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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e86f0bf8e9800ac4f9aa7afc1098697ee3806b3d | eca17aa44d903fb95c24412ae5d342662be0e01f | /scripts/utils.R | 1060a67e4efcdfed8dfad169e4f372eb09b573c5 | [] | no_license | scworland/wu-waterbudget | 936409c5a119f32ea31dff0303cb8d3e0e05eae4 | 6d72fa17e05def76ae7f229f149778c94f13e760 | refs/heads/master | 2021-09-20T11:04:15.224889 | 2018-08-08T16:26:29 | 2018-08-08T16:26:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,451 | r | utils.R |
sw_weighted_interp <- function(from,value,to,to_group_id,weight_var,sum=TRUE){
# several checks
if(inherits(weight_var,"VeloxRaster")==FALSE) stop("'weight_var' must be of class VeloxRaster")
if(inherits(from,"sf")==FALSE) stop("'from' must be of class sf")
#if(all(st_is(from, "MULTIPOLYGON"))==FALSE) stop(... |
c1cf630e760340b28aed1a8259843464e3a5eb17 | a0f4a9476114a466b21771a2ac7eec86d1b0e0d2 | /Jason_detectWideScopeST.R | 3a7bf05409890db4e6cf9caad337ab0f78f37bb0 | [] | no_license | jmostowy1/RS_Acoustics | 88b4f4043b16f79b80492a28753fbae7023a5d61 | 75a99f6ee8db68820c1aa2fac5e0d1d0cd632acf | refs/heads/master | 2021-02-11T00:48:05.520669 | 2020-03-26T00:36:49 | 2020-03-26T00:36:49 | 244,425,045 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,596 | r | Jason_detectWideScopeST.R | source("init_library.R")
met = read_csv(local.EVMetadata.loc)
met = met %>% filter(has_gps == TRUE, category %in% c("ES_Transect", "Non_Transect_Useable"))
for(i in 1:nrow(met)){
ev = ev.restart(ev)
EV.File = ev$OpenFile(met$evs[i])
aco.var.list = EVListAcoVars(EV.File)
#Check to see if the wide-scope s... |
7ed150c2784b836e7609f32c76c73efd669089de | a81e1ca6fe4c13be28d29f639ec768b51d016501 | /tests/testthat/test-extradefault.R | bdf873087d2f092107d16d33ddc052efc5b4bdd0 | [] | no_license | cran/bain | d5f139bd8e644ffc339f8e6eea9c702cddfa5329 | 5bfb947c1569788eb959f5b45a39126b7f9b0ed6 | refs/heads/master | 2021-12-25T13:33:41.105293 | 2021-12-06T12:20:02 | 2021-12-06T12:20:02 | 169,492,749 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,640 | r | test-extradefault.R | # test construction prior and posterior with group_parameters=2 and joint_parameters=2
data(sesamesim)
est1 <-c(1,2)
est2 <-c(3,4)
est3 <-c(-1,1)
estimate <- c(est1,est2,est3)
names(estimate) <- c("pre1", "post1","pre2", "post2","a1", "a2")
ngroup<-c(100,50)
cov1 <-matrix(c(1,0,0,0,
0,1,0... |
eab7ebca6293ae05474f363e692591dda45cbe63 | 4e0d6c32e666ddcf17963f8615c736d5fc3eb301 | /man/cc05-1-TNoMSummary-class.Rd | c186d90c2bcc006fb976ad388846daa01e9cdbad | [] | no_license | cran/ClassComparison | ff522e3ab4bdf6d38be6956f0f72c05ebb980f1d | 6118a8471bbaad8167ed206ce3fd770855435e5e | refs/heads/master | 2020-06-24T14:29:47.094027 | 2019-05-06T15:40:12 | 2019-05-06T15:40:12 | 96,940,058 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 970 | rd | cc05-1-TNoMSummary-class.Rd | \name{TNoMSummary-class}
\alias{TNoMSummary}
\alias{TNoMSummary-class}
\alias{show,TNoMSummary-method}
\docType{class}
\title{Class "TNoMSummary"}
\description{
An implementation class. Users are not expected to create these objects
directly; they are produced as return objects from the summary method for
... |
f95e1f75fed5f815d9613eefe33185eecbedfa28 | 38027635e4309eaa7850984657c9b62c966ff313 | /man/Inf_criteria.Rd | 750ab8a606d1b63d531598b81a9d2d1c7f1b5535 | [] | no_license | emrahgecili/BPReg | 4ccd989aaf8b005de30f813884dee17c8c6805f8 | e07c73107d93947110ad030fc9930134a37e9d1e | refs/heads/master | 2023-04-12T12:30:09.857196 | 2022-10-02T20:40:09 | 2022-10-02T20:40:09 | 287,898,182 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 389 | rd | Inf_criteria.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Inf_criteria.R
\name{Inf_criteria}
\alias{Inf_criteria}
\title{Returns information criteria such as AIC and BIC.}
\usage{
Inf_criteria(M)
}
\arguments{
\item{M}{Final MCMC output after burnin.}
}
\value{
AIC, BIC, log-likelihood.
}
\descripti... |
02f94d77b084f59f20c6b4a0098b628a2e8d93ef | 608adcf47ef5c776429dfe2e555c20c0ef54547a | /inst/doc/widals.R | 3f16f8efaff5d5a930b5067eceeaa78c6edff3e0 | [] | no_license | cran/widals | b722ad1e1e0938998461d8fe83e8b76437cbc031 | c431b52c0455ad4568072220838b571bacc3b6ba | refs/heads/master | 2021-05-15T01:43:27.321897 | 2019-12-07T21:20:02 | 2019-12-07T21:20:02 | 17,700,881 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,778 | r | widals.R | ### R code from vignette source 'widals.Snw'
###################################################
### code chunk number 1: widals.Snw:151-154
###################################################
options(width=49)
options(prompt=" ")
options(continue=" ")
###################################################
### code c... |
94d1ea2b5b504c9fb2a393a3ff5bbbcaafc7bbe3 | 533b2cf6461e41d128530a76a529777a33a41bd8 | /man/eda_locationDrift.Rd | faaa06c943428436e9984d20e0ba026bfeed4ab3 | [
"MIT"
] | permissive | minbad/quickEDA | dd0cce8c5959505332258a29104ee0cdf26b9166 | d0cf5a8a4b515448c787b39b03a3aa4786a3b696 | refs/heads/master | 2021-08-23T21:52:59.145093 | 2017-12-06T18:25:10 | 2017-12-06T18:25:10 | 112,795,316 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 428 | rd | eda_locationDrift.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/eda.R
\name{eda_locationDrift}
\alias{eda_locationDrift}
\title{EDA Assumptions Testing - Drift in Location}
\usage{
eda_locationDrift(y)
}
\arguments{
\item{y}{Numeric vector.}
}
\description{
This function takes in a vector of numerical dat... |
8da265dedda8d6a7a93ce81779dbd8a87211e426 | bffd2afc5e5717528138b497b923c0ba6f65ef58 | /man/ex09.65.Rd | 1790d88b27d88a92d0e17164d0df03c1c1868147 | [] | no_license | dmbates/Devore6 | 850565e62b68e9c01aac8af39ff4275c28b4ce68 | b29580f67971317b4c2a5e8852f8218ecf61d95a | refs/heads/master | 2016-09-10T21:47:13.150798 | 2012-05-31T19:32:53 | 2012-05-31T19:32:53 | 4,512,058 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 622 | rd | ex09.65.Rd | \name{ex09.65}
\alias{ex09.65}
\docType{data}
\title{data from exercise 9.65}
\description{
The \code{ex09.65} data frame has 2 rows and 4 columns.
}
\format{
This data frame contains the following columns:
\describe{
\item{Method}{
a factor with levels
\code{Fixed}
\code{Floating}
}
... |
0af5d1e26e6c23f73d08ae7dc827c2e1835b9ae7 | 604cae5509a1fa049f64d1fcad18a2b005bcb4c7 | /Day 3 - visualization/Maps.R | 57f9ddcb796f8e02b123a4c876f4903fea612dbf | [] | no_license | ammarjabakji/R-training | 797d2556f4fad86de0e17d44387c2375b1bb6b41 | 94df4d1e7e1298120b75dd3be15827a00d12f64f | refs/heads/master | 2020-08-12T20:16:39.402010 | 2019-10-18T06:16:57 | 2019-10-18T06:16:57 | 214,836,534 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 818 | r | Maps.R |
library(tidyverse)
library(leaflet)
m <- leaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addMarkers(lng=174.768, lat=-36.852, popup="The birthplace of R")
m # Print the map
orstationc <- read_csv("http://geog.uoregon.edu/bartlein/old_courses/geog414s05/data/orstationc.csv")
leaflet(or... |
30f5ced78093c3f69fba4cc29b6b23e0b287a6f0 | e8327d77350b80110fb20a5506b180155a108e7b | /ED_Workflow/2_SAS/SAS.ED2.R | 9e3678834c6a42f74b5974e91a601adfbd59327a | [] | no_license | MortonArb-ForestEcology/URF2018-Butkiewicz | c537fe28c2eeb886d324b9b8e565d100187fb9ff | d5f3f630045e24bd165bc2a35885a5a6e3d0c2c4 | refs/heads/master | 2021-06-23T12:27:08.987348 | 2019-06-20T17:49:56 | 2019-06-20T17:49:56 | 136,949,391 | 0 | 0 | null | 2018-07-12T18:37:05 | 2018-06-11T16:00:18 | R | UTF-8 | R | false | false | 24,130 | r | SAS.ED2.R | ##' @name SAS.ED2
##' @title Use semi-analytical solution to accellerate model spinup
##' @author Christine Rollinson, modified from original by Jaclyn Hatala-Matthes (2/18/14)
##' 2014 Feb: Original ED SAS solution Script at PalEON modeling HIPS sites (Matthes)
##' 2015 Aug: Modifications for greater s... |
7e6c563587d3c54119ed1618d5c812e2d13d6a2c | 7f86f568dab6279e6f2d987c77a023bed055a11c | /man/simPPe.Rd | 2e439325add5fd4f3dba4be7a7284c97db30c970 | [] | no_license | cran/AHMbook | b6acd2ed71319be2f0e3374d9d8960a8b04e21bf | d8f8ad8bef93120f187bef494b9ac1ad8200c530 | refs/heads/master | 2023-08-31T21:13:00.618018 | 2023-08-23T21:10:03 | 2023-08-23T22:30:32 | 88,879,777 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,099 | rd | simPPe.Rd | \name{simPPe}
\alias{simPPe}
\encoding{UTF-8}
\title{
Simulate a spatial point pattern in a heterogeneous landscape
}
\description{
The function simulates a spatial point pattern in a heterogeneous landscape simulated on a square landscape. The study area ('core') is simulated inside the larger landscape that includes... |
ea0aecc1d1c6a5633b0b1db328bca82e68b8df13 | 99144fe0beb697c124e5271a1d395ab6477d405a | /man/footnote.decorated.Rd | adf2da8875dc450d40f66136dbc6293865ebed15 | [] | no_license | cran/yamlet | 233e29fc38d75205d4cc04db5a81af49dc05a5d5 | 3f494a19ab2e1cdb426606af40304309c78603ca | refs/heads/master | 2023-09-04T00:52:18.417901 | 2023-08-24T05:00:02 | 2023-08-24T06:31:30 | 236,960,454 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,124 | rd | footnote.decorated.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xtable.R
\name{footnote.decorated}
\alias{footnote.decorated}
\title{Footnote Decorated}
\usage{
\method{footnote}{decorated}(x, ..., equal = ":", collapse = "; ")
}
\arguments{
\item{x}{decorated}
\item{...}{passed to \code{\link... |
