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bb847a1effb4b21a416874ccb62b00d288f6d873 | 05c6c5192018fa59f713e894dc3c3cf0d588036f | /man/fst.one.plink.Rd | 68d9efa5736d5b8740217ad056dec22d86975cc3 | [] | no_license | dyerlab/gwscaR | 029dd73185098375d6f136757cd4cf4855c8f605 | 226fef34caeceff472af8894ff5062a58ab77e5e | refs/heads/master | 2023-03-22T08:30:42.405029 | 2019-11-13T02:47:17 | 2019-11-13T02:47:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,081 | rd | fst.one.plink.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gwscaR_fsts.R
\name{fst.one.plink}
\alias{fst.one.plink}
\title{Calculate pairwise fsts from a dataset in ped format}
\usage{
fst.one.plink(raw, group1, group2, cov.thresh = 0.2, loc_names = NULL)
}
\arguments{
\item{raw}{A data.frame with da... |
6a6d4f18176c3564394b0478c8500de6139e54d1 | 9de3b2b8b28f89cfb13723b6be99f157fc13a313 | /3_Analysis/1_Social/1_Experimental_analyses/Causal_inference/laavan example.R | 3bf040de1116d7171db29d1c353240bf99436215 | [] | no_license | WWF-ConsEvidence/MPAMystery | 0e730dd4d0e39e6c44b36d5f9244a0bfa0ba319b | 6201c07950206a4eb92531ff5ebb9a30c4ec2de9 | refs/heads/master | 2023-06-22T04:39:12.209784 | 2021-07-20T17:53:51 | 2021-07-20T19:34:34 | 84,862,221 | 8 | 1 | null | 2019-07-24T08:21:16 | 2017-03-13T18:43:30 | R | UTF-8 | R | false | false | 3,356 | r | laavan example.R | library(lavaan)
library(qgraph)
library(semPlot)
data(HolzingerSwineford1939)
HS.model <- 'visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model, data = HolzingerSwineford1939)
summary(fit, fit.measures = TRUE)
data(PoliticalDemocracy)
?PoliticalDemo... |
53f3cfb72d4c3c5a60e1da0accf8863f7eaabedd | 55e8db068fbb5fae93e946b4d94ca7820a8b88b9 | /man/getHeight.monerod.Rd | 59c0afa932f6fd685a710dfbbb75a71887679bb6 | [
"WTFPL",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | cnxtech/moneRo | cc80786ba5b85d21a1aeaaa5d39f2d8c47d770f1 | f78f82a9714f8dd214e2b556d94615163268c70a | refs/heads/master | 2020-07-02T02:02:34.994096 | 2017-09-24T04:08:43 | 2017-09-24T04:08:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 744 | rd | getHeight.monerod.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/monero.R
\name{getHeight.monerod}
\alias{getHeight.monerod}
\alias{getHeight}
\title{getHeight}
\usage{
getHeight.monerod(ip = getOption("monerod.ip", "127.0.0.1"),
port = getOption("monerod.port", 18081))
getHeight(ip = getOption("monerod... |
ddbca7f45ace08fd15601b9266bbe4a396782189 | 0b3e9ec793e6eaff23ab3275be43326acf76f5aa | /rcppreqtl/man/makeXmatr.Rd | e34b7bc8fa1dedf2c99c38036457954c65794f3e | [] | no_license | Sun-lab/rcppreqtl | ce213847efb32a5f7cab55db435fbe5f07d9b631 | bc172c3fd5edec8ea5017d95540529613d27402b | refs/heads/master | 2020-03-27T18:36:55.770576 | 2019-05-07T13:52:22 | 2019-05-07T13:52:22 | 146,932,849 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 815 | rd | makeXmatr.Rd | \name{makeXmatr}
\alias{makeXmatr}
\title{Create example design matrix for simulations}
\description{
Produces a design matrix of several sample sizes which can be used to generate simulated dataset
}
\usage{
makeXmatr(ss)
}
\arguments{
\item{ss}{Sample size class: ss=1 implies dample size 32, ss=2 implies sample s... |
414fa9d05cf0a35314de5c858edbcc055c42689b | 224cdcad8036bc820f778be22097161d3adac513 | /meetup-events/2meetupIBMBigData/codes/labs-codes/labs-bigr/BigRLab4.R | 11ffbcf41eeaa3ba69e739ce0ce0db16cbc4ab68 | [] | no_license | caiomsouza/BigDataScienceEvents | 4681ecb07a45c9340bbc716288a04a81545aaae1 | 5642a42a84fdb36e4121034d6397162a3523cb2b | refs/heads/master | 2021-01-19T07:43:41.938455 | 2015-07-17T14:27:57 | 2015-07-17T14:27:57 | 39,254,948 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,897 | r | BigRLab4.R | #___________________________________________________________________________#
# Start BigInsights
# From the Desktop, click on the “Start BigInsights” icon. This action will
# start up various BigInsights components, including HDFS and Map/Reduce, on
# your machine. A Terminal window will pop up that will indicate pr... |
d7c5118de1261662f3096b6c4d7ebd4619c046cb | 0a906cf8b1b7da2aea87de958e3662870df49727 | /diversityForest/inst/testfiles/numSmaller/libFuzzer_numSmaller/numSmaller_valgrind_files/1610037631-test.R | 5805bf334456dd77492b501dc0df420979a40427 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,852 | r | 1610037631-test.R | testlist <- list(reference = c(NaN, NA, NaN, 3.03578768055078e-111, NaN, NaN, 3.4602157353272e-102, 1.36240696149202e-105, 9126801005.92792, -3.38087344363186e+221, -3.27638994300565e+296, NaN, NaN, NaN, 1.32024272918069e-192, NaN, 8.01967015274577e+168, NaN, -2.93379056240461e+304, NA, 8.81442357825871e-280, 1.308... |
be949621c03e07542025c5cae7d4f09b54df50a2 | a1445458bcd29f5f04ee367392d0ba0424710b8d | /j/ints/intsScript.R | ca3ca3adfdeab7edce13784cb0855a5133d6b92f | [] | no_license | jamesmungall/jScripts | 87e81b285f79cd707435aeb24676c515f96dc99f | 4cfe9121ac1a0511ac87a9b406aab17974da593b | refs/heads/master | 2021-01-10T10:34:36.309682 | 2015-06-02T12:01:39 | 2015-06-02T12:01:39 | 36,662,415 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,421 | r | intsScript.R | # Integer Functions
# ==================================
# Start date: 21st July 2012
j.ints.startup=function(showPrints=FALSE){
# dependent packages: sfsmisc
library(sfsmisc);
if(showPrints){
print("loading j/ints/intsScript.R");
print("dependent packages: sfsmisc");
}
}
#
# Aim: Functions ... |
b85b749ebd9d66c93ccd48d247dc848d360eb088 | 1e018375afab08fc10bc5456448234c788ff1aae | /packages/gist/R/gists.R | 13af9742f1ca1046a8cf79e0a1d3da5442b273d1 | [
"MIT"
] | permissive | att/rcloud | 5187a71e83726e9e7425adde8534cf66690cac7f | 3630ec73cebfc8df1b2ee4bd4a07fbe81cb03bb0 | refs/heads/develop | 2023-08-23T18:14:45.171238 | 2022-08-25T23:49:52 | 2022-08-25T23:49:52 | 5,250,457 | 322 | 138 | MIT | 2023-05-22T19:46:48 | 2012-07-31T19:32:52 | JavaScript | UTF-8 | R | false | false | 490 | r | gists.R | get.gist <- function (id, version = NULL, ctx = current.gist.context())
UseMethod("get.gist", ctx)
fork.gist <- function (id, ctx = current.gist.context())
UseMethod("fork.gist", ctx)
get.gist.forks <- function (id, ctx = current.gist.context())
UseMethod("get.gist.forks", ctx)
modify.gist <- function (id,... |
d716f9b95a25fedff87c01f5739b9e55054ccf46 | bfe4fa7d35d25dbd4749c7db18284630743f943b | /for channels/compare_num_samples_variability.R | b12ac78bbe18f7944b77f9f48130dc9c895d28a1 | [] | no_license | innertron/REU_Kam | 0ccfe2d4e178b241cdf836d9c066188dbbd65e82 | bf4028b193f13cc202f66cd28963290722b312ac | refs/heads/master | 2021-01-17T20:16:07.187792 | 2016-08-02T21:29:38 | 2016-08-02T21:29:38 | 61,396,055 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,046 | r | compare_num_samples_variability.R | #calculate the cross map skill for different sample sizes in order to
#find most efficient sample size to use that does not compromise accurazy
#Import the rEDM and igraph libraries
library(rEDM)
#you need to import the neural_data.txt file by hand.
time_span = 1:1000
#get the first second of the data
nd <- neural_d... |
a56fffae39e1f6b49e9888d31145f33c2e11b1a9 | fbc5705f3a94f34e6ca7b9c2b9d724bf2d292a26 | /edX/DS R Basics/Indexing/counting obs below average.R | 97f903c5079e0af9a37644d2e682f5283a17e953 | [] | no_license | shinichimatsuda/R_Training | 1b766d9f5dfbd73490997ae70a9c25e9affdf2f2 | df9b30f2ff0886d1b6fa0ad6f3db71e018b7c24d | refs/heads/master | 2020-12-24T20:52:10.679977 | 2018-12-14T15:20:15 | 2018-12-14T15:20:15 | 58,867,484 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 299 | r | counting obs below average.R | # Store the murder rate per 100,000 for each state, in murder_rate
murder_rate <- murders$total/murders$population*100000
# Compute average murder rate and store in avg using `mean`
avg <- mean(murder_rate)
# How many states have murder rates below avg ? Check using sum
sum(murder_rate < avg)
|
0aa4882692705ae48887433f4a063c803743fe41 | ba9c2741339f66bfd24c6dda7cd40b30919a0984 | /chapter_1/analysis_scripts/kmer_pl/007a_gc_cor_filter.R | 940119845dc266071481c93dafca1374e3f661c2 | [
"Unlicense"
] | permissive | cjfiscus/2022_Fiscus_Dissertation | 928b38d55135e125ace47088eddad4615613cc00 | 25110d7120d52d94c99616ebd4eed40da5aed3bf | refs/heads/main | 2023-04-17T19:35:12.737005 | 2022-09-05T20:26:09 | 2022-09-05T20:26:09 | 532,716,601 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 371 | r | 007a_gc_cor_filter.R | #!/usr/bin/env Rscript
# gc corr filter
# cjfiscus
args = commandArgs(trailingOnly=TRUE)
# new data
df<-read.table(args[1])
# threshold
h<-quantile(df$V2)[4] + 1.5*IQR(df$V2)
l<-quantile(df$V2)[2] - 1.5*IQR(df$V2)
# new bad list
df2<-df[df$V2 > h,]
df3<-df[df$V2 < l,]
out<-rbind(df2, df3)
write.table(out$V1, arg... |
9a116ac6249020da4d2b9147b9d662e865f4426d | a829fe89f88c687ff86f9a4a2939ebaf968c1c15 | /predictRatings.R | a1e54958635e9a9b6414d03ea10cc853bd7b30f6 | [] | no_license | arman1371/Simple-Recommender-With-R | d49e267e1b6c475ea6f47b99d8be319a3fd64734 | 9ad99d35f967cff4693bd5f84664e508cfb75a1b | refs/heads/master | 2016-09-15T23:11:31.487727 | 2015-08-23T07:45:38 | 2015-08-23T07:45:38 | 41,089,902 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 479 | r | predictRatings.R | predictRatings <- function(userCors, usersItems, userMean, userSd){
pred <- data.frame(ItemID = colnames(usersItems), Predict = rep(NA, ncol(usersItems)), stringsAsFactors = F)
for(i in 1:ncol(usersItems)){
sharingNums <- !is.na(usersItems[,i]) & !is.na(userCors[,2])
pred[i, 2] <- sum(usersItems... |
b1cb854ad1eecbb82830855f63baacdec899caff | fa657cc59114f6a53a59f01471132076f115033f | /R/biblio_cocitation.R | 622df7c6f68ce89cb6d7d391f135bc3f964cce96 | [
"MIT"
] | permissive | agoutsmedt/biblionetwork | c53142e8ce6eae15efd9ce023730bece1ebd9c7d | c05eb9a093bb3f63c1665f25550990479a448397 | refs/heads/master | 2023-04-17T13:49:06.551025 | 2023-02-25T18:50:23 | 2023-02-25T18:50:23 | 334,688,094 | 4 | 2 | NOASSERTION | 2021-02-26T14:21:10 | 2021-01-31T15:27:49 | R | UTF-8 | R | false | false | 6,082 | r | biblio_cocitation.R | biblio_cocitation <- function(dt, source, ref, normalized_weight_only=TRUE, weight_threshold = 1, output_in_character = TRUE)
{
#' Calculating the Coupling Angle Measure for Edges in a Co-citation Network
#'
#' `r lifecycle::badge("experimental")`
#'
#' @description This function is basically the same as the ... |
28941e06342526d7fe413ccb2bddf078b37e3364 | 5eb1c6bf57c65103d0e51508a94f7631df52f857 | /man/TNRS.Rd | a7f616029f7399589308cdf1a6e5c0baceb71377 | [] | no_license | EnquistLab/RTNRS | 54f90ff42157afe6c3860cffd04a2124ee12a578 | 4f40c76d34f4be8a15282c0d0c146c3e4713c9a5 | refs/heads/master | 2023-06-25T01:29:56.950510 | 2023-06-13T00:28:19 | 2023-06-13T00:28:19 | 196,649,263 | 8 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,207 | rd | TNRS.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TNRS.R
\name{TNRS}
\alias{TNRS}
\title{Resolve plant taxonomic names}
\usage{
TNRS(
taxonomic_names,
sources = c("wcvp", "wfo"),
classification = "wfo",
mode = "resolve",
matches = "best",
accuracy = NULL,
skip_internet_check = ... |
ac3958d79328079afe5b9e268380ea279807dd40 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/MGDrivE/R/RcppExports.R | a2f6b11fb70065aa03986f7a4e3efb22c3aae306 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,222 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#' Dirichlet Distribution
#'
#' Make a single draw from a Dirichlet distribution with the shape parameter
#' one.
