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de5daeb60d46f8310d022b57d70b7b9906d6e743 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/MCPAN/examples/Simpsonci.Rd.R | 8748b76ec55baa7f1407684d6e4c00cf557875e4 | [] | 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 | 1,299 | r | Simpsonci.Rd.R | library(MCPAN)
### Name: Simpsonci
### Title: Confidence intervals for differences of Simpson indices
### Aliases: Simpsonci
### Keywords: htest
### ** Examples
data(HCD)
HCDcounts<-HCD[,-1]
HCDf<-HCD[,1]
# Rogers and Hsu (2001), Table 2:
# All pair wise comparisons:
Simpsonci(X=HCDcounts, f=HCDf, type = "Tukey... |
186ea51d74e2c7bdb83c06f5339b6a98b2ecf481 | 117fa6d4f8a1b13ef6b8193124592324816d5ca5 | /man/number_range.Rd | b00d6bfd3e80ec46a3b52724cfb76dfa6eb89ff0 | [] | no_license | applied-statistic-using-r/rebus.numbers | 6ae8ce7d9f68da7caeba7af1b6ba313324021725 | 721599e9b44e0a99fe3f611f7846f9277b34c1b7 | refs/heads/master | 2021-01-19T12:46:18.000591 | 2017-05-01T15:21:04 | 2017-05-01T15:21:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 971 | rd | number_range.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/number_range.R
\name{number_range}
\alias{number_range}
\title{Generate a regular expression for a number range}
\usage{
number_range(lo, hi, allow_leading_zeroes = FALSE, capture = FALSE)
}
\arguments{
\item{lo}{An integer.}
\item{hi}{An in... |
87afe95837f7cd0c29509a5ad31413fe46674363 | 8491183d56c8fc70ac58f8af10626875845b9dee | /Week 3/hw3/hw_R/hw3.R | 5f951470c13ef2730a5aff5f470ba21150c823ea | [] | no_license | yifeitung/HUDM_5126 | 7f0a48088f4df801fb5c998aef3863bda3b207b9 | 6c51d67b613f0ea98ae0e15f2b87247a36400574 | refs/heads/master | 2023-02-07T18:09:55.512673 | 2020-12-28T19:30:34 | 2020-12-28T19:30:34 | 307,020,222 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,902 | r | hw3.R | # Linear Models and Regression Analysis HW3
################ Data Preparation #################
setwd("/Users/yifei/Documents/Teachers College/Linear Models and Regression/Week 3")
getwd()
library(ggplot2)
library(lmtest)
library(hrbrthemes)
library(extrafont)
# 1
# This question is adapted from Q3.3. Refer to Grade... |
60361dfcc316c8eb57ea7a5512cfc7998c4e4890 | fb4ee97814efccd540d909a1a19cec8c170646fd | /Python/python_mini_batch/pomoc.R | 81820ac81546701b79b9e1320a296ef57dc075f3 | [] | no_license | potockan/Text-clustering-basing-on-string-metrics | 6ba1ac23f5d29a2cf59e8ea57f7ea43985dc614e | babdf79db3a0ab875cc8641160fe41533e3ec6e3 | refs/heads/master | 2021-01-15T17:46:10.962750 | 2016-05-16T10:52:52 | 2016-05-16T10:52:52 | 25,786,834 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,051 | r | pomoc.R | library(RSQLite)
library(dplyr)
library(stringi)
source("./R/db_exec.R")
nazwy <- c('_', '_lcs', '_dl','_jaccard','_qgram',
'_red_lcs','_red_dl','_red_jaccard','_red_qgram',
'_red_lcs_lcs','_red_dl_dl','_red_jaccard_jaccard','_red_qgram_qgram')
for(n in nazwy){
print(n)
i <- 1
if(n == "_"... |
9598913a3812420a46740a3ffbd20facd4d9cf50 | 2c64352c0495f9b12466a0e99d02804f3454c398 | /man/mod_function.Rd | 07c71eb4a710fa8786163a55cd5b690555a1430b | [] | no_license | cran/nmm | b8596e22b6b680ab3e8300273e1047b1ad48bc74 | 154ad5c5cdffb916fb015aaa10469bfa13a1f17b | refs/heads/master | 2023-02-23T00:53:03.370934 | 2021-01-07T10:20:03 | 2021-01-07T10:20:03 | 334,164,780 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 349 | rd | mod_function.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/007functions.R
\name{mod_function}
\alias{mod_function}
\title{Modifies function to optimize sigma and rho}
\usage{
mod_function(obj)
}
\arguments{
\item{obj}{nmm object}
}
\value{
Function
}
\description{
Modifies function to optimize sigma ... |
2f933e3fd634ede162f560df9817e9d709e046db | 690c2ec7e296ea6073db3e45dbfc8ee04e7789b0 | /man/Tinamus_solitarius_env.Rd | cae93d47a7fc3bbc350813d62ab41d3219e1c2a8 | [] | no_license | cran/bossMaps | e42bf359733dcba0f1a1eaebe40a5510b6e47974 | 6779fe6fdd1445b0511cfa5a1c770ffce326670f | refs/heads/master | 2021-04-29T10:22:02.824527 | 2016-12-30T00:07:50 | 2016-12-30T00:07:50 | 77,647,518 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 530 | rd | Tinamus_solitarius_env.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{Tinamus_solitarius_env}
\alias{Tinamus_solitarius_env}
\title{Expert range environmental data for the Solitary Tinamou (Tinamus solitarius)}
\format{A rasterStack object}
\usage{
Tinamus_solitarius_env
}
\descripti... |
8cfe948b465564645f29ea4abb149b849ed4ce78 | 4f1c86ccd1daa1e7129cdfeb33b1e795362acfad | /09-case_study_03.R | ea6c55f024134a0a98d710ed91e5259fdd21e4b4 | [] | no_license | ua-dt-sci/fall2020_002_class_scripts | 5ff323f176d5643bfe13dd01fb93ee5dd94f1c2f | 3797a44c1e0f9e46496a4ece70eb1ead10995c2f | refs/heads/main | 2023-01-22T22:28:08.165438 | 2020-12-08T20:43:37 | 2020-12-08T20:43:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,116 | r | 09-case_study_03.R | # load libraries
library(tidyverse)
# read data in
url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-10-20/beer_awards.csv"
beer_awards <- read_csv(url)
# alternatively
beer_awards <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/202... |
7619610471fddf11c903aa3991f7ccb78092f605 | 6954021dc0a2e5d648fcc5fc6b549ad85d41e46b | /Diurnal Separation Code.R | 86c296d06f67e282f994c183ce60d31a00967965 | [] | no_license | aaronzsun/weather | 8ad92e2796aaf12cc0a181191d868f6d61871fe3 | 95936b3e36ef0a806711dd28a93149e52af65cb5 | refs/heads/master | 2020-07-29T05:03:59.353778 | 2020-01-25T17:39:44 | 2020-01-25T17:39:44 | 209,679,746 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,609 | r | Diurnal Separation Code.R | #7/2/19 Dinural Separation Code | Aaron Sun
#Attached dataset (change name throughout program for different sets)
#insert dataset name
name <- "BR1"
#insert table name
DATA <- BR1
attach(DATA)
#insert timestamp column
DATA$timestamp <- DATA$V1
#insert desired data columns / column names
DATA$column1 <- V7
DATA$colu... |
7ee547aa7f70d4f769e5f921c1a5b09147c1ebba | 345813866b606f3f3faf9ac9096479214a0df3c6 | /Dataset.R | 64cfd2d9d1997491da13a756d89e8ef6c1854252 | [] | no_license | cooma/Auction_price-of-real-estate | ce7cea14de268d6955fc0da52d43f96ecffeb37b | 2ce61b3ec7c8b92c3351876a6b331400ab93336c | refs/heads/master | 2020-04-28T17:03:26.023342 | 2019-03-13T14:46:12 | 2019-03-13T14:46:12 | 175,432,839 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,360 | r | Dataset.R | suppressMessages({
library(readr)
library(dplyr)
library(caret)
})
setwd("C:/Users/seol/Desktop/Analysis/DACON")
##### Data set Making ######
Auction_master_train <- read_csv("Auction_master_kr/Auction_master_train.csv",
col_types = cols(Appraisal_company = col_skip(),
... |
e3b2168118e1eff5b2fb520b86cbe20338a46781 | aef437f42d60224cb103037538ddaa0fd1281024 | /plot3.R | f8cbde63563088968150d2c382d58871dbb92a50 | [] | no_license | armelad/ExData_Plotting1 | 2a67db74a23f7f20adff1efee64f1837d1c1009c | ccdfeb2c4befbd0253fd48125acf9700406b2472 | refs/heads/master | 2020-09-19T16:51:26.109027 | 2017-06-23T02:53:18 | 2017-06-23T02:53:18 | 94,495,127 | 0 | 0 | null | 2017-06-16T02:01:54 | 2017-06-16T02:01:53 | null | UTF-8 | R | false | false | 2,074 | r | plot3.R | #load library for quick load of the csv data
library(readr)
if (!file.exists("data.zip")) {
#assign the location of the file
fileUrl <-
"https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
# download the file
download.file(fileUrl, dest... |
a935127a4ff626acc0069891417bf9989a3844e7 | 8beadb1abfb098d312f2db476d7aae71d3ebcc2a | /install-packages.R | 29c7c1b6a40c229de3af9006f63035a947437391 | [] | no_license | qianli10000/image-metabolomics-r | 3b455042ba2a701cfbce79b7bca062ba811f7094 | 108d2fff4661c968f6db2985a870e0c8c741e216 | refs/heads/master | 2021-08-22T10:54:13.233606 | 2017-11-29T21:41:25 | 2017-11-29T21:41:25 | 112,549,843 | 0 | 0 | null | 2017-11-30T01:42:45 | 2017-11-30T01:42:45 | null | UTF-8 | R | false | false | 485 | r | install-packages.R | install.packages(c(
'Rserve',
'ptw',
'gplots',
'baseline',
'hyperSpec',
'ggplot2',
'erah',
'mclust', 'matrixStats',
'glmnet'))
source("https://bioconductor.org/biocLite.R")
biocLite(c(
'xcms',
'CAMERA',
'PROcess',
'targetSearch',
'limma',
'RUVnormalize',
'RUVSeq',
'sva'))
# show ins... |
87ef79e372fe06441b97662292bc95f48cc411a2 | cf681440d20cde6f629d96c10f2a1496a22b99dc | /man/build_M_Lambda.Rd | f76c9aac6a8328c2dbe12806dfcdb4875ef60cd1 | [] | no_license | mmkuang/mfbvar | b15ddd881b8d411455b8be322d30d0dcc8061027 | 85f25f00bc4060dd3e2944b6a26bd0b78fffc7c3 | refs/heads/master | 2022-04-12T08:23:24.242462 | 2020-03-19T06:46:46 | 2020-03-19T06:46:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 949 | rd | build_M_Lambda.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/builders.R
\name{build_M_Lambda}
\alias{build_M_Lambda}
\title{Build the \eqn{M_t\Lambda} matrices}
\usage{
build_M_Lambda(Y, Lambda, n_vars, n_lags, n_T)
}
\arguments{
\item{Y}{The data matrix of size \code{(n_T + n_lags) * n_vars} with \cod... |
dbf6cb45d6eb0261b2f043c60ab28ce3eb88b437 | 4b55b60d764da1051c38e6e4b93627335afa0150 | /Flight delay prediction and analysis/knn_kknn.R | ab5a883ae14f6d7b726f09184bdb4a5b54be97e7 | [] | no_license | vyadav06/R-Project-for-Statistical-Computing | f87b09c79c71e5650fbe34154844fdf85ac5eb3e | 0e92e2db05d3bba0f6ec9e358d740d416aed06ef | refs/heads/master | 2021-01-12T06:00:26.228654 | 2016-12-24T07:48:32 | 2016-12-24T07:48:32 | 77,271,457 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,853 | r | knn_kknn.R | #Course : CS 513
