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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
320004f8633eb9ae713b40f2818dc9b147dfa00c | 63b6a027aaab886b940025d821e994e69fa6ddf7 | /Estimation.R | 20935c6119effb825bdbf045816f909185a0ae54 | [] | no_license | lurui0421/CauseSel | 6af4f17ca81e92aed047b867b0e045374cfe8196 | 29fba08b8e8e71ac578ef57f8571a517b2c15d86 | refs/heads/master | 2022-12-10T01:43:33.229305 | 2020-08-10T21:59:04 | 2020-08-10T21:59:04 | 227,627,761 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,961 | r | Estimation.R | ######################################################################################
## Treatment effect estimation ##
#######################################################################################
##: This is the treatment effect estimatio based on variou... |
6f93399f31ace34a084ac277a2a63a7913aa1194 | 6eb6be10dfb00975aa041b19b47ef2511808096d | /ExData_Plotting1-master/plot3.R | a9f028a54f28a5a4735ef4442b9df57e9f291806 | [] | no_license | yashika-sindhu/datasciencecoursera | 5e72af030f83d7ba90433da32af1bc2940b50b54 | 971ceb4526935374250fa646d8722b7e94bb0ed6 | refs/heads/master | 2022-01-22T04:59:26.645366 | 2019-07-22T09:35:49 | 2019-07-22T09:35:49 | 116,703,715 | 0 | 1 | null | 2018-01-09T05:04:23 | 2018-01-08T16:57:01 | null | UTF-8 | R | false | false | 1,321 | r | plot3.R | ## Download the dataset
download.file(
"https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",
destfile="Electric_Power_dataset.zip"
)
## Unzip the data
unzip("Electric_Power_dataset.zip")
## Read the relevant data into R
install.packages("sqldf")
library(sqldf)
my_data<-read.csv.s... |
1ef4e41c84bc21b658ae254b93148f3e7350a8b3 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/m2r/R/m2.R | 116136ff31ec3cd86e60e0537b2480b807cc43ca | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,084 | r | m2.R | #' Call and reset a Macaulay2 process
#'
#' Call and reset a Macaulay2 process
#'
#' @param port port for Macaulay2 socket
#' @param timeout number of seconds before aborting
#' @param attempts numer of times to try to make connection
#' @param cloud use a cloud?
#' @param hostname the remote host to connect to; defaul... |
cbaa348202ec817b6be595e341250f836d5d66e3 | 2a066a86ddbc4546381397171f7f7483251f9990 | /R/reg.diff1M.R | 7b00b6ffd33f1bd805d869576ae589266de98b91 | [] | no_license | cran/clogitLasso | 1df71301a875bfb1474e28a35dbeb0761a7156c4 | 4d8f4af9c9f3fc76682012f15a7f3a919cfb9008 | refs/heads/master | 2020-02-26T14:48:46.306401 | 2018-06-27T20:34:49 | 2018-06-27T20:34:49 | 64,686,532 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,250 | r | reg.diff1M.R |
reg.diff1M = function(X,
y,
strata,
fraction = NULL,
nbfraction = 100,
nopenalize = NULL,
BACK = TRUE,
standardize = FALSE,
maxit = ... |
edb87ccfa6f669ae30735a8a7a295929e97ff725 | 50828550091c6c7cd646e28b07466cde80055d9d | /absences.r | 1b9dc6421a9e9db1842574857a88fda0ca645fcf | [] | no_license | DanielNery/summary-measures-two-dimensional-analysis-ifsp | 390345d02df33f330c5d988c345b9c52c63c22f2 | 46a58c04a561fc0947a0f3bc03d03fa462a497d0 | refs/heads/main | 2023-02-15T04:29:50.529732 | 2020-12-30T00:18:21 | 2020-12-30T00:18:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,613 | r | absences.r | # LENDO ARQUIVO COM AMOSTRA DE DADDOS
data_frame <- read.csv2('~/Documentos/mat_estudantes.csv', sep = ";")
absences = data_frame$absences
ausencias = function (absences) {
# CRIANDO UMA MATRIZ COM SEUS VALORES.
mat <- matrix(c(1, 2))
print(mat)
# DEFININDO O LAYOUT DO MEU HISTOGRAMA.
layout(mat, c(1,1), c(2.5, 1))
... |
7c1471360ad3ff916912551dbd8b09abf7805650 | 24126baa896ba65e54afc16e7ed5a002de9d1173 | /R/user_mod_ui.R | 673e6efced422a394886759fd278aed2bd5bae62 | [] | no_license | ashbaldry/reddit-analysis-app | 9dd52c6dd1dba0a4fe471f7fa09cff8eac4a570e | d9a64de7e1b41f284f757ed68dff8ec169a7caca | refs/heads/master | 2023-08-28T01:48:08.659491 | 2021-11-02T18:37:22 | 2021-11-02T18:37:22 | 348,464,225 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,748 | r | user_mod_ui.R | user_page_ui <- function(id) {
ns <- NS(id)
div(
class = "ui container",
div(
class = "ui stackable grid padded-grid",
div(
class = "two column stretched row",
div(
class = "column",
div(
class = "ui horizontal fluid card",
uiOutput(... |
14c54616f15c22df9f02fd63e50b444eef4a0fb9 | 38e22dcf20dd6e9b2cd745c1871c318b238c27ca | /R/spml.R | b7f7af8bdaed9ff2d89fd6322e50595bd229d3a8 | [] | no_license | cran/splm | 5f6d21bf534ea13dd96e371a41f8f2e698cb2ba0 | 62b73c011ed69da35c92d4cf713feeb8871bb9d1 | refs/heads/master | 2023-08-02T12:21:20.641346 | 2023-07-20T16:00:02 | 2023-07-20T17:31:05 | 17,700,055 | 10 | 9 | null | 2017-12-05T04:27:11 | 2014-03-13T06:31:08 | R | UTF-8 | R | false | false | 3,018 | r | spml.R | spml <- function(formula, data, index=NULL, listw, listw2=listw, na.action,
model=c("within","random","pooling"),
effect=c("individual","time","twoways"),
lag=FALSE, spatial.error=c("b","kkp","none"),
...) {
## wrapper function for all ML models
... |
019dd98d8de9bb6633306d44854c7f39cde672d0 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/TeachingSampling/examples/PikPPS.rd.R | b00bfb055bde3743b91217d666983d4a0728cedd | [] | 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 | 1,392 | r | PikPPS.rd.R | library(TeachingSampling)
### Name: PikPPS
### Title: Inclusion Probabilities in Proportional to Size Sampling Designs
### Aliases: PikPPS
### Keywords: survey
### ** Examples
############
## Example 1
############
x <- c(30,41,50,170,43,200)
n <- 3
# Two elements yields values bigger than one
n*x/sum(x)
# With thi... |
e71c84ab2a474ad7b4f97dc986e18bf86f31187d | 0f104ea64886750d6c5f7051810b4ee39fa91ba9 | /inst/test-data/specific-redcapr/read-oneshot/specify-fields-without-record-id.R | d01b1c6dfa2021b048883f83cb76040a1823d2aa | [
"MIT"
] | permissive | OuhscBbmc/REDCapR | 3ca0c106e93b14d55e2c3e678f7178f0e925a83a | 34f2154852fb52fb99bccd8e8295df8171eb1c18 | refs/heads/main | 2023-07-24T02:44:12.211484 | 2023-07-15T23:03:31 | 2023-07-15T23:03:31 | 14,738,204 | 108 | 43 | NOASSERTION | 2023-09-04T23:07:30 | 2013-11-27T05:27:58 | R | UTF-8 | R | false | false | 800 | r | specify-fields-without-record-id.R | structure(list(name_first = c("Nutmeg", "Tumtum", "Marcus", "Trudy",
"John Lee"), address = c("14 Rose Cottage St.\nKenning UK, 323232",
"14 Rose Cottage Blvd.\nKenning UK 34243", "243 Hill St.\nGuthrie OK 73402",
"342 Elm\nDuncanville TX, 75116", "Hotel Suite\nNew Orleans LA, 70115"
), interpreter_needed = c(0, 0, ... |
1f0b35f546810a5a95a3172278d42fb02b32a2bd | 9655ed9c073e16922159846964374adf934019ac | /plot3.R | 3b3c98d6046c782b366b60078d680af2befb9cfe | [] | no_license | Rub123/ExData_Plotting1 | 49fa294e040c2c6f1cb8551f4f2a089b10c77209 | de131578b06b3fdd4deea553ba0598da6af7de96 | refs/heads/master | 2020-12-02T17:55:52.422727 | 2017-07-09T14:25:59 | 2017-07-09T14:25:59 | 96,449,470 | 0 | 0 | null | 2017-07-06T16:16:42 | 2017-07-06T16:16:42 | null | UTF-8 | R | false | false | 1,542 | r | plot3.R |
library(tidyverse)
dataUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
File_name <- "household_power_consumption.txt"
Zip_name <- "exdata_data_household_power_consumption.zip"
# if the data file doesn`t exist theh: if Zip file exist theh unzipped the file
# el... |
3f0cedbfd643caaadf39022407e93d847ced8aaf | 6fa24cca5ba1dc15f9f206beb97c260786efe732 | /script/old/Distance-1028.R | dbd0b53e366b8c94e081c1093c3bf6909cda7f71 | [] | no_license | wetinhsu/Macaca-population-trend | fe14e5dfa6290b4a982175feae290053c60446b9 | a0e738ec817ae70b8ea042eeb7b64df4c9b5cb10 | refs/heads/master | 2023-07-19T12:38:45.198058 | 2023-07-11T01:13:59 | 2023-07-11T01:13:59 | 190,343,813 | 0 | 0 | null | 2019-06-05T07:07:49 | 2019-06-05T07:07:49 | null | UTF-8 | R | false | false | 2,279 | r | Distance-1028.R | library(Distance)
library(data.table)
library(magrittr)
library(ggplot2)
library(readxl)
setwd("D:/R/test/Macaca-population-trend")
M.data <-
read_xlsx("data/clean/data_for_analysis_1519.xlsx") %>%
setDT %>%
.[ Distance <20 , ] %>%
.[, Year := as.numeric(Year)] %>%
.[, Year.re := Year - min(Year) + 1] ... |
721f2ac4047df1cc6b99881709ae16c65d7a6288 | 768bf50e03d36e04bcc6efd248917becea958cc3 | /Rsuite/FudgeIO/ReadNC.R | 60d6c23b761d8ecd2fc51b03cd311b5c111c1fed | [] | no_license | cwhitlock-NOAA/FUDGE | 62b3d3665cf8d979bf097cd6781f480068364c32 | 8c1ee4013a83ad044792a0066dcd97e35cdb9047 | refs/heads/master | 2021-01-23T18:52:24.149002 | 2015-04-02T14:20:56 | 2015-04-02T14:20:56 | 35,306,320 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,873 | r | ReadNC.R | # Aparna Radhakrishnan 08/04/2014
ReadNC <- function(nc.object,var.name=NA,dstart=NA,dcount=NA,dim='none',verbose=FALSE, force_3_dimensions=FALSE) {
#'Reads data from a variable of a netCDF file object
#'Returns netCDF variable data and, if dim!= 'none', one or more dimensions of that
#'netCDF object
#'-----Arg... |
18219192a50b57d5a8e80e40be258aeffb2bb07c | dd5b4b21b5fd3e4a443f0a7bac5445d24439e841 | /R/study/Linerar Algebra.R | a8b6aeb96a2cc59b5dbb5944d9c49bfddfd946a4 | [] | no_license | qkdrk777777/DUcj | 466a6c519cfe296a1c6753b52c1f49f949c5a894 | 510c80e18dfaa4c8723b79a1aab286f418b3c901 | refs/heads/master | 2020-03-22T18:39:02.326444 | 2018-07-10T18:44:13 | 2018-07-10T18:44:13 | 117,794,983 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,546 | r | Linerar Algebra.R | #package('pracma');package('Matrix')
package('installr')
updateR()
#비동차연립방정식(nonhomogeneous system of linear equations) 해 구하기
#b=O인경우 동차연립방정식 아닌경우를 비동차연립방정식이라 함.