#'
#' @param migrationPoint Vector of weights for draws. Must be positive.
#'
... |
3c56718faaa0b7d481199dda8aa1a3f5293d5c5e | 7bb3f64824627ef179d5f341266a664fd0b69011 | /Statistics_For_Psychology_by_Arthur_Aron,_Elliot_J._Coups,_And_Elaine_N._Aron/CH2/EX2.3a/Ex2_3a.R | 81593723f9174b7dc38e589b35ea1f49b9eb95bb | [
"MIT"
] | permissive | prashantsinalkar/R_TBC_Uploads | 8bd0f71834814b1d03df07ce90b2eae3b7d357f8 | b3f3a8ecd454359a2e992161844f2fb599f8238a | refs/heads/master | 2020-08-05T23:06:09.749051 | 2019-10-04T06:54:07 | 2019-10-04T06:54:07 | 212,746,586 | 0 | 0 | MIT | 2019-10-04T06:03:49 | 2019-10-04T06:03:48 | null | UTF-8 | R | false | false | 790 | r | Ex2_3a.R | # Page no. : 58 - 60
# Worked - out Exapmles
# a) Mean
scores <- c(8, 6, 6, 9, 6, 5, 6, 2)
value <- mean(scores)
cat("Mean of scores is", value)
# b) Median
scores <- c(1, 7, 4, 2, 3, 6, 2, 9, 7)
value <- median(scores)
cat("Median of value is", value)
#... |
584ea60d8d1e5380687d5351b4516045657f755e | 0075a16293acba55aeacc0defb3a566e3eaa3740 | /plot4.R | dc59897888f3231c55427ce51cbd9dc3326b61ef | [] | no_license | charlesott/ExData_Plotting1 | 8343493809f5f7ea81dfb9435fca4f951ebbb660 | 922189372f53e61bfd2beb1c63f33514322f9814 | refs/heads/master | 2021-01-15T20:42:48.915543 | 2014-07-10T17:20:56 | 2014-07-10T17:20:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,123 | r | plot4.R |
# Get the data into memory
hp <- read.csv2("household_power_consumption.txt", header = T)
# Cast the Date column
hp$Date <- strptime(paste(hp$Date,hp$Time), format="%d/%m/%Y %H:%M:%S")
# Subset to get the dates important to this project
hp.sub <- subset(hp, Date >= as.POSIXlt('2007-02-01 00:00')
& ... |
5d22523a373ab9a5cf65f387fe92fc607ee884bb | 34e79c005fbf68149d27283f7749a247992e0b35 | /getting_and_cleaning_data/run_analysis.R | 7d6b660419929637d6661e82c93e968ded1ea33c | [] | no_license | brockwebb/datasciencecoursera | f9e2c0a90091f5cb3cbd785cd4176dbceadb77bc | 7dc18a6f03c591b9d653b5e2f35652a0574bff1b | refs/heads/master | 2016-09-16T14:16:02.231300 | 2014-09-21T22:31:38 | 2014-09-21T22:31:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,688 | r | run_analysis.R | # This script was generated for the Coursera Getting and Cleaning Data class.
# The data file was obtained from the URL:
# https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
# to run the script, first unzip the data in your working directory
# The assignment is, (source: Getting ... |
8830c9de044a3e1d49853d87254c8a527dbbe298 | 779c1eb6debd083c70271e1e6d38fc540c66a0ee | /main.r | be31ece884b9a8443c501d0bb7742def1c6c7a47 | [] | no_license | dvillaunal/Dia_55_10mayo | 3cb51c3c071c241100393e92d9226edc4aac8a75 | c9e067a8765cdd12490ec6ce2e2263b7f30aa9da | refs/heads/main | 2023-04-19T21:42:43.856878 | 2021-05-11T06:41:32 | 2021-05-11T06:41:32 | 366,227,759 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,528 | r | main.r | ## ----Protocolo, eval=FALSE, include=TRUE----------------------------------------------------------------
## "Protocolo:
##
## 1. Daniel Felipe Villa Rengifo
##
## 2. Lenguaje: R
##
## 3. Tema: Manejo de archivos *.xlsx
## (realice al menos dos ejercicios que
## requieran cargar archivos externos,
## leer y p... |
42d26dcb5dd04aee1cc33fb58ad31be863764b37 | 2726e7bdfc2b688ccf7ecaac75a259bcaa0e9817 | /R/analysis/caselist.R | 3d1fb3be3920e1b6e3572378b66789a0e618fe13 | [
"MIT",
"GPL-3.0-only",
"LicenseRef-scancode-warranty-disclaimer",
"LGPL-2.0-or-later",
"GPL-3.0-or-later",
"GPL-1.0-or-later",
"LGPL-2.1-or-later",
"LicenseRef-scancode-other-copyleft",
"LicenseRef-scancode-unknown-license-reference",
"AGPL-3.0-or-later"
] | permissive | marvinmarnold/oipm_annual_report_2018 | e62c292c4398bc8f1222b75bb1003b6c5bc0100f | da5e5bf06b38280552ea8029cfa0ab4d6dc17c66 | refs/heads/master | 2023-03-12T06:24:14.192280 | 2022-02-14T19:47:34 | 2022-02-14T19:56:01 | 172,810,954 | 1 | 1 | MIT | 2023-02-28T12:16:21 | 2019-02-27T00:09:56 | HTML | UTF-8 | R | false | false | 9,218 | r | caselist.R | case.list <- read.csv(file = "data_public/caselist.csv", header = FALSE)
trimws(case.list$V1)
allegations.for.year %>% distinct(PIB.Control.Number)
matching.cases <- allegations.all %>%
filter(
trimws(PIB.Control.Number) %in% trimws(case.list$V1)) %>%
mutate(
district = case_when(
Assigned.division ... |
24d7fcb378ea20bf37a766b418c95a1818462b66 | 21428ae02e7a3181462ed2020a3b24f4cc65e53e | /man/write_s3.Rd | af8d67643dc351012a5d2dc1f4c5a8b14ceead25 | [] | no_license | plataformapreventiva/dbrsocial | 9eadcafea990f27c5ba2e278bc5b5eb243ee0835 | f20fea2580c99ab0205b9860295f4618ee4ebe5d | refs/heads/master | 2021-09-19T10:31:13.443347 | 2018-06-22T22:53:11 | 2018-06-22T22:53:11 | 126,061,421 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 500 | rd | write_s3.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/retrieve.R
\name{write_s3}
\alias{write_s3}
\title{write_s3}
\usage{
write_s3(dataf, name, s3bucket = Sys.getenv("S3_DIR"))
}
\arguments{
\item{dataf}{The data.frame objet to write as a CSV}
\item{name}{The name of the file in the S3 bucket}... |
0ba9b33c7fffe216c996fdda2a7091a2477d537e | 57fdd20c9dc7ab94268be24fa69f59c1e09a22cf | /tests/testthat/test-est_power_root_test.R | 9d347af9e93a7b97895ebc2720524527ddf43b6f | [] | no_license | slzhao/RnaSeqSampleSize | bd991b858b432f097612c5563d03dcfcc52f23df | 24a51fa945ec44d09cf17a54251dc9220684b023 | refs/heads/master | 2023-02-05T06:16:17.389538 | 2022-02-25T15:44:49 | 2022-02-25T15:44:49 | 16,217,256 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 95 | r | test-est_power_root_test.R | test_that("est_power_root", {
expect_equal(est_power_root(n=65),0.1538854,tolerance=0.001)
})
|
1dc2b6909bcdc65675018a9ac803c502d3f4d46e | a9b64bf34b7d5fd005e4fc24c26a2c5d5500650c | /Dia19.R | d66b258e68f5654502f9a6208f269e23234a6290 | [] | no_license | gamerino/30diasdegraficosRFDS | 877ed7a8bf70df88a557c6dc45e503897bdc1623 | f3593e51baba0797ea290ae77f0b0b66452c7a93 | refs/heads/master | 2022-10-15T19:39:25.690351 | 2020-06-11T00:25:52 | 2020-06-11T00:25:52 | 263,490,070 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,269 | r | Dia19.R | library(dplyr)
library(ggplot2)
library(tidyr)
library(lubridate)
#### my black theme
my_theme_black<- function (base_size = 14, base_family = ""){
theme_minimal() +
theme(line = element_line(colour = "black", size = 0.5, linetype = 1,
lineend = "butt"),
re... |
a1f9856164b68269085e0a46d42e39046e80ea1e | 88c996723dd3fba59692eadc9cbd0a14634e0d12 | /man/nNormal.Rd | 3ed6215dfe8e8ff8da8aa93309a5536830c104cf | [] | no_license | AEBilgrau/gsDesign | 58fc02d16e3034baf613c85c6f45a076cb27abeb | f2e1d13bc35aa5a0dc04fc8ecfa6c8a2573921f1 | refs/heads/master | 2020-04-09T11:37:22.043117 | 2017-09-12T18:59:05 | 2017-09-12T18:59:05 | 160,317,205 | 2 | 0 | null | 2018-12-04T07:33:26 | 2018-12-04T07:33:26 | null | UTF-8 | R | false | false | 3,592 | rd | nNormal.Rd | \name{nNormal}
\alias{nNormal}
\title{Normal distribution sample size (2-sample)}
\description{\code{nNormal()} computes a fixed design sample size for comparing 2 means where variance is known. T
The function allows computation of sample size for a non-inferiority hypothesis.
