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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
bfd2e3c6b7a9d9528f01eb34aede0cd7a9835eb8 | cb62ed00ef0ebe7e779acebde313a173a43cb345 | /scripts/mlr-settings/resampling.R | 6bec9b701c55b867b0c0f21c28dd9565a7166d5b | [] | no_license | wlandau/pathogen-modeling | a23fb68f099c977b04fff15fd3b70e974c7bbe11 | 0e9e720b83f8acb985b755319fb2152ee1953928 | refs/heads/master | 2020-04-11T11:05:16.771982 | 2018-12-13T17:27:35 | 2018-12-13T17:27:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 136 | r | resampling.R | spcv_inner_fiveF <- makeResampleDesc("SpCV", iters = 5)
spcv_outer_fiveF_hundredR <- makeResampleDesc("SpRepCV", folds = 5, reps = 100)
|
ea72779bb31382bb00509c7f62e539def314c6f5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/pdmod/examples/calculateResponse.Rd.R | 001957a30fff23c6b3f74f914c563bd42342875c | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 183 | r | calculateResponse.Rd.R | library(pdmod)
### Name: calculateResponse
### Title: Calculate response from the estimate
### Aliases: calculateResponse
### ** Examples
calculateResponse(0.8, 10, runif(20))
|
d0074191c5e30fca8c9dbebd251e9f9d547bcc2f | 981cbaf799599f6d23bf79cdeb4ef72a8f3eb8a5 | /script/5_imbalance.R | 4c021139a5752a24e2402d65d24cf73ff38c020c | [] | no_license | noahhhhhh/Santander_Customer_Satisfaction | 51249cdc53ef6fcf545cd0e069e3b5e3458857af | 2cce8e82ab12659445818f42316cdd8e7ae9d8b6 | refs/heads/master | 2021-01-17T17:14:28.761063 | 2016-05-10T02:38:32 | 2016-05-10T02:38:32 | 54,017,892 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,306 | r | 5_imbalance.R | setwd("/Volumes/Data Science/Google Drive/data_science_competition/kaggle/Santander_Customer_Satisfaction/")
rm(list = ls()); gc();
require(data.table)
require(purrr)
require(caret)
require(xgboost)
require(Ckmeans.1d.dp)
require(Metrics)
require(ggplot2)
require(DMwR)
source("utilities/preprocess.R")
source("utilities... |
728159bc6f12e898782e547160841de56f7b0529 | c19103d2e850ad2f0f4f5dfad67d6b83bdabcf37 | /R/survcorr.R | 12930bad4140590649cf3b1c34a8207e25ecde64 | [] | no_license | cran/SurvCorr | 05941fb13797b181447ff3cb08697df1a50e7c00 | f57dfa76184e4372a3a9785e7b37aff08c8d7106 | refs/heads/master | 2022-11-18T22:49:39.717957 | 2022-11-08T13:10:12 | 2022-11-08T13:10:12 | 31,345,431 | 1 | 3 | null | null | null | null | UTF-8 | R | false | false | 10,681 | r | survcorr.R | ## Helper function for bivariate pearson correlation.
pearson = function(data) {
cormatrix = cor(data, method="pearson")
cormatrix[1, 2]
}
survcorr = function
(
formula1, # Formula for defining time-to-event 1, e.g. Surv(TIME1, STATUS1) ~ 1.
formula2, # Formula for defining time-to-event 2, =Surv(TI... |
f7581ee6a8caf1ec2ba78eba86e8399d7840563f | d68031a6d44fb8e7ee8f5ddbcbf57b08af875df0 | /global.R | 29d43bb70035ea4a30f9d43ebcac53fb5e014bc2 | [] | no_license | bradley-pearson6597/Appsilon-Marine-App | 4e98d6c32728dcfdd8d948f7a22fe33eec4cfd3f | 1c7f0a3ab87bd33142b06d009d64c1ba9e2ab790 | refs/heads/main | 2023-02-11T09:45:55.695181 | 2021-01-06T13:56:49 | 2021-01-06T13:56:49 | 310,593,716 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,177 | r | global.R | #####################################################################################################
###################################### GLOBAL FILE ####################################################
#######################################################################################################
# Load li... |
17cbd92d622ff08e611251e223d9b9af76fbf766 | 7a8ae73cfd590fd83273e3ec47d37011ab4d8089 | /tsne/man/tsne-package.Rd | cb6410611c64dc1e5f2e5c1eaa64913fa1b7b9c4 | [] | no_license | jdonaldson/rtsne | 3dc0bc8f8d82cae1508fb06c40f03dda97195817 | a33cc0087dea7dfa7671d4d6f0049dbc7b2f77c9 | refs/heads/master | 2021-01-17T08:37:47.280295 | 2019-08-27T17:50:58 | 2019-08-27T17:50:58 | 4,193,788 | 56 | 25 | null | 2019-08-21T23:40:49 | 2012-05-01T16:02:53 | R | UTF-8 | R | false | false | 1,133 | rd | tsne-package.Rd | \name{tsne-package}
\Rdversion{1.1}
\alias{tsne-package}
\docType{package}
\title{The tsne-package for multidimensional scaling}
\description{
This package contains one function called \link[tsne]{tsne} which contains all the functionality.
}
\details{
\tabular{ll}{
Package: \tab tsne\cr
Type: \tab Package\cr
Version... |
af92b01d96c93ad1469c611a5c48dfec1d41199b | c024630d8b0ed0f6d1ab731bcea878f6835fbe0e | /2020-12-07/test_converter2_er.R | 2bba7e7ee96c5aa41008ab3aab9d8344cf1ce6d5 | [] | no_license | kassandra-ru/model | ef8fb89acb8492a98608cb57fb2fecf294f7a640 | e5f54d82885c0eb036349e94df55d08d9e28ca67 | refs/heads/master | 2021-12-07T21:56:36.663564 | 2021-08-28T03:14:08 | 2021-08-28T03:14:08 | 145,807,033 | 0 | 5 | null | 2021-01-25T06:34:58 | 2018-08-23T05:53:58 | R | UTF-8 | R | false | false | 2,422 | r | test_converter2_er.R | access_date = Sys.Date()
indprod = rio::import("ind_baza_2018.xlsx", skip = 2, sheet = 1)
indprod_vector = t(indprod[2, 3:ncol(indprod)])
indprod_ts = stats::ts(indprod_vector, start = c(2015, 1), frequency = 12)
indprod_tsibble = tsibble::as_tsibble(indprod_ts)
indprod_tsibble = dplyr::rename(indprod_tsibble, date = i... |
0d33df9200dc50c5d76a04213e05b51dec79895a | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Applied_Statistics_And_Probability_For_Engineers_by_Douglas_C._Montgomery_And_George_C._Runger/CH2/EX2.30/EX2_30.R | 6ff176c32483091d05c858bae86e020330c72c87 | [] | permissive | FOSSEE/R_TBC_Uploads | 1ea929010b46babb1842b3efe0ed34be0deea3c0 | 8ab94daf80307aee399c246682cb79ccf6e9c282 | refs/heads/master | 2023-04-15T04:36:13.331525 | 2023-03-15T18:39:42 | 2023-03-15T18:39:42 | 212,745,783 | 0 | 3 | MIT | 2019-10-04T06:57:33 | 2019-10-04T05:57:19 | null | UTF-8 | R | false | false | 402 | r | EX2_30.R | #install.packages("MASS")
library(MASS)
#Flaws and functions(Pg no. 49)
defective_and_surface_flawed = 2
total_defective_parts = 20
total_surface_flawed = 40
P = fractions((defective_and_surface_flawed*defective_and_surface_flawed)/(total_defective_parts*total_surface_flawed))
print(P)
cat("probability of surface fla... |
f426659f28ab3a07e6149740816ce77ee82e6ce2 | 623ae5f90fd8f7d10c22b61d1bf425a435a00a07 | /foo.R | b9a9bca092e2979e778ca1ac9947c79b78a1f756 | [] | no_license | bkkkk/my_testing_repo | bd5dcc66c08f7713fdc8f5a7c6cf58395fd0d6db | e94c117b5f48ba1b4d66cadb5da144a8726c87bc | refs/heads/master | 2020-08-10T11:14:54.889973 | 2019-11-14T05:39:50 | 2019-11-14T05:39:50 | 214,331,275 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 158 | r | foo.R | x <- 1
y <- 2
sum_of_xy <- y + x
diff_of_xy <- y - x
increase_x <- x + 2000
my_favourite_color <- "Blue"
ggplot(mpg) +
geom_point(aes(x = cyl, y = mpg)) |
0f998106208c9358ebffb2101175d673026ed27c | 47760e76ac09abff3f7133c679318aa69e21ce00 | /3-follow-up/a-make-dataset/e-pheno-preprocess.R | cb9af6c70979e2f4003cf53dc81839abacc8f90e | [] | no_license | MRCIEU/PHESANT-MR-pheWAS-smoking | a50c3ea7c8df3885952845738602f5d90b494de1 | be941325fe3185772193f3cbd9b7e7453acb366a | refs/heads/master | 2020-04-01T01:28:00.745725 | 2019-05-16T14:41:44 | 2019-05-16T14:41:44 | 152,739,725 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,603 | r | e-pheno-preprocess.R |
## Adapted from Robyn Wootton's script for the paper - Causal effects of lifetime smoking on risk for depression and schizophrenia: Evidence from a Mendelian randomisation study
##########################################################################
#1. Read in data
##############################################... |
52f4b035a9c90878f11bcc866cfab4501c005da8 | 3262afc1872d983c36fe27720c41052ef87c10aa | /run_analysis.R | 16e12d930fa3f44eb761960ae4fd6b4291f0d3b9 | [] | no_license | kbppdummy/gcdcourseproject | ce3a6d732b1d2c1b1a8e5ce5e50e48c9615f93fa | 6f88851451e1131a031d91bc3e2f7dd1bf37ef7a | refs/heads/master | 2020-04-14T10:39:16.245231 | 2019-01-02T04:36:28 | 2019-01-02T04:36:28 | 163,792,586 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,923 | r | run_analysis.R | #
# run_analysis.R
# Code Description:
# This is the main file that must be called.
# Created for a Coursera Course Project.
run_analysis <- function(){
#loads the libraries needed by the script, just in case it is not yet loaded
#also loads the user-defined scripts: functions.R and merge.R
prin... |
f6e2776145452c4cc13f4be1dae45683f90af683 | f43931a3d2fe0075098a13662c3497e6dbc49115 | /R/dataLoaders.R | 70fc3c68ab0afa9713fdf4d51edd26b080ffabd9 | [
"MIT"
] | permissive | JDMusc/READ-TV | 43f25df5659d28a044cea5765855a4aab7bec773 | 8ddceec04563f5586bbc35eb9918eda8ed06cf6d | refs/heads/master | 2021-07-04T13:57:40.323156 | 2021-01-19T00:01:36 | 2021-01-19T00:01:36 | 214,256,737 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,621 | r | dataLoaders.R |
loadFileExpr = function(f_name, ...) {
n_args = nargs()
f_name %>%
file_ext %>%
tolower %>%
switch(
'rds' = expr(read_rds),
'csv' = if(n_args > 1)
expr(read_csv(!!!(rlang::list2(...))))
else
expr(read_csv),
'tsv' = if(n_args > 1)
expr(read_tsv(!!!(rlang:... |
97bb8842b7a57b99d40c941c9c03b99a333a523e | 7f478ef249f587dd3f68751f00c45def9f171629 | /NLo_eda.R | 68d8a88484c2606e4bf41f88b9afac6e1a6c9aa7 | [] | no_license | ngone8lo/Project-Loans-Pay-Off-Status | 51964c980518d19738e9cc5845809eadf5e1082e | 73a445cdb5f92b64ef72bc0075f0c71a903f1647 | refs/heads/main | 2023-04-16T20:14:33.663862 | 2021-04-30T08:35:34 | 2021-04-30T08:35:34 | 363,072,868 | 1 | 0 | null | null | null | null | WINDOWS-1250 | R | false | false | 10,910 | r | NLo_eda.R | ## @knitr eda
##Ngoné Lo
## March 2020
#### Set up workspace ###
#Importing libraries
library(tidyverse)
library(janitor) # Helps with initial data cleaning and pretty tables
#Loading dataset
loan_data <- read_csv("outputs/datasets/loan_data_cleaned.csv")
#First we take a look at the prediction variabl... |
7bbc39690ddf596da93092936773e8b368a3cf25 | a5ea9d5ec0d70bfa722cfd5e49ce08119e339dda | /man/grasp.mod.anova.Rd | adc719f0c013b75abd319ed946ca3bb921f44577 | [] | no_license | cran/grasp | c46f16a28babb6cbed65aadbe2ddecc1a7214fd2 | d57d11504ee99616e55a1a9c49e337cf1caf139d | refs/heads/master | 2021-01-23T16:35:38.670044 | 2008-10-10T00:00:00 | 2008-10-10T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 699 | rd | grasp.mod.anova.Rd | \name{grasp.mod.anova}
\alias{grasp.mod.anova}
\title{ Internal GRASP function }
\description{
An ANOVA table is constructed by testing the significance of removing in turn each predictor from the selected model.