A<-matrix(c(2,-6,-8,3,-8,-14,4,-11,-17),ncol=3,byrow=T)
b<-matrix(c(10,8,15),nrow=3)
solve(A,b)
#해가 없는 경우
A<-matrix(c(2,1,1,1,-2,-7,4,3,5),nrow=3,byrow=T)
... |
18803124c8e452238d9e5475c968d91c00b3e249 | 178087fd666375abeb10fc4f9f23230d2438dc21 | /R/boxformat.r | 655f290bef8bfa6c77028840e9bdd2a93c971e89 | [] | no_license | cran/sdtoolkit | 736adf447b9c59c9a795f7f4735d34985dfac0a2 | 8e9767f73b1266de8edf37744c7325a7e36c6497 | refs/heads/master | 2020-05-29T13:14:47.569985 | 2014-02-16T00:00:00 | 2014-02-16T00:00:00 | 17,699,521 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,543 | r | boxformat.r | `boxformat` <-
function(box, dimlist, morestats, pvallist, style = "ineq"){
d <- ncol(box)
mat <- t(box)
colnames(mat) <- c("low","high")
#Sort the pvalues according to ranking so they line up with more stats
#There should be a one line way vectorized way to do this but I can't think of it... |
04be5857f610a2dc51f1077046dcae5083299d34 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/PeerPerformance/examples/alphaScreening.Rd.R | 482b5f9b6ad6a51ec9a75460bfc6019750c5635f | [] | 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 | 485 | r | alphaScreening.Rd.R | library(PeerPerformance)
### Name: alphaScreening
### Title: Screening using the alpha outperformance ratio
### Aliases: alphaScreening
### Keywords: htest
### ** Examples
## Load the data (randomized data of monthly hedge fund returns)
data("hfdata")
rets = hfdata[,1:10]
## Run alpha screening
ctr = list(nCore =... |
8734652a292b7da26e0f9e7fc60f6de2cc7cf06a | 2eafc112ae88a8a790ad585e8458718fe581ce78 | /type.R | daee9c4120cc80fa47d4c432b0d23865b2119bba | [
"MIT"
] | permissive | PRL-PRG/sle22-signatr-artifact | c5047d8ed2f01f6939cb767984a184aedeb65bc2 | 88982d670ac1c746b142fd2435acd1a0eeb7ea15 | refs/heads/master | 2023-04-15T03:15:31.667611 | 2022-11-22T14:53:05 | 2022-11-22T14:53:05 | 548,820,754 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 379 | r | type.R | #!/usr/bin/env Rscript
traces_file <- commandArgs(trailingOnly = TRUE)[1]
fun_name <- basename(traces_file)
types <- signatr::traces_type(traces_file, signatr:::type_system_tastr, "data/cran_db")
types <- types[[fun_name]]
types <- subset(types, select=c(fun_name, id, signature))
if (length(types) > 0) {
qs::qsav... |
7c02214db533b23d9ea13cca66b856f68b1a5540 | 14305a42ea3fbd2791399aa7f100f2c1a935cf21 | /combineAseReadCountFiles.r | 865af81f2812fd009b2495d203ecdd6be8a699a1 | [
"MIT"
] | permissive | baoqingding/verta_jones_elife_2019 | 58fc55a0b21b27a4392daf444f48d63858f07f40 | 87a1bb2685260856c18bdff05bd5da608767f4cf | refs/heads/master | 2023-03-19T20:05:07.027372 | 2019-05-24T11:56:53 | 2019-05-24T11:56:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,060 | r | combineAseReadCountFiles.r |
# load bed files
# define a unique set of positions
# parse individual bed file into a dataframe of all unique positions
# select lines where the is no NA's
inds = c('c172_F1_20_F', 'c172_F1_20_M', 'c172_F1_04_F', 'c172_F1_04_M', 'c172_F1_10_F', 'c172_F1_10_M', 'c172_F1_13_F', 'c172_F1_13_M', 'c172_F1_01_F', 'c172_F1... |
27bec4bc7f3435b77a8414443015aabc4965513d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/glm.predict/examples/basepredict.Rd.R | 76a13b7d35d412c13d9088c5e3ddd878b1deeff3 | [] | 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 | 370 | r | basepredict.Rd.R | library(glm.predict)
### Name: basepredict
### Title: predicted value
### Aliases: basepredict
### Keywords: models
### ** Examples
model1 = glm(Sex ~ Height + Smoke + Pulse, data=MASS::survey, family=binomial(link=logit))
summary(model1)
# comparing a person with the height 150cm to 151cm
basepredict(model1, c(1,1... |
cb951a789414bda79b12186bbf100a909405013d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/POUMM/examples/plot.summary.POUMM.Rd.R | fe54d162bcc70febe9bd5f5da5382be3bb37616e | [] | 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 | 1,022 | r | plot.summary.POUMM.Rd.R | library(POUMM)
### Name: plot.summary.POUMM
### Title: Plot a summary of a POUMM fit
### Aliases: plot.summary.POUMM
### ** Examples
## Not run:
##D library(POUMM)
##D
##D set.seed(1)
##D
##D N <- 1000
##D
##D # create a random non-ultrametric tree of N tips
##D tree <- ape::rtree(N)
##D
##D # Simulate the e... |
900dbc6d819d4d4b9704aed852af99508ff66e90 | b50a1fa9d4c855c648709c6fe75c83d2ca5851cb | /R/learner.R | bae5c4531adb221f0d067f29d09a68d4a5239592 | [] | no_license | cran/boost | 852ebe4469654e8af1adb2e10c4a944a42c78ef8 | a6c8d51ba9ddb7b07b635ae1cc2bbc53b3c7ce13 | refs/heads/master | 2021-01-10T21:31:25.928552 | 2004-12-09T00:00:00 | 2004-12-09T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,447 | r | learner.R | learner <- function(y, w, xlearn, xtest, method, args, bag)
{
## Definitions
learn <- dim(xlearn)[1]
test <- dim(xtest)[1]
blearn <- matrix(0, bag, learn)
btest <- matrix(0, bag, test)
## Currently only stumps as learners are supported, no choice of args!!!
cntrl <- rpart... |
4d520c99b2e20bdccaacb9091a2360c7743a0653 | 6199b3da924058b8bc2ec245ab815feb25e5aac1 | /R/execute_sort_read_pairs_from_stacks.R | 3384865a20d94801268a105f7106b7ef0844c097 | [] | no_license | abshah/RADseqR | f3611803aaf07fbfd247d7aa49f89eb3317cc49e | 629d31177d77a933c563f61a769172a9410b7d51 | refs/heads/master | 2020-12-31T02:14:11.459982 | 2016-04-11T22:41:11 | 2016-04-11T22:41:11 | 37,906,098 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 806 | r | execute_sort_read_pairs_from_stacks.R | #' Execute sort read pairs from stacks
#' @param stacks_output_files output files from stacks. default ="stacks/"
#' @param sample_files sample files directory. default="samples/"
#' @param whitelist_file which whitelist to use. default="whitelist.txt"
#' @param where to store the output files. default="paired/"
#' @ex... |
460d6f3957fe5302039150b3f0bf3a5cdc10c047 | 8f2b6b2cd7876713e26794f84f5d41e1f01e2683 | /man/get_sample_size.Rd | 4118fc4f50931e1e4d73d01733eeb373abad0ff9 | [
"MIT"
] | permissive | Shicheng-Guo/catalogueR | 909a3bf27a71a06a89f0cebbaec4c5bfdba98bd5 | bdaf36272f54077f24de52c0b7b93851ed8bcd3e | refs/heads/master | 2023-03-27T12:25:06.437278 | 2021-03-29T11:22:37 | 2021-03-29T11:22:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 478 | rd | get_sample_size.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{get_sample_size}
\alias{get_sample_size}
\title{Infer (effective) sample size from summary stats}
\usage{
get_sample_size(subset_DT, sample_size = NULL, effective_ss = T, verbose = T)
}
\description{
Infer (effective) sample siz... |
5d2f09547345db6c14f258df9f550edf7ae16eb6 | b88a9c576e7d59abfb3b0bd693d3e96ba54d156a | /Selection-genome-variable_v2.R | 58b5f07d3e201de45c7c2c58db1ff1aa280744ae | [] | no_license | SlimEKDev/Ececorum | dc9ec5f005562a1c43674858e4e100978cf14409 | 64f059d7fbb39a7e462e6e255e9b9ae887eb6466 | refs/heads/master | 2021-01-22T18:08:09.785789 | 2017-03-15T13:57:19 | 2017-03-15T13:57:19 | 85,061,398 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,793 | r | Selection-genome-variable_v2.R | setwd("~/Ececorum")
# Chargement Comparaison Pathway E. cecorum
GenEcec <- read.csv(file = "Ececorum_all_PATRIC_pathways.csv", header = TRUE, sep = "," , fill = TRUE)
summary(GenEcec)
# Sélection Pathway si retrouvé 16 fois (soit tous les génomes sélectionnés) = core-pathwome de E. cecorum
CoreGenEcec <- GenEcec[ GenEc... |
02f50cce30034e911bfdb9da06e4818329299fff | d8b9a7ecd42d91c5bfac7bcb329aa618a8a849ca | /CRISPRScreenPlots/SyntheticLethality_GenesOfInterest.R | c70eb6b0c01d93a8459adaa7f0efca17497df482 | [] | no_license | MFMdeRooij/CRISPRscreen | 3e77af211b0008591b68e90febb11711dfeff3d0 | 27e4d755d350d9c00b5a5fa928749b8cb6cbee75 | refs/heads/master | 2023-08-30T13:56:37.186502 | 2023-08-22T13:09:36 | 2023-08-22T13:09:36 | 226,073,806 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,784 | r | SyntheticLethality_GenesOfInterest.R | # Use the CRISPRScreenAnalysis.R output files of a synthetic lethality screen, adjust the settings, and run the script in R studio.
# This script can normalize the median log2 fold change to the essential and non-essential genes of a synthetic lethality screen, and plots T1control/T0 against T1treated/T0.
# This nor... |
c066419b80bcc70d63b97106f7a668bc0f677478 | 8a736317e9732b939803d041f2448c125ff49e5f | /man/Input_output.Rd | b91d4ce07c15d4140855c668f1e761f16fcdc5ee | [] | no_license | mbojan/isnar | f753c9d6a6c2623e7725c2f03035c1f9cc89ba85 | 56177701d509b267eff845e15514e4cf48e69675 | refs/heads/master | 2021-06-10T15:32:48.345605 | 2021-02-17T20:00:40 | 2021-02-17T20:00:40 | 19,937,772 | 8 | 3 | null | 2015-04-03T12:41:31 | 2014-05-19T10:28:23 | R | UTF-8 | R | false | true | 384 | rd | Input_output.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Input_output.R
\docType{data}
\name{Input_output}
\alias{Input_output}
\title{Input-output table for U.S. economy}
\format{22-by-22 numeric matrix with dimnames.}
\source{
TODO
}
\description{
Based on data from TODO
}
\references{
TODO: Boja... |
4dc37ebd19ae2700f3d05bee1088d5f8006215c1 | 9f5ccb4b451aa7c7e91f21942e828c237d5e8c0f | /cachematrix.R | de48a247339e1ce93f4626635ff1e5e576e5c95c | [] | no_license | jdpacheco/ProgrammingAssignment2 | 70b0de7f306833861c103be2d070654579cd9e89 | 8156b69fc92708187bec42f3389c81bd48eebfe5 | refs/heads/master | 2021-01-20T09:36:14.221357 | 2015-04-26T06:06:20 | 2015-04-26T06:06:20 | 34,598,817 | 0 | 0 | null | 2015-04-26T05:11:46 | 2015-04-26T05:11:46 | null | UTF-8 | R | false | false | 888 | r | cachematrix.R | ## Due to the costly nature of the 'solve' function, and finding
## inverses of Matrices in general, to save some cycles, I have created
## a matrix which can cache its solve result
## This is the new "data structure", which is the matrix with a memory cache
makeCacheMatrix <- function(x = matrix()) {
i <- NULL
s... |
6f787743ec33636b5ab97a8e4fd46e74578314d4 | 29585dff702209dd446c0ab52ceea046c58e384e | /AllPossibleSpellings/R/batch.possSpells.fnc.R | 157f1ec9656f4c6475efb79b3e786804068cd17b | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 382 | r | batch.possSpells.fnc.R | batch.possSpells.fnc <-
function(fn=list.files(pattern=".*\\.rda")){
for(file in fn){
sink(file=paste(gsub("(.*)\\.rda","\\1",file),"_log.txt",sep=""),split=TRUE)
cat("loading file",file,"\n")
load(file)
possible.spellings=possSpells.fnc(words=words)
write(possible.spellings,file=paste(gsub("(.*)\... |
abc07ea37d4260812c6f567260c8315fb16cc7bb | 29585dff702209dd446c0ab52ceea046c58e384e | /SciencesPo/R/stratified.R | 5b5a423c94cc348b29a3c731beae1c86970e2cb7 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,121 | r | stratified.R | #' @encoding UTF-8
#' @title Stratified Sampling
#'
#' @description A handy function for sampling row values of a data.frame conditional to some strata.