Note that you may wish to investigate othe... |
22f296992a5bd4b7432a550905adb1733dbb2dca | 0d52cf571dbc0a15096357a46c7a52b954ee090d | /karinaScripts/KarinaDateTimeScript.R | 1266f04e17d315295c36edc45ea5228714cca2d7 | [] | no_license | arixha/MVTEC-Stats-Project1 | 6da88ca4dcfc767945e00ea54696117bc148dc06 | 6619df7860673f79c75471b005af8a5c2cf936bf | refs/heads/main | 2023-02-15T20:07:54.299459 | 2021-01-08T18:51:54 | 2021-01-08T18:51:54 | 316,367,309 | 0 | 4 | null | 2020-12-04T15:24:43 | 2020-11-27T00:56:30 | R | UTF-8 | R | false | false | 4,486 | r | KarinaDateTimeScript.R |
#Time Management
setwd("D:/karina/docencia/01areferenciesPPT/0DadesPractiques/planta")
#planta<- read.table("depdani41dates.csv",header=T);
planta<- read.table("plantaVminitabMisOK.csv",header=T, sep=";");
names(planta)
n<-dim(planta)[1]
sapply(planta,class)
planta<- read.table("plantaVminitabMisOK.csv",header=T, se... |
76b761ef924b967b80fe967d07cc0c7d523a4e93 | ff9a90bbc356f91b643fc89e7360c4e69172b030 | /R_code_pca_remote_sensing.r | cc0ce4a42c1c96d446469461ab2608cf14c3dfa7 | [] | no_license | GiuliaAdeleTuci/repository1 | 4e80d6b76067e6d488dcda3ef7e7ec7af0060de9 | e73692ebd91e773f4e7fb2609d7c8a3b7633a464 | refs/heads/master | 2021-04-23T23:09:01.259237 | 2020-06-22T14:25:59 | 2020-06-22T14:25:59 | 250,028,045 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,351 | r | R_code_pca_remote_sensing.r | # R code for pcs remote sensing
setwd("/Users/giulia/lab")
library(raster)
library(RStoolbox)
# now we use the function brick that is used to import the whole image of the satellite.
p224r63_2011 <- brick("p224r63_2011_masked.grd")
#b1 blue
#b2 green
#b3 red
#b4 NIR
#b5 SWIR (short-wave infrared)
#b6 thermal infr... |
dc0f828681dbcbdb13369ce600020c73836734a4 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /elo/R/fitted.R | f06e439a515bc21f4982a845ddce9b8f64569138 | [] | 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 | 1,248 | r | fitted.R |
#' Extract model values
#'
#' Extract model values from \code{elo} functions.
#'
#' @param object An object.
#' @param ... Other arguments
#' @param running logical, denoting whether to use the running predicted values.
#' @name fitted.elo
NULL
#> NULL
## stats:::terms.default and stats:::weights.default also work
#... |
8ae708b8ab67de3295bf7c80dd0186eca33fa31f | 60128cee7625b0fb020c4387931964af7e498b36 | /man/refineryLP.Rd | 21f8d810f13725d1b51f86060d07d2440f017ed6 | [] | no_license | fmair/RTL | 7b91cfde5d690dce43b28107e6fcf445a994b642 | 3892587a12caa1b403ccf10f99c1dc200fe5392b | refs/heads/master | 2023-03-04T00:42:39.117858 | 2021-01-26T01:10:56 | 2021-01-26T01:10:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 554 | rd | refineryLP.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/refineryLP.R
\name{refineryLP}
\alias{refineryLP}
\title{\code{refineryLP}}
\usage{
refineryLP(crudes = ref.opt.inputs, products = ref.opt.outputs)
}
\arguments{
\item{crudes}{Data frame of crude inputs}
\item{products}{Data frame of product... |
942c6b0cb67256bdd976d1cbf24006bed7260686 | 13c0687d2111ea43aad32f319b46a6f67bd2f53a | /__09_week_01/__09_week_serialize_02blobs/__09_go_createxAudioTable_03segsFun.R | 0acf904e52d287e4090204ffd9387c38af4476b3 | [] | no_license | davezes/MAS405_S2021 | 0a672892ca320dbad952697948b610adcffc0656 | 025a3ea1de268c05c1a426bdb8944d2367789847 | refs/heads/master | 2023-05-10T22:14:20.857391 | 2021-06-05T21:33:26 | 2021-06-05T21:33:26 | 350,537,508 | 4 | 6 | null | null | null | null | UTF-8 | R | false | false | 3,234 | r | __09_go_createxAudioTable_03segsFun.R |
######################### The directory that encloses this file
######################### is your working directory
######################### __09_audioFiles_01 must be a sibling
######################### efficient
options(stringsAsFactors=FALSE, width=200)
projpath <- getwd()
if(!exists("xdbsock")) {
xdbso... |
384c86d495980a8e1d806e1c099e74e95d135264 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Devore7/examples/ex13.32.Rd.R | ad348c280cbf0d203c711a54f6dc04cf54929eab | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 160 | r | ex13.32.Rd.R | library(Devore7)
### Name: ex13.32
### Title: R Data set: ex13.32
### Aliases: ex13.32
### Keywords: datasets
### ** Examples
data(ex13.32)
str(ex13.32)
|
c3fc668ad9f5c8548d6536bb0a516bddcc5917b6 | e4755d1e2207edc616f4f20eb2d4e5fb65a71c42 | /man/DiversityCurves.Rd | f0ce1bec0f56d6731867cc447b7a1bdb01592c54 | [
"CC0-1.0"
] | permissive | dwbapst/paleotree | 14bbfd5b312848c109a5fc539a1e82978a760538 | 95c2f57e91c4204c04cd59d9662ba94c43c87a60 | refs/heads/master | 2022-09-23T03:57:35.959138 | 2022-08-25T18:29:50 | 2022-08-25T18:29:50 | 3,827,289 | 20 | 11 | null | 2022-08-25T18:30:48 | 2012-03-25T21:01:38 | R | UTF-8 | R | false | true | 11,061 | rd | DiversityCurves.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DiversityCurves.R
\name{DiversityCurves}
\alias{DiversityCurves}
\alias{taxicDivCont}
\alias{taxicDivDisc}
\alias{phyloDiv}
\title{Diversity Curves}
\usage{
taxicDivCont(
timeData,
int.length = 1,
int.times = NULL,
plot = TRUE,
plot... |
df0f729fb01441cb974de8cc61d30f0b7532de24 | 9901e0bb747feddc319e723071a2723b424f1f1a | /sample30_transformdemo.R | 2e3cf17787bb230cc3ee815d9843a1f6ed9dd87a | [] | no_license | paeoigner/rps_citi_ml_r2018 | 9707bf35196b5dfbf34326aae5026d33a4d0326f | 2dff20f380606b505c0eed9f93d64fd45955aaf3 | refs/heads/master | 2020-05-16T10:46:51.350889 | 2018-04-13T12:24:49 | 2018-04-13T12:24:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 222 | r | sample30_transformdemo.R |
attach(airquality)
airquality
transform(Ozone, logOzone = log(Ozone)) # marginally interesting ...
transform(airquality, Ozone = -Ozone)
transform(airquality, new = -Ozone, Temp = (Temp-32)/1.8)
detach(airquality)
|
c6cde54892742bdd6f5d9669db4d9635422254df | 21095827293e28a04cee3d8c13b1ea24080e7334 | /man/pichor.Rd | 4b213e36bd2e12da76d5c32810dcfcddf0fc7e41 | [] | no_license | mikldk/pichor | 791d572caaa9c8097b98bbcd70f1de719418a402 | 3f02903ac651dd70c2fe4a9200e58bd42307165d | refs/heads/master | 2023-02-06T00:02:28.986768 | 2023-01-22T13:08:29 | 2023-01-22T13:08:29 | 184,456,422 | 11 | 3 | null | null | null | null | UTF-8 | R | false | true | 198 | rd | pichor.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pichor.R
\docType{package}
\name{pichor}
\alias{pichor}
\title{\code{pichor} package}
\description{
Piano chords in R
}
|
698e8317e30c4936067dcea594252f8d500359ff | cec39fbac1e0086f328739d63b7a946b0efbd82a | /ShinyApp/data/ShinyAppStuff/app.R | 3fa5981d6b2a1f2648f015c743f00c4d0428e9e1 | [] | no_license | DSPG-Young-Scholars-Program/dspg21zimbabwe | 88c3c2feabb3d48ef14aa875d4ef67bf5ea3244c | 3807aaf4cbc5b627d2169bf755dbe234b9880149 | refs/heads/main | 2023-07-12T20:20:59.691496 | 2021-08-04T22:15:44 | 2021-08-04T22:15:44 | 392,812,259 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 25,927 | r | app.R | # Set Working Directory
setwd("/Users/mattb24/Zimbabwe/2021_DSPG_Zimbabwe/ShinyApp/ShinyAppStuff")
# clean the memory
rm(list=ls())
library(shiny)
library(ggplot2)
library(shinydashboard)
library(DT)
library(shinyWidgets)
library(shinydashboardPlus)
library(leaflet)
library(dashboardthemes)
library(readr)
library(co... |
3b3bdeb080c0b585ebd745a59078f397092fcca1 | cfdf91f84e6d70e4c26f41e335a78cb553dd8e3e | /Desktop/binomial/man/bin_distribution.Rd | d48d3cab731bc19984355e83a3e68e252973a6c4 | [] | no_license | stat133-sp19/hw-stat133-eunicesou-1 | e2747697708fabad9f18118a7d180bf6394df32d | bf5649a29b29a53bf943c4aebe5662fc23671ab1 | refs/heads/master | 2020-04-28T19:53:11.229228 | 2019-05-02T22:07:02 | 2019-05-02T22:07:02 | 175,525,218 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 608 | rd | bin_distribution.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/binomal.R
\name{bin_distribution}
\alias{bin_distribution}
\title{Binomial distribution Function}
\usage{
bin_distribution(trials, prob)
}
\arguments{
\item{trials}{Total number of trials}
\item{prob}{Probability of success}
}
\value{
A data... |
cff2711635a55e4733d779ee1a39980ad31041bd | 6393059185fa3456b16d4e69193828686bcc3a22 | /Code/step1_dictionary/step1_dictionary_PITF.R | 5f431c3e46ba44ab20e4ba8f627fd53442ddb244 | [] | no_license | zhukovyuri/xSub_ReplicationCode | 49c6f092a8dfdd44762e493ed4155dedcc37c371 | a8c83d60ed1076d5a22f4d3fb3bc28fbbbcd0a81 | refs/heads/master | 2021-05-03T10:21:31.921202 | 2019-06-19T00:33:06 | 2019-06-19T00:33:06 | 120,532,879 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,956 | r | step1_dictionary_PITF.R | rm(list=ls())
