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f3af7f397fed085928c79f31493314c959884b06 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /sppmix/R/datasets.R | f9b3229c1352cf4715a68cdf54f3cac313d0a31f | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,479 | r | datasets.R | #' @title Datasets
#' @aliases Datasets
#' @description
#' Several datasets have been included in the
#' \code{sppmix} package. They are all open source
#' datasets that have been processed into
#' \code{\link[spatstat]{ppp}} objects. Many examples and tutorials
#' use these datasets.
#'
#' For basic examples ... |
e69d55e30aa05ff018cbf30c470cd7008b265149 | ca6282054a9875d49d67dcd74896ad13d121b9bd | /UConn/Machine_Learning_With_R/Exercises/DaigleHomework7.R | 2b870ac7259bc9ffa945cc9f40615fa60cc06a8a | [
"Unlicense"
] | permissive | ChristopherDaigle/Learning_and_Development | 131c0a8e6b682b4cd6e1cd0bcc34fe1713704307 | 3d225f4b5c0261275b0f949873e71bdf2f420e6a | refs/heads/master | 2023-07-19T00:24:22.740897 | 2020-02-11T02:22:21 | 2020-02-11T02:22:21 | 211,148,685 | 0 | 0 | Unlicense | 2022-06-21T23:08:40 | 2019-09-26T17:47:56 | Jupyter Notebook | UTF-8 | R | false | false | 2,143 | r | DaigleHomework7.R | #' ___
#' Chris Daigle
#'
#' Homework 7
#' Bootstrapping
#'
#'
#' ___
#'
# Exercise
#
# Another bootstrap method in a linear regression model is to
# resample residuals instead of (X, Y). The former is called Residual
# Bootstrapping and the latter is called Pairwise Bootstrapping. I beleve that
# boot() is based ... |
99f4ba6d0a74534c0a8b3bea3f7eacf23497f326 | 20dc05ee5ca66121017f7382ee4097c4ff2c37a2 | /data/pride/pre-processing/potholes.R | b048632f171632747b80aebde235abebebdeccf9 | [
"MIT"
] | permissive | traffordDataLab/dashboard | 6d885f26213a1177b8f2e3a411a0f914c5c92beb | 83e9bc0cd14d1fac19c743c1e5eece54e0145458 | refs/heads/master | 2021-07-10T20:23:26.488619 | 2020-08-20T15:57:45 | 2020-08-20T15:57:45 | 182,110,692 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,418 | r | potholes.R | # Potholes #
# Source: fixmystreet.com
# URL: https://github.com/mysociety/fms_geographic_data
# Licence: Open Data Commons Attribution License
# NB for category lookup: https://github.com/mysociety/fms_meta_categories
library(tidyverse)
urls <- c("https://github.com/mysociety/fms_geographic_data/raw/master/LAD/fixm... |
730489ca37d752adaca3f17f2dde8515f8351b7b | 5c1e2c6d5f10a0dbf01d2ac39e722a9977840f3b | /sandbox-h2o-features.R | e7dfcec1a5bfc431df19a26b5d84846f0b7e101f | [] | no_license | sarikayamehmet/pumpprediction | 740756cd45961aa3d22146a41815b11944447a3b | 6b2aca076f758bae8b61cd39a3d08492ac982253 | refs/heads/master | 2021-01-18T23:33:00.618008 | 2016-08-17T09:10:31 | 2016-08-17T09:10:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,068 | r | sandbox-h2o-features.R | ## Deep features
library(h2o)
localH2O <- h2o.init(min_mem_size = '4G', nthreads = 4)
trainset.hex <- h2o.uploadFile(path = 'trainset_values.csv', destination_frame = 'trainset.hex', sep = ',', header = TRUE)
labels.hex <- h2o.uploadFile(path = 'trainset_labels.csv', destination_frame = 'labels.hex', sep = ',', header ... |
ee2cad4dab5a85f34759340ee57920bf2b43dcea | 8f0bdf09f4559f87b031cf0b3ceb0e42a5d879a2 | /R/visualization.R | 303be0bb4b14489e37fe33947f5042a424a2b9d6 | [] | no_license | wenbostar/proteoQC | 501db6fac02101accecb386599e10dae9a38edfa | d2c05b5b2b317b0ab8947d5377e703881f7b1dff | refs/heads/master | 2021-01-10T01:39:08.531525 | 2020-11-18T19:10:52 | 2020-11-18T19:10:52 | 36,590,446 | 3 | 6 | null | 2020-11-18T19:10:53 | 2015-05-31T06:09:21 | R | UTF-8 | R | false | false | 32,059 | r | visualization.R |
##' @title Barplot in different level for each fraction
##' @description Barplot in different level for each fraction
##' @param res An object of msQCres
##' @param level 1: total spectrum, 2: identified spectrum,
##' 3: identified peptide, 4: identified protein.
##' @return The name of the figure
plotFractionIDResul... |
7cf16cf7009e2f853be7171cd41a76382f82b849 | 8ddcbd0386df1621dde9df910896158930dd3254 | /Week2/Week2-withanswers.R | b36c819b6834e9a46155ba01d4325c60fd56abe3 | [] | no_license | hunry4068/INF6027 | 83387ed2e3266ef6a6989b4b6a12399f74862b6c | efa7f6563d81979b4f96155cd76acc2a35e8c03c | refs/heads/master | 2020-04-29T16:39:29.873507 | 2019-03-18T11:50:25 | 2019-03-18T11:50:25 | 176,269,196 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,926 | r | Week2-withanswers.R | # **************************************************************#
# R/Rstudio Practical Week 1
# 21 Sept 2017
# Instructor: Paul Clough
# **************************************************************#
# **************************************************************#
# Recap
# ***************************************... |
3ddfc53675eb4b907b3ce74b53ed7e2a11568dd6 | d132be580a6c765d6b5d27541431361aa06f422b | /plot2.R | 785bce442e578a855dd668439217ee401cb63023 | [] | no_license | JonJuneau/ExData_Plotting1 | 4fea6aa84e6b4063443c613aa61a2f99ba5d11d3 | 781ccce1e71d1fa0f1cad0dc007d99e4be72b592 | refs/heads/master | 2021-01-22T15:00:04.048921 | 2014-11-08T04:09:56 | 2014-11-08T04:09:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,140 | r | plot2.R | # plot2.R
clean <- function(x) {as.Date(strptime(x,"%d/%m/%Y"))}
#-------------------------------------------------------------------------------
# Load the data
#-------------------------------------------------------------------------------
PlotData <- read.table("household_power_consumption.txt",
... |
2f253140533eef32798229348a0df346bb1fbffd | dc3f1a4ba2311b757ba72072f4c54af961ffcc86 | /hiv/parse_names.R | 3f60fc9bd9cbfe4f0db01faf784387e84f75914a | [] | no_license | sebastianduchene/virus_analyses | 2d002ee383af98d92eca126d15603bf9343ae41f | b35b13f8e2634badb9a7100c4dedea9c045896fc | refs/heads/master | 2020-04-04T12:29:35.336776 | 2015-08-04T08:56:17 | 2015-08-04T08:56:17 | 19,427,816 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 470 | r | parse_names.R | names.raw <- rownames(alin)
names.split <- strsplit(names.raw, "@|/")
names.taxa <- sapply(names.split, function(x) x[1])
get.date <- function(x){
date <- as.numeric(x[2])
ifelse(date < 14 && date > 0, date <- date + 2000, date <- date + 1900)
return(date)
}
names.dates <- sapply(names.split, function(x) get... |
cbe74822c3c23948fd62bb0672631fdbddcb4af0 | 0a7cbb70d624894ecf0356ac7b0c2c207a06ae11 | /R projects/carddeck.R | bb158f6cb29d21d16a4af2bacbb2f5e5aafa7228 | [] | no_license | 103percent/analbits | fd9f5d6ae1a6ce8d60ff56b4e289e0f7fc140383 | 4f7af26d2a95c21ffa0955c2b1b4fc4c3382b3fe | refs/heads/master | 2021-01-19T00:02:55.296435 | 2017-01-24T22:12:06 | 2017-01-24T22:12:06 | 72,867,847 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 667 | r | carddeck.R | createDeck <- function(totalNumOfDecks = 1)
{
suits <- c("Diamonds", "Clubs", "Hearts", "Spades")
cards <- c("Ace", "Deuce", "Three", "Four","Five",
"Six", "Seven", "Eight", "Nine", "Ten",
"Jack", "Queen", "King")
values <- c(0,2,3,4,5,
6,7,8,9,10,
10,10,10)... |
2b80cbd7e38bedb4e7e8c600b0fadffc7751cf86 | 05dd76193e90344846de715b26228c4afa827d73 | /ADS/lr.r | 9a6857d146347d324611aae78191d0274435e1aa | [] | no_license | jiun0201/project | 87920e7413864a87b010075a5eedc967e3373bd2 | a3fd274604501eab069a329b1953e8fa832d49de | refs/heads/master | 2021-03-12T19:24:31.854372 | 2014-05-17T16:30:44 | 2014-05-17T16:30:44 | 18,280,580 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,765 | r | lr.r | rm(list = ls())
theURL="https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
data=read.table(theURL, header=FALSE,sep=",")
names(data)=c("age","workclass","fnlwgt","education","edunum","maritalstatus","occupation","relationship","race","sex","capitalgain","capitalloss","hoursperweek","nativecou... |
94c34981a7654b6a4f224d1d378f23ee25487841 | 769898772e7225264fd942b2e5a666af3105d3a1 | /R/dahi.R | d3e02d0e5131117e9ac96cdb482ca19da3adf5be | [] | no_license | cran/spatialEco | 3fa4393496453b091c547cc7601a984e54bf2be6 | 22944d790b25451c848d420b61d386471073b1ee | refs/heads/master | 2023-07-08T05:04:12.117110 | 2023-06-30T07:40:02 | 2023-06-30T07:40:02 | 30,218,937 | 5 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,449 | r | dahi.R | #' @title Diurnal Anisotropic Heat Index
#' @description
#' Simple approximation of the anisotropic diurnal heat (Ha) distribution
#'
#' @param x An elevation raster of class terra SpatRaster
#' @param amax The Alpha Max (amax) parameter in degrees defined
#' as: minimum = 0, m... |
f1db2659e4be94c9082d1a1c64466d454da6c20f | 63ce3e48f2217972353de20d58bf2a56d58a31b0 | /R/subsets.R | 4db1909f6b1b08a90c86f7402274f7f02b73322a | [] | no_license | cran/car | 6c8c284a58be9798daa0407f65cd5dd1fc808d34 | d67a2f5ed4013c8766e8d3e827dcaf6aaaf7f0fa | refs/heads/master | 2023-04-06T03:29:23.872258 | 2023-03-30T09:40:02 | 2023-03-30T09:40:02 | 17,694,945 | 8 | 20 | null | 2022-06-06T07:18:20 | 2014-03-13T04:11:37 | R | UTF-8 | R | false | false | 1,930 | r | subsets.R | # Plot optimal subsets regressions -- output from regsubsets
# function in leaps package
# last modified 2015-01-27 by J. Fox
subsets <- function(object, ...){
# if (!require(leaps)) stop("leaps package missing")
UseMethod("subsets")
}
subsets.regsubsets <- function(object,
names=abbreviate(object$xnames, minlen... |