#Vandna Yadav
rm(list=ls())
#********************loading the dataset**************************
data<-read.csv("C:/Users/vandna/Desktop/Stevens/SEM 2/513/Project/Code/dataset/2008.csv")
odataset<-data
attach(odataset)
#********************categorizing Departure Delay *****************
odataset$DepDela... |
167b7930d61cc92dc996b2ce2757d397be032fec | 44b28da4d2fa37ca7542a575c2d1be2a8716efa6 | /Efficient Behavior Mapper/test_smts.r | 942cf93cbac271b792d41b0fbde44d4a60157ed9 | [] | no_license | mertedali/ISDC2018SummerSchool | bee492e7f1c84cd673221904ca790d82c07b5667 | 920dbb7c4bc3e8fda95887f8fddc3a137a973ee5 | refs/heads/master | 2020-03-24T21:06:57.247039 | 2018-08-06T11:31:24 | 2018-08-06T11:31:24 | 143,014,133 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 398 | r | test_smts.r | params = result[, 1:2]
result = result[, -c(1:2)]
result = cbind(1, result)
testdata = result
source("prepare_test.r")
test_terminal=attr(predict(RFins, as.matrix(finaltest[,2:ncol(finaltest)]) ,nodes=TRUE), "nodes")
codetst=matrix(generatecodebook(RFins$forest$nodestatus,test_terminal,nofnode,ntestobs),n... |
1f01c3e440be9c2eb206a577316af7f43c3891d4 | 7312924a61cc00cac1c9f2d4ed082ad8ce551d03 | /data-raw/nga_highlights.R | ebceb72048fcce4748636a371fbc3041744510e7 | [] | no_license | mdlincoln/breadbox | 33d2f31d9b657b030aa924a93a33983aa54cd9be | 0b20f4c23587d1ae9debde3dab310b2987428f43 | refs/heads/master | 2020-03-17T09:12:59.058451 | 2018-05-15T05:56:56 | 2018-05-15T05:56:56 | 133,466,130 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 445 | r | nga_highlights.R | library(tidyverse)
library(stringr)
nga_highlights <- clean_collection_data %>%
filter(onview == "true") %>%
filter(!is.na(area)) %>%
mutate(
label = str_wrap(glue("{artists.0.name}, \"{title}\""), width = 30),
long_label = glue("{artists.0.name}, \"{title}\" (National Gallery of Art)", width = 30)) %>%
... |
71dbd087f07d27474ec994ed9ad1038b9e1e3c98 | 4b4c0c48f4004383b728dce2654b56ec7a4c6851 | /Exploratory-Analysis/Summary.R | 7b3d8f05656dd54a73bc7ea40cbabdbec5d45eb3 | [] | no_license | AmirrorImage/INFO-201-Final-Project | 5c5e0a3a713f75a7e21c649ba3b8d4053f2f6b85 | 4e331f3e67ccf73f1fb36723ae4604947a7d33e8 | refs/heads/main | 2023-03-23T13:48:34.641730 | 2021-03-18T02:00:24 | 2021-03-18T02:00:24 | 334,546,015 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,015 | r | Summary.R | library(tidyverse)
data <- read.csv("https://raw.githubusercontent.com/AmirrorImage/INFO-201-Final-Project/main/Data/Use_Of_Force.csv")
# This file shows some facts I calculated from the data
total_observations <- print(nrow(data))
total_features <- print(ncol(data))
num_male_female <- data %>%
count(Subject_Gen... |
220d94168b5896d22e2ad9ee05e1ba5ab110fc9f | d931c381ae927719bfed81a3b9ea16aeaf78022f | /data_analysis/plotting/logistic_plot.R | a3fe7150d145959dd39f1b1ef9a45a8b6288f52a | [] | no_license | samcheyette/transfer_learning_v1 | ce5e3109411f36e26d481ef49263b474f6e2dc8d | daad6d7261199b7a59dad7d896a358a403d3c9f1 | refs/heads/master | 2020-04-05T13:04:33.994131 | 2017-08-04T14:48:38 | 2017-08-04T14:48:38 | 95,061,035 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,803 | r | logistic_plot.R | library(ggplot2)
library(reshape)
library(grid)
library(dplyr)
library(aod)
data <- read.csv("outR.csv")
head(data)
t1 <- theme(axis.text=element_text(size=18),
strip.text.x = element_text(size = 20),
plot.title=element_text(size=18),
#axis.text.x=element_blank(),
axis.text.x=element_text(size=15),
axis.... |
0dc35358f6847dcc8a422ca7751eeba1960dbbc9 | 6287bd279463ebc8ab78da4f84eb5adee88f7c8c | /R/fit_models_JRSSA.R | 66ec4ed13d04ff7f401d9e1eadca969c38417c51 | [] | no_license | spatialstatisticsupna/Dowry_JRSSA_article | ac7803418bd63e66d6c1e9cfc901c71851e698a5 | 0bc961b558c72ec2c7330ddbb60d4e00c7218fc6 | refs/heads/master | 2022-05-04T21:06:55.990162 | 2022-03-29T11:19:19 | 2022-03-29T11:19:19 | 220,030,538 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,578 | r | fit_models_JRSSA.R | ##########################################################################################
## Title: Crime against women in India: unveiling spatial patterns and temporal trends ##
## of dowry deaths in the districs of Uttar Pradesh ##
## ... |
87f686bf7187fc562e1a8a6e8b5350c0089a5887 | 827c9202f56d6a50b357ccc37d81fdc1683a5955 | /tests/testthat.R | 9e0e489989efa7367dbd3068f583ceafec9c8f9b | [] | no_license | AlexPiche/DPsurv | 7fef615c1d97bc7da2f924542cf8b4a41fa32e29 | b696cd9fc1fda6d45c1f2640f2f3818b4ee8543e | refs/heads/master | 2020-04-01T19:27:12.926209 | 2016-11-29T20:51:34 | 2016-11-29T20:51:34 | 62,817,221 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 54 | r | testthat.R | library(testthat)
library(DPsurv)
test_check("DPsurv") |
acc227a7d42d78c6eb90687ad3e1044293f7fdf2 | cc30a22201e5f3ddce6e53d60c651c210ef3fe00 | /Cap.7/07_03/DM_07_03.R | ae800913c3ba35ab1f327cfb43a70b477ccf6539 | [] | no_license | Varnei/Ciencia_de_Dados_LL_Fundamentos_da_Ciencia_de_Dados_Mineracao_de_Dados_Barton_Poulson | 2357667cd2d59d128f9b6977bd98c9e92638df83 | f33902949cd7d5a4dfa9dcd4e6f848334972e4b9 | refs/heads/main | 2023-07-04T11:56:27.829806 | 2021-08-07T03:12:15 | 2021-08-07T03:12:15 | 389,764,836 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,730 | r | DM_07_03.R | # DM_07_03.R
# INSTALAR E CARREGAR PACOTES ##############################
pacman::p_load(lars, caret) # Importando bibliotecas
# DADOS ####################################################
# Importar os dados
data = read.csv("~/Desktop/winequality-red.csv")
# Definir grupos de variáveis
x <- as.matrix(data[-12])
y ... |
91e13532b0b212e393a093ca1a478d64e0d56a75 | cd19071385b5760d51c5c9fa687d4c8bf74e5d57 | /scripts/helper_file.R | 4f43847fa2cf97f2e950a29c1768e8cf0e021763 | [] | no_license | shiva-g/The-Cube | b7ee893b6fb04b6b1e1921e756bc3005548b3c6b | ab235a5cc4367abf587fdaf1d152122e41ec7de2 | refs/heads/master | 2022-12-25T23:08:30.661267 | 2020-09-14T15:22:58 | 2020-09-14T15:22:58 | 197,828,206 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,695 | r | helper_file.R | library(optparse,quietly = T)
library(yaml,quietly = T)
library(tidyverse, quietly = T)
library(dplyr, quietly = T)
library(ggplot2,quietly = T)
library(ggridges,quietly = T)
library(ggrepel, quietly = T)
if(exists("input.yaml")){
input.yaml <- input.yaml
}else{
message('Input YAML not found.\n')
break;
... |
681ac6df393f6d43801b948c9eb6951f235e008a | bb9b8b991fecd538c5f93a8303373030b31e59d9 | /scripts/coverage_plots_g.R | f417877f6a966104d9db0a0b0f5971c5400d0243 | [] | no_license | LuffyLuffy/assembly_pipeline | 54a4b8cdb91302cee9250764c0ea25fdf30ef128 | ce8d1b5a291cb00b79484da1f59d9869c81572ca | refs/heads/master | 2023-09-03T10:11:54.106672 | 2021-11-02T05:40:52 | 2021-11-02T05:40:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 907 | r | coverage_plots_g.R |
#coverage.files<-list.files("~/coverage_plotting", full.names = TRUE, pattern = ".txt")
#coverage.names<-list.files("~/coverage_plotting", full.names = F, pattern=".txt")
args<- commandArgs(trailingOnly = TRUE)
coverage.file <-args[1]
#setwd("/Volumes/Georgia's Hard drive/temp_work")
pdf.file <- gsub("txt","pdf", cove... |
a9257a11b22a30477ff0d80c845bd1326e59bb9d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/imaginator/examples/ClaimsByFirstReport.Rd.R | c34fb1763eaf5ea9fb59552e73a85f626c12fe57 | [] | 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 | 456 | r | ClaimsByFirstReport.Rd.R | library(imaginator)
### Name: ClaimsByFirstReport
### Title: Claims by first report
### Aliases: ClaimsByFirstReport
### ** Examples
# This will generate a claim data frame which has 1,000 records
# each of which has a severity of 100
dfPolicy <- NewPolicyYear(100, 2001)
dfClaims <- ClaimsByFirstReport(
... |
6925899de6e992c035f8b0f5a889077120cea2d9 | 9e26b0d278981b82487e0c60fbf7f2b975d63fea | /R/register_smacof.R | e338c4f69846732f6e308775f4c001a521af10db | [] | no_license | cran/seriation | 1ea5b4817e7edca61f2e252753f5763b0c4e2d73 | 84790e8861d5a41c56dffb7761fd38070f284291 | refs/heads/master | 2023-08-27T21:40:36.310265 | 2023-07-20T21:20:02 | 2023-07-20T21:30:42 | 17,699,608 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,766 | r | register_smacof.R | #######################################################################
# seriation - Infrastructure for seriation
# Copyright (C) 2015 Michael Hahsler, Christian Buchta and Kurt Hornik
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as pub... |
8e1f0208a97ec1bcc0d524abdec8324495a82541 | 6ab148f7967e3de987c9b87ea7f9b0092ab0dc8e | /01-EDA-e-vis/1-eda-dados-posgraduandos.R | 1978435bbb633bfd714753c5fa785de152a7afb1 | [] | no_license | guilhermemg/fpcc2 | 6d53a8a12ed066ba64e9158724cc0a4fe54b239a | c1e8cb98f3ef7141cb64f891f19bd88dbb73c3a5 | refs/heads/master | 2020-03-25T15:48:56.349295 | 2018-05-28T12:11:10 | 2018-05-28T12:11:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,650 | r | 1-eda-dados-posgraduandos.R | # Você precisará instalar esses pacotes. Faça install.packages("nome") para cada um.