#'
#' @param .data The data.frame from which the sample is desired.
#' @param group The grouping factor, may be a list.
#' @param size The sample size.
#' @param selec... |
218b00d9696a76627dcad60b75ca4cfd2a827e38 | dc98c78d24a63b9d6420b3883a2d3d427c74b292 | /man/box.scale.Rd | e07d265d69ade24de572cb8a170b3a04eae40e3c | [] | no_license | vjcitn/parody2 | 1af69344a19ba841233cb2ddaf5efda1353190bb | 190ccc2306197ca4ddd7eea13cda91ffd93c66bd | refs/heads/main | 2023-02-19T11:12:29.859406 | 2021-01-23T10:21:28 | 2021-01-23T10:21:28 | 330,368,610 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 835 | rd | box.scale.Rd | \name{box.scale}
% [1] "al" "box.scale" "calout.detect" "ckesd"
% [5] "gesdri" "hamp.scale.3" "hamp.scale.4" "hampor"
% [9] "hampoutinds" "lamtab" "logit" "prompt.default"
%[13] "rouor" "rououtinds" "shorth" "shorth.scale"
%[17] "... |
c3ba2c49e4f713e4e7ceafaa94d4f0c8233fbc04 | 8d9cc3035e8daf324a5a29f1908a1b33cc56938f | /scripts/01_data.R | 339b76223fa7c1d136501efcf5e81e3c36901d4f | [] | no_license | JClingo/data-science-methods-final | 5973bad51224c4c548086906a1ebe988dc1a14f3 | ebd0ebe00275976b8c0544548fdd08ba8edc0687 | refs/heads/main | 2023-01-30T02:06:47.556128 | 2020-12-18T00:37:42 | 2020-12-18T00:37:42 | 316,864,205 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,159 | r | 01_data.R | #' ---
#' title: Data processing of 'An exploration of cross-cultural research on bias against atheists'
#' author: "Joshua Clingo"
#' email: "jclingo@ucmerced.edu"
#'
#' output:
#' rmarkdown::html_document:
#' toc: false
#' code_folding: "hide"
#' ---
## This script cleans the data and stores it for late... |
b524592c2b6f01a34ea7948a1b678473bc77c4da | 67337094711ea45a7734f825a36951b28f7ab7ba | /man/plot_comparison.Rd | b2f636f494613fc79f1d5aae71a4f2369e33ef5c | [] | no_license | jashu/itrak | 74ca4046df8862ec16ce037f496b5bb55e603ce8 | 6a57c4bd2f6deba8cefd6786406428e0350b601d | refs/heads/master | 2021-01-18T23:54:58.656427 | 2020-04-30T23:01:01 | 2020-04-30T23:01:01 | 46,810,030 | 5 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,248 | rd | plot_comparison.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_comparison.R
\name{plot_comparison}
\alias{plot_comparison}
\title{Plot Comparison of Time Series}
\usage{
plot_comparison(data, time, pre, post, trial = NULL)
}
\arguments{
\item{data}{Data frame containing both time series.}
\item{tim... |
b6b3e7d627fe577f6c84eca2c9aadd1acd8cc6ce | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/MRMR/examples/CreateEvaluationDates.Rd.R | 05918b21d0e77efd6f58af4934af35e4e0129cb8 | [] | 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 | 546 | r | CreateEvaluationDates.Rd.R | library(MRMR)
### Name: CreateEvaluationDates
### Title: Create triangle evaluation dates
### Aliases: CreateEvaluationDates
### ** Examples
## Not run:
##D OriginStart = c(mdy("1/1/2000"), mdy("1/1/2000"), mdy("1/1/2001"))
##D OriginEnd = c(mdy("12/31/2000"), mdy("12/31/2000"), mdy("12/31/2001"))
##D OriginPeriod... |
d28cafd282a89881f3d07eb8fa21ceb4dd582a09 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /biwavelet/inst/testfiles/rcpp_row_quantile/libFuzzer_rcpp_row_quantile/rcpp_row_quantile_valgrind_files/1610556933-test.R | aef77b100532824b52904b7b117352af0a6b8213 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 282 | r | 1610556933-test.R | testlist <- list(data = structure(c(3.34809087500923e-115, 3.33821168147722e+151, 3.94604863549254e-114, 4.6343369826479e+252, 6.69422745814845e+223, 4.86113721284491e-63, 0, 0, 0, 0, 0, 0), .Dim = 3:4), q = 0)
result <- do.call(biwavelet:::rcpp_row_quantile,testlist)
str(result) |
a587e3df358ccb51c99b7c4a65d30812ab9ee49e | d603c9dfc5a7268c5bfda58327cfb0bfc61e6d74 | /code/R/test_model.R | c946d96db0a4b2a64d6b3d857f27a70e0e69cd79 | [
"MIT"
] | permissive | sammorris81/extreme-decomp | ee60f2913eda7e664324ad4abccfa53c9f1fb334 | a412a513f2b9cde075ecc8842729cc0f7ec32150 | refs/heads/master | 2021-01-20T19:00:10.370167 | 2018-02-22T07:03:36 | 2018-02-22T07:03:36 | 37,218,705 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 178,565 | r | test_model.R | rm(list=ls())
library(fields)
library(Rcpp)
library(emulator)
library(microbenchmark)
library(SpatialExtremes)
library(numDeriv)
library(fields)
#### testing beta ####
source("../../../usefulR/usefulfunctions.R", chdir = TRUE)
openblas.set.num.threads(3)
source("auxfunctions.R", chdir = TRUE)
source("updatemodel.R", c... |
70c82c2c306ac59e8e3786231b959ba3a2cde540 | 4b670987944f024846e8d170b73b372df1710fcf | /man/dli.xform.Rd | ee1b06e7f85813059bd9e25c95ceb4f2b880b2bb | [] | no_license | beckyfisher/custom_functions | 94bed392501b807d6546dab9aa3c243bd83042b6 | 291748ef9be93dd05deff8ff99cb3292c741521c | refs/heads/master | 2020-04-08T10:59:47.805179 | 2019-09-02T04:18:54 | 2019-09-02T04:18:54 | 159,289,189 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 334 | rd | dli.xform.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dredging_wq_transformations.R
\name{dli.xform}
\alias{dli.xform}
\title{dli.xform}
\usage{
dli.xform(x)
}
\arguments{
\item{x}{A numeric vector of raw DLI values}
}
\value{
A numeric vector of transformed values.
}
\description{
Applies a tra... |
7f0a686bd29b4cf9492d616f4720cd7315a56053 | 29585dff702209dd446c0ab52ceea046c58e384e | /ontologySimilarity/inst/doc/ontologySimilarity-guide.R | d680e33018043811501e2a9953a1f7a5f4869a40 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,244 | r | ontologySimilarity-guide.R | ## ------------------------------------------------------------------------
suppressPackageStartupMessages(library(ontologyIndex))
suppressPackageStartupMessages(library(ontologySimilarity))
data(hpo)
set.seed(1)
## ------------------------------------------------------------------------
#random set of terms with ance... |
6bdbd1fde5f211a84418c78c057c354ccdb21f89 | f5e3ef688de15b483f97518626d75086a62efd6f | /histo.R | 68a46679e880ed9a1a0b710742e928d49cc02b2a | [] | no_license | maxerickson/osm_ms_buildings | 99cb42bd006b4270bbd8d6191050ce21ee63403b | 5441dc2fe1a65575d0428b50b5b5d0f540ec8900 | refs/heads/master | 2021-01-18T18:04:05.090768 | 2018-10-24T21:33:32 | 2018-10-24T21:33:32 | 86,838,437 | 21 | 0 | null | null | null | null | UTF-8 | R | false | false | 233 | r | histo.R | d=read.table('/home/max/bingbuildings/overlaps.txt')
res<-hist(d[,1],seq(0,1,0.1))
binnames=format(res$breaks, digits=1)
print(data.frame(paste(binnames[1:10],binnames[2:11],sep='-'),
res$counts),
right=FALSE,
row.names=FALSE) |
f5413b6be7bcdf33d73a367c797aeb5bc55e8ee1 | 95a5cd4c339aeaca2f7e612b55726bab0369e090 | /inst/examples/app.R | 981d4c510f0943ecf2566593507d98290c66e7ed | [] | no_license | bright-spark/shinyUIkit | 88b6434cc34a0e02413c31d65c5159785e55fd1a | 66fb6a640fa41ab5f93fc58bde26fd5259093eb3 | refs/heads/master | 2023-03-19T03:40:24.968413 | 2019-07-22T14:55:04 | 2019-07-22T14:55:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,147 | r | app.R | library(shiny)
shiny::shinyApp(
ui = UIkitPage(
title = "My UIkit application",
UIkitSwitcher(
#animation = "scale-up",
mode = "switcher",
UIkitSwitcherItem(
tabName = "tab1",
UIkitSortable(
UIkitSortableItem(
UIkitCard(
width = NULL,
... |
01c69e7432c2c2ead9dafe4cc1a8bffa70beb578 | 7bbf674e12365b31eff3aaaf9dc25a7548436919 | /tests/prove.R | 31d5e3b6cd93ca0064012cf301584d9c5c623dcd | [] | no_license | phaverty/bigmemoryExtras | d61abed1ee1cd52dbb35bf9b32a850743150ded1 | 033d144545c5fd1637e2c76d2c86cdef776aad22 | refs/heads/master | 2021-07-05T18:51:43.136409 | 2020-12-07T16:20:43 | 2020-12-07T16:20:43 | 22,368,191 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 103 | r | prove.R | require("bigmemoryExtras") || stop("unable to load bigmemoryExtras package")
bigmemoryExtras:::.test()
|
1ac4811c4343c48d1c3f5f237e75f12aad126cd7 | 84e7b589d3d8b05e52e927dc7ce77b79515e71fa | /ch19 - Tidymodel(최적모델)/II-3.Neural Network.R | 2db43b6391ab6bf2283c9ff8c9670d57f875d4ca | [
"MIT"
] | permissive | Lee-changyul/Rstudy_Lee | d1e0f28190de74643d5c0a14f178b41250db7860 | 837a88d6cb4c0e223b42ca18dc5a469051b48533 | refs/heads/main | 2023-06-29T20:21:10.968106 | 2021-08-02T01:48:00 | 2021-08-02T01:48:00 | 325,493,003 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,152 | r | II-3.Neural Network.R | ## II.개별 모델 만들기
## II-3.Neural Network
## 05.Tuning Model 만들기
nnet_model <-
mlp(
hidden_units = tune(),
penalty = tune(),
activation = "relu") %>% # 탐색에서 많이 사용하는 방법
set_engine("nnet") %>%
set_mode("classification")
nnet_model
# 하이퍼파라미터 그리드 만들기
# dials:: grid_regular
# cost_complexity 5개 X tree... |
8a5a7c26e6040396c5de4bccd4f8e750b7d2c619 | cf0c1e0c96c05b2d2359f344e1b0f070ef49d728 | /cachematrix.R | dec828ee96f3ea20d79752d137a5225b112be514 | [] | no_license | eprinjo/ProgrammingAssignment2 | bd98cd87460cef64a49dfd94ae9b49316fd9809d | cd6f8beb1d389f53296c6c4667d2a88509b6993d | refs/heads/master | 2021-01-13T06:53:05.884962 | 2015-07-25T19:44:56 | 2015-07-25T19:44:56 | 39,461,487 | 0 | 0 | null | 2015-07-21T18:01:42 | 2015-07-21T18:01:42 | null | UTF-8 | R | false | false | 937 | r | cachematrix.R | ## makeCacheMatrix contains two functions and stored them in a list
## 1. inverse - to inverse a matrix given in input
## 2. get - to retrieve the inversed matrix
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
## inverse an input matrox using solve()
inverse <- function(){
inv <<- solve(x)
}
... |
e77c03020c75fc784b7f05161314c5460ce4a119 | cc0f711dbf151f5bd65a0c563cac0c1bec04481a | /man/pq.diagnostics.Rd | d8f30ffeb4e6331a59778d27ad6d533ba857f74d | [] | no_license | cran/lmem.qtler | a9a3652c084fd4a4b5508ac506563fd7ac0db847 | 6b056e4021d772421b2dc028b295a9c4c690e30a | refs/heads/master | 2021-01-21T14:48:35.448280 | 2016-07-12T07:49:42 | 2016-07-12T07:49:42 | 58,163,717 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,536 | rd | pq.diagnostics.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/diagnosis_functions.R
\name{pq.diagnostics}
\alias{pq.diagnostics}
\title{Performs phenotypic data quality diagnostics.}
\usage{
pq.diagnostics (crossobj, boxplot = TRUE, qqplot = FALSE,
scatterplot = TRUE,heatplot = TRUE)
