]) || call[[3]][[1]] != quote(`{`)){ # call[[3]] <- as.call(list(quote(`{`), call[[3]])) # } # call[-1] <- lapply(as.list(call[-1]), repair_call) # return(call)} # call[] <- lapply(call, repair_call) # call # } # instead of the complicated route we take, we could just substitute # "^\\s*##(.*)" by "`#`('\\1') but this is not robust to string or ops containing prefix # It's a lot of work to handle a rare situation but that's the simpler I got # add_comment_calls0 <- function(fun, prefix = "##"){ # if(is.null(prefix)) return(fun) # # attach each relevant comment to the following call # fun <- nest_comments(fun, prefix) # body(fun) <- repair_call(body(fun)) # body(fun) <- unnest_comments(body(fun)) # fun # } add_comment_calls <- function(fun, prefix = "##"){ if(is.null(prefix)) return(fun) src <- deparse(fun, width.cutoff = 500, control = "useSource") pattern <- paste0("^\\s*(", prefix, ".*?)$") src <- gsub(pattern, "`#`(\"\\1\")", src) src <- paste(src, collapse = "\n") src <- str2lang(src) eval(src) }

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EDA.R

library(nycflights13) library(tidyverse) library(dplyr) library(ggplot2) library(ggpubr) library(hexbin) library(ggstance) #Q1 - Look at the number of cancelled flights per day. Is there a pattern? Is the proportion of cancelled flights related to the average delay? #part 1- find number of flights scheduled vs number of flights cancelled flights_per_day <- flights %>% mutate(cancelled_flights = (is.na(dep_delay) | is.na(arr_delay))) %>% mutate(date = paste(month, day, year, sep = '-')) %>% group_by(date) %>% summarise(numflights = n(), numcancelled = sum(cancelled_flights)) %>% ungroup() #plot number of flights per day vs. number of flights cancelled ggplot()+ geom_point(flights_per_day, mapping = aes(numflights, numcancelled)) #delay - part 1 - correlation between delay and cancellation cancelled_vs_delay <- flights %>% mutate(cancelled_flights = (is.na(dep_delay) | is.na(arr_delay))) %>% mutate(date = paste(month, day, year, sep = '-')) %>% group_by(date) %>% summarise( avg_cancelled = mean(cancelled_flights), avg_arr_delay = mean(arr_delay, na.rm = TRUE), avg_dep_delay = mean(dep_delay, na.rm = TRUE) ) %>% ungroup() #delay - part 2 - plot to show correlation between arr_delay/dep_delay and the proportion of cancelled flights p1 <- ggplot(cancelled_vs_delay) + geom_point(aes(avg_dep_delay, avg_cancelled)) p2 <- ggplot(cancelled_vs_delay) + geom_point(aes(avg_arr_delay, avg_cancelled)) p <- ggarrange(p1, p2, nrow = 1) #final plot p #Q2 - Which flight(tailnum) has worst on-time record? #general def. of on time --> flights which are not late are shown on-time # point 1 - if we consider overall average delay flights_ontime <- flights %>% mutate(date = paste(month, day, year, sep = '-')) %>% #select flights that have arrival time(means they landed) filter(!is.na(arr_time)) %>% select(date, arr_delay, tailnum)%>% group_by(tailnum) %>% summarise(num_flights = n(), avg_arr_delay = mean(arr_delay, na.rm = TRUE) ) %>% filter(num_flights >= 23) %>% arrange(desc(avg_arr_delay)) # ans - N203FR 41 59.12195 flights_ontime #point 2 - if we consider flights(tailnums) that are not late (i.e. avg_arr_delay <= 0) flights_ontime <- flights %>% mutate(date = paste(month, day, year, sep = '-')) %>% #selecting only flights that have arrival time(means they landed) filter(!is.na(arr_time)) %>% select(date, arr_delay, tailnum)%>% filter(!is.na(tailnum))%>% filter(arr_delay <= 0)%>% group_by(tailnum) %>% summarise(num_flights = n(), avg_arr_delay = mean(arr_delay, na.rm = TRUE) ) %>% filter(num_flights >= 23) %>% arrange(avg_arr_delay) flights_ontime # ans - N423AS 25 -33.44000 #Q3 - What time of day should you fly if you want to avoid delays as much as possible? flight_hour <- flights %>% mutate(date = paste(month, day, year, sep = '-')) %>% #filtering only the flights that are delayed filter(!is.na(dep_delay), !is.na(arr_delay)) %>% select(date, flight, tailnum, hour, minute, dep_delay, arr_delay) %>% #filtering only the flights that have positive delay filter(dep_delay > 0 & arr_delay > 0) %>% #creating time column with hour and minute mutate(time = paste(hour, minute, sep = ':') ) %>% group_by(time) %>% summarise(avg_delay = mean(arr_delay)) %>% arrange(desc(avg_delay)) %>% filter(min_rank(desc(avg_delay)) <= 25) flight_hour # without considering minutes flight_hour <- flights %>% mutate(date = paste(month, day, year, sep = '-')) %>% #filtering only the flights that are delayed filter(!is.na(dep_delay), !is.na(arr_delay)) %>% select(date, flight, tailnum, hour, minute, dep_delay, arr_delay) %>% #filtering only the flights that have positive delay filter(dep_delay > 0 & arr_delay > 0) %>% group_by(hour) %>% summarise(avg_delay = mean(arr_delay)) %>% arrange(desc(avg_delay)) flight_hour # without considering positive delay flight_hour <- flights %>% mutate(date = paste(month, day, year, sep = '-')) %>% #filtering only the flights that are delayed filter(!is.na(dep_delay), !is.na(arr_delay)) %>% select(date, flight, tailnum, hour, minute, dep_delay, arr_delay) %>% group_by(hour) %>% summarise(avg_delay = mean(arr_delay)) %>% arrange(desc(avg_delay)) flight_hour #Q4 - For each destination, compute the total minutes of delay. For each flight, compute the proportion of the total delay for its destination. total_delay <- flights %>% mutate(date = paste(month, day, year, sep = '-')) %>% filter(arr_delay > 0) %>% group_by(flight, dest) %>% select(dest, flight, arr_delay)%>% mutate(total_delay = sum(arr_delay), delay_prop = arr_delay/total_delay ) total_delay #if we consider carriers also: proportion <- flights %>% filter(arr_delay > 0) %>% group_by(dest, carrier, flight) %>% summarise(total_delay = sum(arr_delay)) %>% group_by(dest) %>% mutate( prop = total_delay / sum(total_delay) ) %>% arrange(dest, desc(prop)) proportion #Q5-Explore the distribution of each of the x, y, and z variables in diamonds. What do you learn? Think about a diamond and how you might decide which dimension is the length, width, and depth. p11 <- ggplot(diamonds) + geom_histogram(mapping = aes(x), na.rm = TRUE, binwidth = 0.01)+ theme_minimal() p12 <- ggplot(diamonds, mapping = aes(x = "", y = x))+ geom_boxplot(na.rm = TRUE) + coord_flip() p1 <- ggarrange(p11, p12, ncol = 1) p1 p21 <- ggplot(diamonds)+ geom_histogram(mapping = aes(y), na.rm = TRUE, fill = "lightblue", binwidth = 0.01)+ theme_minimal() p22 <- ggplot(diamonds, mapping = aes(x = "", y = y))+ geom_boxplot(na.rm = TRUE) + coord_flip()+ scale_y_continuous(limits =c(0, 60), breaks = seq(0, 60, 5)) p2 <- ggarrange(p21, p22, ncol = 1) p2 p31 <- ggplot(diamonds)+ geom_histogram(mapping = aes(z), na.rm = TRUE, fill = "lightgreen", binwidth = 0.01) + theme_minimal() p32 <- ggplot(diamonds, mapping = aes(x = "", y = z))+ geom_boxplot(na.rm = TRUE) + coord_flip()+ scale_y_continuous(limits =c(0, 40), breaks = seq(0, 40, 5)) p3 <- ggarrange(p31, p32, ncol = 1) p3 #x - length; y - width; z - depth summary(select(diamonds, x, y, z)) filter(diamonds, min_rank(desc(x)) <= 3) filter(diamonds, min_rank(desc(y)) <= 3) filter(diamonds, min_rank(desc(z)) <= 3) filter(diamonds, min_rank((x)) <= 5) filter(diamonds, min_rank((y)) <= 5) filter(diamonds, min_rank((z)) <= 5) #reducing the limits p11 <- ggplot(diamonds) + geom_histogram(mapping = aes(x), na.rm = TRUE, binwidth = 0.01)+ theme_minimal()+ scale_x_continuous(limits =c(0, 10), breaks = seq(0, 10, 1)) p12 <- ggplot(diamonds, mapping = aes(x = "", y = x))+ geom_boxplot(na.rm = TRUE) + coord_flip() p1 <- ggarrange(p11, p12, ncol = 1) p1 p21 <- ggplot(diamonds)+ geom_histogram(mapping = aes(y), na.rm = TRUE, fill = "lightblue", binwidth = 0.01)+ theme_minimal()+ scale_x_continuous(limits =c(0, 10), breaks = seq(0, 10, 1)) p22 <- ggplot(diamonds, mapping = aes(x = "", y = y))+ geom_boxplot(na.rm = TRUE) + coord_flip()+ scale_y_continuous(limits =c(0, 10), breaks = seq(0, 10, 2)) p2 <- ggarrange(p21, p22, ncol = 1) p2 p31 <- ggplot(diamonds)+ geom_histogram(mapping = aes(z), na.rm = TRUE, fill = "lightgreen", binwidth = 0.01) + theme_minimal()+ scale_x_continuous(limits =c(0, 10), breaks = seq(0, 10, 1)) p32 <- ggplot(diamonds, mapping = aes(x = "", y = z))+ geom_boxplot(na.rm = TRUE) + coord_flip()+ scale_y_continuous(limits =c(0, 10), breaks = seq(0, 10, 2)) p3 <- ggarrange(p31, p32, ncol = 1) p3 #Q6 - Explore the distribution of price. Do you discover anything unusual or surprising? (Hint: Carefully think about the binwidth and make sure you try a wide range of values.) summary(select(diamonds, price)) price1 <- ggplot(diamonds)+ geom_histogram(aes(price), na.rm = TRUE, binwidth = 1)+ scale_x_continuous(limits =c(0, 20000), breaks = seq(0, 20000, 2000)) price1 price2 <- ggplot(diamonds)+ geom_histogram(aes(price), na.rm = TRUE, binwidth = 5)+ scale_x_continuous(limits =c(0, 20000), breaks = seq(0, 20000, 2000)) price2 price3 <- ggplot(diamonds)+ geom_histogram(aes(price), na.rm = TRUE, binwidth = 10)+ scale_x_continuous(limits =c(0, 20000), breaks = seq(0, 20000, 2000)) price3 price4 <- ggplot(diamonds)+ geom_histogram(aes(price), na.rm = TRUE, binwidth = 50)+ scale_x_continuous(limits =c(0, 20000), breaks = seq(0, 20000, 2000)) price4 price5 <- ggplot(diamonds)+ geom_histogram(aes(price), na.rm = TRUE, binwidth = 50)+ scale_x_continuous(limits =c(0, 8000), breaks = seq(0, 8000, 2000)) price5 price6 <- ggplot(diamonds)+ geom_histogram(aes(price), na.rm = TRUE, binwidth = 50)+ scale_x_continuous(limits =c(0, 3000), breaks = seq(0, 3000, 300)) price6 #Q7- How many diamonds are 0.99 carat? How many are 1 carat? What do you think is the cause of the difference? carat <- diamonds %>% filter(carat == 0.99 | carat == 1) %>% group_by(carat) %>% summarise(count_0.99 = n()) carat carat_price <- diamonds %>% group_by(carat) %>% summarise(count = n(), min_price = min(price), max_price = max(price), avg_price = mean(price) ) %>% arrange(carat) carat_price smallset <- carat_price %>% filter(carat >= 0.9, carat <= 1) smallset #Q8 - What variable in the diamonds dataset is most important for predicting the price of a diamond? How is that variable correlated with cut? Why does the combination of those two relationships lead to lower quality diamonds being more expensive? # Carat of the diamond is measured from the dimensions of the diamond. Hence, the other variables cut, color, clarity along with carat can be considered as the factors that effect the price of the diamond. #Correlation between each variable and price #2 continuous variables - scatter/hex/box plots #1 Carat price_vs_carat1 <- ggplot(diamonds)+ geom_hex(aes(carat, price)) price_vs_carat1 #hard to see - try boxplot price_vs_carat2 <- ggplot(diamonds, aes(carat, price))+ geom_boxplot(aes(group = cut_width(carat, 0.1))) price_vs_carat2 #2 - Cut1 #one cont. one categorical price_vs_cut <- ggplot(data = diamonds, mapping = aes(x = cut, y = price)) + geom_boxplot() price_vs_cut #freq. poly. price_vs_cut1 <- ggplot(data = diamonds, mapping = aes(x = price, y = ..density..)) + geom_freqpoly(mapping = aes(colour = cut), binwidth = 500) price_vs_cut1 #3 - Color #categorical and cont. #boxplot price_vs_color2 <- ggplot(diamonds)+ geom_boxplot(aes(color, price)) price_vs_color2 #price increasing as the color - quality worsens #4- Clarity price_vs_clarity <- ggplot(diamonds)+ geom_boxplot(aes(clarity, price)) price_vs_clarity #5 - carat vs. cut # 2 categorical variables carat_cut <- ggplot(diamonds)+ geom_boxplot(aes(cut, carat))+coord_flip() carat_cut #Extracredit #coord_flip() carat_cut <- ggplot(diamonds)+ geom_boxplot(aes(cut, carat))+ coord_flip() carat_cut #ggstance carat_cut2 <- ggplot(diamonds)+ geom_boxploth(aes(carat, cut)) carat_cut2 #Q9 - How could you rescale the count dataset above to more clearly show the distribution of cut within colour, or colour within cut? #given count_dataset <- diamonds %>% count(color, cut) count_dataset #rescaling from count to proportion/density # Cut within colour rescale_cutWcol <- diamonds %>% count(color, cut)%>% group_by(cut)%>% mutate(density = n/sum(n)) rescale_cutWcol cutWcol <- ggplot(rescale_cutWcol)+ geom_tile(aes(color, cut, fill = density))+ scale_fill_distiller(palette = "RdYlBu", limits = c(0,1)) cutWcol # Colour within cut rescale_colWcut <- diamonds %>% count(color, cut)%>% group_by(color)%>% mutate(density = n/sum(n)) rescale_colWcut colWcut <- ggplot(rescale_colWcut)+ geom_tile(aes(color, cut, fill = density))+ scale_fill_distiller(palette = "RdYlBu", limits = c(0,1)) colWcut #Q10- Use geom_tile() together with dplyr to explore how average flight delays vary by destination and month of year. What makes the plot difficult to read? How could you improve it? plot1 <- flights %>% select(year, month, day, dest, dep_delay, arr_delay)%>% group_by(month, dest)%>% summarise(avg_dep_delay = mean(dep_delay, na.rm = TRUE), avg_arr_delay = mean(arr_delay, na.rm = TRUE)) plot_dep <- ggplot(plot1)+ geom_tile(aes(factor(month), dest, fill = avg_dep_delay)) plot_dep plot_arr <- ggplot(plot1)+ geom_tile(aes(factor(month), dest, fill = avg_arr_delay)) plot_arr #improvise improvise <- flights %>% select(year, month, dest, dep_delay, arr_delay) %>% filter(!is.na(arr_delay), !is.na(dep_delay)) %>% group_by(month, dest)%>% filter(dep_delay > 0, arr_delay > 0) %>% summarise(avg.dep.delay = mean(dep_delay, na.rm = TRUE), avg.arr.delay = mean(arr_delay, na.rm = TRUE) ) %>% ungroup() %>% group_by(dest) %>% filter(n() == 12) #view(improvise) p1 <- ggplot(improvise) + geom_tile(aes(factor(month), dest, fill = avg.arr.delay))+ scale_fill_distiller(palette = "RdYlGn")+ xlab("month") + ylab("destination") + ggtitle(label = "Arrival Delay") p1 p2 <- ggplot(improvise) + geom_tile(aes(factor(month), dest, fill = avg.dep.delay)) + scale_fill_distiller(palette = "RdYlGn") + xlab("month") + ylab("destination") p2 #Q11 - Instead of summarising the conditional distribution with a boxplot, you could use a frequency polygon. What do you need to consider when using cut_width() vs cut_number()? How does that impact a visualisation of the 2d distribution of carat and price? cutnumber <- ggplot(data = diamonds) + geom_freqpoly(aes(color = cut_number(carat, 5), x = price)) + labs(color = "Carat") cutnumber cutwidth <- ggplot(diamonds)+ geom_freqpoly(aes(price, color = cut_width(carat, 1.5, boundary = 0))) + labs(color = "Carat") cutwidth #Q12 - Visualise the distribution of carat, partitioned by price. #Carat, price - 2 cont. #scatter/box #too many vars/ can't show distribution using scatterplot distribution <- ggplot(diamonds)+ geom_boxploth(aes(carat, cut_number(price, 10)))+ labs(y = "Price", x = "Carat") distribution #Q13 - How does the price distribution of very large diamonds compare to small diamonds? Is it as you expect, or does it surprise you? price_distribution <- diamonds %>% arrange(desc(price)) %>% select(carat, depth, price, x, y, z) price_distribution summary(select(diamonds, price, carat, depth, x, y, z)) #Q14 - Combine two of the techniques you've learned to visualise the combined distribution of cut, carat, and price. technique_boxplot <- ggplot(diamonds, aes(x = cut_width(carat, 0.8), y = price, colour = cut))+ geom_boxplot() technique_boxplot technique_hexbin <- ggplot(diamonds)+ geom_hex(aes(carat, price, fill = cut), alpha = 1/3, na.rm = TRUE)+ scale_y_continuous(limits = c(0, 20000), breaks = seq(0, 20000, 2000)) technique_hexbin #Q15 - Two dimensional plots reveal outliers that are not visible in one dimensional plots. For example, some points in the plot below have an unusual combination of x and y values, which makes the points outliers even though their x and y values appear normal when examined separately. ... Why is a scatterplot a better display than a binned plot for this case? binned_boxplot <- ggplot(diamonds)+ geom_boxploth(aes(x, cut_width(y, 1.5))) binned_boxplot scatterplot <- ggplot(data = diamonds) + geom_point(mapping = aes(x = x, y = y)) + coord_cartesian(xlim = c(4, 11), ylim = c(4, 11)) scatterplot