}
\usage{
grasp.mod.anova(gr.Yi)
}
\arguments{
\item{gr.Yi}{A vector containing the selected re... |
cfa474fe79ae1a2c32a375c00c801a7fb00cb6ae | 43e5b951fb89dff9d5f9c043706cff6489e2628f | /man/convert_date_seepolizei.Rd | fd678a1e70d5b9f51706c9a4881aec36c7a021f2 | [
"MIT"
] | permissive | Ostluft/rOstluft | dedf9f0c329b9fc2c06c3d175748ec96ac9d96be | a231c4d0219f488be9cb3e57471014dd80dc2008 | refs/heads/master | 2022-05-28T07:51:41.383789 | 2022-03-16T21:00:35 | 2022-03-16T21:00:35 | 153,456,317 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 448 | rd | convert_date_seepolizei.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read-seepolizei.R
\name{convert_date_seepolizei}
\alias{convert_date_seepolizei}
\title{Helper function to ensure correct string format}
\usage{
convert_date_seepolizei(x)
}
\arguments{
\item{x}{date as string, POSIXct, POSIXlt or Date Object... |
d09e0d161c367c14c66c0160de427b8db83773ec | 87bd3070d4c2aefaf0e343a096da12d66b8c119e | /R/summary.mgm.R | 2c33f0e689cdba94e0252b87d67c3adc4eb08c53 | [] | no_license | bottleling/mgm | d03f0371d651d08fbc1fbca0cfa7b1048474af6d | 01484ec9d668e0fe4050e70716ca2802985b4f6e | refs/heads/master | 2021-01-20T00:50:35.843531 | 2017-02-06T11:23:05 | 2017-02-06T11:23:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,406 | r | summary.mgm.R |
summary.mgm <- function(object, data = NULL, ...)
{
# ---------- Loop over Time Steps ----------
out_list <- list()
# stationary or time varying?
if('tv.mgm' %in% class(object) | 'tv.var' %in% class(object)) {
tsteps <- object$call$tsteps
} else {
tsteps <- 1
}
... |
6d9f8551e3688639274829203c82c4df57497d5d | fbe57536cc2d84e69a5bf799c88fcb784e853558 | /man/dispersion.ADM.Rd | 5235a46e6630ec20735ef269044fb640b379a383 | [
"MIT"
] | permissive | burrm/lolcat | 78edf19886fffc02e922b061ce346fdf0ee2c80f | abd3915791d7e63f3827ccb10b1b0895aafd1e38 | refs/heads/master | 2023-04-02T11:27:58.636616 | 2023-03-24T02:33:34 | 2023-03-24T02:33:34 | 49,685,593 | 5 | 2 | null | 2016-10-21T05:14:49 | 2016-01-15T00:56:55 | R | UTF-8 | R | false | true | 380 | rd | dispersion.ADM.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dispersion.ADM.R
\name{dispersion.ADM}
\alias{dispersion.ADM}
\title{Absolute Deviation From Median}
\usage{
dispersion.ADM(x)
}
\arguments{
\item{x}{Vector to compute ADM values for.}
}
\value{
Vector with results of ADM calculation.
}
\desc... |
78d6be44033979b5b3b4895d5e7b706b7c0f37d5 | 9315926fd58d03a3373ff36fbe6a4ce9e30c42c6 | /code/email-domain.R | 18b06bd42e64bd16769d37247cbc1f83eaddcd28 | [] | no_license | hanhy/linux-history | b59af4b3f7248987c6c8033e1ca2bca35986325b | f4ee09e918ca5106c07e24c61b1b0ed18a8f6629 | refs/heads/master | 2021-01-16T22:09:18.942770 | 2016-03-02T16:36:22 | 2016-03-02T16:36:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,787 | r | email-domain.R | # yearly
mods <- c('drivers', 'arch', 'net', 'sound', 'fs', 'kernel', 'mm')
tsel <- delta$mod %in% mods #& delta$y >= 2010
res <- t2apply((1:numofdeltas)[tsel], delta$y[tsel], delta$mod[tsel], function(x) {
# t <- c(length(x), numOfUnique(delta$aid[x]), numOfUnique(delta$cid[x]),
# numOfUnique(delta$ae[x]), nu... |
54ea6d920cc578cf248a078dffebdc2839576c69 | 86422f71fb0db244ea0c49909563a9420c584128 | /R/loops.R | 2efae148f5883820f35d3e2c2071a55f17c3e51e | [] | no_license | Decision-Stats/s15_codes | 0d69fb9e95faabf35d41fd327ed490968cf2dbac | 673a8078163a2eddd02fe418744a71542326c2e0 | refs/heads/master | 2021-01-19T06:58:29.421813 | 2015-07-10T17:16:21 | 2015-07-10T17:16:21 | 38,714,623 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 257 | r | loops.R | for (number in 1:5)
{
print(number)
}
# the contents go under the {}
for(i in 1:5)
{
print (i^2)
}
for (i in 1:5)
{
print (rnorm(i,10,10))
}
#this generates data but does not print anything, unlike the above code.
for (i in 1:5)
{
rnorm(i,10,10)
}
|
eb130f4a3f2202468b8417eff31417ff1f90ad64 | dfc525a68a2319d8906045dda6f4db48ae26b6a5 | /Result_Maps.R | e3b5fb9aa4a33d1c281946b6f90d3171b4e3ba9f | [] | no_license | geoffreylarnold/Election-2018 | 141b16492a70f8ad0773469ce355600dc246a538 | 91b36fef192408a8077234bf9250c0c93ae0f285 | refs/heads/master | 2020-04-06T15:32:47.188137 | 2018-11-14T17:44:17 | 2018-11-14T17:44:17 | 157,582,597 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,738 | r | Result_Maps.R | require(rgdal)
require(readxl)
require(dplyr)
require(leaflet)
require(htmltools)
pittwards <- readOGR("https://services1.arcgis.com/vdNDkVykv9vEWFX4/arcgis/rest/services/VotingDistricts2017_May_v6/FeatureServer/0/query?where=Muni_War_1+LIKE+%27Pittsburgh%25%27&objectIds=&time=&geometry=&geometryType=esriGeometryEnvel... |
587d2a9ce57f264097b8ae661169ef14ff981c99 | 4755427593f4e0f5a162640d6de1041110e63763 | /cursus/data/sigmoid.R | a93e137d5df72f2c9ce284220b0a0513dfc8d6bc | [] | no_license | HoGentTIN/onderzoekstechnieken-cursus | 5e642d984ab422f1d001984463f0e693f89e9637 | bd7e61aa8d2a0a4525de82774568954c76dd33ae | refs/heads/master | 2022-06-28T05:09:34.694920 | 2022-06-21T13:35:59 | 2022-06-21T13:35:59 | 80,239,413 | 21 | 59 | null | 2020-05-25T06:56:06 | 2017-01-27T19:35:24 | HTML | UTF-8 | R | false | false | 97 | r | sigmoid.R | sigmoide <- function (alfa, beta, x){
z <- alfa + beta * x;
e <- exp(z);
return(e/(1+e));
} |
06e2c8bd8faf64d037e2d1f13297209892b9197d | 30ee5256f363954bcacf452ab94249ddf04b270e | /apply_functions.r | 773937ac7fbec0618261bbff2bc719f1883d32f4 | [] | no_license | tiborh/r | 70c45812347a65786c5bf95eccc7376f8caf7f72 | 8de2d56608b2e52faaf554f3cc955a456c58f57f | refs/heads/master | 2022-11-01T05:54:55.451584 | 2022-10-27T15:15:37 | 2022-10-27T15:15:37 | 36,147,838 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,286 | r | apply_functions.r | ## lapply can be used on lists or vectors
l <- list(a=51648794597,b=c("alpha","beta","gamma","delta","epsilon"),c="something else")
lapply(l,class)
## -------------------- ##
## create a vector of random strings
source("common.r")
v <- make.string.v(10)
## unlist to make the returned list a vector
vls <- unlist(la... |
e11fae76661de970c58734721a96a42892622539 | 97b6e598ef6970eed3efd64033ca0a34fd0a594c | /R/00_gen_messy_data.R | 29ff1234d1a1eb014831c97d069295a374648668 | [] | no_license | eugejoh/messy_data_tutorial | 15b49e58e2a62c8cbd7dc83222b5f49e02b72905 | 707cbd2b5c55c045ef01cfaa534e98d66be0e34e | refs/heads/master | 2020-05-26T06:46:11.992206 | 2019-06-05T15:50:43 | 2019-06-05T15:50:43 | 188,139,538 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,024 | r | 00_gen_messy_data.R | # Make Messy Data ---------------------------------------------------------
# Eugene Joh
# 2019-05-21
# generate random data
# - age
# - name
# - sex
# - area
# - hypothetical immune status
# - hypothetical Ab serum concentration
# install.packages("randomNames")
library(randomNames)
make_messy_data <- functio... |
7f599082474f85c84b2af8bc2fb466d61c105f40 | 87bee99b5742c75d392186800f845d44c9e48fcf | /CH.9./SVM_NonLinear.R | 5f8cca35e215010aa3777d606f2ec59567036cf0 | [] | no_license | ssh352/ISLR-5 | 087e84294f2e7ac6fd42472a8d2b6a9dcebf59fa | 1fff21344a7f6e2f379b9618143925bcb4c6f6b7 | refs/heads/master | 2022-08-21T15:36:12.105031 | 2020-05-28T11:12:42 | 2020-05-28T11:12:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,335 | r | SVM_NonLinear.R | set.seed(1)
library(e1071)
X = matrix(rnorm(200*2),ncol=2)
X[1:100,]=X[1:100,]+2
X[101:150,]=X[101:150,]-2
Y = c(rep(1,150),rep(2,50))
plot(X,col=Y)
data1=data.frame(X=X,Y=as.factor(Y))
train=sample(200,100)
# lower cost #
svmfit_r = svm(Y~.,data=data1[train,],kernel='radial',gamma=1,cost=1)
summary(svmfit_r... |
4a14145ecf4d105bf0e9872645fdea4a16b1609b | 7ab8eafb68413a4d1a22262dfbbf31e19245bfc5 | /script/script/outlier_plt_parallel.R | 8ce7579194a7d1e5e3f0217493a99fdd16b4a7f0 | [] | no_license | Ryosuke-Kawamori/medicare | cb604954f18a44cc30ecb54c66aff0887b36d99f | 8ce914b21a24a57bb7a8eee883393d0ed1e3c7d5 | refs/heads/master | 2020-04-22T09:36:34.637741 | 2019-02-14T06:56:12 | 2019-02-14T06:56:12 | 170,278,409 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,371 | r | outlier_plt_parallel.R | lof_provider_stat_result <- read_rds("data/lof_provider_stat_result.rds")
lof_provider_hcpcs_result <- read_rds("data/lof_provider_hcpcs_result.rds")
lof_providermesh_stat_result <- read_rds("data/lof_providermesh_stat_result.rds")
lof_providermesh_hcpcs_result <- read_rds("data/lof_providermesh_hcpcs_result.rds")
lof_... |
b5318d2d32adc716390c5086dc59cd89435ad84f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/lordif/examples/runolr.Rd.R | 986a951e30890f04ac87d9c4bc035cee9d7bbefb | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 184 | r | runolr.Rd.R | library(lordif)
### Name: runolr
### Title: runs ordinal logistic regression models
### Aliases: runolr
### Keywords: ~kwd1 ~kwd2
### ** Examples
## Not run: runolr(rv, ev, gr)
|
59cc9f466117ba3e55356a662b88051f0d30eca3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/FMAdist/examples/famfit.Rd.R | 8b57d3ff379eed414f62f636d0452ea87e5ed1ca | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 332 | r | famfit.Rd.R | library(FMAdist)
### Name: fmafit
### Title: Building frequentist model averaging input models
### Aliases: fmafit
### Keywords: ~kwd1 ~kwd2
### ** Examples
data<-rlnorm(500,meanlog=0,sdlog=0.25)
Fset<-c('gamma','weibull','normal','ED')
type<-'P' #by default type<-'Q'
J<-5 #by default J<-10
myfit<-fmafit(data,Fset... |
05159d8fa8a5f80a42effc61d610f6fa50e10cce | 8e9808c789fc646a66f9b4844e56d6de2a95c405 | /R/tests.R | e91e3d88d416b697cf16c12862ee9a0ce4107389 | [] | no_license | ddarmon/MUsaic | 5aa809e1a696effee03411542a2dba9412e273d6 | 039f326fd7b93e2bc9f3d7c8c5be6e720d97bf7f | refs/heads/master | 2022-04-30T16:49:19.645681 | 2022-03-17T00:02:59 | 2022-03-17T00:02:59 | 200,889,827 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,661 | r | tests.R | #' Welch's two-sample t-test with summary statistics
#'
#' Welch's two-sample t-test for testing claims about the different between two population means without assuming the population variances are equal.