34ca1ad24f239e5abd69e86f65384b2ff0aab3cc | 31d620ab706c29c31157f90d0073062ce09400e6 | /inst/scripts/data.R | 49752966ee447dd863b781b37a2481c2291f9cd6 | [] | no_license | demar01/RIC | fc7295bf22a6b497f3b644621b54e32245f5d421 | e443d8f30147bb0a79f256b3190f09a2d88dbe97 | refs/heads/main | 2023-03-02T23:54:52.443238 | 2021-02-04T13:06:30 | 2021-02-04T13:06:30 | 325,583,265 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 416 | r | data.R | WCLpeptidesfilesmall_path<- system.file("extdata","WCL_peptides_small.txt", package = "RIC")
j <- str_which(colnames(WCLpeptides),str_c(c("Intensity.((\\D)).18_M_4","Intensity.((\\D)).4_18_M","Intensity.((\\D)).M_4_18"), collapse="|"))
QWCLpeptidessmall <- readQFeatures(WCLpeptidesfilesmall_path, ecol = j, sep = "\t", ... |
05961a04ea483da8b9d982eb7001e7ffb1ac96fa | 59a35695a0f692ae874b582604d4440275900953 | /tests/testthat/test-campelo.R | 24d3a9c815a7ee26a13b549eb11cbeb6ecfb4a87 | [] | no_license | cran/iadf | b14a34f0435e7156c6c309af8f895c4943b3df07 | 227b8332f30c8d1b1cbee2fb8b479b885556b480 | refs/heads/master | 2021-06-04T04:07:36.736805 | 2021-05-24T14:40:02 | 2021-05-24T14:40:02 | 93,506,041 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,532 | r | test-campelo.R | context("campelo frequency")
testiadf <- data.frame("AbcAA01a" = c(1, 1, 0, 0, NA),
"AbcAA02a" = c(NA, 1, 1, 1, NA),
row.names = as.character(seq(2000, 2004, 1)))
testrwl <- data.frame("AbcAA01a" = c(10, 10, 20, 20, NA),
"AbcAA02a" = c(NA, 10, 10, 10,... |
98a99dea8f5b07bef20c9f521cb07af02ac1a104 | 3d9ccac22dd69bbf85806fb4b28088d946ef241a | /man/RadiusCredibleSet.Rd | f5518252f53fb3f85f6d14bd96dc548c6dcbc7c3 | [] | no_license | cran/BayesSurvival | 97098d50949e6dcf70b56f11ae60aab9698852a3 | 0c88791b4bba979887578266b9a0b2d6519fcb0c | refs/heads/master | 2023-03-23T11:08:31.188313 | 2021-03-12T20:00:13 | 2021-03-12T20:00:13 | 262,509,134 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,467 | rd | RadiusCredibleSet.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RadiusCredibleSet.R
\name{RadiusCredibleSet}
\alias{RadiusCredibleSet}
\title{Computes the radius of a fixed width credible set for the
survival or the cumulative hazard}
\usage{
RadiusCredibleSet(draws, post.mean, alpha = 0.05)
}
\arguments{... |
c92b5bd32be4878d9e783c97e4a239a79367e72e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/VGAM/examples/binom2.or.Rd.R | cf5d7b16dc3c1d15f02d3f24e0a909a46e8a748c | [] | 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 | 2,078 | r | binom2.or.Rd.R | library(VGAM)
### Name: binom2.or
### Title: Bivariate Binary Regression with an Odds Ratio (Family Function)
### Aliases: binom2.or
### Keywords: models regression
### ** Examples
# Fit the model in Table 6.7 in McCullagh and Nelder (1989)
coalminers <- transform(coalminers, Age = (age - 42) / 5)
fit <- vglm(cbind... |
290c4bed3f431efab8f5251e7b69b5f56cbd197c | c63fc5e6607e2cd5d62464a72c78b06191277eb6 | /man/geonom2fao.Rd | c48cf8d860ae2fed317ff194ae75e4742a3bc90b | [] | no_license | SWS-Methodology/faoswsTrade | 6ce400e545fc805fe1f87d5d3f9d5ba256a8a78c | 2145d71a2fda7b63d17fa7461ec297f98b40756c | refs/heads/master | 2023-02-17T08:40:21.308495 | 2023-02-09T13:53:56 | 2023-02-09T13:53:56 | 55,507,302 | 4 | 1 | null | 2020-05-15T14:45:59 | 2016-04-05T12:49:03 | R | UTF-8 | R | false | true | 784 | rd | geonom2fao.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{geonom2fao}
\alias{geonom2fao}
\title{Eurostat Geonomenclature to FAO area codes mapping}
\format{A data frame: code (integer) Geonom code,
faostat (integer) - FAO area code,
active (integer) - FAO area code to use... |
6387469e8a10a1e9e81d687d97ecd88e962c339a | 7672493ea65b7c97dfebb1fef2d4da4132f2bbf3 | /codes/home_circos_ggplot_chiapet_inter.R | 2b3331d2367b6ff539a521216be6758c83edf004 | [
"Artistic-2.0"
] | permissive | andronekomimi/SNPVizualTools | a49c9869e651970aa9e288c1e1cb7b54eb78f13d | 504b70be6d2d42eef33be499eb984690f630e7f9 | refs/heads/master | 2016-09-11T02:55:29.744674 | 2015-04-09T13:06:27 | 2015-04-09T13:06:27 | 33,318,730 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,436 | r | home_circos_ggplot_chiapet_inter.R |
######################### ARGUMENTS ###############################
args <- commandArgs(trailingOnly = TRUE)
#
# if(length(args) < 5) {
# stop("Argument missing! Usage : scrip.R chrX start stop target_name path_to_snp_list [highlight_region fac. start:end]")
# }
#
#
# current_chr = args[1]
# current_start = as.nu... |
fe5345794c31590c1da7f00d4d346d229ae80377 | 0bbead7aac0c282df7f84ee70f8a83018a3b5d3c | /０ShinyBook-梅津/ShinyBook-master/chapter04/05-app-version3.0/global.R | f30856830f97b3084b7d86cae417003ed5a54bc7 | [] | no_license | luka3117/JcShiny | 284fa9ee29c5b9b86ee022b179e41e3cca153911 | 2c7c9fb410eac2c0f3acf6350a6daca780648d5a | refs/heads/master | 2022-12-12T00:35:35.684502 | 2020-09-17T17:42:00 | 2020-09-17T17:42:00 | 290,563,226 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 339 | r | global.R | library(shiny)
library(shinydashboard)
library(ggmap)
library(leaflet)
# 以下追加部分
library(rgdal)
map <- readOGR(dsn = "./data", layer = "sample", encoding = "UTF-8",
stringsAsFactors = FALSE)
attribute_data <- read.csv("./data/attribute.csv")
# library(readr)
# attribute_data <- read_csv("./data/attribut... |
f87a08a6a8ed7f3bc39a4db367f4f1a4ef0ce34f | c12faab13a3d9eb9cd2c2d9dd6462046fc0ef711 | /whatsapp.R | 619cf0b865b5aeff22f6a2be27692d34fb371161 | [] | no_license | planetdata/somuchdata | 3c0fabe085b3c9d7faf294ff87eed6eaf614fff1 | 9ad6c826c2ff83b8570728760524d8f804ea91fc | refs/heads/master | 2020-04-18T08:03:21.170759 | 2016-10-30T13:13:18 | 2016-10-30T13:13:18 | 65,972,463 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,765 | r | whatsapp.R | install.packages("ggplot2")
install.packages("tidyr")
install.packages("lubridate")
install.packages("readr")
install.packages("stringr")
install.packages("dplyr")
library(ggplot2)
library(tidyr)
library(lubridate)
library(readr)
library(stringr)
library(dplyr)
path<- file.path("C:","Users","Amita","Downloads","cha... |
3e93941da050ff333dca2622e1f3a003bea71799 | fa0f811c827e23874f142583a7129ad2b1404357 | /man/percent.Rd | 9fe37f08981d742f0640a5b8bc093063ec2bb2ec | [
"CC-BY-4.0"
] | permissive | Lifebrain/gbhs | 66203c9240387f2fddf0684ba318c1af748e051d | abf44acba1b05e3b98868e6c6a5806fc071d817a | refs/heads/main | 2023-04-18T19:11:52.442089 | 2022-09-28T08:50:52 | 2022-09-28T08:50:52 | 535,578,726 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 463 | rd | percent.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{percent}
\alias{percent}
\title{Pretty percent displaying}
\usage{
percent(x, accuracy = 1, ...)
}
\arguments{
\item{x}{vector of numbers}
\item{accuracy}{accuracy of the percent}
\item{...}{other arguments to \code{\link[scal... |
4746624ee79502013971f37381bc0083dddf1851 | abcb78ddbbc03256a9e40fbd7f4790a735b91a52 | /tests/testthat/test-rename-Spat.R | f3b0c2d5bb154edee5ed849cec890003764ab319 | [
"MIT"
] | permissive | dieghernan/tidyterra | f94f0b8544b24f231022570fa51e2f2acc09238a | b0214a95186c1d63eb89ce92f7704ab0396d0c3b | refs/heads/main | 2023-09-04T11:47:01.063904 | 2023-08-16T06:19:58 | 2023-08-16T06:19:58 | 488,274,429 | 134 | 4 | NOASSERTION | 2023-09-10T07:27:12 | 2022-05-03T15:59:17 | R | UTF-8 | R | false | false | 2,962 | r | test-rename-Spat.R | test_that("Rename SpatRasters", {
file <- system.file("extdata/cyl_tile.tif", package = "tidyterra")
raster <- terra::rast(file)
s2 <- rename(raster, b1 = cyl_tile_1)
expect_identical(
names(s2),
c("b1", "cyl_tile_2", "cyl_tile_3")
)
expect_true(compare_spatrasters(raster, s2))
# Several rename... |
4c40ac7ecec72a0f3949d0a143273b34dc087ee2 | bea658ce947a90ea17f96ec62221adffa6ea66bd | /chp5.R | 4f2109457f1dfbb14b2a96a0604db2db7111c7e1 | [] | no_license | harborwang/R4DS | f9f28f2274660c7b7ae3eec9acefe0bfcf86e31d | 4809fe4ca18532f84c88ef76132558c85af9438f | refs/heads/master | 2020-03-11T22:35:34.786524 | 2018-04-20T03:02:49 | 2018-04-20T03:02:49 | 130,296,393 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,095 | r | chp5.R |
# Code Head ---------------------------------------------------------------
chp5.R
# libraries ---------------------------------------------------------------
install.packages(nycflights13)
library(nycflights13)
library(dbplyr)
library(tidyverse)
# 5.6.1 -----------------------------------------------------------... |
e52a2055e9475d77f0e0f3a44977c43d2bc44ea3 | c3acaf321474968b8cce7b0961bc0ecc229608d3 | /plot5.R | b3e5311916929137b4d5bc421a93305fa38cdfdf | [] | no_license | fvon61/Exploratory-Data-Analysis-Project-2 | 570cdf6aaf971967fec4c6f81f2b63dc72e794ba | e0d8bef4d06cb490056ffb926c68b5c904e93659 | refs/heads/master | 2020-04-06T03:41:45.342296 | 2015-02-06T19:52:03 | 2015-02-06T19:52:03 | 29,829,953 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,123 | r | plot5.R | ##EDAP Project 2 Plot 5
## This first line will likely take a few seconds. Be patient!
if(!exists("NEI")){
NEI <- readRDS("~/R/data/summarySCC_PM25.rds")
}
# 24510 is Baltimore fips, see plot2.R
# Searching for ON-ROAD and OFF-ROAD type in NEI
subsetNEI <- NEI[NEI$fips=="24510" & (NEI$type=="ON-ROAD" | NEI$type=="O... |
15f37d51073b34cf7a392cc28cd186906489c24b | 387df2ef56aec161c1d835fe9dec44c265c8698d | /samples/foreach/batch_inferencing/batch_inferencing.R | 99d7b4a70000ae0859ba887aa5998adc05016a9a | [
"MIT"
] | permissive | Azure/azureml-sdk-for-r | 072d4e72ad7667a848fc61d50655f7632b6c69cf | 19301e106c0cec69dac0931e98af7e3f713b7bf7 | refs/heads/master | 2023-08-16T16:01:25.257198 | 2022-09-14T16:35:20 | 2022-09-14T16:35:20 | 199,087,253 | 102 | 47 | NOASSERTION | 2022-07-25T19:03:47 | 2019-07-26T22:29:14 | R | UTF-8 | R | false | false | 1,397 | r | batch_inferencing.R | # Copyright(c) Microsoft Corporation.
