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d9c60de72a4f49f77893f149e7385f9316e8963d | d8ea9459151048d15cf421881cf793688aa5f9ca | /rscripts/final/1.1_mss_processing.R | b0a445a6b69a64a2f847aa4ef35d8a34c7b57ef3 | [] | no_license | cschwab1/Berlin-Gentrification-Project.github.io | e74b9698af4c96c119e33750401ed08d17016e6c | 93f3dd70de4cda9e7a8e0699ca7a5251fb9ba76f | refs/heads/main | 2023-06-02T23:40:17.414279 | 2021-05-17T18:42:05 | 2021-05-17T18:42:05 | 364,145,592 | 0 | 0 | null | 2021-06-28T05:55:19 | 2021-05-04T05:07:54 | HTML | UTF-8 | R | false | false | 11,150 | r | 1.1_mss_processing.R | ########################################
# Thesis Script IV: MSS Processing
########################################
library(tidyverse)
library(sf)
library(areal)
setwd("~/Desktop/Code/Thesis")
########## Subsetting variables and cleaning data
##### MSS03
mss_2003 <- read.csv("~/Desktop/Code/Thesis/demographic/2003_... |
efca53d5f5120f18f4483a09d5d1b14985feff68 | 3bef70f4b3d6283f2b2bfb44ccdfbf9b28c6429d | /man/tidy_factor.Rd | e6a2cefe718d129313321bc36f32f33ac2576d5c | [
"MIT"
] | permissive | KWB-R/dwc.wells | 4c1594ea66b1792c6c955b98418982edf80675c1 | 45e8670647c4771fe70d59db0f7cfd1e80242361 | refs/heads/main | 2023-04-10T01:24:40.973815 | 2022-07-12T13:42:20 | 2022-07-12T13:42:20 | 351,021,733 | 0 | 0 | MIT | 2022-10-16T09:17:19 | 2021-03-24T09:35:15 | R | UTF-8 | R | false | true | 579 | rd | tidy_factor.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/prepare_functions.R
\name{tidy_factor}
\alias{tidy_factor}
\title{turn character into factor, sort factor levels and replace NA level}
\usage{
tidy_factor(x, level_sorting = c("frequency", "alphabet")[1])
}
\arguments{
\item{x}{character vect... |
7f78107bd7387418bcf06e6d50b42c9442325ea5 | 555f124ae8496518e510726b84e7f97c78af589b | /SANDBOX/Bikes Markdown.R | 5cfb50acb0f40748a73caca6c24be7787adffb5b | [] | no_license | qilixiang007/CityBikeNYC | 1056c0a1e21f2c54e50b34f221756f936ccf4e0f | 8e4a8d4598eff8a7afe2b0b470c7c041dbf1193f | refs/heads/master | 2022-04-12T03:07:39.490675 | 2020-03-20T00:03:53 | 2020-03-20T00:03:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,559 | r | Bikes Markdown.R | ---
title: "Mapping NYC Citi Bike Routes"
output:
html_document:
keep_md: true
theme: cerulean
highlight: haddock
---
# Setup
Add this code to set global options. Always initialize all of your packages up-front.
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning=F, message=F,... |
8c9a1cbfb43318db349bbad04e62d7c1ee0311b3 | 6cab50985daf1a272f7f8e5b5cd18ec2b210341f | /label_maf.R | 5b8ba2a1331f973a5dc95731ac37ff9eb3e96461 | [] | no_license | wooyaalee/vcf_to_tidy_tsv | f9880181164c2efcf0ecebdc4d8e76159b75e915 | 0dd4dc2f6ef9e21e57b9d01f3848357c210cc4d3 | refs/heads/master | 2022-09-04T03:10:46.819835 | 2020-05-18T02:31:12 | 2020-05-18T02:31:12 | 267,899,335 | 0 | 0 | null | 2020-05-29T16:03:39 | 2020-05-29T16:03:38 | null | UTF-8 | R | false | false | 1,366 | r | label_maf.R | library(GetoptLong)
library(dplyr)
library(tidyr)
library(readr)
## A script to add Tumor/Normal sample barcodes and Study
## to a MAF file.
## Future feature: arbitrary field setting with a -C (custom) flag.
read_maf <- function(fi){
return(readr::read_tsv(fi,
comment="#",
... |
8891a4476a1634858ff650b14f26c9cba203ccf6 | 16073509499c165a47add2639fe8abf8be1acb74 | /sensitivity_simulation.R | 169ce51e7d98d2647b575925e93924f90914750e | [] | no_license | nsvitek/observer-free-morphotype-characterization | f64b2c7aca21b0abfe522d33d431af7037fc4199 | 921852e7b2a36719dc464b945ecee7d6ad7a05e4 | refs/heads/master | 2022-07-08T06:04:41.698122 | 2022-06-24T19:06:12 | 2022-06-24T19:06:12 | 58,686,205 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,771 | r | sensitivity_simulation.R | locateScripts<-"C:/cygwin/home/N.S/scripts/observer-free-morphotype-characterization/"
# location of data
locateData<-"D:/Dropbox/Documents/Dissertation/sensitivity_analysis/data"
# Load Dependencies, Common Objects ------------------------------------------------------------------
setwd(locateScripts)
source("sensiti... |
b7f5c363a3d8f6df3d959f4db2a7149a9c3c6a58 | 7f7a0f2774e61833f5944c7af798c86e224e3339 | /man/Board_colors.Rd | 2c093f3bf3b91aa48379d4af720717c9c37eaf30 | [] | no_license | cconley/SchoolR | a83f6bee0e1c7b3ab7f8a27cc79310d26f45f82b | 1c9f563fac86f58aad783e57814d1901870e283e | refs/heads/master | 2020-03-21T10:13:14.737678 | 2018-06-23T23:24:34 | 2018-06-23T23:24:34 | 138,439,166 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 723 | rd | Board_colors.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Colors.R
\docType{data}
\name{Board_colors}
\alias{Board_colors}
\title{These function loads the Board Color palettes which includes:}
\format{An object of class \code{character} of length 7.}
\usage{
Board_colors
}
\description{
Board_cols()... |
6b5e1f8154388c8d75cb58a2ab2da1905e27f279 | 8cfb4de65cda8ececc9d6547a2bf1238b1ca4c81 | /Matrix Models/Examples/Two-site matrix models for black-headed gulls.R | 8930950df1bcbb7d28110bd9716979822a76da25 | [] | no_license | kanead/white-backed-vulture-population-dynamics | d299a475c1beb0505aac6c2aa5aa490f6e99cdb4 | 4ca7f04dbda31771e51b29f6134f1aa4129ba1f6 | refs/heads/master | 2021-01-10T23:21:42.608761 | 2017-10-20T16:29:10 | 2017-10-20T16:29:10 | 70,612,534 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,095 | r | Two-site matrix models for black-headed gulls.R | # Matrix Models for Population Management & Conservation
# 2014
# CNRS CEFE workshop, Montpellier, France
# Jean-Dominique Lebreton, Olivier Gimenez, Dave Koons
# EXERCISE 3: Two-site matrix models for black-headed gulls
rm(list=ls(all=TRUE)) # clears the R memory, which is sometimes useful
#######################... |
837f9eeded62a2735291381fe99a6a2fa01cc546 | 2b4762251cadb659e03f4fcb4f3ad14be098b75a | /ui.R | 12e3d92579fc443712d9a6b2373d4d3558690adc | [] | no_license | bcheggeseth/ArtistDiversity | 67a77cff52ff328430d86cc06c3778a9c43875a5 | 17b3105cf348788ed90f5439d095fdc541243f59 | refs/heads/master | 2020-04-08T20:27:04.386624 | 2019-01-23T20:20:33 | 2019-01-23T20:20:33 | 159,699,654 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,811 | r | ui.R | library(shiny)
library(DT)
library(ggplot2)
library(shinyWidgets)
library(markdown)
shinyUI(
navbarPage(
title=a(tags$b("Diversity of Artists in Major U.S. Museums by Topaz et al."), href='http://www.plosone.org') ,
windowTitle="Artist Diversity",
id="mytabs",
tabPanel(title="Artist Demographics",
... |
548ff1e5d7546c6fd778f2583dce66270edf6d08 | 77c56c957ebbf937761880000a850574eb7d65aa | /randomForest.R | 56a08306dd99ac3831a61dad1413da37b1305758 | [] | no_license | martinmontane/CienciaDeDatosClases | 28d2dd848882b663b8bb570ab9be4441aaee6c12 | dbbd403bcb51440550ffaea3397450892d2e3ada | refs/heads/master | 2022-11-14T16:01:46.727802 | 2020-07-09T19:08:39 | 2020-07-09T19:08:39 | 263,441,648 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,887 | r | randomForest.R |
load(file=url("https://github.com/martintinch0/CienciaDeDatosParaCuriosos/raw/master/data/independientes.RData"))
library(caret)
library(randomForest)
library(tidyverse)
library(furrr)
plan(multiprocess)
# Intentamos tener los mismos resultados
set.seed(10)
# Lista que guarda los resultados
resultados <- lis... |
b75dafd86074570a0dce36007b6c6b1c049bbe75 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/GGEBiplots/examples/ExamineGen.Rd.R | 2729e6723ac6f9afaf8560c1f6f4067d5bfde971 | [] | 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 | 226 | r | ExamineGen.Rd.R | library(GGEBiplots)
### Name: ExamineGen
### Title: Examine a genotype biplot
### Aliases: ExamineGen
### Keywords: GGE
### ** Examples
library(GGEBiplotGUI)
data(Ontario)
GGE1<-GGEModel(Ontario)
ExamineGen(GGE1,"cas")
|
dabb9f529ac41e243026e31ba1dcd705abf64670 | 3f36e3afc25870cf6e9429de4a5b0604d52dc03a | /R/TrajectoryArguments.R | 9c7a48725802c803e60892a70818d07ef6f532f7 | [] | no_license | Patricklomp/VisualisingHealthTrajectories | 4077a62b7da7b92ad2c7aa99a918aaf15585788e | 98e69c50d354a693f0e9e8a3d76c81e3e5088a7a | refs/heads/master | 2023-05-31T16:21:50.435675 | 2021-06-04T07:43:42 | 2021-06-04T07:43:42 | 317,466,951 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,532 | r | TrajectoryArguments.R |
#' Creates an object to hold analysis-specific data
#'
#' @param mode Indicates whether the analysis is run in DISCOVERY or VALIDATION mode. In VALIDATION mode, the package tries to validate predefined event pairs. In DISCOVERY mode, it tries to identify all directional event pairs from the data.