## Set directory
setwd("~/Dropbox2/Dropbox (Zhukov research team)/XSub/Data/")
# setwd("C:/Users/nadiya/Dropbox (Zhukov research team)/XSub/Data")
## Install & load packages (all at once)
list.of.packages <- c("gdata","countrycode","maptools","foreign","plotrix","sp","raster","rgeos","gdata")
new.packa... |
6d040f4e1759d807ba1eda44281cad51799dc30b | 7715230204185f9f0c8d7f1fe2d480c1e7056779 | /Scripts/Personal Stuff/Lyse van Wijk/RYouReady/Scripts/RYouReady_visualization.R | 5c4cb543b7423a5ce223ba20b9363cf293feedd8 | [] | no_license | lysevanwijk/RCourseHWStudyGroup | c8900133bb3f5518ab24bf582f935d2fc5e1aa0f | 5af6054e405805503bcfaf9cd723c84349393e39 | refs/heads/main | 2023-08-30T18:11:15.073722 | 2021-10-20T09:08:48 | 2021-10-20T09:08:48 | 323,101,186 | 0 | 0 | null | 2020-12-20T15:17:53 | 2020-12-20T15:17:53 | null | UTF-8 | R | false | false | 4,551 | r | RYouReady_visualization.R | #clear global environment
rm(list=ls())
#load packages
library(tidyverse)
library(here)
library(janitor)
library(janeaustenr)
library(ggbeeswarm)
library(RColorBrewer)
#plotten
Example_LongFormat %>%
ggplot(aes(x=Geslacht, y=vasPijnGemiddeld_1))+
geom_point()
Example_LongFormat %>%
ggplot(aes(x=Geslacht, y=v... |
17bb6d0c99ad298ddb27c857298cba4aceae3929 | 992b81fbb548f19eae4af8be17a37ba3d4cdfb8a | /lab03.R | 6fedfc147f72382488e1bd6f7955e30888df359b | [] | no_license | OsamaNadeeem/lab03 | 39da5c0a6c7e8fa7646bf7ab40d877a23c23d742 | 3be0e7c647f7bbc78ef388ede148c51f93d498b2 | refs/heads/master | 2021-07-07T19:49:50.020055 | 2017-09-29T12:08:38 | 2017-09-29T12:08:38 | 105,267,901 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 576 | r | lab03.R |
barplot(col=rainbow(1), mtcars$mpg, ylab="MPG", mtcars$hp,
xlab="Horse Power", main="Miles/h Vs Horse Power",
names.arg=mtcars$hp, las=2,cex.names=0.8)
barplot(mtcars$qs,ylab="Qsec",xlab="EngineCC",col=rainbow(1),
names.arg=mtcars$disp,main="Engine Vs Qsec",las=2)
barplot(mtcars... |
11f814613fc54c2241b1a5c5010fcb00f7e95ea5 | 4951e7c534f334c22d498bbc7035c5e93c5b928d | /reliability/round_prob.R | ec90bd09a289f140aa19b2a8bcb4eb724630f758 | [] | no_license | Derek-Jones/ESEUR-code-data | 140f9cf41b2bcc512bbb2e04bcd81b5f82eef3e1 | 2f42f3fb6e46d273a3803db21e7e70eed2c8c09c | refs/heads/master | 2023-04-04T21:32:13.160607 | 2023-03-20T19:19:51 | 2023-03-20T19:19:51 | 49,327,508 | 420 | 50 | null | null | null | null | UTF-8 | R | false | false | 863 | r | round_prob.R | #
# round_prob.R, 23 Apr 18
# Data from:
# Example
#
# Example from:
# Evidence-based Software Engineering: based on the publicly available data
# Derek M. Jones
#
# TAG example round-number number_round
source("ESEUR_config.r")
pal_col=rainbow(4)
# Formula from:
# Very, Many, Small, {Penguins}: {Vaguely} Related T... |
f2804e3e7de8e94588b61a10bd8caf95376162a9 | 7d43fe7a590ee1e03cc8629a1086603b20b3733e | /forecasting/03 - exponential.smoothing/06 - ets.R | be08dce16c3d5cd3ce94b66bcb0b0b8065625aea | [] | no_license | aliawaischeema/DataCamp | 5a0f27b26fe26f8f171fa3e4a2f865841eb82304 | 9f86505b46637fb299b7b2e9391c607ee0d1c285 | refs/heads/master | 2023-03-17T15:30:29.538126 | 2019-08-26T20:48:33 | 2019-08-26T20:48:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 697 | r | 06 - ets.R | # Fit ETS model to austa in fitaus
fitaus <- ets(austa)
# Check residuals
checkresiduals(fitaus)
# Plot forecasts
autoplot(forecast(fitaus))
# Repeat for hyndsight data in fiths
fiths <- ets(hyndsight)
checkresiduals(fiths)
autoplot(forecast(fiths))
# Which model(s) fails test? (TRUE or FALSE)
fitausfail <- FALSE
f... |
dbfe94fa60257eeed51d0082b06aae6739697e18 | e5cff5c16e1ddbcd759da049b2614cdb5123cb1e | /Exploratory Data Analysis/week4/plot6.R | 7a1940f32a9830b58a2be2233447e997e0032a67 | [] | no_license | kronik/datasciencecoursera | 88afe7f2a9181237ea9774fbdf383529373be6d6 | 91e53c671e634f814c22b28b99008922afc76605 | refs/heads/master | 2021-01-21T13:49:36.792143 | 2016-05-22T09:26:42 | 2016-05-22T09:26:42 | 51,052,908 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 710 | r | plot6.R | library(dplyr)
library(ggplot2)
#Set new working directory
setwd("~/work/datasciencecoursera/Exploratory Data Analysis/week4/")
NEI <- readRDS("summarySCC_PM25.rds")
cities <- NEI[(NEI$fips == "06037" | NEI$fips == "24510") & NEI$type=="ON-ROAD",]
s <- summarise(group_by(cities, fips, year), Emissions = sum(Emission... |
57925b86db36231da6506db68d82823d7f8d6a1c | 8ec42d6b5fd354f587bd666e5bae8f6e755af7fb | /basic/rep_repint.R | f4d3bd0610264f90e91cc48734ae934e94e7cb1c | [] | no_license | hihumi/Learn-R | 55c1451b67e53fa2fdbbdbf25b917d43ad0cd9d1 | 040c75f1ef6d2cf48c2ee02d34d5b6eb7f93bc3f | refs/heads/master | 2021-01-10T10:41:53.651518 | 2016-10-19T06:02:00 | 2016-10-19T06:02:00 | 52,103,830 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 891 | r | rep_repint.R | # 要素を繰り返す数列
# rep(), rep.int()
# rep()
x1 <- c(1, 5, 10)
rep(x1, 10)
typeof(rep(x1, length = 5))
class(rep(x1, length = 5))
# length引数で長さを指定
rep(x1, length = 5)
# each引数でx1の各要素の繰り返しを指定
rep(x1, each = 5)
# times引数でx1全体の繰り返しを指定
rep(x1, times = 5)
# length.out引数はlengthと同様の結果
rep(x1, length.out = 5)
# 上記の組み合わせ例
rep(x1, le... |
90e944b2466d507446866c8c9483f7a459318b26 | 06402333efefe2b1fd6a85eee57ece90e2374afe | /02_download_data.R | f69da04a5eb99fa025c2166c90a5f0e4b2362b44 | [] | no_license | chuckwong13/MISCADA_CoreIIA_Classification | 6d36c19dfc5126415b6e84b1200e81c69e1fd359 | bad63f4463d886cbaec36fa9253434d551c7c8f0 | refs/heads/master | 2021-03-12T02:47:34.798831 | 2020-03-20T11:30:29 | 2020-03-20T11:30:29 | 246,583,241 | 0 | 0 | null | 2020-03-11T13:47:07 | 2020-03-11T13:47:06 | null | UTF-8 | R | false | false | 1,495 | r | 02_download_data.R | # Write code here to download the data you are using for your report.
# DO NOT push the data to your Github repository.
# For example, to download the simple Iris data from the UCI Machine Learning
# Repository
# uci.wine <- read.csv("https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data",header = ... |
dce729cc9a6ef8fc18560f03a360602e18b9a5c3 | 445e123499826e42b0770384ba3a1fe9086f6875 | /R/makeTranscriptDbFromBiomart.R | 9f772de1cf987fe923c735dcdc46f3b79a3ead92 | [] | no_license | genome-vendor/r-bioc-genomicfeatures | aa5d2f9b0c7e3844546de95aaeddac3ffa5cb269 | a9ffc2fb3682122dc28939aba0403364092e27b1 | refs/heads/master | 2016-09-06T01:33:31.530482 | 2013-02-14T17:34:25 | 2013-02-14T18:03:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 33,143 | r | makeTranscriptDbFromBiomart.R | ### =========================================================================
### makeTranscriptDbFromBiomart()
### -------------------------------------------------------------------------
###
### For people who want to tap BioMart.
### Typical use:
### txdb <- makeTranscriptDbFromBiomart(biomart="ensembl",
### ... |
fb41194749d72de2cbd168fc18ce12daf07c8b20 | 0d193831c182d0860777a08e4a923f33c42702bf | /cachematrix.R | 5e67ee06d829e0e4fb057d7ef6bf83cf15cca6d3 | [] | no_license | wjbolles/ProgrammingAssignment2 | 947fb21ed007b057ce462eec24c1bb37bb2f1c64 | 91ac9158cd472a75f7d99ecd999655a98051b73c | refs/heads/master | 2021-01-18T03:01:58.476316 | 2015-07-26T20:45:38 | 2015-07-26T20:45:38 | 39,698,217 | 0 | 0 | null | 2015-07-25T18:46:43 | 2015-07-25T18:46:41 | null | UTF-8 | R | false | false | 1,117 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Write a short comment describing this function
# Returns a list of 4 functions for returning precomputed inverse matrices:
# get,
# set,
# setinverse,
# and getinverse.
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set... |
ab07295a2f2063cbc3a6555e8129f2135b36307a | 0363e9059653e5ce2a8fd4dfa1bcfe981072ea82 | /R/getLapplyIndex.R | b28ca46f0ae0990574fb999499045bd98f03e021 | [] | no_license | mwrowe/microRutils | 7725bd4d5e2ac60337932f384562ed39abcf86a1 | 654cd867bafe126593089441f63c88906ecf60ed | refs/heads/master | 2021-07-07T19:59:43.732449 | 2021-06-10T16:59:33 | 2021-06-10T16:59:33 | 245,310,935 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,267 | r | getLapplyIndex.R | #' getLapplyIndex: Access Index of List Element Within lapply()
#'
#' Get name/index of current list element from within FUN argument of lapply()
#'
#' \itemize{
#' \item This is meant to be called from within the FUN argument passed to
#' lapply(); it cannot be used as the FUN argument itself.