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pathway_names.Rd

\name{pathway_names} \alias{pathway_names} \docType{data} \title{Pathway names in KEGG Database} \description{All pathway names we used in this method} \keyword{datasets}

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svgPanZoom.Rd

% Generated by roxygen2 (4.0.2): do not edit by hand \name{svgPanZoom} \alias{svgPanZoom} \title{Pan and Zoom R graphics} \usage{ svgPanZoom(svg, ..., width = NULL, height = NULL) } \description{ Add panning and zooming to almost any R graphics and hopefully and eventually other htmlwidgets. }

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/Codes/concat_and_gapfill_climate_data_from_Kenya.R

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concat_and_gapfill_climate_data_from_Kenya.R

# concatenate climate data from redcap and gapfill missing logger data -------- rm(list=ls()) #remove previous variable assignments # load libraries library(tidyverse) library(plyr) # load and format data redcap data --------------------------------------------------------------------- # import redcap data as 'redcap_clim_vec' source("Codes/REDCap_import_climate_data.R") # create site and date variables redcap_climate$site <- gsub("_arm_1", "", redcap_climate$redcap_event_name) redcap_climate$Date <- redcap_climate$date_collected # subset data to weather variables of interest climate_subset <- redcap_climate[,c("Date", "site", "temp_mean_hobo", "rainfall_hobo", "daily_rainfall", "rh_mean_hobo")] # load and format NOAA GSOD (Global Summary of the Day) from ------------------------------------------ # https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd?datasetabbv=GSOD&countryabbv&georegionabbv kisumu_gsod <- read.delim("Kenya/Climate/GSOD_Kisumu.txt", header = TRUE, sep = ",") mombasa_gsod <- read.delim("Kenya/Climate/GSOD_Mombasa.txt", header = TRUE, sep = ",") # format temperature and site gsod.files <- list(kisumu_gsod, mombasa_gsod) gsod.files <-lapply(gsod.files, function(x) cbind(x, mean_temp_gsod = (x$TEMP-32)*(5/9))) gsod.files <-lapply(gsod.files, function(x) cbind(x, Date = as.Date(paste(substr(x$YEARMODA, 1, 4), substr(x$YEARMODA, 5, 6), substr(x$YEARMODA, 7, 8), sep="-"), "%Y-%m-%d"))) # split, subset data, and merge data kisumu_gsod <- gsod.files[[1]][,c("Date", "mean_temp_gsod")] mombasa_gsod <- gsod.files[[2]][,c("Date", "mean_temp_gsod")] wide_data <- merge(kisumu_gsod, mombasa_gsod, by="Date", all=T) colnames(wide_data) <- c("Date", "kisumu_mean_temp_gsod", "mombasa_mean_temp_gsod") # reshape from long to wide sites <- unique(climate_subset$site) for (i in 1:length(sites)){ x <- subset(climate_subset, site == sites[i]) x <- x[, !names(x) %in% c("site")] colnames(x)[2:5] <- paste(sites[i], colnames(x)[2:5], sep='_') wide_data <- merge(wide_data, x, by="Date", all=T) } # make sure every date is included ------------------------------------------------------------------ minDate <- as.Date('2013-06-01', '%Y-%m-%d') maxDate <- as.Date('2019-02-11', '%Y-%m-%d') dates <- as.data.frame(seq.Date(minDate, maxDate, by=1)) colnames(dates)[1] <- "Date" wide_data <- merge(wide_data, dates, by="Date", all=T) wide_data <- subset(wide_data, Date >= minDate & Date <= maxDate) # fill in missing temperature data ------------------------------------------------------------------ # Chulaimbo fill_ch_w_hosp <- lm(chulaimbo_village_temp_mean_hobo ~ chulaimbo_hospital_temp_mean_hobo, data = wide_data) fill_ch_w_gsod <- lm(chulaimbo_village_temp_mean_hobo ~ kisumu_mean_temp_gsod, data = wide_data) wide_data$chulaimbo_Temperature <- ifelse(!is.na(wide_data$chulaimbo_village_temp_mean_hobo), wide_data$chulaimbo_village_temp_mean_hobo, round(coef(fill_ch_w_hosp)[[1]] + coef(fill_ch_w_hosp)[[2]] * wide_data$chulaimbo_hospital_temp_mean_hobo, 1)) wide_data$chulaimbo_Temperature <- ifelse(!is.na(wide_data$chulaimbo_Temperature), wide_data$chulaimbo_Temperature, round(coef(fill_ch_w_gsod)[[1]] + coef(fill_ch_w_gsod)[[2]] * wide_data$kisumu_mean_temp_gsod, 1)) # Kisumu wide_data$kisumu_estate_temp_mean_hobo <- ifelse(wide_data$kisumu_estate_temp_mean_hobo < 18, NA, wide_data$kisumu_estate_temp_mean_hobo) # remove suspect temperature values fill_ki_w_obama <- lm(kisumu_estate_temp_mean_hobo ~ obama_temp_mean_hobo, data = wide_data) fill_ki_w_gsod <- lm(kisumu_estate_temp_mean_hobo ~ kisumu_mean_temp_gsod, data = wide_data) wide_data$kisumu_Temperature <- ifelse(!is.na(wide_data$kisumu_estate_temp_mean_hobo), wide_data$kisumu_estate_temp_mean_hobo, round(coef(fill_ki_w_obama)[[1]] + coef(fill_ki_w_obama)[[2]] * wide_data$obama_temp_mean_hobo, 1)) wide_data$kisumu_Temperature <- ifelse(!is.na(wide_data$kisumu_Temperature), wide_data$kisumu_Temperature, round(coef(fill_ki_w_gsod)[[1]] + coef(fill_ki_w_gsod)[[2]] * wide_data$kisumu_mean_temp_gsod, 1)) # Msambweni fill_ms_w_uk <- lm(msambweni_temp_mean_hobo ~ ukunda_temp_mean_hobo, data = wide_data) fill_ms_w_gsod <- lm(msambweni_temp_mean_hobo ~ mombasa_mean_temp_gsod, data = wide_data) wide_data$msambweni_Temperature <- ifelse(!is.na(wide_data$msambweni_temp_mean_hobo), wide_data$msambweni_temp_mean_hobo, round(coef(fill_ms_w_uk)[[1]] + coef(fill_ms_w_uk)[[2]] * wide_data$ukunda_temp_mean_hobo, 1)) wide_data$msambweni_Temperature <- ifelse(!is.na(wide_data$msambweni_Temperature), wide_data$msambweni_Temperature, round(coef(fill_ms_w_gsod)[[1]] + coef(fill_ms_w_gsod)[[2]] * wide_data$mombasa_mean_temp_gsod, 1)) # Ukunda wide_data$ukunda_temp_mean_hobo <- ifelse(wide_data$ukunda_temp_mean_hobo >= 34, NA, wide_data$ukunda_temp_mean_hobo) # remove suspect temperature values fill_uk_w_gsod <- lm(ukunda_temp_mean_hobo ~ mombasa_mean_temp_gsod, data = wide_data) wide_data$ukunda_Temperature <- ifelse(!is.na(wide_data$ukunda_temp_mean_hobo), wide_data$ukunda_temp_mean_hobo, round(coef(fill_uk_w_gsod)[[1]] + coef(fill_uk_w_gsod)[[2]] * wide_data$mombasa_mean_temp_gsod, 1)) # fill in missing rainfall data ------------------------------------------------------------------- # calculate 30 days aggregated rainfall values wide_data$chulaimbo_rainfall_hobo <- wide_data$chulaimbo_village_rainfall_hobo wide_data$chulaimbo_daily_rainfall <- wide_data$chulaimbo_hospital_daily_rainfall sites2 <- c("chulaimbo", "obama", "msambweni", "ukunda") for (j in 1:length(sites2)){ wide_data[paste0("Monthly_rainfall_", sites2[j])] <- NA wide_data[paste0("Monthly_rainfall_", sites2[j], "_noaa")] <- NA for (k in 30:nrow(wide_data)){ wide_data[k,paste0("Monthly_rainfall_", sites2[j])] <- sum(wide_data[(k-29):k, paste0(sites2[j], "_rainfall_hobo")]) wide_data[k,paste0("Monthly_rainfall_", sites2[j], "_noaa")] <- sum(wide_data[(k-29):k, paste0(sites2[j], "_daily_rainfall")]) } } # Chulaimbo fill_ch_w_noaa <- lm(Monthly_rainfall_chulaimbo ~ Monthly_rainfall_chulaimbo_noaa, data = wide_data) wide_data$chulaimbo_Rainfall <- ifelse(!is.na(wide_data$Monthly_rainfall_chulaimbo), wide_data$Monthly_rainfall_chulaimbo, round(coef(fill_ch_w_noaa)[[1]] + coef(fill_ch_w_noaa)[[2]] * wide_data$Monthly_rainfall_chulaimbo_noaa, 1)) # Kisumu fill_ki_w_noaa <- lm(Monthly_rainfall_obama ~ Monthly_rainfall_obama_noaa, data = wide_data) wide_data$kisumu_Rainfall <- ifelse(!is.na(wide_data$Monthly_rainfall_obama), wide_data$Monthly_rainfall_obama, round(coef(fill_ki_w_noaa)[[1]] + coef(fill_ki_w_noaa)[[2]] * wide_data$Monthly_rainfall_obama_noaa, 1)) # Msmabweni fill_ms_w_noaa <- lm(Monthly_rainfall_msambweni ~ Monthly_rainfall_msambweni_noaa, data = wide_data) wide_data$msambweni_Rainfall <- ifelse(!is.na(wide_data$Monthly_rainfall_msambweni), wide_data$Monthly_rainfall_msambweni, round(coef(fill_ms_w_noaa)[[1]] + coef(fill_ms_w_noaa)[[2]] * wide_data$Monthly_rainfall_msambweni_noaa, 1)) # Ukunda fill_uk_w_noaa <- lm(Monthly_rainfall_ukunda ~ Monthly_rainfall_ukunda_noaa, data = wide_data) wide_data$ukunda_Rainfall <- ifelse(!is.na(wide_data$Monthly_rainfall_ukunda), wide_data$Monthly_rainfall_ukunda, round(coef(fill_uk_w_noaa)[[1]] + coef(fill_uk_w_noaa)[[2]] * wide_data$Monthly_rainfall_ukunda_noaa, 1)) # fill in missing humidity data ------------------------------------------------------------------- wide_data$Month_Day <- format(wide_data$Date, "%m-%d") humidityMeans <- ddply(wide_data, .(Month_Day), summarize , chulaimbo_rh_ltm = round(mean(chulaimbo_hospital_rh_mean_hobo, na.rm=T), mean(chulaimbo_village_rh_mean_hobo, na.rm=T)) , kisumu_rh_ltm = round(mean(obama_rh_mean_hobo, na.rm=T), mean(kisumu_estate_rh_mean_hobo, na.rm=T)) , msambweni_rh_ltm = round(mean(msambweni_rh_mean_hobo, na.rm=T)) , ukunda_rh_ltm = round(mean(ukunda_rh_mean_hobo, na.rm=T))) wide_data <- merge(wide_data, humidityMeans, by="Month_Day", all=T) wide_data$chulaimbo_Humidity <- ifelse(!is.na(wide_data$chulaimbo_village_rh_mean_hobo), wide_data$chulaimbo_village_rh_mean_hobo, wide_data$chulaimbo_rh_ltm) wide_data$kisumu_Humidity <- ifelse(!is.na(wide_data$obama_rh_mean_hobo), wide_data$obama_rh_mean_hobo, wide_data$kisumu_rh_ltm) wide_data$msambweni_Humidity <- ifelse(!is.na(wide_data$msambweni_rh_mean_hobo), wide_data$msambweni_rh_mean_hobo, wide_data$msambweni_rh_ltm) wide_data$ukunda_Humidity <- ifelse(!is.na(wide_data$ukunda_rh_mean_hobo), wide_data$ukunda_rh_mean_hobo, wide_data$ukunda_rh_ltm) # calculate 30 day aggregated rainfall values ----------------------------------------------------- wide_data <- wide_data[order(wide_data$Date),] sites2 <- c("chulaimbo", "kisumu", "msambweni", "ukunda") for (j in 1:length(sites2)){ weatherdf <- wide_data[, c("Date", paste0(sites2[j], "_Temperature"), paste0(sites2[j], "_Rainfall"), paste0(sites2[j], "_Humidity"))] weatherdf$Monthly_rainfall <- NA weatherdf$Monthly_rainfall_weighted <- NA weatherdf$Monthly_rainy_days_25 <- NA for (k in 30:nrow(weatherdf)){ rainSub <- subset(weatherdf, Date >= Date[k] - 29 & Date <= Date[k]) rainSub$exDec <- 30:1 rainSub$exDec <- rainSub[,paste0(sites2[j], "_Rainfall")] * (1/rainSub$exDec) weatherdf$Monthly_rainfall[k] <- sum(rainSub[paste0(sites2[j], "_Rainfall")]) weatherdf$Monthly_rainfall_weighted[k] <- sum(rainSub$exDec) weatherdf$Monthly_rainy_days_25[k] <- sum(rainSub[paste0(sites2[j], "_Rainfall")] > 2.5) if (is.na(weatherdf[k,paste0(sites2[j], "_Temperature")])){ # fill in the few missing temperature days with the mean of the 2 days before and after date with missing data weatherdf[k,paste0(sites2[j], "_Temperature")] <- mean(weatherdf[(k-2):(k+2),paste0(sites2[j], "_Temperature")], na.rm=T) } } colnames(weatherdf) <- gsub(paste0(sites2[j], "_"), "", colnames(weatherdf)) weatherdf$Site <- paste0(toupper(substr(sites2[j],1,1)), substr(sites2[j],2,nchar(sites2[j]))) weatherdf <- weatherdf[30:nrow(weatherdf),] assign(sites2[j], weatherdf) } # merge data into long format and save ----------------------------------------------------------- weatherdf <- do.call(rbind, list(chulaimbo, kisumu, msambweni, ukunda)) write.csv(weatherdf, "Concatenated_Data/climate_data/gapfilled_climate_data_Kenya.csv", row.names = F)