}
\argumen... |
756d9a16c7d443f550a0975323dc8878d690434a | 86772a78af6ca3567ed333c9a4cd68c5af73848d | /supplementaries/Mode Jumping MCMC/supplementary/examples/Simulated Data (Example 1)/mode_jumping_package_class_simulated_bas_data_3211.r | 049941e97d163a4c940e36a74e800ce2ecf847d8 | [] | no_license | aliaksah/EMJMCMC2016 | 077170db8ca4a21fbf158d182f551b3814c6c702 | 3954d55fc45296297ee561e0f97f85eb5048c39e | refs/heads/master | 2023-07-19T16:52:43.772170 | 2023-07-15T16:05:37 | 2023-07-15T16:05:37 | 53,848,643 | 17 | 5 | null | 2021-11-25T14:53:35 | 2016-03-14T10:51:06 | R | UTF-8 | R | false | false | 8,106 | r | mode_jumping_package_class_simulated_bas_data_3211.r | rm(list = ls(all = TRUE))
# install the required packges if needed
#install.packages("INLA", repos="http://www.math.ntnu.no/inla/R/testing")
#install.packages("bigmemory")
#install.packages("snow")
#install.packages("Rmpi")
#install.packages("ade4")
#install.packages("sp")
#install.packages("BAS")
#install.packages("ht... |
c58044f22df1b784b3d8790c4dc5a66cad7bac33 | 58ac7af9a85b288580401ff386e025f3d0c9fd43 | /DDPSC/Library_Statistics/fastqc_summarize.R | 5e75fbd0d242a102547f4acb4f54eeec9147329a | [] | no_license | calizarr/Misc | 431f0a22f82ec029b6fc5d39db722259d564e71f | 037b1ce92c0d9776dcd5a4e993083c355e1558bb | refs/heads/master | 2022-07-09T19:12:13.958079 | 2022-05-20T23:06:44 | 2022-05-20T23:06:44 | 36,998,913 | 0 | 0 | null | 2021-01-20T22:04:00 | 2015-06-06T23:56:39 | R | UTF-8 | R | false | false | 4,120 | r | fastqc_summarize.R | #!/usr/bin/Rscript
library(tidyr)
library(argparser, quietly = TRUE)
p <- arg_parser("Take files, a single file, or a directory then a directory path (for files) and an output filename to summarize FastQC")
p <- add_argument(p,
arg = "--input",
help = "Give a space separated list of... |
bc3b4429c62cac6e4b3dcfc6c8cf4b29f4d92b07 | 7101abfc448961275c411ee68fe229100ffd4a7f | /02.Rcode/80.지도만들기.R | 3bc14271d22831c58be616a653fd07ec37517679 | [] | no_license | Gwangil/YourHomeMyHome | 239a087dfc19d4b97f25347836687e4bad4b62dd | bab1c9d5d32f5369f681556a8c8d356db2c9fc53 | refs/heads/master | 2021-01-01T19:05:35.272984 | 2019-05-15T00:34:54 | 2019-05-15T00:34:54 | 98,506,459 | 0 | 0 | null | null | null | null | UHC | R | false | false | 7,102 | r | 80.지도만들기.R | #install.packages('data.table')
#install.packages('dplyr')
#install.packages("XML") # XML 다루는 패키지
#install.packages("stringr")
#install.packages("ggplot2") # 그림 그리는 패키지
#install.packages("leaflet") # 지도관련 패키지
#install.packages("jsonlite") # json 관련 패키지
#install.packages('RCurl') # url... |
46c7f04c81bebdeffa4201e2558a1ae004808c34 | 1fab782e96a803e221f16434b80656232f88ac74 | /main.R | 2aa1a5091528d9f498d20caa10d500bd686edbf0 | [] | no_license | superelastic/FAA03 | a330858124df378653b98c269500c62a76a0be0f | a6f90711e0e61fcd2d048d9b3f4d5085fb09f1d6 | refs/heads/master | 2021-01-23T11:50:02.350443 | 2015-04-19T20:02:00 | 2015-04-19T20:02:00 | 33,827,396 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,737 | r | main.R | rm(list=ls())
setInternet2(TRUE)
con = gzcon(url('http://www.systematicportfolio.com/sit.gz', 'rb'))
source(con)
close(con)
load.packages("TTR,PerformanceAnalytics,quantmod,lattice")
source("C:/Users/rf6994/Documents/R/FAA03/FAAreturns.R")
require(quantmod)
require(PerformanceAnalytics)
mutualFunds <- c("CSD", "E... |
c0a96f2be34a943fbcd091065a10e986189cf356 | b59771808f850041ae5fb9a01266a37cbab6c112 | /man/CompTransform.Rd | 513e74685397118d92f1701b424fbc21a3b456d1 | [
"MIT"
] | permissive | TroyHernandez/alignmentfreer | a3ab610186a194ce03dcbd3fd761cbf41ea66700 | d9f54535e85ac11ae961c0644f94b24972a39ef0 | refs/heads/master | 2016-09-06T00:47:52.144677 | 2014-03-23T20:46:58 | 2014-03-23T20:46:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 602 | rd | CompTransform.Rd | \name{CompTransform}
\alias{CompTransform}
\title{Calculate the composition vector from a generalized vector sequence.}
\usage{
CompTransform(vec, kmer = 3, statistic = 3)
}
\arguments{
\item{vec}{a generalized vector sequence as derived from
\code{Vectorizer} with kmer greater than 1.}
\item{kmer}{an integer th... |
a8205d7765ee68162416fae8f0068f7494ea1eac | 6a68c8a50933b31dbd274aa0dccd7753adf2def8 | /processing/2b_descriptives.R | ea77ce6695df96dbac6a148c1a316a91f78485ca | [] | no_license | ballardtj/recruitment | 81583adf28154d9113921b5c6e847fd4b0fff06e | d8520d365fdede756f86aa70e9ea7996aa9fb6e7 | refs/heads/master | 2020-03-23T11:55:57.940010 | 2019-02-22T01:58:10 | 2019-02-22T01:58:10 | 141,527,292 | 0 | 0 | null | 2018-08-09T05:41:08 | 2018-07-19T05:10:39 | R | UTF-8 | R | false | false | 1,811 | r | 2b_descriptives.R | rm(list=ls())
#load packages
library(tidyverse)
#load data
load("../clean_data/trimmed_data_exp2.RData")
#response rates
trimmed_data %>%
#our program logs rt and response for non-responses as -1.
#Here, we replace -1 with NA.
mutate(rt = if_else(time>0,time,NA_integer_),
response = if_else(response>0... |
708c7361dda162b55a8b96cf37201c83abb46fa5 | 396df2552224ffcb0294fe6e297b231aa2e59e68 | /_working/0157-fitting-bins.R | 420fa0a6b6715ca0efdf139a9e208d5a55704d5e | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | ellisp/blog-source | d072bed980a5074d6c7fac03be3635f70ab5f098 | 1227f83df23af06da5280214ac7f2e0182be5707 | refs/heads/master | 2023-09-05T07:04:53.114901 | 2023-08-27T21:27:55 | 2023-08-27T21:27:55 | 122,695,494 | 17 | 8 | null | 2023-08-27T21:15:33 | 2018-02-24T02:36:45 | HTML | UTF-8 | R | false | false | 8,724 | r | 0157-fitting-bins.R | library(tidyverse)
library(multidplyr)
library(frs)
library(fitdistrplus)
library(knitr)
library(readxl)
library(kableExtra)
library(clipr)
#-----------------simulated data-------------
set.seed(123)
simulated_rate <- 0.005
volumes <- tibble(volume = rexp(n = 10000, rate = simulated_rate))
volumes <- volumes %>%
m... |
fb147a58a7634572a5204e61a8d42b3348d0b15b | b143351b8b602b51213c5cac34488594f9aa07a0 | /2-groups-t-test-analysis.R | 157e0ae4d42054887a9a7b3422531a4f89d08ea7 | [] | no_license | jpfolador/ttest | 62f111c55d1019fe519e6ebbe107102442a08b79 | 9a05fe93d27a7a2b12b1c9ea5b0b869bb4f10d30 | refs/heads/master | 2023-02-23T22:32:11.523492 | 2021-01-30T22:04:21 | 2021-01-30T22:04:21 | 253,340,649 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,226 | r | 2-groups-t-test-analysis.R | #-----------------------------------------------
# Comparison between the ages of two groups
#
# Author: Joao Paulo Folador
# email: jpfolador at gmail.com
#-----------------------------------------------
if (!require("tidyr")) { install.packages('tidyr') }
if (!require("irr")) { install.packages('irr') }
if ... |
7295adf926e2a90907a334e4f0335a04be4295c2 | 8ed441ee034ab9f22ed248645f8f6ba2606b6e5b | /ecoseed/arabidopsis/arabidopsis_late.R | 0aef08c76494e92574b6b6312d3f8cfe4dced56f | [] | no_license | CathyCat88/thesis | 51b33ddf4f86255f1c143f68a8e57ad4dc98726c | a1f311f4b95d4ef40006dd5773d54c97cb295ea7 | refs/heads/master | 2020-09-28T12:39:58.624508 | 2016-11-13T16:17:16 | 2016-11-13T16:17:16 | 66,710,427 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,428 | r | arabidopsis_late.R | graphics.off()
remove(list = ls())
data <- read.csv("arabidopsis_late.csv", header = TRUE)
blank <- data[data$temperature == "blank",]
mean.blank <- mean(blank$OD)
data2 <- data[data$temperature != "blank",]
Concentration <- function(OD, weight) {
return((OD - mean.blank)/ (weight*24200*2*10^-6))
}
data2$result ... |
ba5997494d8deb3d0204290bc673ebf4d5856c41 | 7d192b2ddb740d0a3882b5f66b3453bf217b6b72 | /src/analysis/atom-committers.R | f2fec525f0f665163b4e1f880d031a2f1b8d748f | [
"MIT"
] | permissive | dgopstein/atom-finder | b497fe634e217fb2b682892728e42b6bc83e3d68 | 4198a0113c42da6f38710f72f84a7fa592e87fec | refs/heads/master | 2023-07-08T13:16:49.140429 | 2023-06-26T03:44:53 | 2023-06-26T03:44:53 | 71,188,915 | 4 | 7 | null | 2017-09-28T23:13:06 | 2016-10-17T23:11:51 | C++ | UTF-8 | R | false | false | 1,484 | r | atom-committers.R | library(data.table)
library(ggplot2)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
source("util.R")
atom.committers.gcc <- data.table(read.csv("data/atom-committers_gcc_2017-10-31_1-3.csv")); atom.committers <- atom.committers.gcc
atom.committers.linux <- data.table(read.csv("data/atom-committers_linux... |
8326f3eb364c5ed81fbcb8ed148be5d4dec913bd | 750c83ade014709a7a1d7cec05c190625bff9be3 | /app.R | 6a39db4752202187d15ceec0803d5349dc01366e | [] | no_license | mrc-ide/shinyq | 07b0b7a6101b836b36af8be1c620f1848926bfe1 | 3db138a3573ddd33ad62731926097770ea82f179 | refs/heads/master | 2021-07-04T22:40:27.094856 | 2020-08-28T13:40:22 | 2020-08-28T13:41:20 | 138,864,036 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 75 | r | app.R | source("common.R", local = TRUE)
shiny::shinyApp(ui, server(workers = 1L))
|
27e6aa7d04ac46cba20fdb2360465ddb6d610dec | c1beee1b69f30c33de46a6b1d56ef06af465a747 | /iris.R | 44f974f02a8dd094d7f2c47985317d8daf373ca5 | [] | no_license | Mayankagupta/R-programs | 7db41c985a5dda6aec0f8463b7c251af91ca0425 | 142adb8f960950a19dff4a0719f0c84760462862 | refs/heads/master | 2022-12-23T22:38:07.688110 | 2020-09-28T10:58:42 | 2020-09-28T10:58:42 | 299,279,017 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 224 | r | iris.R | names(iris)
is.vector(names(iris))
vec<-names(iris)
length(vec)
nrow(iris)
ncol(iris)
dim(iris)
iris(,1)
iris[,1]
sum<-iris[,1]
length(sum)
sum(sum)
sum(sum)/length(sum)
mean(iris[,1])
iris[,5]
summary(iris)
|
305beaa99ceb11e7b73c26d3703b7e6ba64749bf | ebee9629abd81143610a6352288ceb2296d111ac | /R/pairwise_vectors.R | ecdd0de3af6a2156a14157d0d78195ac0c57dd88 | [] | no_license | antiphon/Kdirectional | 76de70805b4537a5aff0636486eb387cb64069b0 | 98ab63c3491f1497d6fae8b7b096ddd58afc4b29 | refs/heads/master | 2023-02-26T02:19:41.235132 | 2023-02-12T13:07:11 | 2023-02-12T13:07:11 | 37,183,574 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,058 | r | pairwise_vectors.R | #' Compute pairwise distances and angles
#'
#' Computes pairwise vectors of a 2d or 3d point pattern. Returns
#' circular- or spherical coordinates, depending on the dimension.