bcdd51f25512ebdd93d817fd7cbe0f389b10a937 | 30554897707057f2739e63d0abdfa6dd9f105401 | /R-package/mtrToolkit/man/KRCRT.Rd | def3efcf06fe5aa42e2757d228d7b31318f0bcbe | [] | no_license | hugoabonizio/mtr-toolkit | 14471fefda51e56485619b2695a944bff723483f | 480b8aeaddbd4e1b7f988b65e12367c475971f71 | refs/heads/master | 2020-03-26T23:54:17.918999 | 2018-06-18T13:28:01 | 2018-06-18T13:28:01 | 145,576,635 | 0 | 0 | null | 2018-08-21T14:37:17 | 2018-08-21T14:37:16 | null | UTF-8 | R | false | true | 869 | rd | KRCRT.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/KRCRT.R
\name{KRCRT}
\alias{KRCRT}
\title{Creates a k-Random Clusters Regression Trees (k-RCRTRF) model.}
\usage{
KRCRT(X, Y, k = 2, max.depth = Inf, var.improvp = 0.01, min.size = NULL)
}
\arguments{
\item{X, }{Y The input features and targe... |
810d14277e18b3c59b4814d3a9c87dbaceb15efb | a35a018fcc041d18e440b9b77a21bbd35f2dda01 | /tests/testthat/test-colours.R | 29e549e1fd405b83776c23fc07d384bddacd295b | [] | no_license | Hey-Lees/ftplottools | 366f91bd8967686f6e5cdd9fcf7d15f50fdf2adb | 31d2c1e178a30b468f531964015874ddc0d3b83b | refs/heads/master | 2023-08-26T09:30:47.874860 | 2021-11-04T09:20:12 | 2021-11-04T09:20:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 869 | r | test-colours.R | context("test-colours")
test_that("ft_colour returns expected values", {
expect_equal(ft_colors("paper"), c("#FFF1E5"))
expect_equal(ft_colors("claret"), c("#990F3D"))
expect_equal(ft_colors("black-30"), c("#B3A9A0"))
})
test_that("ft_colors returns the correct length",{
expect_equal(length(ft_colors("paper",... |
e7a98db1bfdcf0b3fb31ef480207eaaaec910041 | f4f54eb0a4dc5e6b70f46d72a25793f4ae42d339 | /man/bra.Rd | 1585bf44e90b8ad862890c3f3c1f846aa5479f5e | [] | no_license | krv/blockra | b0275ad14f9eca0598ba83b237730ea6f2447845 | ec5c922537191fcd276848193b18d0971d7dfa1e | refs/heads/master | 2021-01-10T07:58:10.276472 | 2015-07-30T09:55:30 | 2015-07-30T09:55:30 | 36,675,669 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,035 | rd | bra.Rd | % Generated by roxygen2 (4.1.0.9001): do not edit by hand
% Please edit documentation in R/bra.r
\name{bra}
\alias{bra}
\title{Block rearrangement algorithm}
\usage{
bra(X, epsilon = 0.1, shuffle = TRUE, fix.first = TRUE, obj = var,
seed = 1)
}
\arguments{
\item{X}{numeric array or matrix}
\item{epsilon}{target vari... |
dc5bf19fe8ffc00a4826f5a4974ae4edaf0db1da | a37a3e54565806ee4c9d1f925ce87cd06de5f254 | /analysis/models/model_cox.R | bc5231e94867a17dc3f9e54ea15e7880ebb2ada2 | [
"MIT"
] | permissive | opensafely/comparative-ve-research | cdec66752a31294016a9d9b857bea359fbc8abfd | cf3118545d18a5777dc43d153a6261bd68de56bd | refs/heads/main | 2023-08-23T10:47:30.506829 | 2022-05-06T11:36:00 | 2022-05-06T11:36:00 | 367,404,609 | 1 | 1 | MIT | 2022-05-06T11:36:01 | 2021-05-14T15:27:50 | HTML | UTF-8 | R | false | false | 10,226 | r | model_cox.R |
# # # # # # # # # # # # # # # # # # # # #
# This script:
# imports processed data
# fits some Cox models with time-varying effects
#
# The script must be accompanied by three arguments,
# `outcome` - the dependent variable in the regression model
# `timescale` - either "timesincevax" or "calendar"
# `censor_seconddose... |
fe3e21afd229e278dd6430ec02ad0794580f192e | c207e66e9c50316a22a3df5661912d83ab6c1fd0 | /R/tsml.cara.rct.R | 363ac22858934847ef60048132bd2c105b388608 | [] | no_license | achambaz/tsml.cara.rct | ab8587af760a6e2f8346c7bdbaa1b2c30cdd9f87 | 2b2aa282d4a11c601b37cacb368b67d03f6e8fc9 | refs/heads/master | 2021-01-14T12:15:07.323927 | 2016-09-29T18:51:00 | 2016-09-29T18:51:00 | 68,332,455 | 2 | 2 | null | 2016-09-22T23:21:08 | 2016-09-15T21:23:44 | R | UTF-8 | R | false | false | 22,141 | r | tsml.cara.rct.R | setMethodS3("update", "TSMLCARA", function(#Updates a TSMLCARA Object
### Updates a TSMLCARA object.
this,
### A \code{TSMLCARA} object, as created by \code{TSMLCARA}.
flavor=c("parametric", "lasso"),
### A \code{character} indicatin... |
d305a6ca7bdae1de6825de396ed5f16d1d1a39cb | c1439351216e4cd99ba17f3f0cdc7290e4ba6fe3 | /man/removeCerror.Rd | 3b9b864d5ca305d02c597334ec9454857e07fe5c | [] | no_license | cran/piecewiseSEM | cb751749c7ba0485eb81840b366dd8aae3dbe12d | c8264234681c9954c88c5926d477f5dd181112cf | refs/heads/master | 2023-03-08T20:59:05.204323 | 2023-03-04T17:00:02 | 2023-03-04T17:00:02 | 48,085,794 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 310 | rd | removeCerror.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/basisSet.R
\name{removeCerror}
\alias{removeCerror}
\title{Remove correlated errors from the basis set}
\usage{
removeCerror(b, modelList)
}
\description{
Remove correlated errors from the basis set
}
\keyword{internal}
|
897a439476c9153d0c603a1efb9093cfbab87ad2 | ef79aa2916012d0db5bf74b02cdd21266d06a934 | /man/jaccard.mean.Rd | 0366530edf83be07fb3f6dd4cad6110f516790a7 | [] | no_license | yijuanhu/LDM | c39619ce3d1ec08bcd2d3e9f8a67f9eab345b02d | 649c49ec530f926a8420b37555266d7efc3f0de5 | refs/heads/main | 2023-08-27T20:28:33.777430 | 2023-08-27T07:26:44 | 2023-08-27T07:26:44 | 126,239,042 | 22 | 4 | null | null | null | null | UTF-8 | R | false | true | 1,428 | rd | jaccard.mean.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/LDM_fun.R
\name{jaccard.mean}
\alias{jaccard.mean}
\title{Expected value of the Jaccard distance matrix}
\usage{
jaccard.mean(
otu.table,
rarefy.depth = min(rowSums(otu.table)),
first.order.approx.only = FALSE
)
}
\arguments{
\item{otu.... |
fa17a86d2a54b7d64dc4febd86dd731ea570748b | 07ca789edc86a0df1ccfc4b7fe89eb4b416f6e78 | /SCRIPTS/rspo3/robust_validation_plot.R | d55d754d6fcc3509c4e925bd66568942d5e45d15 | [] | no_license | niaid/h5n1-chi-010 | 1316b5cbcb34b9699ef0405d0d97d66b9bfbbf0d | 35487afdd368bb91c9693d5b79750c98b326614c | refs/heads/main | 2023-05-24T14:47:00.117636 | 2021-06-23T21:24:04 | 2021-06-23T21:24:04 | 379,733,109 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,739 | r | robust_validation_plot.R | library(tidyverse)
# Load robust correlation validation results.
val_result <- read.table(file.path("RESULTS", "rspo3","val_result_all_with_pre_z_norm.txt"), header = T, sep = "\t")
# val_mean_sd <- val_result %>% group_by(subject) %>% summarize(z_mean = mean(z_score), z_sd = sd(z_score)/sqrt(length(z_score)))
val_me... |
e77e103b850d1d8acd67cb350cd00996792ea01f | 4dc3c38381cd3074c51b0c1746d4fe594f8306d7 | /MEETUPS/CRUG/APPS/GUI_R_GWidgets.R | 72b3c472bdc688903caae37011e1b44d2599726c | [] | no_license | ParfaitG/WORKSHOPS | d77918d4ab2e27e9edbf670509797b1c6ab3701b | b81a2581132d120c61a041cfc0dfca000b968b47 | refs/heads/master | 2021-06-07T05:23:05.096122 | 2020-07-28T03:47:26 | 2020-07-28T03:47:26 | 135,520,592 | 8 | 4 | null | null | null | null | UTF-8 | R | false | false | 4,143 | r | GUI_R_GWidgets.R | options(connectionObserver = NULL)
library(RSQLite, quietly = TRUE)
library(RGtk2, quietly = TRUE)
library(gWidgets2, quietly = TRUE)
library(gWidgets2RGtk2, quietly = TRUE)
options(guiToolkit="RGtk2")
setwd("/home/parfaitg/Documents/CRUG")
getList <- function(){
conn <- dbConnect(SQLite(), dbname = "Data/CTA_Da... |
302839c2ccfaa033a9215cf2adb51b91aa997e33 | 9f06adddff3d1003c405a77a5fb57b9153c9ff61 | /man/percent_to_k.Rd | 3059824f63555774132d71f8f4576a3daae60e03 | [] | no_license | kimberlyroche/rulesoflife | c7372572b74a964db2fb585824bf6e1c23f7793a | 2173f2404e22c7fd6c1bf0fdf94e56905503f41d | refs/heads/main | 2023-05-15T08:41:16.997396 | 2023-04-29T16:00:08 | 2023-04-29T16:00:08 | 355,001,176 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 383 | rd | percent_to_k.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utility.R
\name{percent_to_k}
\alias{percent_to_k}
\title{Converts a percent to a relative count}
\usage{
percent_to_k(percent, n_features)
}
\arguments{
\item{percent}{percent}
\item{n_features}{total number of features}
}
\value{
relative ... |
9187ad7db968edeb655dcda168c2ae2dd74645e6 | d83745053905580ccc87478db3b1a1dbaee9b80d | /scripts/script.R | 87e79b8f6f276fac7ed9861a1d799081e5ec0912 | [] | no_license | pburgov/M3_Actividad_Colaborativa | ea14a93adb19a7a85a23d9dd286a6cfaa2cd51c2 | 3c0d337ebfb897c1ef3958b86dbff904a491f6ed | refs/heads/master | 2021-08-07T02:59:36.526798 | 2017-11-07T10:32:08 | 2017-11-07T10:32:08 | 109,650,388 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 6,807 | r | script.R |
# Comprobamos y establecemos directorio de trabajo
getwd()
pathToMain <- "/Users/pburgo/Documents/MBD/M3_Actividad_Colaborativa"
setwd(pathToMain)
# Función que toma una cadema de fecha en formato YYYYmmdd o mmddYYYY
# y devuelve otra en formato YYYY-mm-dd
CustomDateFormatting <- function(x){
x <- as.Date(parse_dat... |
138da0b0380dbeba0ba174ca6848cfabb855957a | 666aeddc20c72fc498e31c733d23bc1180baaaf2 | /man/opt_design.Rd | d8a529c230f878891c0702c6217e48a0e155ccb8 | [] | no_license | MatheMax/RegReSample | 1af5ccb90e1c9f56ac9852526659814b93182e95 | e5613c1898a473563fcc40dadcf148c20fc5ddda | refs/heads/master | 2020-04-07T16:29:02.063078 | 2018-11-27T12:56:42 | 2018-11-27T12:56:42 | 158,530,765 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,161 | rd | opt_design.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/optimization.R
\name{opt_design}
\alias{opt_design}
\title{Compute an optimal design}
\usage{
opt_design(alpha, beta, lambda, weighted.alternative = FALSE,
delta.mcr = 0, delta.alt = 0.3, tau = 0.1, n.max = Inf)
}
\arguments{
\item{alpha}{M... |
e330aa37d8233f088d984ec8d5a8322b993e1f76 | 7717b280fcd6fd9343a36f04c08398b153b37e40 | /man/step_hdoutliers.Rd | 90105f88f6a0311bf95efc27e20e07cdb06ac20d | [