#'
#' @param xbar the sample mean of the first sample.
#' @param ybar the sample mean of the second sample.
#' @pa... |
16debcd8855f7230e3ded39e8bff2c49292a42cd | a6364e9cf520b508475803801355a9f95d15ec55 | /line_prof_test.R | 56c3dfab9af6dd4d16f97254ceffac19ed79895f | [] | no_license | chiu/practice-R | d6da6c5b6972429d02142321e0be6313483664a8 | bf50f616fd93d89b308f7bc40bfed637b20f19c2 | refs/heads/master | 2020-04-09T16:50:50.353874 | 2016-05-19T21:31:40 | 2016-05-19T21:31:40 | 51,965,712 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 162 | r | line_prof_test.R | # devtools::install_github("hadley/lineprof")
# devtools::install_github("hadley/lineprof")
library(lineprof)
source("profiling-example.R")
l <- lineprof(f())
l |
b75c5b29754ff1491648619315f6cbd7046a0b03 | 80badebbbe4bd0398cd19b7c36492f5ab0e5facf | /man/Polygons-class.Rd | 4dfa4bd23063914fc585fd9636cfa2df4968f587 | [] | no_license | edzer/sp | 12012caba5cc6cf5778dfabfc846f7bf85311f05 | 0e8312edc0a2164380592c61577fe6bc825d9cd9 | refs/heads/main | 2023-06-21T09:36:24.101762 | 2023-06-20T19:27:01 | 2023-06-20T19:27:01 | 48,277,606 | 139 | 44 | null | 2023-08-19T09:19:39 | 2015-12-19T10:23:36 | R | UTF-8 | R | false | false | 1,865 | rd | Polygons-class.Rd | \name{Polygons-class}
\docType{class}
\alias{Polygons-class}
\title{Class "Polygons"}
\description{ Collection of objects of class \code{"Polygon"} }
\section{Objects from the Class}{
Objects can be created by calls to the function \code{Polygons}
}
\section{Slots}{
\describe{
\item{\code{Polygons}:}{Object of c... |
8c7eb7ff4c44675eb1384e6f3c34078c7b472ee1 | 98005bc40a0a85089d167900f6ff1aaec6fd57f1 | /Aplicacion_Contaminantes/server.R | 9bc5c0b364b40ed1dd8518ce17bd8148b325373b | [] | no_license | AdrianLandaverde/Contaminantes_en_la_CDMX | fb681483578715c9f9ab7bc3f6a6e16f2b094437 | 61a50cc48e1cb855f1a3ea8b7856ddc1dbe564e4 | refs/heads/main | 2023-06-02T08:01:18.605204 | 2021-06-23T19:08:46 | 2021-06-23T19:08:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 55,253 | r | server.R |
library(shiny)
library(tidyverse)
library(dplyr, quietly = TRUE)
library(gapminder)
# Define server logic required to draw a histogram
shinyServer(function(input, output) {
output$regresionLinealGrafico <- renderPlot({
contaminante = "SO2"
periodo_Regresion_Lineal<- input$Periodo... |
e00cdbd98817a422cef49024933c5e492eb89d41 | 5fb4e9f81f7bb146ec228000245025717e54b776 | /Scripts/Area_information_species.R | 4e3067888cc3a79448042a57399528640a757233 | [] | no_license | alipal89/Chapter_1_analysis | 3b23c8905ebb4cf8d3fe62d7e769a5703751cf68 | 98eedcf87e20fc62a706f510eae1cdee30a9a317 | refs/heads/master | 2021-01-13T02:50:05.616648 | 2016-12-22T15:08:47 | 2016-12-22T15:08:47 | 77,153,362 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,723 | r | Area_information_species.R | ############### Need to calculate the area for occupied for all species
############### Annual area
############### Seasonal area
rm(list = ls())
setwd("C:/Alison/Chapter 1/Analysis")
# This is the species occurences for all species
species_occ<-read.table("AllGridded_FDB_CPH_filtered.txt",sep = "\t", header=T)
sp... |
731834459a2cf7922cd1491b719ffab5b89cb1b9 | 9822a9ade61745cdee439169c3533ce74f46b873 | /R/Old/Explore/Analysis1_summary_stats.R | 14f5f0fa78ad294312266f075c016e068f3da9eb | [] | no_license | MatthewKatzen/NEM_Battery | 97c6bea3aad4897ff3aa0b4894f4f70171549c93 | 18fe985f872933970cafb2a539780c93e346749d | refs/heads/master | 2023-02-07T11:00:13.521649 | 2021-01-04T08:01:09 | 2021-01-04T08:01:09 | 267,240,061 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,257 | r | Analysis1_summary_stats.R | #Analysis1
#buy low sell high
#alpha beta
#load packages
library(tidyverse)
library(tidyr)
library(lubridate)
library(data.table)
library(janitor)
library(readxl)
library(writexl)
library(xtable)
library(nlme)
Sys.setenv(TZ='UTC')
### Load data
full_data <- fread("D:/Battery/Data/full_lmp.csv") %>% mutate(settlementd... |
7ccf611e1bd923fdc4ea22d23195c386cd8be016 | 7c3f2e88c0273324f42c5175795931c668631520 | /R/cls_ecog_repo.R | a852f3f2a2fe6df3f62bee673561b9c2f7992e94 | [] | no_license | dipterix/rave | ddf843b81d784a825acf8fe407c6169c52af5e3e | fe9e298fc9f740a70f96359d87987f8807fbd6f3 | refs/heads/master | 2020-06-26T19:52:26.927042 | 2019-07-30T22:47:36 | 2019-07-30T22:47:36 | 110,286,235 | 0 | 0 | null | 2018-06-20T03:18:47 | 2017-11-10T19:45:40 | R | UTF-8 | R | false | false | 17,316 | r | cls_ecog_repo.R | #' Baseline signals
#' @param el Tensor or ECoGTensor object
#' @param from baseline start time
#' @param to baseline end time
#' @param method mean or median, default is mean
#' @param unit "\%" percent signal change or "dB" decibel unit
#' @param data_only return array or tensor object?
#' @param hybrid if return ten... |
920dc858a16b47f8739fe118f130052ce054d888 | 80da7a81e82713dcdedd6f785589e93623fae885 | /Explorarory.R | 686447c0b75dde88f22201937d422ff47a37fe5f | [] | no_license | MiG-Kharkov/HR-training | 9a9599b18d6ab277fb19bbb035945bccef6917ca | c91f4ef7baab091a0f07b1a6d497eb971c8373ed | refs/heads/master | 2021-01-12T04:30:38.035087 | 2017-01-20T14:35:38 | 2017-01-20T14:35:38 | 77,630,755 | 0 | 3 | null | 2017-01-20T14:35:39 | 2016-12-29T18:05:20 | R | UTF-8 | R | false | false | 18,063 | r | Explorarory.R | # Exploratory data
# Preliminary analysis real work is supposed to be in main.R
install.packages("corrplot")
install.packages(ROCR)
#Load librarys
library(ggplot2)
library(caret)
library(corrplot)
library(e1071)
library(ROCR)
#clear global environment
rm(list = ls())
dataset <- read.csv("HR_comma_sep.csv")
dataset$le... |
ce2efbc683f8dfb1a3f2df2f2fdfd804a5d60074 | 831309ea5419fd7a365403c27b9f98606deca04b | /R/hpaBinary_generics.R | 37c56c5ac37786079a49fc5e1c3ac08f940ac398 | [] | no_license | cran/hpa | 574f4a8ada8cc7dc911983344543a1bc9df4f566 | 2fe73d9bd0a8d97394989bcf6e017cd5598c6d03 | refs/heads/master | 2023-06-01T21:41:55.956317 | 2023-05-07T14:20:02 | 2023-05-07T14:20:02 | 236,612,370 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,217 | r | hpaBinary_generics.R | #' Predict method for hpaBinary
#' @param object Object of class "hpaBinary"
#' @template newdata_Template
#' @param is_prob logical; if TRUE (default) then function returns
#' predicted probabilities. Otherwise latent variable
#' (single index) estimates will be returned.
#' @template elipsis_Template
#' @retu... |
8cd83783f46e5c7140fb202a16f98d7bbd9fb99b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/grt/examples/gqc.Rd.R | b933cfe6a9c54dd2b62fff5a150b14fd26230529 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 258 | r | gqc.Rd.R | library(grt)
### Name: gqc
### Title: General Quadratic Classifier
### Aliases: gqc print.gqc
### Keywords: multivariate
### ** Examples
data(subjdemo_2d)
fit.2dq <- gqc(response ~ x + y, data=subjdemo_2d,
category=subjdemo_2d$category, zlimit=7)
|
f576fcdd26da6ffd106fec27c95f3977a1f042f7 | a94308678716ab60f03956e503f554a767e73733 | /man/DIF.Logistic.MG.Rd | 9e0b839bc1cadeda2bdacdc58d05fccce4ba8510 | [] | no_license | cswells1/MeasInv | 9f9cb20da68b695cc1f65fc5c80f92ea31b030e7 | b74acffcf8ec0d6886f7081882aa3965306eb4af | refs/heads/master | 2023-07-14T21:35:32.915150 | 2021-09-12T22:50:49 | 2021-09-12T22:50:49 | 405,707,567 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 739 | rd | DIF.Logistic.MG.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DIF.Logistic.MG.R
\name{DIF.Logistic.MG}
\alias{DIF.Logistic.MG}
\title{Logistic regression DIF method for more than two groups}
\usage{
DIF.Logistic.MG(data, group, sig.level, purify, output.filename)
}
\arguments{
\item{data}{numeric: eithe... |
4a52ca4d5e39ae3daf7b75b6576152cd6ae0672a | 1aa41ed59a7ccc6b0cf001cd3bc8ed1f77b2162b | /R/user.R | 67d2fe1c695125e1962f93d0082dda5872547d06 | [] | no_license | yutannihilation/wunderlistr | a30fcf401eec1d2174e0cc49988a514f16ebf798 | d98b815d5687f1e1563d0aace6769686753aa193 | refs/heads/master | 2021-01-10T01:10:59.563351 | 2016-03-28T22:06:33 | 2016-03-28T22:06:33 | 54,811,791 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 484 | r | user.R | #' User API
#'
#' All info related to the currently signed in user.