#'
#' @param x matrix of coordinates.
#' @param from Indices from which to compute
#' @param to Indices to which to compute
#' @param asMatrix... |
edebaf2159d794da6435289447734b2ab52e9572 | d7ff71e8ffb07419aad458fb2114a752c5bf562c | /tests/testthat/line_breaks_and_other/pipe-line-breaks-in.R | a600216696ad43e3e6f343f1e65b1933cb306e64 | [
"MIT"
] | permissive | r-lib/styler | 50dcfe2a0039bae686518959d14fa2d8a3c2a50b | ca400ad869c6bc69aacb2f18ec0ffae8a195f811 | refs/heads/main | 2023-08-24T20:27:37.511727 | 2023-08-22T13:27:51 | 2023-08-22T13:27:51 | 81,366,413 | 634 | 79 | NOASSERTION | 2023-09-11T08:24:43 | 2017-02-08T19:16:37 | R | UTF-8 | R | false | false | 1,811 | r | pipe-line-breaks-in.R | c(a %>% b)
c(a %>% b())
c(a + b %>% c)
c(
a %>% b)
c(a %>% b()
)
c(a %>% b() # 33
)
c(
a + b %>% c
)
c(
a + b %>%
c)
c(a + b %>%
c)
c(
a + b %>% # 654
c
)
c( # rr
a + b %>%
c
)
c(
a +
b %>% c
)
c(a +
b %>% c
)
a %>% b(
)
a %>% b(
) %>% q
a %>%
b()
a %>% b() %>% c
... |
d62f64cacb12359187f59e5ba2365322ee9e3736 | e2c212336cd141635e163aadb4b068c57c5cffb1 | /R/connect_engine.R | efd0f71068fca4be7c03361cd29aac352a7ceeb2 | [] | no_license | abyanka/myWrdsAccess | cbf863d9c14243478b90b644e291e9458e5aa740 | fd817f09c3989aca36616a71966b6f1e852f53fd | refs/heads/master | 2023-03-15T18:45:38.413098 | 2019-05-17T13:54:05 | 2019-05-17T13:54:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 149 | r | connect_engine.R | wrds_connect <- function(){
wrds <<- DBI::dbConnect(odbc::odbc(), "wrds-postgres")
}
wrds_disconnect <- function(){
DBI::dbDisconnect(wrds)
}
|
af2f65640835b66eb25f5ceb6fbd6d49b8cab773 | 9c3c1d8e918dd6108a819eee5c0da53c8f21636e | /man/datasets.Rd | 8af30fc7734a66b40cb5d37eda5f8ec05394b3d0 | [] | no_license | cran/chronosphere | 054d466fcfaa3c3555f810780ba48952161beb97 | 09988de91d2f520fc8e3ccf3d01f284a5d5dcbc9 | refs/heads/master | 2023-08-17T21:44:32.330628 | 2023-08-17T12:33:00 | 2023-08-17T12:44:55 | 236,570,939 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,330 | rd | datasets.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/datasets.R
\name{datasets}
\alias{datasets}
\title{Download a database extract from \code{chronosphere} remote server}
\usage{
datasets(
src = NULL,
datadir = NULL,
verbose = FALSE,
master = FALSE,
greetings = TRUE,
all = FALSE
)
... |
482eea9e871e0be5d5d53b223be27a719c489da3 | e7c3e0886bf01da80252301c541f87cb6c80cf84 | /scripts/PlotMaggie.R | 6bb0546f092306dcf6b9d3130028b6334f178f6d | [] | no_license | zengfengbo/nextgenseq_pipeline | 6ae8599228b36c49442d9da5409888209b2e3b5e | 6070e223252499b7447b854c538c416a353eaf52 | refs/heads/master | 2021-05-22T17:40:19.547802 | 2019-10-18T13:53:14 | 2019-10-18T13:53:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,862 | r | PlotMaggie.R | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library("optparse"))
suppressPackageStartupMessages(library("stringr"))
suppressPackageStartupMessages(library("gridExtra"))
option_list <- list(
make_option("--input", help="bcftools covrage compare metrix, required"),
make_option("--patient", help="patient I... |
8d8b6b345c47fd3903e492d331440e31d4f50146 | 7d3fe7aca728be7f701ef0ec595222b608798696 | /Reservoir_Layer/Tools/Functions.r | f2feb076282dac8bfb4fe6d856527f7cf19cae4e | [] | no_license | 54481andrew/pathogen-spillover-forecast | dbf9e9c5d0e864e0ea87ecb72ab6c2fa55d15e5b | 210c002bd3db7556560479fb3153aebf90e7b1cf | refs/heads/master | 2022-01-23T04:45:36.030327 | 2022-01-06T05:01:22 | 2022-01-06T05:01:22 | 278,423,939 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,479 | r | Functions.r |
## Return interpretable predictor names for plots
pretty.labels <- function(x){
x.out <- c()
for(xi in x){
xi.name <- switch(xi,
TreeCover = 'Tree Cover',
ShrubCover = 'Shrub Cover',
... |
9b09ee91cac2d82a4365086a58d49195b3ff2b35 | 6727bd3ae9437a309f029af586e43625cb4f804a | /BMW y VW/bmw y vw.R | 1d7f68d8b554bd12dabf9692e3c04ab2ecf2b606 | [] | no_license | miguelcobaleda/prediccion | 34092fe589ca0bcbee368c151b61e4e23ce94525 | 5534528715edb9bcef80b20ebd7ac6f6e3c1c4b4 | refs/heads/main | 2023-02-02T03:27:54.740200 | 2020-12-13T17:19:23 | 2020-12-13T17:19:23 | 311,446,776 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,731 | r | bmw y vw.R | library("quantmod") #Package to download financials historical data
library(forecast)
library("fGarch")
library(vars)
library(depmixS4)
library(TTR)
library(ggplot2)
library(reshape2)
library(xts)
library(extrafont)
#funciones
archTest <- function(rtn,m=10){
# Perform Lagrange Multiplier Test for ARCH ... |
3af5e528b0704c313303e3fb0777f5fb4af9710d | 191d38b3f528a316e51aeef8683799435ac3d2b7 | /chapter4.R | 6c02ce0f6e8b0f311078312837d7ca88ba96167b | [] | no_license | sonirishi/Mclreath_Bayes | 7d7e18b1c103fa45a89bba7eeee4ae3d5ec83ddf | 9b1655de8ffcf649e0c54a70033b57b47a6e9dd6 | refs/heads/master | 2020-07-06T14:51:36.492867 | 2019-09-15T10:54:06 | 2019-09-15T10:54:06 | 203,057,669 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,189 | r | chapter4.R | rm(list=ls(all=T))
library(rethinking)
library(dplyr)
random_walk <- replicate(1000,sum(runif(16,-1,1)))
plot(density(random_walk))
hist(random_walk)
mults <- replicate(10000,prod(1 + runif(12,0,0.1)))
plot(density(mults))
logmults <- replicate(10000,log(prod(1 + runif(12,0,0.1)))) ## log of big ... |
7a760fc991f64ebaf0a859277e592e66e33a69ee | 923f808538d02bea3a3b0125f93d94cf720b090c | /.tmp/hrapgc.collectLCs4.R | e663b5ba245f9e4c49a8bc37cb8fab088bf533d6 | [] | no_license | Tuxkid/PNZ_EF | e557d8d2a08c92fb33db6faf4862927b4a7beec0 | 219cdd3139b0a05928d5b3c32d6606ceff3e5aa2 | refs/heads/master | 2021-01-23T07:04:05.411294 | 2016-02-24T20:39:25 | 2016-02-24T20:39:25 | 40,269,470 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,239 | r | hrapgc.collectLCs4.R | collectLCs4 <- function(adjust.cont = FALSE,
xO = feb16OffCI.df, #xOCT = sepOffCI_CT.df,
xW = feb16WithCI.df, #xWCT = sepWithCI_CT.df,
ab.off = ab.sept15OffAll$concJ,
ab.with = ab.sept15WithAll$concJ,
zO =... |
ccfd81e21aec2d0acbbf195dcdd98fe429dabcf8 | dddb431f9b34f1d048180ebadbbd7fb7d9fe73f5 | /man/ordASDA.Rd | 0c4305c687954141c3fd469d19bce8c3edc08c15 | [] | no_license | gumeo/accSDA | b091b7f20febe61501920854bad3618d9daf61ed | fdc0c29bc02fe69f8726896ca7c6771059807dad | refs/heads/master | 2022-09-06T01:50:43.733060 | 2022-09-02T08:01:43 | 2022-09-02T08:01:43 | 57,045,995 | 7 | 1 | null | 2022-04-05T23:10:55 | 2016-04-25T13:57:33 | R | UTF-8 | R | false | true | 3,935 | rd | ordASDA.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ordinalFunctions.R
\name{ordASDA}
\alias{ordASDA}
\alias{ordASDA.default}
\title{Ordinal Accelerated Sparse Discriminant Analysis}
\usage{
ordASDA(Xt, ...)