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cum_flux_summaries_GWP.R

mydata <- copy(data) # calcultate GWP CO2 eq mydata[, CH4.4replicates:= CH4] mydata[is.na(CH4.4replicates), CH4.4replicates:= 0] gwpCH4 <- 25 gwpN2O <- 298 mydata[,GWP.CO2:= CO2] mydata[,GWP.CH4:= CH4.4replicates/1000*16/12 * gwpCH4 * 12/44] mydata[,GWP.N2O:= N2O/1000*44/28 * gwpN2O * 12/44] mydata[,GWP.all:= GWP.CO2 + GWP.CH4 + GWP.N2O] mydata[,GWP.CO2eq.mgCo2m2h:= GWP.all / 14/3/24 * 1000 * 44/12] # summaries s.total <- mydata[,.( NO = mean(NO, na.rm=T), NO.se = sd(NO, na.rm=T)/sqrt(sum(!is.na(NO))), N2O = mean(N2O, na.rm=T), N2O.se = sd(N2O, na.rm=T)/sqrt(sum(!is.na(N2O))), NH3 = mean(NH3, na.rm=T), NH3.se = sd(NH3, na.rm=T)/sqrt(sum(!is.na(NH3))), CO2 = mean(CO2, na.rm=T), CO2.se = sd(CO2, na.rm=T)/sqrt(sum(!is.na(CO2))), CH4 = mean(CH4, na.rm=T), CH4.se = sd(CH4, na.rm=T)/sqrt(sum(!is.na(CH4))), NO.N2O = mean(NO.N2O, na.rm=T), NO.N2O.se = sd(NO.N2O, na.rm=T)/sqrt(sum(!is.na(NO.N2O))), NO.N2O.NH3 = mean(NO.N2O.NH3, na.rm=T), NO.N2O.NH3.se = sd(NO.N2O.NH3, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3))), NO.N2O.NH3filled = mean(NO.N2O.NH3filled, na.rm=T), NO.N2O.NH3filled.se = sd(NO.N2O.NH3filled, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3filled))), GWP.CO2 = mean(GWP.CO2, na.rm=T), GWP.CO2.se = sd(GWP.CO2, na.rm=T)/sqrt(sum(!is.na(GWP.CO2))), GWP.CH4 = mean(GWP.CH4, na.rm=T), GWP.CH4.se = sd(GWP.CH4, na.rm=T)/sqrt(sum(!is.na(GWP.CH4))), GWP.N2O = mean(GWP.N2O, na.rm=T), GWP.N2O.se = sd(GWP.N2O, na.rm=T)/sqrt(sum(!is.na(GWP.N2O))), GWP.all = mean(GWP.all, na.rm=T), GWP.all.se = sd(GWP.all, na.rm=T)/sqrt(sum(!is.na(GWP.all))), GWP.CO2eq.mgCo2m2h = mean(GWP.CO2eq.mgCo2m2h, na.rm=T), GWP.CO2eq.mgCo2m2h.se = sd(GWP.CO2eq.mgCo2m2h, na.rm=T)/sqrt(sum(!is.na(GWP.CO2eq.mgCo2m2h))) )] s.core <- mydata[,.( NO = NO, N2O = N2O, NH3 = NH3, CO2 = CO2, CH4 = CH4, NO.N2O = NO.N2O, NO.N2O.NH3 = NO.N2O.NH3, NO.N2O.NH3filled = NO.N2O.NH3filled, GWP.CO2 = GWP.CO2, GWP.CH4 = GWP.CH4, GWP.N2O = GWP.N2O, GWP.all = GWP.all, GWP.CO2eq.mgCo2m2h = GWP.CO2eq.mgCo2m2h ), by=.(fertilization, precipitation, tillage, incubation, treatment)] s.treat <- mydata[,.( NO = mean(NO, na.rm=T), NO.se = sd(NO, na.rm=T)/sqrt(sum(!is.na(NO))), N2O = mean(N2O, na.rm=T), N2O.se = sd(N2O, na.rm=T)/sqrt(sum(!is.na(N2O))), NH3 = mean(NH3, na.rm=T), NH3.se = sd(NH3, na.rm=T)/sqrt(sum(!is.na(NH3))), CO2 = mean(CO2, na.rm=T), CO2.se = sd(CO2, na.rm=T)/sqrt(sum(!is.na(CO2))), CH4 = mean(CH4, na.rm=T), CH4.se = sd(CH4, na.rm=T)/sqrt(sum(!is.na(CH4))), NO.N2O = mean(NO.N2O, na.rm=T), NO.N2O.se = sd(NO.N2O, na.rm=T)/sqrt(sum(!is.na(NO.N2O))), NO.N2O.NH3 = mean(NO.N2O.NH3, na.rm=T), NO.N2O.NH3.se = sd(NO.N2O.NH3, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3))), NO.N2O.NH3filled = mean(NO.N2O.NH3filled, na.rm=T), NO.N2O.NH3filled.se = sd(NO.N2O.NH3filled, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3filled))), GWP.CO2 = mean(GWP.CO2, na.rm=T), GWP.CO2.se = sd(GWP.CO2, na.rm=T)/sqrt(sum(!is.na(GWP.CO2))), GWP.CH4 = mean(GWP.CH4, na.rm=T), GWP.CH4.se = sd(GWP.CH4, na.rm=T)/sqrt(sum(!is.na(GWP.CH4))), GWP.N2O = mean(GWP.N2O, na.rm=T), GWP.N2O.se = sd(GWP.N2O, na.rm=T)/sqrt(sum(!is.na(GWP.N2O))), GWP.all = mean(GWP.all, na.rm=T), GWP.all.se = sd(GWP.all, na.rm=T)/sqrt(sum(!is.na(GWP.all))), GWP.CO2eq.mgCo2m2h = mean(GWP.CO2eq.mgCo2m2h, na.rm=T), GWP.CO2eq.mgCo2m2h.se = sd(GWP.CO2eq.mgCo2m2h, na.rm=T)/sqrt(sum(!is.na(GWP.CO2eq.mgCo2m2h))) ), by=treatment] s.till <- mydata[,.( NO = mean(NO, na.rm=T), NO.se = sd(NO, na.rm=T)/sqrt(sum(!is.na(NO))), N2O = mean(N2O, na.rm=T), N2O.se = sd(N2O, na.rm=T)/sqrt(sum(!is.na(N2O))), NH3 = mean(NH3, na.rm=T), NH3.se = sd(NH3, na.rm=T)/sqrt(sum(!is.na(NH3))), CO2 = mean(CO2, na.rm=T), CO2.se = sd(CO2, na.rm=T)/sqrt(sum(!is.na(CO2))), CH4 = mean(CH4, na.rm=T), CH4.se = sd(CH4, na.rm=T)/sqrt(sum(!is.na(CH4))), NO.N2O = mean(NO.N2O, na.rm=T), NO.N2O.se = sd(NO.N2O, na.rm=T)/sqrt(sum(!is.na(NO.N2O))), NO.N2O.NH3 = mean(NO.N2O.NH3, na.rm=T), NO.N2O.NH3.se = sd(NO.N2O.NH3, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3))), NO.N2O.NH3filled = mean(NO.N2O.NH3filled, na.rm=T), NO.N2O.NH3filled.se = sd(NO.N2O.NH3filled, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3filled))), GWP.CO2 = mean(GWP.CO2, na.rm=T), GWP.CO2.se = sd(GWP.CO2, na.rm=T)/sqrt(sum(!is.na(GWP.CO2))), GWP.CH4 = mean(GWP.CH4, na.rm=T), GWP.CH4.se = sd(GWP.CH4, na.rm=T)/sqrt(sum(!is.na(GWP.CH4))), GWP.N2O = mean(GWP.N2O, na.rm=T), GWP.N2O.se = sd(GWP.N2O, na.rm=T)/sqrt(sum(!is.na(GWP.N2O))), GWP.all = mean(GWP.all, na.rm=T), GWP.all.se = sd(GWP.all, na.rm=T)/sqrt(sum(!is.na(GWP.all))), GWP.CO2eq.mgCo2m2h = mean(GWP.CO2eq.mgCo2m2h, na.rm=T), GWP.CO2eq.mgCo2m2h.se = sd(GWP.CO2eq.mgCo2m2h, na.rm=T)/sqrt(sum(!is.na(GWP.CO2eq.mgCo2m2h))) ), by=tillage] s.fert <- mydata[,.( NO = mean(NO, na.rm=T), NO.se = sd(NO, na.rm=T)/sqrt(sum(!is.na(NO))), N2O = mean(N2O, na.rm=T), N2O.se = sd(N2O, na.rm=T)/sqrt(sum(!is.na(N2O))), NH3 = mean(NH3, na.rm=T), NH3.se = sd(NH3, na.rm=T)/sqrt(sum(!is.na(NH3))), CO2 = mean(CO2, na.rm=T), CO2.se = sd(CO2, na.rm=T)/sqrt(sum(!is.na(CO2))), CH4 = mean(CH4, na.rm=T), CH4.se = sd(CH4, na.rm=T)/sqrt(sum(!is.na(CH4))), NO.N2O = mean(NO.N2O, na.rm=T), NO.N2O.se = sd(NO.N2O, na.rm=T)/sqrt(sum(!is.na(NO.N2O))), NO.N2O.NH3 = mean(NO.N2O.NH3, na.rm=T), NO.N2O.NH3.se = sd(NO.N2O.NH3, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3))), NO.N2O.NH3filled = mean(NO.N2O.NH3filled, na.rm=T), NO.N2O.NH3filled.se = sd(NO.N2O.NH3filled, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3filled))), GWP.CO2 = mean(GWP.CO2, na.rm=T), GWP.CO2.se = sd(GWP.CO2, na.rm=T)/sqrt(sum(!is.na(GWP.CO2))), GWP.CH4 = mean(GWP.CH4, na.rm=T), GWP.CH4.se = sd(GWP.CH4, na.rm=T)/sqrt(sum(!is.na(GWP.CH4))), GWP.N2O = mean(GWP.N2O, na.rm=T), GWP.N2O.se = sd(GWP.N2O, na.rm=T)/sqrt(sum(!is.na(GWP.N2O))), GWP.all = mean(GWP.all, na.rm=T), GWP.all.se = sd(GWP.all, na.rm=T)/sqrt(sum(!is.na(GWP.all))), GWP.CO2eq.mgCo2m2h = mean(GWP.CO2eq.mgCo2m2h, na.rm=T), GWP.CO2eq.mgCo2m2h.se = sd(GWP.CO2eq.mgCo2m2h, na.rm=T)/sqrt(sum(!is.na(GWP.CO2eq.mgCo2m2h))) ), by=fertilization] s.rain <- mydata[,.( NO = mean(NO, na.rm=T), NO.se = sd(NO, na.rm=T)/sqrt(sum(!is.na(NO))), N2O = mean(N2O, na.rm=T), N2O.se = sd(N2O, na.rm=T)/sqrt(sum(!is.na(N2O))), NH3 = mean(NH3, na.rm=T), NH3.se = sd(NH3, na.rm=T)/sqrt(sum(!is.na(NH3))), CO2 = mean(CO2, na.rm=T), CO2.se = sd(CO2, na.rm=T)/sqrt(sum(!is.na(CO2))), CH4 = mean(CH4, na.rm=T), CH4.se = sd(CH4, na.rm=T)/sqrt(sum(!is.na(CH4))), NO.N2O = mean(NO.N2O, na.rm=T), NO.N2O.se = sd(NO.N2O, na.rm=T)/sqrt(sum(!is.na(NO.N2O))), NO.N2O.NH3 = mean(NO.N2O.NH3, na.rm=T), NO.N2O.NH3.se = sd(NO.N2O.NH3, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3))), NO.N2O.NH3filled = mean(NO.N2O.NH3filled, na.rm=T), NO.N2O.NH3filled.se = sd(NO.N2O.NH3filled, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3filled))), GWP.CO2 = mean(GWP.CO2, na.rm=T), GWP.CO2.se = sd(GWP.CO2, na.rm=T)/sqrt(sum(!is.na(GWP.CO2))), GWP.CH4 = mean(GWP.CH4, na.rm=T), GWP.CH4.se = sd(GWP.CH4, na.rm=T)/sqrt(sum(!is.na(GWP.CH4))), GWP.N2O = mean(GWP.N2O, na.rm=T), GWP.N2O.se = sd(GWP.N2O, na.rm=T)/sqrt(sum(!is.na(GWP.N2O))), GWP.all = mean(GWP.all, na.rm=T), GWP.all.se = sd(GWP.all, na.rm=T)/sqrt(sum(!is.na(GWP.all))), GWP.CO2eq.mgCo2m2h = mean(GWP.CO2eq.mgCo2m2h, na.rm=T), GWP.CO2eq.mgCo2m2h.se = sd(GWP.CO2eq.mgCo2m2h, na.rm=T)/sqrt(sum(!is.na(GWP.CO2eq.mgCo2m2h))) ), by=precipitation] s.fert.till <- mydata[,.( NO = mean(NO, na.rm=T), NO.se = sd(NO, na.rm=T)/sqrt(sum(!is.na(NO))), N2O = mean(N2O, na.rm=T), N2O.se = sd(N2O, na.rm=T)/sqrt(sum(!is.na(N2O))), NH3 = mean(NH3, na.rm=T), NH3.se = sd(NH3, na.rm=T)/sqrt(sum(!is.na(NH3))), CO2 = mean(CO2, na.rm=T), CO2.se = sd(CO2, na.rm=T)/sqrt(sum(!is.na(CO2))), CH4 = mean(CH4, na.rm=T), CH4.se = sd(CH4, na.rm=T)/sqrt(sum(!is.na(CH4))), NO.N2O = mean(NO.N2O, na.rm=T), NO.N2O.se = sd(NO.N2O, na.rm=T)/sqrt(sum(!is.na(NO.N2O))), NO.N2O.NH3 = mean(NO.N2O.NH3, na.rm=T), NO.N2O.NH3.se = sd(NO.N2O.NH3, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3))), NO.N2O.NH3filled = mean(NO.N2O.NH3filled, na.rm=T), NO.N2O.NH3filled.se = sd(NO.N2O.NH3filled, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3filled))), GWP.CO2 = mean(GWP.CO2, na.rm=T), GWP.CO2.se = sd(GWP.CO2, na.rm=T)/sqrt(sum(!is.na(GWP.CO2))), GWP.CH4 = mean(GWP.CH4, na.rm=T), GWP.CH4.se = sd(GWP.CH4, na.rm=T)/sqrt(sum(!is.na(GWP.CH4))), GWP.N2O = mean(GWP.N2O, na.rm=T), GWP.N2O.se = sd(GWP.N2O, na.rm=T)/sqrt(sum(!is.na(GWP.N2O))), GWP.all = mean(GWP.all, na.rm=T), GWP.all.se = sd(GWP.all, na.rm=T)/sqrt(sum(!is.na(GWP.all))), GWP.CO2eq.mgCo2m2h = mean(GWP.CO2eq.mgCo2m2h, na.rm=T), GWP.CO2eq.mgCo2m2h.se = sd(GWP.CO2eq.mgCo2m2h, na.rm=T)/sqrt(sum(!is.na(GWP.CO2eq.mgCo2m2h))) ), by=.