"MIT"
] | permissive | mattsq/straystep | b8bb1ad0205252e3650442bf15cfa9e678cbf9c9 | 69ea1291f1bdc82abbf303447a44d8664f22ebe1 | refs/heads/master | 2022-12-24T19:37:12.680766 | 2020-09-23T03:19:55 | 2020-09-23T03:19:55 | 296,797,506 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,724 | rd | step_hdoutliers.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/step_hdoutliers.R
\name{step_hdoutliers}
\alias{step_hdoutliers}
\title{XXXX}
\usage{
step_hdoutliers(
recipe,
...,
role = NA,
trained = FALSE,
reference_colnames = NULL,
outlier_bounds = NULL,
outlier_cutoff_threshold = 0.01,
... |
176fd5cfb55594a7b34056408be43b021c798210 | 6e32987e92e9074939fea0d76f103b6a29df7f1f | /googlemlv1.auto/man/GoogleCloudMlV1__Capability.Rd | e51e633d362f9bb8f67a04e1c74d2f323b137454 | [] | no_license | justinjm/autoGoogleAPI | a8158acd9d5fa33eeafd9150079f66e7ae5f0668 | 6a26a543271916329606e5dbd42d11d8a1602aca | refs/heads/master | 2023-09-03T02:00:51.433755 | 2023-08-09T21:29:35 | 2023-08-09T21:29:35 | 183,957,898 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 640 | rd | GoogleCloudMlV1__Capability.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ml_objects.R
\name{GoogleCloudMlV1__Capability}
\alias{GoogleCloudMlV1__Capability}
\title{GoogleCloudMlV1__Capability Object}
\usage{
GoogleCloudMlV1__Capability(type = NULL, availableAccelerators = NULL)
}
\arguments{
\item{type}{No descrip... |
4f1d9d6e3e6b7384d7399065e101a51cac7f2aa5 | e738305b10944199874d5220cb24d067625a049d | /Workshop code 2. Working with data.R | 30533b0daa4cf1a84e08771980df3d2e53cd214c | [] | no_license | seanchrismurphy/A-Guided-Tour-of-R | 01334c89968716cd32ed7dbbebd045c1e0272c2b | b0f93f603640a389b37ebc73bbcd505099454a24 | refs/heads/master | 2020-03-16T13:02:50.672414 | 2018-05-09T00:54:32 | 2018-05-09T00:54:32 | 132,680,212 | 26 | 8 | null | null | null | null | UTF-8 | R | false | false | 18,074 | r | Workshop code 2. Working with data.R | # Now that you're familiar with the R interface, let's get our hands on some data! #
# Before we do, we'll need to load up a few packages. The base version of R comes with a lot of functionality, but over the years
# people and organisations have built modules that improve over the basics in particular areas. In parti... |
e43fcc584698ad0cc207586bb9d15cf382b4f558 | 0a6c0442a585875b2e7d5edf738b98e4abae4a14 | /plot3.R | 7631e54178803ffc780fa33ad238764904058795 | [] | no_license | gesserta/ExData_Plotting1 | 665e611de64c7814385622a69e3178c3c20092cc | 327d7900aab833ce908a57dd05225ccb770f7846 | refs/heads/master | 2020-12-25T09:17:48.788209 | 2014-06-06T22:48:48 | 2014-06-06T22:48:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 995 | r | plot3.R | plot3 <- function() {
## Read data; set your working directory where file sits
epc <- read.table("household_power_consumption.txt",header=TRUE,sep=";",stringsAsFactors=FALSE)
epc$DateTime<-paste(epc$Date,epc$Time)
epc$DateTime.c<-strptime(epc$DateTime, format="%d/%m/%Y %H:%M:%S")
epc$Date<-as.Date(as.chara... |
2e1c285cee24e25e021c0429be20d9e0e936a845 | bdb594aad445bb6826d2fed16af0bdc355c80da8 | /pathway.analysis.R | ffaa676716b3f76b7c0893f2e0c38167381f8b49 | [
"MIT"
] | permissive | lanagarmire/pretermBirth_metabolomics | ebe67342655fe9d79df33d9422a89a4727f69490 | eec536ba5c4c3a35b29b913a852472d5b39ebc00 | refs/heads/main | 2023-06-18T23:10:53.137138 | 2021-07-15T12:45:52 | 2021-07-15T12:45:52 | 386,132,331 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,455 | r | pathway.analysis.R |
rm(list = ls())
wkdir <- 'C:/Users/evely/Google Drive/summer intern/Preterm birth metabolomics project'
setwd(wkdir)
#run pathifier
library(pathifier)
library(foreach)
library(doParallel)
organize_pathifier_result = function(pds,re_scale=T) {
scores = list()
pathway.names = character()
for (i ... |
9567e74f3e7d88911be148d38b9ffe1172580bf7 | ca3cee27c33debd51d59aa5d0a98bf3c2cefe4de | /temp/tsample-E-codes-dump.R | bb65528e33c4e9fc6cdcbc3d853a556e62152ca8 | [] | no_license | zmdg11/wkFocus | 132294c28f27ad10d06fe6fb2c3553a1756ac6a8 | e42d961b918b2d5aa0248adf0b4cd7e8b94708ef | refs/heads/master | 2021-04-26T23:23:34.990177 | 2018-03-10T20:26:49 | 2018-03-10T20:26:49 | 123,984,811 | 0 | 0 | null | 2018-03-07T18:47:53 | 2018-03-05T22:05:44 | HTML | UTF-8 | R | false | false | 322 | r | tsample-E-codes-dump.R | err_inx <- which(agree_df$d == "E")
err_inx
err_t <- agree_df$t[err_inx]
err_t
tmp <- t(agree_list)
tmp <- as.data.frame(tmp)
tmp$t <- as.character(tmp$t)
err_t <- as.character(err_t)
err_list <- filter(tmp, t %in% err_t)
c1_err <- filter(coder1_df, t %in% err_t)
c1_err
c2_err <- filter(coder2_df, t %in% err_t)
c2_err
... |
72410339aa647dfb2b82529a1972a592bc30a6ac | 840944dacec0eb78b5989a2d2e4f69898ac17967 | /R/dplyr_custom_functions.R | 3b473859cb3d52bae84e5a1c9737c38266f7eb10 | [
"MIT"
] | permissive | Sorenson-Impact/sorensonimpact | e5104516366aca205f9f5c7dccf8a23487006bca | 78796d0a720037a866160ca62d8734d48a2aaff3 | refs/heads/master | 2021-11-13T16:06:01.147657 | 2021-11-04T16:40:13 | 2021-11-04T16:40:13 | 108,036,549 | 12 | 7 | null | 2020-01-28T18:02:53 | 2017-10-23T20:36:58 | R | UTF-8 | R | false | false | 5,529 | r | dplyr_custom_functions.R | #' Extract duplicate rows
#' @description
#' \lifecycle{defunct}
#' Extract all rows with duplicated values in the given columns
#' @importFrom magrittr "%>%"
#' @param ... Columns to evaluate for duplication. Works via \code{group_by()}.
#' @return Filtered dataframe with duplicates in given columns
#' @examples
#' \d... |
e097de43a7ee239da956affc4c532a2aafc5312b | 52a6cea02ee8ac8c53e1049a1df8c31494aaadd0 | /perceptron_demo.R | c323e293862a57c5e10a9966e3ff940233e24e66 | [
"MIT"
] | permissive | ControlNet/ml-algorithms | ccd8ed592e8dfa90ca0a15b9f4aa7b1843e07cf0 | 16e37eae032250ecda7a12d84839d5ad72753635 | refs/heads/main | 2023-04-14T14:59:06.459439 | 2021-04-16T17:58:02 | 2021-04-16T17:58:02 | 358,675,339 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,789 | r | perceptron_demo.R | library(MASS) # generates multivariate Gaussian sampels
library(ggplot2)
library(reshape2)
source("perceptron.R")
## Generative parameters
c0 <- '+1'; c1 <- '-1' # class labels
mu0 <- c(4.5, 0.5); p0 <- 0.60
mu1 <- c(1.0, 4.0); p1 <- 1 - p0
sigma <- matrix(c(1, 0, 0, 1), nrow=2, ncol=2, byrow = TRUE) # shared covarian... |
a16b51446d55e4c19ea6cde0fdfc3659093a3359 | 4f8a5e8a24267857ea2cb81a514f1709eee18a4f | /R/svg.close.R | 9452dda428c89dc58db37923cf39d34e052c6eb2 | [] | no_license | kashenfelter/svgViewR | c68518377bc9cf43159b298141ffbc21875c7b07 | cf98a1d084ac04f8355ad81252d0943fdedea80e | refs/heads/master | 2021-08-06T04:44:32.680098 | 2017-11-03T03:19:40 | 2017-11-03T03:19:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 374 | r | svg.close.R | svg.close <- function(){
# Give error if svg.new has not been called
if(is.null(getOption("svg_glo_con"))) stop("svg.new has not been called yet.")
# Get current connection
file <- getOption("svg_glo_con")
# Close
svgviewr.new(file=file, conn.type='close', layers=file$layers, fdir=file$fdir, debug=file$debug)... |
f311d6903edf0ec5ddfa48188f216c02504ad0a6 | 158754ef260ab3521fe71c70d391e0337f69f37a | /man/dgnorm.Rd | 49a33066684b90933390bff46c63d4d29e779684 | [] | no_license | bmasch/salmonIPM | 0ac41b05a0680a522f206f883d8e7966cf4c5038 | 374cd56323d1309fc0101372cef58abf9f6dca20 | refs/heads/master | 2021-05-14T18:02:40.901920 | 2017-12-04T19:11:53 | 2017-12-04T19:11:53 | 116,062,168 | 4 | 0 | null | 2018-01-02T22:09:35 | 2018-01-02T22:09:35 | null | UTF-8 | R | false | true | 474 | rd | dgnorm.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dgnorm.r
\name{dgnorm}
\alias{dgnorm}
\title{Density function for generalized normal (Subbotin) distribution}
\usage{
dgnorm(x, mu = 0, sigma = 1, shape = 2)
}
\arguments{
\item{x}{variate}
\item{mu}{mean of the distribution}
\item{sigma}{s... |
6f1236cdaef8781d5a59d256bbf2a794f086467b | 079fb9926646dfb61bd59bb66388ff68b15fe4bb | /shared_code/heatmap.r | 0f3c4f1d8f0774ddb49965998f9bcdee0d928df9 | [] | no_license | BioinformaticsArchive/chen_elife_2013 | ed256f0574061349a2d9dcb22fe35f6b4b2aaa74 | e3f04dccf2ce6b157f2721c536ffc1f0dc539b2f | refs/heads/master | 2020-04-05T23:46:37.818338 | 2013-08-15T21:25:35 | 2013-08-15T21:25:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,596 | r | heatmap.r | library(GenomicRanges)
library(lattice)
# given a data frame of intervals (columns chr, region_start and region_end), returns a matrix
# of values in those intervals with row names taken from id.column
read_matrix <- function(loc.df, cov.object, id.column="fb_gene_id") {
if(length(unique(loc.df$region_end - loc.df$r... |
e45c3d68786fc402d76af382377a42a9cf566bdd | cd6a84f096cf47c8d0e6e727d6b5564b9caf8d96 | /R/reliabilityIRT.R | ddb21cdcb487a3f02b795b5919c0369f5d4c71ca | [
"CC-BY-4.0"
] | permissive | DevPsyLab/petersenlab | c14a24bafeb68c5f0eca886f6264391f97d66668 | 9ee5242aa04c09053ed70dd23d47312d9af25cb4 | refs/heads/main | 2023-09-04T02:48:36.288068 | 2023-09-01T05:15:51 | 2023-09-01T05:15:51 | 597,009,425 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,105 | r | reliabilityIRT.R | #' @title
#' Reliability (IRT).
#'
#' @description
#' Estimate the reliability in item response theory.
#'
#' @details
#' Estimate the reliability in item response theory using the
#' test information (i.e., the sum of all items' information).
#'
#' @param information Test information.