\method{ordASDA}{default}(
Xt,
Yt,
s = 1,
Om,
gam = 0.001,
lam = 1e-06,
... |
80b18b0a09b52b39751dfcec4f20ef0735c24502 | 763682fcbdb7430bed317aa94b0ea2f014f52ec4 | /fig1/plotPRStimeGroup.R | ad460dd675a46b78ba183c5910321b31fd6ce2c9 | [] | no_license | mathilab/SkinPigmentationCode | 10ce7b5c8fb05bf2263d5f420cae27c1468adef7 | e829dfcc1ee1311c3701b1e4a27ecfaa5ec17e61 | refs/heads/master | 2022-12-28T16:44:34.598601 | 2020-10-16T06:22:01 | 2020-10-16T06:22:01 | 258,414,584 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,711 | r | plotPRStimeGroup.R | ## Plot PRS time series stratified by European ancestral group
plotPRStimeGroup <- function(data.df,avg.df,use_beta,out) {
# Partition dataset by ADMIXTURE proportions
# Early Farmer
ef.df <- data.df[data.df$ANA>0.6, ]
filter_ef <- ef.df$ID[which(ef.df$Date<5000 & ef.df$YAM>0.3)]
ef.df <- ef.df[!(ef.df$ID %i... |
966a630d61d4659ceeb85ecc3c87b5e20ca337b4 | 0e6f323fffa7de3eafe39767bd8dc1aa7c762e1b | /R/bit-package.R | 906b1ed21ad08ebffa7d279f3b9bcc43650a7432 | [] | no_license | cran/bit | a3fa7153016147b81826381b2c2f44261b7d8769 | 02ef361a415bb7268767da034fb56c0018b59e1f | refs/heads/master | 2022-11-18T16:22:02.293270 | 2022-11-15T20:20:16 | 2022-11-15T20:20:16 | 18,805,509 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,055 | r | bit-package.R | # Package documentation
# (c) 2008-2017 Jens Oehlschägel
# Licence: GPL2
# Provided 'as is', use at your own risk
#' bit: Classes and methods for fast memory-efficient boolean selections
#'
#' Provided are classes for boolean and skewed boolean vectors, fast boolean
#' methods, fast unique and non-unique integer sor... |
a528460e7215b372bd31b4c033651dcf5d7caaf8 | 29585dff702209dd446c0ab52ceea046c58e384e | /shinystan/inst/ShinyStan/global_utils.R | 45acfd3ef1b2ff50da7d6b6b6dcc2a45f4f51079 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,122 | r | global_utils.R | # give ShinyStan app access to ggplot functions
ggplot_fns_file <- if (packageVersion("ggplot2") < "2.0.0")
"ggplot_fns_old.rda" else "ggplot_fns.rda"
load(ggplot_fns_file)
lapply(ggplot_fns, function(f) {
try(assign(f, getFromNamespace(f, "ggplot2"), envir = parent.frame(2)),
silent = TRUE)
})
helpers <- ... |
9515428874486c1f960a791b1c56e2e8116d8922 | 4e263337af30425e2bfc61284f45f611cec6cd0e | /Analysis/Mixed_Model_Group.R | 742acba1f826591a0b12fcd2e6a91fb7f4b05fb1 | [] | no_license | yeatmanlab/Parametric_speech_public | c9ce4f443783c11355a07d4d5c3c87f5a0936bb6 | 8df268acda5c9e425c6df43291191207082d91a4 | refs/heads/master | 2020-04-23T17:47:01.871970 | 2019-02-18T19:39:35 | 2019-02-18T19:39:35 | 171,344,531 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,343 | r | Mixed_Model_Group.R | # This script does linear models on the fit psychometric functions
library(dplyr)
library(lme4)
library(pbkrtest)
library(ggplot2)
rm(list = ls())
psychometrics <- read.csv("../cleaned_psychometrics.csv")
## set deviation contrasts
psychometrics$duration<- factor(psychometrics$duration, levels=c("100", "300"))
durati... |
b1af20f3d31c73bae00fe66f2bd3cd766f9c5390 | c78d381271668ae9fcb74afd00ece39348e349b1 | /per-poll-simulations/0193-reid-research/config.R | 72e519e4fc976b1f338d23d6362e8de7a546b9c2 | [] | no_license | nzherald/nz-election-prediction | b1c1464e2ee0f3cb8bfd109b4ff9f244937f2424 | 5bbafe06a4a1cea09782c4f57210e84a5600b7df | refs/heads/master | 2021-01-01T04:58:12.745919 | 2017-09-21T10:48:16 | 2017-09-21T10:48:16 | 97,279,096 | 0 | 0 | null | 2017-09-16T15:02:30 | 2017-07-14T23:13:56 | R | UTF-8 | R | false | false | 29 | r | config.R |
MaxSims = 5000
DaysTo = 193
|
515be796e3305129c94dbde3803a1ebf06de6be9 | 103cefcd0a90175d953b11b1a13a6c76adb28aef | /analyses/photoperiod/photoperiodfig.R | b2b672d8bd7830a72e3afd6803f00053b672d1b3 | [] | no_license | lizzieinvancouver/ospree | 8ab1732e1245762194db383cdea79be331bbe310 | 9622af29475e7bfaa1b5f6697dcd86e0153a0a30 | refs/heads/master | 2023-08-20T09:09:19.079970 | 2023-08-17T10:33:50 | 2023-08-17T10:33:50 | 44,701,634 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,801 | r | photoperiodfig.R | #Figure of temporal and spatial effects on daylength for OSPREE photoperiod paper:
#Started 18 Sept 2017 by Ailene
#examples from geosphere package
#https://www.rdocumentation.org/packages/geosphere/versions/1.5-5/topics/daylength
library(geosphere)#daylength(lat, doy)
#generate a vector of daylengths from January 1-... |
525131f7081ebcfc46df61a502179aed4d4fb191 | 32e0458f7a034d1bbc63b2e251ed485c8672fc53 | /man/prunePed.Rd | 48f3056f8ab4ac3cb9af95044e6acc18ce5eaddd | [] | no_license | matthewwolak/nadiv | 8ac285b4d5d5e1de558b3de9019db1c81bdd6bce | 4d60f7c2a71149780c0cd33aee2b7735e8650619 | refs/heads/master | 2023-08-02T13:14:04.450579 | 2023-06-16T02:00:38 | 2023-06-16T02:00:38 | 33,896,065 | 16 | 7 | null | 2023-06-16T02:00:39 | 2015-04-13T21:52:53 | R | UTF-8 | R | false | true | 2,646 | rd | prunePed.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/prunePed.R
\name{prunePed}
\alias{prunePed}
\alias{prunePed.default}
\alias{prunePed.numPed}
\title{Prunes a pedigree based on individuals with phenotypes}
\usage{
prunePed(pedigree, phenotyped, ...)
\method{prunePed}{default}(pedigree, phen... |
a31e8890f0f220074022dee98cda48af482f490a | 507d088e311f38ac0e7d97251957d34205d0aafb | /train.R | 7c8045c1a54919c023f3943ea0a6b5494e7f94d0 | [
"MIT"
] | permissive | mrecos/R_keras_Unet | 81e18d24d1405db2a7113030b8579ce12e93912c | 58818811a0f0ee9cdaaafe02a5ec985c932517d3 | refs/heads/master | 2021-10-09T03:23:26.241715 | 2018-12-20T15:51:20 | 2018-12-20T15:51:20 | 160,965,416 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,898 | r | train.R | library("config")
library("magick")
library("abind")
#library("parallel")
#library("doParallel")
#library("foreach")
config <- config::get(config = "testing", file = "./R_Unet/R_Unet_config.yml")
source(file = "./R_Unet/R_Unet_functions.R")
source(file = "./R_Unet/R_Unet.R")
model <- get_unet_128(input_shape = c(conf... |
ae2b3110a8151c3708241053cdc59e0f2d413acd | 59e78bdb65a0e75bfdb25bf7a2db7d3cbd4f5785 | /man/boral_coefs.Rd | 6a075d6c02b5378386208c0075583ebeb4fd3df2 | [] | no_license | mjwestgate/boralis | 15e7b7b23ae4f3796f93c39fb003df5259b0e4ba | a216cd43a3a0ff7e7da3b4d4ecf66c70e60e4cf0 | refs/heads/master | 2020-04-17T07:01:50.400845 | 2019-05-23T05:11:15 | 2019-05-23T05:11:15 | 166,350,510 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,696 | rd | boral_coefs.Rd | \name{boral_coefs}
\alias{boral_coefs}
\title{Extract regression coefficients from a boral model}
\description{Extracts the data necessary for drawing a caterpillar plot of regression coefficients from a boral model. Intended as an internal function called by \code{\link{boral_coefsplot}}, but included here in for user... |
b591773e9d70abe37773139aa663627101d7c428 | 2ade465c017ed359b649bc958d6d3c50dc37db0c | /Task_02/task02 (Autosaved).R | e50341e573341c9512e61052aa61ba847bed4909 | [] | no_license | CheyenneMarie07/Tasks | 7286a9a85e89695188fbbbf01a534e3d58b42072 | 1a93e838dcd2af6970efd8e168c80f37ea8e885c | refs/heads/master | 2023-04-14T18:31:59.309751 | 2021-04-30T13:57:45 | 2021-04-30T13:57:45 | 332,087,185 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,817 | r | task02 (Autosaved).R | setwd('//Users//cheyenneyoung//Desktop//Evolution//Tasks//Task_02')
Data <- read.csv('http://jonsmitchell.com/data/beren.csv',stringsAsFactors=F)
write.csv(Data,"rawdata.csv",quote=F)
length(Data)
nrow(Data)
ncol(Data)
print(Data)
head(Data)
colnames(Data)
Data[1, ]
Data[2, ]
Data[1:3, ]
Data[1:3, 4]
Data[1:5, 1:3]
Dat... |
ccd788b1d632780a431091cf05965a7d8140c1ca | 3e14540a1ad52f1a26b2a6102c6d6e478bf2065a | /map_test1.r | 6c725103f6d1c2ac376abed7218e2f0358a7d22e | [] | no_license | vedapragna/germany_map | d4aeae9693317f48e933115d22161c9d02ba680c | cf3afbd3ce4dd7d9d9566e3a1abc89a8c214dfa1 | refs/heads/master | 2020-12-02T15:24:32.974788 | 2018-02-04T17:38:09 | 2018-02-04T17:38:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,715 | r | map_test1.r |
rm(list=ls(all=TRUE))
library(choroplethr)
library(dplyr)
library(ggplot2)
library(rgdal)
library(maptools)
library(gpclib)
library(readr)
library(R6)
#setwd("~/achim/statistik_r/geo/plz-gebiete.shp/")
#sf <- readOGR(dsn = ".", layer = "plz-gebiete")
#ari lamstein example
setwd("~/Documents/entwickeln/statistik_r/m... |
a6460cb0a38489649110a79ac55dab996de283e4 | 17224ed7e6b364814c3a0b3cd55ccb1d3000296d | /plot4.R | c406453dcc2d9b7794c72e00154002cb60568838 | [] | no_license | andycook/ExData_Plotting1 | 9c145582c78539701adf161d7950a031eb089013 | 3f186bdc37e63e0a04c3baf5612605ee03b791bf | refs/heads/master | 2020-12-25T01:43:14.745523 | 2014-06-09T00:02:33 | 2014-06-09T00:02:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,182 | r | plot4.R | library(data.table)
testData <- data.table(read.table("./data//household_power_consumption.txt", sep = ";", header = TRUE, na.strings="?"))