# Licensed under the MIT license.
library(azuremlsdk)
library(foreach)
# needed to load register_do_azureml_parallel() method.
# this won't be required when register_do_azureml_parallel() method is public.
devtools::load_all()
ws <- load_workspace_from_config()
# create AmlComp... |
c7063d5f6d614d10cf476b26150f4369c32a7141 | f196029056a88cb374fb22712ec622a76b9707f9 | /final_report.R | 1c5376c6462332f28254c042ecc1ddde8ff67803 | [] | no_license | samyin/case-study | 59231f43a68abc5bb789db5bc8efea03c991b390 | 467ff2d07de6342b9a0fab0376180902981fe304 | refs/heads/master | 2021-01-20T08:07:46.519443 | 2017-05-03T03:33:58 | 2017-05-03T03:33:58 | 90,103,021 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,864 | r | final_report.R | lymph <- read.table(gzfile("lymph.fr.gz"), sep = ",")
gene_names <- vector(length = ncol(lymph) - 2)
for (i in 1:length(gene_names)) {
gene_names[i] <- paste0("gene_", i)
}
colnames(lymph) <- c("survtime", "status", gene_names)
remove(gene_names)
lymph_uncensored <- lymph[lymph$status == 0, ]
lymph_censored <- lymph... |
4eb431adc139864805082b7f734afd4fea84daa2 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/pop/examples/transfun.Rd.R | 9e1055de8db619379941933fd8c5a293a63a54e9 | [] | 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 | 563 | r | transfun.Rd.R | library(pop)
### Name: transfun
### Title: transfun objects
### Aliases: *.transfun is.transfun parameters.transfun
### parameters<-.transfun print.transfun transfun
### ** Examples
prob <- p(0.3)
is.transfun(prob)
prob
(compound <- prob * r(4.3))
# extract the transfun parameters
(param_prob <- parameters(prob... |
2e9e325cd0c529fc1d540f0220c89ad84c4b177a | d62e53a63bff37661c83345102128d993f103934 | /cachematrix.R | 5590dea4d469c31eda1f01925b62cca307a465d3 | [] | no_license | cibermania5/ProgrammingAssignment2 | 51a7f21175e827e805243374ea028d68de727953 | d21d95958e473539a2ed07855446c1aa616c3964 | refs/heads/master | 2021-01-15T17:46:41.251495 | 2014-07-27T07:21:33 | 2014-07-27T07:21:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,404 | r | cachematrix.R | ## cachematrix.R
## Purpose:
## To cache -store- the value of the inverse of a given matrix.
## How it works:
## we will use two functions, the first one to cache the value of the inverse; the second one to actual calculate the inverse values, if already calculated for a given matrix, it wi... |
6d578fecad90dbae333a1d997faa6975fc12471f | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /ETAS/R/poisstest.R | 6f637c8e9fd49d5d1fc41843ef47b59aef7e4910 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,640 | r | poisstest.R | # the code for joint spatio-temporal test is obtained and
# adapted from
# http://statistics.berkeley.edu/~stark/Code/Quake/permutest.r
# based on
# Luen, B., & Stark, P. B. (2012).
# Poisson tests of declustered catalogues.
# Geophysical journal international, 189(1), 691-700.
poiss.test <- function(object, ... |
d82d4fc8fb67e8daaab495e72c8b105d626e68df | e0ce97b158f5b1fe8f19d0ef500dede26d8711e7 | /src/data_imputation/job_zones.R | fc5ba33c7cdf3c99881de924ba60866fd91d05cc | [] | no_license | DSPG-Young-Scholars-Program/dspg20STW | 478041402a6f94cada96ef5151c6887069ccdfc5 | 5c21fa8e7b088e6e22ad4ac877a3e6a4c1681300 | refs/heads/master | 2023-05-31T17:12:58.937203 | 2021-06-10T21:02:42 | 2021-06-10T21:02:42 | 269,649,328 | 0 | 0 | null | 2020-10-30T15:51:07 | 2020-06-05T13:40:20 | HTML | UTF-8 | R | false | false | 2,302 | r | job_zones.R | job_zones <- function(years){
library(dplyr)
library(tidyr)
conn <- RPostgreSQL::dbConnect(drv = RPostgreSQL::PostgreSQL(),
dbname = "sdad",
host = "postgis1",
port = 5432,
use... |
9119294bf7198a96999a2d29438cac2e7dc9f0e0 | f445fe1c05a8a343d32787d2e6815bd80546cbfa | /R/waynedata.R | cc29f3d7eaab828ba2d2379aed1986a5dfcbe7e0 | [] | no_license | dillon4287/CodeProjects | f6d99986c811c7df7bb27b0fab6196861741bac7 | b7a9fa9f30cad84c5bdc58c757f051ebcfd4db73 | refs/heads/master | 2023-07-20T23:44:39.088933 | 2023-07-18T19:40:46 | 2023-07-18T19:40:46 | 77,807,396 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 715 | r | waynedata.R | waynedata <- read_excel("~/Downloads/963-972 (raw) (sorted by ID and INC_RANK).xlsx")
rows_cols = dim(waynedata)
stopgrabbingrows <- 5
isfirstperiod <- 1
rowcount <- 1
newdf <- as.data.frame(matrix(nrow=rows_cols[1], ncol=rows_cols[2], 0))
for(i in 1:rows_cols[1]){
if( (waynedata$INC_RANK[i] == '.') && (isfirstperiod... |
3af4278ee729d3fab6e2f04966087cef9ca3ed06 | 3bcf964805203e8bfd3bd0138d738cef3d2930b8 | /busevi_iz_Splita.R | 809e4b273e1eea4138abf0b1e1db1a120c7d6a80 | [] | no_license | ipuzek/bits_n_R | a9fe3be68641657f644163f79e8e4d5a07b8378c | 0af639c099c37861de42e75661e3edc1685267d6 | refs/heads/master | 2021-01-22T08:02:29.118729 | 2017-05-29T15:59:16 | 2017-05-29T15:59:16 | 92,598,417 | 0 | 0 | null | 2017-05-29T15:59:17 | 2017-05-27T12:50:23 | R | UTF-8 | R | false | false | 950 | r | busevi_iz_Splita.R | # setup #
library(lubridate)
library(dplyr)
# read & wrangle #
buss <- readxl::read_excel("Desktop/busevi.xlsx")
names(buss) <- c("polazak", "dolazak", "peron", "prijevoznik")
correct_trajanje <- function(x.df) {
x.df.trajanje <- mutate_at(x.df,
vars(polazak, dolazak),
... |
82dc22037b787792e79d4886f676d6b5426d75fb | 6098a529a53fd983c94801f6cd0138c65d99bd7d | /R/Analyses/PD_case_vs_controls_repeated.R | 41056ee581dcfd4418547a8828031e37302c1964 | [
"MIT"
] | permissive | Jason-lee-lxx/mPowerRerun | 2793670af667236fd8bcb36a202f6ff35ce462bb | b4e4c252a2392764170cc07b6e0c609cba043e80 | refs/heads/main | 2023-06-20T12:31:17.187565 | 2021-07-22T17:39:14 | 2021-07-22T17:39:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,805 | r | PD_case_vs_controls_repeated.R | ###########################################################
#' Script for creating intermediary data
#' PD case vs controls repeated measurements
#' @author: Elias Chaibub Neto, Aryton Tediarjo
#' @author_email: echaibub@synapse.org, aryton.tediarjo@sagebase.org
#######################################################
... |
619c9af65ad43fbc7ef943f03d8b9bd66ab9d75d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/dataMaid/examples/allVisualFunctions.Rd.R | ef009700d0ceb17c85028512e7c0ca31afa82e79 | [] | 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 | 177 | r | allVisualFunctions.Rd.R | library(dataMaid)
### Name: allVisualFunctions
### Title: Overview of all available visualFunctions
### Aliases: allVisualFunctions
### ** Examples
allVisualFunctions()
|
cdc59b2bfca914007267c0f39a0eb97aef9880ee | d8249f2cd682483afb8f29c3a3a634c6897158c8 | /backend_OEM/2_r_scripts/valores_iniciales.R | 55e063d904fe7b1529d799acce24823b89dcdd57 | [] | no_license | JoelGL23/OEM_site | 8ff8cf451f112d28cb375450d993c96a4987c38f | 31cd0fb775df0c9ddf126d5eefe5eca3990af6e1 | refs/heads/main | 2023-07-03T11:14:25.008644 | 2021-08-13T03:34:37 | 2021-08-13T03:34:37 | 395,173,229 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,606 | r | valores_iniciales.R | library(lubridate)
id_semanal <- "CAS01"
fecha_inicial <- ymd("2021-01-01")
descarga_semanal <- 2
descripcion_archivo_semanal <- "pendiente"
valores_fecha <- obtener_valores_fecha(fecha_inicial)
nombre_tabla_semanal <- paste("covid19mexico",valores_fecha$anio_ab, valores_fecha$mes, valores_fecha$dia, sep = "")
arch... |
d2bbaa915178910e866e71b610a7618a4065606a | 2e74c7339c63385172629eaa84680a85a4731ee9 | /mort_hiv/02_adult_mortality/45q15/09_graph_all_stages.r | 4c1e7bd577eeff01fc84d022f076cfabc52b07a5 | [] | no_license | zhusui/ihme-modeling | 04545182d0359adacd22984cb11c584c86e889c2 | dfd2fe2a23bd4a0799b49881cb9785f5c0512db3 | refs/heads/master | 2021-01-20T12:30:52.254363 | 2016-10-11T00:33:36 | 2016-10-11T00:33:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,999 | r | 09_graph_all_stages.r | ###############################################################################
## Description: Graph all stages of adult mortality prediction, data, and other
## pertinant information
###############################################################################
#source("strPath/09_graph_all_stages.r")... |
e08634932fb33182439440cb2cc8aeebb0d667a9 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.management/man/licensemanagerusersubscriptions_register_identity_provider.Rd | 3ad2caab4b2b6207f5764b9c08674dd2b3cdecd2 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 997 | rd | licensemanagerusersubscriptions_register_identity_provider.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/licensemanagerusersubscriptions_operations.R
\name{licensemanagerusersubscriptions_register_identity_provider}
\alias{licensemanagerusersubscriptions_register_identity_provider}
\title{Registers an identity provider for user-based subscriptio... |
8ffac8b2fbb3bf84aa5a0c6f6e4351446ac732a4 | 703ebad03de39fb3dccef3b63b0c80b94e927506 | /Plot4.R | 321ac3b1ca9fce86da4ffba62639e1b02e030451 | [] | no_license | motazel/ExData_Plotting1 | b4309c207319d7777a982a572e2429e48f905f9a | da3cf44e5253c63370b0619350ffbef2ddfd8ac7 | refs/heads/master | 2021-01-15T16:52:48.449815 | 2015-12-22T16:05:25 | 2015-12-22T16:05:25 | 31,593,778 | 0 | 0 | null | 2015-03-03T10:55:20 | 2015-03-03T10:55:20 | null | UTF-8 | R | false | false | 3,899 | r | Plot4.R | setwd("C:\\Users\\motazel\\OneDrive\\Documents\\Courses\\DataScienceJHUCoursera\\ExploratoryData_Course\\Project1\\ExData_Plotting2")
library(dplyr)
#read data from file "household_power_consumption.txt"
library(lubridate)
#exdata_data_household_power_consumption <- read.csv("exdata_data_household_power_consumption\\... |
03b4afd0444772b27f5e257092aedc410a533056 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/BioGeoBEARS/examples/mix_colors_for_states.Rd.R | 81655f767978161f4e5c8bad6adb9593dde5fc17 | [] | 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 | 193 | r | mix_colors_for_states.Rd.R | library(BioGeoBEARS)
### Name: mix_colors_for_states
### Title: Mix colors logically to produce colors for multi-area ranges
### Aliases: mix_colors_for_states
### ** Examples
testval=1
|
03140d1734194041ce8e3cc6f41ad404113f5f22 | 6f1fd8f953c4b755962d460c06e1d1d21e6da078 | /man/PROBA_INTERVAL.Rd | 87ac06959f6efad6d8feae2381edd01cbd451220 | [] | no_license | cran/HYRISK | cfae5d3f3cdef3f442f3908bc9aa974fe75f8119 | 2f2a9730af0e386328ae8a4a5a905d03924ed529 | refs/heads/master | 2021-09-28T00:25:25.129898 | 2017-04-04T14:38:45 | 2017-04-04T14:38:45 | 87,205,473 | 0 | 2 | null | 2021-09-24T14:02:15 | 2017-04-04T15:42:15 | R | UTF-8 | R | false | false | 971 | rd | PROBA_INTERVAL.Rd | \name{PROBA_INTERVAL}
\alias{PROBA_INTERVAL}
\title{
Interval of probability of exceedance.