library(dplyr, warn.conflicts = F)
library(readr)
library(ggplot2)
# theme_set(theme_bw()) # você pode preferir os gráficos assim
library(gmodels)
# ====================================
# LER, ARRUMAR, LIMPAR
# =======================... |
38b5372ea37c49d69fbc59794f982f7c3cfa47f4 | 9fbaf8e2920166916c5c038ca17c6c055b9ef135 | /tests/testthat/test_errorfunctions.R | b963e9537cbae7597fb29f671a7f51e4e7cc8f0e | [] | no_license | RMHogervorst/heisertransform | 4fcff64d0440990818dc1505a3c87010237a04ec | 60066346a4c227728cf32875d184822c8bdc83b9 | refs/heads/master | 2021-01-18T21:47:17.601914 | 2016-06-01T21:05:48 | 2016-06-01T21:05:48 | 48,912,939 | 0 | 0 | null | 2016-12-06T13:18:56 | 2016-01-02T14:50:01 | R | UTF-8 | R | false | false | 1,995 | r | test_errorfunctions.R | context("general functioning of errors and warnings")
## creation of errorsdataset
n <-15
var1 <- rnorm(n, mean = .40, sd = .04)
var2<- rnorm(n, .30, 0.02)
delete<-var1+var2 >1
var1<-var1[!delete]
var2<-var2[!delete]
var3<- 1-(var1+var2)
varfactor<-as.factor(var3)
varcharacter<-as.character(var3)
vartoomuch<-var2*2 #... |
f88f515cf5a0ad0005bf1957bafe09f8e7a4b1ba | 5b7a0942ce5cbeaed035098223207b446704fb66 | /R/lsGetSummary.R | 52847cf0e925c53ec7afc8fd5e279ce8ebd5f06e | [
"MIT"
] | permissive | k127/LimeRick | 4f3bcc8c2204c5c67968d0822b558c29bb5392aa | a4d634981f5de5afa5b5e3bee72cf6acd284c92a | refs/heads/master | 2023-04-11T21:56:54.854494 | 2020-06-19T18:36:05 | 2020-06-19T18:36:05 | 271,702,292 | 0 | 1 | null | 2020-06-12T03:45:14 | 2020-06-12T03:45:14 | null | UTF-8 | R | false | false | 1,709 | r | lsGetSummary.R | #' Get survey summary, regarding token usage and survey participation
#'
#' @param surveyID ID of the survey
#' @param status \emph{(optional)} To request a specific status (\code{"completed_responses"},
#' \code{"incomplete_responses"}, \code{"full_responses"}, \code{"token_count"}, \code{"token_invalid"},
#' \cod... |
9978d83ef9847ccff3886cc5c4e21a6316879a35 | a4801f5f15cfe478585286cd1986ca04bcc65eef | /tests/testthat/test-updateBindingConstraint.R | 795fea7a12f2452188738929b806d42248b3e281 | [] | no_license | rte-antares-rpackage/antaresEditObject | acfa8ad126149fb6a38943919e55567a5af155f8 | 452b09e9b98d4425d6ee2474b9bbd06548e846d2 | refs/heads/master | 2023-08-10T21:01:21.414683 | 2023-07-13T13:06:02 | 2023-07-13T13:06:02 | 96,431,226 | 10 | 16 | null | 2023-09-08T09:31:25 | 2017-07-06T13:07:19 | R | UTF-8 | R | false | false | 1,730 | r | test-updateBindingConstraint.R | context("Function editBindingConstraint")
sapply(studies, function(study) {
setup_study(study, sourcedir)
opts <- antaresRead::setSimulationPath(studyPath, "input")
#Create a new binding constraint
createBindingConstraint(
name = "myconstraint",
values = matrix(data = rep(0, 8760 * 3), ncol = 3... |
a88c1a395b14224400f76478980465db26239831 | b12b4d4c22f4aae84bcd2621654aa010ca8f26f5 | /scripts/figures/Bnapus_figure2_venn.R | 89f4f4b7fc06db3a910cfd70570c3ac4c2ec381c | [] | no_license | gavinmdouglas/canola_pseudomonas_RNAseq | 20fa3e0ad33e3bc859225ba57cac40b1777723d3 | d92849ab758c5490144fa78e4626ce7d0d55f66d | refs/heads/master | 2021-03-30T15:51:16.800416 | 2021-02-14T22:26:32 | 2021-02-14T22:26:32 | 99,141,112 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,944 | r | Bnapus_figure2_venn.R | # Figure of main venn diagrams
rm(list=ls(all.names=TRUE))
setwd("/home/gavin/projects/pseudomonas_RNAseq/canola_pseudomonas_RNAseq/")
library(cowplot)
library(ggVennDiagram)
# Venn diagrams for each tissue of overall DE genes (lfc > 2) by day. Four panels in total for up/down in both shoots and roots
# Panel A -... |
dd6afde292f47760752d92d0810957cb74ef290f | fc7841b1e7c4cc7b3839fb57ccd9802c6f4d3d8f | /test/fixtures/test.number.R | b81ea48a1b35418e8658527d044456668ac90f54 | [
"MIT"
] | permissive | distributions-io/hypergeometric-pmf | d74810fc581a8d3a67a9758bf4e8596704f04114 | bf6c5a64a8118c366a152cf88fe24c25c7937544 | refs/heads/master | 2021-01-22T07:35:17.913997 | 2015-10-24T01:44:10 | 2015-10-24T01:44:10 | 39,271,045 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 296 | r | test.number.R | options( digits = 16 )
library( jsonlite )
m = 5
n = 5
k = 3
x = c( -1, 0.5, 0, 1, 2, 3, 4, 5)
y = dhyper( x, m,n,k )
cat( y, sep = ",\n" )
data = list(
m = m,
n = n,
k = k,
data = x,
expected = y
)
write( toJSON( data, digits = 16, auto_unbox = TRUE ), "./test/fixtures/number.json" )
|
a002482eb485f9cf4f46c27f93bfabb4ccbea983 | 2448d4800d4336b53489bcce3c17a32e442a7716 | /tests/test-that.R | 764f4a7e4c73cdcb050d48f19c40b983b6c6ee37 | [] | no_license | vsbuffalo/devtools | 17d17fd1d2fb620fef8d9883dffed389f80e39fb | 782e6b071d058eea53aae596a3c120d61df2f0b4 | refs/heads/master | 2020-12-24T10:41:24.637105 | 2016-02-18T14:03:05 | 2016-02-18T14:03:05 | 52,121,375 | 2 | 0 | null | 2016-02-19T22:42:43 | 2016-02-19T22:42:43 | null | UTF-8 | R | false | false | 41 | r | test-that.R | library(testthat)
test_check("devtools")
|
42ff03322a699e4a895403a107cf10db11a1c00e | 10898984bdd86ccff61363d2aff9c1fc5fbb1545 | /man/computeLTA.Rd | 343fe54800bc987e078fce759182ecc19ecc9eb7 | [] | no_license | victoriaknutson/SpatioTemporal | 69e2b251f2b861f88d3d06c91695f61fffdc4736 | 9980dc005018fd18abeea5eec6228ef4522791f5 | refs/heads/master | 2023-07-10T06:44:58.350806 | 2021-04-14T01:49:02 | 2021-04-14T01:49:02 | null | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 2,462 | rd | computeLTA.Rd | \name{computeLTA}
\alias{computeLTA}
\title{Computes the Long Term Average for Each Sites.}
\usage{
computeLTA(object, transform = function(x) {
return(x) })
}
\arguments{
\item{object}{A \code{predCVSTmodel} object, the result
of \code{\link{predictCV.STmodel}}.}
\item{transform}{Transform observation... |
1f19f306e86ae083a0ed7427b9f17c28e5701a6c | dd1102ed8f681e5dfb675075b870ee948f017ccc | /paths.R | 143eff092ac9582e1562e38bbed650742b260930 | [] | no_license | garridoo/ljsphere | a726ec88922bd967bcee1c44ff13f73de8e146dc | 647a1bc7d6a8ae15f50a4f751c94baae89727771 | refs/heads/master | 2021-06-12T19:59:49.903325 | 2021-05-20T11:25:13 | 2021-05-20T11:25:13 | 254,386,539 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 284 | r | paths.R |
# path to the project folder
project_folder <- "/biodata/dep_psl/grp_rgo/ljsphere/"
# paths to sub-directories
results.dir <- paste(project_folder, "/results/", sep="")
data.dir <- paste(project_folder, "/data/", sep="")
figures.dir <- paste(project_folder, "/figures/", sep="")
|
a00d99e9c6cd76048e76e9e10eadd683dc81f572 | 5390b30d1f233b024479c7e5199a39ccab75db24 | /man/recencySendReceiver.Rd | 698f1c0d49fb5077b84504e8b053b6944d5528bc | [] | no_license | TilburgNetworkGroup/remstats | 65d5c6046612bc6b954a61f8e78f8471887400c9 | 19799f91a9906312e89ba7fe58bef33e49a6b6f1 | refs/heads/master | 2023-07-19T18:21:13.674451 | 2023-07-13T14:11:23 | 2023-07-13T14:11:23 | 248,442,585 | 4 | 1 | null | 2023-09-05T08:36:29 | 2020-03-19T07:54:52 | R | UTF-8 | R | false | true | 1,472 | rd | recencySendReceiver.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/effects.R
\name{recencySendReceiver}
\alias{recencySendReceiver}
\title{recencySendReceiver}
\usage{
recencySendReceiver(consider_type = FALSE)
}
\arguments{
\item{consider_type}{logical, indicates whether to compute the recency
separately fo... |
57793493bc861a339019dc5e3429032804fac6c0 | a83fe101098fad2b7da530ce7c867df7ac7226dd | /Video.R | 70f969229bde5e2d04011cbc1e711ec62a2cb380 | [] | no_license | orzkng2015/R | 3fb0a6cf98d0d38a5fcf6bb5f5327bc713dc6c62 | aba78fcf739914c4aba5764bcf069df1f2934da1 | refs/heads/master | 2020-12-24T20:51:40.722461 | 2016-05-01T22:50:05 | 2016-05-01T22:50:05 | 56,644,079 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,246 | r | Video.R |
####progress
CourseView <- function(courseID, playVideo){
# Used to filter the course_id.
index <- list()
for(i in 1:lengths(playVideo)[1]){
# If the playVideo$course_id equals to courseID, the index set to true.
index[length(index)+1] <- ifelse(playVideo$course_id[i] == courseID, T, F)
}
# Get the... |
3d5ef46471ead9d8c7da630ccd34f3cd5b076491 | 1cad4dcc0c0f921644be13ebcccf9d8c45c5dc83 | /run_analysis.R | f4da7a182bf4be75130615a4d6820b1bab8077b7 | [] | no_license | renato145/GettingandCleaningDataCourseProject | 31b2e54b5e235cef40f6b7280b0a227a0628c53b | 6aaed5d74be852868f6ae93777b8d95a3cbb4bf6 | refs/heads/master | 2021-01-24T14:27:58.898901 | 2015-06-15T21:55:05 | 2015-06-15T21:55:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,650 | r | run_analysis.R | library(plyr)
library(dplyr)
#1 - Merges the training and the test sets to create one data set.
mergedSet <- rbind(read.table("UCI HAR Dataset/train/X_train.txt"),
read.table("UCI HAR Dataset/test/X_test.txt"))
#2 - Extracts only the measurements on the mean and standard
# deviation for each mea... |
927e3e1f893c928d91bb6196ebdcb51b7a977a6d | c750c1991c8d0ed18b174dc72f3014fd35e5bd8c | /pkgs/oce/man/ctdRaw.Rd | f4a6cd60e143f87a67a8fed374b1ffab5a9f6018 | [] | no_license | vaguiar/EDAV_Project_2017 | 4b190e66fe7a6b4078cfe1b875bccd9b5a594b25 | 288ffaeec1cfdd873fe7439c0fa0c46a90a16a4f | refs/heads/base | 2021-01-23T02:39:36.272851 | 2017-05-01T23:21:03 | 2017-05-01T23:21:03 | 86,010,131 | 1 | 0 | null | 2017-05-01T23:43:04 | 2017-03-24T00:21:20 | HTML | UTF-8 | R | false | true | 2,398 | rd | ctdRaw.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ctd.R
\docType{data}
\name{ctdRaw}
\alias{ctdRaw}
\title{Seawater CTD Profile, Without Trimming of Extraneous Data}
\usage{
data(ctdRaw)
}
\description{
This is sample CTD profile provided for testing. It includes not just the
(useful) porti... |
cf0ceddf03387c38279bf96e510f18df85702aac | 9d221239bfd8e36f09fb151c492f2cd1c21348e7 | /R/pvaluer.R | c5df60f088c1173e122bd53dd4e40d45a074a6c9 | [] | no_license | wmhall/cul_hsp | d877e031a792eb4f8504fd45869277de73037287 | f5af9abc690d4a6fe360bde85180760989475d12 | refs/heads/master | 2021-01-19T21:42:02.945696 | 2017-04-19T02:50:41 | 2017-04-19T02:50:41 | 88,691,012 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 764 | r | pvaluer.R | #functions for working with p values.