#'
#' @seealso \url{https://developer.wunderlist.com/documentation/endpoints/user}
#'
#' @param list_id List ID
#'
#' @export
wndr_get_user <- function(list_id = NULL) {
if(is.null(list_id)) {
wndr_api(verb = "GET",
path = "/api/v1/u... |
6702ebcc35b31ea29c3e7a0e66290b06e0e46003 | 4d2c2e6c274dc94b9b418fbf397cbb5bd06a23ed | /pbdr/tutorial3/tutorial3/u4-mcmc_glmm_mclapply.r | ee15e6ae1bdd7e53f82b2e586c27bff387f18f83 | [] | no_license | snoweye/snoweye.github.io | 5991f8d533e0a0b1428f1c179768925a21bb47b9 | e5d35e49aca7520f97d0719c829df4fc11ff63e1 | refs/heads/master | 2023-07-22T13:35:24.683834 | 2023-07-06T22:09:43 | 2023-07-06T22:09:43 | 64,176,642 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 951 | r | u4-mcmc_glmm_mclapply.r | library(nlme)
library(MASS)
propose.glmm.random.mclapply <- function(param, da.mcmc, tau = 1){
f <- function(i.random){
### Random walk.
random.new <- param$random
random.new[i.random] <- rnorm(1, mean = param$random[i.random],
sd = tau * da.mcmc$sd.random)
logL.new ... |
3148b5c706ec4e0022c8e4a88179fb9f6db5fd15 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/OasisR/examples/xPy.Rd.R | 8bfb033c297fa397b60995337ead5284c45a8e28 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 160 | r | xPy.Rd.R | library(OasisR)
### Name: xPy
### Title: A function to compute interaction index (xPy)
### Aliases: xPy
### ** Examples
x <- segdata@data[ ,1:2]
xPy(x)
|
ff3b4ffe9176690bd7302d6d3bd67eafe332dd4a | 763e605372290000e74bcda4db7f0e1cd24b6b46 | /plot2.R | ede89768ec4ab9aa31baa92eef6de1b9ae8c6649 | [] | no_license | davidmanero/ExData_Plotting1 | a03c131eab81064c6e32172d8504c07e791b715a | 5a1d3ab1c404ecb03d199f0bcc2720d5ed0a68ae | refs/heads/master | 2021-01-14T08:54:53.642379 | 2015-04-12T20:36:09 | 2015-04-12T20:36:09 | 33,830,763 | 0 | 0 | null | 2015-04-12T19:36:49 | 2015-04-12T19:36:48 | null | UTF-8 | R | false | false | 752 | r | plot2.R | ## This code is used for creating the plot2 in the Course Project 1 for the Exploratoy
## Data Analysis curse from Coursera
## The downloaded file with the information has been manually cut with days 1st and 2nd
## Of Febrary 2007 in the local file "1st_2nd_Feb2007.txt"
datafile <- "./data/1st_2nd_Feb2007.txt"
data <... |
998223e413b7737dca6df3dc142ac98589c6ddd9 | 4052545c292db46b6363f299828dfc1d8d7f8b9a | /shiny2/leaflet/choropleths.R | 91ea91e442cfc74bee824249f5e34a22dc2a6791 | [] | no_license | uvesco/studioEpi | 972324a5af179912d1ab2c60ea21f0f04cddfa33 | ed0849185460d628aa1fdc73eb13e67555a87a75 | refs/heads/master | 2020-12-12T05:03:04.953612 | 2020-04-04T23:26:09 | 2020-04-04T23:26:09 | 234,048,695 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 124 | r | choropleths.R | library(geojson)
# https://rstudio.github.io/leaflet/choropleths.html
states <- geojson_read("us_states.json", what = "sp")
|
3d12a3a06670eecff6c182fc15b3b899d4618d7f | 65553a50702fb30c40ec78f75ec305a8fab298f8 | /man/Guitar-package.Rd | 47c6afb599f7232a743d05df32782ca142ad5099 | [] | no_license | lzcyzm/Guitar | e22f1b9a34952b48b219a5bfcb8a91a9d1283408 | cb1e1146c86ce8cf9caff505d1b22dca2bc37a3e | refs/heads/master | 2021-01-10T07:42:46.819016 | 2015-10-22T02:55:25 | 2015-10-22T02:55:25 | 44,791,600 | 1 | 0 | null | 2015-10-23T04:54:56 | 2015-10-23T04:54:56 | null | UTF-8 | R | false | false | 1,391 | rd | Guitar-package.Rd | \name{Guitar-package}
\alias{Guitar-package}
\alias{Guitar}
\docType{package}
\title{
Guitar
}
\description{
RNA Landmarks Guided Transcriptomic View of RNA-related Genomic Features.
}
\details{
The package is designed for transcriptomic visualization of RNA-related genomic features represented with genome-... |
957a22355f87e0d1aa95ac492bbf70bce42ef6a7 | ece7ca8a7491a99c92d51a1afc3a053014fa1b3f | /R/idxstatsBam.R | e61854242f7f8dbf9e0ec045f3f2c127b6ad92b7 | [
"MIT"
] | permissive | Bioconductor/Rsamtools | a31c353bb24545fc5736fd8e99042bbe8068ac1c | cded55f6bd1d8363b16f60b79f74f707c8f90b77 | refs/heads/devel | 2023-05-12T18:01:23.101002 | 2023-04-25T13:50:37 | 2023-04-25T13:50:37 | 102,150,322 | 22 | 24 | NOASSERTION | 2023-09-02T09:43:11 | 2017-09-01T20:24:35 | R | UTF-8 | R | false | false | 206 | r | idxstatsBam.R | setMethod(idxstatsBam, "character",
function(file, index=file, ...)
{
index <- .normalizePath(index)
bam <- open(BamFile(file, index), "rb")
on.exit(close(bam))
idxstatsBam(bam, ...)
})
|
af93d88cea344a801a4022f669340b61acb4eb7c | 3f985066ad0d90af692f53afdbc00a7f7ca91688 | /day5Part1.R | 82cb9da2ce9addae5970d04f423924ad49ddcbe4 | [] | no_license | leibensperger/Advent.of.Code | e36c2ffac730e947733e8a84cc7a9b6f2e1afc02 | 26301a4c44f9083d696f40c323fe15354f99923c | refs/heads/master | 2020-04-12T07:58:17.093258 | 2019-01-02T18:58:18 | 2019-01-02T18:58:18 | 162,377,248 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,925 | r | day5Part1.R | #--- Day 5: Alchemical Reduction ---
# You've managed to sneak in to the prototype suit
# manufacturing lab. The Elves are making decent progress,
# but are still struggling with the suit's size reduction
# capabilities.
#
# While the very latest in 1518 alchemical technology
# might have solved their problem eventuall... |
050d4cc55f4f646f7e9b8297cc0f78ddfa4ee861 | fd97957ee8a1434b5f43b2cadd38217644797e5b | /man/print.hint.test.Rd | 5f28fb3bb7fba64aa9757fa64cf0ddb1d60ba7a7 | [] | no_license | cran/hint | a2fe4fdf50fc73d92a7a7746e28eae31761c7b24 | b0b532db7fc788082182f793ce86f479573c95d6 | refs/heads/master | 2022-02-13T23:26:10.763801 | 2022-02-02T13:40:02 | 2022-02-02T13:40:02 | 17,696,655 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 410 | rd | print.hint.test.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hint_main.R
\name{print.hint.test}
\alias{print.hint.test}
\title{print.hint.test}
\usage{
\method{print}{hint.test}(x, ...)
}
\arguments{
\item{x}{An object of class `hint.test`.}
\item{...}{Additional arguments to be passed to `print`.}
}
... |
efe93f9319b154021ff628908bb380e1b8d72a24 | 1ca51889971d9fc4c759c6cf2a7b100f8b621592 | /R/class_cost.R | 01b82c5c5f768956d606385050c193ae1efe1ff3 | [
"MIT"
] | permissive | mgaldame/baguette | 2734f150247f1103394b25c7af555bfd21f0dc7a | 7bf898b766da8d65c6f848e05341d0f5554b9ec6 | refs/heads/master | 2023-05-05T06:21:51.381009 | 2021-05-27T15:12:33 | 2021-05-27T15:12:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,131 | r | class_cost.R | #' Cost parameter for minority class
#'
#' Used in `bag_treer()`.
#'
#' @param range A two-element vector holding the _defaults_ for the smallest and
#' largest possible values, respectively.
#'
#' @param trans A `trans` object from the `scales` package, such as
#' `scales::log10_trans()` or `scales::reciprocal_trans()... |
e64384b1d221384f906b067e57da86e646bbc3c4 | bb8a54bb3ae1a527ad66786d2aed62bb60a51277 | /man/year_oldest.Rd | 5955598ab7e6173629f293b7efed08dd0f15e752 | [] | no_license | JialingMa/NBAsstat1 | bfa9ba2a6be979193f91de0de93d4a19fc153c4c | 6a521c92a9fd9194187c119efa3f3292685941b1 | refs/heads/master | 2021-02-16T16:27:23.048808 | 2020-03-04T23:18:13 | 2020-03-04T23:18:13 | 245,024,104 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 418 | rd | year_oldest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/year_oldest_function.R
\name{year_oldest}
\alias{year_oldest}
\title{A Year Age Function}
\usage{
year_oldest(year)
}
\arguments{
\item{year}{an individual year}
}
\value{
character
}
\description{
This function allows you to find the name of... |
3d4764b181235ba68bef346432f224423f4848d5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RcmdrMisc/examples/discretePlot.Rd.R | 14b8e2f030d75d39b26eff83c3fdeab4ac63557a | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 361 | r | discretePlot.Rd.R | library(RcmdrMisc)
### Name: discretePlot
### Title: Plot Distribution of Discrete Numeric Variable
### Aliases: discretePlot
### Keywords: hplot
### ** Examples
if (require(datasets)){
data(mtcars)
mtcars$cyl <- factor(mtcars$cyl)
with(mtcars, {
discretePlot(carb)
discretePlot(carb, scale="percent")
... |
97c7df7c92e59248ffc3c3371d2ed67b57f208e9 | 21960cbad6a8d83b8e394513cfed63a96093f7fb | /man/convert_Q.Rd | de96325dbff982c6b3f44b43b38ba79de42e3816 | [] | no_license | mattreusswig/convertUnits | fbbff61c7fba579862b5b733fa2bfb8ea2401b57 | e8764a0e7fe94bade7941294dd69bc8fa91a1f90 | refs/heads/master | 2021-03-06T05:34:10.347027 | 2020-03-18T01:46:01 | 2020-03-18T01:46:01 | 246,182,675 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 749 | rd | convert_Q.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/convert_Q.R
\name{convert_Q}
\alias{convert_Q}
\title{Convert between flow units typically seen in wastewater and streamflow calculations--cfs, acre-ft/d, mgd, lpm, cms}
\usage{
convert_Q(x, from, to)
}
\arguments{
\item{x}{A vector of number... |
fbe583da994d5d092d527bd5d4d76d8f7dca4484 | 1abf8398ec048750d230f77b5467c0d3cf508349 | /man/LoadRequiredPackage.Rd | 016e0bd964a523e7bace75c135f7951850829f35 | [] | no_license | bioinformatics-gao/ChipSeq | 97e8453cb74663bd2b4f35e44846311ca962850d | dde9e5a4f82142657f22d281cb10509715c0ef78 | refs/heads/master | 2021-01-12T00:03:09.798091 | 2017-01-11T17:32:19 | 2017-01-11T17:32:19 | 78,662,917 | 1 | 0 | null | 2017-01-11T17:35:08 | 2017-01-11T17:35:08 | null | UTF-8 | R | false | true | 260 | rd | LoadRequiredPackage.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/LoadRequiredPackage.R
\name{LoadRequiredPackage}
\alias{LoadRequiredPackage}
\title{Title}
\usage{
LoadRequiredPackage()
}
\description{
Title
}
\examples{
LoadRequiredPackage()
}
|
2aed9fa85e1037306b89832350a987f9a6998ec6 | 87472097e88f2e3aef1e9f003add2aa149c50233 | /man/addDemographicFields.Rd | 09d6a18efd1c0c1a9021a0c6485bfb63c8df440a | [] | no_license | RGLab/ImmuneSignatures2 | f1feca1e5f05f99419a8aca00b0d68928e1b8e82 | 15fc078c4475ae421142aa4b6271c9143db04eda | refs/heads/main | 2023-04-18T07:08:56.765734 | 2022-12-05T22:52:42 | 2022-12-05T22:52:42 | 252,603,828 | 1 | 1 | null | 2022-07-28T23:05:21 | 2020-04-03T01:27:26 | R | UTF-8 | R | false | true | 419 | rd | addDemographicFields.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sharedMetaData.R
\name{addDemographicFields}
\alias{addDemographicFields}
\title{Add fields from demographic data to meta-data}
\usage{
addDemographicFields(dt, demographicData)
}
\arguments{
\item{dt}{meta-data data.table}
\item{demographic... |
fb48c710fc36cb346de4d98b555d51bc72f9635a | be7347efe4e197e441039642bb61cad76aadd54c | /R/random.R | b89dbcd07544778c428fdfeab911af3febc39fed | [
"MIT"
] | permissive | SimonGoring/giphyR | 1e95aa960b38d7845c0bf10e4f332562ab82d24f | 979d204c498fd661495dba16a6ecf5c00a589008 | refs/heads/master | 2021-01-22T18:37:47.875982 | 2019-03-14T19:02:46 | 2019-03-14T19:02:46 | 85,097,720 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,524 | r | random.R | #' @title Return a random GIF
#' @description Returns a random GIF from Giphy, which may be limited using tags.