#' @param varTheta Variance of th... |
b7d41e75d30a37e1f873e06b7f7c94c808184df8 | a70a98b37f9f88ef8b6e0ed930d0060b4777f134 | /Code/SimulationFunctions.R | 0eee11cd77f6306d8f2c9f0e11d711a0eaf117ec | [] | no_license | PolCap/DemographicResilience | febf9139de3e3ab1db3f8b47631f5b4e49e9c1a4 | 037f217b8969244854877f114d747aa53ea26f2c | refs/heads/master | 2023-04-11T10:34:51.527162 | 2022-03-08T12:30:59 | 2022-03-08T12:30:59 | 436,702,812 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,025 | r | SimulationFunctions.R | # Function to simulate random matrices
random_matrices <- function(dimension=NA, upd=FALSE){
# Simulate x transitions from 0 to 1
matUvector <- runif(dimension^2,0,1)
# Transform into a matrix
matU <- matrix(matUvector,nrow=dimension)
# Remove the first row of the matU
matU[1,2:dimension] <-... |
176c02f6701f7c49b937d19a719a71ada8ada6b5 | 27b70fed2f828b777dba07a6a2ad881087cf7006 | /ui.R | 9777c9ad25bfde8cfc0bdbf1320faac8798f28a0 | [] | no_license | catwizard/data_product | 79f3ae43f4af04d53efe921757766c7e36f88231 | cbaf4e683822c01ff16c415b3104ed11584ebf3f | refs/heads/master | 2020-05-25T10:15:07.873440 | 2015-06-28T02:25:36 | 2015-06-28T02:25:36 | 37,263,883 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,230 | r | ui.R | library(shiny)
library(ISLR); data(Wage); dataset <- Wage
fluidPage(
navbarPage("Wage Exploration",
tabPanel("Variables",
sidebarPanel(
HTML("Choose X, Y, Z and Facet to exploring the relationship"),
selectInput('x', 'X', names(dataset), names(dataset)[[2]]),
selectI... |
33281f8a03280e446c471cdb3e25369d4019fc11 | f5c81db2ecd5464b3ea09efb3e6a5a9d0484f8a6 | /R/utility_functions.R | d91c9baac56791dfa3c8996c259856cf3460b13d | [] | no_license | dstanley4/fastInteraction | 5095e69682872890c5a90f1116fa5c389801eb62 | 8461d57bea2a0b868cf081404501024a710b722b | refs/heads/master | 2023-06-08T21:50:54.188804 | 2023-06-04T18:18:09 | 2023-06-04T18:18:09 | 220,544,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,664 | r | utility_functions.R | calculate.surface <- function(lm_object, criterion, predictor, moderator, criterion.name, predictor.name, moderator.name) {
mx <- mean(predictor, na.rm = TRUE)
mm <- mean(moderator, na.rm = TRUE)
sdx <- sd(predictor, na.rm = TRUE)
sdm <- sd(moderator, na.rm = TRUE)
x.range <- c( (mx - 2*sdx), (mx + 2*sdx))
... |
7fd7b5c0fefbcfcf6969df37021d542d67067f82 | c0f1ad567a5f8ab8fb376242dc1a990d2ab6b3e8 | /Propensión/SPViajes.R | f3f86fa13779001e46d5d2809dcc147b43d19a2d | [] | no_license | RAS-WB-Uniandes-Urban-Cycling/proBikePolicies | edda6596b693f68b22c4ad50d6746833cef167e3 | 5c82094420a38421748bbb1f997550df4852fd17 | refs/heads/master | 2021-06-06T17:44:25.098109 | 2021-05-09T18:06:08 | 2021-05-09T18:06:08 | 135,209,976 | 0 | 2 | null | null | null | null | WINDOWS-1250 | R | false | false | 9,916 | r | SPViajes.R | library(tidyverse)
library(lubridate)
library(sf)
EncuestaM <- Encuesta %>% mutate(id_manzana=as.character(id_manzana),SecCodigo = paste0("00",str_sub(id_manzana,7,10)),ManCodigo = paste0(SecCodigo,0,str_sub(id_manzana,13,14)))
Data <- Personas %>% select(moviliza_bicicleta,id_encuesta,numero_persona,sexo,edad,nivel_... |
5770322905e1277e526962634d9ae442dfdd70ef | 95e564d41cea0c341c1b1f84f9c15938728d9ea8 | /Initial Statistical Analysis.R | 164c32f2c36dbe4a1b2492bfa802d42f4870942f | [] | no_license | UVAHealthSystemCapstone/UVAHealthSystemCapstone | f838dbc41e6d9e8615e0d4fc79423a21d3d48229 | d8c7158d2a7263d09a4bbe483f8d449e0ab87a62 | refs/heads/master | 2022-04-16T05:14:25.332396 | 2020-04-15T01:02:50 | 2020-04-15T01:02:50 | 211,167,990 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,081 | r | Initial Statistical Analysis.R | library(tidyverse)
library(dplyr)
library(ggplot2)
#load data
load("//galen.storage.virginia.edu/ivy-hip-chru/ekd6bx/Carilion_ind_weather_merged")
individuals <- weather_merged_car
# Creating data frame with just diabetes patients (based on ICD codes)
library(stringr)
# ICD 9 Codes
icd9_main <- as.characte... |
6aa8e39514ea7cf69648be8cd38960df4bd7b0b2 | 1685e0fcd453743bf2806af06b9adb86aaaf705c | /server.R | 36bf4975dbd16a7ed6350f432abfd97eee6a67b7 | [] | no_license | NZF85/Coursera-Developing-Data-Products | 53e065af233a9e88461813135113efca0f4da06c | 9779ff2215688945117b2530c38a869707ee3de9 | refs/heads/master | 2016-09-05T22:39:44.683525 | 2015-09-20T02:46:10 | 2015-09-20T02:46:10 | 42,778,404 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,213 | r | server.R | library(shiny)
# Load data processing file
source("data.R")
Country <- sort(unique(data$Country))
# Shiny server
shinyServer(
function(input, output) {
output$setid <- renderText({input$setid})
output$address <- renderText({
input$goButtonAdd
isolate(paste("http://data.un.org/Data.aspx?d=UN... |
b6ebe427c50a266fe094f851cee343fba084643f | faf431062499cce160bf7ef6ee34737c5091ff3d | /finalAssigment/ui.R | 92b20d9403c11d907cdd1775b20bd8716eff97ec | [] | no_license | mcastrol/dataProductFinalAssignment | f8593832c9ac47bf0d3814083c8a2f140112e1a0 | e1c8f14911dcf0eca63e0942d3880a41f19f7177 | refs/heads/master | 2020-03-11T12:56:48.216696 | 2018-04-18T06:38:41 | 2018-04-18T06:38:41 | 130,011,239 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,599 | 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)
# Define UI for application that draws a histogram
shinyUI(fluidPage(
... |
0023b4a4ce98a14e9ba98bebfb47075d1b22120e | 07028f1e1126661a945e35ef3a7baa0f4745da0e | /cachematrix.R | 7cb615f81bd6ce56f9fcd6abf02f4e036a59b33d | [] | no_license | hpalenqueoroz/ProgrammingAssignment2 | 00934512b91b1339171bd471f0179949240c834d | 50a7056e5e9d938a99dacf3b9ec2ad07df1fb644 | refs/heads/master | 2022-12-20T08:07:09.061590 | 2020-10-20T17:37:51 | 2020-10-20T17:37:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,249 | r | cachematrix.R | ## The function makeCacheMatrix creates a special vector
## that works saving the inverse of a matrix in a cache (this func).
makeCacheMatrix <- function(x = matrix()) {
## Here we are creating a "inv" value that it's undefined by now.
##
inv <- NULL
## 1. set the value of the matrix
set <- function(... |
c61718cba976e3e265d9f15f484b0454981a81b2 | 56b32941415e9abe063d6e52754b665bf95c8d6a | /R-Portable/App/R-Portable/library/igraph/tests/test-notable.R | 40906ae84c2e0d078ab54313ed0bf50a5825f92b | [
"LicenseRef-scancode-unknown-license-reference",
"GPL-2.0-only",
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-newlib-historical",
"GPL-2.0-or-later",
"MIT"
] | permissive | voltek62/seo-viz-install | 37ed82a014fc36e192d9a5e5aed7bd45327c8ff3 | e7c63f4e2e4acebc1556912887ecd6a12b4458a0 | refs/heads/master | 2020-05-23T08:59:32.933837 | 2017-03-12T22:00:01 | 2017-03-12T22:00:01 | 84,758,190 | 1 | 0 | MIT | 2019-10-13T20:51:49 | 2017-03-12T21:20:14 | C++ | UTF-8 | R | false | false | 936 | r | test-notable.R |
context("Notable graphs")
test_that("notable graphs work with make_graph", {
g <- make_graph("Levi")
g2 <- graph.famous("Levi")
expect_true(identical_graphs(g, g2))
})
test_that("make_graph for notable graphs is case insensitive", {
g <- make_graph("Levi")
g2 <- make_graph("levi")
expect_true(identica... |
3e3eb97c49f057b349cc395b274b43fd5a97aec0 | 1c50623e94dd4bdf27ba0140002e367426261dc1 | /RNASeqAna/man/edgeRAnaRPKM.Rd | 19ac02d7f43436c8f4ccc5306688646aa53ed9e7 | [] | no_license | fxy1018/EdgeR_RNA_Analysis_R_Tools | cee772e8262a68ce0079cbeb6da8faf70dba60ac | 5a47792a42effa183aeea1862d0dce1160c2feb2 | refs/heads/master | 2021-08-06T05:49:01.763325 | 2017-11-03T15:34:22 | 2017-11-03T15:34:22 | 104,395,098 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 627 | rd | edgeRAnaRPKM.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/edgeRAnaRPKM.R
\name{edgeRAnaRPKM}
\alias{edgeRAnaRPKM}
\title{A edgeRAnaRPKM Function}
\usage{
edgeRAnaRPKM(files, dir, group, outprefix, spe)
}
\arguments{
\item{files}{a dataframe which contain edgeR pathway analysis results}
\item{dir}{d... |
30bce0c5ddd8ac20e8b0607f69ccb64ad2e31a91 | eb4667b178e418d936c35569383e5cb0663f93ad | /R/multtest.gp.bin.R | f4e260ec39a25f2ba4208d2b47523ab4cfc2e7ee | [] | no_license | cran/RVAideMemoire | 21081d49de9999a7438c40de05ab67a145336a02 | 6a48aaa7facac606e954b06a9cc1ea46b387d575 | refs/heads/master | 2023-08-31T00:44:09.327145 | 2023-08-23T07:30:05 | 2023-08-23T09:30:39 | 17,692,998 | 7 | 7 | null | null | null | null | UTF-8 | R | false | false | 3,249 | r | multtest.gp.bin.R | # grDevices: n2mfrow
# car: Anova
multtest.gp.bin <- function(tab,fac,test=c("LRT","Fisher"),p.method="fdr",ordered=TRUE,...) {
test <- match.arg(test)
tab <- as.data.frame(tab)
fac <- droplevels(factor(fac))
nlev <- nlevels(fac)
if (nlev<2) {stop("at least 2 groups are needed")}
gp.prop <- as.mat... |
85875cc3b5675eb18dc46b6f58472b5d18432e2d | 17d582790e37f4a1fa3cfcfc531fdf5c4f4086d4 | /packrat/lib/x86_64-redhat-linux-gnu/3.5.1/lme4/tests/testthat/test-rank.R | 2f9bcb44ddc267a25e5292b87ae72a263d0b6411 | [] | no_license | teyden/asthma-research | bcd02733aeb893074bb71fd58c5c99de03888640 | 09c1fb98d09e897e652620dcab1482a19743110f | refs/heads/master | 2021-01-26T08:20:58.263136 | 2020-02-27T04:12:56 | 2020-02-27T04:12:56 | 243,374,255 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,906 | r | test-rank.R | library("testthat")
library("lme4")
context("testing fixed-effect design matrices for full rank")
test_that("lmerRank", {
set.seed(101)
n <- 20
x <- y <- rnorm(n)
d <- data.frame(x,y,
z = rnorm(n),
r = sample(1:5, size=n, replace=TRUE),
y2 = y + c(0.001, rep(0,n-1)))
expect_messa... |
feaa13746a9a648f1e9ee8b1e568c0fc6b2f9d97 | 3f3e0d69fd9d9c8e9c9555756949568037971a8b | /Ch. 4/Results/Table_movements.r | 9e46ab07bf72d8fe53b626186f20c399cf93d9e8 | [] | no_license | anasanz/MyScripts | 28d5a6f244029674017d53d01f8c00307cb81ecb | d762b9582d99c6fc285af13150f95ffd2622c1a8 | refs/heads/master | 2021-05-10T08:56:54.228036 | 2021-03-08T07:11:51 | 2021-03-08T07:11:51 | 118,910,393 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,819 | r | Table_movements.r | rm(list = ls())
library(dplyr)
library(rgdal)
library(tidyr)
setwd("D:/PhD/Fourth chapter/Results/Analisis3_bottleneck_effect")