#' \item It ... |
1da6a42f5eebd5dfe7b6defc5fe61400ca698788 | 63e1231faa30a4cea6dd9f25e87c2372383aa2f4 | /man/Choose.Rd | 3a74b2254c4916a018ff7b2463763175d0fe4d60 | [] | no_license | cran/MSEtool | 35e4f802f1078412d5ebc2efc3149c46fc6d13a5 | 6b060d381adf2007becf5605bc295cca62f26770 | refs/heads/master | 2023-08-03T06:51:58.080968 | 2023-07-19T22:10:23 | 2023-07-20T01:47:18 | 145,912,213 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,325 | rd | Choose.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Sketch_Functions.R
\name{Choose}
\alias{Choose}
\alias{ChooseEffort}
\alias{ChooseM}
\alias{ChooseSelect}
\title{Manually map parameters for the historical period of operating model}
\usage{
ChooseEffort(Fleet, Years = NULL)
Choos... |
9db708cdd6fc5bd9050881539ab520bd93cc537b | dc4ee159f747caade7c36cb7ee673fd5819f6e3b | /Exploratory Data Analysis/EDA1/plot3.R | 7984a57a91aecadbc18e5cc41bb22073134cf875 | [] | no_license | SampurnR/Coursera | d59b1b4cb2c5b4826e24a34ae812225159fcb6a0 | 09db173f326a49d6d9a751803c86a74ac5482f5d | refs/heads/master | 2020-06-04T19:35:35.176125 | 2015-04-22T03:23:12 | 2015-04-22T03:23:12 | 34,346,454 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,109 | r | plot3.R | setwd("C:\\Workspace\\R\\Codes\\Coursera\\EDA")
########## reading data
consumption_All <- read.table("household_power_consumption.txt", header=TRUE, sep=';', na.strings="?", stringsAsFactors=FALSE)
consumption_All$Date <- as.Date(consumption_All$Date, format = "%d/%m/%Y")
############## subsetting data
consumption_s... |
17f487dea2016e5fdadba0d0faa51bd78ae78783 | 5532bd62517bbadf3a402e637eff9896024a44eb | /man/RcmdrPlugin.SM-internal.Rd | 2f779c73ed717250a9738628db198610d9b61aab | [] | no_license | cran/RcmdrPlugin.SM | f78e7a1a75c918e26644d157b6721e207e4442b0 | d79f4c0800be272583f84f0cb7d5ea850d652a82 | refs/heads/master | 2021-01-01T19:11:09.974424 | 2012-12-21T00:00:00 | 2012-12-21T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 677 | rd | RcmdrPlugin.SM-internal.Rd | \name{RcmdrSMPlugin-internal}
\title{Internal RcmdrSMPlugin objects}
\alias{dotGraph}
\alias{dotchartTable}
\alias{barGraph2}
\alias{pieChart2}
\alias{frequencyDistribution2}
\alias{Histogram2}
\alias{boxPlot2}
\alias{QQPlot2}
\alias{barCode}
\alias{barCode2}
\alias{signifNC}
\alias{oneWayAnova2}
\a... |
a21d4a945627cd0a545ef05bb5224c794a30a62b | 26e26aca4102f40bc848120c4ebc99bb40d4a3c1 | /R/Archive/June 2020 (removed code files)/207-Homemade.R | d8226721bec89fab81ff0432897dc0395d6f3d4b | [] | no_license | IPRCIRI/IRHEIS | ee6c00dd44e1e4c2090c5ef4cf1286bcc37c84a1 | 1be8fa815d6a4b2aa5ad10d0a815c80a104c9d12 | refs/heads/master | 2023-07-13T01:27:19.954174 | 2023-07-04T09:14:58 | 2023-07-04T09:14:58 | 90,146,792 | 13 | 6 | null | 2021-12-09T12:08:58 | 2017-05-03T12:31:57 | R | UTF-8 | R | false | false | 1,638 | r | 207-Homemade.R | # 35-Homemade.R
# Builds the HomemadeIncome data.table for households
#
# Copyright © 2017: Arin Shahbazian
# Licence: GPL-3
rm(list=ls())
starttime <- proc.time()
cat("\n\n================ HHHomemadeIncome =====================================\n")
library(yaml)
Settings <- yaml.load_file("Settings.yaml")
library(da... |
1db5e1edbb36ef1b48ee4ccbb96dd45883437467 | a252fb33f4fa269e78ce3fd35fd6af169d624acb | /model/lauren/mleFits/models/noToM_model.R | 4f7d6160aa987d2e2e35f90bbe78d7373db9b415 | [] | no_license | la-oey/Bullshitter | a55347378c6293c78dc50f02476060bb96437388 | 38a068660e7dca8a369c0e18c3e7324abcf451b5 | refs/heads/master | 2021-11-27T17:34:24.706342 | 2021-11-22T07:21:15 | 2021-11-22T07:21:15 | 157,911,292 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,930 | r | noToM_model.R | setwd("/Users/loey/Desktop/Research/FakeNews/Bullshitter/model/lauren/mleFits")
source("ToMModelFunctions.R")
library(tidyverse)
noToM.s.pred <- function(alph, eta.S, weight){
weight = logitToProb(pmin(10, pmax(-10, weight)))
predS = mapply(function(i, j){
p.L_ksay.k.r(j, alph, eta.S, i, lastlvl=TRUE, rep(0... |
f4ced9679297ee5a6341a83596d6bb7bb1bd340a | 8dc01e5b732f84181089d0b36be6edf049459c3f | /run_analysis.R | dce8b31ff52d0e6c54c8149fe9137322ae0f3815 | [] | no_license | clankford/getting-and-cleaning-data-project | 2afa57d3c937e55ffbfcaf298ca97d38f2d3241f | 40fb46b0406c998ec6e0ec1e9096ee6c9d506801 | refs/heads/master | 2021-01-10T13:43:00.421124 | 2015-05-24T06:27:18 | 2015-05-24T06:27:18 | 36,158,876 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,543 | r | run_analysis.R | ##setwd("~/Dropbox/Data Science Specialization/Getting and Cleaning Data/data")
rawFeatures <- read.table("UCI HAR Dataset/features.txt")
rawActivityLabels <- read.table("UCI HAR Dataset/activity_labels.txt")
##Removing factor levels and treat data as characters
rawActivityLabels$V2 <- as.character(rawActivityLabels$V2... |
2bd789d7bacc88d25484fa0eeb6931bcf7ed0da3 | 86e4124e3a2884a0a2bec25915c9476ecc97ddbf | /plot6.R | fd19c75bde94bab9e4bfd3cf44c650b42a52ae93 | [] | no_license | sjrocks/ExData_Plotting2 | bdeda2f24ba479a39856a45394fbc63a83fa0e20 | 2b8a049ff14ebcd08b5dc8808ce944ae100cd912 | refs/heads/master | 2021-01-10T03:22:34.775229 | 2016-02-15T00:56:05 | 2016-02-15T00:56:05 | 51,721,915 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 863 | r | plot6.R | library(dplyr)
library(ggplot2)
#clear session
rm(list=ls())
# Read the data
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
SCC.motorVehicle <- filter(SCC, grepl("vehicle", EI.Sector, perl = T, ignore.case = T))
NEI.motorVehicle <- inner_join(NEI, SCC.motorVehicle, by="SCC")
... |
87b42cb4710b26d0ff6fba929bafe43664cd491d | 6aead4c3fbbff49544c12c0d1b02d944cc3fce85 | /makingValuesCategorical.r | a61f8989eb6780618f07e5725343ff5d46fc48b8 | [] | no_license | mycakmak/acikakademikodlari | d619ad69697193abaf7d5709e20f85931aaee232 | 4712c15a07ddd6714849b11934f9530b159a9117 | refs/heads/master | 2021-01-23T21:17:20.822709 | 2017-11-16T13:59:42 | 2017-11-16T13:59:42 | 102,891,158 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 187 | r | makingValuesCategorical.r | frame2 <- maml.mapInputPort(1)
frame2[, c("customer_id","product_id","weekday")] = lapply(frame1[,c("customer_id","product_id","weekday")], as.factor)
maml.mapOutputPort("frame2");
|
41dc6257d7ada41819bd8f4ffefb203ffd7fe523 | ab2749ed7092f3d79690a2495867b109daf9a48b | /R/app_ui_results.R | e9028e76d6f697627461e7f29a1dd8954e9b121a | [
"MIT"
] | permissive | TileDB-Inc/gtexplorer | 49f93d47f055fd1498dd52ed25ee1df1a972b2d5 | df9a9814d095e53d8b93e97164f81ca42a8847d0 | refs/heads/master | 2023-08-26T22:16:00.507436 | 2021-09-30T16:00:45 | 2021-09-30T16:01:07 | 368,531,835 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 217 | r | app_ui_results.R | app_ui_results <- function() {
div(
id = "options",
shiny::fluidRow(
DT::DTOutput("table_genes")
),
shiny::fluidRow(
plotly::plotlyOutput("plot_results", height = "550px")
)
)
}
|
7c0968b03b3a179b52084615a26c9cdfcaefbc38 | 701b77156f818bc155617253405eeac6fe80b6a1 | /misc/pdf_to_text.R | c86d566c43dfbd56578ec62cfbb212a235bf4a14 | [
"MIT"
] | permissive | kingwatam/csrp_hku | 63c6f367587114d44f82215d15bfa78138ba6b86 | 5688966281dd0a7ea5cda793303268833f09274c | refs/heads/main | 2023-03-19T19:58:14.538540 | 2021-03-05T09:31:16 | 2021-03-05T09:31:16 | 308,496,334 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 326 | r | pdf_to_text.R | rm(list=ls())
graphics.off()
# par(mar=c(0,0,0,0)) # set plot margins to 0
setpath <- "/MEGAsync/Work/HKU/CSRP"
setwd(sprintf("~%s", setpath))
source("helper_functions.R")
library(pdftools)
library(magrittr)
library(readr)
setwd(sprintf("~%s/misc", setpath))
text <- pdf_text('example.pdf')
cat(text, file = 'example.... |
4a9d08790a96ec3d3bf0686f735b00c44d026752 | 92630399e7e476b1307eba0deceeab6048c69687 | /R/optimaltrees.R | 439433576f23fdcf327ee5952a27eea4b8f92d07 | [] | no_license | cran/iai | 53bbc8fa096659614e4b288902cec2021feb35d0 | 591d213c1a7f9cafa0673e3f897f2c589f3e0435 | refs/heads/master | 2023-06-23T03:16:43.953505 | 2023-06-13T15:10:06 | 2023-06-13T15:10:06 | 197,544,870 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,047 | r | optimaltrees.R | #' Learner for training Optimal Classification Trees
#'
#' Julia Equivalent:
#' \href{https://docs.interpretable.ai/v3.1.1/OptimalTrees/reference/#IAI.OptimalTreeClassifier}{\code{IAI.OptimalTreeClassifier}}
#'
#' @param ... Use keyword arguments to set parameters on the resulting learner.
#' Refer to the Ju... |
efdc90dcc7598ffb2e4cf128d7728c46339ec763 | 6edb95a7572e2248c2c772e55953a452ee699dbc | /Web Crawling/한빛미디어 1.R | 754c29d3da18f22d7ef73c1084086d84b0428c02 | [] | no_license | wotjr5477/R-Lecture-2021 | 064f53854f4d17a0c87cfdcc683f3723bc3d3b86 | d1871cf852eda6fc5417db7ae62e785b649f8ece | refs/heads/main | 2023-08-27T14:02:06.012255 | 2021-10-25T04:38:15 | 2021-10-25T04:38:15 | 359,694,757 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,437 | r | 한빛미디어 1.R | # 210510
# 한빛 미디어 사이트로 웹 크롤링 연습하기
library(rvest)
library(stringr)
library(dplyr)
# 웹 사이트 읽기
base_url = "https://www.hanbit.co.kr/media/books"
sub_url = "new_book_list.html"
url = paste(base_url, sub_url, sep="/")
url
html = read_html(url)
container = html_node(html, "#container") # id = "container"
book_list = htm... |
147791936161453950589a3f9f866324c52402a1 | 73590bd798f17fbc859c89726c3eb2c293394384 | /curation/ovarian/curatedOvarianData/man/GSE19829.GPL570_eset.Rd | 7dc312ba0b9d179f3e679b37417a2acb61d6299e | [] | no_license | tgerke/catd | b8f4d68f61d92ad3d5e6264325137637d65079cd | ba2b6298532cf86cfb8d8223ca35f688b9c332f9 | refs/heads/master | 2021-05-11T19:45:09.159306 | 2018-01-17T20:39:26 | 2018-01-17T20:39:26 | 117,888,686 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,550 | rd | GSE19829.GPL570_eset.Rd | \name{GSE19829.GPL570_eset}
\alias{GSE19829.GPL570_eset}
\docType{data}
\title{Gene Expression Profile of BRCAness That Correlates With Responsiveness to Chemotherapy and With Outcome in Patients With Epithelial Ovarian Cancer.}
\description{To define a gene expression profile of BRCAness that correlates with chemother... |
4426d9259adabd39cbcba0c36a21c4417d1fc53a | 6c2e5e53628f944665176749b7b29491789acc8d | /testes_hipotese.r | e2279c4dab68c94ea34a317de098f19628feb079 | [] | no_license | anselmobattisti/loadbalance_tests | 367ea1fb1ac516b43dbda10ca88ada362a77a0d5 | 9be81726a59fd2ed2a55523fa8a9789ada282c9e | refs/heads/master | 2020-04-07T00:38:31.727399 | 2018-11-29T23:09:13 | 2018-11-29T23:09:13 | 157,912,717 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 126 | r | testes_hipotese.r | wilcox.test(dados_4$Round.Robin, dados_4$Round.Robin.Weight, paired = TRUE, exact = FALSE, conf.int = TRUE, conf.level = 0.95) |
4890548047615cd66d8608cffd1199623aa8780d | caf56f313d6e34f4da4c5a0a29d31ff86262533a | /R/all-equal.r | 003491d26a4a3f7cf4ed85c019406b3b02f26141 | [] | no_license | bhive01/tibble | c00b4894e4067a2d6443a33808649bf327367b3a | 7c0aca252cba66ff02e48e9a9dffea816ffe4d4f | refs/heads/master | 2021-01-17T04:56:06.995980 | 2016-03-19T00:43:30 | 2016-03-19T00:43:30 | 54,232,754 | 0 | 1 | null | 2016-03-19T00:43:30 | 2016-03-18T21:34:01 | R | UTF-8 | R | false | false | 3,409 | r | all-equal.r | #' Flexible equality comparison for data frames.
#'
#' When comparing two \code{tbl_df} using \code{\link{all.equal}}, column and
#' row order is ignored by default, and types are not coerced. The \code{dplyr}
#' package provides a much more efficient implementation for this functionality.