#' @param tag a single, or multiple text strings.
#' @param rating - A rating for age appropriate gifs. Options include \code{"y"}, \code{"g"}, \code{"pg"}, \code{"pg-13"} and \code{"r"}.
#' @param sticker S... |
88514c86467ca332e4ccd9c119210717953ff78a | e92f5c95c8c17f4b2dc8d754fd212fd4fcc4c8b4 | /data-raw/frutas.R | 8c0939124fdf82cdeed1c10efd2d260e80179926 | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | cienciadedatos/datos | de1cab523ce46ed1b1d86dbb4f947ba9e58ff432 | 6008b75bfc68e2e8332fe92c37f5ec59361fa4f4 | refs/heads/main | 2023-07-20T12:01:18.032448 | 2023-07-17T12:50:37 | 2023-07-17T15:10:53 | 140,963,726 | 37 | 34 | CC0-1.0 | 2023-07-17T12:52:14 | 2018-07-14T17:07:44 | R | UTF-8 | R | false | false | 448 | r | frutas.R | # From `dput(datos::frutas)` (245b4af)
frutas <- c(
"banana",
"papaya",
"uva",
"ar\u00e1ndano",
"frutilla",
"mora",
"pl\u00e1tano",
"anan\u00e1",
"pi\u00f1a",
"manzana",
"pera",
"sand\u00eda",
"melocot\u00f3n",
"mel\u00f3n",
"damasco",
"durazno",
"frambuesa",
"mango",
"guayaba",
... |
7ce6b47b858d8fdf8b77f67cc8a35a7121d48da1 | 37520057f8324bbddcebaa3276fdc5c7390bca14 | /eda/osm_exp_2.R | a453327f1d785417e5f242a2031bfed07d7f8d39 | [] | no_license | JordanJamesSands/melbourne_housing | 8aa9eac49f5f6ae25a2de4df5793d07a06d150b3 | e00ed6b81c48b67f5b88bdba102902f5ead1e749 | refs/heads/master | 2020-04-29T10:29:44.704673 | 2019-04-30T06:54:41 | 2019-04-30T06:54:41 | 176,063,789 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,396 | r | osm_exp_2.R | #other scripts
source('project_functions.R')
#new pipeline
source('clean/read_data.R')
source('clean/clean2.R')
sdf = n_neighbours(osm_data$school,2000,'nschools_2000')
newdata = merge(property_data,sdf,by='ID')
newdata = merge(newdata,min_dist(osm_data$train,1,'traindist',1000),by='ID')
newdata = merge(newdata,n_neig... |
37c7759f167cacd080d79e9dd0d036cf4d5eda91 | 83df92b20aebeeb409aea013078275f77660f4e7 | /aoc2020_day9.R | a254ab0b7b4015e5a004d1762aae0b2eb0e18fa4 | [] | no_license | nalsalam/aoc2020 | 2ce80f9fe63c52c6142ffe626965c89620d6d607 | 54cc20bb75eb02acb34a849b8d9471f947da11d5 | refs/heads/main | 2023-02-01T21:26:34.616763 | 2020-12-22T10:26:53 | 2020-12-22T10:26:53 | 317,584,138 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,065 | r | aoc2020_day9.R | # Day 9 - encryption
library(tidyverse)
### Part 1
### Example
input <- read_lines(file = "data-naa/input9_test.txt") %>% as.numeric()
valid <-
map_lgl(6:length(input),
~ input[[.x]] %in% combn(input[(.x - 5):(.x - 1)], 2, FUN = sum))
input[6:20][!valid]
### Puzzle
input <- read_lines(file = "data-naa/input9.tx... |
ef9f91689f0b6e565b1857b4b79a52b7832674d0 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/chemometrics/examples/prm.Rd.R | 95befc604fbae8fcf82b66d0a60bae9437520954 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 163 | r | prm.Rd.R | library(chemometrics)
### Name: prm
### Title: Robust PLS
### Aliases: prm
### Keywords: multivariate
### ** Examples
data(PAC)
res <- prm(PAC$X,PAC$y,a=5)
|
f63346c54d2b558387b2af27b310f32cc55b720d | d4608310406b4a60580c47c0ccdfaf8c7e58cf22 | /Paper1_three_way_interaction_graph_jan2021.R | a28d1b12c1092841e5685a2f762defd921070e26 | [] | no_license | marieleyse/paper-Fall-2020 | d4c511a0cd318e6a10e547e5ce0fb697fb4bfa9d | 2e543d6c28015dbb2c9255eed0aa19d1588e18a7 | refs/heads/master | 2023-05-06T01:58:51.277656 | 2021-06-02T19:23:07 | 2021-06-02T19:23:07 | 297,434,580 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,486 | r | Paper1_three_way_interaction_graph_jan2021.R | setwd("/Users/Marie-Elyse/Downloads")
NEW = read.csv("MAVAN_48M_and_up_jun2020_new.csv")
#CALLED MEAN_CENTERED BUT ZSCORE
NEW$mean_centered_ADHD = c(scale(NEW$ADHD))
NEW$mean_centered_PRS_0_001_adhd_child = c(scale(NEW$PRS_0_001_adhd_child))
NEW$mean_centered_auc_post_cesd = c(scale(NEW$auc_post_cesd))
NEW$mean_cente... |
35d8a43320ebb75110a484121c037c1eab786836 | fa6e9410f2b66410e68c906b459574501e040ce8 | /man/deprivation_decile.Rd | 51f3c0545cac691e7f035407663c1137e7c135f6 | [] | no_license | carlganz/fingertipsR | 9df8df727b86ee73cf3081ca1d1171655d9f7c32 | 4e882aec8fd924011f5e29c9bd5ebbe0c3680c6c | refs/heads/master | 2021-08-30T00:37:00.760310 | 2017-12-15T11:39:36 | 2017-12-15T11:39:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,617 | rd | deprivation_decile.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/deprivation_decile.R
\name{deprivation_decile}
\alias{deprivation_decile}
\title{Deprivation deciles}
\usage{
deprivation_decile(AreaTypeID = 102, Year = 2015)
}
\arguments{
\item{AreaTypeID}{Numeric value, limited to either 102 (counties and... |
f69c45c5a1e68642b70bec596a1c32d8f370668c | 8089f496afe6bf15f774539d0199331e9ad1e337 | /man/restrict_coef.Rd | 934597f19292b6e0f1afe5681132abfc3f9a9232 | [] | no_license | MHaringa/insurancerating | 365235e34bc294053502590afb8f3070a99d7aba | 8ce689ee8f6d0df0902f496a57813889f9c245f9 | refs/heads/master | 2023-01-07T14:43:12.863949 | 2022-12-23T09:47:49 | 2022-12-23T09:47:49 | 147,171,263 | 58 | 14 | null | null | null | null | UTF-8 | R | false | true | 2,620 | rd | restrict_coef.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/model_refinement.R
\name{restrict_coef}
\alias{restrict_coef}
\title{Restrict coefficients in the model}
\usage{
restrict_coef(model, restrictions)
}
\arguments{
\item{model}{object of class glm/restricted}
\item{restrictions}{data.frame wit... |
7e8ff0c39248b75b2974b34a81dba2cee2c50141 | a1f5c0a6e87880dda823d6f9d73dd9c8becbe660 | /scripts/archive/modelingNo90CorrCovv2.R | c0060eb5cedb9f0005dd2f316f8913ff809706d8 | [] | no_license | karistenneson/lidarNew | da2296cfef98641838035d54550e68cb3c9ce4ec | 5eeef887d69ee7f734e8a7c844707250d2165304 | refs/heads/master | 2021-09-07T12:12:11.493762 | 2018-02-22T17:43:18 | 2018-02-22T17:43:18 | 104,791,628 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 48,385 | r | modelingNo90CorrCovv2.R | ### This file is for running the BMA components of the analysis
## Written by MS Patterson (maspatte@uw.edu) and Karis Tenneson (krtenneson@fs.fed.us)
# Last updated: Oct 20, 2017
### Bring in data
#setwd('C:\\Users\\krtenneson\\Desktop\\lidarPaper\\lidarNew\\scripts')
#setwd('\\\\166.2.126.25\\rseat\\Programs\\Reimbu... |
2c3b09a67417f42e3b2094691a1fb641ff4d99fc | 1ae5b7089e9d2c52c81104c89fa1145c6646d881 | /plot2.R | b9ee718cd7e7c6825539831b60b84e4b58651913 | [] | no_license | ekuchar/ExData_Plotting1 | 2bc5d6376708b474c600461d374db27dad032535 | de843e4fd8c56ba6fe6c370654093c362b9646e7 | refs/heads/master | 2021-01-14T12:40:26.582707 | 2014-05-07T18:58:56 | 2014-05-07T18:58:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 681 | r | plot2.R | #reading from local repository because of slow/unacessible internet
setwd( "C:/Users/tata/WORK/Data")
# use English (e.g. for week days) insted of Czech
Sys.setlocale("LC_TIME","English")
#install if necessary
#install.packages("sqldf")
library(sqldf)
x <- read.csv.sql("household_power_consumption.txt", sep=";", s... |
516abd11f4ba84915f0a001b24e491d16a784a73 | b8c9bbba211bcb9c2f0caf79d22ccedacd0c9192 | /GetCleanCourseProject.R | fe7f83cddd7bc0dc2cc6342904848943ad34f449 | [] | no_license | tarekanis/Getting_and_cleaning_data_project | ee1c8ded3bc5d55e64b3b6d3fade98ac52d79453 | c84d7fff3b4168ebb592688cde1c1f9da58e9322 | refs/heads/master | 2021-01-19T18:05:55.390182 | 2014-07-27T13:36:33 | 2014-07-27T13:36:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,128 | r | GetCleanCourseProject.R | # Setup file name variables
folderName <- "UCI HAR DataSet"
columnNamesFile <- paste(folderName,"/features.txt",sep = "")
## Test files
testSub <- paste(folderName,"/test/subject_test.txt",sep = "")
testY <- paste(folderName,"/test/y_test.txt",sep = "")
testX <- paste(folderName,"/test/X_test.txt",sep = "")
## Training... |
8c2bd5160b5134fc4daa521d877b0681afca6bc2 | 4df908dd007b35ebb9c6ead3eb30f30517fae57a | /man/dir.create.adf.Rd | 419b05d0536345c2bb8e71d8562b27695a648834 | [] | no_license | cran/adfExplorer | 2d821b94cbd32856671b6fe2b43c1fe40fb07641 | 8d6c0f9eac8bc2b59cd5799b493b202dff5c38e3 | refs/heads/master | 2021-09-08T02:04:15.568010 | 2021-09-05T07:50:02 | 2021-09-05T07:50:02 | 108,872,575 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,487 | rd | dir.create.adf.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/04fileOperations.r
\docType{methods}
\name{dir.create.adf}
\alias{dir.create.adf}
\alias{dir.create.adf,amigaDisk,character,missing,missing-method}
\alias{dir.create.adf,amigaDisk,character,POSIXt,missing-method}
\alias{dir.create.adf,... |
5399c4ab427b748d8d5c2a02634db6dbab8e6cd6 | f484b55f35e230b94321beddc3aab06aa5cb55b2 | /tests/testthat/test-serializeJSON-S4.R | 99c1f7d95ca36e91301c0ce110953de5710aac3b | [
"MIT"
] | permissive | FlexShopper/jsonlite | 24e59fb1f71c386b665ed890fc549aac49084423 | 633e438608c9798cb3bd00fde71ef006a19dd064 | refs/heads/master | 2023-01-16T03:14:29.172849 | 2020-11-30T15:09:57 | 2020-11-30T15:09:57 | 317,259,057 | 0 | 0 | NOASSERTION | 2020-11-30T15:09:59 | 2020-11-30T15:04:36 | null | UTF-8 | R | false | false | 1,382 | r | test-serializeJSON-S4.R | context("Serializing S4 objects")
test_that("Simple S4 serialization", {
setClass("myClass", slots = list(name = "character"))
obj <- new("myClass", name = "myName")