fixed_digits <- function(xs, n = 2) {
formatC(xs, digits = n, format = "f")
}
fixed_zero <- . %>% fixed_digits(n=0)
remove_leading_zero <- function(xs) {
# Problem if any value is greater than 1.0
digit_matters <- xs %>% as.numeric %>%
abs %>% magrittr::is_greater_th... |
d66b8819407b58ddc99111c41e528ce3a9071067 | 36844710bcf289e8c056550767f4fb508b6395ff | /src/Exercise5_1.R | 81bc5f7f3a9844935da77334c4626dbf6d78f880 | [] | no_license | 3100/do_bayesian | 500ac3da587f82d4fd10342320bb3078db92702f | 1114f5bd9c0b1965bbe54a3b1170e0896d7c5657 | refs/heads/master | 2021-01-19T05:45:08.728766 | 2017-08-19T23:00:11 | 2017-08-19T23:00:11 | 100,582,198 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 961 | r | Exercise5_1.R | ## 陽性だった後の再検査で陰性になったとき、その人が病気である確率を求める。
# p(D=陽|θ=病) = 0.99 # ある人が病気の時に、陽性になる確率
pPositiveWhenTrue = 0.99
# 病気でないのに陽性になる確率0.05
# p(D=陽|θ=無)
pPositiveWhenFalse = 0.05
# p(θ=病) = pPositive = 0.001
pTrue <- 0.001
# p(D=陽) = Σp(D=陽|θ*)p(θ*) # すべてのθ値での和 : 周辺確率
pPositive <- pPositiveWhenTrue*pTrue + pPositiveWhenFalse*(1-pTr... |
5525faa064c75659f6d955da554d7952170444b2 | 1828faa2103627f8e8d50af213efed04d10a67d4 | /cachematrix.R | fa169a21727243d7b84dd8dbb8bf4a8aa1ed974a | [] | no_license | mcs2712/ProgrammingAssignment2 | 871dfc10075bdd6217544d98a975955f92b6e4a9 | b7f8f35f31fbe15ca501d0967db3566763e4a50c | refs/heads/master | 2020-12-14T09:57:43.290307 | 2015-05-24T09:15:52 | 2015-05-24T09:15:52 | 36,126,503 | 0 | 0 | null | 2015-05-23T14:20:27 | 2015-05-23T14:20:26 | null | UTF-8 | R | false | false | 2,113 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
# -- DESCRIPTION --
# makeCacheMatrix
# - creates an object with four function elements: set, get, setinv and getinv
# - you can give the function a matrix argument
# if no argument given, it takes an empty matrix as default
# cach... |
375d2184f165b3c56ce52ea81a107268681c8ed5 | b749e2826f9c85a87dc3b1270c45ddbf83c10809 | /SimulationLoop.R | 9b105fa49d377a15a99822acd05396736dd76a58 | [] | no_license | moedancer/SequentialDesigns | 4e7a53d2356549b3804c6013932fe072548757ac | 59f0294242a73b613e93810256918f629fb615f8 | refs/heads/master | 2020-08-23T07:35:28.859385 | 2019-10-21T13:55:58 | 2019-10-21T13:55:58 | 216,572,182 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,125 | r | SimulationLoop.R | #Execute simulations for all scnearios to be considered in the mauscript
#"setup_[...].R" scripts will be sourced in the loop according to the currently investigated distribution
require(Hmisc) #function: rcorr
#setwd() according to folder containing scripts
set.seed(1)
#########################################... |
66366c6d33166d16b7f797b41851d9a3c51f1e55 | 75db022357f0aaff30d419c13eafb9dddfce885a | /inst/IP/GLORYS_ESS_Shrimp.r | 5eeeb57f87f3a8760e6a4e9837ee61732bf50eeb | [] | no_license | LobsterScience/bio.lobster | d4c553f0f55f561bb9f9cd4fac52c585e9cd16f8 | b2af955291cb70c2d994e58fd99d68c6d7907181 | refs/heads/master | 2023-09-01T00:12:23.064363 | 2023-08-23T16:34:12 | 2023-08-23T16:34:12 | 60,636,005 | 11 | 5 | null | 2017-01-20T14:35:09 | 2016-06-07T18:18:28 | R | UTF-8 | R | false | false | 541 | r | GLORYS_ESS_Shrimp.r | #Pruning GLORYS to ESS Shrimp
require(bio.lobster)
require(satin)
require(tidyr)
require(PBSmapping)
setwd(file.path(project.datadirectory('bio.lobster'),'data','GLORYS'))
y1 = read.cmems('GLORYS1993')
a = y1$bottomT
image(a@lon, a@lat, t(a@data[,,1,]))
po = data.frame(X=c(-62,-62,-57,-57),Y=c(44,46,46,44),PID=1,POS... |
b1f6f869ba02c5350a3c67ba9ca1034b302b1540 | df4614ac59318c9c284d1d0337812e169ef74a59 | /45-SimpleLinearRegression.R | ff6f212efed973079869b00d0c5796c93aabafbe | [] | no_license | kushalpoudel35/r-scripts | 354e9a97e2f2df0e12cdda5f5fc16b7ea04f6d9b | 6d1c762a47f6500842e2fa93f7494b3d36289b9b | refs/heads/master | 2021-06-27T14:22:48.885772 | 2021-06-04T12:55:11 | 2021-06-04T12:55:11 | 231,202,478 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,152 | r | 45-SimpleLinearRegression.R | LungCapData = read.table(file='data/LungCapData.txt', header=TRUE, sep='\t')
head(LungCapData)
names(LungCapData)
LungCap = LungCapData$LungCap
Age = LungCapData$Age
Height = LungCapData$Height
Smoke = LungCapData$Smoke
Gender = LungCapData$Gender
Caesarean = LungCapData$Caesarean
class(Age)
class(LungC... |
bed8241f50813c74b0e66aac05d51ff2116448ea | 6ae574fc7fa9b720c361b9a47c51684cdd302f96 | /R/print.CADFtestsummary.R | 53d505700bbec2bf6bcb6611b84eadb1720b6f70 | [] | no_license | cran/CADFtest | 7e498795d51f7bf72394d634daf327426c68dde8 | 405d9d90df237f6c5dbaa4da644172c178c1f8ef | refs/heads/master | 2021-01-21T12:23:33.687891 | 2017-06-02T16:10:31 | 2017-06-02T16:10:31 | 17,678,203 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 265 | r | print.CADFtestsummary.R | print.CADFtestsummary <- function(x, ...)
{
# x is an object of class `CADFtestsummary'
ttype <- "Covariate Augmented DF test"
if (nrow(x$test.summary)==3) ttype <- "Augmented DF test"
cat(ttype, "\n")
print(x$test.summary, ...)
print(x$model.summary, ...)
}
|
2825d9682289f488843079ac217e68c7e1f6fda8 | 2f4503595629446439b6c794a34840c6ce5d66a3 | /If-else.R | a15249aa5bec14e9d685e68a644492052ccddfa3 | [] | no_license | nikhil4111995/R-for-Data-Analysis | caeed4530cc49e81143d420ea21c426a5a434df6 | bc2a4c0ca75145b59fce64ae3096fe7d16a12ed0 | refs/heads/main | 2023-02-02T22:27:45.944739 | 2020-12-23T03:50:23 | 2020-12-23T03:50:23 | 323,795,553 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 176 | r | If-else.R |
rm(answer)
x <- rnorm(1)
if(x > 1){
answer <- "greater than one"
}else if (x<1 & x>0){
answer <- "Between 0 and 1"
} else {
answer <- "Less than 0"
} |
222c82ae5ea6d9db263b6392dea1fd69f25f1b19 | 77157987168fc6a0827df2ecdd55104813be77b1 | /MGDrivE/inst/testfiles/calcCos/libFuzzer_calcCos/calcCos_valgrind_files/1612727253-test.R | 7c815f29f669d6d36be34e8d105831c0c4e22099 | [] | no_license | akhikolla/updatedatatype-list2 | e8758b374f9a18fd3ef07664f1150e14a2e4c3d8 | a3a519440e02d89640c75207c73c1456cf86487d | refs/heads/master | 2023-03-21T13:17:13.762823 | 2021-03-20T15:46:49 | 2021-03-20T15:46:49 | 349,766,184 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 211 | r | 1612727253-test.R | testlist <- list(latLongs = structure(c(2.12687638151216e-310, 4.87418056037875e-241, 2.12687638151216e-310), .Dim = c(1L, 3L)), r = 1.668805394687e-307)
result <- do.call(MGDrivE::calcCos,testlist)
str(result) |
63b2bbab93feaee6599831b27547e10c6ae78c14 | 4fed9d47a2af0bd99de61068b7ab54f08b109ebd | /Rmetapop/inst/examples/simulate_metapop.R | 87e8f7867666bee74b4cd4beed48d11f7cc0454b | [] | no_license | dkokamoto/Rmetapop | 402d5dde93b103df757d54e1852ce20e61c490f1 | 281c1fbf4c233c1504ba0c116ffbbff9836cf351 | refs/heads/master | 2022-10-12T14:55:11.954234 | 2018-05-28T18:48:43 | 2018-05-28T18:48:43 | 38,710,185 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,733 | r | simulate_metapop.R | #' @param n_loc number of locations at which a stock assessment is implemented
# #n_subloc <- rep(10,5) ### vector of number of sublocations
#' @param n_iter number of years to simulate, including the warmup period
#' @param n_stages number of stages including eggs and recruits
#' @param stage_mat stage # that indica... |
ce1947925c4a729ec181556ead2c543b7f7267e2 | f67acc22852d59399366ed9d1453c6ee39c9e7e1 | /fancy_plot_survival.R | dc80813f1e5c0ce772a0eddaa9cbeb6017e5ffdc | [] | no_license | daboe01/fancy_plot_survival | aee4df56b2c5c39fe410dc83a992cf505ed0f17a | 6bf81e480e52e0a833fa5f854d5b2fa413a066f3 | refs/heads/master | 2020-06-06T13:04:35.292247 | 2015-07-01T11:52:19 | 2015-07-01T11:52:19 | 38,367,704 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,378 | r | fancy_plot_survival.R | # 1.9.2008: survival plot tool by dr. boehringer
# todo: option to append the n= per group to the legends
# rework jitter.groups to give deterministic results
library(ggplot2)
library(survival)
plot.survival.fancy=function(s, conf.int=F, auto.scale=F, xmax=0, marker=c("point","blank"), displace.groups=F, levels=c(),... |
8eb00d8d47673630b66bb856562729d79124ce55 | 8ec902c3b48d757aa459caf17f3fac723b20187c | /R/make.thresholds.character.R | c9c838ee5d28090ae68f6595192ba7d164aba535 | [
"BSD-2-Clause"
] | permissive | david-ti/wrightmap | 47d34578a087d890033a47c4e8d62e261f29c532 | daed7f30eb01d065a2c4ae7c245154abdaf7cb1c | refs/heads/master | 2022-06-30T09:07:29.029247 | 2022-05-15T22:44:32 | 2022-05-15T22:44:32 | 17,348,470 | 2 | 5 | NOASSERTION | 2022-05-15T22:44:33 | 2014-03-02T22:24:15 | R | UTF-8 | R | false | false | 159 | r | make.thresholds.character.R | make.thresholds.character <-
function(item.params,design.matrix="normal",...) {
#print("character")
return(make.thresholds(CQmodel(show=item.params),...))
}
|
c8eb739901ce8fb1ceac2176a194cc3a926a6794 | d58b6bcfc847a1f8fafa5de52f308d6546a422ac | /2020_WTCW/0-1.Illustrations.R | 298c9c98b1ba26a931e6921d6be4249ca31b48ef | [
"MIT"
] | permissive | baruuum/Replication_Code | 814d25f873c6198971afcff62b6734d3847f38f6 | 23e4ec0e6df4cf3666784a0d18447452e3e79f97 | refs/heads/master | 2022-03-01T19:42:13.790846 | 2022-02-15T07:32:47 | 2022-02-15T07:32:47 | 138,830,546 | 9 | 10 | null | null | null | null | UTF-8 | R | false | false | 3,384 | r | 0-1.Illustrations.R | ###############################################################################
## ##
## Hypothetical Scenarioes, Graphics for Illustration ##
## ... |
91c3ae3889ebeb8ce1dcb6fdabd9e47e268d96bd | 0ff5853af9fd557f6591979a834d9fe82708d234 | /R/hatvalues.drc.R | d26fb78f36ea2cb2c0b1aa8428e7f5a7a008ac44 | [] | no_license | csetraynor/drc | 4c6deed9b783802852cfd3ed60c87dec6afc0ce5 | 8719d43a09711250cd791d7d2bc965558d3e62a6 | refs/heads/master | 2023-04-11T03:15:00.881506 | 2021-05-04T02:42:06 | 2021-05-04T02:42:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 195 | r | hatvalues.drc.R | hatvalues.drc <- function(model, ...)