## -------------------------------------------------
## TABLE MOVEMENTS
## -------------------------------------------------
## -------------------------------------------... |
5e2f6da2e5818986cac4849ffa3a2d519aaad926 | 650f02c3d940eac1f33db33d0320e46aa44868cc | /code/04train_model.R | 975373bbdb512049b6be72af44e56583403dd5e5 | [
"MIT"
] | permissive | lucasjamar/pokeML | 4c9be2297956ac6ccec309b49c144706ce087279 | 060169314d89f478516865b05a27286794fe42da | refs/heads/master | 2022-09-08T10:47:16.419354 | 2020-06-04T11:38:09 | 2020-06-04T11:38:09 | 267,602,922 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,670 | r | 04train_model.R | #' ---
#' title: "Model Training"
#' author: "Lucas Jamar"
#' ---
#' Using the optimal parameters found during our grid search, we train our final
#' [lightGBM](https://lightgbm.readthedocs.io/en/latest/)
#' L2 regressor of the difference between the true and expected portion of remaining
#' HP of player 1. Once again,... |
ab5303afd0beddccb93cbbc02d549f7b46eecbee | 1c0ffc7bb3953258e728cd5640b6dc3467c3d0df | /tests/test-all.R | c9e52698ba250a89bad8507db59a4d9675d7512c | [] | no_license | isabella232/aws.cloudtrail | 47fe3ac177f0ae23e52bc058f24a15bc9d9eeccb | 815ad6f2f4ab5a7994730db50f50c54b903e2e2d | refs/heads/master | 2022-04-12T01:48:40.943779 | 2020-01-12T14:24:44 | 2020-01-12T14:24:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 197 | r | test-all.R | library("testthat")
k1 <- Sys.getenv("AWS_ACCESS_KEY_ID")
k2 <- Sys.getenv("AWS_SECRET_ACCESS_KEY")
if ((k1 != "") && (k2 != "")) {
library("aws.cloudtrail")
test_check("aws.cloudtrail")
}
|
e5cb48d773dd64e5029d11f00cb04fe011a64e45 | 0c383177e26bd40f4bced4a571e47e0b3062cc41 | /man/has_file_search_pat.Rd | 6477ecd7a2e195779cab08b7f6f2687d7c6b1256 | [] | no_license | charlotte-ngs/rmddochelper | 3e8eca2b52f56e049137737c0398fc67214ebdba | 8bffa21dd725c00cc8127f16d65382589024869a | refs/heads/master | 2021-01-17T17:37:40.906734 | 2019-06-26T06:29:04 | 2019-06-26T06:29:04 | 60,537,436 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 712 | rd | has_file_search_pat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/odg_graphics.R
\name{has_file_search_pat}
\alias{has_file_search_pat}
\title{Check whether ps_pattern is found in ps_file}
\usage{
has_file_search_pat(ps_file, ps_pattern)
}
\arguments{
\item{ps_file}{name of file in which search is done}
\i... |
1523e9d94dda76060f90b1064a3157daa68fdbe7 | 9dbe86526c0a7edf52050c59146a7cc6d1b9a306 | /R/HB/FLIC_clocklab_protocol.R | 5078bb0db2d6ee3fadc11ffb2bdd4cad4b646cd9 | [] | no_license | austindreyer/FLIC | a7593fb90cc733bb7b9ade6eabf70e22d5a97772 | 67b03c2e05bd3987e2259013e0b09cb3184ced65 | refs/heads/master | 2021-04-15T10:22:49.987515 | 2020-01-31T19:32:04 | 2020-01-31T19:32:04 | 126,266,561 | 0 | 0 | null | 2018-05-17T19:32:41 | 2018-03-22T02:14:00 | R | UTF-8 | R | false | false | 7,952 | r | FLIC_clocklab_protocol.R | #### FLIC_clocklab_protocol ####
### Last updated 6/11/2019 ###
## The following protocol is also available in the FLIC_Protocols file in the
## "Protocols" folder of the OneDrive -> Feeding Project directory
# ClockLab Analysis
### 1 ###
# Go to the ClockLab software and open the text files created in R using txt... |
46b548cadd26f34a37e4ac22c2f123ce77239297 | 69884df13130b6d843ab5eb51d9ad4bd6a38d705 | /man/mod_mainDataOutput.Rd | 99692d547ab94f7b72536272a2c6cacd1bb8a1f9 | [] | no_license | MalditoBarbudo/nfiApp | a1a625ec9207e3272ff3c373d453b95b430c8f42 | baebe9fea0fb018cb002bf84803834928e089ae8 | refs/heads/main | 2023-06-08T01:38:34.382460 | 2023-04-28T06:56:28 | 2023-04-28T06:56:28 | 249,689,113 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 311 | rd | mod_mainDataOutput.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mod_mainDataOutput.R
\name{mod_mainDataOutput}
\alias{mod_mainDataOutput}
\title{mod_mainDataOutput and mod_mainData}
\usage{
mod_mainDataOutput(id)
}
\arguments{
\item{id}{}
}
\description{
Shiny module to get the data as tbl_sql
}
|
14ffa4f6afdaca62ec1a14d07e712efd18dbe6f7 | 593f8cf964d4e1280f559a4f600938b95f3cd81f | /TucsonAZ.R | 65c8e0633493886ea334e17b5f91a50bdae2619d | [
"MIT"
] | permissive | QUAY17/stats_correlation | 11a0c177341b3aaf2402435b601ca83bf75ac110 | cfe22b371513f685874c9beeb55884d0ffed53ac | refs/heads/main | 2023-07-03T09:00:11.372024 | 2021-08-12T03:10:12 | 2021-08-12T03:10:12 | 395,137,544 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 95,860 | r | TucsonAZ.R | month,day,year,temp,residual
1,1,1995,48.1,-3.8
1,2,1995,55.1,3.3
1,3,1995,51.8,0.1
1,4,1995,50.6,-1
1,5,1995,53.3,1.7
1,6,1995,47.2,-4.3
1,7,1995,48.1,-3.4
1,8,1995,51.8,0.4
1,9,1995,53.5,2.1
1,10,1995,56.7,5.4
1,11,1995,54.9,3.6
1,12,1995,54.4,3.1
1,13,1995,53.7,2.5
1,14,1995,52.4,1.2
1,15,1995,55.5,4.... |
8be7847c6dd09bcb220cff7559acbbb2d64e87b0 | 64178eeec231869fd9fd03f67cfbd8f59647a53b | /V2/analyse.r | 0887e8063c48de601fed357413534cadd98d659e | [] | no_license | drtrev/sigeng | 30174e9ea5791186460505ce62bebcf6896297be | a14aee8ebc5cabce080edf73c0bc2d7a011bf2b1 | refs/heads/master | 2021-07-14T03:38:56.323884 | 2016-10-02T12:04:10 | 2016-10-02T12:04:10 | 6,940,352 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,588 | r | analyse.r | load.packages <- function()
{
library(lme4)
library(ez)
library(nortest)
library(plyr)
}
analyse <- function(dat, analysis)
# Given analysis id, do a specific analysis/order:
{
test <- F; if (test) analysis <- analyses[1,]
outliers <- function(dat, analysis, norm=T)
{
outliers.sub... |
56d17a7fdb5d8c2a3316f93f39b0a064e4b4cca5 | 3375e9c749a9e096ae7a89dc53cf3188e3d1b599 | /hospital-CA.R | d062b98b390a025431eeb5f3b272b37014d1196d | [] | no_license | BattaLiu/referral-network | f57815b52c274694d075f14769e8c94eb558f67a | 9c62449c2e904ec454b394b31ca16e3fdffeb15d | refs/heads/master | 2022-05-25T16:21:06.180862 | 2016-11-28T06:12:57 | 2016-11-28T06:12:57 | 74,306,379 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,386 | r | hospital-CA.R | # dig into the hospital data of CA
library(dplyr)
df.hospital.ca <- read.csv("/Users/batta/Dropbox/phd4/nursing-homes/read/Data/hospital-CA.csv")
system <- df.hospital.ca %>%
group_by(PARENT_NAME,PARENT_ZIP_9) %>%
tally
system$initial.name <- gsub("^([[:alpha:]]+)([[:blank:]]|[[:punct:]]).*", "\\1", system$PARENT... |
f91e0a8f67eb0fa3fe9cb8712feda994a1d0277f | 708bcae7afadb711182594f8beafd655ab6d4884 | /Rscript/3.1/part 1/complexity_bench.R | 84c5c821141091dbe3bf84a020469e05b5619dac | [
"Unlicense"
] | permissive | wzzlcss/wzzlcss.github.io | be7905b625090a0fc4e444803d5508cf8b5d7da5 | 5262f9313ecec37e0e04f45b65dfb2ceacc3b316 | refs/heads/master | 2021-07-15T12:27:10.974511 | 2020-06-19T05:46:17 | 2020-06-19T05:46:17 | 175,936,935 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,650 | r | complexity_bench.R | # in order to run this script #
# please get a version of sgdnet on the batch-opt branch #
# and download optB.R to your working dir #
#------ Helper function ------#
library(lattice)
library(latticeExtra)
library(grid)
lattice.options(default.theme = list(fontsize = list(points = 4, text = 8)))
complexity_plot <- f... |
16e35ac0885697a597d956ea1d1587960d37ff42 | 631b105158e5fb7317b1c3f6b607bfb5147339db | /base-scripts/all_varType_counting.R | 64229ffa4b328227e4dca6a1aedf39055d491aeb | [] | no_license | Amfgcp/neoseq_shiny | c314e15b37b547615e8943fa793e61d776c5e7b5 | e528a0b08dda967dbae198c532dbea27bb0cd58b | refs/heads/master | 2023-02-08T12:19:03.797815 | 2020-12-31T23:31:22 | 2020-12-31T23:31:22 | 295,021,823 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,231 | r | all_varType_counting.R | library(dplyr)
library(ggplot2)
# Data handling
# Hardcoded pep results folder
#pep.info <- list("path" = paste0('O:/ImmunoGenomics/ngsdata', '/results'))
pep.info <- list("path" = paste0('/mnt/patharchief/ImmunoGenomics/ngsdata', '/results'))
# List all .pep.txt files
pep.info$samples = list.files(pep.info$path, patt... |
2f71da7e292b515cf1741d00204bab57ec9c283d | 77949294d765a96e46fc2c71deb3da2fb82bf12f | /案例演示/3、图形初阶/1、使用图形.R | 0eab254aa2d696095082275ecc0f3cb8d4858fa1 | [] | no_license | ocxz/RLange | 7715cb8c2b6b21995ac948f65036dbb043634026 | 9e68822e1eb8aed048f6b917db91121707c84097 | refs/heads/master | 2020-08-07T23:00:35.700567 | 2019-10-14T07:28:48 | 2019-10-14T07:28:48 | 213,614,549 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 681 | r | 1、使用图形.R | # 保存图形
# 保存为pdf()、png()、jpeg()……
png("F:/R语言/案例演示/3、图形初阶/mypng.png")
# 加载mtcars数据库
attach(mtcars)
# 根据wt(x坐标) mpg(y坐标) 画出散点图
plot(wt, mpg)
# 添加一条最优拟合曲线
abline(lm(mpg~wt))
# 给散点图添加标题
title("Regression of MPG On Weight")
# 解绑数据库
detach(mtcars)
# 关闭输出
dev.off()
# 打开一个新的窗口
dev.new()
# 加载mtcars数据库
attach(mtcars)
# 根据wt(x... |
da705a25847670ddcde85a2e9cda4fef3d9353ed | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/pmr/examples/leisure.white.Rd.R | c9c8c7f56f3c8af7ad486dccd289e6cee0227ba6 | [] | 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 | 209 | r | leisure.white.Rd.R | library(pmr)
### Name: leisure.white
### Title: leisure.white
### Aliases: leisure.white
### Keywords: datasets
### ** Examples
data(leisure.white)
## maybe str(leisure.white) ; plot(leisure.white) ...
|
16a340582eb61e3dff93651f9886ae2e59e41f6b | 363b2ecc5154498e9587c9213bde894684775637 | /s.r | fee48975ae061f559a45e2b2193d53073b630e16 | [] | no_license | kennybob/RepData_PeerAssessment1 | f0bd21492fd60377da7696cd9414f9c4bf635eb0 | a4341405d943afa3cb871ade9647083fc37f0ad7 | refs/heads/master | 2020-03-29T17:41:36.228222 | 2015-04-12T03:00:45 | 2015-04-12T03:00:45 | 33,689,006 | 0 | 0 | null | 2015-04-09T19:45:04 | 2015-04-09T19:45:03 | null | UTF-8 | R | false | false | 2,255 | r | s.r |
getData <- function() {
if (!file.exists("activity.zip")) {
unzip("activity.zip")
}
read.csv("activity.csv")
}
get_mean_per_day <- function(data) {
total_steps_per_day <- tapply(data$steps, data$date, sum)
hist(total_steps_per_day)
cat("mean: ",mean(total_steps_per_day, na.rm=TRUE), "\n")
... |
56dc1bccdbdb4001388cdf6b8bf5d9098321d484 | 75ed4d67d5f2315344c824183847dd3f382e3e1b | /plot1.R | 561c70ada34056ec5e4e0d9aa2f74e57f6572c66 | [] | no_license | wanyx2015/ExData_Plotting2 | 75c29e848c6afdeca6e49ea0fc5e58b6e7fc8bc2 | 37285e34af487885d2d8388a57871e1b7521f338 | refs/heads/master | 2021-01-10T01:38:09.657336 | 2015-12-27T03:40:26 | 2015-12-27T03:40:26 | 48,618,011 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 892 | r | plot1.R | ##