out <- jsonlite::unserializeJSON(jsonlite::serializeJSON(obj))
expect_identical(obj, out)
removeClass("myClass")
})
test_that("Serialize optio... |
ff17daf74200337654207f9a41700a0384db3eb5 | 6464efbccd76256c3fb97fa4e50efb5d480b7c8c | /cran/paws.application.integration/man/sns_create_platform_application.Rd | 7f0eabbc9d8191aea322c76a5c7f79a5b36809b8 | [
"Apache-2.0"
] | permissive | johnnytommy/paws | 019b410ad8d4218199eb7349eb1844864bd45119 | a371a5f2207b534cf60735e693c809bd33ce3ccf | refs/heads/master | 2020-09-14T23:09:23.848860 | 2020-04-06T21:49:17 | 2020-04-06T21:49:17 | 223,286,996 | 1 | 0 | NOASSERTION | 2019-11-22T00:29:10 | 2019-11-21T23:56:19 | null | UTF-8 | R | false | true | 2,411 | rd | sns_create_platform_application.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sns_operations.R
\name{sns_create_platform_application}
\alias{sns_create_platform_application}
\title{Creates a platform application object for one of the supported push
notification services, such as APNS and FCM, to which devices and mobil... |
adda6bb69c255004743d71e2fbb9fcd252d8c044 | 1a56429cc99500152d6239b9bc9778c47006f094 | /data-raw/amp-ad.R | 95a9130dd9fd12eef1cb22f4de22d25db00247a1 | [
"MIT"
] | permissive | labsyspharm/driad-website | bd019dd1aa7df3c94841f10e13f1d559f7911297 | 9d5bf30e6d30798c9008133aa4a574e52cb2a402 | refs/heads/master | 2023-03-29T07:13:42.105040 | 2021-04-05T14:12:00 | 2021-04-05T14:12:00 | 303,443,350 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,007 | r | amp-ad.R | # Run on O2
library(DRIAD)
library(tidyverse)
library(here)
library(batchtools)
wd <- file.path("/n", "scratch3", "users", "c", "ch305", "driad")
dir.create(wd)
fnROSMAP <- wrangleROSMAP(tempdir())
# fnMSBB <- Sys.glob(here::here("data-raw", "msbb*.tsv.gz"))
fnMSBB <- wrangleMSBB(tempdir())
prediction_tasks_all <- ... |
6f00b2cbb9edbb61c52e26214795413b233a8984 | efc3930c5c08799cb78381d167c9beff0cff20be | /05.Data-tranformation/05.Add-new-variables-with-mutate/00.a.mutate.R | b251d20fb3a52e95935d7dabca69581fb25ee67f | [] | no_license | ReneNyffenegger/R-for-Data-Science | 5309d5aa07da7f524087b7312b0ab43f228a8795 | 5dc4185176978a1eacc4a560e10a3d70a263a4dd | refs/heads/master | 2020-05-15T18:12:03.492070 | 2019-05-06T19:47:56 | 2019-05-06T19:47:56 | 182,419,875 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 359 | r | 00.a.mutate.R | library(nycflights13)
library(tidyverse )
flights_sml <- select(flights,
year:day,
ends_with("delay"),
distance,
air_time
)
mutate(flights_sml,
gain = dep_delay - arr_delay,
hours = air_time / 60,
# It's possible to refer to columns that are created in
# the same call to mutate... |
77a5294f1e04ab8c9e359d254ebc9bc7b4ea6442 | 83f5e78e0446003f9ef164fce4aa3899d8753a68 | /man/mybin.Rd | 63b6d7e2c9cf29e18f19b64e019e4bf0d7656271 | [] | no_license | mclaunts/MATH4753 | d4df46c12260eb3e2813d891f97f97c1674c3790 | bac0cb3b9f5bb87ce6817fc989133abbacb259c7 | refs/heads/master | 2023-01-18T22:25:04.975915 | 2020-11-17T23:30:16 | 2020-11-17T23:30:16 | 298,446,598 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 428 | rd | mybin.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mybin.R
\name{mybin}
\alias{mybin}
\title{Mybin}
\usage{
mybin(iter = 100, n = 10, p = 0.5)
}
\arguments{
\item{iter}{the number of iterations}
\item{n}{the number of Bernoulli trials}
\item{p}{the probability of success in each trial}
}
\v... |
2ef4c3ac214d91d8566ced2e62be01a6082b16c0 | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googlespectrumv1explorer.auto/man/PawsGetSpectrumRequest.Rd | b064be457041aace96fb7dae89f2c308e0be9010 | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,395 | rd | PawsGetSpectrumRequest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/spectrum_objects.R
\name{PawsGetSpectrumRequest}
\alias{PawsGetSpectrumRequest}
\title{PawsGetSpectrumRequest Object}
\usage{
PawsGetSpectrumRequest(antenna = NULL, capabilities = NULL,
deviceDesc = NULL, location = NULL, masterDeviceDesc =... |
2f97612c47659b8c5b45411b0f00621d553368ca | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/clinDR/examples/emaxalt.Rd.R | aaec7b8c91a91a09ded91ab0f1963e6909f7ff20 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 598 | r | emaxalt.Rd.R | library(clinDR)
### Name: emaxalt
### Title: Fit 4- or 3-parameter Emax model substituting simpler curves if
### convergence not achieved.
### Aliases: emaxalt
### Keywords: nonlinear
### ** Examples
save.seed<-.Random.seed
set.seed(12357)
doselev<-c(0,5,25,50,100)
n<-c(78,81,81,81,77)
dose<-rep(doselev,n)
###... |
3ea33bdafe81d6213836f6a7da8f5bb1a5f4aaa7 | ed5c385b23b4316e4fd0afb73c6d32aab76bc154 | /R/resultados_todos.R | b4ac475a4717792f5e8fcde0118430331b97c174 | [] | no_license | loreabad6/analisis_elecciones_EC | a7149d5b460b67bae221ea7967f85733b0908074 | b946067d065e96c571292b74d88f4060d95c9a29 | refs/heads/main | 2023-04-01T20:37:36.745974 | 2021-04-12T07:16:31 | 2021-04-12T07:16:31 | 339,191,850 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,155 | r | resultados_todos.R | library(rtweet)
library(tidyverse)
library(lubridate)
library(gganimate)
# Extraer tweets de Carlos Oporto
resultados = search_tweets(
q = "Resultados AND Oficiales AND CNE AND carlosoporto",
include_rts = F
)
resultados_tidy = resultados %>%
# Seleccionar columnas de interes
select(created_at, text) %>%
# ... |
d6428a85839bc2df8f17d44a435e73c2fa8f61df | ebbe08d58a57ae2e9d308a12df500e1e0ef8d098 | /microbiome/alpha_diversity_gender.R | 878772e269dd13832f9c5a52a4fdbe61455f6608 | [] | no_license | Drizzle-Zhang/bioinformatics | a20b8b01e3c6807a9b6b605394b400daf1a848a3 | 9a24fc1107d42ac4e2bc37b1c866324b766c4a86 | refs/heads/master | 2022-02-19T15:57:43.723344 | 2022-02-14T02:32:47 | 2022-02-14T02:32:47 | 171,384,799 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,095 | r | alpha_diversity_gender.R | # alpha diversity analysis
library(amplicon)
file.in <- '/home/drizzle_zhang/microbiome/result/4.Alpha_Diversity/alpha_index_table/alpha_estimator_summary.csv'
mat.alpha <- read.table(file.in, sep = '\t', header = T, row.names = 1)
type.dose <- c(0, 1, 2, 3)
# meta file
meta.file <- '/home/drizzle_zhang/microbiome/re... |
d3a72bbd2d936476c723df55c370e8ab15fe2ae5 | 8c0013662db894bbd34454a1fd49506e31377d34 | /TRS/newer R to segmented and SLM workflow/tests/collect & output exercise data at percent of max WR/collectExeData.r | bcb51f9196dbeb8144ead4c9625bed4bb586696e | [
"MIT"
] | permissive | tudou2015/DOS-Segmented-Regression-Tools | a9285ea3cfc30d269ee353f5186a57778db764c3 | 556744f567b5188f97f43061cdd433ba6b57e236 | refs/heads/master | 2020-06-11T07:00:31.346740 | 2016-07-04T17:27:41 | 2016-07-04T17:27:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,635 | r | collectExeData.r | # Function to find data at exercise levels of interest
# Given an input of percentage e.g. (.5 for 50%) of work-rate:
# locate the equivalent exercise data at that percentage
# average +/- span for key variables
# Output: average W, VOK, HR, VE + standard deviations
collectExeData <- function(argv){
# Percentage of m... |
273da2cce81d6074b8fd2457cbd15af3318b3f63 | 3f3a25ade89f7ee32537d959cd312bd4f657c7ac | /sto_analysis/shanghai_index/index1.r | cb6de2487200a03dc850cfe610d13421a6413ad0 | [] | no_license | davidyuqiwei/davidyu_v1 | 9b913b835b773888375f3351a2336f04ae2858c9 | f4906ede802cabeafd50109d80669090211b38cd | refs/heads/master | 2021-10-26T09:01:31.406090 | 2021-10-21T00:29:46 | 2021-10-21T00:29:46 | 138,952,029 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,685 | r | index1.r | setwd("G:/stock/data/shanghai_shenzhen_index")
df1=read.csv("shanghai_index_1991-01-01_2017-11-21.csv")
df1=df1[complete.cases(df1),]
close1=df1[,5]
vol=df1[,6]
n1=which(vol>0&vol<10000000)
df2=df1[n1,]
year1=substr(df2[,1],1,4)
month1=substr(df2[,1],6,7)
n1=which(year1>=2012&year1<=2016)
vol=df2[n1,6]
close1=df... |
bf691da5de1fb5b284301909f495a12f46cb8ad4 | b5680157471d9eefd15175390cc577036e905fc2 | /scripts/normalise_matrix_a.R | 64cb21e2c2ba68841dd443fa45762864bb87a9ab | [] | no_license | KIRILLxBREAK/bioinformatics | 4af57fb0313664acdc5c6a80dea696f05068d172 | 02e001b2268a4ae1db3e6ef9961f948e78d49b4b | refs/heads/master | 2021-01-09T23:36:09.218293 | 2019-01-09T18:29:27 | 2019-01-09T18:29:27 | 73,214,752 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 451 | r | normalise_matrix_a.R | #!/usr/local/bin/Rscript
library(magrittr)
library(dplyr)
#dfA <- read.csv('../analysis/csv/A.csv')
load('../data/temp_rdata/dfA.rd')
rownames(dfA) <- dfA[['entrezgene_id']]
dfA %<>% dplyr::select(-entrezgene_id)
dfA <- dfA - rowMeans(dfA)
path_to_A_norm <- "../analysis/csv/A_norm.csv"
write.table(dfA, file=path... |
bb37a9603df162a4dae4f08d22fbb57f6f8d2b98 | 6b4aeb90da899e7053191cf20c2617cae0d46fee | /plot3.R | 009f87df8eb401a25f8d290d6a05cb2068d71ba5 | [] | no_license | tanmayshishodia/ExData_Plotting1 | 1b497c1e6c1320820bbb91500b0fdf48984cfd4c | d5f314fe3df040b59ddd5f564ed5815cf3783969 | refs/heads/master | 2020-07-25T22:23:35.192891 | 2019-09-15T11:41:19 | 2019-09-15T11:41:19 | 208,441,241 | 0 | 0 | null | 2019-09-14T13:03:06 | 2019-09-14T13:03:06 | null | UTF-8 | R | false | false | 864 | r | plot3.R | library(data.table)
#Load the file
dt <- fread("household_power_consumption.txt", na.strings = "?")