{
xmat <- model$der
diag(xmat %*% ginv(t(xmat) %*% xmat) %*% t(xmat))
# names(hvector) <- as.character(1:length(hvector))
# hvector
}
|
a03a697f5cffcca2405cceb09beaf6b8dfbc6ace | 66aba2d5193e0a918e558f8681d9814f3472ad31 | /run_analysis.R | d6feb52ed037203777bfccd8904052f6b6e96410 | [] | no_license | jonleogane/Getting-and-Cleaning-Data-project | 7a5889a68ae8aea4d2abcf073e57de9c5566a709 | 15f6714e9c92387088f3461339e840b0b3e13b19 | refs/heads/master | 2021-01-25T10:28:43.225677 | 2015-07-22T17:07:18 | 2015-07-22T17:07:18 | 39,506,016 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,008 | r | run_analysis.R |
library(plyr)
# Step 1 - Merge the test and training datasets
# read data into variables
x_train <- read.table("data/getdata-projectfiles-UCI HAR Dataset/train/X_train.txt")
y_train <- read.table("data/getdata-projectfiles-UCI HAR Dataset/train/y_train.txt")
subject_train <- read.table("data/getdata-projectfiles-UCI ... |
3a373db105659406b6e15b5ebdc5688c55f937ce | 7a89c1c240c0935f0d7ea809717961ad28657769 | /R/approximate_entropy.R | d6d217d7385292dd007f64b058562a1d2a7f3c6f | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | abhishektiwari/Rptsentropy | ae5a7315fb1efebf64be311c0df20424cba15ab8 | 9787d53ebbd965ebcebc370ddfa4ee5d0c6d6894 | refs/heads/master | 2020-06-05T08:56:39.109622 | 2011-02-25T15:01:20 | 2011-02-25T15:01:20 | 1,380,223 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 488 | r | approximate_entropy.R | #! /usr/bin/env Rscript
# Copyright © 2010-2011 Abhishek Tiwari (abhishek@abhishek-tiwari.com)
#
# This file is part of ptsentropy.
#
# Files included in this package ptsentropy are copyrighted freeware
# distributed under the terms and conditions as specified in file LICENSE.
ApEn <- function(entropy3) {
cmr <- Cmr... |
65223ab3691ca6d575bf16caf409f665014d62a0 | effe14a2cd10c729731f08b501fdb9ff0b065791 | /paws/man/pinpoint_get_user_endpoints.Rd | 4b0056439fc52f6a101443aeeabd8b2fd2fcd51c | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | peoplecure/paws | 8fccc08d40093bb25e2fdf66dd5e38820f6d335a | 89f044704ef832a85a71249ce008f01821b1cf88 | refs/heads/master | 2020-06-02T16:00:40.294628 | 2019-06-08T23:00:39 | 2019-06-08T23:00:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 697 | rd | pinpoint_get_user_endpoints.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pinpoint_operations.R
\name{pinpoint_get_user_endpoints}
\alias{pinpoint_get_user_endpoints}
\title{Returns information about the endpoints that are associated with a User
ID}
\usage{
pinpoint_get_user_endpoints(ApplicationId, UserId)
}
\argu... |
fe382a32faa13aa7680ca97eee7d53c399376cbe | d1cc6e32ab5af2ac5b974b0ad0ce2a2a4b453e31 | /man/split_path.Rd | 3438b1bbc2d661fa0b4eb6e3fe6946f52e102911 | [] | no_license | CarragherLab/ImageXpressR | ed5e192328acee9586e04d7b0b317151c2e56a73 | 262086967ad4158cf494ed8777ae1582323c903c | refs/heads/master | 2021-01-02T09:23:19.279811 | 2017-08-03T07:16:44 | 2017-08-03T07:16:44 | 99,202,967 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 322 | rd | split_path.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parse.R
\name{split_path}
\alias{split_path}
\title{split path into components by file separator}
\usage{
split_path(path)
}
\arguments{
\item{path}{string, ImageExpress file path}
}
\description{
split path into components by file separator
... |
36c1c1c1f25d542293775f5369c31d1e091c47d0 | 17f2a5bda68e2df016bfc0833e29b4ff7841d517 | /R/Functions for PSMD.Psychometrics (JC).R | 465c3d5420d5798c0aeda9f1bf1ce44f913032eb | [] | no_license | PSMD-Psychometrics/-old-psychometricsPSMD-old- | 3a5b6b51896c41154f547b7251a9d0edef1b28fc | 3a5c817fd6e0ddc357590e94c5ca2b7f803ad3c4 | refs/heads/master | 2021-07-03T09:22:30.848231 | 2017-09-22T09:03:09 | 2017-09-22T09:03:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,001 | r | Functions for PSMD.Psychometrics (JC).R |
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#### Jo's Functions ####
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
ef500fd11635dd56a5f292fa07656066e2d6d611 | 883a4a0c1eae84485e1d38e1635fcae6ecca1772 | /nCompiler/tests/testthat/test-nCompile_nFunction.R | d04041ec0cf5589cc524fd6bc883aef10dd2abd2 | [
"BSD-3-Clause"
] | permissive | nimble-dev/nCompiler | 6d3a64d55d1ee3df07775e156064bb9b3d2e7df2 | 392aabaf28806827c7aa7b0b47f535456878bd69 | refs/heads/master | 2022-10-28T13:58:45.873095 | 2022-10-05T20:14:58 | 2022-10-05T20:14:58 | 174,240,931 | 56 | 7 | BSD-3-Clause | 2022-05-07T00:25:21 | 2019-03-07T00:15:35 | R | UTF-8 | R | false | false | 3,074 | r | test-nCompile_nFunction.R | context("Testing nFunction compilation")
nc <- nClass(
Cpublic = list(
go = nFunction(
fun = function(x = 'numericVector') {
y <- x
for(i in 1:10) {
y[i] <- 2 * x[i]
}
return(y)
},
returnType = 'numericVector'
)
)
)
Cnc <- nCompile_nClass(nc)
Cnc$... |
b33d7e314c86bda8af35e9c134f8d630f1df49d8 | df40822e65bfcdb5996b068cc53964d6c752e567 | /R/StoredProcedures.R | 25d518d5b859403f7f3726f4ce67318c36ec1fdb | [] | no_license | mfalbertsGMail/Solvas-Capital-R-Utility | 6f420cd1d42d73b70465faf3a93d9d8b644998f3 | c8c6079a8b00103938abc097d7e94a1edff9eb1e | refs/heads/master | 2021-01-12T10:13:21.543777 | 2016-11-23T01:06:53 | 2016-11-23T01:06:53 | 76,387,933 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,790 | r | StoredProcedures.R | # Low-level access to the database stored procedures. Not public - use
# the object model to access these functions. All functions that directly
# call any SPs should be at this level.
#
# Note: @keywords internal keeps the documentation from being published.
#
# Initializes the financial instrument re... |
05baee565676728dca4a57e78a6a45441c6984e8 | d1a4917b7dca113817df475ed0a44525de77d994 | /man/sidarthe.Rd | d68bb90242ecce8b7835e0af1d3ab068c2f5a3de | [
"MIT"
] | permissive | shill1729/odeSolveR | 8cdf673a7af96bc1cc081b7b66b2c02f7e15e6c0 | 183bc0ddbcd81c4ea3b7fd57c0677708db90c2fe | refs/heads/master | 2023-03-27T12:42:04.861729 | 2021-03-24T20:16:33 | 2021-03-24T20:16:33 | 258,375,739 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 5,422 | rd | sidarthe.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sidarthe.R
\name{sidarthe}
\alias{sidarthe}
\title{Euler scheme for the SIDARTHE model}
\usage{
sidarthe(
parameters,
initial_conditions,
t0 = 0,
tn = 1,
n = 1000,
verbose = TRUE
)
}
\arguments{
\item{parameters}{named list of par... |
480cd7f236349ad81207690cd5c482146a007011 | 6412622f5b2ba024096e04fc506135cb4f61695c | /Lectures/Lesson 04 Seasonal Models.R | 1e44ff811b01bacfcd08612ede4830766c847cdc | [] | no_license | PyRPy/tsa4_textbook | 4de940ca2b93989c6e4706591447b238568ce010 | e6b1e347c3d2334b112eca78af1f74a4e21b90ad | refs/heads/master | 2021-06-08T06:09:30.262389 | 2021-05-13T21:05:49 | 2021-05-13T21:05:49 | 159,443,131 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,767 | r | Lesson 04 Seasonal Models.R | # Lesson 4: Seasonal Models
# 4.1 Seasonal ARIMA models -----------------------------------------------
# Example 4-2: ARIMA(1,0,0) x (1,0,0)12
thepacf=ARMAacf (ar = c(.6,0,0,0,0,0,0,0,0,0,0,.5,-.30),lag.max=30,pacf=T)
plot (thepacf,type="h")
# 4.2 Identifying Seasonal Models and R Code ---------------------... |
c3e9c3e17c9a7594cd350efe87ad1a69dd41acd3 | 551b9335dcc91791535095126beb86b4bd132a06 | /man/imageplot_output.Rd | af99c10127ea230b00aa805de7f8fa4d99d46325 | [
"MIT"
] | permissive | dungates/ImagePlotting | 214a8d4488327f8e0e6e16c85fd119277c641cf8 | b8bf80a4a086e6938fa0c159147a9822a13d489c | refs/heads/master | 2023-07-15T15:50:44.380292 | 2021-08-11T17:52:30 | 2021-08-11T17:52:30 | 325,050,470 | 1 | 2 | NOASSERTION | 2021-06-25T20:32:55 | 2020-12-28T15:41:32 | R | UTF-8 | R | false | true | 513 | rd | imageplot_output.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Functions.R
\name{imageplot_output}
\alias{imageplot_output}
\title{function that allows you to pass alpha to a GG plot that also encodes other things}
\usage{
imageplot_output(Q, X, Y, A)
}
\arguments{
\item{X}{is the X var}
\item{Y}{is the... |
515be618d897d8cc54b3a47cb542a0293cc9afdf | 91f211177a9fc2b20a2b808c6200e5a8dfaa21b1 | /Options Pricing Code.R | 2d244bb48455da556a554faa8bd71b46aac0ad79 | [] | no_license | SaifRehman11/Options-Pricing | 8f63f3725886e5b4abbb13165357c2981bd56d93 | d9f81b251e36e92dad773a60d3bb2f3f82df85ec | refs/heads/master | 2022-11-04T02:53:26.322193 | 2020-06-18T23:54:43 | 2020-06-18T23:54:43 | 273,350,026 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,205 | r | Options Pricing Code.R | setwd("E:/Google Drive/Saif/USC MBA/Statistics/Project")
data <- read.csv("option_train.csv")
str(data)
testing <- read.csv("option_test.csv")
summary(data)
summary(testing)
stddata <- scale(data$Value)
stddata[which(abs(stddata)>3)]
library(MASS)
cor(data)
plot(data)
boxplot(data$Value, ... |
c814525408855c4b972eb8aee539795dbd3a43b2 | c28b23b0a89094f2024b796879b60a364168aacf | /quiz2.R | b21c30939b6ce72be465e57f33fe779f30f5f5e0 | [] | no_license | ofirsh/BiostatisticsBootCamp2 | b60ae32d949d5d3d03635c71a83dc3ce2f2726c8 | 5b7ac11390e64eed6792c7258fd4110f08fde384 | refs/heads/master | 2021-01-10T19:57:56.198814 | 2015-02-08T04:24:39 | 2015-02-08T04:24:39 | 30,019,487 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,511 | r | quiz2.R | # Solution by Ofir Shalev, February 2015
----------------------------------------
# Question 1
# ----------
#
# What is the delta method asymptotic standard error of sqrt(phat) where phat is X/n where
# X∼Binomial(n,p)?
# Answer 1 (pseudo code)
# ----------------------
# pseudo algorithm, just to show the method -... |
3cf3bb55f7d90d2bc50086d13097c2c85ee0eb57 | f67d1d9e539d11d907423710ddc49095b9845891 | /R/accessMSigDB.R | e532e1ffc87a63db057ac6a51bd4751e3c1a7050 | [
"CC-BY-4.0"
] | permissive | bhuvad/msigdb | 9ef5b2692474b395ef02a0e935b3e618563c505c | a33991bcb143de33dd7303164a16755cdd0851c7 | refs/heads/main | 2023-07-04T09:11:01.269999 | 2021-08-12T02:19:49 | 2021-08-12T02:19:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,895 | r | accessMSigDB.R | #' Retrieve MSigDB data hosted on the hub
#'
#' Download molecular signatures database (MSigDB) hosted on the ExperimentHub
#' or retrieve pre-downloaded version from cache. This package currently hosts
#' versions greater than 7.2 for human and mouse with both symbol and Entrez
#' identifiers.