#'
#' @param target,current ... |
f600993c3d5c17b427af6d279091f22713b34348 | 9e1a32fb410bbd83a8c3935eb820d5fd5e97b668 | /analysis.R | cc0e159198524e05f88030bd23745ab4c96abd64 | [
"MIT"
] | permissive | inti/ASE | 3f72b72c3cce8302c4f9a6f6eab67e7a94a13207 | 4f7d40487f80643a7981945598c03932c7d4a38e | refs/heads/master | 2020-03-26T17:04:49.435801 | 2018-11-13T19:20:03 | 2018-11-13T19:20:03 | 145,140,411 | 0 | 0 | MIT | 2018-10-04T12:58:01 | 2018-08-17T16:00:34 | null | UTF-8 | R | false | false | 770 | r | analysis.R | library(brms)
library(VGAM)
library(plyr)
library(ggplot2)
library(lmerTest)
info = read.delim("SraRunTable.txt")
data = read.delim("test_all_10K",header=T)
data$x = as.integer(data$alpha_post)
data$n = as.integer(data$beta_post)
head(data,3)
s = subset(data, name =="LOC100650155")
s2 = merge(s[,c("bam","pASE","x",... |
be21fd57d4165cd4fb46e3978758444b80cf935a | 04b2447224d6321e0eba324b1b786eab2e3c5f10 | /digit_f.r | 24a394c250fb15ea530f5a0bb373973aaeb56759 | [] | no_license | woofer30/tkfd_model | 811b607cdd4cd93e8e9eb6d208cfd8ff4dc7f2ff | 0af91bac794b10470902b403fbd64466ae6a0b39 | refs/heads/master | 2020-09-27T14:05:07.463202 | 2016-09-04T07:21:05 | 2016-09-04T07:21:05 | 67,331,323 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 515 | r | digit_f.r | ## シナリオ変数 財政
base$DMY_F_DLT_MID = ifelse(index(base)>2016,ifelse(index(base)==F_DLT_Y0_MID,1,0),0)
base$F_PRB_FIX_RGDP = dexpd(0*base$one, 0.01*base$one ,2013)
base$F_DLT = dexpd(base$F_DLT,base$F_DLT+as.numeric(F_DLT_P1)*base$one,as.numeric(F_DLT_Y1)-1)
base$F_DLT = dexpd(base$F_DLT,base$F_DLT+as.numeric(F_DLT... |
a0cbfceca7292f6437c6d92ce25cccb3c35d9af8 | 0b2690313a9a81596ea6ef899dabd192ca050a9d | /data-raw/build_fips.R | 55d9c297f537b380d6f955ae117db5db9cb3fc73 | [
"MIT"
] | permissive | jeffreyawright/censable | e151daf5c7e1305d874741ef94a6a16e6a421252 | 127bfa87242155e0f18240426c5273c44d0116bf | refs/heads/main | 2023-08-17T12:04:20.569014 | 2021-10-07T14:59:28 | 2021-10-07T14:59:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,427 | r | build_fips.R | # Create FIPS 2000
fips_2000 <- readxl::read_excel('fips_2000.xlsx', col_names = FALSE)
names(fips_2000) <- 'raw'
fips_2000 <- fips_2000 %>%
dplyr::mutate(
raw = stringr::str_squish(raw),
state = stringr::str_sub(raw, 1, 2),
county = stringr::str_sub(raw, 3, 5)
)
fips_2000 <- fips_2000 %>%
dplyr::muta... |
b2c0019afa5fb47d647830e3ae73321020f184a1 | 77b27c431d919ff6c463b1eb70576b811c5a84dc | /paper_specific_analyses/regional_select_countries_prevalence_and_counts.R | 7e96d330fae5216eac01273d03538fafe15fbb8a | [] | no_license | ihmeuw/cgf | 5db05acb4778c6a1aa03e314d69f0ea3f3426939 | 0cf00065a42cc7e2bc824d2eb4ee3d9b5a4ae066 | refs/heads/main | 2023-04-07T09:31:00.515179 | 2022-03-01T22:06:52 | 2022-03-01T22:06:52 | 463,323,720 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,956 | r | regional_select_countries_prevalence_and_counts.R |
# load functions and libraries
invisible(sapply(list.files("filepath", full.names = T), source))
library(data.table)
library(ggplot2)
library(dplyr)
library(scales)
library('cowplot', lib.loc = "filepath")
library(gridExtra)
library("ggridges", lib.loc = "filepath")
"%ni%" <- Negate("%in%")
# loading populations
p... |
ae9ae16a1f67019f1e99a1661c39508302c60666 | 92445969b4bde82452b46bc4745a760d11f80863 | /Exerciese/Ch9/Ch9-4.R | 5597a7cd223426dd3f73c47f5a7366b2ff581470 | [] | no_license | ChenPH0522/ISLR | c44c3cd2ee5e080da922f4d9551bf5fed31b03cb | cfc961e5b3f26144ee60c8a0174d19773ae5387e | refs/heads/master | 2020-04-22T18:52:36.910641 | 2019-08-08T01:54:35 | 2019-08-08T01:54:35 | 170,590,311 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 986 | r | Ch9-4.R | library(e1071)
set.seed(1)
# ------------------------------------
n = 100
p = 2
x = matrix(rnorm(n*p), ncol=p)
y = ifelse( x[,1]^2+x[, 2]^2 < 1, 1, 0 )
plot(x, col=(2-y))
# SVM: polynomial kernal
dat = data.frame(x=x, y=as.factor(y))
svm.poly = svm(y~., data=dat, kernel='polynomial', degree=2, cost=1)
summary(svm.po... |
8943a47c9b1fc7636ad15b89ab01bd02dbf84b3d | 95e7aff56e3ca1db4a7968e24b8fcaa178a57237 | /XGBOOST_importance_plots.R | 0a4e28698ea805f3b773bb4cda391f51b7ba6116 | [] | no_license | eherdter/Seatrout_ENV_Chapter2 | 2525954cde07b41e8805523bb6b94f1975edac94 | 6bf05d4bd2efacd5ac2160cd878e424d4bb578f2 | refs/heads/master | 2021-10-08T14:59:07.902986 | 2018-12-13T19:59:08 | 2018-12-13T19:59:08 | 109,744,498 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,701 | r | XGBOOST_importance_plots.R | #importance plots for BRT and GLM
#"Seatrout_ENV_Chapter2/xgboost_results",
rm(list=ls())
# Set Location
IS_HOME = FALSE
if (IS_HOME == TRUE) {
data = "~/Desktop/PhD project/Projects/Seatrout/Data/Exported R Dataframes/Seatrou"
source_location= "/Desktop/PhD project/Projects/Seatrout/Seatrout_ENV_Chapter2"
... |
33c834b1e1f9e362e1b31f819d56372185997aef | 6454ad6876db56ce0bdf0a6cbc45efe7502cecaa | /R/final_model.R | 16ccec4c3626af7588001bf44d5a6740a95c1993 | [] | no_license | dpoursanidis/modleR | aa148a01556f3dbf2299f837bd90a7bf36725e2d | 97ea4cfc95297cd244fbd58fd252f1cacd1bcd5f | refs/heads/master | 2020-07-30T18:08:55.220380 | 2019-08-15T13:54:41 | 2019-08-15T13:54:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,802 | r | final_model.R | #' Joins ENM from several partitions, creating a model per algorithm.
#'
#' This function reads the output from dismo.mod and creates a model per species
#' @param species_name A character string with the species name
#' @param algorithms Which algorithms will be processed. If no name is given it
#' will process all al... |
116536b70e927b12d835c3581e13761ff4c25014 | a6cf1dcd8e192497e2a78176d3d08a60db501b27 | /WBYU.R | b2f5dba1c987f5ee5de8614454309610e403c0fa | [] | no_license | aaronabraham311/World-Bank-Youth-Unemployment-EDA | 360438a01fa0a97b11c56540188a12e815e05621 | 506e58fac7338cdbedb5f33b26faef005668b7a0 | refs/heads/master | 2021-06-29T18:27:18.401846 | 2017-09-19T14:46:16 | 2017-09-19T14:46:16 | 103,993,583 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,334 | r | WBYU.R | # Including libraries
library(tidyverse)
library(rworldmap)
# Loading and viewing data
data <- read_csv("API_ILO_country_YU.csv")
View(data)
# Notice that there are rows that are not countries. Rather, it is a classification (based on income or continent)
# First question: how does youth unemployment change over yea... |
18fa640bfd24b0d9de393960a016431b9ab12dac | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/fmf/R/RcppExports.R | d87eb08404524491c5ced4f8de882043b50cbc3a | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 512 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
knn_index_dist_rcpp <- function(MATRIX, TEST_DATA, k, method, threads, eps = 1.0e-6) {
.Call(`_fmf_knn_index_dist_rcpp`, MATRIX, TEST_DATA, k, method, threads, eps)
}
DIST_MATRIX_knn <- fu... |
04874714d56ad3a245db7c623488d85ca5f7fc52 | d4830fc3ad754c22929791f9b8153fdcdcf8cc11 | /ELFGEN/internal/elf_assemble_batch.R | 3dccb3e6fec35dd28d6d451050e7243acd6b1173 | [] | no_license | HARPgroup/r-dh-ecohydro | 9afc44c5c332ca2db6fdfb5c3151f2c27364d1a9 | f7c43a3da6fe68da8ec1e62f34e4f67147db63d2 | refs/heads/master | 2022-05-08T11:05:45.134772 | 2022-04-20T13:51:28 | 2022-04-20T13:51:28 | 104,229,291 | 1 | 1 | null | 2022-04-20T13:51:29 | 2017-09-20T14:53:28 | HTML | UTF-8 | R | false | false | 34,233 | r | elf_assemble_batch.R | library(quantreg);
library(ggplot2);
library(ggrepel);
library(ggpmisc);
library(grid);
library(httr);
library(data.table);
library(scales);
library(rgeos); #used for geospatial processing
library(sp); #contains SpatialPolygonsDataFrame()
library(ggsn); #used for adding scale bar and north arrow to map
library(tidyve... |
7e6a2eb6ff5b775d27c753638262cd3dbdb3ee36 | 02127f6ff5483e535efb0e0dd487e0d5bbdbe102 | /Practise Assignment.R | 1f576d3792b2caeaf1423c641b513ecfc7d8013f | [] | no_license | helenatan/R_Programming_Github | 25081548ca4f096878cb3d50f86b15989f2ff1c8 | 6229b6753d2731e607f72d177c222823bdc880e6 | refs/heads/master | 2020-05-15T06:03:40.150335 | 2015-03-05T08:09:42 | 2015-03-05T08:09:42 | 30,334,995 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,229 | r | Practise Assignment.R | ## Task Description
## Date: Feb 5, 2015
## 1. Download the file "diet_data.zip" from http://s3.amazonaws.com/practice_assignment/diet_data.zip
## and unzip it into your R working directory
## 2. Get Steve's weight on the last day, and determine if he has lost any
## weight compared to the first day
## 3. Among all 5 ... |
9252ff4a40d6967b933f9973a2838086c2900973 | f609028e25e06a84cffe690261f5f828fea2e857 | /tests/testthat/test-request-object.R | e7ab08422a1807eab5be28522b8a7247994a2adc | [] | no_license | nteetor/prairie | 89dd08bf751a5c896e9b17d6355b07763265d655 | f275f64392b0c52c0b6b905c156c33ee9e5fc57b | refs/heads/master | 2020-04-16T04:51:56.702687 | 2017-07-08T20:09:54 | 2017-07-08T20:09:54 | 40,051,783 | 39 | 3 | null | 2017-07-21T01:51:55 | 2015-08-01T15:56:12 | R | UTF-8 | R | false | false | 1,076 | r | test-request-object.R | context('request object / as.request')
template_request <- list2env(
list(
HTTP_ACCEPT = "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8",
HTTP_ACCEPT_ENCODING = "gzip, deflate, sdch",
HTTP_ACCEPT_LANGUAGE = "en-US,en;q=0.8",
HTTP_CACHE_CONTROL = "max-age=0",
HTTP_CONT... |
ba9cd0b03770065f1087c41dfc96383bd250848a | f5d675843979c36e841b4940453147717806d0d3 | /plot2.R | 8a48c64d04c84d93392e476a6e18b8b5fbd870f0 | [] | no_license | nryedida/ExData_Plotting1 | 870512a845a27db3b10f1d0fab2a1a603f47cd6c | 5e038c91a6a45af2df928357176a75db95f45da0 | refs/heads/master | 2020-03-20T06:37:56.825480 | 2018-06-13T18:28:30 | 2018-06-13T18:28:30 | 137,254,834 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 455 | r | plot2.R | hpc <- read.table("household_power_consumption.txt", sep=";", header = TRUE, na.strings = "?")