}
\description{
Function for summarizing the uncertainty propagation's results in the form of an interval of probability of exceedance for a given threshold.
}
\usage{
PROBA_INTERVAL(Z0, threshold)
}
\arguments{
\item{Z0}{
Ou... |
a7845d797704dfe20cc926f6e2507d5e800c80bd | 5e64de04d8b75d96759d50e28d83654827a7682b | /R/power.R | 25f3321fe71a1465701be165d9c80924e60e092e | [] | no_license | xww52526/tiger | 3fce67043c04992ddb2e11ed763257a58450e891 | 08918fdad21d29de0529a4ca9db2b914af7d3a6f | refs/heads/master | 2021-08-29T20:59:38.156342 | 2017-12-15T01:03:40 | 2017-12-15T01:03:40 | 114,311,009 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 161 | r | power.R | #' generate squared values
#'
#' @param x A number
#' @return The square of \code{x}
#' @export
#' @examples
#' square(5)
square <- function(x){
return(x^2)
}
|
98de159ebe0cd498481426a74a7e19ba46708c20 | 82a04ac219608d776f5fc6f89325e1d99fe6c04e | /3.4 Solution.R | 3c79935891c776c5d666496ada37c7a3fc143197 | [
"MIT"
] | permissive | PacktPublishing/Programming-for-Statistics-and-Data-Science | ba3ee85580430769b9a81184505a71e50527f162 | 08cb273ca132658a92f3ccd3e66349d495174d97 | refs/heads/master | 2023-02-09T01:42:05.078048 | 2023-01-30T09:34:51 | 2023-01-30T09:34:51 | 185,159,458 | 11 | 16 | null | null | null | null | UTF-8 | R | false | false | 880 | r | 3.4 Solution.R |
# recreate the yugioh vector if you don't have it ready
cards <- c("Blue-Eyes White Dragon", "Exodius", "The Winged Dragon of Ra", "Raigeki",
"Slifer the Sky Dragon", "Obelisk the Tormentor", "Black Luster Soldier",
"5-Headed Dragon", "Exodia the Forbidden One", "Dragon Master Knight")
a... |
44bb1148752e74855907d8e940c802e72f47eac7 | 3463be9b8916d7d73505d3a1ce1cccf7f2c2fd8e | /man/as.character.vctrs_address.Rd | f0ae1b85b5a775b0c5a890d0a0c96e616a9cc6ea | [] | no_license | ColinFay/emayili | 51d4a9ecfa9d1708304ceacbc085b0f9d02ee35e | a7ec347fbd126a00de3777589bca2fdb918acacb | refs/heads/master | 2023-09-02T15:56:14.794628 | 2021-10-22T06:14:54 | 2021-10-22T06:19:02 | 420,019,281 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 462 | rd | as.character.vctrs_address.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/address.R
\name{as.character.vctrs_address}
\alias{as.character.vctrs_address}
\title{Convert address object to character}
\usage{
\method{as.character}{vctrs_address}(x, ...)
}
\arguments{
\item{x}{A vector of \code{address} objects.}
\item... |
3e81191d4730c5f6e74b27d80d01ab5449d9994f | a613ce35a0211308221e0b4b577911655dc308d9 | /R/print.confScore.R | 27e71d39a107bf369b2e260ce838eef96ccfcf06 | [] | no_license | cran/ModelGood | 0497553e7c1147012cad3ef1fc101979246c06ad | 5ca274b06328d48232bc46649bca88141646d7e3 | refs/heads/master | 2021-01-10T20:56:09.347883 | 2014-11-10T00:00:00 | 2014-11-10T00:00:00 | 17,680,985 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 211 | r | print.confScore.R | print.confScore <- function(x,...){
print(x$splitMethod)
cat("\n")
avscore <- do.call("rbind",lapply(x$models,function(x){
mean(x$score)
}))
colnames(avscore) <- "MeanConfScore"
print(avscore)
}
|
a2fe7e760bf4d3069e793fb8d135d681d9a4ce14 | e3fc93c18399b8e7a39120060511a63c8935f998 | /run_analysis.R | 10855fa38a1870548e729d738c50871ed937c53d | [] | no_license | chsels/Course03_project | d77ebe2c69a3ee92a386c9b4314047b42f341d49 | 21b4f171a6ed3345965f82d9e483d74fefc1de65 | refs/heads/master | 2020-04-01T16:45:49.623226 | 2014-11-17T01:18:19 | 2014-11-17T01:18:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,317 | r | run_analysis.R | ##load library
library(dplyr)
##read in measurement names
nam<-read.table("features.txt")
##Read in the measurements
testX<-read.table("test/X_test.txt", col.names=nam[,2]) ##test data
trainX<-read.table("train/X_train.txt", col.names=nam[,2]) ##train data
##Read in activities
testY<-read.table("test/Y_te... |
4fa5ea4b83f082748060294236f4d65d7ca49709 | de5442463a0a5060fa70bdf4fdf60ca6e1a29f2a | /artigo3/kerneloptimizer/test.R | 0eeaf10b46b20d4035efcb4e3a31eb8eb3d83fcc | [] | no_license | vicrsp/rna-ppgee | cab49001f3e392252c77b4ca13ea582950b392f4 | 0b85be5c709e32d1ae5065d94457d6827e4d0b91 | refs/heads/main | 2023-05-04T19:58:48.632248 | 2021-05-27T22:08:56 | 2021-05-27T22:08:56 | 319,147,974 | 0 | 0 | null | 2021-05-27T21:54:59 | 2020-12-06T22:48:35 | Jupyter Notebook | UTF-8 | R | false | false | 1,098 | r | test.R | library(ggplot2)
library(mlbench)
library(reticulate)
# use_virtualenv("myenv/")
kerneloptimizer <- import("kerneloptimizer")
optimizer <- kerneloptimizer$optimizer$KernelOptimizer
p <- mlbench.spirals(500,1,sd=.03)
#p <- mlbench.2dnormals(500) # Base com superposição
X <- p$x
y <- as.numeric(p$classes)
d <- ncol(X)
... |
436030058f03cab3d2e60cfcc7755e9dfa6e4b57 | c9e0c41b6e838d5d91c81cd1800e513ec53cd5ab | /man/gtkRangeGetUpperStepperSensitivity.Rd | 8651647ba342f73bbdaea3958b61284e6e75b2be | [] | no_license | cran/RGtk2.10 | 3eb71086e637163c34e372c7c742922b079209e3 | 75aacd92d4b2db7d0942a3a6bc62105163b35c5e | refs/heads/master | 2021-01-22T23:26:26.975959 | 2007-05-05T00:00:00 | 2007-05-05T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 562 | rd | gtkRangeGetUpperStepperSensitivity.Rd | \alias{gtkRangeGetUpperStepperSensitivity}
\name{gtkRangeGetUpperStepperSensitivity}
\title{gtkRangeGetUpperStepperSensitivity}
\description{Gets the sensitivity policy for the stepper that points to the
'upper' end of the GtkRange's adjustment.}
\usage{gtkRangeGetUpperStepperSensitivity(object)}
\arguments{\item{\code... |
3be7ed3c724cec4d79002df53f8dcf1a4e24701d | 7bf9f78285e2d8c9cbbb371f6359795fa7683004 | /R/transio_pgs_h_4.R | 29481e051d9c7cb589e97a56362cf6efbcf6dd5f | [
"MIT"
] | permissive | tleblevecIMP/TransPGS | 16cd32b396fb812f13245aa30bc11646f308db63 | 188227b90ec67deb0841cd9f76b6d6b5a6868dbd | refs/heads/master | 2020-04-06T07:07:58.222809 | 2017-08-15T13:04:08 | 2017-08-15T13:04:18 | 60,359,662 | 1 | 0 | null | 2017-08-15T12:52:40 | 2016-06-03T15:49:54 | R | UTF-8 | R | false | false | 1,925 | r | transio_pgs_h_4.R | #' to compute the transiograms with four facies
#'
transio_pgs_h_4<-function(pF1,pF2,pF3,a,b,rho1a,cor_h,rho,shift,h){
pF4 = 1-pF3-pF2-pF1
rho1h = cor_h[1]
rho2h = cor_h[2]
rho12h = cor_h[3]
rho21h = cor_h[4]
sigma = matrix(c(1,rho1h,rho,rho12h,rho1h,1,rho21h,rho,rho,rho21h,1,rho2h,rho12h,rho,rho2h,1),4... |
09fcbd718fdd562c8139e3fc44b196fc832f7495 | d4887d46f8c364792d56431fc452b98b59ae86f1 | /data/tf_idf model based on title.R | 8dd46498ba3d433cd78dccae2b26ea343c27b4ad | [] | no_license | Rev-Jiang/IE583-group5 | 71cfa22ea70de9bc3b0ae051472fbee1c70598e2 | dd3bf8f6e3820292b6abea4ddf3992282c76c338 | refs/heads/master | 2021-05-18T23:54:59.172097 | 2020-04-29T03:50:17 | 2020-04-29T03:50:17 | 251,484,982 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,567 | r | tf_idf model based on title.R | data<-read.csv("/Users/Anesthesia/Downloads/metadata.csv")
title.text <- as.character(data$title)
title.df<-data.frame(title=title.text,journal=data$journal,id=data$doi)
tidy_title <- title.df %>% ## pipe operator in dplyr, can be used to chain code together.