#'
#' @param org a chara... |
de682b19e089c3a5abcc5ae4fd25922f3026b9e1 | ef55cbf38be57a866e520e2d13ad85df5f32e9a3 | /server.R | c13ddfa749b612cb8c67892eacb29bad749deddd | [] | no_license | Chihengwang/RShinyApp | 85f6c191d49453f863fb72134e87253a67df359e | c6401de03419e7cf33047dff86b2e121887ac374 | refs/heads/master | 2021-08-24T13:19:45.076849 | 2017-11-21T07:24:53 | 2017-11-21T07:24:53 | 111,513,833 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,935 | r | server.R | #===========================================================================
# Library
#===========================================================================
library(shiny)
library(dplyr)
library(data.table)
library(RCurl)
library(rjson)
#==========================================================================... |
8a485361f45a3b83212362cdf261e57b7def4423 | 583143369a62d0af35cba7dcdbab7094b2a63b57 | /man/hello.Rd | feb1710de964d9d81640c492671a672e7df25ffd | [] | no_license | weekendwarri0r/HowToMakeRProject | 5b2242701baa808cbd1e0bc532fd070f189c51aa | bc0853b738c90e496f2bf9f4f63d9fbaba31ba93 | refs/heads/master | 2021-07-24T14:18:04.812882 | 2017-11-04T09:15:17 | 2017-11-04T09:15:17 | 109,371,566 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 324 | rd | hello.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hello.R
\name{hello}
\alias{hello}
\title{Say "Hello" to arg}
\usage{
hello(name)
}
\arguments{
\item{whom}{the function says "Hello" to}
}
\value{
chr
}
\description{
Say "Hello" to arg
}
\examples{
\dontrun{
hello("Bob")
hello("My friend")
... |
f4ecf6b179341f5a4458719da9067e0bab8ddcdf | 93933cc91cea975577d5a693fe047bc1939e88ec | /server.R | 2056ba07ce609866e5f5b05c6e6e2623361b647a | [] | no_license | Gayathriramanathan13/EPA-SO2-Emissions-Visualization-using-R-Shiny | 123e6be2358d6126ab1f2bbbcca29c38b5ca8e35 | c8c71f4684b743beabca3d07369d9463f92aabb3 | refs/heads/master | 2021-01-18T22:53:50.758025 | 2017-04-03T14:10:37 | 2017-04-03T14:10:37 | 87,080,365 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,679 | r | server.R | library(shiny)
library(shinydashboard)
library(dplyr)
library(tidyr)
library(ggplot2)
library(googleVis)
library(xlsx)
library(plotly)
#Start of Server side code
shinyServer(
function(input, output, session) {
output$stateEmission<-renderPlotly({
state.years.so2.tdy$hover <- with(state.years... |
c49946fb1b4732b0830bbd5f976cf03348b400ec | 1ff8cc01489c730dd6cd0750f1018e29e02fc493 | /man/read_eseal_meta.Rd | 026456a3ad88578f9c1e01e4bd9b9f1f237fd6e2 | [
"MIT"
] | permissive | abfleishman/esealUltrasounds | ff7fd9e2da96428934f3dae2d0fe346255ff2303 | e0baa6fbd6b258d49482d96f2c0eb723c435b3b7 | refs/heads/master | 2020-04-27T15:38:39.161240 | 2019-03-22T06:12:35 | 2019-03-22T06:12:35 | 174,454,088 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 546 | rd | read_eseal_meta.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read_eseal_meta.R
\name{read_eseal_meta}
\alias{read_eseal_meta}
\title{Read Ultra Sound Metadata}
\usage{
read_eseal_meta(image_dir)
}
\arguments{
\item{image_dir}{path to a Session_* directory}
}
\value{
a data frame with ultrasound metadat... |
2fdd97e60b6c44b1b5c1970831e70f522d49c617 | 785955fd58c7c8cfd20d60c0e3d2f9a787d95bc0 | /install_dga.r | be5548d70c9f2a37df16b083f98c74c6abb5a9d8 | [] | no_license | mienkofax/research-base | cba6ba3e813fc24ef69495b6dcf7bc9810de4ab4 | 5319c87160f0cf6d49e71889403923ab8209f751 | refs/heads/master | 2023-02-10T17:23:31.867674 | 2021-01-15T07:48:13 | 2021-01-15T07:48:13 | 324,749,272 | 0 | 0 | null | 2020-12-27T12:23:57 | 2020-12-27T11:43:28 | null | UTF-8 | R | false | false | 277 | r | install_dga.r | install.packages("devtools", dependencies = TRUE, lib = "/usr/lib/R/site-library")
install.packages("randomForest", dependencies = TRUE, lib = "/usr/lib/R/site-library")
library(devtools)
devtools::install_github("jayjacobs/dga", lib = "/usr/lib/R/site-library", force=TRUE)
|
c6f6eec68a61660ea3affb818cc891604c29a29e | 9110e4952edfbb758826c2f2d05751247181c8ac | /Codigo R/ordenarCartas.R | cffc70c75f2f531cc8dcf4b6d5586a11e6d54cba | [] | no_license | Aokaen/Poker_Simu | db7b0665d0b3709e6a0e276729ad18ca854dd460 | 6c3c6cc3ed66d6cb47312dc9b91b6ebe3afe2880 | refs/heads/master | 2022-11-09T13:37:18.903883 | 2020-06-25T23:58:58 | 2020-06-25T23:58:58 | 216,742,645 | 0 | 0 | null | 2020-06-24T15:00:30 | 2019-10-22T06:48:15 | R | WINDOWS-1250 | R | false | false | 394 | r | ordenarCartas.R | #funcion para juntar y ordenar las cartas de mano y mesa en una única jugada
ordenarCartas<-function(Mano,Mesa)
{
jugada<-rbind(Mano,Mesa)
tamano<-nrow(jugada)
a<-tamano-1
b<-tamano
for(i in 1:a)
{
for(j in i:b)
{
ni<-as.numeric(jugada[i,1])
nj<-as.numeric(jugada[j,1])
if(ni<nj)
{
aux<-jugada[i,]
... |
6ef233d2f366c4b1918cf4abc2568bf220dc6928 | 2dfa786120788b2faaa655928ac94b3f8d3c78c1 | /Bayesian Statistics/Linear_Regression_Quiz7A.R | 805946143961c7c1e78ab16aa8a26d3debefd5cb | [] | no_license | heihei2015/ai | cdf7cb8fa91cbe9abb6996457aabd192a3bfc86e | d5885b1b8edf701957a84574ac9c5e9b899efdf8 | refs/heads/master | 2022-12-04T20:37:53.678364 | 2020-07-29T11:05:12 | 2020-07-29T11:05:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,864 | r | Linear_Regression_Quiz7A.R | library("car") # load the 'car' package
data("Anscombe") # load the data set
?Anscombe # read a description of the data
head(Anscombe) # look at the first few lines of the data
pairs(Anscombe) # scatter plots for each pair of variables
#linear model non-informative
lmod = lm( Anscombe$education ~ income + young +... |
ac26d0a350ceb0aee76c8653402fb25239c63404 | c1213fbeb2b3a509c370630a05738030edf1b4c2 | /R/max_rowlength.R | e1a1e71ee46437e1035edcf8d6310ded47c9b7a1 | [
"MIT"
] | permissive | moshejasper/lampshade | 79d31cd26c9ed4790f342a89ba4595518aa2efab | 96c4becb90f093b39d633ce4c94245ab143372b9 | refs/heads/main | 2023-08-12T23:00:49.774854 | 2021-10-04T12:27:06 | 2021-10-04T12:27:06 | 413,237,471 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 294 | r | max_rowlength.R | #' Max rowlength
#'
#' @param n Number of bases in DNA sequence
#'
#' @return Returns integer number of bases in a DNA row (currently under A5 page assumptions)
#' @export
#'
#' @examples
#' max_rowlength(100)
max_rowlength <- function(n){
rnum <- sqrt(n / 12.5) * 12.5
return(rnum %/% 1)
} |
b9538e74427059c5863fb9b2041342d5897817ad | e2c4df2516f7a5743bfd2491f30d5f1150ef691c | /GetPValue.R | cd9bdbd7767e786fc12f459e6a6474ea220064b6 | [] | no_license | LaurenSamuels/BOOM | 4be62bf0109dbb3966d1ac319d08e28efa2f295a | c418a15017a6a53197584269727ddbe5c5c2a82d | refs/heads/master | 2021-01-17T06:07:49.092508 | 2016-11-14T15:05:11 | 2016-11-14T15:05:11 | 54,785,430 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 97 | r | GetPValue.R | GetPValue <- function(TE, SE){
zval <- abs(TE / SE)
2 * pnorm(zval, lower.tail= FALSE)
}
|
c3832d9271d386545e1b647a88db274c0d66632d | 5a411c9d7b3edd84ef34b94eede3ce85fbc7909c | /man/HRM.Rd | f36693c14a52ee45fd2bec2794c49b9436f56a07 | [] | no_license | happma/HRM | e10d039c178ce396a42d6b90e87ea4e3c7a48dec | 69b77d40431cd70763acab54aa813268a394e94d | refs/heads/master | 2020-03-12T02:42:19.292507 | 2018-08-10T11:15:39 | 2018-08-10T11:15:39 | 100,510,794 | 0 | 0 | null | 2018-02-01T23:12:24 | 2017-08-16T16:34:50 | R | UTF-8 | R | false | false | 1,443 | rd | HRM.Rd | \name{HRM-package}
\alias{HRM}
\docType{package}
\title{
Inference on low- and high-dimensional multi-group reapeted-measures designs with unequal covariance matrices.