hpc$Date <- as.Date(hpc$Date, format="%d/%m/%Y")
hpc_df <- hpc[(hpc$Date=="2007-02-01") | (hpc$Date=="2007-02-02"),]
hpc_df$x <- strptime(paste(hpc_df$Date, hpc_df$Time), "%Y-%m-%d %H:%M:%S")
plot(hpc_df$x, hpc_df$Global_acti... |
e7a3eab83b472a0f7bedba67b639c2612ae69ffa | 9aaacc9395f7950012c60deb494eae3b35eed805 | /field_abundances_and_field_fitness.R | 93641a74b36173791bc85bf9d531acdef7161966 | [] | no_license | jensculrich/coexistence_manuscript_2019 | 4a78fe95fbdb29ae18246852da688465ffb087b8 | 28569319c540ffba42edb16ceb9e8b3a9b4cbde4 | refs/heads/master | 2022-12-02T21:52:56.322247 | 2020-08-12T22:23:02 | 2020-08-12T22:23:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,813 | r | field_abundances_and_field_fitness.R | # this file contains code to analyze relationships
# between fecundity and hetero-/con-specific abundance
# and between abundance and hetero/conspecific abundance
library(tidyverse)
library(lme4)
#################################
## Data Structuring #############
#################################
fitness_and_abunda... |
a7a4c192109123a860c3734a7b52dc919cfc989f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/GEOmap/examples/plotnicetix.Rd.R | 575764566f476697d90cb45b7488a2ab0c5bf3ce | [] | 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 | 329 | r | plotnicetix.Rd.R | library(GEOmap)
### Name: plotnicetix
### Title: Plot Lat-Lon tick marks
### Aliases: plotnicetix
### Keywords: misc
### ** Examples
proj = setPROJ(7, LAT0 = 0 , LON0= -93)
rx = c(652713.4, 656017.4)
ry = c(1629271, 1631755)
plot(rx, ry, type='n', asp=1, axes=FALSE , ann=FALSE)
plotnicetix(rx, ry, proj, PMAT=N... |
f845f8f45255bcdf0f8cf40c0b8fb0825adbb21d | 8a1f7a8d83ed93b69195864d08024798fda75957 | /sandbox/GGally/ggallyTest6-Plots.R | c9a6fbda054e4ab0d2a25347ee0bb7b13a103ddd | [] | no_license | hudsonchaves/diss-floss | 9f40c84ca041870f05bdade3808bbf9313f359ee | ed048c3a547c67216768e283922c971afc662664 | refs/heads/master | 2020-12-26T05:02:01.682801 | 2014-12-04T05:33:21 | 2014-12-04T05:33:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,634 | r | ggallyTest6-Plots.R | # GGally @examples for correlation plots
rm(list = ls(all.names = TRUE))
library("GGally")
PRJ_HOME <- Sys.getenv("DISS_FLOSS_HOME")
source(file.path(PRJ_HOME, "utils/data.R"))
MERGED_DIR <- file.path(PRJ_HOME, "data/merged")
MERGED_FILE <- "flossData" # default
RDS_EXT <- ".rds"
fileName <- paste0(MERGED_FILE, ... |
da6dc9d6c22d0146e31d8a54017819a4abda2743 | e01a015d086bb9f0029e2c77070e3dc168a88562 | /2-R programming/week4/assignment3/common.R | c90d74b4f79cdc6f3c5ee1d50e51c18838b737f1 | [] | no_license | YvesKamdjo/coursera | f471d1a9ec3267238c014624c7d31ecd99a8f57f | ba839d616ff4bf9c75cabd77dd060a9844a29c80 | refs/heads/master | 2021-01-21T01:43:47.186568 | 2016-06-29T06:04:24 | 2016-06-29T06:04:24 | 62,199,176 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 138 | r | common.R | readfile <- function(fname="outcome-of-care-measures.csv"){
outcome <- read.csv(fname, colClasses = "character")
return(outcome)
} |
ec22ef399ee1d52a182e24815149615a72d590c9 | 3fd2648f81e40341fc3581efea365c9203b8fd90 | /plot4.R | 946858173d4333fc98fad49216c2adeb2a43af26 | [] | no_license | TimWise/Coursera-ExploratoryDataAnalysis-Plotting1 | 7c61d44cbaa8b127cbf1f97302fae129aee3464a | c94fd3ea896ccfeb36341f168d3fc28ad081cbb8 | refs/heads/master | 2020-12-11T09:23:53.402875 | 2014-11-04T01:41:02 | 2014-11-04T01:41:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,453 | r | plot4.R |
# Whoa, this package helped with the two new plots looking really black
#
# Using this we can control the linewidth easily so that we don't get large blocks
# of black when points are dense
#
# It also gives the gray box and black axis look we were trying to get
# manually
#
install.packages("Cairo")
library(Cairo)... |
9ac5fdf1af569371c6f94b23e0a3ac5b3dda457f | 0bc1712f713ba17764248c1c9f496c411d265861 | /script/CategoryBox.R | 9d18b317908f2ed644a374839179240b4f1d8c15 | [] | no_license | ZhikunWu/PGC | 012dc3867189f35330265204308885c5629916a3 | 8925d020a8f74bfda8e83daa6607c1ca6e9cfac6 | refs/heads/master | 2023-04-07T06:39:46.462351 | 2021-12-30T06:56:01 | 2021-12-30T06:56:01 | 284,568,907 | 0 | 3 | null | 2021-09-22T08:48:00 | 2020-08-03T00:44:06 | Python | UTF-8 | R | false | false | 3,115 | r | CategoryBox.R | #!/usr/bin/Rscript
library(ggplot2)
library(argparser)
library(reshape2)
library(readr)
#usage: Rscript /home/wuzhikun/github/NanoHub/script/CategoryBox.R --input /home/wuzhikun/Project/Population/population/genotype/Sample_SV_type_heter2homo.txt --pdf /home/wuzhikun/Project/Population/population/genotype/Samp... |
c18b2210ba04cb09ad088fd62ba095207488b5da | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/EurosarcBayes/examples/binom_one_alpha.Rd.R | f1536405bca26060bdfffc7d142695d74c105ab5 | [] | 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 | 510 | r | binom_one_alpha.Rd.R | library(EurosarcBayes)
### Name: binom_one_alpha
### Title: Single arm, exact p-value calculator for single or multi-stage
### binomial trials.
### Aliases: binom_one_alpha
### ** Examples
# Simon's two stage design
failure=c(0,3)
success=c(NA,4)
n=c(7,18)
p0=0.1
result.success=4
result.n=18
# without accountin... |
4985e119cd94577a70485709fe9ecc7bb6877d06 | 5c0e5ff92e0ce763bce555f2da2522043aabdf0a | /survey/code/r/fa_04102020.R | f1943a2c9ce9e8a3f9d5c7053858211a94df91f2 | [] | no_license | ellelang/DCM | faad6cb457e43614a365d63ce2adb3a2241cad93 | 4c9b8a3d045606bbefb4d9b2ba47b685f444b4d6 | refs/heads/master | 2023-07-06T08:45:54.881813 | 2023-06-22T03:45:00 | 2023-06-22T03:45:00 | 159,539,900 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,245 | r | fa_04102020.R | rm(list = ls())
setwd("C:/Users/langzx/Desktop/github/DCM/survey/data")
#setwd("~/Documents/github/DCM/survey/data")
library(psych)
library(FactoMineR)
library(dplyr)
library(tidyverse)
library(fastDummies)
library(sem)
library(factoextra)
library(lavaan)
library(likert)
library(missMDA)
library(corrplot)
library(poly... |
94d39977c3cb01ff0de493ae3dc533756fb38105 | 8b063cda337ef9be263e3ebb1a132cbcbf453022 | /assignment/9.15/Assignment 03.R | 02cbf98a181edc8d13b76c17d90b9564563102d9 | [] | no_license | linleiwen/Course_Programming-_for_analytics | 7285946965ff5521d841e22ceaf7d2c3e6f7532a | 4b08a89ce0e49e1eb04b1c4128294de37deb67b0 | refs/heads/master | 2021-08-23T16:59:31.038610 | 2017-12-05T19:46:30 | 2017-12-05T19:46:30 | 111,733,693 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 358 | r | Assignment 03.R | rnorm2 <- function(n,mean,sd) { mean+sd*scale(rnorm(n)) }
set.seed(1239)
r1 <- rnorm2(100,25,4)
r2 <- rnorm2(50,10,3)
samplingframe <- c(r1,r2)
hist(samplingframe, breaks=20,col = "pink")
mean(sample(samplingframe,size = 50, replace = T))
a= 1:50
a = apply(a,2)
for(i in 1:50) a[i]=mean(sample(samplingfra... |
077e029b6c51f166a6b2c87281b0121b66ccada2 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/greyzoneSurv/examples/mydata.Rd.R | 6e58b683665d82a8c0962e116443a13b1791568d | [] | 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 | 119 | r | mydata.Rd.R | library(greyzoneSurv)
### Name: mydata
### Title: Package Data
### Aliases: mydata
### ** Examples
data(mydata)
|
87403e3cfe8d613b8f58baa14c3a49d3e941a78d | c555092c911699a657b961a007636208ddfa7b1b | /man/qplot.Rd | bbe53e883e7d493720f330bd082054aa6a7075c6 | [] | no_license | cran/ggplot2 | e724eda7c05dc8e0dc6bb1a8af7346a25908965c | e1b29e4025de863b86ae136594f51041b3b8ec0b | refs/heads/master | 2023-08-30T12:24:48.220095 | 2023-08-14T11:20:02 | 2023-08-14T12:45:10 | 17,696,391 | 3 | 3 | null | null | null | null | UTF-8 | R | false | true | 2,971 | rd | qplot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/quick-plot.R
\name{qplot}
\alias{qplot}
\alias{quickplot}
\title{Quick plot}
\usage{
qplot(
x,
y,
...,
data,
facets = NULL,
margins = FALSE,
geom = "auto",
xlim = c(NA, NA),
ylim = c(NA, NA),
log = "",
... |
36ed1a7ce1c12cedee12f34d619fa403ff337a59 | 419a2bf110b5bfe2a0ae3e56d26a32347cb2e191 | /My_func.R | f379e868d892061b477736701650aec68c472474 | [] | no_license | mshariful/TestRipo | 83af60701fadef8c562c5c6b9d51849f453e6bc4 | 4c2fabae607f41ef5128715602af74f02ed7898e | refs/heads/master | 2020-12-17T21:04:41.346951 | 2020-01-21T15:04:59 | 2020-01-21T15:04:59 | 235,295,546 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 181 | r | My_func.R | library(dplyr)
str(iris)
summary(iris)
# This is a line from RStudio
plot(iris$'Sepal.Length',iris$'Sepal.Width')
# This looks better
# This looks better 2
# This looks cool 3 |
339a98f94b6a94bbd4151019810887ef74d7f87e | 55ebbf72246f8c4addab7acfd0a2fbaae20eb5e1 | /man/sensitivitiesSymb.Rd | 0662b6f517f60407ac1d90c004d3352e97c53882 | [
"MIT"
] | permissive | dkaschek/cOde | 07571c98397610f8bea5c2187eced6f9defc7b55 | 3c9ddff4108262615f148858848ba583fc1dde86 | refs/heads/master | 2022-05-07T07:06:44.496680 | 2022-04-19T07:30:18 | 2022-04-19T07:30:18 | 43,353,323 | 11 | 3 | null | 2019-07-09T11:44:54 | 2015-09-29T07:59:42 | R | UTF-8 | R | false | true | 7,254 | rd | sensitivitiesSymb.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/derivedEquations.R
\name{sensitivitiesSymb}
\alias{sensitivitiesSymb}
\title{Compute sensitivity equations of a function symbolically}
\usage{
sensitivitiesSymb(
f,
states = names(f),
parameters = NULL,
inputs = NULL,
events = NULL,... |
e7f2c8552ecd0e4bcf3683d14df8bb895b052480 | 287f022f6e04b20aaa27f165d1dea80994b0e492 | /curvefit/man/curvefit-package.Rd | 7b2b8fd2b31bd137782ebbc2089e7585c9c748aa | [] | no_license | prsteele/curvefit | 6559d72d746dc1035eb3860f591b3ab597ae4831 | 720276f5263e966bfb95ef9d2e296ceaa361f634 | refs/heads/master | 2020-05-30T15:45:57.799241 | 2012-05-08T15:06:42 | 2012-05-08T15:06:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,333 | rd | curvefit-package.Rd | \name{curvefit-package}
\alias{curvefit-package}
\alias{curvefit}
\docType{package}
\title{Bayesian curve fitting}
\description{
Fits data using Bayesian techniques.