unnest_tokens(output = word, input = title,
... |
fd690c1920a06c1f0126512a1c89be85332bc394 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /easyVerification/tests/testthat/test_veriUnwrap.R | 9d43f354b2c7a1914a0b4e78cbcff85a101b68ac | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | false | 541 | r | test_veriUnwrap.R | library(easyVerification)
context('Unwrapping')
xxx <- array(1, c(1,3,5))
xx <- array(1, c(3,5))
x <- rep(1, 15)
test_that("input arguments are valid", {
expect_error(veriUnwrap(xxx, 'EnsMe'))
expect_error(veriUnwrap(x, 'EnsMe'))
expect_error(veriUnwrap(xx, 'fdkl'))
expect_error(veriUnwrap(xx, EnsMe))
})
te... |
77e8dac33b4f69f8514fced9efc467f6d237cd09 | 65516b6967007b4fa12173d0af58ec1dc3b58fd2 | /server.R | 2a3e1834c761067ba4a1cbd810ffe858762e0b31 | [] | no_license | lsteo/exponential | bf3d185d657dea1ca3d4dce4369c09bfce2b86f1 | b683a544dbb64b2cc36220f7d5ced7d98e4d5579 | HEAD | 2016-09-05T10:30:08.788959 | 2015-04-26T10:28:24 | 2015-04-26T10:28:24 | 34,600,472 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,514 | r | server.R | library(ggplot2)
## Simulate exponential distribution
simulation <- function(number, lambda, times) {
mean.s <- NULL
var.s <- NULL
for (i in 1:times) {
value.s <- rexp(number, lambda)
mean.s <- c(mean.s, mean(value.s))
var.s <- c(var.s, var(value... |
b65833166dac46935ded74d6a19bdf738f6c1c88 | b2b45d59c8b8068a25a093dadf40f0e472c831a4 | /과제1/2-1merge_D.R | 363753fd65678b9f1a6b6728ec05a5d400c0ceb7 | [] | no_license | jeong-ah313/R_study | 1dfdba9fd8087a128134181c9d294e066e0c3864 | 834ac3743d3248c8152f515615d13452656ed640 | refs/heads/main | 2023-09-03T14:32:50.235551 | 2021-11-22T15:47:43 | 2021-11-22T15:47:43 | 430,758,997 | 0 | 0 | null | null | null | null | UHC | R | false | false | 1,099 | r | 2-1merge_D.R | #20191015화 201811944성정아
#2.1 병합정렬 (Merge Sort) 프로그램을 작성하시오. (프로그램 코드 출력)
Merge_Sort_D <- function(V) {
if(length(V)==0) stop("No elements to sort")
merge_fn_D<-function(first_half,second_half){
result<-c()
while(length(first_half)>0 && length(second_half)>0){
if(first_half[1]>=second_ha... |
ad793f785863d3f93b36677856701c816a3662c7 | 97932fb906650536ff644f4b57e1b05a74695e1d | /man/sq_delete_item.Rd | 580f94b3240b9a5b227b527fd6c49431480817db | [] | no_license | muschellij2/squareupr | 350ed186d711182abfb6ad5dde0c068e5325ee29 | 37bf28750127235c09f7f57278faf484b04aac0d | refs/heads/master | 2021-05-26T23:23:03.404648 | 2019-07-11T20:50:36 | 2019-07-11T20:50:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 816 | rd | sq_delete_item.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/items.R
\name{sq_delete_item}
\alias{sq_delete_item}
\title{Delete Item}
\usage{
sq_delete_item(location, item_id, verbose = FALSE)
}
\arguments{
\item{location}{character; the Square ID or name associated to a location.
This must be an exac... |
3152fcf33817ad47cb0fa7482acb7eb107e68dd5 | 322076953a568c56e618ff4927c34470fd2f7de9 | /man/rawdensity_kth.Rd | 30ce9c603596cc499957ad1ff5ea03aab7f863d8 | [] | no_license | cran/LIHNPSD | c102634f6e34afdee6a8ab789f3ab6d16b3dfca3 | 872584eab249dd7a6f5b8031a44afdfd5d9c1a78 | refs/heads/master | 2020-03-29T19:42:01.862788 | 2012-04-12T00:00:00 | 2012-04-12T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 547 | rd | rawdensity_kth.Rd | \name{rawdensity_kth}
\alias{rawdensity_kth}
\title{
The k-th item of the raw PDF
}
\description{
The k-th item of the raw PDF. This is used primarily to understand
the internal structure of the subordination.
}
\usage{
rawdensity_kth(d, x, k)
}
\arguments{
\item{d}{ A fully specified PSD S3 object... |
72ba43c532daccd8b2dce64a60335ba1f1116fef | 573611c1c4fb728749abc203bcb6ebdd229bf394 | /SmallAnalyses.R | 585575192555e848b6f13f5e8c2478eef2533df0 | [] | no_license | ppgibson/MSQ_DataPaper_Anl | 6dc96edd3dbb76aaec8960e1008e67cf58b14ce9 | b1e66642d7172907d56f6c4cedaa2cb26e92897e | refs/heads/master | 2021-01-10T04:39:42.958811 | 2016-02-19T21:48:17 | 2016-02-19T21:48:17 | 48,077,958 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,553 | r | SmallAnalyses.R | ######################################
# Minor analyses for results section #
######################################
library(gridExtra)
#### 1. Site CVs ####
# Calculate cv among individual point samples/core samples within a site (ie, n<=4)
byyrsite <- group_by(samples, sample_year, category, site_number)
siteyr... |
573df3745db911c37c8e5206d2eba5c2f3ff6783 | 23f331debdc1d0c244686b22852720cf14c83801 | /bryan/cyanoPCA.R | 7a2d3133bacf767e98016ce638e7c7e524f435d6 | [] | no_license | willbmisled/cyanoLakes | 96ceb15e3d81a6ca869a70a08d13eb08d188ee07 | 345fc108ca5df96333a518ae819cd3dd80420ffb | refs/heads/master | 2021-01-18T08:12:05.803687 | 2018-07-26T14:46:41 | 2018-07-26T14:46:41 | 18,803,295 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,916 | r | cyanoPCA.R | v<-'cyanoPCA.r'
####################Load the NLA Data and Biovolume Data
#Data Definitions:
#
browseURL('https://github.com/jhollist/cyanoLakes/blob/master/bryan/cyanoBioVolData.md')
#Get the Data
load(url('https://raw.github.com/jhollist/cyanoLakes/master/bryan/cyanoBioVolData.rda'))
#Assign Colors for plots
... |
61d763f2ed1b96d07198bf283df2b4b8967d3b85 | 3c01db1a4b2f29873dab7ff00919ac529645b848 | /R/somobj_doc.R | 1fc16933f680bfe80b84c3af325817c3482a64d1 | [] | no_license | somdisco/SOMDisco | 8d48740702069f43260d2ec50595f0854bd9416a | 2e653ab4aa5af6aa2b9c960ea2c5ae4df60a59cc | refs/heads/master | 2022-12-29T03:08:06.403448 | 2020-10-16T16:16:13 | 2020-10-16T16:16:13 | 288,159,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 41,120 | r | somobj_doc.R | #' @name SOM
#' @title The SOM Object Class
#'
#' @field age The current age of the SOM (the number of training steps that have been performed thus far).
#' @field alpha The strength of the prototype update, as set in the LRAS, given the current age of the SOM.
#' @field beta The strength of the win frequency update, ... |
e81e25647dee301af3faf6acd86d278ea2bb62aa | ed6e434234e898f721243756bebfcb76a7be9f22 | /modules/predixcan/fqtl/transpheno.R | 13bb7472f95ab120bbc2af7015543d3051965d32 | [] | no_license | drveera/mgenie | 72cb59d81247c44ea79d8e07193cb5c716b83ed2 | 0f12a92c81fa5eb4610b42f6932db2c5c42b5163 | refs/heads/master | 2021-04-15T08:26:04.179844 | 2019-09-24T08:16:06 | 2019-09-24T08:16:06 | 126,526,161 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 495 | r | transpheno.R |
args <- commandArgs(trailingOnly = TRUE)
bed.file <- args[1]
out.file <- args[2]
library(data.table)
##format bed file
ebed <- fread(paste0("zcat ",bed.file))
names(ebed)[1] <- "chr"
ebed <- ebed[chr==22]
ebed <- ebed[,7:ncol(ebed)]
ebed <- t(ebed)
famids <- rownames(ebed)
ebed <- data.table(ebed)
pheno <- cbind(f... |
a5166d98c9dce0cbe8bae97cf9d81d1b942e4ec7 | 2da593c68a2bb1fd6ee94b8e5e68a894d37f6c10 | /R/conf.R | 80f8ceb48843b110406870c13f0828aeec3cda2e | [] | no_license | anu-bioinfo/neoantigenR | 78e3f1e3ceb63234d43f8a3495fd12364b7daa98 | f9a4533b18ba4086e5b5a37a26f940c5915083ad | refs/heads/master | 2020-04-05T03:42:03.272403 | 2018-04-18T20:38:55 | 2018-04-18T20:38:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,001 | r | conf.R | ##--------------------------------
## required input file names
##--------------------------------
#' the organism used to search protein database
#' @examples
#' org="hg19"
#' @return the organism used to search protein database
org="hg19"
#' the path for reference protein database file (Swiss-uniprot)
#' @... |
2608e55016722b4219bc2372d1a694472c948ca5 | 96316eeaf890d9454c7f7cc34e409b02e7aa51fa | /scripts/input_to_tibble.R | 5a25a61c66aefb381013744463daaf0f00aacd45 | [] | no_license | EdwinTh/tennis | bf89c73c11165447c7549d56412a28d59a7ff8ad | 8dec0ec67acc9a1f5d2e32fc403166665c27bc10 | refs/heads/master | 2021-01-21T10:42:12.304695 | 2020-01-12T11:28:39 | 2020-01-12T11:28:39 | 83,474,532 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 932 | r | input_to_tibble.R | # Bind all the inidividual csv files to tibbles, so we have a complete table
# for each subject
read_tennis_data <- function(year,
file = "atp_matches",
subdir = "tennis_atp") {
readr::read_csv(
sprintf(
"~/Documents/R_packages/tennis/data_input/%s/%... |
68ce6f41a16dfb6821512a8a1327315ed59b1619 | 7190079db1dcbfbb904ee17261f77767873b41a8 | /R/14_wood.R | 4d85652d5e5b841828d331dbf2520411773273e9 | [
"MIT"
] | permissive | jlmelville/funconstrain | 5830cd2ff1bd7f1df0a58986c0171c24a5723a37 | 62f918da58764b197c8aba8b9de008b3d834bb4b | refs/heads/master | 2022-12-12T04:24:24.152380 | 2022-06-02T03:53:06 | 2022-06-02T03:53:06 | 90,517,223 | 3 | 0 | NOASSERTION | 2022-11-25T13:55:36 | 2017-05-07T07:52:44 | R | UTF-8 | R | false | false | 3,857 | r | 14_wood.R | #' Wood Function
#'
#' Test function 14 from the More', Garbow and Hillstrom paper.
#'
#' The objective function is the sum of \code{m} functions, each of \code{n}
#' parameters.
#'
#' \itemize{
#' \item Dimensions: Number of parameters \code{n = 4}, number of summand
#' functions \code{m = 6}.
#' \item Minima: \... |
dd61e025c63b22be3a9a53e1ffc6fdca49d2e308 | b353f7d6f9f514b889c88b29f8b03f6eb219366a | /man/build_yahoo_url.Rd | ea6177ab2d1d39ff5b0f504ad780c38672d35cea | [] | no_license | ces0491/companyDataScrapeR | 806b83fa2068267bf6435e51096e4c4125642962 | 76ba4ddb4a42003a966cce84dff238a6e7eb89fd | refs/heads/master | 2023-06-16T04:05:15.530547 | 2021-07-15T17:22:13 | 2021-07-15T17:22:13 | 291,532,715 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 852 | rd | build_yahoo_url.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/build_yahoo_url.R
\name{build_yahoo_url}
\alias{build_yahoo_url}
\title{construct URLs required to scrape relevant data from Yahoo Finance}
\usage{
build_yahoo_url(
tickers,
type = c("price", "IS", "BS", "CFS"),
start_date = NULL,
end... |
06d49df3d67d7f522b1738af77e5395faea9f166 | b1bdf858d1de49e354562ff4d411fc2f43c3d27e | /plot4.R | 439329e37200470e9f40805a2bd64d13a10809fb | [] | no_license | JocZ/ExData_Plotting1 | fe28583e606210f3bc38d4a0ef3fbadab5c602ac | a41f3e04946484ef1f8382730e95e68b76119cdd | refs/heads/master | 2021-01-17T08:46:07.217523 | 2014-08-10T21:20:48 | 2014-08-10T21:20:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,071 | r | plot4.R | ## Read the data set
pwr<- read.table("household_power_consumption.txt",header=T, sep=";", na.strings="?")