#Paste date and time and change char to date format
dt1 <- dt[, DateTime := as.POSIXct(paste(Date, Time), format = "%d/%m/%Y %H:%M:%S")]
#Extract the required dates
dt1 <- dt[dt$DateTime >= "2007-02-01" & dt$... |
dfd4db06a950167e82f29b1851c978971cdc09fb | 32413897f497fd21258a034f737dac9d1904e0d6 | /regression.R | f88dd81b7829a89d3e96d9be406a8eb9bef16dd6 | [] | no_license | liaison/RnD | e984c75c33e3d93d84fb8d80fa2a604932e7cc23 | 0c6e2ebff2aa74f72e03b2e86620a4d3c7027677 | refs/heads/master | 2021-01-22T10:56:35.714984 | 2017-04-10T09:35:37 | 2017-04-10T09:35:37 | 49,974,551 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,227 | r | regression.R | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... |
b6fa4bf7270a992f30b9255393bd071ea2526a80 | bdc8c4e780a8c23831cc6b640faa504c4c516679 | /SVM_cv_demo.R | 1be4f4d1a098d83e43641cb9b32772471df9cee3 | [] | no_license | maccalvert/SVM_cv_demo | 52432f0cf7ad1e92d2e012f9785d13f165f19ebf | 2c287864d093f4117f8ed58084287d6d307e8ea2 | refs/heads/main | 2023-01-12T00:52:26.629252 | 2020-11-09T16:25:04 | 2020-11-09T16:25:04 | 311,397,178 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,217 | r | SVM_cv_demo.R | library(e1071)
library(ggplot2)
n <- 5000
set.seed(10111)
x <- matrix(rnorm(n*2), ncol = 2)
x[1:round(n/3),] <- x[1:round(n/3),] + 2
x[(round(n/3)+1):(round(n/3)*2),2] <- x[(round(n/3)+1):(round(n/3)*2),2] - 2
x[(round(n/3)+1):(round(n/3)*2),1] <- x[(round(n/3)+1):(round(n/3)*2),1] + 2
y <- c(rep(1,round(n/3,)*2), rep... |
c4cd7494d4ecdb354d3fc74ea79fb146e1193ac5 | 26fa9a756f6b769b678ecdf1e2acf91b18ff5ee9 | /man/show_ex_toc.Rd | ac9fe55df4165da78de2dc4196159af77643d889 | [
"MIT"
] | permissive | petzi53/learnitdown | ef14d62cb5890abcb40d93ce86465ccc676b92c9 | d53732fcaabba5a5d934675b65a88f8de1be9502 | refs/heads/master | 2023-06-06T21:02:12.221453 | 2021-07-05T16:45:21 | 2021-07-05T16:45:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 801 | rd | show_ex_toc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/show_ex_toc.R
\name{show_ex_toc}
\alias{show_ex_toc}
\title{Insert a table of content for the exercises at the end of a bookdown chapter}
\usage{
show_ex_toc(header = "", clear.it = TRUE)
}
\arguments{
\item{header}{A Markdown text to place a... |
752620b78fff02ecac86b1212ef4fe78077128c5 | 3fd7e629ef19625f3b2f9db01bb9ab62c17aaf40 | /Hospital_Care (2).R | 72656b244815c542ed74ddc7f396f4b262ca274f | [] | no_license | erabhay85/Hospital_Care_Analysis_Case_Study | 2caf3ea847e2476a6747ecaad057f4b75fea459a | cb9838529ace796583e779168a6fa993c8a1eba4 | refs/heads/master | 2021-08-29T23:07:41.892264 | 2017-12-15T07:27:40 | 2017-12-15T07:27:40 | 114,340,035 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 603 | r | Hospital_Care (2).R |
data <- read.csv("C:/Python27/Fall_Data11.csv",header = TRUE,sep = ",")
View(data)
##data$majorinjury =ifelse(data$InJuryLevel_Key==3,1,0)
str(data)
summary(data)
CTDF$random <- runif(nrow(CTDF), 0, 1);
CTDF <- CTDF[order(CTDF$random),]
CTDF.dev <- CTDF[which(CTDF$random <= 0.7),]
CTDF.val <- CTDF[which(CTDF$random ... |
3893f9c56f9f8f5cd339f92cd02955810c4e5cb8 | a447fc11752764aef2ba535e530255b615b2f6d5 | /R/wrappers_sparse.r | 28925178f22182a59077902cc92cf8988de91fef | [
"BSD-2-Clause"
] | permissive | wrathematics/coop | 8dec1727de8c3f007d1d749c24def6c9e9079bbc | 3a0d91311fc172fda52f4f82a10aaf1691a9460e | refs/heads/master | 2021-11-24T03:46:08.206976 | 2021-11-23T12:20:19 | 2021-11-23T12:20:19 | 44,967,170 | 31 | 7 | null | 2017-06-19T17:33:08 | 2015-10-26T12:35:17 | C | UTF-8 | R | false | false | 923 | r | wrappers_sparse.r | #' @useDynLib coop R_co_sparse
co_sparse <- function(n, a, i, j, index, type, use, inverse)
{
check.is.flag(inverse)
if (!is.double(a))
storage.mode(a) <- "double"
if (!is.integer(i))
storage.mode(i) <- "integer"
if (!is.integer(j))
storage.mode(j) <- "integer"
use <- check_use(use)
if (us... |
ce34c53175c49729385f7fa62d82e5b0844df301 | 5dc8cb48cfc061d84cab2e1593067b696ea69990 | /aula7/exemplo6.R | 8253cc66ac74cf8df876e365bca23b56f9149527 | [
"MIT"
] | permissive | rodrigoesborges/slides2017 | 838d2eae2ce67dfd3f514ac8628443f47ac4732e | 434b21d318d49bd7c6ce4677d578ca22bf3c0da3 | refs/heads/master | 2021-06-12T10:21:43.072406 | 2017-02-22T17:15:33 | 2017-02-22T17:15:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,431 | r | exemplo6.R | library(purrr)
dormir_secs <- function(secs = 1L) {
Sys.sleep(secs)
return(sprintf('dormi %d segundo(s)', secs))
}
# ------------------------------------------------------------------
## Progress bar
vetor <- rep(1, 4)
prog <- dplyr::progress_estimated(length(vetor))
dormidas <- map(vetor, ~{
print(prog$tick(... |
376d8d52880e4e67dcfc4844bb6cf401044162d4 | d38fe70c8d6e30c1cf1e9be7f49b4c709b353528 | /demo/archivist_jss.r | 18edb2da1e1c1551d44422cd6c3b11dffa768f0c | [] | no_license | pbiecek/archivist | 1c1d4f9880255a67a6fd9d926668504b61958abb | 18841c9bd216e792299beb4b8d72f287006aee76 | refs/heads/master | 2021-06-06T00:03:18.169650 | 2021-05-20T13:39:49 | 2021-05-20T13:39:49 | 12,585,738 | 80 | 23 | null | 2021-05-20T13:28:47 | 2013-09-04T08:23:59 | HTML | UTF-8 | R | false | false | 4,521 | r | archivist_jss.r | # Intro
#This is the replication script for 'archivist: An R Package for Managing, Recording and Restoring Data Analysis Results' (Przemyslaw Biecek, Marcin Kosinski) submitted to JSS.
#First, make sure that `archivist` is installed.
if (!require(archivist)) {
install.packages("archivist")
library(archivist)
... |
321e8081aa42789b6cd15000262bb88a9691d3b8 | 9a5a28781aeeeb1e3629e5c59425802c7ddae77f | /man/LR.inference.Rd | 754ab6a25f399aa39c053947336f0b2cb4ae8c61 | [] | no_license | jlstiles/sim.papers | a2b94682e02ea4b8031a3040ea3dac2b03d416bf | 380608511079eea2c519f25e22cae81a48c14f4e | refs/heads/master | 2021-10-25T04:19:51.264694 | 2019-03-31T21:01:21 | 2019-03-31T21:01:21 | 95,248,313 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,013 | rd | LR.inference.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/LR.inference.R
\name{LR.inference}
\alias{LR.inference}
\title{LR.inference}
\usage{
LR.inference(W, A, Y, Qform, alpha = 0.05, simultaneous.inference = FALSE)
}
\arguments{
\item{W, }{matrix or data.frame of covariates}
\item{A, }{a binary ... |
b47dab5f160e7b988230b118c9857183c3d21045 | 7e34930737b7bfe9746a3c4b5340e23a80482691 | /cachematrix.R | cd8e63278a65f005c9a885eb3db1e6e3bc8aa3e1 | [] | no_license | ehodder/ProgrammingAssignment2 | 1b9b176eac15d6fb4fa41dceb88e32de9d4a4472 | 25b82a2a307010cf35742efcd542c8af620efb3b | refs/heads/master | 2021-01-18T02:11:30.711826 | 2014-04-23T16:26:03 | 2014-04-23T16:26:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,126 | r | cachematrix.R | ## These functions provide a way to create and cache an inverse of a matrix
## so that the inverse only has to be calculated once. Create a new matrix object
## by calling makeMatrix with the original matrix then call cacheSolve with the new
## matrix object to get the inverse
## makeCacheMatrix creates a matrix objec... |
42250e2a276ab6ae850138f48b41274ef4e90d6e | 7dc5e6dac1531a024b48e1871bfbc8ab923f7e18 | /getggmap.R | 4dd5ab3be0afb4c428ab1dede559e364b203db0d | [] | no_license | spacetimeecologist/RangeFilling | b5c46b6f27614d4a1d788408381ae4606c4d5f44 | 2a20b88411319081e8d0ad1508b4d7435921956a | refs/heads/master | 2020-05-17T17:03:18.742685 | 2015-06-22T15:31:16 | 2015-06-22T15:31:16 | 22,317,074 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,998 | r | getggmap.R | require(ggplot2)
require(maptools)
require(plyr)
require(raster)
# raster <- raster('C:/Users/ben/Documents/Data/Output/Yuccabrevifolia/06-24.1705/maxent.1705.envi')
# points <- read.csv('C:/Users/ben/Documents/Data/Output/Yuccabrevifolia/06-24.1705/presence.csv')
# points <- points[, 2:3]
# poly <- shapefile('C:/User... |
4ffcd8a52bdc12c9b6d2bf31860d3750966853fd | 3fa8c5984304c79e988d32a2ce6eb9c5983e3cba | /R_assignment_11/Assignment-11-Vizualization.R | 88347761999e833a4b0bea3d2f06e31e395899d7 | [] | no_license | nlad-gmu/Lad_AIT580 | 8a62ae5abbd5ed6ff11ed06ebb060985d7bd83a0 | e5dc2a6751aacb4eab39f7d64f91bd22ebfe9c7c | refs/heads/master | 2020-07-24T11:29:18.151181 | 2019-12-17T09:36:46 | 2019-12-17T09:36:46 | 207,908,848 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 469 | r | Assignment-11-Vizualization.R | ###------------------
###Visualization
###------------------
###Students Name:Neha Lad
###GNumber:G01169261
rm(list=ls())
data <- read.csv(file.choose())
install.packages("ggplot2")
library(ggplot2)
#1. Create Histogram for Age using R (10 points)
ggplot(data, aes(x=Age)) + geom_histogram(binwidth=1,color="darkblue... |
9dc131a97ddfcc536d54e73305b5a31e435a57b0 | 6550e0e725f2e7b6ff9dd55714f37a8a75807980 | /scripts/regression_experiments.R | fac48eef130c8cb785783fdd8168dfd99ae2c129 | [
"MIT"
] | permissive | tuner/turbine-analysis | ee1b4521e45cdf9e79f4577cd76b4d016d88752b | 652de26ea7690bd9d29390404c206048a37e3bad | refs/heads/master | 2020-03-31T17:21:41.598780 | 2018-12-05T05:56:53 | 2018-12-05T05:56:53 | 152,419,890 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,118 | r | regression_experiments.R | ###