}
\details{
\tabular{ll}{
Package: \tab curvefit\cr
Type: \tab Package\cr
Version: \tab 1.0\cr
Date: \tab 2012-04-22\cr
License: \tab GPLv3\cr
LazyLoa... |
ef93b0e8f1f9e58359f9a79077f3ca59a3cb936b | e96286a6c47c377596b8857a6661d113ad75b5b8 | /Code/Source/predictive_fit_hmnl.R | deed4c8977d4f8dc4d5a28613e149a90b679e5cc | [
"MIT"
] | permissive | marcdotson/conjoint-ensembles | 1dcaf6413b12851d9bfd2d05285703f6ebf199bc | fbb1163b03e77d14238584565247630f934fdefa | refs/heads/main | 2022-06-17T07:16:00.905779 | 2021-09-16T21:55:34 | 2021-09-16T21:55:34 | 134,318,937 | 0 | 1 | MIT | 2023-08-30T16:59:39 | 2018-05-21T20:00:33 | R | UTF-8 | R | false | false | 2,483 | r | predictive_fit_hmnl.R | predictive_fit_hmnl = function(hmnl_draws, test_X, test_Y, test_Z){
# Compute the hit rate for the indicated model.
# hmnl_fit - hmnl output with log_lik, betadraws, gammadraws, and Omegadraws
# test_Y - choices (hold-out sample)
# test_X - design matrices (hold-out sample)
# test_Z - matrix of covari... |
2060cc26143da1249455659e0523e1edf8d48155 | 0c4914d3c503a936f0f7531aefd56de756012a9d | /man/simData2.Rd | c75d521085db54e6d9d5e6b6972f94db35873192 | [] | no_license | feiliu135/FADPclust | 88ebf030a00692b15ec3536fd7dbef628f782435 | d81d53f13ef261b51e132e65802a09a983732cf1 | refs/heads/master | 2023-08-19T05:00:59.197574 | 2021-10-08T06:10:09 | 2021-10-08T06:10:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 439 | rd | simData2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simData2.R
\docType{data}
\name{simData2}
\alias{simData2}
\title{Simulated multivariate functional data for method FADPclust}
\format{fd, see FDA R package for details.}
\description{
Simulated three-dimensional multivariate functional data,... |
6dc97f617e255d435e9e2aafcf9ddb07d36bebfe | 3e51c995b0766e621819e8d5804156e7d29b2c43 | /R2OpenBUGS_ex1.R | 7f25a4ed15d5285049eacab84cfbbccc98c82a5d | [] | no_license | miaomiao7777/Bayesian-Stats | 39efd0a0eb0440ecf6a638b402b2f74b37996eeb | 94a38b26b8d95eba725f104c1aff7346d9f44f5a | refs/heads/master | 2020-04-29T14:45:05.246126 | 2019-03-18T04:49:35 | 2019-03-18T04:49:35 | 176,206,264 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 670 | r | R2OpenBUGS_ex1.R | install.packages("R2OpenBUGS")
library(R2OpenBUGS)
bugsex1 <- function() {
pi ~ dbeta(1,1)%_%I(0.2,0.45)
r ~ dbin(pi,N)
}
N <- 14
r <- 4
filename <- file.path("C:/Users/wangsaja/Desktop/openbugs/","bugsex1.txt")
write.model(bugsex1,filename)
filename
data <- list("N","r")
#inits <- function() {li... |
0eb8d053270a39eb6476f0a14ce0d0e4f1953d9e | 25d6eec99ea583edb64b56cc113e59364de65038 | /eibshiny2018/app2/ui.R | 2a8117620fd888829cce1014f983d2f164815deb | [
"MIT"
] | permissive | ShinyHub/ShinyEiB | 3c4af4d9c559440ebf66e6af3f45f02311a7eae7 | d2fcf437fdd59d8fd61b55b77c7457df6ef6df45 | refs/heads/master | 2020-03-31T04:07:06.001451 | 2018-10-07T01:05:22 | 2018-10-07T01:05:22 | 151,891,829 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,441 | r | ui.R | #
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(ggplot2)
# Define UI for application that draws a histogram
shinyUI... |
e2b23aec2a7ee3b2399f7d521fdea72cc36b9fe9 | a0e4276f3f4d002263655d00f67d0878cacf6d3c | /lib/PKfit/R/plotting.sim.R | 802754c06f9a3951e98480a5b2a043bb5f4d034a | [] | no_license | shanmdphd/mypkfit | a4dff0b1181d89acce8ba7838dffde081a825ace | 508012aea85b47f77976a7d6cd8dfe55f090332c | refs/heads/master | 2021-01-01T16:43:18.814912 | 2017-07-21T03:20:37 | 2017-07-21T03:20:37 | 97,901,499 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,347 | r | plotting.sim.R | ### ----------------plot for simulation----------------
### Draw 2 windows, and consider that 2 more windows are coming
plotting.sim <- function(i,x,y,xaxis,yaxis,MD=FALSE)
{
### dev.new()
### par(mfrow=c(2,1),ask = FALSE) ### moved to each module since v1.3.7
main<-paste(c("Subject:- ", i),collapse="")
if(MD){
... |
113fd1067680a8e54374ee2a2b88411eb1983a58 | 3b1e41af7baf9feec33697fc842268af76625f41 | /17. Figure S8.R | bfb93b5f72ef0718513acfbb5a7e17de5a246c75 | [] | no_license | Barardo/ASM_HumanLT | 4daa8b1f659f57d8ce33c916dedea125a6395a5e | d6bf86afbf62a615ed9393f8a5d7e062e8e291a3 | refs/heads/master | 2023-01-02T11:49:37.071803 | 2020-10-30T09:52:25 | 2020-10-30T09:52:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,322 | r | 17. Figure S8.R |
# Clean up
rm(list=ls())
# Working directory
wd<-"/Users/alistairsenior/Dropbox (Sydney Uni)/Human lifetables and Nutrition" # Work iMac
#wd<-"/Users/asenior/Dropbox (Sydney Uni)/Human lifetables and Nutrition" # Macbook
wd<-"/Users/asenior/Dropbox (Sydney Uni)/Human lifetables and Nutrition"
setwd(wd)
# Load librar... |
4bc18033e1c516287363309049c2b13f307d274d | 95fe9b8378d3a5d70037ea0fe1e52f8840ac2587 | /tree/prob.R | d369f60169987ce176c24fae28f79efc5753b34f | [
"MIT"
] | permissive | tor4z/RKDD98 | 6780f1d9fa8ef68819150010d9b75574d507d44a | 0e80ffd1c341bd880ff76317c3cc0605836e30f3 | refs/heads/master | 2021-01-11T14:13:47.311165 | 2017-02-12T07:15:25 | 2017-02-12T07:15:25 | 81,192,609 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 185 | r | prob.R | source("./const.R")
cup98.chisqTest <- function(name) {
tb <- table(cup98pos[,name], cup98pos$TARGET_D2)
plot(tb, main = name, las=1)
print(name)
print(chisq.test(tb))
} |
bde4c7de6c4f0c5d2802b8ebd8e60cebe42d7914 | 4a7f4e3c1f830349bbf52fbf73ae4dbc4114375b | /tmev_package/mevr/man/predict.mevr.Rd | 3573922707efa89547a16c6a83f37d9b68a790d1 | [
"MIT"
] | permissive | Falke96/extreme_precipitation_austria | f3f96328db1fd3459f8b6beff57703c0cb2c5e7e | e001fcea31c7e1fb2d30bf8ea3a2bb56707a18ec | refs/heads/main | 2023-08-16T23:40:06.534867 | 2023-08-08T13:05:36 | 2023-08-08T13:05:36 | 611,209,754 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,747 | rd | predict.mevr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mevr.R
\name{predict.mevr}
\alias{predict.mevr}
\title{TMEV prediction}
\usage{
\method{predict}{mevr}(object, newdata, term, ...)
}
\arguments{
\item{object}{Object of class \code{mevr}, fitted with the TMEV.}
\item{newdata}{A data frame wi... |
486a7f98718904af9001b6ce7e27e6dced090668 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.management/man/cloudtrail_list_import_failures.Rd | 36a7ad243a01bf2fe846f8fb5d793d7cb44c63a7 | [
"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 | 742 | rd | cloudtrail_list_import_failures.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cloudtrail_operations.R
\name{cloudtrail_list_import_failures}
\alias{cloudtrail_list_import_failures}
\title{Returns a list of failures for the specified import}
\usage{
cloudtrail_list_import_failures(ImportId, MaxResults = NULL, NextToken ... |
f7a0e183ef0eebaf0ca033f7f936aa3c79380069 | 265606ff19843908088e74461a2e3f2c2153cdb5 | /Section_4_Cross_validation.R | eb1ca4d6fdcfcd3f875823f894ea78bd8805bbc8 | [] | no_license | masa951125/machine_learning | 474b598a94f48c690d2bf60f3884d22112e76d8b | b0e647c4d61a2604d4a3cac0c7f0c2ad3ee3ba48 | refs/heads/main | 2023-05-03T02:32:42.272870 | 2021-05-26T14:06:09 | 2021-05-26T14:06:09 | 343,765,007 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,491 | r | Section_4_Cross_validation.R | library(tidyverse)
library(caret)
set.seed(1996, sample.kind="Rounding")
n <- 1000
p <- 10000
x <- matrix(rnorm(n*p), n, p)
colnames(x) <- paste("x", 1:ncol(x), sep = "_")
y <- rbinom(n, 1, 0.5) %>% factor()
x_subset <- x[ ,sample(p, 100)]
fit <- train(x_subset,y, method="glm")
fit
install.packages("BiocManager")
... |
9a73bd7ff666ed00db435ee19af5cacc498cd462 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /KoulMde/R/MdeFunc31.R | 7def768aaf2e01b27c7e201afb2e750247c47226 | [] | 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 | 19,293 | r | MdeFunc31.R |
#' Minimum distance estimation in linear regression model.
#'
#' Estimates the regression coefficients in the model \eqn{Y=X\beta + \epsilon}.
#'@param Y - Vector of response variables in linear regression model.
#'@param X - Design matrix of explanatory variables in linear regression model.
#'@param D - Weight ... |
f1aa84a074ecc65aef71ceebd543d41f944817e9 | 2a811f8be6af323af6d0fe823a3f1e62b2b79a46 | /man/plot.hglasso.Rd | 1c2109839655de47e0eef1a65d111d141a7615b3 | [] | no_license | cran/hglasso | 681f0fe36f1a10c5e53dbf144d144b13a4a5111d | da7e8d5f000b176dffff2d68d5a0118ea78e1d24 | refs/heads/master | 2022-06-01T20:17:07.281739 | 2022-05-13T07:20:02 | 2022-05-13T07:20:02 | 18,368,817 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,025 | rd | plot.hglasso.Rd | \name{plot.hglasso}
\alias{plot.hglasso}
\alias{plot.hcov}
\alias{plot.hbn}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Plot an object of class \code{hglasso}, \code{hcov}, or \code{hbn}
}
\description{
This function plots an object hglasso or hcov --- graphical representation of the estimate... |
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