#convert into date format
pwr$ndate<- paste(pwr$Date,pwr$Time)
pwr$ndate<- strptime(pwr$ndate,"%d/%m/%Y %H:%M:%S")
pwr$Date<- as.Date(pwr$Date,"%d/%m/%Y" )
## subset the two days fron the data set
s<-pwr$Date=="20... |
e3a5b18676bfa82b58cdaa2f29d1bf4944c974e9 | a0830531052bd2330932c3a2c9750326cf8304fc | /vmstools/man/summarizeTacsat.Rd | f05d1ea5795a9f7ddd3cb0ba2718e1c2b11719d1 | [] | no_license | mcruf/vmstools | 17d9c8f0c875c2a107cfd21ada94977d532c882d | 093bf8666cdab26d74da229f1412e93716173970 | refs/heads/master | 2021-05-29T20:57:18.053843 | 2015-06-11T09:49:20 | 2015-06-11T09:49:20 | 139,850,057 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 441 | rd | summarizeTacsat.Rd | \name{summarizeTacsat}
\alias{summarizeTacsat}
\title{
Summarize primary aspects of tacsat file}
\description{
Summary nr vessels, countries, spatial range, temporal range, effort from tacsat}
\usage{
summarizeTacsat(tacsat)
}
\arguments{
\item{tacsat}{
tacsat dataframe
}
}
\author{
Niels T. Hintzen}
... |
1a25c6e0a29bfd488434660b603cb5a40655ae79 | 7f7c55fce129ce299358e22b4f82757d5bfb111e | /R/bitly_links.R | 51cc315561c20e149fe3dfd86134b222174fbb0b | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | RickPack/urlshorteneR | 32c43e797b7720d647947b23277d230c0de8b5a0 | 040c218e3670ed21ee44363f5edd580271759298 | refs/heads/master | 2020-04-28T00:47:33.950190 | 2019-03-10T17:50:24 | 2019-03-10T17:50:24 | 169,720,605 | 0 | 0 | Apache-2.0 | 2019-02-21T10:53:57 | 2019-02-08T10:48:18 | R | UTF-8 | R | false | false | 10,413 | r | bitly_links.R | #' @title Query for a Bitlink based on a long URL.
#'
#' @description See \url{http://dev.bitly.com/links.html#v3_link_lookup}
#'
#' @param url - one long URLs to lookup.
#' @param showRequestURL - show URL which has been build and requested from server. For debug
#' purposes.
#'
#' @return url - an echo back o... |
5be4307759bcce64a0999ed7b4f02f76a42dceec | 5a74de235c00f492e368c7653579c7a8ced84164 | /man/modify_yaml_front_matter.Rd | 2e14f3e7ce476111a1d80b0e21023aab98c0ccb0 | [] | no_license | rlugojr/rgallery | b5a4350b3450440123926571d5dc01fb686086c6 | 271daf585e9c8f718d71426d3a99d5f0469569cc | refs/heads/master | 2020-04-05T22:45:11.586988 | 2015-03-30T17:35:54 | 2015-03-30T17:35:54 | 62,098,319 | 0 | 0 | null | 2016-06-28T00:50:58 | 2016-06-28T00:50:58 | null | UTF-8 | R | false | false | 543 | rd | modify_yaml_front_matter.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/utilities.R
\name{modify_yaml_front_matter}
\alias{modify_yaml_front_matter}
\title{Modify yaml front matter of a file}
\usage{
modify_yaml_front_matter(infile, outfile = infile, ...)
}
\arguments{
\item{infile}{file to be modified}
... |
e4731d154584ab9333d4df6468f0dc0ae0ad8552 | 59ff127da27e4cad25c6c4a0e22eb936770105ec | /R/dscore_plot.R | 99cd8f58a3fb33c604373d998dc85966f66c9690 | [] | no_license | lgl15/cnmtf | ca3445367057b782428104660a2de406914239a7 | 8e7cb76e2a2f08bd652fd59ba25354e7d3e6753d | refs/heads/master | 2020-03-10T13:43:45.447310 | 2019-03-21T18:54:36 | 2019-03-21T18:54:36 | 129,406,824 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,023 | r | dscore_plot.R | ###############################################################
# cNMTF
# 3. Delta score functions
# 3.3 Functions for Manhattan plots, Venn diagrams and clusters
#
# Corresponding author:
# Luis Leal, Imperial College London, email: lgl15@imperial.ac.uk
###############################################################
... |
530e3e32284bf7f779bbd7dbb40d4728c610fcfe | 9488c78bb4997787d27d804d78bdd15f1ab21327 | /improve_knn.R | b3e98c5045726953b930ea0d513ba3987b2fb48f | [] | no_license | zisvan/insight_project | ac9ac967f49423a084993a8402cfbbf017e5bc16 | 8f33471f9fe7623ad3681737e66ac17c911e0e6c | refs/heads/master | 2021-01-22T05:57:36.698807 | 2017-02-22T19:09:41 | 2017-02-22T19:09:41 | 81,724,080 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,812 | r | improve_knn.R | ## Improve kNN
library(dplyr)
library(class)
library(caret)
#function
#get a vector of k
# for each set of train+test data, run knn with each k
# rank
# return list ranked by performance
#function - performance of knn
#make combinations of 11 variables 3-10 and make a vector with each set of variables... |
5c85b053f15d13e2539973f9b07a563f26b1b840 | e6db5b835c6e41ff587f96d2664ac684c2d7c466 | /R/complete.R | 252ffc26a14a119c80d7428da81d9b6960874725 | [] | no_license | javamonkey79/datasciencecoursera | 91fef425b2c6cd7fc04a12b32fc823b2b0fc4858 | 1af886f3f32762be26dbb765ca495c8c2b8fb628 | refs/heads/master | 2020-05-17T07:47:43.604117 | 2014-08-24T21:25:18 | 2014-08-24T21:25:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 338 | r | complete.R | complete <- function(directory, id = 1:332) {
result<-sapply(id, function(i){
csvFileName<-sprintf("%s/%.3d.csv", directory, i)
csv<-read.csv(csvFileName)
# c(i,sum(lapply(csv[,2,3], function(x){!is.na(x)})))
c(i,sum(complete.cases(csv)))
})
id = result[1,]
nobs = result[2,]
data... |
6cc9d99bc372c2c1faa6a24d114b4bc7fa0c72bc | 5f0f6e20e8bb02dab7e2bd32c58fb6393c8d0451 | /5 - Data Challenge - Carrefour.R | 40e939c196d223d8c14bd5bd6149793b0a882314 | [] | no_license | dnsfaccini/data-challenge-carrefour | a6a6608593a64914c201ae4e618c70ce0565ddef | 5a07beaf568c3c670998c5d044de453a3da3d432 | refs/heads/main | 2023-08-29T12:25:50.704819 | 2021-09-27T01:16:47 | 2021-09-27T01:16:47 | 410,683,140 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,026 | r | 5 - Data Challenge - Carrefour.R | ##Carregando pacotes
library(dplyr)
library(rtweet)
library(tm)
library(wordcloud)
library(syuzhet)
# Buscando Tweets com função search_tweets() do pacote rtweet
carrefour_tweets <- search_tweets(
"#carrefour",
include_rts = FALSE
)
save(carrefour_tweets, file = "Carrefour_Tweets.RData")
# Qua... |
21a47955e4c94a1fda765e978ffda030334279d6 | 7a08cd1d405ddec1545b548860b18a68b19fa1ac | /functional_tests/ft_web_example_1.R | f55b1b1984dc5d4ac4795a26b697b3cf08945d08 | [] | no_license | wxwx1993/GPSmatching | b27d419ce33c0b516b10713a4bd428f639a1caf1 | d6661dca3a027e00cdea0cc30ada0ed42649bc7b | refs/heads/develop | 2023-08-24T03:11:08.438445 | 2023-02-17T21:53:27 | 2023-02-17T21:53:27 | 233,946,225 | 21 | 8 | null | 2023-02-17T21:52:44 | 2020-01-14T22:12:28 | R | UTF-8 | R | false | false | 2,935 | r | ft_web_example_1.R | ## author: Naeem Khoshnevis
## created: October 2022
## purpose: Reproducing examples in https://nsaph-software.github.io/intro.html.
# Load libraries
library(ggplot2)
# CausalGPS: Matching on Generalized Propensity Scores with Continuous Exposures
# Example 1: Use all data and default values.
#
data... |
4475438d838eee1fcd272d4b2527f56fe1430f23 | 727a052968125e92a3a0ff11154a1dcc00974627 | /man/anova1f_3c.Rd | c6dacd3126f959f0ac1d7ce5989c50e43044aaf3 | [
"MIT"
] | permissive | nbrosowsky/pwr2ppl | a358292901ec0939af53cbe9577d08570a8a44f2 | 197c89557655469dd46f9d1259dab978d18402f2 | refs/heads/master | 2020-05-04T22:55:47.099107 | 2019-04-02T19:37:37 | 2019-04-02T19:37:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,485 | rd | anova1f_3c.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/anova1f_3c.R
\name{anova1f_3c}
\alias{anova1f_3c}
\title{Compute power for a One Factor ANOVA with three levels and contrasts.
Takes means, sds, and sample sizes for each group. Alpha is .05 by default, alterative values may be entered b... |
aacf95666f48528d2c9b64919d7ac86141f2f011 | 6211752fd3b0f509a4052e67d0c7ff693c4fe710 | /Q3/1/crimewise_rape.r | 6c667c693cea55853302ac046197ff3b79679736 | [] | no_license | PurvishaThakkar/datascience-project-in-R-studio | 1ad05d9194638c7c0ac5e3050e2ee7ebd6c0a9f6 | db9ecbb722eec57b51ba02c38644a899062e68b2 | refs/heads/master | 2020-06-30T02:12:24.514949 | 2019-08-05T16:22:16 | 2019-08-05T16:22:16 | 200,689,518 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 532 | r | crimewise_rape.r | library(dplyr)
library(ggplot2)
table=read.csv("E:/project/crimes.csv")
View(table)
number_of_rapes=table %>% summarise(number_of_rapes = sum(Rape))
head(number_of_rapes)
data2=table %>% group_by(STATE.UT) %>% summarise(number_of_rapes = sum(Rape))
View(data2)
#========================ggplot of the data which is gro... |
7eb610198d324f77272c897e0c93621a0c07e2ae | 29585dff702209dd446c0ab52ceea046c58e384e | /LICORS/R/compute_LC_coordinates.R | ee662377e804d8d8136ba35ea45e676e88f5ffd2 | [] | 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 | 4,362 | r | compute_LC_coordinates.R | #' @title Computes coordinates of PLC and FLC relative to origin
#'
#' @description
#' Computes the space-time coordinates of PLC and FLC given control settings
#' relative to the origin \eqn{(\mathbf{r}, t) = (\boldsymbol 0, 0)}.