}
\description{
Tests for main and simple treatment effects, time effects, as well as treatment by time interactions in possibly high-dimensional multi-... |
566796477bac9a49c27e412a93cab9c79c2a5fdb | afdde8a124424dbc1c66112834be4723a3984406 | /R/additional_analysis/cox.R | bca59e964e2f16f5d42bd65dfc290831d289cee4 | [] | no_license | nnh/NHOH-R-miniCHP | 70fa4baa33db7bad1fd5e83e0a54147f6c13f5eb | baf796d1f1e675994815dc038fc553bd07db20e9 | refs/heads/master | 2020-04-19T09:20:54.569562 | 2019-05-17T01:23:03 | 2019-05-17T01:23:03 | 168,107,576 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,896 | r | cox.R | # cox.R
# Created date: 2019/3/26
# Author: mariko ohtsuka
#' @title
#' round2
#' @description
#' Customize round function
#' Reference URL
#' r - Round up from .5 - Stack Overflow
#' https://stackoverflow.com/questions/12688717/round-up-from-5
#' @param
#' x : Number to be rounded
#' digits : Number of decimal places
... |
b2af64d4845004b10e9c2323943eaa932d52369b | 512cc7446bfc05b392ba7e697d316eb00c620c01 | /bin/R/benchmark_mnn.R | 8ef0ff4ef253a1ddbbc782f4ba13539e45ec0956 | [
"MIT"
] | permissive | brianhie/scanorama | b4ce1c947b097a5098850aeafa92cb0126791ad1 | 3fbff622d8c6c0122e699e2e72e9ab4e2a531c7f | refs/heads/master | 2022-12-13T05:23:11.764455 | 2022-11-28T18:50:30 | 2022-11-28T18:50:30 | 141,593,152 | 228 | 47 | MIT | 2022-11-28T18:48:41 | 2018-07-19T14:43:41 | Python | UTF-8 | R | false | false | 1,463 | r | benchmark_mnn.R | library(methods)
library(scran)
names = list(
"data/pancreas/pancreas_inDrop_table.txt",
"data/pancreas/pancreas_multi_celseq2_expression_matrix_table.txt",
"data/pancreas/pancreas_multi_celseq_expression_matrix_table.txt",
"data/pancreas/pancreas_multi_fluidigmc1_expression_matrix_table.txt",
"dat... |
c008a0e0fbe664fea4f9dae44302ddab583a2d67 | 321d64b8075c68a8472aa712114ea9f5131607d1 | /plot4.R | 6f22481f470f081e15b1068803ad8f11fab8d7ae | [] | no_license | fengkehh/Exploratory_Data_Final | 9ac6a4239450a70cfec0be4373512346e2a3e6e3 | c28fb065569451be4397e307f8c40f0a59a1618f | refs/heads/master | 2021-01-19T11:18:30.936074 | 2017-02-18T22:19:54 | 2017-02-18T22:19:54 | 82,237,867 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,484 | r | plot4.R | # Plot 4
# Load data if necessary
data_load <- function() {
# Check that both data files exist
if (!prod(c('Source_Classification_Code.rds', 'summarySCC_PM25.rds') %in% dir())) {
download.file('https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip',
destfile = 'datas... |
32f7fbce68a0c26fb9b5501a9976cf5432d62344 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/fulltext/examples/ft_search.Rd.R | 100489aa6bcaac5fac80d3be9d939d5da5cbbedf | [] | 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 | 2,291 | r | ft_search.Rd.R | library(fulltext)
### Name: ft_search
### Title: Search for full text
### Aliases: ft_search ft_search_ls
### ** Examples
# List publishers included
ft_search_ls()
## Not run:
##D # Plos
##D (res1 <- ft_search(query='ecology', from='plos'))
##D res1$plos
##D ft_search(query='climate change', from='plos', limit=50... |
86e0ee66fd8ad32d65d9ef6a2460d7c3462505f7 | 2da2406aff1f6318cba7453db555c7ed4d2ea0d3 | /inst/snippet/prob-cdf02.R | 73ac2f7c535997962329e451e29f294ebe84f25a | [] | no_license | rpruim/fastR2 | 4efe9742f56fe7fcee0ede1c1ec1203abb312f34 | d0fe0464ea6a6258b2414e4fcd59166eaf3103f8 | refs/heads/main | 2022-05-05T23:24:55.024994 | 2022-03-15T23:06:08 | 2022-03-15T23:06:08 | 3,821,177 | 11 | 8 | null | null | null | null | UTF-8 | R | false | false | 150 | r | prob-cdf02.R | # compute the variance using E(X^2) - E(X)^2
value(integrate(f, k=2, lower = 0, upper = 2)) -
value(integrate(f, k=1, lower = 0, upper = 2))^2
|
43d2c6a727417f5318fa4cda11a8f85242378643 | 444654820df65d00eeb06d1ffdc9c29e968642ec | /scenario_0/scripts/0.setup.R | cdc4b7bfd8fb67ee13610407500c86df2cd221a6 | [] | no_license | michaelgras/MSE_CSH_monitoring_TAC2 | 207f7949dbea4dd9185e0c2307e7417dcae7eb9d | 494d91bd142ce4562bf5b84e1f1741b2dbf273c7 | refs/heads/master | 2020-08-06T12:52:25.462201 | 2019-10-05T10:37:57 | 2019-10-05T10:37:57 | 212,981,950 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,219 | r | 0.setup.R | #initial environment setup
#install.packages("devtools")
#install.packages("FLCore", repo = "http://flr-project.org/R")
#install.packages(c("ggplotFL"), repos="http://flr-project.org/R")
#library(devtools)
#install_github("ices-tools-prod/msy")
#pathR<-paste("C:/Program Files/R/R-",substr(R.Version()$version.string, ... |
06e46e14040432fa8243ee1f6bdd7d73068a1bde | 53ad81079abbff55ee82b8905d005f250897844c | /GMHomework/man/remove_contradict.Rd | a513a7fb39cfef517beda117f7717881634daeef | [] | no_license | isfong1/Gradient_Metrics_Assigment | 0aa475ccaffd09992426713604b324107805d425 | f0d012be70bb1bddde800f2d03578dfe5699dfea | refs/heads/main | 2023-01-30T00:45:52.191987 | 2020-12-04T02:19:54 | 2020-12-04T02:19:54 | 317,860,587 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 638 | rd | remove_contradict.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/remove_contradict.R
\name{remove_contradict}
\alias{remove_contradict}
\title{Remove contradict ID}
\usage{
remove_contradict(data, ..., score, id)
}
\arguments{
\item{data}{a data frame}
\item{...}{variables or computations to group by.}
\... |
0ef3a5a0b038cfef8d388da368de3c9240469ef8 | 11e5a075d5b0da27d5f3563bdc7ee573e20a6574 | /man/ctpopulator.Rd | 3a6fc34071af5bd33c82d910df80a6f8082581a2 | [] | no_license | carlosvirgen/ctnamecleaner | 4d7767b6ae06054954f66d9532c0fce98899371e | ef17c564b4cb51df5a1b420090739c9db6ba266b | refs/heads/master | 2021-01-12T02:05:34.207937 | 2016-01-26T18:39:52 | 2016-01-26T18:39:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 747 | rd | ctpopulator.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/cpopulator.R, R/ctpopulator.R
\name{ctpopulator}
\alias{ctpopulator}
\title{CT Population Appender}
\arguments{
\item{name}{Column with town names}
\item{data}{Name of dataframe}
\item{name}{Column with town names}
\item{data}{Name... |
b5c4ba33c665f81ffa25f39ebbe5343894994e02 | c4670bf1594581622401a727791cd4d8283c5f4e | /2015_MLR/RiskAdj_Enroll_Match.R | e4a6d87010a5299d426ee40b59bcaaaf26f44c86 | [] | no_license | conor-ryan/Imperfect_Insurance_Competition_Code | db274b3818a97b240de08f05e79a5dedee246da1 | e9ed4927f6a7a7670ec235a669b61b23509cc372 | refs/heads/master | 2023-07-20T05:51:29.180132 | 2023-07-05T23:06:10 | 2023-07-05T23:06:10 | 112,538,023 | 0 | 3 | null | null | null | null | WINDOWS-1252 | R | false | false | 3,817 | r | RiskAdj_Enroll_Match.R | rm(list = ls())
library(doBy)
library(noncensus)
setwd("C:/Users/Conor/Documents/Research/Imperfect_Insurance_Competition/")
##### Firm IDs ####
firms = read.csv("Data/2015_MLR/MR_Submission_Template_Header.csv",stringsAsFactors=FALSE)
firms = firms[,c("ï..MR_SUBMISSION_TEMPLATE_ID","BUSINESS_STATE","GROUP_AFFILIATION... |
9d2aae17de8ad0d4a298f17b67d8fdc71f966b23 | 191c6535c0c2b4a3d7025569eee619ad3c1ad0fc | /p3-timeseries/ingest-osx.R | df66481149ad3a441df8b6dc1bec20b3ee1cf6d9 | [] | no_license | pj201/csc791-projects | 4e8c60c5d5a183a0971e8dc93fb5b851ef4063e4 | c8fb1ad7b712c11bbc05c390fb6d96d7e61a5ac9 | refs/heads/master | 2020-05-31T01:26:28.868547 | 2015-05-06T03:44:36 | 2015-05-06T03:44:36 | 32,999,761 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,045 | r | ingest-osx.R | ############################################################
# CSC791 P3: Function to ingest data from OSX Journaling project
# Kshitij Sharma, ksharma3@ncsu.edu, Last updated: 4/2/2015
############################################################
#library(Rcurl)
#library(TimeSeries)
library(jsonlite)
library(httr)
lib... |
507481e08b0aedd4559f5fe0b82eaab3c8707059 | 685d6ca2be8ac49f81584fa157a60a801d8d1f60 | /R/dwplot.R | a982c722d617cba251f690c6eabbf74cd983c4ca | [] | no_license | cran/dotwhisker | eff2c85a13b87bab8e2a32ad4f0a017612cf02bb | 748c1d47fd2123c9756005450c9c6ae43a240c44 | refs/heads/master | 2023-04-08T12:38:30.053853 | 2021-09-02T13:50:35 | 2021-09-02T13:50:35 | 39,861,333 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 23,836 | r | dwplot.R | #' Dot-and-Whisker Plots of Regression Results
#'
#' \code{dwplot} is a function for quickly and easily generating dot-and-whisker plots of regression models saved in tidy data frames.
#'
#' @param x Either a model object to be tidied with \code{\link[broom]{tidy}}, or a list of such model objects, or a tidy data f... |
28ca4b0073e46e7adc6ae74947a5d9e74435a1d2 | d86762bc2c7fc458ff3968532b40e1305191fff7 | /sub_scripts/covertToCSV.R | fa5125ccc5080aa982b146eeb08af4430be1f224 | [] | no_license | hjakupovic/SNPextractor | 4a8bfe47d8f1f4eed7d77e96516a364071cea191 | c8c0b35f11f58a1646f022913072792c80099771 | refs/heads/master | 2022-06-22T11:51:15.544028 | 2018-02-09T11:38:26 | 2018-02-09T11:38:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,707 | r | covertToCSV.R | #!/usr/local/bin/Rscript
args <- commandArgs(trailingOnly=T)
folder <- args[1]
outputName <- args[2]
userSNPsFile <- args[3]
geno <- read.table(paste(folder,'genoFile.noHead',sep=''),h=F,as.is=T)
genoHeader <- read.table(paste(folder,'newHeader',sep=''),h=F,as.is=T)
colnames(geno) <- t(genoHeader)
#Writing info as nu... |
bfc52ad7e58b414fdfd093506790694e072e9350 | 3f6beaa6c2c36d7ee730c38e80553342171a984a | /Assignment2/Mandatory2_Code.R | 08772863f936ba9f6449c5c4b40d44e03a3eb33a | [] | no_license | vladmaksyk/StatisticalModeling | ed2368aae19e120bced057f7db6fd223469156ca | 678b66bdfb15ce8a49ab124b4c63f44d22fbe5a8 | refs/heads/master | 2020-06-11T23:57:20.344173 | 2019-06-27T16:33:55 | 2019-06-27T16:33:55 | 194,128,624 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,937 | r | Mandatory2_Code.R | rm(list=ls()) # clear all variables
graphics.off() # clear all figures
cat("\014") # clear console window
###################################################################
##################### Problem 1c ###################################
###################################################################
cat("Pr... |
2e85c5d416cd3625501e9323d24a29ee3473f6a7 | 4213a7ada3a3c816876b8094f3903c746410a95f | /transposes.r | 71d0ad3b910352ab2e44f3272c4e164ce7e128be | [
"CC0-1.0"
] | permissive | sarahsmason/Health | d90bf8809c29318d6524094bea187e5d1e936561 | 73f13bf0755ff27b7ea8ec46c7d48223b6a0de99 | refs/heads/master | 2023-04-19T22:10:17.302888 | 2021-05-10T00:20:58 | 2021-05-10T00:20:58 | 284,592,708 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 476 | r | transposes.r | install.packages("dplyr")
install.packages("stringr")
install.packages("ggplot2")
install.packages("data.table")
library(dplyr)
library(stringr)
library(ggplot2)
library(class)
library(data.table)
setwd("#your.dir#")
#Load data
tableGenes <- read.csv("#your.file.csv", sep=",",
... |
d275c834f55b9efb5a25e9535c6100112bf2ed06 | 35d850f46b513c3d9abb71e84e4d1eead94c6914 | /man/DataValidation.Rd | 7b201d81738cb3c4fea67bc0aee66685c6490411 | [] | no_license | smockin/RedcapData | 4690dc8696a1214c94b3a99b53b94ad9844755e1 | 63b14a033f866ac7a9511b92e36ea8ed74e07d90 | refs/heads/v1.1.1 | 2023-02-08T01:19:15.523402 | 2023-01-23T06:41:50 | 2023-01-23T06:41:50 | 182,263,613 | 2 | 1 | null | 2020-12-19T13:08:11 | 2019-04-19T12:46:25 | R | UTF-8 | R | false | true | 648 | rd | DataValidation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_checks.R
\name{date_can_be_validated}
\alias{date_can_be_validated}
\alias{data_can_be_validated}
\alias{data_missing}
\title{Check whether a date can be used for validation}
\usage{
date_can_be_validated(var)
data_can_be_validated(var)... |
0e99cea06f8801c361eb121123f530fe20a6867e | 42a5c6d0fb7ab6263e6f86bbc6d46b5231ee7215 | /make.map.R | 54804812a618829401a03c05349c91f1c743c143 | [] | no_license | mcrossley3/whiteflyPopGenReview | a4412b313aa61ca6112ea62623ed3bd04170f64c | 6fef655807f763bc8b94778022ba40b57363a28b | refs/heads/master | 2023-01-24T08:22:14.511251 | 2020-12-04T20:54:36 | 2020-12-04T20:54:36 | 298,055,616 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,886 | r | make.map.R |
setwd('C:/Users/mcros/Desktop/Postdoc UGA/Whitefly project 2020/manuscripts/review Insects special issue')
library(rgdal)
library(rgeos)
library(tidyverse)
library(sf) # for manipulation of simple features objects
library(lwgeom)
winkel_tripel = "+proj=wintri +datum=WGS84 +no_defs +over"
world = sp... |
b24fb52c324cd5ff917b3f7be3b173ddae825af7 | 87bc2495102f8555b1c4ec66f561c868e2d5495b | /man/calculaVolumeDefault.Rd | 675e0e2ef9ce0c8cd72c4368e411b0afad5edc19 | [] | no_license | cran/Fgmutils | a4b716bfb5eccff5cb89133314ab18eb01047875 | 52b9c01a4ee269acc66a2efa29df8411033f0306 | refs/heads/master | 2020-05-21T04:22:25.850012 | 2018-11-17T23:50:22 | 2018-11-17T23:50:22 | 48,080,087 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 583 | rd | calculaVolumeDefault.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calculaVolumeDefault.R
\name{calculaVolumeDefault}
\alias{calculaVolumeDefault}
\title{calculates Volume Default}
\usage{
calculaVolumeDefault(ht, dap, ...)