(fertilization, tillage)] s.fert.rain <- mydata[,.( NO = mean(NO, na.rm=T), NO.se = sd(NO, na.rm=T)/sqrt(sum(!is.na(NO))), N2O = mean(N2O, na.rm=T), N2O.se = sd(N2O, na.rm=T)/sqrt(sum(!is.na(N2O))), NH3 = mean(NH3, na.rm=T), NH3.se = sd(NH3, na.rm=T)/sqrt(sum(!is.na(NH3))), CO2 = mean(CO2, na.rm=T), CO2.se = sd(CO2, na.rm=T)/sqrt(sum(!is.na(CO2))), CH4 = mean(CH4, na.rm=T), CH4.se = sd(CH4, na.rm=T)/sqrt(sum(!is.na(CH4))), NO.N2O = mean(NO.N2O, na.rm=T), NO.N2O.se = sd(NO.N2O, na.rm=T)/sqrt(sum(!is.na(NO.N2O))), NO.N2O.NH3 = mean(NO.N2O.NH3, na.rm=T), NO.N2O.NH3.se = sd(NO.N2O.NH3, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3))), NO.N2O.NH3filled = mean(NO.N2O.NH3filled, na.rm=T), NO.N2O.NH3filled.se = sd(NO.N2O.NH3filled, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3filled))), GWP.CO2 = mean(GWP.CO2, na.rm=T), GWP.CO2.se = sd(GWP.CO2, na.rm=T)/sqrt(sum(!is.na(GWP.CO2))), GWP.CH4 = mean(GWP.CH4, na.rm=T), GWP.CH4.se = sd(GWP.CH4, na.rm=T)/sqrt(sum(!is.na(GWP.CH4))), GWP.N2O = mean(GWP.N2O, na.rm=T), GWP.N2O.se = sd(GWP.N2O, na.rm=T)/sqrt(sum(!is.na(GWP.N2O))), GWP.all = mean(GWP.all, na.rm=T), GWP.all.se = sd(GWP.all, na.rm=T)/sqrt(sum(!is.na(GWP.all))), GWP.CO2eq.mgCo2m2h = mean(GWP.CO2eq.mgCo2m2h, na.rm=T), GWP.CO2eq.mgCo2m2h.se = sd(GWP.CO2eq.mgCo2m2h, na.rm=T)/sqrt(sum(!is.na(GWP.CO2eq.mgCo2m2h))) ), by=.(fertilization, precipitation)] s.till.rain <- mydata[,.( NO = mean(NO, na.rm=T), NO.se = sd(NO, na.rm=T)/sqrt(sum(!is.na(NO))), N2O = mean(N2O, na.rm=T), N2O.se = sd(N2O, na.rm=T)/sqrt(sum(!is.na(N2O))), NH3 = mean(NH3, na.rm=T), NH3.se = sd(NH3, na.rm=T)/sqrt(sum(!is.na(NH3))), CO2 = mean(CO2, na.rm=T), CO2.se = sd(CO2, na.rm=T)/sqrt(sum(!is.na(CO2))), CH4 = mean(CH4, na.rm=T), CH4.se = sd(CH4, na.rm=T)/sqrt(sum(!is.na(CH4))), NO.N2O = mean(NO.N2O, na.rm=T), NO.N2O.se = sd(NO.N2O, na.rm=T)/sqrt(sum(!is.na(NO.N2O))), NO.N2O.NH3 = mean(NO.N2O.NH3, na.rm=T), NO.N2O.NH3.se = sd(NO.N2O.NH3, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3))), NO.N2O.NH3filled = mean(NO.N2O.NH3filled, na.rm=T), NO.N2O.NH3filled.se = sd(NO.N2O.NH3filled, na.rm=T)/sqrt(sum(!is.na(NO.N2O.NH3filled))), GWP.CO2 = mean(GWP.CO2, na.rm=T), GWP.CO2.se = sd(GWP.CO2, na.rm=T)/sqrt(sum(!is.na(GWP.CO2))), GWP.CH4 = mean(GWP.CH4, na.rm=T), GWP.CH4.se = sd(GWP.CH4, na.rm=T)/sqrt(sum(!is.na(GWP.CH4))), GWP.N2O = mean(GWP.N2O, na.rm=T), GWP.N2O.se = sd(GWP.N2O, na.rm=T)/sqrt(sum(!is.na(GWP.N2O))), GWP.all = mean(GWP.all, na.rm=T), GWP.all.se = sd(GWP.all, na.rm=T)/sqrt(sum(!is.na(GWP.all))), GWP.CO2eq.mgCo2m2h = mean(GWP.CO2eq.mgCo2m2h, na.rm=T), GWP.CO2eq.mgCo2m2h.se = sd(GWP.CO2eq.mgCo2m2h, na.rm=T)/sqrt(sum(!is.na(GWP.CO2eq.mgCo2m2h))) ), by=.(tillage, precipitation)] ################################################################################ # write.summaries mydata <- copy (s.core) no.format <- names(mydata)[names(mydata) %in% c("incabation", "treatment", "fertilization", "precipitation", "tillage")] to.format4 <- names(mydata)[! names(mydata) %in% c(no.format)] mydata[,(to.format4):= lapply(.SD, function(x) formatC(x, format = "f", digits = 4)), .SDcols = to.format4] myfile <- paste0(folder.GWP, "/GWPcumFlux_bycore.dat") write.table(mydata, file= myfile, row.names = FALSE, sep = "\t", quote = FALSE) mydata <- copy (s.treat) to.format2<- c(grep(".se",names(mydata), value=T)) no.format <- names(mydata)[names(mydata) %in% c("treatment", "fertilization", "precipitation", "tillage")] to.format4 <- names(mydata)[! names(mydata) %in% c(to.format2, no.format)] mydata[,(to.format2):= lapply(.SD, function(x) formatC(x, format = "f", digits = 2)), .SDcols = to.format2] mydata[,(to.format4):= lapply(.SD, function(x) formatC(x, format = "f", digits = 4)), .SDcols = to.format4] myfile <- paste0(folder.GWP, "/GWPcumFlux_bytreat.dat") write.table(mydata, file= myfile, row.names = FALSE, sep = "\t", quote = FALSE) mydata <- copy (s.till) to.format2<- c(grep(".se",names(mydata), value=T)) no.format <- names(mydata)[names(mydata) %in% c("treatment", "fertilization", "precipitation", "tillage")] to.format4 <- names(mydata)[! names(mydata) %in% c(to.format2, no.format)] mydata[,(to.format2):= lapply(.SD, function(x) formatC(x, format = "f", digits = 2)), .SDcols = to.format2] mydata[,(to.format4):= lapply(.SD, function(x) formatC(x, format = "f", digits = 4)), .SDcols = to.format4] myfile <- paste0(folder.GWP, "/GWPcumFlux_bytillage.dat") write.table(mydata, file= myfile, row.names = FALSE, sep = "\t", quote = FALSE) mydata <- copy (s.fert) to.format2<- c(grep(".se",names(mydata), value=T)) no.format <- names(mydata)[names(mydata) %in% c("treatment", "fertilization", "precipitation", "tillage")] to.format4 <- names(mydata)[! names(mydata) %in% c(to.format2, no.format)] mydata[,(to.format2):= lapply(.SD, function(x) formatC(x, format = "f", digits = 2)), .SDcols = to.format2] mydata[,(to.format4):= lapply(.SD, function(x) formatC(x, format = "f", digits = 4)), .SDcols = to.format4] myfile <- paste0(folder.GWP, "/GWPcumFlux_byfert.dat") write.table(mydata, file= myfile, row.names = FALSE, sep = "\t", quote = FALSE) mydata <- copy (s.rain) to.format2<- c(grep(".se",names(mydata), value=T)) no.format <- names(mydata)[names(mydata) %in% c("treatment", "fertilization", "precipitation", "tillage")] to.format4 <- names(mydata)[! names(mydata) %in% c(to.format2, no.format)] mydata[,(to.format2):= lapply(.SD, function(x) formatC(x, format = "f", digits = 2)), .SDcols = to.format2] mydata[,(to.format4):= lapply(.SD, function(x) formatC(x, format = "f", digits = 4)), .SDcols = to.format4] myfile <- paste0(folder.GWP, "/GWPcumFlux_byrain.dat") write.table(mydata, file= myfile, row.names = FALSE, sep = "\t", quote = FALSE) mydata <- copy (s.fert.till) to.format2<- c(grep(".se",names(mydata), value=T)) no.format <- names(mydata)[names(mydata) %in% c("treatment", "fertilization", "precipitation", "tillage")] to.format4 <- names(mydata)[! names(mydata) %in% c(to.format2, no.format)] mydata[,(to.format2):= lapply(.SD, function(x) formatC(x, format = "f", digits = 2)), .SDcols = to.format2] mydata[,(to.format4):= lapply(.SD, function(x) formatC(x, format = "f", digits = 4)), .SDcols = to.format4] myfile <- paste0(folder.GWP, "/GWPcumFlux_by_fert_till.dat") write.table(mydata, file= myfile, row.names = FALSE, sep = "\t", quote = FALSE) mydata <- copy (s.fert.rain) to.format2<- c(grep(".se",names(mydata), value=T)) no.format <- names(mydata)[names(mydata) %in% c("treatment", "fertilization", "precipitation", "tillage")] to.format4 <- names(mydata)[! names(mydata) %in% c(to.format2, no.format)] mydata[,(to.format2):= lapply(.SD, function(x) formatC(x, format = "f", digits = 2)), .SDcols = to.format2] mydata[,(to.format4):= lapply(.SD, function(x) formatC(x, format = "f", digits = 4)), .SDcols = to.format4] myfile <- paste0(folder.GWP, "/GWPcumFlux_by_fert_rain.dat") write.table(mydata, file= myfile, row.names = FALSE, sep = "\t", quote = FALSE) mydata <- copy (s.till.rain) to.format2<- c(grep(".se",names(mydata), value=T)) no.format <- names(mydata)[names(mydata) %in% c("treatment", "fertilization", "precipitation", "tillage")] to.format4 <- names(mydata)[! names(mydata) %in% c(to.format2, no.format)] mydata[,(to.format2):= lapply(.SD, function(x) formatC(x, format = "f", digits = 2)), .SDcols = to.format2] mydata[,(to.format4):= lapply(.SD, function(x) formatC(x, format = "f", digits = 4)), .SDcols = to.format4] myfile <- paste0(folder.GWP, "/GWPcumFlux_by_till_rain.dat") write.table(mydata, file= myfile, row.names = FALSE, sep = "\t", quote = FALSE) mydata <- copy (s.total) to.format2<- c(grep(".se",names(mydata), value=T)) no.format <- names(mydata)[names(mydata) %in% c("treatment", "fertilization", "precipitation", "tillage")] to.format4 <- names(mydata)[! names(mydata) %in% c(to.format2, no.format)] mydata[,(to.format2):= lapply(.SD, function(x) formatC(x, format = "f", digits = 2)), .SDcols = to.format2] mydata[,(to.format4):= lapply(.SD, function(x) formatC(x, format = "f", digits = 4)), .SDcols = to.format4] myfile <- paste0(folder.GWP, "/GWPcumFlux.dat") write.table(mydata, file= myfile, row.names = FALSE, sep = "\t", quote = FALSE) ### rm(mydata)