#' @param minimumDays... |
7b2d30a8ae0407fff86587ca621aafebfffc80ce | 4d034cff18c2b990cb987b1ddb84666d2591dfa4 | /titanic-survivors/script/04_check.R | 6c98f805dfa189e1177d079db65f6fb61553df37 | [] | no_license | GiuseppeTT/fooling-around | f40d755ebab8198f995d496fb7819799e0304567 | b25a98f8619f6ad1e4870d89d41d6e0095521b36 | refs/heads/main | 2023-07-11T10:40:49.695509 | 2021-07-21T21:08:19 | 2021-07-21T21:08:19 | 384,141,688 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,005 | r | 04_check.R | # TODO:
# - Read test data and load fit model
# - Check it's performance
# - Plot ROC
# - Check AUC
# Set up -----------------------------------------------------------------------
## Load libraries
library(tidyverse)
library(tidymodels)
## Source auxiliary R files
# source("R/constants.R")
# source("R/functions.... |
09b03773e395175f48689f443e4613f9aab592a4 | 759f30b784192b3baf83f8c1e0e7c0b88f3af4a9 | /rankhospital.R | 3b1d5a3e097fa692c0882b16856e688f077921ba | [] | no_license | RQuinn78/coursera_assignments | 2f1d72826bc32252690c2b10579db43cedfb92e4 | 609c17aea0fce392df8ed02322d9f89712a2c90a | refs/heads/master | 2021-01-10T16:10:28.443617 | 2016-02-16T19:10:20 | 2016-02-16T19:10:20 | 51,860,844 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,312 | r | rankhospital.R | rankhospital <- function (state, outcome, num= "best"){
care_meas <- read.csv("outcome-of-care-measures.csv", colClasses="character")
if (!any(state == state.abb)){
stop ("invalid state")}
if (!any(outcome == c("heart attack", "heart failure", "pneumonia"))){
stop ("invalid outcome")}
#... |
99f6f5a0d8a5bfc068064f0b989efc174da6fbf1 | 4ede1bcfdebc480e41b8bc384787cf3fdb2c3692 | /Code/10_visualization_several_estimates.R | 7311ed1a065a8bccc5d1340e2b5ecbd1a1d08418 | [] | no_license | kikeacosta/optimal_vaccination_age_varies_across_countries | fa21dde907176fb1d7a22be6165fcf60945ac97f | 7343089fcdbb8070e75512c76ea24e8485b61f71 | refs/heads/master | 2023-06-22T08:32:02.234361 | 2021-07-22T00:58:47 | 2021-07-22T00:58:47 | 351,043,960 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,457 | r | 10_visualization_several_estimates.R | #=================================================================================================#
#=================================================================================================#
#
# YLL & Vaccines
# Visualization
#
#==============================================================================... |
aec100152bc03a6c65bc1671de515bb12d6d9505 | d23337c36e2f2b4d55c2d60cc818b905296fcf53 | /R/cdf_plots.R | 5c476b4c16dd3c3fbafb69c0c60f9fa64cf84678 | [] | no_license | tywagner/EDC_LandscapeBias | 873af5b322423bbb20be810d0971bb4b67e2011a | 75d8edc8b33299c1c35c7cdead5186f5e3ce82cf | refs/heads/master | 2021-06-22T11:35:19.323719 | 2017-07-31T14:56:56 | 2017-07-31T14:56:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,286 | r | cdf_plots.R | # rm(list=ls())
library(data.table)
# Read in census catchments
cens <- fread('FINAL_EDC_NHD_Summaries_V6.csv')
cens[, .N]
head(cens)
dim(cens)
# summary(cens)
# Read in sample reaches
samp <- fread('Immediate_Catchment_Summaries_V6.csv')
samp[, .N]
head(samp)
dim(samp)
# remove site names
samp <- samp[, c("SiteNm1"... |
8d6fe46b07d2e4f91cd99462bd4b6d5450899252 | 63cb78527bcb90f984788587a29f8f115e94ab64 | /R/dashbioManhattan.R | 84d1c7e80ace4f3eaca6ac466b4c65c73c24d02a | [
"MIT"
] | permissive | plotly/dash-bio | 2b3468626c7f021c083c8b9170e61862d5dc151d | 8a97db7811cc586d7e0bf1d33c17b898052b2e8f | refs/heads/master | 2023-09-03T13:30:45.743959 | 2023-08-16T15:26:27 | 2023-08-16T15:26:27 | 141,365,566 | 505 | 228 | MIT | 2023-08-23T01:28:46 | 2018-07-18T01:40:23 | Python | UTF-8 | R | false | false | 1,609 | r | dashbioManhattan.R | dashbioManhattan <- function (dataframe, chrm = "CHR", bp = "BP", p = "P",
snp = "SNP", logp = TRUE, title = "Manhattan Plot", showgrid = FALSE, xlabel = NULL,
ylabel = "-log10(p)", point_size = 5, showlegend = FALSE, col = c("#969696", "#252525"),
suggestiveline_value = -log10(1e-05), suggestiveline_color = "blue",... |
e5466753f0401adf67c4bd83af5524bfc55e9942 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/utiml/examples/subset_correction.Rd.R | a649aec5852af786f6fc58c67435cf1b6a329b2b | [] | 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 | 235 | r | subset_correction.Rd.R | library(utiml)
### Name: subset_correction
### Title: Subset Correction of a predicted result
### Aliases: subset_correction
### ** Examples
prediction <- predict(br(toyml, "RANDOM"), toyml)
subset_correction(prediction, toyml)
|
bf71377b2024c573015f4cbdf102925ca468b556 | 87d67d1370f9eff8d02e661a754cfc68765b6311 | /cachematrix.R | 57d42ee363983bb8f3c21ba5cd4a9ed127151eb6 | [] | no_license | dhdhdh/ProgrammingAssignment2 | 68bff4d87e52a350afa06db09455726dc2b30e8a | 8f8cb2947ce53c349b238a19012baaa14edfc374 | refs/heads/master | 2021-01-14T10:31:15.200265 | 2015-06-18T11:53:50 | 2015-06-18T11:53:50 | 37,654,766 | 0 | 0 | null | 2015-06-18T11:07:45 | 2015-06-18T11:07:44 | null | UTF-8 | R | false | false | 935 | r | cachematrix.R | ## These functions calculates the inverse of an invertible square matrix and
##caches the result as long as the matrix did not change
## This function allows the input square invertible matrix to cache its inverse
makeCacheMatrix <- function(x = matrix()) {
Inv<-NULL
set <- function(y) {
x <<- y
Inv <<- N... |
845f076b94f860888cc05080788177c572a29a1d | 92fe75798aff7836c17cc9cc439fef0bdc9bfcb6 | /R/naiveIntegration.R | 988a8486acaf89c01be44116826a762433d30557 | [
"BSD-2-Clause"
] | permissive | pmartR/peppuR | a036e07fe230a66195190b90d4a8c96e733083c6 | d1dae01782c69bc49749b405b36fb82416226515 | refs/heads/master | 2020-04-12T16:28:48.210435 | 2020-01-13T21:23:09 | 2020-01-13T21:23:09 | 162,613,771 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,189 | r | naiveIntegration.R | #'Combine probabilities from multiple data sources.
#'
#'@param probabilities_by_source a list of data.frames with one column for each
#' predicted class corresponding to the probability of that class label given
#' the data source. Each data frame corresponds to a data source. All
#' probabilities in each row must ... |
d538b4c82aa504786a3c5d2e9e1e6ef975b4acce | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/mdscore/examples/lr.test.Rd.R | c06ffc2b0eac39c74ac1cb38182a3641718d84f8 | [] | 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 | 374 | r | lr.test.Rd.R | library(mdscore)
### Name: lr.test
### Title: Likelihood ratio test for generalized linear models
### Aliases: lr.test
### Keywords: likelihood ratio glm
### ** Examples
data(strength)
fitf <- glm(y ~ cut * lot, data = strength,family = inverse.gaussian("inverse"))
fit0 <- glm(y ~ cut + lot, data = strength, family... |
026f85de4f47e4899663e0b0335e165b7a59e568 | 0392b09ae91b7a4fdbddb014cd57238202241b62 | /plot4.R | 72be6672325a5be02de4bab6f7ef1644cc68e9e3 | [] | no_license | JeremiahMurphy/ExData_Plotting1 | 484f68925c5a48dd9331305f0610555f3eadf9ee | a17526e40cb11bf304bcfdb1ffc21583861580c3 | refs/heads/master | 2020-05-20T05:49:24.073652 | 2019-05-07T14:12:32 | 2019-05-07T14:12:32 | 185,415,407 | 0 | 0 | null | 2019-05-07T14:11:00 | 2019-05-07T14:11:00 | null | UTF-8 | R | false | false | 1,039 | r | plot4.R | install.packages("dplyr")
library(dplyr)
my_data <- read.csv2("household_power_consumption.txt")
days <- filter(my_data, Date == "1/2/2007" | Date == "2/2/2007")
DateTime <- strptime(paste(days$Date, days$Time), "%d/%m/%Y %H:%M:%S")
png(file="plot4.png",width=480,height=480)
par(mfrow=c(2,2))
plot(DateTime,as.numeric(d... |
1cab1d82e99eb4bf34b9e4abcd9ecca04a4a5a7c | 872b1c8e4ccaf58ffeb863e3726913a3ca8511c9 | /demo/WriteDot.R | 23de076b947f8c1c2138bbf975846fe3969a8d2c | [] | no_license | rdiaz02/BML | ad49b5903c131e10cdd06baebcc29f3989326aca | 50e8793b45730262db60e658708e63992d787a08 | refs/heads/master | 2020-05-23T00:43:51.624777 | 2019-01-16T12:38:05 | 2019-01-16T12:38:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 97 | r | WriteDot.R | library(BML)
col_resic <- bml(BML::col_resic, 5, 0.5, 5)
writeDotFile(col_resic, 'COL_RESIC.dot') |
b1809e7d8f6025da4d16ae956434ec88bd5711c5 | 863259383c00396558454c1e9f78e993f65391d9 | /AuthorNetwork_kirkversion.R | ca09d6e58d1abb7e7dbb334eaab36125ccf69542 | [] | no_license | RotmanCodingClub/Project0 | f36cc04ebe5bea69c854196de04fa9c9bfd8f030 | 191fccb9b33699cb989835a062bb9bf0992b0e5d | refs/heads/master | 2021-01-12T08:09:21.344479 | 2017-04-20T16:53:57 | 2017-04-20T16:53:57 | 76,487,569 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 943 | r | AuthorNetwork_kirkversion.R | library("broom", lib.loc="~/R/R-3.3.1/library")
library("tidyr", lib.loc="~/R/R-3.3.1/library")
library("ggplot2", lib.loc="~/R/R-3.3.1/library")
library("gridExtra", lib.loc="~/R/R-3.3.1/library")
library("dplyr", lib.loc="~/R/R-3.3.1/library")