# Playing with the data. Just some random hacks..
###
# Clear the workspace
rm(list=ls())
source("common.R")
data <- load_data()
explain_variable(data$turbine$`Local wind power should be produced in Helsinki`)
explain_variable(data$turbine$`I would like to buy local wind power in Helsinki, even if cheaper elec... |
419a193ad45561e15c07038c7d6a9789370c9e1c | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /metafolio/inst/testfiles/est_beta_params/libFuzzer_est_beta_params/est_beta_params_valgrind_files/1612988827-test.R | 0db2932d4fca6e5eac6116247a3390523ff1798f | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 140 | r | 1612988827-test.R | testlist <- list(mu = 2.99205734114282e+21, var = 4.55931121056418e+169)
result <- do.call(metafolio:::est_beta_params,testlist)
str(result) |
171b67463c1b6a22af67ac18067b6d25fade9a51 | 1374a5344c2818fa97ba234a37087596664ba0e2 | /analysis/robustness_results.R | 3ea5b24b5cb77538a76ac8a0e8fa0c845a593282 | [] | no_license | kennyjoseph/ORCID_career_flows | d0cb36fbd0b713e0b2571f17d49159348cedbc2d | bfefedfba52c43ed3b5dec1d50e3c72b8176e5c4 | refs/heads/master | 2023-03-17T07:09:47.648304 | 2021-03-22T17:29:55 | 2021-03-22T17:29:55 | 297,714,897 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,314 | r | robustness_results.R | source("util.R")
theme_set(theme_light(20))
run_sim <- function(trans_file){
trans <- fread(trans_file)
trans <- melt(trans, id =c("from_matched_field","to_matched_field"))
full_trans <- rbindlist(apply(trans, 1,function(m){data.table(from_matched_field=rep(m[1],m[4]),
... |
2be7b013ba0e3b0f2ff7aaa59b63b2b32d755f41 | 3fd97955d533167594a314730eafc1a9d13e8b1d | /scripts/04_calculate_lsm_NA.R | a2304713e1b1b3a9836e615b939f1e77510d5e69 | [] | no_license | mhesselbarth/Borthwick_et_al_2019_Front_Genet | ea59e9c9f181a63f62dffe7645ab2444d61fceae | 89b90c73188d3f923f36c44f7f4f0a5b0b37344e | refs/heads/master | 2021-07-20T13:23:16.577675 | 2020-05-01T17:32:11 | 2020-05-01T17:32:11 | 153,736,815 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,896 | r | 04_calculate_lsm_NA.R | # load libraries
library(clustermq)
library(suppoRt) # devtools::install_github("mhesselbarth/helpeR")
library(landscapemetrics)
library(raster)
library(sp)
library(tidyverse)
source(paste0(getwd(), "/scripts/00_calculate_lsm_helper.R"))
source(paste0(getwd(), "/scripts/00_clip_and_calc.R"))
#### Load data ####
# # ... |
77b5b27fba681ed372ed3140668927d654176ddd | 3b06a15e9a14a27dee2049c10dd2904e792fae1e | /R/heuristic_contention_model.R | 74e6a356bcaefec4b9d9b73a54259026c9c459f0 | [] | no_license | jorgerodriguezveiga/WildfireResources | 7cf965e7fcfa45a9921b27fb08d8f73212f50da2 | 73d02124c0ea6001ab2bb88c81d64ebed39a2fe3 | refs/heads/master | 2021-05-09T18:11:01.921116 | 2018-05-06T16:52:58 | 2018-05-06T16:52:58 | 119,157,188 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 14,756 | r | heuristic_contention_model.R | # ---------------------------------------------------------------------------- #
# Model 1: Contention_model
# ---------------------------------------------------------------------------- #
#' Contention model
#'
#' @description Solve the contention model.
#'
#' @param W_fix Work matrix.
#' @param S_fix Start matrix.
#... |
da24fc23cb2e521f2711bd0ab99e300e86d4a91a | cb24924846351f3ac98aa4bcd96a7288917622f8 | /204.R | 9be4116114dd30f810d6bc176fe5f0ee6f202fcb | [] | no_license | Miffka/RAnalysis1 | e6a2400783a9870d52d3dafc86efbff07ac8ed8f | 867b84f1a8953ae605af6298447bf8aacc3092d1 | refs/heads/master | 2020-03-28T18:35:31.624730 | 2018-09-15T11:01:50 | 2018-09-15T11:01:50 | 148,894,199 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,667 | r | 204.R | setwd(as.character("C:/Users/miffka/Documents/!DataMining/RAnalysis1"))
getwd()
library(psych)
library(ggplot2)
library(Hmisc)
# свои функции
my_calc <- function(x, y){ #задаем аргументы в скобках
s <- x + y #задаем внутренние переменные и пишем тело
return(s) #возвращаем результат
}
result <- my_ca... |
f83e7b4643e725d13b64234fe0863fbbe2aee963 | 2e5b80db0556d23b88870d1afab2f75ad84f18f3 | /published-data-extraction/R/combine-recruit-data.R | 7fd7bf383d69a8de0f07bb068f42970dd8aad684 | [
"MIT"
] | permissive | MRCIEU/ewascatalog | dfbdc1e57dc65fa066810eb1a208bd9fc3d32add | 90f95fc663ca86a56696d633d5049c2e09463318 | refs/heads/main | 2023-06-25T08:39:25.506215 | 2023-06-11T18:34:17 | 2023-06-11T18:34:17 | 329,965,100 | 2 | 0 | null | 2021-01-15T18:16:33 | 2021-01-15T16:28:55 | R | UTF-8 | R | false | false | 4,247 | r | combine-recruit-data.R | #
#
#
# pkgs
library(tidyverse) # tidy code and data
library(readxl) # reading in excel files
library(openxlsx) # writing out excel file
## CHANGE ME
date_of_extraction <- "2022-07-25"
mkdir <- function(path) system(paste("mkdir -p", path))
mkdir(paste0("recruits-data/combined-data/", date_of_extraction))
mkdir(pas... |
a49dafeb39dc7ff7a4ae999392a9f9354f5bc42f | d7ff455d68ab5aec6f90cc7f2a0d26dd594e4348 | /R/big_main_simulation_additional.R | 8b906972acd455bfa35c3e5297d4ddc2ace19eb9 | [] | no_license | stijnmasschelein/complementarity-simulation | 5f4c6c9794b820e88291e4b9f4140ad73a5ad0e0 | e9a2f88a3c358585762aba3accbeff063b068a73 | refs/heads/master | 2021-06-04T01:58:52.011673 | 2020-02-04T10:55:21 | 2020-02-04T10:55:21 | 147,630,411 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 546 | r | big_main_simulation_additional.R | if (!require("remotes")){
install.packages("remotes")
library(remotes)
}
if (!require(simcompl2)){
remotes::install_github("stijnmasschelein/simcompl2",
ref = "only_files")
library(simcompl2)
}
source("R/big_parameters.R")
data_params_1 <- data_params
data_params_1$b1 = list(c(.5, .5, ... |
2ee3bb29c648fee99f6366152eb97410db99b583 | 524847bb282dc11701351f268b83f2642a95fc7b | /plot2.R | f58b3b58b83cf0775c2a45dd9b7609e570ab0c32 | [] | no_license | paras1605/ExData_Plotting1 | 25316f0687d174ddd2a40c303062dba35c48c95d | af694c3922294a1f0770f5cc0b6b04860b7ea0ea | refs/heads/master | 2020-04-30T00:54:59.735121 | 2019-03-20T17:45:55 | 2019-03-20T17:45:55 | 176,514,709 | 0 | 0 | null | 2019-03-19T13:10:47 | 2019-03-19T13:10:46 | null | UTF-8 | R | false | false | 588 | r | plot2.R | table <- read.table("household_power_consumption.txt", header = TRUE, sep = ";", stringsAsFactors = FALSE)
subsetdata <- table[table$Date %in% c("1/2/2007","2/2/2007"), ]
subsetdata$Date <- as.Date(subsetdata$Date, format="%d/%m/%Y")
# Date has to be used with time and hence combining date and time as one
datetime <- ... |
8674d2bdb5ef71233283341f26ee8b422a4c71b5 | 799c09fde62544672faadbf5ea81242e6f5d001f | /Binom Max Lik.R | cb48ee31fa88d2985ff94131b684c0b267ceb57b | [] | no_license | dcossyleon/Stats-Simulations | 6494901abec5db3c6e5bbe79aff3c8039c5375fc | 99227ba624fbbca68f3d10be79e7b436da0bc1fe | refs/heads/master | 2020-04-25T00:19:38.828044 | 2019-02-24T18:33:25 | 2019-02-24T18:33:25 | 172,373,275 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 860 | r | Binom Max Lik.R | #Jan 22, 2018
#Maximum likelihood Binomial distrib
#1) Generate binomial data
n=1000
size=1
X1 <- runif(n, min=0, max=1)
B0 <- 1
B1 <- 2
Y <- rbinom(n=n, size=size, prob=(exp(B0+B1*X1)/(1+(exp(B0+B1*X1))))) #not good
Y2 <-rbinom(n=n, size=size, prob=(1/(1+exp(-(B0+B1*X1)))))
Y3 <-rbinom(n=n, size=size, prob=(1/(1+exp(... |
607e45b1e6113364a7ae9e65a21e9a6a188a10a5 | 7075471c1b29d89c7d08cc3c72ef888609e76dd1 | /server.R | 7b120f4a5467b0c5193bb8b5be9b82a190df41ff | [] | no_license | manshrestha/Shiny-Application-and-Reproducible-Pitch | ddee2a7157a4666f70710a205aebad701007854e | f340a695ef3566abe56b68b6dd5dee4c4aca9d99 | refs/heads/master | 2020-12-30T22:32:18.419172 | 2016-05-12T11:11:18 | 2016-05-12T11:11:18 | 58,634,522 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,184 | r | server.R | # Perform all necessary initialization
init <- function(){
library(shiny) # load Shiny Library
data(mtcars) # load mtcars dataset
set.seed(888) # set seed for reproducibility
}
regression <- function(){
mtcars$am[mtcars$am == 0] <- "Automatic"
mtcars$am[mtcars$am ... |
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