## Question 1: Have total emissions from PM2.5 decreased in the United States from
## from 1999 to 2008? Using the base plotting system, make a plot showing the total
## PM2.5 emission from all sources for each of the years 1999, 2002, 2005, and 2008.
##
library(dplyr)
# preparing the data
rm(list = ls())
NEI <- ... |
e62be065dc619a3163419ced37d950033ee9e41f | 5dd5fbe1e7fc1854b507b7dec048a2e0d5232510 | /man/plot_return_residual_cox.Rd | 90182b22ea7ce0c664f291f9a4362910a76b727a | [] | no_license | cran/packDAMipd | 33fc903b293f9fd63fd587925cd898287a09b7cf | bd61a85bf1171c1f97625486279de0fbdb6f3538 | refs/heads/master | 2023-03-18T21:22:37.105435 | 2021-03-03T08:20:14 | 2021-03-03T08:20:14 | 312,234,163 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,041 | rd | plot_return_residual_cox.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/help_parameter_estimation_survival.R
\name{plot_return_residual_cox}
\alias{plot_return_residual_cox}
\title{Plotting and return the residuals after cox proportional hazard model}
\usage{
plot_return_residual_cox(
param_to_be_estimat... |
0b31186af3065833257094fca9d0671cac7edf55 | c0fb7f572c90e5314c319d688649cc17224c4e88 | /split_data.R | 217c3a149c19c3e61ca35d312839ced0176b5994 | [] | no_license | toshkaexe/prediction-in-R | 915bdca459bade8b5460bde6108868563353e5eb | 50ed898c3e02ef2f07e5f7b78b842d816decfc3b | refs/heads/master | 2021-01-20T06:44:25.597576 | 2017-08-29T15:01:46 | 2017-08-29T15:01:46 | 101,512,062 | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 2,183 | r | split_data.R |
#ï..created_at
file_Name <- "C:/Users/azeltser/Desktop/Projekt/prediction_07082017/scanned_beacons.csv"
#ScannedBeacons <- read.csv(file = "C:/Users/azeltser/Desktop/Projekt/prediction_07082017/scanned_beacons.csv",head=TRUE,sep=",")
ScannedBeacons <- read.csv(file = file_Name, head=TRUE, sep=",")
ScannedBeaco... |
a67c59a483818c85b5e8034d399638fa1f5c8f2b | c1b3f39f75bc72a4e5273ca9892a3e154508f918 | /R/01_model_inputs_functions.R | f41e3ae863bd495e881ed515c0e46f8e57017c0c | [
"MIT"
] | permissive | W-Mohammed/cdx2cea | 4d6eaa58c6ab69601db0ee06b76c4fd39f2999da | ba40e252744a671cdca589973de3fb9d8366c3b1 | refs/heads/master | 2023-09-02T02:34:31.364339 | 2021-11-02T16:36:19 | 2021-11-02T16:36:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,651 | r | 01_model_inputs_functions.R | #' Base-case initial parameter set
#'
#' \code{load_params_init} generates the initial values of the CDX2 CEA model
#'
#' @param n_age_init Initial age of the cohort.
#' @param n_age_max Oldest age of the cohort.
#' @param n_cycles_year Number of cycles per year
#' @param d_c Discount factor for costs
#' @param d_e Di... |
86864437e527d5fdf152d742d0c932a358cfbf37 | a71b7fe35d652d86f136823cd1801eb51d902839 | /highway.R | 31bc91b6244b92d95152d38a429aefcc6116d6c6 | [] | no_license | StaThin/data | 9efd602022db768b927c3338e5ce7483f57e3469 | d7f6c6b5d4df140527c269b032bb3b0be45ceeeb | refs/heads/master | 2023-03-29T18:40:09.694794 | 2023-03-15T09:32:42 | 2023-03-15T09:32:42 | 29,299,462 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,637 | r | highway.R | "highway" <-
structure(list(inc = c(4.58, 2.86, 3.02, 2.29, 1.61, 6.87, 3.85,
6.12, 3.29, 5.88, 4.2, 4.61, 4.8, 3.85, 2.69, 1.99, 2.01, 4.22,
2.76, 2.55, 1.89, 2.34, 2.83, 1.81, 9.23, 8.6, 8.21, 2.93, 7.48,
2.57, 5.77, 2.9, 2.97, 1.84, 3.78, 2.76, 4.27, 3.05, 4.12), lun = c(4.99,
16.11, 9.75, 10.65, 20.01, 5.97, 8.... |
bb25bdaec49128c35fa2634460dd58577a19d3b7 | c353229e39ed2709bcc73fc918269ed88a2588ce | /tests/testthat/test-annoplot_accessors.R | 11ebe3e31ff01740e44447630bee054a2ed8ea54 | [] | no_license | amytildazhang/annoplots | 282236f21fd848fce2e643a147c52201de970f55 | 865301c0d8a35e85dac6a33f347c0f29f0bce2ba | refs/heads/main | 2023-08-14T20:22:35.773416 | 2021-09-13T23:27:09 | 2021-09-13T23:27:09 | 406,158,736 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,207 | r | test-annoplot_accessors.R | source("../../data-raw/test-objects.R")
test_that("Can access base plots", {
expect_silent(ap_plots(test_ap))
expect_silent(ap_plots(test_apfade))
ps <- ap_plots(test_ap)[[1]]
expect_s3_class(ps, "gg")
})
test_that("'highlight' returns plots", {
# expect_silent(ap_highlight(test_ap, 1:10)) # current... |
1df40dd89396ad0990a07f85a7a511ccd150c10c | 9cb71c08fb66a2b5bd6d24b50737392e33663e78 | /scripts/data_transform.R | 21752f4095e89f43a6aa73c874c38a5b7f801827 | [] | no_license | lulzzz/agro | d49562a6af10bd61f14952e4011d195c7f77b1a2 | b6c88bf052a68daeb8fc7423221b61190ab4d015 | refs/heads/master | 2021-01-21T10:46:18.846667 | 2015-10-13T20:46:06 | 2015-10-13T20:46:06 | 83,484,832 | 4 | 1 | null | 2017-02-28T22:16:58 | 2017-02-28T22:16:58 | null | UTF-8 | R | false | false | 144 | r | data_transform.R | require(tidyr)
# creating ndvi avrage value time series for all fields
ndvi_avg <- spread(veg_sample[, c(1,2,4)], key = date, value = ndvi_avg)
|
e256bea79c599dde12b22a8c136387bb8885edc0 | 7584c4b6119cf7985b1ea152f03de0a2619fe13b | /man/root.Rd | 4a9bad5e1dbc92fd4efc83756dc65db58db8c987 | [] | no_license | blueraleigh/macroevolution | 2e380d14d91c7312d6ce1298d808f32b4b1becbd | bfa0644f4941940d7812106914add06fd5540656 | refs/heads/master | 2021-12-09T20:12:57.472284 | 2021-11-10T22:43:14 | 2021-11-10T22:43:14 | 213,418,210 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 283 | rd | root.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/treeio.R
\name{root}
\alias{root}
\title{Root node index}
\usage{
root(phy)
}
\arguments{
\item{phy}{An object of class \code{tree}.}
}
\value{
The index of the root node
}
\description{
Root node index
}
|
3338eddbc3fe37e9eeda5ce594af65a3cdaeb3b6 | 3f4cedffbce92b6bb385b5c6b597531d5b1b868d | /R/merge_duplicate_alerts.R | dabc3d69e3fd04dfb2cb2fa6ae7aa4c488eacf86 | [
"MIT"
] | permissive | theagent/promedr | 32f35896039e46b7cded6bec9b24d3c0b65d4522 | 0782feab24307774327e1967f513a0abcf63c520 | refs/heads/master | 2021-04-21T07:08:29.291426 | 2020-03-09T15:40:53 | 2020-03-09T15:40:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,450 | r | merge_duplicate_alerts.R | ##' Merge rows with duplicate alerts
##'
##' Each record in ProMED and HealthMap data feeds is (in principle)
##' associated with
##' a unique alert-id. Occasionally, we get multiple rows that have
##' the same alert-id. In such instances, we want to merge these rows
##' into a single row in a meaningful way. For the m... |
59a50a2878356936a3542945bc922573444a008c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/gsscopu/examples/sscopu.Rd.R | cb29e86f4fd8a3c25a8b4abc0718fdc0285d04f7 | [] | 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 | 500 | r | sscopu.Rd.R | library(gsscopu)
### Name: sscopu
### Title: Estimating Copula Density Using Smoothing Splines
### Aliases: sscopu sscopu2
### Keywords: smooth models distribution
### ** Examples
## simulate 2-D data
x <- matrix(runif(200),100,2)
## fit copula density
fit <- sscopu(x)
## "same fit"
fit2 <- sscopu2(x,id=fit$id)
## ... |
c73398882972c25dda137584f4a896b05e359552 | 809619e09165bb59d4b068eb8bad833d0a30c411 | /man/result_inspector.Rd | bd190d1c93d6dbe056aaa789a422520858fd9e69 | [] | no_license | cran/GWASinspector | 2910c12799e24c0c7e9f34df871f7d19c658c36a | 5fabba85bf8d9ce8eb30c51344be4cb4a59489fe | refs/heads/master | 2023-05-24T16:53:12.048188 | 2023-05-15T17:30:02 | 2023-05-15T17:30:02 | 236,609,635 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 725 | rd | result_inspector.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/result_inspector.R
\name{result_inspector}
\alias{result_inspector}
\title{Displays a brief report after running the QC pipeline}
\usage{
result_inspector(inspector)
}
\arguments{
\item{inspector}{An instance of \linkS4class{Inspector} class.... |
403c8f4edd725e165f551acea9cfc30676aa6d4c | 04f349102910e5052ea34d3e7744e4d79a2fbb4f | /R/cof_lv_ugb.R | 25c5a488fd803da9423350a23e9fa4f1262c26fa | [
"MIT"
] | permissive | scoultersdcoe/CNAIM | f0728b00f0d0628e554975c78d767ee2c472fb3b | 5c77ce4c50ef92fd05b9bb44b33fdca18302d020 | refs/heads/master | 2023-08-23T22:54:59.450292 | 2021-03-12T15:52:54 | 2021-03-12T15:52:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,973 | r | cof_lv_ugb.R | #' @title Financial cost of Failure for LV UGB
#' @description This function calculates financial consequences of failure
#' (cf. section 7.3, page 75, CNAIM, 2017). Financial consequences
#' of failure is used in
#' the derivation of consequences of failure see \code{\link{cof}}().