library(stringr)
library(networkD3)
#read in csv file downloaded ... |
c22e5015b5a454e4e7a80b7448672884c0471695 | 960e994f0ba2f7db9821cbad3490a579aaaba136 | /man/probe_simple_take_off_velocity.Rd | fea0f6aee1c92d7edfe4c270545bc3ca222940de | [
"MIT"
] | permissive | ouzlim/vjsim | 1a3aaabf5d93bc1be72c9fe069d80f00eb5d8755 | 456d771193463ef00efb91085ef8782ca57f9f21 | refs/heads/master | 2022-11-26T22:14:19.172622 | 2020-08-03T22:39:53 | 2020-08-03T22:39:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,253 | rd | probe_simple_take_off_velocity.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simple_optimal_profile.R
\name{probe_simple_take_off_velocity}
\alias{probe_simple_take_off_velocity}
\title{Probe Simple Take-off Velocity}
\usage{
probe_simple_take_off_velocity(
L0,
TOV0,
bodyweight,
change_ratio = seq(0.9, 1.1, le... |
6bd3330ee8f7650939bdd12ec042c6256ad78fdd | cb789e80d114f84215838f53dba8123050780546 | /plot3.R | 16bc853fe9c79dc0b0028de72ebb7f0c134c1202 | [] | no_license | Minnovate/ExData_Plotting1 | 15fe78cbbc31cb4d283f3ddbcb1cdf913d54ef27 | 655e4fd38e8ee471228d4ee88265ceb9b9bb7a38 | refs/heads/master | 2021-01-12T17:15:32.726195 | 2016-10-25T05:03:32 | 2016-10-25T05:03:32 | 71,530,385 | 0 | 0 | null | 2016-10-21T04:40:55 | 2016-10-21T04:40:55 | null | UTF-8 | R | false | false | 2,081 | r | plot3.R | #Set the Working directory to the right location
defaultWD <- "//Users/gamelord/Documents/OneDrive/Coursera.org/4. Exploratory Data/ExData_Plotting1/"
setwd(defaultWD)
fileurl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(fileurl,destfile="./power.zip",method="... |
9fbe17a207756d5293437f186f24c2f77f5d3c57 | a17b69940bbd695d068e9e811583bc0010ee336e | /man/predict.Rd | 84d6e22ad6c7b33c575c0551ddafe639f1f67562 | [] | no_license | iwonasado/sgPLS | e825050d12b954af7c5e126f15ef0a07a112b53f | 8a358b4dfb2dea3b25b83a242b17c8cbad2e5f71 | refs/heads/master | 2021-01-18T06:06:37.691777 | 2015-03-14T00:00:00 | 2015-03-14T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,061 | rd | predict.Rd | \name{predict}
\encoding{latin1}
\alias{predict.sgPLS}
\alias{predict.gPLS}
\alias{predict.sPLS}
\title{Predict Method for sPLS, gPLS or sgPLS}
\description{
Predicted values based on sparse PLS, group PLS, sparse group PLS models. New responses and
variates are predicted using a fitted model and a new matrix of obs... |
7f48348f87145f4120fc6aa509588c7ff0cd6ad4 | e3b4c2ebe67b68abdbc2190353493a382962ba10 | /man/conan_check.Rd | b259eb62d671ce944a1407446486763e9db06e9b | [
"MIT"
] | permissive | mrc-ide/conan | 9f219f18a8f65e9a0e625506e376f3d0606871cc | 312a42e3086966697931e3b1fe0b60735c051669 | refs/heads/main | 2023-09-01T17:10:26.435970 | 2021-05-07T16:29:58 | 2021-05-07T16:29:58 | 352,555,128 | 4 | 0 | NOASSERTION | 2023-08-22T08:26:31 | 2021-03-29T07:34:17 | R | UTF-8 | R | false | true | 984 | rd | conan_check.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/check.R
\name{conan_check}
\alias{conan_check}
\title{Check if packages in library}
\usage{
conan_check(packages, library)
}
\arguments{
\item{packages}{A character vector of package names or pkgdepends
"references"}
\item{library}{A path to... |
e92fd8675d3d850e26792e8f26512433a33ff0c3 | 0e4457fb2de5f700ba75ea97e70a8570fe00513d | /tests/testthat/test-sample-mc-binary-cov.R | 4066d228d29660d14fc55bfaca0d30e412692393 | [
"MIT"
] | permissive | skgallagher/InfectionTrees | 63043f87b206dfc25f98a629bc832cc1db8a4fab | cfe4f5d4d3f6eca11c1cce56261857b4d1f44f24 | refs/heads/master | 2023-06-28T07:55:12.273475 | 2021-07-24T21:05:42 | 2021-07-24T21:05:42 | 277,616,855 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,340 | r | test-sample-mc-binary-cov.R |
test_that("summarize_binary_trees", {
mc_trees <- data.frame(cluster_id = c(1, 1, 1,
2, 2, 2,
3, 3, 3),
n_inf = c(2, 0, 0,
1, 1, 0,
... |
92dc97bbd755f09e9ceff10dcc9268d64c0050a6 | 0a677c67824ad812542e8625126be1dd3ed7c711 | /tools/torchgen/man/method_cpp_exceptions.Rd | 389d9280cb5bbba9844a531daa2e32c29f514d91 | [
"LicenseRef-scancode-warranty-disclaimer",
"MIT"
] | permissive | dfalbel/torch | 48ff1b38ffdef9abe4873364c26b8abbae3ba330 | ae317db8ec1392acd9f1a2d5f03cef9ad676f778 | refs/heads/master | 2021-07-08T04:41:59.099075 | 2020-08-07T22:21:43 | 2020-08-07T22:21:43 | 151,864,442 | 58 | 8 | NOASSERTION | 2019-10-24T21:35:26 | 2018-10-06T17:27:42 | C++ | UTF-8 | R | false | true | 247 | rd | method_cpp_exceptions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods_cpp.R
\name{method_cpp_exceptions}
\alias{method_cpp_exceptions}
\title{Get the exceptions}
\usage{
method_cpp_exceptions()
}
\description{
Get the exceptions
}
|
de4fa80c7b66febd9c3ca88b07d0a65ff9b3e2aa | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/permutations/examples/sgn.Rd.R | 55bae69663625272c7a90fee268af63cb6d237ce | [] | 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 | 319 | r | sgn.Rd.R | library(permutations)
### Name: sgn
### Title: Sign of a permutation
### Aliases: sgn is.even
### ** Examples
sgn(id) # always problematic
sgn(rperm(10,5))
x <- rperm(40,6)
y <- rperm(40,6)
stopifnot(all(sgn(x*y) == sgn(x)*sgn(y))) # sgn() is a homomorphism
z <- as.cycle(rperm(20,9,5))
z[is.even(z)]
|
11ad3687e848a3039a627c339234a3c8cc88830e | fdc0fa3eda2092bc285ab39542e1e5d65f3140e6 | /plot3.R | 3ec123ee644cfd838d7188d5a493b39e77d77495 | [] | no_license | cdshps/ExData_Plotting1 | 2f37efc140593f2f14cbd7a3f943f3096da2436d | 50e522e086284b803c5c22abf0a788f7dcd54e1d | refs/heads/master | 2020-04-01T04:38:05.241101 | 2018-10-13T12:52:12 | 2018-10-13T12:52:12 | 152,871,219 | 0 | 0 | null | 2018-10-13T12:46:30 | 2018-10-13T12:46:30 | null | UTF-8 | R | false | false | 1,313 | r | plot3.R | plot3 <- function()
{
# Read relevant data (1st and 2nd February 2007)
electricData <- read.table(file="household_power_consumption.txt", header=T, sep=";", na.strings="?")
electricData <- electricData[(electricData$Date=="1/2/2007") | (electricData$Date=="2/2/2007"),]
# Summarize co... |
939d320b17710b62daf384b168fa8ba5f4928819 | d0ea8cdff2e89f2c6e38f7c266c577a2b81d8008 | /lecture8/regression_lecture_students.r | 9e6d49dd757b9707f7f4906c4bbc6f442e451ca0 | [] | no_license | wampeh1/Ecog314_Spring2017 | 5a5b113abf9b6788dcc3f6de334d70271a7672f0 | f4776d26fe04457402a799296fbc912f767fdf12 | refs/heads/master | 2021-05-02T00:53:54.558669 | 2017-04-21T12:39:08 | 2017-04-21T12:39:08 | 78,502,858 | 4 | 7 | null | 2017-03-08T21:37:14 | 2017-01-10T06:02:15 | HTML | UTF-8 | R | false | false | 1,173 | r | regression_lecture_students.r | #install.packages("Lahman")
library(Lahman)
library(dplyr)
library(ggplot2)
# load data from package Lahman
# make subsets of some of the data
teams_small <- Teams %>%
mutate() %>%
select()
batting_small <- Batting %>%
mutate() %>%
select()
# join al... |
31eb42812453f838e1afbdb9c6b10de8b092cceb | 6a5770f0513758cdf4965e78dd36220419e2f316 | /02 Data Wrangling/Data Wrangling.R | f29856345e80e3b9e422c077693902ccdce4320c | [] | no_license | leronewilliams/DV_RProject2 | 0d7db07cb1de8f70d0f0a64eb21af41c0f8c51b0 | 3c1d7e27753f40112c02a37c4a4b8ded224bf975 | refs/heads/master | 2021-01-17T13:46:09.491652 | 2016-05-26T23:52:45 | 2016-05-26T23:52:45 | 30,425,926 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 839 | r | Data Wrangling.R | require("dplyr")
require("tidyr")
head(vocab)
#Generates the "Female" and "Male"columns from the "SEX" column
vocab %>% spread(SEX,EDUCATION) %>% tbl_df
#Average Vocab per Education
vocab %>% group_by(SEX) %>% summarize(Average_Education = mean(EDUCATION)) %>% tbl_df
#Average Vocab per Sex
vocab %>% group_by(SEX) %>% s... |
ff4dfb427d4ca4814256c80cd15b5488e22aa106 | be1cc419487bd3d57e32b4e44e24599a3514b295 | /java-r/src/main/resources/mtcars.R | 301c47526e2baaf4966fa7caff2a2b830624932a | [
"MIT"
] | permissive | bbonnin/talk-r-java-graalvm | f4192a56fd973acedc15208706fd87d3b08057a1 | 644bc68b061585f98ba3d8e2a71a9d721418bb98 | refs/heads/master | 2023-01-23T07:24:11.998457 | 2019-02-27T16:43:37 | 2019-02-27T16:43:37 | 149,638,699 | 0 | 0 | MIT | 2023-01-06T08:15:41 | 2018-09-20T16:28:16 | R | UTF-8 | R | false | false | 1,545 | r | mtcars.R | library(ggplot2)
library(scales)
logger <- java.type("io.millesabords.demos.java_r.Logger")
#############################################################################################
# With plotMtcars, all the context for the execution is provided by the Java app
function(params) {
svg()
logger$log("CODE... |
102bb4adf310cbd1a816f1f41dc4022cb1dc4db2 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/MSQC/examples/archery2.Rd.R | fc2bf0ba0d01a6679f0802d96334b671b65ed3a3 | [] | 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 | 248 | r | archery2.Rd.R | library(MSQC)
### Name: archery2
### Title: Target archery dataset during the elimination stage (used as
### Phase II)
### Aliases: archery2
### Keywords: datasets
### ** Examples
data(archery1)