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/man/summary.binomscreenr.Rd

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summary.binomscreenr.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/binomialScreening.R \name{summary.binomscreenr} \alias{summary.binomscreenr} \title{Print Summaries of \code{binomscreenr} Objects} \usage{ \method{summary}{binomscreenr}(object, diagnostics = FALSE, ...) } \arguments{ \item{object}{an object of class \code{binomscreenr} produced by function \code{binomialScreening}.} \item{diagnostics}{a logical value; plot model diagnostics if \verb{TRUE}.} \item{...}{further arguments passed to or from other methods.} } \value{ Nothing. Summaries are printed as a side effect. } \description{ Print Summaries of \code{binomscreenr} Objects }

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refs/heads/master

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test-summary.R

context("Tests for auxiliary functions") #aux_mean test_that("aux_mean is less than or equal to the first parameter", { expect_lte(aux_mean(10, 0.5), 10) expect_lte(aux_mean(10, 1), 10) expect_lte(aux_mean(10, 0), 10) expect_lte(aux_mean(5, 0.5), 5) expect_lte(aux_mean(5, 1), 5) expect_lte(aux_mean(5, 0), 5) }) test_that("aux_mean produces a single output", { expect_length(aux_mean(10, 0.5), 1) expect_length(aux_mean(13, 0.75), 1) expect_length(aux_mean(20, 1), 1) expect_length(aux_mean(25, 0), 1) }) test_that("aux_mean produces output of class type equal to that of its parameters", { expect_is(aux_mean(10, 0.5), class(10)) expect_is(aux_mean(10, 0.5), class(0.5)) expect_is(aux_mean(17, 0.25), class(17)) expect_is(aux_mean(17, 0.25), class(0.25)) }) #aux_variance test_that("aux_variance produces a single output", { expect_length(aux_variance(10, 0.5), 1) expect_length(aux_variance(13, 0.75), 1) expect_length(aux_variance(20, 1), 1) expect_length(aux_variance(25, 0), 1) }) test_that("aux_variance produces output of class type equal to that of its parameters", { expect_is(aux_variance(10, 0.5), class(10)) expect_is(aux_variance(10, 0.5), class(0.5)) expect_is(aux_variance(17, 0.25), class(17)) expect_is(aux_variance(17, 0.25), class(0.25)) }) test_that("aux_variance produces non-negative output", { expect_gte(aux_variance(10, 0.5), 0) expect_gte(aux_variance(10, 0.75), 0) expect_gte(aux_variance(10, 0), 0) expect_gte(aux_variance(10, 1), 0) }) #aux_mode test_that("aux_mode produces output of class type equal to that of its parameters", { expect_is(aux_mode(10, 0.5), class(10)) expect_is(aux_mode(10, 0.5), class(0.5)) expect_is(aux_mode(17, 0.25), class(17)) expect_is(aux_mode(17, 0.25), class(0.25)) }) test_that("aux_mode produces output with valid length", { expect_length(aux_mode(10, 0.5), 1) expect_length(aux_mode(9, 0.5), 2) expect_length(aux_mode(49, 0.3), 2) }) test_that("aux_mode works with valid parameters", { expect_equal(aux_mode(10, 0.5), 5) expect_equal(aux_mode(92, 0.5), 46) expect_equal(aux_mode(49, 0.4), c(20, 19)) }) #aux_skewness test_that("aux_skewness fails for certain parameters", { expect_error(aux_skewness(0, 0.5)) expect_error(aux_skewness(10, 0)) expect_error(aux_skewness(18, 1)) }) test_that("aux_skewness produces a single output", { expect_length(aux_skewness(10, 0.5), 1) expect_length(aux_skewness(49, 0.4), 1) expect_length(aux_skewness(92, 0.5), 1) }) test_that("aux_skewness produces output of class type equal to that of its parameters", { expect_is(aux_skewness(10, 0.5), class(10)) expect_is(aux_skewness(10, 0.5), class(0.5)) expect_is(aux_skewness(17, 0.25), class(17)) expect_is(aux_skewness(17, 0.25), class(0.25)) }) #aux_kurtosis test_that("aux_kurtosis fails for certain parameters", { expect_error(aux_kurtosis(0, 0.5)) expect_error(aux_kurtosis(10, 0)) expect_error(aux_kurtosis(18, 1)) }) test_that("aux_kurtosis produces a single output", { expect_length(aux_kurtosis(10, 0.5), 1) expect_length(aux_kurtosis(49, 0.4), 1) expect_length(aux_kurtosis(92, 0.5), 1) }) test_that("aux_kurtosis produces output of class type equal to that of its parameters", { expect_is(aux_kurtosis(10, 0.5), class(10)) expect_is(aux_kurtosis(10, 0.5), class(0.5)) expect_is(aux_kurtosis(17, 0.25), class(17)) expect_is(aux_kurtosis(17, 0.25), class(0.25)) })

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/seq ana lab 2/lab4.R

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dlchrom1=234560296; dlchrom3=203221608; dlchrom4=173516952; dlchrom8=150392723; dlchrom12=108540874; dlchrom18=75010233; contig1=1+2698+161+14809; contig3=1+2323+171+11569; contig4=1+9831+71+1664; contig8=1215+1+7950+63; contig12=820+1+28+5707; contig18=690+1+3904+39 dlugosc=c(dlchrom1,dlchrom3,dlchrom4,dlchrom8,dlchrom12,dlchrom18); contig=c(contig1,contig3,contig4,contig8,contig12,contig18); plot(dlugosc,contig, type="b", xlab="dlugosc sekwencji", ylab="ilosc contigow",); title("zaleznosci ilosci contigow od dlugosci sekwencji chromosomu"); lspokrycie=48.0; lscontig=458935; penpokrycie=61; pencontig=230930; varpokrycie=81; varcontig=110959; halpokrycie=103; halcontig=31786; plasmidpokrycie=136; plasmidcontig=349; simpokrycie=180; simcontig=82; pokrycie=c(lspokrycie,penpokrycie,varpokrycie,halpokrycie,plasmidpokrycie,simpokrycie); contigC=c(lscontig,pencontig,varcontig,halcontig,plasmidcontig,simcontig); plot(pokrycie,contigC,type='b',xlab='pokrycie genomu',ylab='ilosc contigow'); title("zaleznosc ilosci contigow od pokrycia genomu");