#'
#' Since these coordinates do not change for different space-time positions, ... |
35ca678e4fca39040b183b82826faf1518adf3c9 | b3c5f225ac5fc4e3280591206bac374565c65a86 | /R/plot_MA.R | d1f4bce025e4ea9f5550e44df1ed998d9aecac92 | [] | no_license | ClaudiaRHD/chipAnalyseR | 4d5c404b51130ab650383022801bb2c50eba3111 | 2e41b66048e3945f90d3ac30be22700d4157ba04 | refs/heads/master | 2021-04-15T10:09:31.490707 | 2020-06-15T09:41:37 | 2020-06-15T09:41:37 | 126,216,813 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,680 | r | plot_MA.R | #' Plot the average of the condition group against the difference of the binary logarithm of the two conditions
#' @description Compare the mean of area under the curve signal for data sets according to the inserted conditions. The conditions are adjusted to the anno-file where the conditions for all data sets are coll... |
62047b6bf3f579c36bcfe0c676364f4798c89555 | 4e5251d0b4f7b9032e99fb64495cd8c1574b31ff | /phylogeny/test.R | ba74f7dcd57dacf7bd88579931016b0df7f4d6b7 | [] | no_license | CourtneyCampany/sporgasm | 58597767e578fd76bd9c114b2e4fa7599dd54af6 | 5933bde053bcec9e026bd8ad151d0947ca715c20 | refs/heads/master | 2021-06-26T16:47:43.081749 | 2021-06-08T17:14:33 | 2021-06-08T17:14:33 | 96,159,207 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 871 | r | test.R | #indepedent constatst examples
obj<-read.csv("constrasts/Centrarchidae.csv",row.names=1)
fit.ols<-lm(gape.width~buccal.length,data=obj)
fit.ols
summary(fit.ols)
plot(obj[,c("buccal.length","gape.width")],
xlab="relative buccal length",
ylab="relative gape width",pch=21,bg="grey",
cex=1.4)
abline(fi... |
b65daded776bf7b0d47ec0ac509af3df5012c297 | 293bdd47ca209c8d61187920048e40605cdc7f3b | /code/data_processing/R/process_data.R | 5b9cff87131a74f064f428856722d0d510c055b5 | [
"MIT"
] | permissive | formidable-family/ffc-humans-in-the-loop | ddc6ce5a8284a1ae511e939cab8822324b083f4b | 36d550654c93904127d2ffd2454fe120b2dd3eb0 | refs/heads/master | 2021-04-27T01:07:48.830811 | 2019-02-13T00:10:39 | 2019-02-13T00:10:39 | 122,667,026 | 2 | 1 | MIT | 2018-03-24T00:53:50 | 2018-02-23T20:02:45 | R | UTF-8 | R | false | false | 1,029 | r | process_data.R | process_data_minimal <- function(data) {
# wrapper that does the most justifiable recoding and conversion
# to the background data
# addresses character NAs,
# converts all labelled variables to factors or numerics
# and converts some character variables to factors or numerics
# does NOT address missings i... |
1535e67072610c78e8bed0625f0c103523881a79 | 975434abfdecff4a32718ae3ef91955c7f0e847a | /R/crossvalDensity.R | ac7528139e33c3ca9ef3a670ef72e1396a040cb5 | [] | no_license | cran/latticeDensity | 281ac309dbe39aa10e7347a09da1401f0d499e3e | 7c3ef14304e955340eaf9613f575304c4760b508 | refs/heads/master | 2021-07-10T17:15:22.314083 | 2021-04-18T15:20:02 | 2021-04-18T15:20:02 | 17,696,990 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,299 | r | crossvalDensity.R | #' UBC crossvalidation for the lattice-based density estimator.
#'
#' A function to perform crossvalidation to determine the smoothing
#' parameter for the lattice-based density estimator. It minimizes the
#' UCV criterion.
#'
#' @details The function computes the k-step diffusion \eqn{p_k = T^kp_0}, then computes th... |
9610a09e3e8787ef5230e5844566e0dd3bfd6c69 | 4b34baa7df15710a577ff86c4c03418e89354fcf | /man/suppinf.Rd | 33d62f71c921c23dc6e2f12e8bde1d6707b58a2c | [] | no_license | shaileshtripathi/ssapbm | c10ae073e77fd711a5a48db2e93ff6055378fb9f | a8621c76c6d78b56274e5eb58b58c2397b172fc2 | refs/heads/master | 2020-04-17T15:42:13.288214 | 2016-11-17T19:57:09 | 2016-11-17T19:57:09 | 66,633,778 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,682,807 | rd | suppinf.Rd | \name{suppinf}
\alias{suppinf}
\docType{data}
\title{
%% ~~ data name/kind ... ~~
}
\description{
%% ~~ A concise (1-5 lines) description of the dataset. ~~
}
\usage{data("suppinf")}
\format{
A data frame with 12625 observations on the following 13 variables.
\describe{
\item{\samp{Gene symbol}}{a factor wit... |
1693c4f41b2de6fa5ec371fca514befcc032e5ff | 0ea852e85df22dde661279d2738f229e2f8511e1 | /R/strategy3.StadoGracza.R | 55d569a1f03a9714ebc9a2f568227fc074dcf85e | [] | no_license | ekarbowiak/SuperFarmerPKEK | 6666768970e682de58a1d7136b744c0a3f642f4f | e660a8a5ef72834d77a64bd8ce7290e9e2c0af8e | refs/heads/master | 2021-01-13T05:31:16.125472 | 2017-01-27T09:08:00 | 2017-01-27T09:08:00 | 80,165,451 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,708 | r | strategy3.StadoGracza.R | #' Strategia 3
#'
#' Funkcja strategy_3.StadoGracza() dokonuje wymiany zwierzat zgodnie ze strategia 3
#'
#' @param StadoGracza wektor zawieracjacy liczby poszczegolnych zwierzat w stadzie gracza
#' @param ceny_w_krolikach wektor opisujacy wartosc zwierzat wyrazona w krolikach
#' @export
#' @return funkcja zwra... |
223d0f776fa09a86709e49f659bce7fb82beb5a2 | 39f3dddfa858fd71584af501a4b1b3cf43091ca9 | /R/get_wgcna_modules.R | 0fda7d9cababecb22222d9ef9cf7af52c34d5feb | [] | no_license | perslab/brown-rausch-iscience-2021 | cc7e4e028b737e9495772f102e5145010855e2d6 | aca131f8bd1eddb9d8416c87e08c461fab7da050 | refs/heads/master | 2023-04-08T00:22:17.860856 | 2021-04-06T07:52:05 | 2021-04-06T07:52:05 | 277,510,849 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,777 | r | get_wgcna_modules.R | ##' .. content for \description{} (no empty lines) ..
##'
##' .. content for \details{} ..
##'
##' @title
##' @param data nested tibble
##' @param networkType signed, hybrid, unsigned; default is signed
##' @param clust_method clustering method for hclust; use complete for method rather than average (gives better resul... |
3606c491f4f52f4c3dc1963ab623106d53b5717c | 0bfd6c66c8e3155ff3e94924a58641701d3f21ff | /research/src/load_workspace.R | ba3ccd7ad9d3c8ab426096f66081570530358a54 | [] | no_license | sahanamd/cqf-final-project | 626bc6dc04223d6d5e43656dbef82cb5a99249e3 | 77e19329f274280c595427506760f22c0af79d10 | refs/heads/master | 2020-09-10T16:46:49.973724 | 2014-07-25T10:09:48 | 2014-07-25T10:09:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,968 | r | load_workspace.R | loadOneName <- function(objName, file, envir = parent.frame(),
assign.on.exit = TRUE) {
tempEnv <- new.env()
load(file, envir = tempEnv)
stopifnot(objName %in% ls(tempEnv))
if(assign.on.exit) {
assign(objName, tempEnv[[objName]], envir = envir)
return(invisible(tempEnv[[ob... |
ee293f2f2f4f97ca5aef98b6af2184f6eff45de3 | 962c154d99151cc8e14ec9d5b9fedf031398f615 | /cachematrix.R | 007ebe2d8b52b82affea1078d7f7bdbed338399d | [] | no_license | pra55n/ProgrammingAssignment2 | 709b3c21cb9d6c51de33cd339a6162e175c6dc85 | 8acf273a6d0b6ab9b707ef3f032ed7a5ddf9fe91 | refs/heads/master | 2020-12-02T16:36:56.785399 | 2014-10-26T16:34:31 | 2014-10-26T16:34:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,405 | r | cachematrix.R | ## The following two functions provide a mechanism to cache the inverse of a matrix.
## The caching trades memory for computation time and can provide significant reduction
## in run time if the inverse of a matrix is calculated repeatedly.
## makeCacheMatrix function returns a list of functions which form a closure... |
cc56d4535c7831d76300c47ec8642732165d0166 | c5c1feb2410b88997e3160d3bfca218aa119e701 | /man/grepv.Rd | 05f138ddeb6929336398bd4dcaf26772c52263b8 | [] | no_license | janlammertyn/jawaka | a888f0e992e44a4a0eb21a1a66cb9eced236867c | 89408d348d835030ac6aa6230730d02f3988d08f | refs/heads/master | 2021-01-10T18:34:01.566977 | 2019-08-23T13:33:11 | 2019-08-23T13:33:11 | 19,488,782 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 484 | rd | grepv.Rd | % Generated by roxygen2 (4.0.0.99): do not edit by hand
\name{grepv}
\alias{grepv}
\title{A grep for variable names Function}
\usage{
grepv(pattern = NULL, data = NULL)
}
\arguments{
\item{pattern}{Character string containing the search pattern.}
\item{data}{Data frame containing the variable names to search through.... |
d807fb9b549eccd42f4f42f0edcdeb68ce2bc224 | 5572e38f1a6a811fc9f484922db496f0a05ba46f | /location_location_code/R/main.R | e566127c35363905b6729a3b6d2fbfce6f8aa327 | [] | no_license | HOAnalyticsCode/pmi_case_study | 7782d8cfc1e43879c7a31603426024c02e294884 | a3a3fbeed0a4f1a953340d2c562122b4c9e23664 | refs/heads/master | 2020-03-14T18:54:48.590787 | 2018-05-01T19:15:03 | 2018-05-01T19:15:03 | 131,751,757 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,203 | r | main.R | # ENVIRONMENT #
library(glmnet)
library(gbm)
library(randomForest)
library(boot)
library(jsonlite)
library(lubridate)
source("project_functions.R")
# DATA and REFORMATTING #
sales_df <- read.csv("sales_granular.csv")
surroundings_json <- jsonlite::read_json("Surroundings.json")
sales_df <- sales_df_reformatting(sales... |
8102fc81c473f9fb02d1ac1c46ef22134502dd03 | f1633d8551fa1861956336ab861fb6a5a584ec64 | /man/restartApp.Rd | be61d21fe023ca053131657f35924780d8b5f0d4 | [] | no_license | rstudio/rsconnect | 8c275158ba17851a6c0cb054f1dfaa35962ebd18 | 8df93143524d3dbfe0ae11d77978f83ca807cbdf | refs/heads/main | 2023-09-01T00:05:20.787374 | 2023-08-31T15:22:40 | 2023-08-31T15:22:40 | 22,077,963 | 126 | 92 | null | 2023-09-12T16:58:45 | 2014-07-21T19:54:19 | R | UTF-8 | R | false | true | 994 | rd | restartApp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/restartApp.R
\name{restartApp}
\alias{restartApp}
\title{Restart an Application}
\usage{
restartApp(appName, account = NULL, server = NULL, quiet = FALSE)
}
\arguments{
\item{appName}{Name of application to restart}
\item{account}{Account na... |
67f6613390a2c0dd2a8f780e54bd6e34bb59009e | 65c93c5e9390c0e04ac8e9578e6ac97397f500f9 | /R/helpers.R | 91f5a272cd5b7feaadf703b514625fa0772a133d | [] | no_license | symbolrush/plot11R | fb2eede64a7f4497a4fa80db06d28d46d6209035 | 8e0d5ded8cd296850bed74b242e1bec2e41e93a2 | refs/heads/master | 2021-01-10T03:12:32.696027 | 2015-11-10T14:22:27 | 2015-11-10T14:22:27 | 46,713,996 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,709 | r | helpers.R | #' fhsblue: brings you the funky fhsblue and makes all graphics corporate design
#' compatible.