## maybe str(archery1) ; plot(archery1) ...
|
1a1efd0a71cde5479130920fbd32cb2d3dd9d860 | 00d3c5d5a17235d7f683bc62d4442b1678b3353b | /word_plots_R/Plots_Words.R | b8bbdd39a0914173998b94c952c9e87c94b1e9e9 | [] | no_license | bowen1993/comp150_project1 | e52c95250934227b54f5bccd93740915111e0517 | ed7687bf6f15ee201b29cc92eedb6c49dc3d1187 | refs/heads/master | 2022-12-08T21:43:18.327174 | 2018-10-22T21:29:39 | 2018-10-22T21:29:39 | 153,384,277 | 0 | 0 | null | 2022-12-08T01:18:49 | 2018-10-17T02:33:38 | Jupyter Notebook | UTF-8 | R | false | false | 5,339 | r | Plots_Words.R | # Processing the labels of the raw data (6.8 in "Deep Learning with R")
group1_dir <- "/Users/sofia/comp150_project1/data/data1"
train_dir <- file.path(group1_dir, "train")
labels <- c()
texts <- c()
for (label_type in c("male", "female")) {
label <- switch(label_type, male = 0, female = 1)
dir_name <- file.path... |
6c8583eba95f5090178c05f318b55212b9b96e53 | 01114541c33a31ff4b1134788ff0815fef397329 | /16S_amplicon/the new way/3_casting_h2o2.r | ab6249bfe9f73121dcc4dc330eca3b5b6c12ae4a | [] | no_license | RJ333/R_scripts | 06b31ad1459bafc68e0c212aa55eb83e5f354be9 | a882732aeb86b10a44f5fedf86401bf20d4618f6 | refs/heads/master | 2021-04-26T22:55:19.096526 | 2019-07-22T08:30:33 | 2019-07-22T08:30:33 | 123,895,394 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,123 | r | 3_casting_h2o2.r | library(reshape2) #load package reshape for cast (not reshape2!)
#calculate sum of reads per sample in excel, then copy new table the relative abundances
#copy wholetax into second column, split one wholetax into taxonomic levels again (in excel)
#read into R
tcast_h2o2_rel<-read.csv(file.choose(... |
cf70c82bf511b6049747dac7da643676be5a5f56 | 961f1a2de9dd6875fb6a86b08dfc917a78f0933d | /61_function.R | d604d5c3067acc5293e9ce6e6d2d032e258309bd | [] | no_license | alex7777777/my_funktion | c9ba83405f4e37438565d559b749ee2980cfca16 | fb1741a910a975f5c2052b6de3ccfa1be39e4a4f | refs/heads/master | 2020-05-29T13:48:15.868690 | 2020-01-09T22:10:13 | 2020-01-09T22:10:13 | 189,173,623 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,346 | r | 61_function.R | #######################################################
# 2019-11-13 - by Alex Gorbach
#######################################################
# Function: heuristic rule.
# The function finds more than two times repeating events and reduces them.
# The same object is returned back, but only with the first and last ev... |
8ce468fcd8fae5277de86ce047ac10f7120f556d | 626e46ac01f44d9a9cf390e3ffa2750daebb7ed4 | /assets/gausman.R | 0bad8dc1152403e32c34ea80ba4131ccdfc63d25 | [] | no_license | pqnelson/pqnelson.github.com | 13913cd8aff1fbc88096688a4f5c39f422cf888c | 1dfa24f2eaff77723761cf3e84fb1828d0d811eb | refs/heads/master | 2023-08-15T13:26:30.210825 | 2023-08-14T14:37:18 | 2023-08-14T14:37:18 | 6,422,157 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 571 | r | gausman.R | alpha0 <- 213 + 22 + 65
beta0 <- 214*3 + 1
x0 <- qbeta(0.05, alpha0, beta0)
x1 <- qbeta(0.95, alpha0, beta0)
coord.x <- c(x0,seq(x0,x1,0.001),x1)
coord.y <- c(0, dbeta(seq(x0,x1,0.001), alpha0, beta0),0)
# https://www.baseball-reference.com/boxes/ANA/ANA201508070.shtml
alpha1 <- 9
beta1 <- 27 - alpha1
p1 <- alpha1/(al... |
d02063b27addcbcd003657c14d71bfc2ec748a86 | c719263832b498a7c7ceb53563ea4b8761ba6a09 | /man/pick.Rd | 99258babaf54c566c03cbedecebd3417e156f18d | [] | no_license | cran/mudfold | f708d4ca8e9fb49dc73b97e00f1fc52565bd0137 | 55b8413294c4f88c59614b3143c1c1ff9b0e3d57 | refs/heads/master | 2022-11-29T16:18:33.523375 | 2022-11-24T08:30:02 | 2022-11-24T08:30:02 | 86,725,488 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,009 | rd | pick.Rd | \name{pick}
\alias{pick}
\title{
Transform items to preference binary data.
}
\description{
Function \code{pick} can be used to transform quantitative or ordinal type of variables, into binary form (i.e., \code{0},\code{1}). When \code{byItem=FALSE}, then the underlying idea is that the individual selects those i... |
0715e748941df334cc77c623170a0862d7472f24 | 93d8d16ecedddea66ef7f51ab56deb85c24f2a7d | /Development/Remove_Micro_Trace.R | 51127f43a29fe6a106c3d2d9e7c6ee26c3037cc1 | [
"CC-BY-4.0"
] | permissive | ItoErika/PBDB_Fidelity_app | b5231ad006f1c81b55972c5b2fa5e1199a74eb7a | a82b1f4e72b0b61fa6bd47a9449e25d1cdb4199e | refs/heads/master | 2021-01-11T04:32:52.433881 | 2018-08-27T18:17:31 | 2018-08-27T18:17:31 | 71,168,019 | 1 | 2 | null | 2018-08-27T18:17:32 | 2016-10-17T18:21:06 | R | UTF-8 | R | false | false | 2,299 | r | Remove_Micro_Trace.R | # REFINE OUTPUT1
# Load the output from Stage 1 which involved extracting DeepDiveData sentences which contain both a word (or words) indicating the occurrence of fossils, and a candidate unit name.
# Load the stage 1 output table from postgres
# Load required library
library("RPostgreSQL")
# Connet to PostgreSQL
Dr... |
6b8d51fe8d6516995b7ce097dae803d39d738d82 | ead6f85cb11adb348eac80da2e4af54bee81bdd5 | /UnderstandingTheLinearRegression.R | 71592644c421121b19fbf9a3d050bf0bfff2f01b | [] | no_license | Softx0/RegresionLineal | 6fbf923380460aab9da8c1dd7d39a36445e942f7 | 320148257edc7b3ccc2c072382ac3210cfdb2d69 | refs/heads/master | 2020-11-26T00:42:51.201849 | 2020-01-25T19:12:42 | 2020-01-25T19:12:42 | 228,910,004 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,248 | r | UnderstandingTheLinearRegression.R | # En R utilizamos lm() para crear un modelo de regresion, para visualizar el modelo utilizamos la funcion summary()
# Para analizar los residuos del modelo, podemos utilziar el comodin $ para referirnos a ellso, con $resid
# Los residuos son diferentes entre la prediccion y el resultado actual que te arroje, entonces ... |
554a1693e4a7bf89c6161d4671b24084ad121af8 | a6089cc3f17e5fc7aa5c0e36daae87d750e54a1d | /man/getShapefile.Rd | 670131f474cfcec184a00fdf1f7493ecfb3590e1 | [] | no_license | nzwormgirl/Amy | 34cb8a35a53392eec25fcc67363ce972755b6bc1 | 05881c5a2ac000d1d4d7fc2b628af23fc57ec36e | refs/heads/master | 2021-06-27T18:35:24.373486 | 2021-05-24T04:26:43 | 2021-05-24T04:26:43 | 67,004,302 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 521 | rd | getShapefile.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getShapefile.R
\name{getShapefile}
\alias{getShapefile}
\title{getShapefile}
\usage{
getShapefile(myShapefile, myDir)
}
\arguments{
\item{myShapefile}{A character string with the shapefile name. Must not end in .shp}
\item{myDir}{The directo... |
580abf629def0aef79d8fe8e272070266284d6ed | 9c2f40ae8269413feb32cffa6d581dfe9f931dd0 | /R/tv.R | 2baa53632fb3ac0f7be47ce1217e8df9d34b9da7 | [
"MIT"
] | permissive | tpetricek/datadiff | ed5ca6cdfe8129ed947c24a42c62ea265aad86ef | 8941269b483da9abcacde804b7f6b6e0a122a57a | refs/heads/master | 2020-07-31T19:09:25.118489 | 2019-09-25T23:55:01 | 2019-09-25T23:55:01 | 210,723,004 | 0 | 0 | MIT | 2019-09-25T00:39:49 | 2019-09-25T00:39:49 | null | UTF-8 | R | false | false | 2,490 | r | tv.R | #' Total variation distance for two discrete variables (as factors)
#'
#' Compute the total variation distance for samples from two discrete
#' distributions, coded as unordered factors. The result is a number in the
#' (closed) unit interval.
#'
#' @param v1,v2
#' A pair of factors. Both must have at least one non-mis... |
d5f1a6b5ef9d1f6d12e650d5af54005a36cf4309 | 3b26ab6bc88a47dfef383d4937558e4bd44da506 | /man/mort.Rd | 6a6e9f0a44dbcc9e99a673c613a1574da288252a | [
"MIT"
] | permissive | SMBaylis/fishSim | affafad3915dad24057895d1b0708bc53dd206bd | 2f98c4545780d4d42f63dd169fb9902c61d0c614 | refs/heads/master | 2021-08-02T18:07:06.651542 | 2021-07-23T06:17:11 | 2021-07-23T06:17:11 | 144,930,871 | 3 | 2 | MIT | 2021-02-15T01:28:04 | 2018-08-16T03:17:48 | R | UTF-8 | R | false | true | 2,429 | rd | mort.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fishSim_dev.R
\name{mort}
\alias{mort}
\title{kill some members of the population}
\usage{
mort(
indiv = makeFounders(),
year = "-1",
type = "simple",
maxAge = if (type == "age") length(ageMort) - 1 else Inf,
maxPop = 1000,
mortRa... |
8af6aee56808473a96e8d5f9e2d6aec301ef36b4 | 533af4bb66e3797c25d86dba3482839827d68a0a | /Create_simulated_predictors.R | 1915df7640b9e1d1b5efc9f173f5fb0287bc57f3 | [] | no_license | Qosine/Final_Seminar | 739c24758e7b2da803910dfd5bb00da5d324414f | d00e7d910a8d779bc3631889cc776bf10c57bdab | refs/heads/master | 2021-04-08T01:49:30.054839 | 2020-03-21T18:36:47 | 2020-03-21T18:36:47 | 248,726,593 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,293 | r | Create_simulated_predictors.R | ########################################################################################
# Author : Pointlogic Team 4, Case Studies in BA&QM
# Description : From an original sample, create a larger simulated population
# by permuting columns (of the original data) independently,
# and t... |
7a609b2a0b8cda779d75c00034840d40682a9615 | b8abb780d06dcc80e73fefe581c0bb6e0985be79 | /server.R | be099414c2436ae92208ca18c9eeb93171eda7ab | [] | no_license | gomugomu0034/Shiny-App | c79433efd479fe5fbfb2607e3e7cf8bc1ec4bcff | 1b2408065db98b7056bc8b6250855534f83c4c7e | refs/heads/master | 2020-06-04T02:32:38.810061 | 2019-06-13T22:21:49 | 2019-06-13T22:21:49 | 191,836,415 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,294 | r | server.R | #
# This is the server logic 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)
# SHiny server function designs two different model based on mtcars dataset
# model 1 (... |
0838661cc5d4ff52ea85f26ec01d88da14442447 | f0ec114929d46453f39ed8903d6c2b9bad28183b | /R/boxplot.R | b01492ed2cbcdb22d723a7c3142cdcfa4b709d38 | [] | no_license | cran/microbenchmark | f3d671db49c18d90a568ffedfa8e1630fa42e0cd | 3054890c0c5cf68d2b4321a4e258e3e41a23b636 | refs/heads/master | 2023-05-13T01:56:44.055467 | 2023-04-28T20:20:02 | 2023-04-28T20:20:02 | 17,697,488 | 3 | 4 | null | null | null | null | UTF-8 | R | false | false | 1,661 | r | boxplot.R | #' Boxplot of \code{microbenchmark} timings.