newTest <- testData[(as.Date(testData$Date, "%d/%m/%Y") == as.Date("01/02/2007", "%d/%m/%Y")) | (as.Date(testData$Date, "%d/%m/%Y") == as.Date("02/02/2007", "%d/%m/%Y")),]
png(file... |
8fad727948203c018b7f68e205f03e4bb7c48271 | 7b2983670bb3f5594ff6dae4fa7228117a00afc2 | /dara_structure_R.R | 03e5e9b1df99ccc3011d8b9a94ed087230a79ed0 | [] | no_license | ee15sa/Intro_to_R | d3d8d90275d98ed259cc85ed3e5c0897de00b966 | 0fcb4d9b58b01a702d8fa29bea32a19e4f23bec8 | refs/heads/master | 2021-05-11T18:45:14.965363 | 2018-01-17T16:15:41 | 2018-01-17T16:15:41 | 117,838,737 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 862 | r | dara_structure_R.R | # Author: Murat Okumah
# Date: 10 January 2018
# Purpose: Script to create and analyse data frames
cats <- read.csv(file = "data/feline_data.csv")
cats
#Address a particular column with $
cats$weight
cats$coat
#Add 2kg to each weight
cats$weight + 2
#Data types
typeof(cats$weight)
typeof(cats$coat)
class(cats)... |
1297fdb8051b7c1c4470d1d2c9405910ed7aa332 | 6783a205b16a9a0edc07d336974588401e8d041d | /man/hgch_bar_DatNum.Rd | 588726ffe40780e545198741caba710a167f3991 | [] | no_license | isciolab/hgchmagic | 91f0307a862700c7af9ff3bcedf314620815c54e | 4c580a9c60a27610e1bc64dc107d43ec55867c00 | refs/heads/master | 2020-05-01T18:42:05.017783 | 2018-08-07T03:06:25 | 2018-08-07T03:06:25 | 177,629,911 | 0 | 1 | null | 2019-03-25T17:05:44 | 2019-03-25T17:05:43 | null | UTF-8 | R | false | true | 888 | rd | hgch_bar_DatNum.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bars.R
\name{hgch_bar_DatNum}
\alias{hgch_bar_DatNum}
\title{Bar (dates, numbers)}
\usage{
hgch_bar_DatNum(data, title = NULL, subtitle = NULL, caption = NULL,
horLabel = NULL, verLabel = NULL, horLine = NULL, horLineLabel = NULL,
verLine... |
43e26d56054dfcae98464d9880eafc78a0ef1ed5 | 1a62b73c8d330398dd74478d1490c07ae7e2f29a | /cachematrix.R | adf296233aa4290f255883e2503d16fb2187300e | [] | no_license | pnovosad/ProgrammingAssignment2 | 329021a17e9d08dab912b157d1891ba7729adcba | b079bcbb28971c1ff143ff6dfdffe3bab7002bab | refs/heads/master | 2021-04-12T03:16:32.534476 | 2018-03-17T20:20:39 | 2018-03-17T20:20:39 | 125,661,593 | 0 | 0 | null | 2018-03-17T19:17:42 | 2018-03-17T19:17:41 | null | UTF-8 | R | false | false | 1,970 | r | cachematrix.R | ## PA2 week 3 by pn, 20180317
## <- : single arrow assignment operator works at the current level
## <<-: double arrow assignment operator can modify variables in parent levels
## a closure is a function written by/in another function
## makeCacheMatrix(): creates a special matrix object that can cache its ... |
6a66176de2914371f9d3e9d2d15b7dc9679fad98 | d422bbcd3a9ca6fcdb8dd1994a550ff988d1990e | /Segmentation and Discriminant Analysis -Car Seats Data.R | 63d5570437aec69d96992770153107e5534152d7 | [] | no_license | Drooz/HW4-BigData | 8416864d85a65695a2e48630e47c4396f0130bda | c9f2d9c2a9b9fa4b7038aec6948132c26c592f3e | refs/heads/master | 2020-04-29T09:26:08.452684 | 2019-03-16T21:08:35 | 2019-03-16T21:08:35 | 176,024,932 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,818 | r | Segmentation and Discriminant Analysis -Car Seats Data.R |
#################################################################
## Marketing analytics ##
## Exercise in Segmentation AND Discriminant Analysis ##
## Carseats Data ##
##################################################... |
55966b8aad94d2c3501342bec8e33e674cc061e8 | 184d33fbe6d0ab73a260d0db9d3849df00d33786 | /tm.plugin.alceste/R/readAlceste.R | 2c2d161f373280444512aca2135450233ecbf47c | [] | no_license | nalimilan/R.TeMiS | 65660d9fbe4c8ca7253aeba5571eab4445736c99 | 3a8398038595807790087c36375bb26417ca606a | refs/heads/master | 2023-04-30T18:04:49.721122 | 2023-04-25T19:45:04 | 2023-04-25T19:45:04 | 81,315,737 | 25 | 7 | null | 2020-06-29T21:45:06 | 2017-02-08T10:07:16 | C | UTF-8 | R | false | false | 1,024 | r | readAlceste.R | readAlceste <- FunctionGenerator(function(elem, language, id) {
function(elem, language, id) {
id2 <- regmatches(elem$content[1], regexec("^([[:digit:]]+) \\*", elem$content[1]))[[1]][2]
# Only override default ID if present
if(!is.na(id2))
id <- id2
starred <- sub("^(\\... |
ee5e3bfbbce596a043ff00b47dc878118bd72d43 | 2935d597895945d2a32b6701f75e918405533a57 | /DMC1/snakemake_ChIPseq/mapped/both/peaks/PeakRanger1.18/ranger/p0.001_q0.01/genomewide/peak_read_counts/RPKM/DMC1_Rep1_ChIP_peaks_ranLoc_read_counts_log2ChIPcontrol_RPKM.R | 785e9b52aa76f24e90964bd187efe36cae1f29c0 | [] | no_license | ajtock/wheat | 7e39a25664cb05436991e7e5b652cf3a1a1bc751 | b062ec7de68121b45aaf8db6ea483edf4f5f4e44 | refs/heads/master | 2022-05-04T01:06:48.281070 | 2022-04-06T11:23:17 | 2022-04-06T11:23:17 | 162,912,621 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 35,423 | r | DMC1_Rep1_ChIP_peaks_ranLoc_read_counts_log2ChIPcontrol_RPKM.R | #!/applications/R/R-3.5.0/bin/Rscript
# Get read counts for each peak and ranLoc (with equivalent width distribution to peaks)
# and plot summaries
# Usage:
# ./DMC1_Rep1_ChIP_peaks_ranLoc_read_counts_log2ChIPcontrol_RPKM.R DMC1_Rep1_ChIP DMC1 MNase_Rep1 MNase 'chr3B'
#libName <- "DMC1_Rep1_ChIP"
#dirName <- "DMC1"
... |
a5390a731cf523ee2a5a916c311308cf717e474d | 1a87d39148d5b6957e8fbb41a75cd726d85d69af | /man/dissimM.Rd | 89e1b86f3a4919689842544bd4dba41cd410c2f8 | [] | no_license | mknoll/dataAnalysisMisc | 61f218f42ba03bc3905416068ea72be1de839004 | 1c720c8e35ae18ca03aca15ff1a9485e920e8832 | refs/heads/master | 2023-01-12T16:49:39.807006 | 2022-12-22T10:21:41 | 2022-12-22T10:21:41 | 91,482,748 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 218 | rd | dissimM.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CNVprofiler.R
\name{dissimM}
\alias{dissimM}
\title{Compute dissim matrix}
\usage{
dissimM(dataChr)
}
\description{
Compute dissim matrix
}
|
1fb98bf59f801e824a26e8e8ddd480a0dd5725f8 | c8cc82425c6d676ae62f09dc9b3107f0ceeaa854 | /R/ReadNC.R | 8d1f742f2d9d28ef5e8118bbd7d34c6ad968a00a | [] | no_license | APCC21/rSDQDM | ceff58d08041c22d30ca6defe68bab64313ca7fc | ef1394e6b1ac32f48add5c1233b844e8c4c4e573 | refs/heads/master | 2021-05-13T20:34:42.707319 | 2018-01-15T04:30:15 | 2018-01-15T04:30:15 | 116,915,534 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,090 | r | ReadNC.R | ReadNC<- function (Folder,NCfileName,Num_days_Cal,Variable,yr_bg,yr_ed,End_Year_ref,...) {
# Reading netCDF files
# Each GCM has different lengths of historical runs and projections.
# According to file names, this function combines all netCDF files into one and extract the reference and projection periods
... |
accd60ffe53056fda15f36ff42d490be4bc84c35 | 71d45d4d5cc85b985e355327a49fd9708b9f6a46 | /plot1.R | 6ab5aeea1a0b8f2e2f2f52bf4ec2aeb9e778b1e3 | [] | no_license | ahmedsamouka/Peer-graded-Assignment-Course-Project-2 | 8565e7c3bff085f1f759d5ab00604a3360aa6185 | 3f88cd4a08af5acf1c943414d941e458f065d8ae | refs/heads/master | 2020-04-26T07:11:52.843834 | 2019-03-02T01:01:24 | 2019-03-02T01:01:24 | 173,387,306 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 338 | r | plot1.R | > NEI <- readRDS("exdata_data_NEI_data/summarySCC_PM25.rds")
> SCC <- readRDS("exdata_data_NEI_data/Source_Classification_Code.rds")
> emissionsyear <- tapply(NEI$Emissions, NEI$year)
> emissionsyear <- tapply(NEI$Emissions, NEI$year, sum)
> barplot(emissionsyear, xlab = "year", ylab = "total emission", main = "total e... |
11bf51a1aa4dcb2b79b76808d50f9baad3d7b8fb | 1ee3625bc622a90c92617d2bb2711abff8e8c74f | /man/snip.Rd | 9c1582800c308cc282f185ec3a0f4f46e69ebab7 | [] | no_license | darrellpenta/APAstyler | d54a49cd328261b448db5afa0dabee4c0d4612c2 | c895a13f0473d76efc15bd42d202c245fe36a021 | refs/heads/master | 2021-01-22T19:08:28.415784 | 2017-10-07T13:55:11 | 2017-10-07T13:55:11 | 85,164,023 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,093 | rd | snip.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/snip-function.R
\name{snip}
\alias{snip}
\title{Remove leading zero and/or trailing zero from a statistic}
\usage{
snip(x, lead = NULL, trail = 3)
}
\arguments{
\item{x}{A numeric value, coercible to a character.}
\item{lead}{Optional number... |
4e337295814e524e2645ad0f96ea39c35c11d87d | c6fc456fad22537821549275fcc7f492c23cf8a6 | /Exemplo Clusterização Hierarquica.R | bb10e97b969da3033e1b9354cd9d458d614608af | [] | no_license | dougvancan/IA-Modelos | c16422ffb5ab457f845f90240ffa3921f8c04dc8 | fb5dce6417909ffd7f50759271f0aebf934f6c5d | refs/heads/master | 2020-05-16T18:56:28.663134 | 2019-04-24T14:24:20 | 2019-04-24T14:24:20 | 183,244,789 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 341 | r | Exemplo Clusterização Hierarquica.R |
#Normaliza os dados do Dataset Iris
irissc = scale(iris[, 1:4])
labels = iris[, 5]
#Calcula a distânca Euclidiana entre todas as amotras
d = dist(irissc)
#Realiza a clusterização Hierarquica por meio da média
output_cluster<-hclust(d,method='average')
#Plota o Dendograma gerado
plot(output_cluster,labels=labels,yl... |
174621cffbbf0bf1feb1a0354e690264109caaea | ada00eed3e808d05bf2a609dee4d14da8af8003a | /spammow/src/data.r | 88d7b98144360441a484dd0a606fe0548f70a4a0 | [] | no_license | felidadae/_archive | 69bf239adea9c6b0471d85c983036e9440c25a01 | 7a32029d99944a1025d881472076b16e504bded7 | refs/heads/master | 2021-01-11T06:18:48.259714 | 2018-03-28T14:02:01 | 2018-03-28T14:02:01 | 70,054,282 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,551 | r | data.r |
#------------------------------------------------------------------
library(CORElearn)
library(tm) # Text mining: Corpus and Document Term Matrix
library(class) # KNN model
#------------------------------------------------------------------
#------------------------------------------------------------------... |
618e33e09d90efd6955a33b284864fbdb7bd8ded | 8ff92ab6946777ce207b845cf8d4377a03d7ded4 | /Process_data.R | fa8ded3c6fded23e54b1f75eaef0a8ba66b9fec9 | [
"MIT"
] | permissive | brainy749/Tiwara-G5-Sahel | aaca655d32e184cdaedb69228bf8f46525dacf9b | 0021099914000c1e3c7b4def1a3fa80e158addd5 | refs/heads/master | 2020-04-25T22:15:53.117078 | 2019-02-28T14:48:15 | 2019-02-28T14:48:15 | 173,105,721 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 25,207 | r | Process_data.R |
# Imports paquets ---------------------------------------------------------
# library(devtools)
#devtools::install_github("RinteRface/bs4Dash")
library(shiny)
library(fontawesome)
library(shinyWidgets)
library(bs4Dash)
library(plotly)
library(r2d3)
library(r2d3maps)
library(rnaturalearth)
library(magick)
library(leafl... |
7028a717d9e070b4a9cf1d205cc6a55c2adee63d | 000a615bc4e146c9c47e8b1fe158df2fe3ae0996 | /Cute3_1/RandomForest/RFWithoutSquareTransform.r | bec8b1d2193a70db49f2cb0f38d295a1c80a10f7 | [] | no_license | SheikMBasha/MLLearning | 4f10da4a354dd897b61f82b1b556d29b00942737 | 525a28875745dd1090cd85da20df8f5bfe3951cc | refs/heads/master | 2021-09-28T17:42:19.309258 | 2018-02-25T18:43:16 | 2018-02-25T18:43:16 | 115,577,096 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,859 | r | RFWithoutSquareTransform.r | mae mse rmse mape
0.15388315 0.06503103 0.25501183 0.91108427
> regr.eval(test_target, pred_test)
mae mse rmse mape
0.2203253 0.1683485 0.4103029 1.1221224
########################
10,10
mae mse rmse mape
0.11273081 0.03794958 0.19480653 0.... |
1be9d59f5ae9c2283e46ca9714f919febc3acdd0 | 424c7098a182cb3f67f334765a225d0531a0d5a5 | /r/man/Scalar.Rd | 21e04c12e088aa660c7d2ea086968739684e82de | [
"Apache-2.0",
"MIT",
"BSD-3-Clause",
"BSD-2-Clause",
"JSON",
"OpenSSL",
"CC-BY-3.0",
"NTP",
"LicenseRef-scancode-unknown-license-reference",
"CC0-1.0",
"LLVM-exception",
"Zlib",
"CC-BY-4.0",
"LicenseRef-scancode-protobuf",
"ZPL-2.1",
"BSL-1.0",
"LicenseRef-scancode-public-domain"
] | permissive | romainfrancois/arrow | 08b7d1ae810438c8507c50ba33a9cabc35b4ee74 | 8cebc4948ab5c5792c20a3f463e2043e01c49828 | refs/heads/master | 2022-03-12T02:08:17.793883 | 2021-12-05T06:19:46 | 2021-12-05T06:19:46 | 124,081,421 | 16 | 3 | Apache-2.0 | 2018-03-06T13:21:29 | 2018-03-06T13:21:28 | null | UTF-8 | R | false | true | 1,229 | rd | Scalar.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scalar.R
\docType{class}
\name{Scalar}
\alias{Scalar}
\title{Arrow scalars}
\description{
A \code{Scalar} holds a single value of an Arrow type.