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/prostateCancerFunctions.R

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prostateCancerFunctions.R

library(survival) library(pec) library(MASS) library(muhaz) library(caret) library(matrixStats) library(gbm,lib.loc ='gbmci') library(glmnet) # read in training and testing data readData <- function(trainFile, testFile) { trainingDF <- read.csv(trainFile) testDF <- read.csv(testFile) time <- read.csv("trainTime.csv") names(time) <- c("time") time <- as.matrix(time) # requires time to be a matrix to work with Surv event <- read.csv("trainEvent.csv") names(event) <- c("event") event <- as.matrix(event) # requires event to be a matrix to work with Surv return(list("time" = time, "event" = event, "trainingDF" = trainingDF, "testDF" = testDF)) } # map data vector x to norm based on quantile MapToNorm <- function(x){ return (qnorm(ppoints(length(x)))[order(order(x))]) } # normalize real-valued columns normalizeData <- function(dataObject, n){ time <- dataObject$time trainingDF <- dataObject$trainingDF event <- dataObject$event testDF <- dataObject$testDF preProc <- preProcess(trainingDF[,1:n], method=c("center", "scale")) # calculate mean and variance of training set trainingDF.norm <- predict(preProc, trainingDF[,1:n]) # apply centering/scaling to training set trainingDF.norm = data.frame(trainingDF.norm, trainingDF[,(n+1):ncol(trainingDF)]) testDF.norm <- predict(preProc, testDF[,1:n]) # apply centering/scaling to test set testDF.norm = data.frame(testDF.norm, testDF[,(n+1):ncol(testDF)]) return(list("time" = time, "event" = event, "trainingDF" = trainingDF.norm, "testDF" = testDF.norm)) } # coxph regression model.coxph <- function(dataObject){ coxfit <- coxph(formula=Surv(dataObject$time, dataObject$event) ~ ., data=dataObject$trainingDF, method="efron") globalRisk <- predict(coxfit, newdata=dataObject$testDF, type="risk") # global (relative risk) # predict survival probability (risk) at 12, 18, 24 months testSurvivalProb <- predictSurvProb(coxfit, newdata=dataObject$testDF, times=seq(366,732,183)) risksDF <- data.frame(globalRisk, testSurvivalProb[,1], testSurvivalProb[,2], testSurvivalProb[,3]) return(risksDF) } # gradient boost machine with coxph model.gbmcoxph <- function(dataObject, numTrees, depth, bagFraction, shrink){ time <- dataObject$time event <- dataObject$event trainingDF <- dataObject$trainingDF testDF <- dataObject$testDF coxphfit.gbm <- gbm(Surv(time, event) ~ ., distribution = "coxph", n.trees = numTrees, data = trainingDF, shrinkage= shrink, interaction.depth = depth, bag.fraction = bagFraction, cv.folds = 5, keep.data = TRUE, verbose = FALSE) best.iter <- gbm.perf(coxphfit.gbm, method = "cv", plot.it=FALSE) # returns test set estimate of best number of trees cumulativeHaz <- basehaz.gbm(time, event, coxphfit.gbm$fit, t.eval = seq(366, 732, 183), cumulative = TRUE) testDF.linPred <- predict(coxphfit.gbm, newdata = testDF, best.iter) globalRisk <- exp(testDF.linPred) # global risk s.12mon <- exp(-cumulativeHaz[1])^globalRisk # Survival prob at 12 months s.18mon <- exp(-cumulativeHaz[2])^globalRisk # Survival prob at 18 months s.24mon <- exp(-cumulativeHaz[3])^globalRisk # Survival prob at 24 months risksDF <- data.frame(globalRisk, s.12mon, s.18mon, s.24mon) return(risksDF) } # gradient boost machine with c-index optimization model.gbmci <- function(dataObject, numTrees, depth, bagFraction, shrink){ time <- dataObject$time event <- dataObject$event trainingDF <- dataObject$trainingDF testDF <- dataObject$testDF coxphfit.gbm <- gbm(Surv(time, event) ~ ., distribution = "sci", n.trees = numTrees, data = trainingDF, interaction.depth = depth, bag.fraction = bagFraction, cv.folds = 5, keep.data = TRUE, verbose = FALSE, shrinkage = shrink) best.iter <- gbm.perf(coxphfit.gbm, method = "cv", plot.it=FALSE) # returns test set estimate of best number of trees cumulativeHaz <- basehaz.gbm(time, event, coxphfit.gbm$fit, t.eval = seq(366, 732, 183), cumulative = TRUE) testDF.linPred <- predict(coxphfit.gbm, newdata = testDF, best.iter) globalRisk <- exp(testDF.linPred) # global risk s.12mon <- exp(-cumulativeHaz[1])^globalRisk # Survival prob at 12 months s.18mon <- exp(-cumulativeHaz[2])^globalRisk # Survival prob at 18 months s.24mon <- exp(-cumulativeHaz[3])^globalRisk # Survival prob at 24 months risksDF <- data.frame(globalRisk, s.12mon, s.18mon, s.24mon) return(risksDF) } # coxph regression used to obtain exact time to event model.coxph.time2event <- function(dataObject){ coxfit <- coxph(formula=Surv(dataObject$time, dataObject$event) ~ ., data=dataObject$trainingDF, method="efron") globalRisk <- predict(coxfit, newdata=dataObject$testDF, type="risk") # global (relative risk) # calculates the survival probabilites for each test patient from day 1 through the maximum survival day of the training set survivalProb <- predictSurvProb(coxfit, newdata=dataObject$testDF, times=seq(1,max(dataObject$time),1)) exactTime2Event <- matrix(data=0,nrow=nrow(dataObject$testDF),ncol=1) # scans through each patient looking for the first day where survival probability is less or equal to 0.502 for (i in 1:nrow(dataObject$testDF)){ firstElementCount <- 0 for (j in 1:ncol(survivalProb)){ if (survivalProb[i,j] <= 0.502) { firstElementCount <- 1 exactTime2Event[i] <- j } if (j == ncol(survivalProb)){ exactTime2Event[i] <- j } if (firstElementCount == 1) { break } } } return(exactTime2Event) } # gradient boost machine with coxph used to obtain exact time to event model.gbmcoxph.time2event <- function(dataObject, numTrees, depth, bagFraction, shrink){ time <- dataObject$time event <- dataObject$event trainingDF <- dataObject$trainingDF testDF <- dataObject$testDF coxphfit.gbm <- gbm(Surv(time, event) ~ ., distribution = "coxph", n.trees = numTrees, data = trainingDF, shrinkage= shrink, interaction.depth = depth, bag.fraction = bagFraction, cv.folds = 5, keep.data = TRUE, verbose = FALSE) best.iter <- gbm.perf(coxphfit.gbm, method = "cv", plot.it=FALSE) # returns test set estimate of best number of trees cumulativeHaz <- basehaz.gbm(time, event, coxphfit.gbm$fit, t.eval = seq(1,max(dataObject$time),1), cumulative = TRUE) testDF.linPred <- predict(coxphfit.gbm, newdata = testDF, best.iter) globalRisk <- exp(testDF.linPred) # global risk exactTime2Event <- matrix(data=0,nrow=nrow(dataObject$testDF),ncol=1) survivalProb <- matrix(data=0,nrow=nrow(dataObject$testDF),ncol=max(dataObject$time)) # calculates the survival probabilites for each test patient from day 1 through the maximum survival day of the training set for (k in 1:ncol(survivalProb)){ survivalProb[,k] <- exp(-cumulativeHaz[k])^globalRisk } survivalProb[is.na(survivalProb)] <- 1 # scans through each patient looking for the first day where survival probability is less or equal to 0.502 for (i in 1:nrow(dataObject$testDF)){ firstElementCount <- 0 for (j in 1:ncol(survivalProb)){ if (survivalProb[i,j] <= 0.502) { firstElementCount <- 1 exactTime2Event[i] <- j } if (j == ncol(survivalProb)){ exactTime2Event[i] <- j } if (firstElementCount == 1) { break } } } return(exactTime2Event) } # gradient boost machine with ci used to obtain exact time to event model.gbmci.time2event <- function(dataObject, numTrees, depth, bagFraction, shrink){ time <- dataObject$time event <- dataObject$event trainingDF <- dataObject$trainingDF testDF <- dataObject$testDF coxphfit.gbm <- gbm(Surv(time, event) ~ ., distribution = "sci", n.trees = numTrees, data = trainingDF, shrinkage= shrink, interaction.depth = depth, bag.fraction = bagFraction, cv.folds = 5, keep.data = TRUE, verbose = FALSE) best.iter <- gbm.perf(coxphfit.gbm, method = "cv", plot.it=FALSE) # returns test set estimate of best number of trees cumulativeHaz <- basehaz.gbm(time, event, coxphfit.gbm$fit, t.eval = seq(1,max(dataObject$time),1), cumulative = TRUE) testDF.linPred <- predict(coxphfit.gbm, newdata = testDF, best.iter) globalRisk <- exp(testDF.linPred) # global risk exactTime2Event <- matrix(data=0,nrow=nrow(dataObject$testDF),ncol=1) survivalProb <- matrix(data=0,nrow=nrow(dataObject$testDF),ncol=max(dataObject$time)) # calculates the survival probabilites for each test patient from day 1 through the maximum survival day of the training set for (k in 1:ncol(survivalProb)){ survivalProb[,k] <- exp(-cumulativeHaz[k])^globalRisk } survivalProb[is.na(survivalProb)] <- 1 # scans through each patient looking for the first day where survival probability is less or equal to 0.502 for (i in 1:nrow(dataObject$testDF)){ firstElementCount <- 0 for (j in 1:ncol(survivalProb)){ if (survivalProb[i,j] <= 0.502) { firstElementCount <- 1 exactTime2Event[i] <- j } if (j == ncol(survivalProb)){ exactTime2Event[i] <- j } if (firstElementCount == 1) { break } } } return(exactTime2Event) }