#'
#' @param x A \code{microbenchmark} object.
#' @param unit Unit in which the results be plotted.
#' @param log Should times be plotted on log scale?
#' @param xlab X axis label.
#' @param ylab Y axis label.
#' @param horizontal Switch X and Y axes.
#' @param ... Passed on... |
4c1b9a6d824276d6cd6195af67221afdf50116c6 | cdb466282c1bede90c67114ae10dacb50f7febdc | /help/Basa_Bleiveis.R | 448ffd040f306ea5c2dd12dba0d17f1362b41be3 | [] | no_license | matejbasa2/R-BasketballScorePrediction | a6279879c04b4ac74e2c5b6c1c75410e71b0a680 | f1d9403ce748fb85740607eed1593675c3fedb47 | refs/heads/master | 2020-12-19T14:50:02.685802 | 2020-01-23T09:55:17 | 2020-01-23T09:55:17 | 235,765,963 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,761 | r | Basa_Bleiveis.R | mae <- function(observed, predicted)
{
mean(abs(observed - predicted))
}
rmae <- function(observed, predicted, mean.val)
{
sum(abs(observed - predicted)) / sum(abs(observed - mean.val))
}
mse <- function(observed, predicted)
{
mean((observed - predicted)^2)
}
rmse <- function(observed, predicted, mean.val) ... |
3e843f19b76d7f8655a7ac5029fa8aacb921f15f | eb4f7e624e5b4b0f3436c75c6711ff4f9455c347 | /run_analysis.R | 675f1efc4c4d28f9dd88cc5068f7870a0f43191b | [] | no_license | shivika3390/Getting_and_cleaning_data_course_project | bd61cf762b7d567b545d55f6e68036c397146ee6 | c6a89df53a6656cc66223be086ed699dde07ef22 | refs/heads/master | 2021-01-10T03:46:57.960997 | 2016-02-04T13:11:20 | 2016-02-04T13:11:20 | 50,766,391 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,000 | r | run_analysis.R | ##Download and unzip the dataset
if(!file.exists("./data")){dir.create("./data")}
fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(fileUrl,destfile="./data/Dataset.zip",method="curl")
unzip(zipfile="./data/Dataset.zip",exdir="./data")
path_rf <- file.pa... |
cc6ed5e23d99efa84ec887c9de0898f0a919142e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/crossdes/examples/williams.BIB.Rd.R | 36a914de2e25e8d5c1ffc4db480fa220e4413174 | [] | 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 | 301 | r | williams.BIB.Rd.R | library(crossdes)
### Name: williams.BIB
### Title: Construction of Carryover Balanced Designs Based on Balanced
### Incomplete Block Designs
### Aliases: williams.BIB
### Keywords: design
### ** Examples
d <- matrix( rep(1:3,each=2), ncol=2)
# # check for balance
# isGYD(d)
williams.BIB(d)
|
34579d355450cd8f87a603d19087235d531efb56 | 0479b5e809beae1d18a9c6b603305d674fd5b12e | /man/Merge_methy_tcga.Rd | 15b331b2f91dec5253b503552f470af17db03f01 | [] | no_license | huerqiang/GeoTcgaData | ecbd292e37df065ae4697c7dd07027c1e665853d | cc85914f2a17177164c7ae426f8f0f09f91e98c1 | refs/heads/master | 2023-04-12T10:04:20.034688 | 2023-04-04T05:57:04 | 2023-04-04T05:57:04 | 206,305,770 | 6 | 0 | null | null | null | null | UTF-8 | R | false | true | 778 | rd | Merge_methy_tcga.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Merge_methylation.R
\name{Merge_methy_tcga}
\alias{Merge_methy_tcga}
\title{Merge methylation data downloaded from TCGA}
\usage{
Merge_methy_tcga(dirr = NULL)
}
\arguments{
\item{dirr}{a string for the directory of methylation data download f... |
996d46c9c01d4a3aec43eb6e3422d3f0b9c297bf | cbfaa5d8817adb478029bc6f3aff5d1545fd42a5 | /Scraper.R | 3a3a0193722ab2af4e3dfe398cb5cb2a1f404648 | [] | no_license | dmolloy3/AFL | cb06cb9d0b4829a5d17f728ff7c83b4702c947b2 | bdc90bc21021ca89026a1c5e863420e7e3e9d211 | refs/heads/master | 2021-05-15T12:23:12.336023 | 2017-10-26T06:26:00 | 2017-10-26T06:26:00 | 108,352,178 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,367 | r | Scraper.R | library(tidyverse)
library(rvest)
library(stringr)
#Import and Tidy all games
all.games <- read_table("All Games.txt", skip = 2, col_names = FALSE) %>%
select(date = X2, round = X3, home.team = X4, home.score = X5, away.team = X6, away.score = X7, venue = X8)
all.games$date <- parse_date(all.games$date, "%d-%b-%Y")
... |
685cab93c9edde722934daa98f1d40af29d02cd8 | 1542b8ef5c6387facf4d49f8fd4f6b5ef5d8e9c0 | /man/xClassifyPerf.Rd | 2d026605e31eca768f81141838a3831c620a0506 | [] | no_license | wuwill/XGR | 7e7486614334b664a05e389cd646678c51d1e557 | c52f9f1388ba8295257f0412c9eee9b7797c2029 | refs/heads/master | 2020-04-12T12:38:04.470630 | 2018-12-19T17:40:30 | 2018-12-19T17:40:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,804 | rd | xClassifyPerf.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xClassifyPerf.r
\name{xClassifyPerf}
\alias{xClassifyPerf}
\title{Function to evaluate the prediction performance via ROC and Precision-Recall (PR) analysis}
\usage{
xClassifyPerf(prediction, GSP, GSN, rescale = F, plot = c("none",
"ROC", "PR... |
086104dd0c67bcf8dc6bddb22c1e9fd1e53e64d6 | ef342e6f6abd0015a63fc864ebe4c092a0a812a0 | /R/install_packs.R | ce760c55ad132f72c0b5b9637d1bbcbbcb5f8944 | [] | no_license | eugejoh/edatools | 58813098382dbef74350b152b61ce5454ec3b601 | 1e26ef7803a17edaf21966f1f8390d467b75e3a5 | refs/heads/master | 2020-04-09T13:59:36.248376 | 2019-01-17T19:58:49 | 2019-01-17T19:58:49 | 160,385,053 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 744 | r | install_packs.R | #' Install Packages
#'
#' This wrapper function checks whether specified packages are installed and installs if neccessary,
#' otherwise loads the package.
#'
#' @param pkg \code{character} string of package names to install or load.
#'
#' @return loads packages
#' @export
#'
#' @importFrom utils installed.packages ins... |
d9736fb6c23507e546abf70e6e39d42909b96ea9 | 35ae1abde4828b315a805ca5ed207bcf4d13722c | /new_combination.R | bb21826a7909f516a4fafca94d3fa2c704f119fe | [] | no_license | DongboShi/chinese_author_disambiguation | a0f49b98123971d7ce7d32457ba83372f14e4ba4 | 0081699c386f73de598941ed1b7aa4a0ffbf90bb | refs/heads/master | 2021-07-07T18:58:38.857284 | 2020-12-16T16:28:08 | 2020-12-16T16:28:08 | 215,990,534 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,352 | r | new_combination.R | library(rhdf5)
library(dplyr)
library(stringr)
library(readr)
library(parallel)
observations<-read_csv('/Users/zijiangred/changjiang/dataset/feature/observations.csv')
train_sample<-observations%>%filter(ntruth>=10)
train_data<-data.frame()
setwd('/Users/zijiangred/changjiang/dataset')
for (i in 1:nrow(tra... |
9f5bf1fa199a7d6cf9d23f02fcf5344bc253b858 | cecced4835b4f960141b85e25eabd8756f1702ea | /R/sc_workflow.R | d03813df8fcd64fbb761d73c5fa2bdff099b1b3d | [] | no_license | LuyiTian/scPipe | 13dab9bea3b424d1a196ff2fba39dec8788c2ea8 | d90f45117bf85e4a738e19adc3354e6d88d67426 | refs/heads/master | 2023-06-23T01:44:20.197982 | 2023-04-17T13:26:42 | 2023-04-17T13:26:42 | 71,699,710 | 61 | 26 | null | 2023-06-12T11:04:49 | 2016-10-23T11:53:40 | HTML | UTF-8 | R | false | false | 12,726 | r | sc_workflow.R | #' create a SingleCellExperiment object from data folder generated by preprocessing step
#'
#' after we run \code{sc_gene_counting} and finish the preprocessing step. \code{create_sce_by_dir}
#' can be used to generate the \link{SingleCellExperiment} object from the folder that contains gene count matrix and QC statist... |
e51aea7a904566ae6a5a377686e6ca51f93208c2 | 30659c6dba27f5e056810f0559456247e8dd29c9 | /Chaos/settingUpChaosByKeystoneness.R | f4cca5b54b23bb56b3f27af23f0c841ba4883c01 | [] | no_license | mcgregorv/CRAM_chaos | 4253d594f82a94011cb245a7944efa765830efbb | bb890f6e65f4c2f10953d176bac9b45926ae23a7 | refs/heads/master | 2020-07-19T16:00:41.907512 | 2019-11-29T00:58:02 | 2019-11-29T00:58:02 | 206,476,644 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,831 | r | settingUpChaosByKeystoneness.R | ####### didn't really end up using this as shifting all by the same amount doesn't really test much..