#' @param lv_asset_category String T... |
3609108c3e61f8cd0b420d4932797bfb31fdb138 | 55cd81bfa7426eab1472c7bda8a98552b16941f3 | /R/helpers.R | 31c90735f62cc22277dc1b295a1130b78afb5f32 | [] | no_license | vonshick/ETLtool | d42e082ffca39286866efa7f667bf83a133a9e07 | a68a9639f2bcb15e1aca6bbb08662e879cea0dfc | refs/heads/master | 2020-08-07T15:17:15.195025 | 2019-10-08T09:58:24 | 2019-10-08T09:58:24 | 213,503,334 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,334 | r | helpers.R | #' @importFrom purrr map2
#' @importFrom dplyr bind_rows %>%
drop_sublists <- function(list) {
list_without_sublists <- list()
names <- names(list)
map2(list, names, function(element, name) {
if (!is.list(element)) {
list_without_sublists[[name]] <<- element
}
})
list_without_sublists %>%
b... |
adf8df5088ffc21641f36a6129ed3d33e18a6e82 | 2a83dfd6f09f9977ba2fd2d97fbb606ebe5494c4 | /rmbl2019/man/mbl_load_data.Rd | 83de548d0bb505cdce543e4a7a4fc4916cf39da8 | [] | no_license | tomsing1/mbl2019 | c9fa801ecddadd38798b4c60c9c8c6e10152e2fa | ff42c461f6a0f8db66ce31f97b9e412dfbd4606c | refs/heads/master | 2020-06-20T05:11:49.935341 | 2019-07-29T05:35:12 | 2019-07-29T05:35:12 | 197,006,213 | 0 | 0 | null | 2019-07-29T05:35:13 | 2019-07-15T13:40:03 | R | UTF-8 | R | false | true | 523 | rd | mbl_load_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mbl_load_data.R
\name{mbl_load_data}
\alias{mbl_load_data}
\title{Load DGEList with expression data from aws.s3}
\usage{
mbl_load_data(organism = c("mouse", "fly", "fish", "planaria", "worm"),
dataset = c("pre_mbl", "mbl"))
}
\arguments{
\i... |
349b6ea30dbb9636e1c3ce70db0c4c12629e566b | 200477836bf1e3ec08131092653e46fd26259136 | /SherryPlot/demo_cellmarker.R | 4da1dd30f058b79db87ffd6063f03e986579ceee | [] | no_license | SherryDong/create_plot_by_R_base | 0d8b4fc074e40993b977008c0c5f27e8702b24bb | 22a52779e5ec17a15767ef84024f594d0c3b7459 | refs/heads/master | 2023-08-31T05:30:04.611672 | 2023-08-22T01:21:28 | 2023-08-22T01:21:28 | 195,830,801 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,206 | r | demo_cellmarker.R | ## markers
panglodb <- read.delim('D:/写写文章/GD-Driver/BrainCortexDriver_project/data/PanglaoDB_markers_27_Mar_2020.tsv')
panglodb_brain <- panglodb[which(panglodb$organ=='Brain'&panglodb$gene.type=='protein-coding gene'&panglodb$species!='Mm'&panglodb$official.gene.symbol%in%feature.data$geneSymbol),]
genes_of_intere... |
40128ab711ff465747e528c41c9ede36d1b8dd85 | 47b1491bdcd0335f6df4cbc750af9b7fcac9c72c | /code/functions/format_training.R | 5a40140e24b7b969cd581856b2504264f71e5f94 | [
"CC-BY-4.0"
] | permissive | UKRN-Open-Research/survey-reports | 772c63e508a84cbf4f6c19830a75361a9d42862e | 701b4d6067df94bafca1fad9b2530b2129958e1c | refs/heads/main | 2023-04-05T03:13:10.905127 | 2021-04-14T15:12:46 | 2021-04-14T15:12:46 | 340,357,501 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,804 | r | format_training.R | format_training_familiarity <- function(df, training){
if(training == "Yes"){
# Subset responses of those who want training
df = df %>%
select(OpenAccess, OpenCode, DataSharing, Preprints, Preregistration, Primary, Unit, randomID) %>%
pivot_longer(cols = OpenAccess:Preregistration, names_t... |
cafdd5f4834a2cfec422031b5296b3e942050e34 | f0afd5a4bdfc8f61d748a2944d75aef9fa361f4a | /plot2.R | 3ab59547fac31ff8a607749b2d0bff5bfec6c922 | [] | no_license | KunalSharma2209/ExData_Plotting1 | 1604aaa5bba26850d7192ff0fcf08e5abf5f9924 | 0012db3f6b2a3377f59a3024da78bc1b50978000 | refs/heads/master | 2020-12-23T23:25:24.311297 | 2020-02-05T11:22:08 | 2020-02-05T11:22:08 | 237,307,718 | 0 | 0 | null | 2020-01-30T21:18:46 | 2020-01-30T21:18:45 | null | UTF-8 | R | false | false | 1,328 | r | plot2.R | ### Writing code and setting your working directory
getwd()
dir()
setwd("~/R/ExploratoryWeek1Assignment")
library(lubridate)
### Read in the data
power_data <- read.delim("household_power_consumption.txt", header=TRUE, sep=";")
head(power_data)
power_data[1,]
### Add a variable to the data tabl... |
8715357c7a94001b0f07693f40f47b2b71f41559 | 18b5b5ea60ef362374e8ed60e651a2cffc4e221c | /inst/rmarkdown/templates/flexible-webframework/skeleton/plan_skeleton.R | 948759b681517d64f58e985e3a03e0410c10aa02 | [
"MIT"
] | permissive | tpemartin/webtemplate | a6bfc468e8d3d64f7ca042676a6d247198e4c5b0 | 061eff584dbc0e8659cb1ca6a00119e4afe76658 | refs/heads/master | 2022-12-30T17:43:34.926760 | 2020-10-10T13:20:39 | 2020-10-10T13:20:39 | 292,805,709 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,393 | r | plan_skeleton.R | # plan_skeleton------------
plan_skeleton=drake::drake_plan(
# > plan begins -----------
# >> frameworkChoice--------------
frameworkChoice = {
menu <- get_menu()
myChoice <- list(
menu$materialize(),
menu$jQuery(),
menu$googleLogin()
)
myChoice
},
# >> body--------------
body = {
tagList(
ta... |
afb9ce3d9b2abfd6fa896dbb97f70cf41219f4de | a320c7353d59c30a3d7bc2c303c5b6793449ae7d | /run_analysis.R | b6a2a95fd91e2d8000c4f679f08021c2c22b4746 | [] | no_license | mitsmis/ProgrammingAssignment4 | 85595e61935eb913391f23238eb1b71c2dbdbc0e | e231a0cfdd8b9295331c3821f1d610ccb74baab9 | refs/heads/master | 2021-01-09T20:01:17.082824 | 2016-07-17T20:19:51 | 2016-07-17T20:19:51 | 63,536,678 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,516 | r | run_analysis.R | # Data was downloaded the old fashioned way and placed in
# appropriate Folder in order to complete the analysis.
# (features) Read and Bind the test and training datasets together
# Leverage 'read.table' and 'rbind' to do this.
x.train <- read.table('./UCI HAR Dataset/train/X_train.txt')
x.test <- read.table('.... |
ec3d0ed286543b281ddce3c672835c11575b0a5e | 1f59624d8e1d90a8232367d901574faf84ee35d6 | /Plot1.R | f17cc67d08e9360ace5a0f7c8f6f76919795128e | [] | no_license | riveraor6/ExData_Plotting2 | d3ceac9eeee1d3ab5072cd599bcdf7d203bbc790 | ea2b53cfe17e6e484f24e87b022632f97d4753c4 | refs/heads/master | 2020-12-25T18:19:38.342239 | 2014-10-27T01:39:18 | 2014-10-27T01:39:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,209 | r | Plot1.R | ##Setting library
library(plyr)
library(ggplot2)
library(data.table)
dir <- setwd("D:/Users/Mad Labs PR/Documents/Exploratory_2")
url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
filename1 <- "exdata-data-NEI_data.zip"
filename2 <- "Source_Classification_Code.rds"
filename3 <- "summarySCC_PM... |
38db7befe04fe63160329eb88696e7c262c74a95 | 38d3251ce2d0a946da522729eb2b6476bfb5d612 | /hw04/code/clean-data-script.R | 4983882765d2f1a91781fe0b2185cee387c47353 | [] | no_license | jennyhdw/stat133-hws-fall17 | 411b243fbf0c1f00a421d28e20110ba101d586b0 | 0bf7616d745b457b7abe74458891fc7407eb829d | refs/heads/master | 2021-08-23T07:10:41.070354 | 2017-12-04T02:31:29 | 2017-12-04T02:31:29 | 103,677,915 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,648 | r | clean-data-script.R | # ==============================================================
# Title: Cleaning Data
# Description: Data preparation
# Input(s): rawscores.csv
# Output(s): cleaned data
# Author: Jenny Huang
# Date: 11-09-2017
# ===============================================================
#packages
library(readr) # import... |
b49f28b26ece7b901b2f553e4951e76efa1a4f95 | f8ef7a9663ea5cdce9776314719eafc44bba24fb | /man/BF.Rd | ce44187fdb797b83bd347af610de7f28a25c5d44 | [] | no_license | jomulder/BFpack | cd00253f44f520ab8a8952807cb361a4c17a0c57 | e0bd669ddb8ebfdc3b8e7f56b77eeafd0ab7cc1a | refs/heads/master | 2023-08-16T10:42:12.051035 | 2023-08-15T09:45:29 | 2023-08-15T09:45:29 | 134,589,180 | 13 | 4 | null | 2021-11-23T15:55:07 | 2018-05-23T15:23:20 | R | UTF-8 | R | false | true | 10,789 | rd | BF.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/BF.gaussian.R, R/BF.lm.R, R/BF_methods.R,
% R/BFttest.R
\name{BF.default}
\alias{BF.default}
\alias{BF.lm}
\alias{BF}
\alias{BF.t_test}
\title{Bayes factors for Bayesian exploratory and confirmatory hypothesis
testing}
\usage{
\method{BF}{d... |
e1e88c9292a697fb65d66e524e562a07a2fbc17f | 2656a078cef4fbb1a32b78dc946e945b5424ba35 | /man/lesmis.Rd | 0d61bda7212b1e7a5bc797c96d1213d63f8a4123 | [] | no_license | fanatichuman/geomnet | fa5ccf2049eae8f6131160815442fff779cb221f | 6aff9ca74a581e773e8b7f502863c8bef483e473 | refs/heads/master | 2021-01-24T20:25:07.135518 | 2016-07-20T16:00:24 | 2016-07-20T16:00:24 | 64,494,837 | 1 | 0 | null | 2016-07-29T16:27:48 | 2016-07-29T16:27:47 | R | UTF-8 | R | false | true | 1,523 | rd | lesmis.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.r
\docType{data}
\name{lesmis}
\alias{lesmis}
\title{Coappearance network of characters in Les Miserables (undirected)}
\format{A list of two data frames:
\itemize{
\item the edges data set consists of three variables of length 254:
\ite... |
1c58e94adc6a07fb73aa1a02f049475ba7ab9d3a | 29585dff702209dd446c0ab52ceea046c58e384e | /RPPairwiseDesign/R/PPrect.R | cebfc25c058fed3717dbe7d78649bcd097628376 | [] | 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 | 605 | r | PPrect.R | PPrect <-
function(n,l) {
M<-3
s<-n*l;a<-c();b<-c();c<-c()
A<-matrix(1:s, ncol = l, byrow=TRUE)
for (i in 1:n) {
for (j in 1:l) {
a1<-A[i,];b2<-A[-i,];b1<-b2[,j]
c1<-A[-i,];c1<-c1[,-j];c2<-as.vector(c1);f<-length(c2)
a<-c(a,a1);b<-c(b,b1);c<-c(c,c2)}}
AA<-matrix(a, ncol = l, byrow=TRUE)
BB<-matrix(b, ncol = ... |
12c76877c95189305b806d8e0e929b9243e1a525 | be429607b3bcbf89d8ced76bd2a3aa4ee8e64301 | /man/dt_stat.Rd | 8fdc921f77860c1477254348dda4a7a6f8035243 | [] | no_license | lshreyas/ehR | b66034d860a5a61b6ad7b38d22a667633c855c01 | a39e1d6c13851be7eb0b123b1a1c6c12329a6e84 | refs/heads/master | 2021-01-18T16:07:00.081227 | 2017-03-30T01:59:13 | 2017-03-30T01:59:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 593 | rd | dt_stat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dt_stat.R
\name{dt_stat}
\alias{dt_stat}
\title{Count number of observations in a data.table over a given timeframe/a given id list.}
\usage{