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ch08.R

#+ Setup remotes::install_github('rmcelreath/rethinking', upgrade=F) library(rethinking) #' ## R code 8.1 #+ R code 8.1 library(rethinking) data(rugged) d <- rugged # make log version of outcome d$log_gdp <- log( d$rgdppc_2000 ) # extract countries with GDP data dd <- d[ complete.cases(d$rgdppc_2000) , ] # rescale variables dd$log_gdp_std <- dd$log_gdp / mean(dd$log_gdp) dd$rugged_std <- dd$rugged / max(dd$rugged) #' ## R code 8.2 #+ R code 8.2 m8.1 <- quap( alist( log_gdp_std ~ dnorm( mu , sigma ) , mu <- a + b*( rugged_std - 0.215 ) , a ~ dnorm( 1 , 1 ) , b ~ dnorm( 0 , 1 ) , sigma ~ dexp( 1 ) ) , data=dd ) #' ## R code 8.3 #+ R code 8.3 set.seed(7) prior <- extract.prior( m8.1 ) # set up the plot dimensions plot( NULL , xlim=c(0,1) , ylim=c(0.5,1.5) , xlab="ruggedness" , ylab="log GDP" ) abline( h=min(dd$log_gdp_std) , lty=2 ) abline( h=max(dd$log_gdp_std) , lty=2 ) # draw 50 lines from the prior rugged_seq <- seq( from=-0.1 , to=1.1 , length.out=30 ) mu <- link( m8.1 , post=prior , data=data.frame(rugged_std=rugged_seq) ) for ( i in 1:50 ) lines( rugged_seq , mu[i,] , col=col.alpha("black",0.3) ) #' ## R code 8.4 #+ R code 8.4 sum( abs(prior$b) > 0.6 ) / length(prior$b) #' ## R code 8.5 #+ R code 8.5 m8.1 <- quap( alist( log_gdp_std ~ dnorm( mu , sigma ) , mu <- a + b*( rugged_std - 0.215 ) , a ~ dnorm( 1 , 0.1 ) , b ~ dnorm( 0 , 0.3 ) , sigma ~ dexp(1) ) , data=dd ) #' ## R code 8.6 #+ R code 8.6 precis( m8.1 ) #' ## R code 8.7 #+ R code 8.7 # make variable to index Africa (1) or not (2) dd$cid <- ifelse( dd$cont_africa==1 , 1 , 2 ) #' ## R code 8.8 #+ R code 8.8 m8.2 <- quap( alist( log_gdp_std ~ dnorm( mu , sigma ) , mu <- a[cid] + b*( rugged_std - 0.215 ) , a[cid] ~ dnorm( 1 , 0.1 ) , b ~ dnorm( 0 , 0.3 ) , sigma ~ dexp( 1 ) ) , data=dd ) #' ## R code 8.9 #+ R code 8.9 compare( m8.1 , m8.2 ) #' ## R code 8.10 #+ R code 8.10 precis( m8.2 , depth=2 ) #' ## R code 8.11 #+ R code 8.11 post <- extract.samples(m8.2) diff_a1_a2 <- post$a[,1] - post$a[,2] PI( diff_a1_a2 ) #' ## R code 8.12 #+ R code 8.12 rugged.seq <- seq( from=-0.1 , to=1.1 , length.out=30 ) # compute mu over samples, fixing cid=2 and then cid=1 mu.NotAfrica <- link( m8.2 , data=data.frame( cid=2 , rugged_std=rugged.seq ) ) mu.Africa <- link( m8.2 , data=data.frame( cid=1 , rugged_std=rugged.seq ) ) # summarize to means and intervals mu.NotAfrica_mu <- apply( mu.NotAfrica , 2 , mean ) mu.NotAfrica_ci <- apply( mu.NotAfrica , 2 , PI , prob=0.97 ) mu.Africa_mu <- apply( mu.Africa , 2 , mean ) mu.Africa_ci <- apply( mu.Africa , 2 , PI , prob=0.97 ) #' ## R code 8.13 #+ R code 8.13 m8.3 <- quap( alist( log_gdp_std ~ dnorm( mu , sigma ) , mu <- a[cid] + b[cid]*( rugged_std - 0.215 ) , a[cid] ~ dnorm( 1 , 0.1 ) , b[cid] ~ dnorm( 0 , 0.3 ) , sigma ~ dexp( 1 ) ) , data=dd ) #' ## R code 8.14 #+ R code 8.14 precis( m8.5 , depth=2 ) #' ## R code 8.15 #+ R code 8.15 compare( m8.1 , m8.2 , m8.3 , func=PSIS ) #' ## R code 8.16 #+ R code 8.16 plot( PSIS( m8.3 , pointwise=TRUE )$k ) #' ## R code 8.17 #+ R code 8.17 # plot Africa - cid=1 d.A1 <- dd[ dd$cid==1 , ] plot( d.A1$rugged_std , d.A1$log_gdp_std , pch=16 , col=rangi2 , xlab="ruggedness (standardized)" , ylab="log GDP (as proportion of mean)" , xlim=c(0,1) ) mu <- link( m8.3 , data=data.frame( cid=1 , rugged_std=rugged_seq ) ) mu_mean <- apply( mu , 2 , mean ) mu_ci <- apply( mu , 2 , PI , prob=0.97 ) lines( rugged_seq , mu_mean , lwd=2 ) shade( mu_ci , rugged_seq , col=col.alpha(rangi2,0.3) ) mtext("African nations") # plot non-Africa - cid=2 d.A0 <- dd[ dd$cid==2 , ] plot( d.A0$rugged_std , d.A0$log_gdp_std , pch=1 , col="black" , xlab="ruggedness (standardized)" , ylab="log GDP (as proportion of mean)" , xlim=c(0,1) ) mu <- link( m8.3 , data=data.frame( cid=2 , rugged_std=rugged_seq ) ) mu_mean <- apply( mu , 2 , mean ) mu_ci <- apply( mu , 2 , PI , prob=0.97 ) lines( rugged_seq , mu_mean , lwd=2 ) shade( mu_ci , rugged_seq ) mtext("Non-African nations") #' ## R code 8.18 #+ R code 8.18 rugged_seq <- seq(from=-0.2,to=1.2,length.out=30) muA <- link( m8.3 , data=data.frame(cid=1,rugged_std=rugged_seq) ) muN <- link( m8.3 , data=data.frame(cid=2,rugged_std=rugged_seq) ) delta <- muA - muN #' ## R code 8.19 #+ R code 8.19 library(rethinking) data(tulips) d <- tulips str(d) #' ## R code 8.20 #+ R code 8.20 d$blooms_std <- d$blooms / max(d$blooms) d$water_cent <- d$water - mean(d$water) d$shade_cent <- d$shade - mean(d$shade) #' ## R code 8.21 #+ R code 8.21 a <- rnorm( 1e4 , 0.5 , 1 ); sum( a < 0 | a > 1 ) / length( a ) #' ## R code 8.22 #+ R code 8.22 a <- rnorm( 1e4 , 0.5 , 0.25 ); sum( a < 0 | a > 1 ) / length( a ) #' ## R code 8.23 #+ R code 8.23 m8.4 <- quap( alist( blooms_std ~ dnorm( mu , sigma ) , mu <- a + bw*water_cent + bs*shade_cent , a ~ dnorm( 0.5 , 0.25 ) , bw ~ dnorm( 0 , 0.25 ) , bs ~ dnorm( 0 , 0.25 ) , sigma ~ dexp( 1 ) ) , data=d ) #' ## R code 8.24 #+ R code 8.24 m8.5 <- quap( alist( blooms_std ~ dnorm( mu , sigma ) , mu <- a + bw*water_cent + bs*shade_cent + bws*water_cent*shade_cent , a ~ dnorm( 0.5 , 0.25 ) , bw ~ dnorm( 0 , 0.25 ) , bs ~ dnorm( 0 , 0.25 ) , bws ~ dnorm( 0 , 0.25 ) , sigma ~ dexp( 1 ) ) , data=d ) #' ## R code 8.25 #+ R code 8.25 par(mfrow=c(1,3)) # 3 plots in 1 row for ( s in -1:1 ) { idx <- which( d$shade_cent==s ) plot( d$water_cent[idx] , d$blooms_std[idx] , xlim=c(-1,1) , ylim=c(0,1) , xlab="water" , ylab="blooms" , pch=16 , col=rangi2 ) mu <- link( m8.4 , data=data.frame( shade_cent=s , water_cent=-1:1 ) ) for ( i in 1:20 ) lines( -1:1 , mu[i,] , col=col.alpha("black",0.3) ) } #' ## R code 8.26 #+ R code 8.26 set.seed(7) prior <- extract.prior(m8.5) #' ## R code 8.27 #+ R code 8.27 d$lang.per.cap <- d$num.lang / d$k.pop

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Example-1.R

# Generate HIMA example based on real-world dataset - 1 (linear outcome) set.seed(1029) p <- 300 # the dimension of mediators q <- 2 # the dimension of covariates n <- 300 # sample size alpha <- matrix(0,1,p) # the coefficients for X -> M beta <- matrix(0,1,p) # the coefficients for M -> Y alpha[1:5] <- 0.5 beta[1:5] <- 0.5 zeta <- matrix(0.01,p,q) # the coefficients of covariates for X -> M eta <- matrix(0.01,1,q) # the coefficients of covariates for M -> Y gamma <- 0.5 # the direct effect gamma_total <- gamma + alpha%*%t(beta) # the total effect X <- matrix(rbinom(n, size=1, prob=0.6),n,1) # expoure: 1=treatment; 0=placebo Z <- matrix(0,n,2) # covariates Z[,1] <- rbinom(n, size=1, prob=0.5) # sex: male=1; female=0 Z[,2] <- sample(18:65,n,replace=TRUE) # age ranging from 18-65 # Z[,2] <- (Z[,2]-mean(Z[,2]))/sd(Z[,2]) # scaled age, so we add a note that age is scaled before modeling e <- matrix(rnorm(n*p, mean = 0, sd = 1.5),n,p) E <- matrix(rnorm(n, mean = 0, sd = 1),n,1) M <- X%*%(alpha) + Z%*%t(zeta) + e # the mediator matrix Y <- X*gamma + M%*%t(beta) + Z%*%t(eta) + E # the response Y colnames(M) <- paste0("M", 1:p) rownames(M) <- paste0("S", 1:n) pheno <- data.frame(Treatment = X, Outcome = Y, Sex = Z[,1], Age = Z[,2]) Example1 <- list(PhenoData = pheno, Mediator = M) usethis::use_data(Example1, overwrite = TRUE)

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reading_NGK.r

n<-64 ############################################################# #What user needs to change: Kernel and path name<-"Gauss" path=paste("/FolderName/Test_",name,"_n=",n,"_p=80.csv",sep="") ############################################################# name<-"Gauss" path=paste("/FolderName/Test_",name,"_n=",n,"_p=80.csv",sep="") A<-as.matrix(read.csv(path)[,-1]) #RSS RSS<-mean(A[,6]/n);RSSsd<-sd(A[,6]/n) #SE SE<-mean(A[,4]);SEsd<-sd(A[,4]) A[which(A!=0)]<-1 count<-colSums(A) #False Positive rate (alpha) FP<-mean(rowSums(A[,13:87])/(rowSums(A[,13:87])+75)); FPsd<-sd(rowSums(A[,13:87])/(rowSums(A[,13:87])+75)) #False negative rate (beta) FN<-mean((5-rowSums(A[,8:12]))/((5-rowSums(A[,8:12]))+5)); FNsd<-sd((5-rowSums(A[,8:12]))/((5-rowSums(A[,8:12]))+5)) MS<-mean(rowSums(A[,8:87])) MSsd<-sd(rowSums(A[,8:87])) count cbind(FP,FPsd,FN,FNsd,MS,MSsd,RSS,RSSsd,SE,SEsd) ############################################################# #What user needs to change: Kernel and path name<-"Poly" path=paste("/FolderName/Test_",name,"_n=",n,"_p=80.csv",sep="") ############################################################# A<-as.matrix(read.csv(path)[,-1]) #RSS RSS<-mean(A[,6]/n);RSSsd<-sd(A[,6]/n) #SE SE<-mean(A[,4]);SEsd<-sd(A[,4]) A[which(A!=0)]<-1 count1<-colSums(A) #False Positive rate (alpha) FP<-mean(rowSums(A[,13:87])/(rowSums(A[,13:87])+75)); FPsd<-sd(rowSums(A[,13:87])/(rowSums(A[,13:87])+75)) #False negative rate (beta) FN<-mean((5-rowSums(A[,8:12]))/((5-rowSums(A[,8:12]))+5)); FNsd<-sd((5-rowSums(A[,8:12]))/((5-rowSums(A[,8:12]))+5)) MS<-mean(rowSums(A[,8:87])) MSsd<-sd(rowSums(A[,8:87])) count1 cbind(FP,FPsd,FN,FNsd,MS,MSsd,RSS,RSSsd,SE,SEsd) ############# par(mfrow=c(1,1),oma=c(0,1,4,0),mar=c(8,4.5,6,2)) plot(count[-(1:7)]/400,xlab="Predictor Index", ylab="Probability",cex=1.,cex.lab=1.5,cex.axis=1.5) points(count1[-(1:7)]/400,pch=20) legend(locator(1),cex=1.5, c("NGK Gauss","NGK linear poly"),pch=c(1,20))

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jumptestday.Rd.R

library(JumpTest) ### Name: jumptestday ### Title: Nonparametric jump test for each interval ### Aliases: jumptestday ### ** Examples orip <- runif(100) testres <- jumptestday(orip) ts <- testres@stat pv <- testres@pvalue

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merging_accuracytable_7.R

dindexname = "sri12" bsnnames = c("soyang", "daecheong", "andong", "sumjin", "chungju", "hapcheon", "namgang", "imha") bsncodes = c(1012, 3008, 2001, 4001, 1003, 2015, 2018, 2002) period.irr = c(4:9) period.nonirr = setdiff(c(1:12), period.irr) bsncase = 1 for (bsncase in c(1:length(bsncodes))){ targetbsn = bsncodes[bsncase]; bsnname = bsnnames[bsncase]; kfoldinfo = read.csv("kfoldinfo.csv", header=T, row.names=1) kk=1 BSfilepath = file.path("./predictResult","BSresult") BS.df = read.csv(file.path(BSfilepath, paste0("BS_allperiod_",dindexname,"_",bsnname,".csv")), row.names=1) BS.df$RPS = NA enstype = "raw" for (enstype in c("raw", "up")){ for (kk in c(1:4)){ kfold = paste0("k", kk) predictfilepath = file.path("./predictResult", kfold) RPSfilepath = file.path(predictfilepath, "RPSresult") rpsprob.ts = read.csv(file.path(RPSfilepath,paste0("RPS_prob_",enstype,"_",dindexname,"_",bsnname,".csv")), row.names=1) %>% xts(., ymd(rownames(.))) rpsdet.ts = read.csv(file.path(RPSfilepath,paste0("RPS_det_",enstype,"_",dindexname,"_",bsnname,".csv")), row.names=1) %>% xts(., ymd(rownames(.))) attach(BS.df) period = "all" BS.df$RPS[which(Predicttype=="prob"&K==kfold&Enstype==enstype&Period==period)] = mean(rpsprob.ts) BS.df$RPS[which(Predicttype=="det"&K==kfold&Enstype==enstype&Period==period)] = mean(rpsdet.ts) period = "irrigation" RPS.irr = month(rpsprob.ts) %in% period.irr %>% rpsprob.ts[.] %>% mean(.) BS.df$RPS[which(Predicttype=="prob"&K==kfold&Enstype==enstype&Period==period)] = RPS.irr RPS.irr = month(rpsdet.ts) %in% period.irr %>% rpsdet.ts[.] %>% mean(.) BS.df$RPS[which(Predicttype=="det"&K==kfold&Enstype==enstype&Period==period)] = RPS.irr period = "non-irrigation" RPS.nonirr = month(rpsprob.ts) %in% period.nonirr %>% rpsprob.ts[.] %>% mean(.) BS.df$RPS[which(Predicttype=="prob"&K==kfold&Enstype==enstype&Period==period)] = RPS.nonirr RPS.nonirr = month(rpsdet.ts) %in% period.nonirr %>% rpsdet.ts[.] %>% mean(.) BS.df$RPS[which(Predicttype=="det"&K==kfold&Enstype==enstype&Period==period)] = RPS.nonirr detach() } } accfilepath = file.path("./predictResult") acc.df = BS.df acc.df %>% write.csv(., file.path(accfilepath, paste0("accuracytable_",dindexname,"_",bsnname,".csv"))) }