##
## similar to settingUpTestingChaos.R, but uses a scalar on the initial conditions based on how well informed we defined the group in the first paper
## with the option of only changes the top XX groups by keysto... |
cad623c47f2c714eb46a584ec54d149f4d8cead8 | 8ce774c79575b5fea9cf60e762afc771ccba4d86 | /R/presupuestochile-package.R | c810e294a6d59a41dd471d0366f0c5ca7a5b23e0 | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | mmc00/presupuestochile | b5755e12138828bdaec62e427f40d2f054f1b8c3 | 6811d76ec2ce15c74d64cfe6a614fbc9f61e9829 | refs/heads/master | 2022-11-12T04:35:10.321633 | 2020-07-05T04:51:21 | 2020-07-05T04:51:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,749 | r | presupuestochile-package.R | #' Presupuestos (2012-2020)
#' Contiene el valor asignado al Presupuesto de la Nacion por anio.
#' @name presupuestos
#' @docType data
#' @author Direccion de Presupuestos (DIPRES)
#' @usage presupuestos
#' @format Un tibble de 9 filas y 8 columnas
#' @references \url{http://presupuesto.bcn.cl/presupuesto/api}
#' @keyw... |
af99ceaaf9333bcba31c6946e6f6416516c0b37b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ph2bye/examples/bayes.design.Rd.R | 5ee14620deaefe549c9d43caee7f4ba1c62de3ff | [] | 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 | 747 | r | bayes.design.Rd.R | library(ph2bye)
### Name: bayes.design
### Title: Bayesian design method for sequentially monitoring patients
### using Beta-Binomial posterior probability based on observing data
### Aliases: bayes.design
### ** Examples
# Using Multiple Myeloma (MM) data example
MM.r = rep(0,6); MM.mean = 0.1; MM.var = 0.0225
a... |
f395fb37aba6f0763fd3fec3ca455b8f2e0e8ca0 | 669cb22798e081bcdd50308251b3dc14715a369d | /man/playNote.Rd | 35cb4c1b1fd80188253a494526c9d8df135caef8 | [] | no_license | cran/music | 1205fdddd3eea8e9c17bb6a33e359862bc269132 | 168954210763189e90a05b3ad35da07e3b7415c9 | refs/heads/master | 2022-07-21T21:17:15.856809 | 2022-07-10T17:30:02 | 2022-07-10T17:30:02 | 171,648,062 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,273 | rd | playNote.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/play.R
\name{playNote}
\alias{playNote}
\title{Play Note}
\usage{
playNote(
note,
oscillator = "sine",
duration = rep(1, length(note)),
BPM = 120,
sample.rate = 44100,
attack.time = 50,
inner.release.time = 50,
A4 = 440,
plo... |
17a86dc951f42786c1764206f9238d450988cf23 | 5b07b36d6fdf9f11d8d75884ae9484d20904f29a | /scripts/understory-stats.R | e3b6e0154d8eaebcd59d575b7d6f22c5e871a5e0 | [] | no_license | schwilklab/understory-ma | 2f35b631fe135dcd2afaeda821df538608566f86 | 69180bfc187b8f44d415b70b71c49cd57b4851ac | refs/heads/master | 2021-03-27T13:07:06.586735 | 2020-11-19T17:35:59 | 2020-11-19T17:35:59 | 17,608,788 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,635 | r | understory-stats.R | library(ggplot2)
library(metafor)
library(dplyr)
options(na.action = "na.omit")
RESULTS_DIR = "../results/plots/"
DATA_DIR = "../data/response-vars/"
# no need to look at graminoids and forbs separately now
EXCLUDES = c("g-richness.csv", "g-cover.csv", "f-richness.csv", "f-cover.csv" ,
"exotic-cover.csv... |
6c27496143ebe9e4fefae2f513bcc540bf63d93a | 0a906cf8b1b7da2aea87de958e3662870df49727 | /grattan/inst/testfiles/anyOutside/libFuzzer_anyOutside/anyOutside_valgrind_files/1610054654-test.R | eaba7f87cf5c626f6d7e433fd4b0269b5d4c5a9a | [] | 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 | 458 | r | 1610054654-test.R | testlist <- list(a = -1L, b = -2236963L, x = c(-8323073L, NA, -1L, -233L, -1L, -1L, -209L, -1L, -1L, -1L, -1879048193L, 0L, 788529152L, 16777007L, -253L, -572662307L, -572653569L, -1L, -1L, -16763648L, 1L, 115L, 1373494749L, -574619649L, -54999L, -52993L, -1L, -1L, -870527796L, 13421595L, 0L, 0L, -9043840L, 0L, 167... |
a27b125a73ba11ffdd4448f9754864e62b1c7264 | e3e319f0bf486394c27587a81bc720807c84602a | /R/countenance-package.R | 589a2d13769184b763e6a41a5d89653248ff264e | [
"MIT"
] | permissive | hrbrmstr/countenance | 2443f7c8cce717813a5425c96a4dbfda426b1cb8 | a3813cd8fa6efa8b838d1db1aab98c8d3b936c6f | refs/heads/master | 2022-04-20T18:46:28.734128 | 2020-04-23T01:31:23 | 2020-04-23T01:31:23 | 258,053,094 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 285 | r | countenance-package.R | #' Tools to Work with the Pi-Hole API
#'
#' Named after a primary synonym for 'pihole', tools are provided
#' to access the Pi-Hole API.
#'
#' @md
#' @name countenance
#' @keywords internal
#' @author Bob Rudis (bob@@rud.is)
#' @import httr
#' @importFrom jsonlite fromJSON
"_PACKAGE"
|
263f726262f70a71e80fa72b1d8299cf73df1dd9 | ba2845eadc8880147e906ab727d322d875226efa | /Analyses/wholegroup_paper1/DirIndEffectsFigure.R | c02e9bb1417c7671fa14e53fb320dd98ce22235b | [] | no_license | AileneKane/radcliffe | 80e52e7260195a237646e499bf4e3dad4af55330 | 182cd194814e46785d38230027610ea9a499b7e8 | refs/heads/master | 2023-04-27T19:55:13.285880 | 2023-04-19T15:15:02 | 2023-04-19T15:15:02 | 49,010,639 | 5 | 2 | null | null | null | null | UTF-8 | R | false | false | 5,645 | r | DirIndEffectsFigure.R | # ------------------------------------------
# Mock figures showing spatial variation in experimental warming
# A. Ettinger, aettinger@fas.harvard.edu
# Description: Mock plot of spatial variation: blocks vs plots vs treatment levels
setwd("~/git/radcliffe")
rm(list=ls())
options(stringsAsFactors=FALSE)
#Here is a d... |
8762344724bdc9bb7d9cf5350530155394ff17cc | d5a9b43b6cce6d03b8ecd5a571b6ba94421064a7 | /HW4_1_Regression_Tree_vs_Linear_Model.R | 89ad6941744eb6faa415aaccda1a6e7ef54a4872 | [] | no_license | garnerat/HW4_CART | 79ef750bda0691ce22ebacdf6b7f7be539665102 | acca514c46f941b9afb11fa9a57349bec59ffa7b | refs/heads/master | 2021-01-13T16:18:11.971993 | 2017-02-14T01:25:55 | 2017-02-14T01:25:55 | 81,395,805 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,315 | r | HW4_1_Regression_Tree_vs_Linear_Model.R | # Data Mining
# HW 4 Problem 1
# Create Linear Model and Regression Tree on Boston Housing data, compare results
##### packages #####
packages<- c("MASS","caret","ggplot2","GGally","boot","rpart","rpart.plot")
install.packages(packages)
lapply(as.list(packages),library, character.only = TRUE)
##### Partition Train... |
aa9d0aad27efe866df91343115a4eb064de56915 | e9d04d8ac6f62fe8ace6a8881e17ea5926548cf4 | /tests/testthat.R | db1af1c916b9eb3fe212268919a2e18dc890768e | [] | no_license | aristoteleo/glmGamPoi | aadd8bcb4521e12c8c312d0b11ffcdcf9c975598 | 6ddb3f4706e525cdf83ce3dd662c9599a90cf9e1 | refs/heads/master | 2022-12-03T14:51:47.867448 | 2020-08-24T14:37:24 | 2020-08-24T14:37:24 | 292,893,581 | 1 | 0 | null | 2020-09-04T16:17:45 | 2020-09-04T16:17:44 | null | UTF-8 | R | false | false | 62 | r | testthat.R | library(testthat)
library(glmGamPoi)
test_check("glmGamPoi")
|
89bfd37b9fa13078c9c47f3957a477ccda7424a9 | dc22b8c55d15e0a627c85d2f3c220541d362f4fc | /man/ENMeval-package.Rd | 589604cacb08124453d06f95eb7c7f42d40c0467 | [] | no_license | darcyj/ENMeval | 742fe4101375bb62e6f56648a38bac6eccc4a73b | 40cc98527d3b032450544f41cc8120d1443164cc | refs/heads/master | 2020-03-18T13:09:55.698293 | 2017-01-10T09:17:38 | 2017-01-10T09:17:38 | 134,764,744 | 1 | 0 | null | 2018-05-24T20:21:53 | 2018-05-24T20:21:53 | null | UTF-8 | R | false | false | 5,584 | rd | ENMeval-package.Rd | \name{ENMeval-package}
\alias{ENMeval-package}
\alias{ENMeval}
\docType{package}
\title{ Automated runs and evaluations of ecological niche models }
\description{Automatically partitions data into bins for model training and testing, executes ecological niche models (ENMs) across a range of user-defined settings, and c... |
71b5d398333f240eb88f8e6027716c41ca4829a5 | 9d211512cc5ff67f0aba6562f9248dc6ad80b673 | /man/calAv.Rd | aba3cc9a2d550ffe9ca9e08b4c6ed0a6812e7440 | [] | no_license | amsszlh/scMC | b4a82f3fb1692fe6c75eb40e3a3b89d3fef51e78 | 40908ae90fc153c2ba270c8da46fe70068c146e5 | refs/heads/main | 2023-02-04T10:27:54.579463 | 2020-12-27T19:35:27 | 2020-12-27T19:35:27 | 324,831,102 | 9 | 5 | null | null | null | null | UTF-8 | R | false | true | 286 | rd | calAv.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/modeling.R
\name{calAv}
\alias{calAv}
\title{compute the projected data}
\usage{
calAv(v, args)
}
\arguments{
\item{v}{correction vectors}
\item{args}{arguments}
}
\description{
compute the projected data
}
|
2a3f733c5227bc58a2395e4c5ba15805d18b987e | de7ae016ab661e1aa34116c644d094b9c552de62 | /server.R | e4f9a8490a3450ec2556697c94fae1c4acc20e9b | [] | no_license | jwyatt85/field_experiments | caca6519c56d4d5e6073ec7ac099082e10064eb5 | 0488a6499df4e772c7074818a9baabad95c56ecd | refs/heads/master | 2021-01-19T02:05:12.155940 | 2017-03-15T15:19:21 | 2017-03-15T15:19:21 | 55,979,787 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,748 | r | server.R | # server.R
source("helpers.R")
options(shiny.maxRequestSize = 30*1024^2)
shinyServer(function(input, output) {
#make the plot - reactive so I can use the function
makePlot <- reactive({
inFile <- input$file1
if(is.null(inFile)) return(NULL)
dat<- read.csv(inFile$datapath)