}
\section{Methods}{
\verb{$ToString()}: convert to a string
\verb{$as_vector()}: convert to an ... |
2020e406ae283261260b9cd2487d75aebca12bea | 0e92c0b362b230341f9cc31207df8139dbc3ac18 | /R/writeAllGDAL.R | 9e1999511d0aebbfe52755cdc6c756df221bd0b6 | [] | no_license | cran/raster | b08740e15a19ad3af5e0ec128d656853e3f4d3c6 | dec20262815cf92b3124e8973aeb9ccf1a1a2fda | refs/heads/master | 2023-07-09T20:03:45.126382 | 2023-07-04T10:40:02 | 2023-07-04T10:40:02 | 17,699,044 | 29 | 35 | null | 2015-12-05T19:06:17 | 2014-03-13T06:02:19 | R | UTF-8 | R | false | false | 1,053 | r | writeAllGDAL.R | # Author: Robert J. Hijmans
# Date : January 2009
# Version 0.9
# Licence GPL v3
.writeGDALall <- function(x, filename, options=NULL, setStatistics=TRUE, ...) {
stat <- cbind(NA, NA)
# if (nlayers(x) > 1) {
# y <- brick(x, values=FALSE)
# levels(y) <- levels(x)
# x <- getValues(x)
## if (setStatist... |
fa35d4f0f0ee530b4a80c90581e10a1373875864 | 6470ce550c26c7cd13245dab8b84623534e78655 | /第10章 网络关系型图表/图10-5-1_蜂巢网络图.R | 8ed007f34eca2fa3d051bc346190c12706fde20e | [] | no_license | EasyChart/Beautiful-Visualization-with-R | 0d73ed4ee1e1855e33048330294335fbad6d2a25 | 27990b9349b697ec4336d3e72bae5f3a08d5f5ea | refs/heads/master | 2023-06-10T07:36:29.289034 | 2023-06-05T03:48:59 | 2023-06-05T03:48:59 | 189,740,776 | 687 | 446 | null | 2020-02-26T08:07:21 | 2019-06-01T14:14:10 | PostScript | UTF-8 | R | false | false | 1,992 | r | 图10-5-1_蜂巢网络图.R |
#EasyShu团队出品，更多文章请关注微信公众号【EasyShu】
#如有问题修正与深入学习，可联系微信：EasyCharts
#reference：
#http://www.hiveplot.net/
#https://www.data-imaginist.com/tags/ggraph/
#http://www.sthda.com/english/articles/33-social-network-analysis/135-network-visualization-essentials-in-r/
library(ggraph)
library(igraph)
library(dplyr)
library(wes... |
ee0394c47cef94e91651b20c8c4f52d85adb7ca6 | f81ac43a1d02013a9cb9eebc2a7d92da4cae9169 | /R/significance_means.R | 6f5a7b009be22e56ae9e14fce38f59a05b2c2f3c | [] | no_license | gdemin/expss | 67d7df59bd4dad2287f49403741840598e01f4a6 | 668d7bace676b555cb34d5e0d633fad516c0f19b | refs/heads/master | 2023-08-31T03:27:40.220828 | 2023-07-16T21:41:53 | 2023-07-16T21:41:53 | 31,271,628 | 83 | 15 | null | 2022-11-02T18:53:17 | 2015-02-24T17:16:42 | R | UTF-8 | R | false | false | 18,586 | r | significance_means.R | MEANS_IND = c(TRUE, FALSE, FALSE)
SD_IND = c(FALSE, TRUE, FALSE)
N_IND = c(FALSE, FALSE, TRUE)
#' @rdname significance
#' @export
significance_means = function(x,
sig_level = 0.05,
delta_means = 0,
min_base = 2,
... |
c4cf06315ed35cfc265cbef6fe9a025fe4ef250c | 08ff8a019901e8f9aab196f0635de156f33af35e | /predJoin_v_0_3.r | 8f1c6843a1d277bbb4f87689b02975a7a6e5b2ce | [] | no_license | squirrelClare/DataAnalysis_R | 63561b514297ab5038c34f707cbb9e28def2f7da | ee7b8f12f26489d380bb192f517797980b41686b | refs/heads/master | 2021-01-10T03:44:31.778737 | 2015-10-21T15:46:38 | 2015-10-21T15:46:38 | 44,686,687 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,057 | r | predJoin_v_0_3.r | # 在数据库DM2015中执行sql，并返回查询的结果
methChoice_exeDm2015<-function(sql) {
library(RMySQL)
conn<-dbConnect(RMySQL::MySQL(),dbname='DM2015',username='dimer',host='192.168.10.87',
port=3306,password='data123')
dbSendQuery(conn,statement="set names 'utf8';")
res<-dbSendQuery(conn,statement=sql)
mydata<-fetch(res,n=-1... |
6d62b077f9283995f9e1d23d3ed078850a7a70fe | 38b5ddfe71331594e45ca40026b57219377b9bac | /PreProcessingHelpers_ImageStream.R | b370fb39817b80c83c9be38e2996e7cde339e80e | [] | no_license | jaywarrick/R-Cytoprofiling | 0602429224c24c63630b339452f8a69727413981 | 88aeb315f95d2f48ca6b50bc92e2a2e9f4fdfc22 | refs/heads/master | 2023-07-20T04:13:57.119807 | 2023-07-14T03:33:10 | 2023-07-14T03:33:10 | 51,033,729 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 27,546 | r | PreProcessingHelpers_ImageStream.R | library(data.table)
library(foreign)
##### Visualization #####
browseShinyData <- function()
{
sourceGitHubFile(user='jaywarrick', repo='R-General', branch='master', file='DataClassBrowser/ui.R')
sourceGitHubFile(user='jaywarrick', repo='R-General', branch='master', file='DataClassBrowser/server.R')
shinyApp(ui=my... |
c82975590c5e45d830f21e0c933dbdf59b6c494e | c6e28546148e8714443e996346c23cd22e6e260a | /At1/methods.R | 6e406dda6f8e5fe3b5efa517f04cfad3bd444030 | [] | no_license | isrvasconcelos/SystemsIdentification-2017.2 | 8d68b20933edaac9c3f30d492ee9819230b4c242 | b6a50572978ec08285ad340cba27d2bf6a2c3802 | refs/heads/master | 2020-03-17T17:25:25.369186 | 2018-05-19T02:23:33 | 2018-05-19T02:23:33 | 126,961,190 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,878 | r | methods.R | source("systemblocks.R")
suppressMessages(library(Metrics))
######################################################################################
## Plot auxiliar
exportMSE_Barplot <- function( data , imgName , title ) {
ylim <- c( 0 , max(data) )
setEPS(width=10, height=7)
postscript(paste(imgName, ".eps", se... |
c0684eb52550efe88a21c5d65f1a5bb92676c0cb | 420938f4f6a85690269001ecce76f08cf3397c4e | /man/bracket_drop.Rd | c7e33c0d547978b6a031fc300f5088fa96c634cf | [] | no_license | cran/table.glue | d50fb96df4bdf322b03e6ec1d13a97b856f4d9d9 | 45af6a4234fa45b2a1c9031205b6bcc49b68e3e1 | refs/heads/master | 2023-02-19T10:14:29.835635 | 2023-02-07T07:30:02 | 2023-02-07T07:30:02 | 300,206,546 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,290 | rd | bracket_drop.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bracket_helpers.R
\name{bracket_drop}
\alias{bracket_drop}
\alias{bracket_extract}
\alias{bracket_insert_left}
\alias{bracket_insert_right}
\alias{bracket_point_estimate}
\alias{bracket_lower_bound}
\alias{bracket_upper_bound}
\titl... |
75484b35b9d5c4aed1ca691683de3a185c386373 | 441b9022b155015fe64e707742bcc47b4dd8b542 | /scripts/land_use/water_use_CA_counties.R | 7385215d300aff06c29b6fdf455e328493ebd5f4 | [] | no_license | sudokita/ClimateActionR | 9f19e61cd3f9cf6b5b878928f91e6f7277c84f26 | 3697477ff00888d28443b09f3998bce2d89af585 | refs/heads/master | 2021-01-21T02:50:37.103083 | 2016-03-19T06:14:59 | 2016-03-19T06:14:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,444 | r | water_use_CA_counties.R | #################################################-
## DOMAIN: LAND USE
## Retrieve California water use data 1985-2010
## URL: http://waterdata.usgs.gov/ca/nwis/wu
## Author: W. Petry
#################################################-
## Preliminaries
library(rdrop2)
library(dplyr)
library(tidyr)
#####################... |
7b68ef78a30d8e33ff774eac58c13b041ea7d470 | 5c21757fb60ca9fa2232f87cc05ade4e34de6466 | /man/randdis.Rd | c54a4acc5689d88e10b5a50b9f6a6145d9ab17a8 | [] | no_license | cran/DBGSA | d2f0c59b50ce4568d98c8acb6aeff7542ed8d5b0 | 5ca177761739df5c3910876059d1b4a7f8fb1183 | refs/heads/master | 2016-09-06T20:06:04.407631 | 2011-12-29T00:00:00 | 2011-12-29T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,732 | rd | randdis.Rd | \name{randdis}
\alias{randdis}
\title{Randomly generating some gene expression profiles by gene resampling}
\description{
A function which is used to generate the required number of gene expression profiles by permutation called gene resampling
}
\usage{
randdis(z,minigenenum,randnum,se... |
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