dt_stat(id_list, empi_list, date_list, dt, dt_date_var, timeframe_day = 365,
buffer_day = 0, timef... |
ba4293a34bcf97e3a4d539917250b34e7e45a1e0 | 4b3e218c2081e897d23e40da99f7767c0ca710af | /man/shinydivajs.Rd | 89d2dd2ebb41d3d59665c0b8352196545c8139de | [] | no_license | byzheng/shinydivajs | 9f410e81f6c77e4ded1f6960ab8050363371c4cd | 54f3b3b6301a5a7d9adccd2dd483f7fd5020a90a | refs/heads/master | 2021-01-20T22:10:15.739909 | 2016-09-24T11:54:15 | 2016-09-24T11:54:15 | 53,198,295 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,110 | rd | shinydivajs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/divajs.R
\name{shinydivajs}
\alias{shinydivajs}
\title{a JavaScript book image viewer}
\usage{
shinydivajs(objectData, inGrid = FALSE, enableAutoTitle = TRUE,
enableFullscreen = TRUE, enableLinkIcon = TRUE, width = NULL,
height = NULL)
}
... |
e1cdcd0d6182daaf51b6c820a781586f5379a8cd | 8c84b89f304133224eac4c4819b3826c9ff84958 | /man/switch.norm.funcs.Rd | bdf3248628f45bf478a5a12927fe7c934c9af431 | [] | no_license | QihangYang/PRECISION.array | 7ca6010838e6dc0e65fec57014e736d1dc91f67c | a413b8ad068c548486d5e8fe20c7fa831aaeb10a | refs/heads/main | 2023-03-27T11:27:55.657639 | 2021-03-15T15:08:53 | 2021-03-15T15:08:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 829 | rd | switch.norm.funcs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/switch.norm.funcs.R
\name{switch.norm.funcs}
\alias{switch.norm.funcs}
\title{Switch Normalization Functions}
\usage{
switch.norm.funcs(norm.list = c("NN", "MN", "QN", "VSN"), norm.funcs = NULL)
}
\arguments{
\item{norm.list}{Switch all the b... |
46da8178685ce57c2616194ddeceddea65b7db95 | 29585dff702209dd446c0ab52ceea046c58e384e | /btergm/R/interpretation.R | 0eee87ca6f2517d00fdb479c01f314007a0cc7f6 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,123 | r | interpretation.R | # interpretation function for ergm objects
interpret.ergm <- function(object, formula = object$formula,
coefficients = coef(object), target = eval(parse(text =
deparse(formula[[2]]))), type = "tie", i, j, ...) {
if (length(j) > 1 && (type == "tie" || type == "dyad")) {
stop(paste("For computing dyadi... |
c17b723895bd07c9cee154f898ea39133d7ce423 | 43348304944732564b0dc500472a95b0ccd7c47d | /cachematrix.R | 696632646e99c1537d30d2ae8eac52e36f7f8161 | [] | no_license | amsdias/ProgrammingAssignment2 | 7aa700d7d8bef392e54a70eb49578a7b6591e25c | fab4ad6014082515c88d9ee1ec88455c3ea036d3 | refs/heads/master | 2021-01-24T15:33:30.019542 | 2015-07-25T17:24:14 | 2015-07-25T17:24:14 | 39,217,658 | 0 | 0 | null | 2015-07-16T20:01:18 | 2015-07-16T20:01:16 | null | UTF-8 | R | false | false | 1,093 | r | cachematrix.R | ## The following two functions are used to cache the inverse of a matrix
## This is useful since it is a costly operation
## This function creates a list containing functions that:
## 1. set the value of the matrix
## 2. get the value of the matrix
## 3. set the value of the inverse matrix
## 4. get the value of the i... |
434b48daa05df78e094ac6b74fff8d18e87de967 | e98ca256c242b0e333e4631905f49fdc0aeb8def | /calc.rmseP.R | e986e7dc21533f3a9bb7deb3a67c8d90330b28e0 | [] | no_license | xime377/RedEdge-calibration | 635268b2003a3f8897cf3dd1f1d903b826c29c7a | 1026cb2732114915c5d6a70436dbb1ac7ad98dea | refs/heads/master | 2021-01-23T01:08:47.344638 | 2018-02-01T12:57:27 | 2018-02-01T12:57:27 | 85,881,512 | 6 | 3 | null | 2017-03-30T10:35:57 | 2017-03-22T22:17:13 | null | UTF-8 | R | false | false | 911 | r | calc.rmseP.R | ##Calculate RMSE%
#Error calculation
#'
#' @param Pred predicted values
#' @param Ref Reference values
#'
#' @return E Error in %
EP<-function(Pred,Ref){
E=(Pred-Ref)/Ref
E
}
#Calculation of SE %
#'
#' @param Pred predicted values
#' @param Ref Reference values
#'
#' @return SE S... |
5f7e42c26b0631ca39302f79c9f9e4d4a1eacc29 | b7955a94f382c890f12ee2506583273548045ec7 | /Combination.r | 03c2c7caa7d1bcb7a6160f2377ea10be4bd9f687 | [] | no_license | DSSAT/glue | 7e9e590ccff4e0204700b7409dde23fb49cfbabb | 139a26d4b8ef752145a04ec81bb552c3a5568dd9 | refs/heads/develop | 2023-08-17T05:28:52.363786 | 2021-08-27T20:01:27 | 2021-08-27T20:01:27 | 120,203,785 | 1 | 12 | null | 2023-08-11T20:17:17 | 2018-02-04T16:50:51 | R | UTF-8 | R | false | false | 463 | r | Combination.r | ## This is the function to combine the likelihood values from different measurements.
Combination<-function(LikelihoodMatrix)
{
#print(LikelihoodMatrix);
Dimension<-dim(LikelihoodMatrix);
#print(Dimension);
if (Dimension[2]==2)
{
CombinedLikelihood<-LikelihoodMatrix[2];
} else
{
CombinedLikelihood<-LikelihoodMat... |
3d3fd4aa96958620aeb68babdbae21f82e85f7ea | f79cd4e052c5cbb24e7ef3e4bec1c39f9ce4e413 | /BEMTOOL-ver2.5-2018_0901/bmtgui/biological/assessment/VITpaths_females/add.VITpaths_females.r | d9a66775cc57b36b728249abc182971c37cefc1c | [] | no_license | gresci/BEMTOOL2.5 | 4caf3dca3c67423af327a8ecb1e6ba6eacc8ae14 | 619664981b2863675bde582763c5abf1f8daf34f | refs/heads/master | 2023-01-12T15:04:09.093864 | 2020-06-23T07:00:40 | 2020-06-23T07:00:40 | 282,134,041 | 0 | 0 | null | 2020-07-24T05:47:24 | 2020-07-24T05:47:23 | null | UTF-8 | R | false | false | 1,245 | r | add.VITpaths_females.r | # BEMTOOL - Bio-Economic Model TOOLs - version 2.5
# Authors: G. Lembo, I. Bitetto, M.T. Facchini, M.T. Spedicato 2018
# COISPA Tecnologia & Ricerca, Via dei Trulli 18/20 - (Bari), Italy
# In case of use of the model, the Authors should be cited.
# If you have any comments or suggestions please contact the following e... |
c964ed4e18bcb187e202c283d51b512e44002214 | b8883d2e0019778f2dd66919d39baad425e7dbe6 | /R/detect titles.R | 753b225247fc18f6a2acf003e88c4bb940e706c0 | [
"MIT"
] | permissive | marissasmith8/Citation-Network-Analysis | e952fb5431b33efea6d898c95dbc1624fe909a30 | e2af77670fd9c21415dfe97fd288962aaf1e7e95 | refs/heads/master | 2023-03-29T05:06:40.350490 | 2021-03-29T15:03:00 | 2021-03-29T15:03:00 | 347,930,282 | 0 | 0 | MIT | 2021-03-17T11:40:53 | 2021-03-15T10:44:26 | HTML | UTF-8 | R | false | false | 1,998 | r | detect titles.R | library(readxl)
library(tidyverse)
library(rebus)
full <- read_xlsx("Refs full (08.04.19).xlsx", sheet = "Full References ")[,1]
clean <- read_xlsx("Refs full (08.04.19).xlsx", sheet = "Clean References ")[,1]
table(is.na(full))
table(is.na(clean))
refs <- colnames(read_xlsx("Refs full (08.04.19).xlsx", sheet = "Cle... |
e37d90ff868fbe471be3981e115bfe34eca3276e | dda08ebff68da583ec11f861cf1d0e75293fd2c5 | /tests/testthat/tests.seqtest.R | f2e33cb9a6f8d7c2a4f5d16a29d3985c7ab177c6 | [] | no_license | lnalborczyk/ESTER | 314f65f1a52d925f475cff2b0b54cbbf85fc5e0a | eee73e59b3e62caa936d64d563b6fa9d69e593b7 | refs/heads/master | 2021-01-11T17:03:10.560357 | 2018-05-19T08:57:35 | 2018-05-19T08:57:35 | 69,504,922 | 1 | 2 | null | 2017-01-26T14:34:38 | 2016-09-28T21:23:41 | R | UTF-8 | R | false | false | 36 | r | tests.seqtest.R | context("Tests for seqtest output")
|
3349e3a4c4548de8b5db99399b3b097d4c341aac | f94f004442845afd0ca9ff5e2881bb57044f8ec0 | /tests/testthat/test-gather.R | d2712d54118909c5247197c33a63743a275da146 | [] | no_license | jreisner/biclustermd | f715ce4f0673c5a7206651d4fc7be459960d2303 | 975af747fce1adf46feba4ec65c05debf6cc8b17 | refs/heads/master | 2021-07-23T19:13:22.140337 | 2021-06-17T14:10:44 | 2021-06-17T14:10:44 | 127,488,714 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,008 | r | test-gather.R | context("gather.biclustermd")
test_that("test that gather() returns all entries in the data provided to biclustermd()", {
sbc <- biclustermd(synthetic)
gsbc <- gather(sbc)
expect_equal(nrow(gsbc), prod(dim(sbc$data)))
})
test_that("test that gather() retains all row names", {
sbc <- biclustermd(synthetic)
... |
39a193a62224a27436270f5f1b0691ca8dec6a9c | d36827d1ae6c78b62ab2b67b2e6b48c7f9f9b4ef | /Legacy/Oldtrain/pr_roc_start.R | 1d94ad51f6151e379d391f28f29aff27989abc74 | [] | no_license | agawes/CNN-iPSC | c6c3f944d32b30ecfd01bbd478712d2e63244c76 | a6485ea11d3cdb7c1fb5dc3abaa3a0cdfb6c34ae | refs/heads/master | 2020-09-07T12:39:59.019667 | 2019-10-04T09:48:02 | 2019-10-04T09:48:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 811 | r | pr_roc_start.R | library(precrec)
library(ggplot2)
library(grid)
library(tidyr)
library(plyr)
library(dplyr)
setwd("~/Oxford 2.0/Scripts/CNN_project/Data/better_train/iter1")
temp = list.files(pattern="roc*")
for (i in 1:length(temp)) assign(temp[i], read.delim(temp[i], header = FALSE))
selected <- ls()[grep('roc', ls())]
roc1.txt$... |
227d38acc193d8a603dc9b432dd38a83335a1c55 | ec1f284fa56cb3270ad64d22615aa63b8da47f6e | /doc/cours/summerSchool/2016CSSS/nonlinear.R | d79ace805bd73e0aad298daf3f4d60272c14d081 | [] | no_license | simoncarrignon/phd | 681d9935d5bf66b9b51a0e852b851a6487deb425 | 542eeaf080b2e779adc86ac8899c503c647d0204 | refs/heads/master | 2021-01-25T20:59:48.073803 | 2019-01-28T15:28:21 | 2019-01-28T15:28:21 | 39,514,198 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 159 | r | nonlinear.R |
f<-function(x0,R,n){
res=
R*x[n-1](1-x[n-1])
}
x=1:10
fq2<-function(x,r){
return(r*x*(1-x))
}
x=seq(0,1,.001)
plot(x,fq2(x,3.1))
line(x,x)
fq2(x,3.1)
|
f6c4ec33fd5d71e13e9f229bf28b60d179a1a3d7 | 5bb2c8ca2457acd0c22775175a2722c3857a8a16 | /man/get_pvalue.Rd | c27840a130b6444d4fd29366713e2e6d02f2491a | [] | no_license | IQSS/Zelig | d65dc2a72329e472df3ca255c503b2e1df737d79 | 4774793b54b61b30cc6cfc94a7548879a78700b2 | refs/heads/master | 2023-02-07T10:39:43.638288 | 2023-01-25T20:41:12 | 2023-01-25T20:41:12 | 14,958,190 | 115 | 52 | null | 2023-01-25T20:41:13 | 2013-12-05T15:57:10 | R | UTF-8 | R | false | true | 356 | rd | get_pvalue.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wrappers.R
\name{get_pvalue}
\alias{get_pvalue}
\title{Extract p-values from a Zelig estimated model}
\usage{
get_pvalue(object)
}
\arguments{
\item{object}{an object of class Zelig}
}
\description{
Extract p-values from a Zelig estimated mod... |
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