#'
#' @return fhsblue The famous color of all colors
fhsblue <- function() {
rgb(0, 102, 153, maxColorValue = 255)
}
#' cpDtService: color palette to paint dots according to their level of service
#' time (DtService).
#'... |
c607a92a630a8aebcb573de6a575ca8546643fee | b0e85e54db18592ddff0680301cd76e5dc7279e4 | /R/preprocess.R | a42ab3e51829cd860c3bdbc80c6aa4cede2f14a4 | [] | no_license | chechir/phantomR | 685aad31458c3cb31913afac9e318ebb9ed36bee | fe528803ba680e71139467745f9ca993a9e364c3 | refs/heads/master | 2022-11-08T08:15:01.888699 | 2020-06-17T14:21:31 | 2020-06-17T15:08:02 | 271,252,804 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,702 | r | preprocess.R | #'Preprocess metabolomics data
#'@description Preprocess: perform sparsity removal or features under the threshold and impute according to a given function
#'@param df Input data frame
#'@param mf_cols Input name of molecular features columns
#'@param sparsity_thershold Only keep MFs where the sparsity is higher that t... |
18b6407313837f78a5f90ccf14611e8f56e75326 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/lazyWeave/examples/ComparisonTable.Rd.R | 127b740d1ed0bf7127a6e160a46b290ead9db1ca | [] | 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 | 3,862 | r | ComparisonTable.Rd.R | library(lazyWeave)
### Name: ComparisonTable
### Title: Comparison Tables
### Aliases: ComparisonTable catconttable cattable conttable
### ** Examples
#Read in the delivery dataset from the lazyWeave package
data(Delivery)
#Use conttable to summarize maternal age, ga weeks, weight (grams)
#and grava by delivery ... |
512bd8decb03dee43935249ee00f50c5d5fc0a7f | 530214433c7cef8858b4debb2ceab21e2a4dba7f | /TestToAdd.R | 76f1d63ba4b9d2f9515f99503b28d0708a2a0922 | [] | no_license | ApittarelloJDM/link_existing | 13f9525aaac21ef990b94f33b35d2d1574d5d272 | 04329728625fa96e17ed6c3272a8f48fc5dc127c | refs/heads/master | 2022-10-06T08:39:44.621564 | 2020-06-07T19:11:35 | 2020-06-07T19:11:35 | 270,404,725 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 123 | r | TestToAdd.R | # this was just a test to learn how to add existing projects to gitHub #
matrix.example<-matrix(c(1,2,3,4),nrow=2,ncol=2)
|
3d228f16e305ded10ea809856356931f39e9a660 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /idefix/man/SeqKL.Rd | a217766bc4e94751b5bb54e09c1e6b44586f1187 | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | true | 3,694 | rd | SeqKL.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/efficiency_algorithms.R
\name{SeqKL}
\alias{SeqKL}
\title{Sequential Kullback-Leibler based algorithm for the MNL model.}
\usage{
SeqKL(des = NULL, cand.set, n.alts, par.draws, alt.cte = NULL,
no.choice = NULL, weights = NULL, allow.... |
d0a8bf770787ddbbd14619997a6f96b454327e1c | fa123d67273a996ebcd2f4aec7b0f4f910de39ee | /plot4.R | a017d3da9f99ec33d1f3edbafc1ab08dc04a4432 | [] | no_license | Karanvas1994/ExData_Plotting1 | 90480626f9172b987f46f3e1288501cfe661af9a | 37b104b49bb23c49bfa4dd588425d83d9997ba80 | refs/heads/master | 2021-01-15T12:42:33.219091 | 2015-06-07T12:48:45 | 2015-06-07T12:48:45 | 37,015,291 | 0 | 0 | null | 2015-06-07T12:12:11 | 2015-06-07T12:12:10 | null | UTF-8 | R | false | false | 790 | r | plot4.R | ## After Starting.R
png(filename = "plot4.png",width = 480, height = 480,units = "px", bg = "transparent")
par(mfrow = c(2, 2))
#1st
plot(newDateTime, Global_active_power,type = "l",xlab = "", ylab = "Global Active Power")
#2nd
plot(newDateTime, Voltage,type = "l",xlab = "datetime", ylab = "Voltage")
#3rd(Just Cop... |
2331866dfbe34e89ef14cc566cffdc2b3d60021d | 29585dff702209dd446c0ab52ceea046c58e384e | /Rpdb/R/distances.R | b4ebdb66ade22253b177af9920c48080bd600d22 | [] | 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 | 2,781 | r | distances.R | distances <- function(...)
UseMethod("distances")
distances.default <- function(dx1 = numeric(0), dx2 = numeric(0), dx3 = numeric(0), basis = "xyz", ...){
if(!is.numeric(dx1) | !is.numeric(dx2) | !is.numeric(dx3))
stop("'dx1', 'dx2' and 'dx3' must be numeric")
if(is.null(dim(dx1))){
if(!is.null(dim(dx2))... |
a792b3174742a4343947ba5f7882c52fb15c82e3 | 520326bc402c8d552def151cc1162d19602b5b59 | /R/zzz.R | 29924f7a586cc5039ee235d7c8ac83f2486edafd | [] | no_license | cran/slasso | 47cd9ecd8d99591a5002243ed794405fe0210c7b | 239cfb53c5d344341ebeb0467148d0b57015d0f9 | refs/heads/master | 2023-08-24T16:45:16.038489 | 2021-10-15T06:40:02 | 2021-10-15T06:40:02 | 417,551,020 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 741 | r | zzz.R | .onLoad<-function(libname, pkgname){
if (!identical(objective@system, R.version$system))
{
objective <- cxxfunctionplus2(methods::signature(), body=objective.body,
includes=objective.include, plugin="RcppArmadillo",save.dso=TRUE)
utils::assignInNamespace("objective", obje... |
caf70ce49709420f52034539361b0aa55690fcdd | a6b1ac269d1834324402e772ec647e7c220bdb14 | /man/MB.pheno.Rd | de6884354b064eac87607d619e777b09d280bae3 | [
"MIT"
] | permissive | BaderLab/netDx.examples | 2dec63276d1c52efe1df8493e9605a3feecac12f | c5786a4b884d484ac390ad8af22c0c85a5f906f7 | refs/heads/master | 2020-06-04T08:37:12.308466 | 2019-08-01T15:26:50 | 2019-08-01T15:26:50 | 191,947,659 | 0 | 0 | null | 2019-06-14T13:40:43 | 2019-06-14T13:27:08 | null | UTF-8 | R | false | false | 395 | rd | MB.pheno.Rd | \name{MB.pheno}
\alias{MB.pheno}
\docType{data}
\title{
Gene expression for medulloblastoma example
}
\description{
data.frame with gene expression values (rows) for all patients (columns)
}
\usage{}
\source{
Northcott et al. (2011). J Clin Oncol. 29 (11):1408.
}
\references{
Northcott et al. (2011). J Clin Oncol. 29 (... |
6f7558a833c73d7be73f91b99538f772538d3e52 | 6ca169ac4dd64e5a7add2f6fad670ac6d7bad69c | /nestedCV/dthybrid/getOptParaDTHybrid.R | d5b7ec799c1622d07821401ca771ea33a269155b | [] | no_license | minghao2016/chemogenomicAlg4DTIpred | 7139d2090373994b19df438e9f3d00fe4f07787a | d1a3ab2ada4d7cf8d041588de3428c0c9dd2115e | refs/heads/master | 2021-05-16T10:42:12.480507 | 2021-04-09T22:30:45 | 2021-04-09T22:30:45 | 104,808,409 | 8 | 4 | null | null | null | null | UTF-8 | R | false | false | 1,745 | r | getOptParaDTHybrid.R |
getOptParaDTHybrid <- function(Yfold, innerFold = 5, numSplitInner = 1,
paraList = list(lambda = seq(0.2, 0.9, by = 0.1),
alpha = seq(0.2, 0.3, by = 0.1)),
sd,
st) {
##
Folds <- doCVPositiveOnly3(Yfold, kfold = innerFold, numSplit = numSplitI... |
58aa4d39341c529ee6e2c23fa51db81836998d51 | 17341802600d5f2fd61a1a665affc0a314336c14 | /plot6.R | 3a8e51a6b93ed11cebd673397a5a593b6ed501ae | [] | no_license | jrw126/Exploratory-Data-Analysis-Project-2 | 4596b5c128048fe5e9ddae8d04049f2d1cc3f9a4 | 9fb5bf095535477a9ed4362724841de13fb5a8b9 | refs/heads/master | 2020-06-04T12:17:35.072909 | 2014-07-23T02:12:23 | 2014-07-23T02:12:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,864 | r | plot6.R | # Exploratory Data Analysis
# Project 2
# Plot 6
# This code assumes that all raw data files are in your working directory
# Load data
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
# Compare emissions from motor vehicle sources in Baltimore City with emissions from motor
# ve... |
58cac20899e9040941997ee10b395238151a1969 | 51ce64716f0eaf246bac2f10638c375cbcbe00b8 | /R/diagnostics.trafo_mod.R | a38882c5687e13a404824a66bb0e8b8415728509 | [] | no_license | cran/trafo | abdb24cf8deb7d7e97a5d98cd4c33f3c98499c6e | 345fda5d48f7b5c2af7fa6758ad00aef5e815ad7 | refs/heads/master | 2020-03-27T04:01:35.520767 | 2018-11-27T15:30:04 | 2018-11-27T15:30:04 | 145,906,336 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,947 | r | diagnostics.trafo_mod.R | #' Diagnostics for an untransformed and a transformed model
#'
#' Returns information about the applied transformation and selected diagnostics
#' to check model assumptions. The return helps to compare the untransformed and
#' the transformed model with regard to model assumptions.
#'
#' @param object an obje... |
3f65ec89b925f6a7230f732b12341c6b1a5f92ea | 4cddedb697460516f12b7b3868f6ab3d16b58c9d | /pa4/agecount.R | d2d4ec0edb47220185a408bd42a4e448105be616 | [] | no_license | ivanyu/compdata-002 | d16bd0dc1613555007fa7f44a85f6bdb04b4fa52 | 57176181ba056512f59f139139177d26181ffd0d | refs/heads/master | 2021-01-17T05:49:20.873441 | 2013-01-29T16:05:37 | 2013-01-29T16:05:37 | 7,407,096 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 507 | r | agecount.R | agecount <- function(age = NULL) {
## Check that "age" is non-NULL; else throw error
## Read "homicides.txt" data file
## Extract ages of victims; ignore records where no age is
## given
## Return integer containing count of homicides for that age
if (is.null(age)) {
stop... |
4f2c398be320a1cb5e73a67514a88c6fab8ec605 | 453daef8cc3ced1f856d4817f180eff5796cfde7 | /R/ona.R | a33c624ca412c8d0701860179bb3ed6e33f4b267 | [] | no_license | onaio/ona.R | 46afeb1ae17571e187318f5ea619752198848878 | 8851e63c2a730646faa5a3efe1c106ef350bba06 | refs/heads/master | 2021-12-08T07:10:57.618719 | 2021-12-07T12:37:32 | 2021-12-07T12:37:32 | 50,027,437 | 5 | 9 | null | 2021-12-07T12:37:33 | 2016-01-20T12:03:02 | R | UTF-8 | R | false | false | 18,139 | r | ona.R | library(RJSONIO)
library(stringr)
library(plyr)
library(RCurl)
library(lubridate)
library(sp)
library(doBy)
setClass("onaData", representation("data.frame", form="data.frame"), contains="data.frame")
#' Produce a data.frame out of a onaDataObj
#'
#' @param the ona object which will be possibly co-erced to a datafra... |
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