}
\arguments{
\item{ht}{is list of height of individuals}
\item{dap}{is l... |
532f0b2cab0ccdb6b56659b7a86ae2f812eb5db0 | 20e30d05121195b59f3ee09a90fc6ba6922418a8 | /Deprecated/To Dropbox/depreciated/figure.scripts/fig3.taxa.R | 9ef2b6b0fb88c5bfd3d03082c84b9a65547d8d3c | [] | no_license | NW-Anderson/Ancestral-Condition-Test | 17fb7ba92221ae273cb9921655c8547761a8692f | c9d344b4185638e125a39ee989e27bb408e20122 | refs/heads/master | 2022-01-27T13:58:16.453852 | 2022-01-12T20:09:10 | 2022-01-12T20:09:10 | 187,305,273 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,860 | r | fig3.taxa.R |
##### Fig 3 #####
load('../results/UnidirectionalTaxaFPResults.RData')
load('../results/UnidirectionalTaxaPowerResults.RData')
x <- seq(from=20, to=200, by=20)
y <- vector()
for(i in 1:10){
y <- c(y, taxa.uni.power.results[1:100, i])
}
probs <- vector()
for(i in 1:10){
probs[i] <- sum(taxa.uni.power.results[1:10... |
c49f8a55befb639a048b68fe18da4e8d161213ec | 6c38c5850f822a151b3930a1574d80718876e69c | /StatLearning for Analytics/Week 4 - Logistic Regression/server.R | d3576f4bb456e3b2140e4dec44c4b9ff699072e0 | [] | no_license | christianmconroy/Georgetown-Coursework | 7eb30f7c3d20a831ade19c21177b0eb7ad30d288 | d301800adc35cb6a509be5c75fc6c46c3263537b | refs/heads/master | 2020-05-09T13:59:33.434366 | 2020-05-07T16:10:16 | 2020-05-07T16:10:16 | 181,173,259 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 524 | r | server.R | shinyServer(function(input, output) {
library(ggplot2)
output$plot<- renderPlot({
x <- seq(0,8,length.out=50)
beta_0 <- input$beta_0
beta_1 <- input$beta_1
beta_2 <- input$beta_2
beta_3 <- input$beta_3
y_hat <- exp(beta_0 + beta_1*x +beta_2 + beta_3*x*beta_2 )
logit <- y_hat ... |
f8260c0913263b72334ef7a3d5d059eec3096792 | f77d4ae139d960f6138e29f4e5e9e39fcba528fb | /R_CODES/Previous/Junk codes/rej2.R | 8ca771c150a01c27a68006050150eab6a5531dc0 | [] | no_license | zenabu-suboi/masters_project | fc80eb077af8e92bf81cda94952d4dec196bb263 | d865eb68e66d35c52229023d7aa83b78fd7518f4 | refs/heads/master | 2022-04-22T15:01:15.063667 | 2020-04-28T10:50:17 | 2020-04-28T10:50:17 | 179,095,292 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,988 | r | rej2.R | ### ABC rejection, we need to run it 1000000 times in the end
nreprej= 100000
tolp= 1 ### see ?ABC_rejection, provides us with the parameters that produce
### summary statistics X% closest to the target
### e.g. when we set it to 0.1, and we start with nreprej=100, we end up with 10.
#?ABC_rejection
set.seed(234)
... |
b41432d787c7e099545a893c2f0df9738566aab3 | 7670b011e2504fc6213456b2f0f5992f056470bb | /Lectures/Lecture_4/advanced_profiling.R | 13d596b7278edde10fdbc2c4668cb7d98edc5adc | [
"MIT"
] | permissive | ogencoglu/R_for_VTT | 448f57387e4131c11d5f47f00b392ba2225e82da | a7f15e1feedc745cadbd7db7d43d192265c047fd | refs/heads/master | 2021-01-01T03:53:28.362701 | 2016-05-15T06:31:06 | 2016-05-15T06:31:06 | 56,995,798 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,126 | r | advanced_profiling.R | # R Lecture to VTT - Lecture 4
# Author : Oguzhan Gencoglu
# Latest Version : 11.05.2016
# Contact : oguzhan.gencoglu@tut.fi
# -------------- Advanced Profiling --------------
# Removing incomplete rows example
fake = matrix(rnorm(200000),10000,20)
fake[fake>2] = NA
f = as.data.fra... |
f6421a01cee8232af1e4f4d9a145546371846593 | da401ba918d2fe3c7f6d558c65e660cdd1535d0e | /R/coxphmulti.R | d3d0bb4c37b490355330eb9748b4f86c5292ecdc | [] | no_license | tomdrake/summarizer | 84d62db144666a90d4eeb1b1ece9306e8bc9f6de | ee714dfe55056eeaccd286346dff6cdcd3a1963e | refs/heads/master | 2021-05-09T01:59:20.619177 | 2018-01-28T17:27:46 | 2018-01-28T17:27:46 | 94,934,964 | 0 | 0 | null | 2017-06-20T21:05:09 | 2017-06-20T21:05:09 | null | UTF-8 | R | false | false | 611 | r | coxphmulti.R | #Survival multivariable with weights - clusters also possible
coxphmulti <- function(df.in, dependent, explanatory, weights = NULL){
require(survival)
result = list()
if (is.null(weights)){
for (i in 1:length(dependent)){
result[[i]] = coxph(as.formula(paste0(dependent, "~", paste(explanatory, collapse="+")... |
11ba7533ac46eaedf1dc68371884f13b931f5960 | b7f0d300ee724bf170f08394257391ea12703945 | /c9-data-products/plotly-demo.R | 9529db68943bb72b126720ce0bedfe15708fdef1 | [] | no_license | TheYuanLiao/datasciencecoursera | c36a62d1f4af6e4cad2d9188cb9814d0d8ed6bd1 | e227991e3ab6169f01476557a8987d1e840b5d64 | refs/heads/master | 2023-01-23T19:02:49.647831 | 2020-10-24T15:31:28 | 2020-10-24T15:31:28 | 281,198,019 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 221 | r | plotly-demo.R | library(plotly)
library(shiny)
set.seed(777)
temp <- rnorm(100, mean=30, sd=5)
pressure <- rnorm(100)
dtime <- 1:100
p <- plot_ly(x=temp, y=pressure, z=dtime,
type='scatter3d', mode='markers', color=temp)
p
|
4107381cbb50e44e608efca6f60bedfc0f96f73f | 3ec39ea137d1aaa0c7106c1ae49ddf395b5fda20 | /R/addSetting.R | c51e30c5c5220686b185f1d4165aeb4b13b1ed78 | [] | no_license | mli1/safetyGraphics | e48c5bee150926f008d6185c764a179d5a3e5a71 | 165651d98c6894646f84884d1c9f7a24336d25f7 | refs/heads/master | 2023-01-22T17:00:10.267847 | 2020-01-16T14:26:14 | 2020-01-16T14:26:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,746 | r | addSetting.R | #' Adds a new setting for use in the safetyGraphics shiny app
#'
#' This function updates settings objects to add a new setting parameter to the safetyGraphics shiny app
#'
#' This function makes it easy for users to adds a new settings to the safetyGraphics shiny app by making updates to the underlying metadata used b... |
5e58b7a50ee617b3ed59f84eba29f8838b67f351 | f5611051efee3fe799b5c7b17eb36a73a19bf37e | /man/get_stringdb.Rd | c16f5e7ddee4027b81f453b076e7b136fd2bfcac | [
"LicenseRef-scancode-proprietary-license",
"MIT",
"GPL-2.0-only"
] | permissive | InfOmics/LErNet | 975104c3845ea91079f1cd4fbfd852efa740f0d2 | 7a1a1652009fc4f99c7c18ece0bc8733a8cb482b | refs/heads/master | 2021-08-17T10:32:54.744114 | 2021-04-07T16:31:10 | 2021-04-07T16:31:10 | 171,709,544 | 0 | 1 | MIT | 2020-10-28T15:43:40 | 2019-02-20T16:32:12 | R | UTF-8 | R | false | true | 1,024 | rd | get_stringdb.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/support.R
\name{get_stringdb}
\alias{get_stringdb}
\title{Retrieving of information from the STRING database}
\usage{
get_stringdb(stringdb_tax = 9606, stringdb_thr = 900)
}
\arguments{
\item{stringdb_tax}{taxa of the species. Default human (... |
6a6da3c7817a074e05862efc549d990f5ecb91e6 | 60a99dc425d9edca7b3dec562f5cf6367d9c61ec | /MExPosition/R/mpDOACT.STATIS.core.R | d34e1583f3e404a8ac71757833074ab44c3f0ed7 | [] | no_license | LukeMoraglia/ExPosition1 | e7718ae848608f1dc3934513c6588f53f2c45a7f | a69da6c5b0f14ef9fd031b98c3b40b34dad5240f | refs/heads/master | 2022-12-31T17:45:10.909002 | 2020-10-22T19:45:49 | 2020-10-22T19:45:49 | 255,486,130 | 0 | 1 | null | 2020-10-22T18:08:38 | 2020-04-14T02:01:12 | R | UTF-8 | R | false | false | 8,860 | r | mpDOACT.STATIS.core.R | mpDOACT.STATIS.core <- function(dataset1, column.design.1, dataset2, column.design.2)
{
num.groups <- dim(column.design.1)[1]
# cross product of dataset 1
CubeSP.1= array(0,dim=c(dim(dataset1)[1],dim(dataset1)[1],dim(column.design.1)[1]))
from = 1
for(i in 1:dim(column.design.1)[1])
{ from = sum(c... |
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