Y = dat[,inpu... |
6ceaf1e3640248e0ef118a1a8b2dd5704bb25097 | 7472860795edf6f7332a1c6acc8dcc6dca966297 | /VectorManipulation/Vdecode/Vdecode.r | e8759486dc1eb05631fccc56c6d3542d6c1aad8a | [] | no_license | selectedacre/Max_Objects | c3de3ae1240625e1d9ebc728bb4a9a623382441f | 1533ab2f00c7d17a51f82b91fbaae4ec390514d0 | refs/heads/master | 2021-01-14T08:35:30.386587 | 2016-04-24T20:49:03 | 2016-04-24T20:49:03 | null | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 505 | r | Vdecode.r | #include "Carbon.r"
#include "QuickDraw.r"
resource 'STR#' (17166, "Vdecode") {
{ /* array StringArray: 3 elements */
/* [1] */
"Converts a mixed-base vector to a number"
".";
/* [2] */
"List input";
/* [3] */
"Result output"
}
};
resource 'vers' (1, "Vdecode") {
0x1,
0x0,
release,
0x0,
0,
"1",
... |
6c109e561ab08b14d1ef78c57bec8f6c58c9786c | 38f396c9d6b7a964e909355ab8978dc72132748a | /20151201/rei8.R | 7c5db4e02f884b8ec07c8df3d1858af27135487c | [] | no_license | shengbo-medley/MiscForStudy | 321417549863208316aab4f2f129eeb6c70184d1 | 9c9cd1bed80efb3756ec3b38d84a06b33de54b23 | refs/heads/master | 2020-04-06T03:40:41.695471 | 2016-01-26T15:53:24 | 2016-01-26T23:42:17 | 42,396,655 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 96 | r | rei8.R | source("rei7.R")
head(modelLookup())
packageVersion("caret")
length(unique(modelLookup()$model)) |
69cb5a4fdf653edb14f7201e420d5111a82d3f27 | 5bd968ab7897d690e57c21d5012c55be22aac23e | /anno.R | b14c63cef3267578b3b8626e7226beeeeb99118d | [] | no_license | gahoo/circRNAmerged | f315f0982056463c2e002fd73eab9a4f51a584b4 | b0f54f03cb4e2ed9ffe8aa6e91ebb405821abb71 | refs/heads/master | 2021-01-10T11:12:55.637230 | 2016-01-09T16:46:27 | 2016-01-09T16:46:27 | 45,912,643 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,387 | r | anno.R | library(dplyr)
library(GenomicRanges)
library(org.Hs.eg.db)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
GeneID_SYMBOL<-AnnotationDbi::select(org.Hs.eg.db,
keys = keys(org.Hs.eg.db),
columns = c("ENTREZID", "SYMBOL")) %>%
rename_(GENEID="ENTREZID")
AnnotationDbi::select(Tx... |
7b3d4bc4dc6264115206de6de8d00145997d784f | 65f3c0ad386a67ed103b0419fadd32a8f201a30a | /inst/examples/time_varying_example.R | 912452172f7dd9e9debac8d1bb309b5cee40c373 | [
"MIT"
] | permissive | r-glennie/CTMCdive | 1d827dd4abe1cf24582deed30d29c986eb78af4d | ca2fb902d0a47eb523691ee911e0c73012e08bcc | refs/heads/master | 2023-07-07T09:48:47.716882 | 2023-06-28T17:02:18 | 2023-06-28T17:02:18 | 160,235,889 | 2 | 4 | MIT | 2022-02-24T14:01:54 | 2018-12-03T18:29:45 | R | UTF-8 | R | false | false | 2,388 | r | time_varying_example.R | library(CTMCdive)
# Simulate data -----------------------------------------------------------
# total observation time
T <- 24 * 60 * 7
# time step
dt <- 0.1
# time-varying intensities
tgr <- seq(0, T, by = dt)
dive_I <- function(t) {
#return(rep(0.06, length(t)))
return(0.01 + 0.2 * (t/T - 1/2)^2)
}
surf_... |
db8f6a2e0811117691fab42c65d544facfeb0aad | b4753e0c5a3c1b61cbdce34f3dd94004ea22d0b4 | /ui.r | 746fe3f3531a28a5291914f76248e434d9592e0a | [] | no_license | MrityunjayKumar123/ShinyApp | b4cea496e0eb3238e0422df63b47e88d35d9ef4e | b63e8b7f02a2bb74e537c2ae6c43bf584e7cfff6 | refs/heads/master | 2020-04-09T22:33:42.379425 | 2018-12-22T18:06:04 | 2018-12-22T18:06:04 | 160,631,629 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,196 | r | ui.r |
if (!require(shiny)){install.packages("shiny")}
if (!require(udpipe)){install.packages("udpipe")}
if (!require(stringr)){install.packages("stringr")}
if (!require(lattice)){install.packages("lattice")}
if (!require(igraph)){install.packages("igraph")}
if (!require(ggraph)){install.packages("ggraph")}
if (!r... |
425f785422389f15e15e48e68927c4f5fa4db99c | 2ad8af2d89893e6a59cb77c55d39f7f1aee1cef1 | /code/paper_figures.R | 475be447be250d57cd4d77e5adcc1ff95ce81d25 | [] | no_license | Armadilloa16/CVgeneralisationCode | f691d1fac875d2c79276306c4b8844908eb05d7c | 93b88a6d85ed396b2008bdb5774b7eb690f90b60 | refs/heads/master | 2021-08-04T14:01:53.304786 | 2021-07-27T08:43:30 | 2021-07-27T08:43:30 | 235,052,456 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,356 | r | paper_figures.R |
rm(list=ls())
library(plyr)
library(ggplot2)
library(latex2exp)
# Load and organise data
loo.results = read.csv(file.path("data", "result_summaries", "SIM_loo_results.csv"))
loo.results$err = loo.results$err / 43
names(loo.results)[names(loo.results) == 'err'] = 'Eloo'
tru.results = read.csv(file.path("data", "res... |
c96d07cd61b17a2eae0584a0002ffedb1401e9a5 | d4a19fdbcf046b82a79491f5b11e83d0c1c0b0ce | /R/my_file_rename.R | 432c82b138d3d586b7d9be639fdcb5b74655fe05 | [] | no_license | Laurigit/libSE | 60289577beb67ddad199b331c8226aa18aa79330 | 11e500e741fe0e614eac6d5b5928c35e33ad1564 | refs/heads/main | 2023-06-01T01:58:14.486995 | 2021-06-09T12:38:03 | 2021-06-09T12:38:03 | 375,009,105 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 483 | r | my_file_rename.R | #https://stackoverflow.com/questions/10266963/moving-files-between-folders
my_file_rename <- function(from, to) {
todir <- dirname(to)
if (!isTRUE(file.info(todir)$isdir)) dir.create(todir, recursive=TRUE)
# from <- "C:/Users/lepistol/OneDrive - Stora Enso OYJ/SOP_data_share/SOPDataAll_May_2021_ver1.xlsx"
# to <-... |
ddef2da15102c87055ff5c1cbb9aa8e21c513cc5 | 553992ae66d19695a240b2c8df4357b09f99bb69 | /ONDRI/Scholars_SEP2019_Ordination/Heatmaps_DecompViz.R | 5606fe242f8a078698c1b79613f172e5d922f9ad | [] | no_license | Alfiew/Workshops | 839ec14d5c4b95cd39474044e9bdb2946d2dece9 | 4ac40823e13ed285bcabc44eb4449d4d1be4cd05 | refs/heads/master | 2023-04-19T05:48:15.096172 | 2021-04-27T01:12:07 | 2021-04-27T01:12:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,872 | r | Heatmaps_DecompViz.R | ## make heatmap & eigen/SVD break downs
library(pheatmap)
# load(file=paste0("/Data/ADNI/Examples/amerge_subset.rda"))
#
#
# # select the continuous only subset
# continuous_data_subset <- amerge_subset[,which(variable_type_map[,"Continuous"]==1)]
vis_pca_data <- wine$objective
rownames(vis_pca_data) <- NULL
colna... |
cec71c1b40fc899e6664e31f0eb66db1337a393f | 78205c1f432baf8dbf03cd7e6b325635d3fbab0c | /tests/testthat.R | 8757b214b6918412d3f631cee3f1677dfa952226 | [
"MIT"
] | permissive | mikldk/popr | 4ec23427cea231bb7c18e8c71f5fcb56481d15f6 | a6a444a5e288fb3f0db99a06b01627e039b7fcf2 | refs/heads/master | 2020-12-25T14:49:20.491710 | 2017-10-05T13:43:15 | 2017-10-05T13:43:15 | 66,831,709 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 52 | r | testthat.R | library(testthat)
library(popr)
test_check("popr")
|
5926280a5f90d58a9b1ad1080d1e19d470fadb77 | da0dc67d2612430c239b0694b58bbb60eb82629d | /Test/MicrobeMetaboliteCorrelation.R | bbde8a9c0a0ca0c0e877b2d583f38244abfd110c | [
"MIT"
] | permissive | omicsEye/microbial_physiology | 5f9ebcdb74ad4c988dd9b4a8261f45a351fd9a52 | 41a711ce9c396fe18b785140c3cdb61ac79fd614 | refs/heads/master | 2023-07-09T09:26:25.649734 | 2021-07-22T14:47:25 | 2021-07-22T14:47:25 | 190,052,643 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,834 | r | MicrobeMetaboliteCorrelation.R | setwd("~/Documents/R_WorkSpace/m2interact")
source('R/Heatmap.R')
sample_data <- load.meta.data('Data/IHMP/hmp2_metadata.csv', tax_column = 2)
# sample_data <- microbe_sample_data[, c(75, 34, 40)]
microbe_abundance_table <- load.abundance.data('Data/iHMP/taxonomic_profiles.tsv_AbundanceTable_2019-07-09.csv')
microbe_a... |
f1feef786c6d03c5edf069c00b5f5c487284047f | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Experiments/Miller-Marin/trafficlight-controller/tlc04-nonuniform-depth-41/tlc04-nonuniform-depth-41.R | 513215bad19a69f1f2182c2e0ea5784f37d43eb3 | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 694 | r | tlc04-nonuniform-depth-41.R | c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 39420
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 39420
c
c Input Parameter (command line, file):
c input filename QBFLIB/Miller-Marin/trafficlight-controller/tlc04-nonuniform-... |
5fcead8b4b8b740f57b98b1ca868a572029be6c5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/solarius/examples/dat50.Rd.R | 9c043e86f124f5e203acefb55c8fd40740657ad2 | [] | 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 | 373 | r | dat50.Rd.R | library(solarius)
### Name: phenodata
### Title: dat50 data set adapted from FFBSKAT R package
### Aliases: genocovdata genodata kin phenodata snpdata
### ** Examples
data(dat50)
str(phenodata)
plotKinship2(2*kin)
str(genodata)
genodata[1:5, 1:5]
str(genocovdata)
genocovdata[1:5, 1:5]
# compare with the genotyp... |
630553d9dc64152f0fff30687a4d8ac4343e61c6 | 89a65b6c63a0b37540925e8addf5e925821fd21e | /run_analysis.R | 3638500aa7a2063fbd438bf9d565b5a3e7aefe2a | [] | no_license | kjitender/Getdata-033 | bec75280feee00ecacf03def674023337dfe87cf | 3ab8fc60a0d60f1808f4cca202e807ea0e9c9df1 | refs/heads/master | 2021-01-10T08:28:28.565205 | 2015-10-25T17:59:07 | 2015-10-25T17:59:07 | 44,920,701 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,678 | r | run_analysis.R | library(plyr)
library(data.table)
library(dplyr)