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Ex3.R

# Required packages library(mitml) # Read data filepath <- "~/desktop/examples/imps.csv" impdata <- read.csv(filepath, header = F) names(impdata) <- c("imputation","id","female","diagnose","sleep","pain","posaff","negaff","stress") # Compute product variable impdata$femxpain <- impdata$female * impdata$pain # Analyze data and pool estimates implist <- as.mitml.list(split(impdata, impdata$imputation)) analysis <- with(implist, lm(stress ~ female + pain + femxpain)) estimates <- testEstimates(analysis, var.comp = T, df.com = (250-3-1)) estimates # Compare models with Wald test emptymodel <- with(implist, lm(stress ~ 1)) testModels(analysis, emptymodel, method = "D1")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/HurdleDistributions.R \name{rGibbsHurdle} \alias{rGibbsHurdle} \alias{cpp_rGibbsHurdle} \title{Sample from a multivariate hurdle model} \usage{ rGibbsHurdle(G, H, K, Nt, burnin = 500, thin = 0.1, tol = 5e-04, Nkeep = 500) } \arguments{ \item{G}{symmetric discrete interaction matrix} \item{H}{unstructured location matrix} \item{K}{Symmetric positive definite conditional precision matrix} \item{Nt}{Number of unthinned samples, including burnin} \item{burnin}{how many samples to discard from burn in} \item{thin}{how many samples to thin} \item{tol}{Numeric tolerance for zero} \item{Nkeep}{(optional) number of samples, post-burnin and thinning} } \value{ matrix of (Nt-burnin)*thin samples } \description{ Sample from a multivariate hurdle model } \examples{ G = matrix(c(-15, 1, 0, 1, -15, 1.5, 0, 1.5, -15), nrow=3) H = diag(5, nrow=3) K = diag(1, nrow=3) y = rGibbsHurdle(G, H, K, 2000, thin = .2, burnin=1000) }

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context("toAST") test_that("call args have correct parent", { result = toAST( call("is.na", 42L) ) # ----- expect_is(result, "Call") expect_is(result$args[[1]], "Integer") expect_identical(result, result$args[[1]]$parent) }) test_that("primitives are converted to Primitives", { result = toAST(sum) # ----- expect_is(result, "Primitive") #expect_is(result$name, "Symbol") pnames = names(formals(args(sum))) expect_equal(names(result$params), pnames) p1 = result$params[[1]] expect_is(p1, "Parameter") expect_equal(p1$default, NULL) p2 = result$params[[2]] expect_is(p2, "Parameter") expect_equal(p2$default$value, FALSE) }) test_that("functions are converted to Functions", { result = toAST(ifelse) # ----- expect_is(result, "Function") pnames = names(formals(ifelse)) expect_equal(names(result$params), pnames) p1 = result$params[[1]] expect_is(p1, "Parameter") expect_equal(p1$default, NULL) p2 = result$params[[2]] expect_is(p2, "Parameter") expect_equal(p2$default, NULL) p3 = result$params[[3]] expect_is(p3, "Parameter") expect_equal(p3$default, NULL) }) test_that("functions with no params are converted to Functions", { result = toAST(function() 42L) # ----- expect_is(result, "Function") expect_equal(length(result$params), 0) }) test_that("function definitions are converted to Functions", { code = quote(function(a, b = 3) 42L) result = toAST(code) # ----- expect_is(result, "Function") expect_equal(names(result$params), c("a", "b")) p1 = result$params[[1]] expect_is(p1, "Parameter") expect_equal(p1$default, NULL) p2 = result$params[[2]] expect_is(p2, "Parameter") expect_equal(p2$default$value, 3) })

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popovers.R

po_reseed_graph <- function() { title <- "Re-seed Graph" content <- "Generate a new random number to seed the graph layout." list(title = title, content = content) } po_graph_layout <- function() { title <- "Graph Layout Algorithms" content <- paste0("Select a network layout for the graph. ", "<code>FR</code> Fruchterman-Reingold ", "<code>KK</code> Kamada-Kawai ", "<code>DH</code> Davidson-Harel ", "<code>LGL</code> Large Graph Layout ", "<code>DrL</code> Distributed Recursive Layout ", "<code>GEM</code> GEM Force-Directed Layout ", "<code>MDS</code> Multidimensional Scaling Layout ", "  ", "<a href = 'https://igraph.org/c/doc/igraph-Layout.html' target = '_blank'>igraph Layouts</a>" ) list(title = title, content = content) } po_cat_filter <- function() { title <- "Categorical Filter" content <- paste0("Categorical variables are identified by graph vertex attributes with names that begin with the ", "prefix code <code>vosonCA_</code>. VOSON Dash does not provide an interface for adding ", "these vertex attributes at this time, so they must be added to the graph in a seperate data ", "coding process. ", "When found these variables appear in the <code>Category</code> select list and can be used to ", "filter graph vertices using the list of category values under <code>View</code>.") list(title = title, content = content) } po_twit_query <- function() { title <- "Twitter Search Query" content <- paste0("A range of search operators and filters can be used in the <code>Search Query</code> input. ", "Simple terms can also entered and used in conjunction with the <code>Additional Filters</code> ", "provided. ", "  ", "<a href = 'https://developer.twitter.com/en/docs/tweets/rules-and-filtering/overview/standard-", "operators' target = '_blank'>Twitter Standard Search Operators</a>" ) list(title = title, content = content) } po_twit_id_range <- function() { title <- "Twitter ID Range" content <- paste0("Set the bounds of a search. <code>Since ID</code> requests the twitter API to return only ", "tweets tweeted after a particular tweet or status ID. <code>Max ID</code> requests the return ", "of only tweets tweeted before a tweet or status ID." ) list(title = title, content = content) } po_twit_lang <- function() { title <- "Tweet Language" content <- paste0("Requests the twitter API to return tweets in the language entered as two character ", "<code>ISO 639-1</code> code. Language detection is best-effort. ", "  ", "<a href = 'https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes'", "target = '_blank'>ISO 639-1 Language Codes</a>" ) list(title = title, content = content) } po_twit_results <- function() { title <- "Search Results" content <- paste0("Requests the twitter API to return tweets of the following result types. ", "<code>Recent</code> Return only the most recent tweets in results ", "<code>Mixed</code> Return mixture of both popular and real time tweets in results ", "<code>Popular</code> Return only the most popular tweets in results" ) list(title = title, content = content) } po_twit_assoc <- function() { title <- "Concept Relations" content <- paste0("<code>Limited</code> option includes only ties between most frequently ", "occurring hashtags and terms. If unchecked the network will include ties between most ", "frequently occurring hashtags and terms, hashtags and hashtags, and terms and terms." ) list(title = title, content = content) } po_yt_url <- function() { title <- "Youtube Video URL" content <- paste0("Enter a Youtube video URL in either long or short format. ", "  ", "<code>https://www.youtube.com/watch?v=xxxxxxxxxxx</code> ", "  ", "<code>https://youtu.be/xxxxxxxx</code>" ) list(title = title, content = content) } po_red_url <- function() { title <- "Reddit Thread URL" content <- paste0("Enter a Reddit thread URL in the following format. ", "  ", "<code>https://www.reddit.com/r/xxxxxx/ comments/xxxxxx/x_xxxx_xxxx</code> ", "Collects maximum 500 comments per thread." ) list(title = title, content = content) } po_web_auth <- function() { title <- "Web Auth Token" content <- paste0("This token creation method is interactive and will open a web browser tab to twitter asking you ", "to login and authorize the twitter app. This allows the app to access the twitter API on your ", "behalf. ", "As <code>VOSONDash</code> only uses the twitter search API an app only requires minimal ", "<code>read</code> access to the users twitter account. ", "Note: Unfortunately a current side-effect of <code>aborting</code> or interupting / not ", "completing the process is ending the <code>VOSONDash</code> session. Please save any work before ", "continuing.", " ", "  ", "<a href = 'https://developer.twitter.com/en/docs/basics/authentication/overview/oauth'", "target = '_blank'>Twitter API: Application-user authentication</a>" ) list(title = title, content = content) }

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/R/makePredictions.R

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makePredictions.R

#'@export makePredictions <- function() { results <- read.csv(file = "data/HistNCAATournamentResults.csv", header = T, stringsAsFactors = F) results$GID <- c(1:nrow(results)) lmmodel <- create_model(fromSample = T, yearToTest = NULL) test <- lmmodel[[2]] lmmodel <- lmmodel[[1]] # test <- get_kp_data(results %>% filter(Year == 2017))[[1]] test$pred <- predict(lmmodel,newdata=test,type='response') test$bpred <- ifelse(test$pred > 0.5, 1, 0) test <- test[order(test$GID),] gids <- test %>% group_by(GID) %>% summarise(gamec = sum(bpred), count = n()) %>% filter(gamec != 1 & count == 2) %>% as.data.frame() %>% select(GID) gids <- as.vector(gids$GID) w <- which(test$GID %in% gids) for(i in 1:length(gids)) { if(test$pred[w[(2*i)-1]] > test$pred[w[2*i]]) { test$bpred[w[2*i - 1]] <- 1 test$bpred[w[2*i]] <- 0 } else { test$bpred[w[2*i - 1]] <- 0 test$bpred[w[2*i]] <- 1 } } test$Correct <- ifelse(test$Result == test$bpred, 1, 0) plyr::count(test$Correct) return(test) # games <- data.frame() # for(i in 1:length(unique(test$GID))) { # t <- test %>% filter(GID == unique(test$GID)[i]) # t <- t[order(-t$bpred),] # temp <- as.data.frame(matrix(ncol = 0, nrow = 1)) # temp$GID <- t$GID[1] # temp$Year <- t$Year[1] # temp$Round <- t$Round[1] # temp$PWinner <- t$Team[1] # temp$PLoser <- t$Team[2] # temp$Winner <- t %>% filter(Result == 1) %>% select(Team) %>% unlist() # temp$Loser <- t %>% filter(Result == 0) %>% select(Team) %>% unlist() # temp$Correct <- ifelse(temp$PWinner == temp$Winner, 1, 0) # # games <- rbind(games, temp) # } }

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/plot3.R

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plot3.R

# # Exploratory Data Analysis # Course Project 1 Extension # Plot 3 # # needs data.table package for fast reading require("data.table") full_data <- fread('household_power_consumption.txt', na.strings="?") target_dates <- as.Date(c("2007-02-01", "2007-02-02")) target_data <- full_data[as.Date(full_data$Date, format='%d/%m/%Y') %in% target_dates] datetime <- as.POSIXct( paste(target_data$Date, target_data$Time), format = "%d/%m/%Y %T") # construct png directly, as dev.copy results in cropped legend png("plot3.png", width = 480, height = 480) # build plot 3 plot(datetime, target_data$Sub_metering_1, type='l', xlab = '', ylab = 'Energy sub metering') lines(datetime, target_data$Sub_metering_2, type='l', col='red') lines(datetime, target_data$Sub_metering_3, type='l', col='blue') legend("topright", col = c("black","blue", "red"), lwd=1, legend = colnames(target_data)[7:9]) dev.off()

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ar_ordered.R

# level gamma acceptance region from ordering of X's at fixed lambda and b # r is the ranking function ar.ordered <- function(lambda, b, gamma, r) { X = 0:170 R = r(X, lambda, b) # get sort indices R.sort.idx = sort(R, decreasing=T, index.return=T)$ix # probability masses px.sort = dpois(X[R.sort.idx], lambda+b) cumprob = cumsum(px.sort) # first index with cumulative probability >= gamma last.x.idx = min(which(cumprob >= gamma)) left = min(X[R.sort.idx[1:last.x.idx]]) right= max(X[R.sort.idx[1:last.x.idx]]) return(data.frame(lambda,left,right)) }

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/man/FSR_standalone.Rd

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FSR_standalone.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/standalone.R \name{FSR_standalone} \alias{FSR_standalone} \title{FSR Standalone} \usage{ FSR_standalone(languages = psyquest::languages(), subscales = NULL, ...) } \arguments{ \item{languages}{(Character vector) Determines the languages available to participants. Possible languages include \code{"en"} (English), and \code{"de"} (German). The first language is selected by default.} \item{subscales}{(Character vector) The subscales to be included in the questionnaire. Possible subscales are \code{"Absorption"}, \code{"Fluency of performance"}, \code{"Demands"}, \code{"Skills"}, \code{"Demand Fit"} and \code{"Importance"}. If no subscales are provided all subscales are selected.} \item{...}{Further arguments to be passed to \code{\link{FSR}()}.} } \description{ This function launches a standalone testing session for the FSS questionnaire. FSR stands for 'Theory of Intelligence'. }