## Getting the data
## Assuming Samsung data is not there in folder.
if(!file.exists("./data")){dir.create("./data")}
fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(fileUrl,destfile="./data/Dataset.zip... |
7f176c7ecd262aecf160f777a39e33e0a0b3dfa0 | 540c8c7f7c84df193d879ac1b56529e1bf11c616 | /week5/quiz5/recycling_program.R | d24acef59472ab177098e4e67753731e8c4a6818 | [
"MIT"
] | permissive | jcontesti/experimentation_for_improvement | 5a0bd4e3be621335c21c3f8ee41fc103f7a21e4d | d64b8d399567c10ef500e91751ae72a729c93629 | refs/heads/master | 2021-05-22T17:05:27.685268 | 2020-04-19T16:51:10 | 2020-04-19T16:51:10 | 253,014,113 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 110 | r | recycling_program.R | H <- c(-1, +1, -1, +1)
S <- c(-1, -1, +1, +1)
y <- c(200, 150, 120, 100)
model <- lm(y ~ H*S)
summary(model) |
7ca86c545c5afbd27cef7c49e466abf42ebcc256 | 3981bd092d1c0c0d64c112da7d36d7ff76fc2098 | /GradientDesent&Correlation/PartialCorrelations.R | ead89a824d88b83f569b336e89fe90c466b4cdad | [] | no_license | GirishGore/Machine-Learning-Tutorials | 42fd198f98fcdda258606147deeb37d412571d92 | deb70bd0bacac2b16018fe29d72cfa25f956b241 | refs/heads/master | 2020-03-08T20:08:56.206601 | 2018-04-23T04:17:40 | 2018-04-23T04:17:40 | 128,374,461 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 164 | r | PartialCorrelations.R | X = c(2,4,15,20)
Y = c(1,2,3,4)
Z = c(0,0,1,1)
mm1 = lm(X~Z)
res1 = mm1$residuals
mm2 = lm(Y~Z)
res2 = mm2$residuals
cor(res1,res2)
##0.919145
cor(X,Y)
##0.9695016
|
a5a3c5aa7714a559c2848b8ff4aaabbd6c240d88 | f559c911e683268f517b25a34a2cbf463f80671d | /ZIP/ZIP_sin7.R | f4944b0eed8969e1abcc194ccb172fee05ff9716 | [] | no_license | Luis-2199/BayesProject | 70ee263fe5aae88b6c1943c2cdd0aeb57ad6c7ef | 3b0cfc05622047122f6da13a0f60757994e0cbc6 | refs/heads/main | 2023-07-08T16:09:29.984859 | 2021-08-15T14:12:15 | 2021-08-15T14:12:15 | 388,851,361 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,181 | r | ZIP_sin7.R | ################# Modelo Ganador sin 7s ##################################
# Base sin 7s
CData_CDMX2_sin7 <- CData_CDMX2 %>% filter(Vic_Rob_As < 7)
# Ajustamos modelo
mod_ZIPsin7 <- zeroinfl(Vic_Rob_As ~ Seg_Mun + Region |
Edad + Mas_Pat_Vil + Region + Sit_Lab,
data= CD... |
cd5d5f2b9dc602b023c425695121117149b2d212 | 2a6b1b93b9388fb6c8f289efec52bb2f50963eb0 | /examples/20111009-kuntien-sukupuolijakauma.R | 79d1ca7557f5b1a183f6b3567144b58c5b4e578e | [] | no_license | louhos/takomo | be80209cf3ee0f1773648d8b127219ad212ae487 | 7af1752f14821b879f80f052bebcc97ba5ff5804 | refs/heads/master | 2021-01-17T12:25:09.186161 | 2016-07-18T12:14:39 | 2016-07-18T12:14:39 | 3,610,040 | 8 | 4 | null | 2015-08-10T19:10:22 | 2012-03-03T10:49:30 | R | UTF-8 | R | false | false | 2,201 | r | 20111009-kuntien-sukupuolijakauma.R | # This script is part of the Louhos-project (http://louhos.github.com/)
# Copyright (C) 2010-2013 Leo Lahti.
# Contact: <http://louhos.github.com/contact>.
# All rights reserved.
# This program is open source software; you can redistribute it and/or modify
# it under the terms of the FreeBSD License (keep this notic... |
2ac820e9e7dcd0e93a3aa32842add6a47fb0d5ed | c63fc5e6607e2cd5d62464a72c78b06191277eb6 | /man/rprt_glimpse0.Rd | 7d0b53297f5000e68c43ee1077ad0814e10d95e3 | [] | no_license | SWS-Methodology/faoswsTrade | 6ce400e545fc805fe1f87d5d3f9d5ba256a8a78c | 2145d71a2fda7b63d17fa7461ec297f98b40756c | refs/heads/master | 2023-02-17T08:40:21.308495 | 2023-02-09T13:53:56 | 2023-02-09T13:53:56 | 55,507,302 | 4 | 1 | null | 2020-05-15T14:45:59 | 2016-04-05T12:49:03 | R | UTF-8 | R | false | true | 344 | rd | rprt_glimpse0.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rprt_glimpse0.R
\name{rprt_glimpse0}
\alias{rprt_glimpse0}
\title{Drops dplyr::glimpse's invisible value}
\usage{
rprt_glimpse0(tbl)
}
\arguments{
\item{tbl}{Data.}
}
\description{
It is required to include glimpse's output into
logging messa... |
9dcf828f0a2e807c200ab8ab39afd8ed205eede3 | 6a9aafcd2a09b13173833f79b7797c1db2ebb42d | /plot1.R | 30ad62431adcf1b7f82ee65618d42c866b09da90 | [] | no_license | aryalsohan0/EDA-CP2 | 37daffa8c86e88eaaddcf1c91cfb3c8ab13b9d80 | f5e335d5a67d8aeddda20ccc8831f89956e4f8fd | refs/heads/master | 2022-12-10T19:58:14.727465 | 2020-08-30T09:42:05 | 2020-08-30T09:42:05 | 291,427,672 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 665 | r | plot1.R | # Reading data
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
# Create data frame yearly emission (YE)
install.packages("tidyverse")
library(tidyverse)
YE <- NEI %>%
group_by(year) %>%
summarise(Yearly_Emissions = sum(Emissions))
# plotting yearly total ... |
54817c8b4e74e19e9eb5bc7a23fbe9a5ce3fbe2c | 4848ca8518dc0d2b62c27abf5635952e6c7d7d67 | /R/f_hv_rab.R | 3a3746a050982ed3aad85edf71dd3c14b386f43b | [] | no_license | regenesis90/KHCMinR | ede72486081c87f5e18f5038e6126cb033f9bf67 | 895ca40e4f9953e4fb69407461c9758dc6c02cb4 | refs/heads/master | 2023-06-28T00:29:04.365990 | 2021-07-22T04:44:03 | 2021-07-22T04:44:03 | 369,752,159 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 699 | r | f_hv_rab.R | #' Heavy Vehicle Factors in Roundabout
#'
#' It follows <Formula 11-4> in KHCM(2013) p.500
#' @param lane Roundabout lane. Choose one from : \code{1}, \code{2}
#' @param P_T Heavy Vehicle Ratio.
#' @keywords Heavy Vehicle Fators Roundabout
#' @seealso \code{\link{E_T_rab}}, \code{\link{V_i_pce_rab}}
#' @export f_hv_rab... |
eae2cceaa78575a56ecc303122f6d2e243a77b7d | 1ba1eada0980537db781a8ccd388533e74d644cd | /cachematrix.R | ff8e279fae84802ff9e24caf67be3ef149055fef | [] | no_license | bmeier01/ProgrammingAssignment2 | c5e234e7495965362f4b1e17f37e3342a19099a7 | 5b227edf24e68b64cc529fcdf82b13c42d9ac4b8 | refs/heads/master | 2021-01-21T16:00:16.351475 | 2015-01-24T22:57:48 | 2015-01-24T22:57:48 | 27,048,429 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,935 | r | cachematrix.R | ## These functions are used to cache the inverse of a matrix rather than computing it every time
## If the contents of a vector are not changing, it may make sense to cache i.e. the value of the mean
## especially when it may take long to compute
## therefore when we need it again, it can be looked up in the cache rath... |
c879d635adc7032134d037b727b4e306e9ccece0 | 4b0238b0dffae1e750d16eb590f15188288c5091 | /graduate/fig_baseline_pattern.R | e494de140689995c282bebc0ff91fdb741a39ea6 | [] | no_license | Kiki1017/gev | 9a9ae1968bf8ccf0624e2bb401bc4e9262406d39 | 1cd15d6257df895f988a6447f4e42e4e6841155b | refs/heads/master | 2022-03-30T00:26:38.132629 | 2020-01-08T06:52:12 | 2020-01-08T06:52:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,659 | r | fig_baseline_pattern.R | # To verify that the initial value (fgev) is working
rm(list=ls())
setwd("~/GITHUB/gev")
source("./lib/sgev3library.R")
source("./lib/pack.R")
# surface base setting
mean_vec = c(0,0)
sig_mat = matrix(c(30,0,0,30),nrow=2)
set_uni = dmvnorm(cbind(x1,x2), mean=mean_vec, sigma=sig_mat)
mean_vec1 = c(5,0); mean_vec2 = c... |
819ca5c55c00f0580aec71d6f99026bd4fc9f33a | 691a1a785b2f0a47a04777ada08cb1a8bf4b94ef | /Sourced_Functions/ProjectIISourceFunctions_v2.R | 7e2161f86cfbfaf0d7a542994bf55aae2cdc909b | [] | no_license | arthurvickie/Multi-Marker_Method | cbafc3e6a9a16c703b3d241d19234d0dfdb38e89 | d1d90c3c6f99d587a987f4be761c8dbe042f176a | refs/heads/main | 2023-07-15T10:17:00.234816 | 2021-08-30T20:49:22 | 2021-08-30T20:49:22 | 377,881,258 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 60,302 | r | ProjectIISourceFunctions_v2.R | ############################################
## Source functions for Project II ###
############################################
###################################
DetermineAssocRSNPs = function(gene, LowLD, percentageAssoc){
#determine which SNPs to actually set as assoc. based on gene
#check gene
if(g... |
5ebce29b24c6170f7509941d0ddf39ba1c010c50 | 4504e2c8fc4aefdaccaac740a9b82f29cd49e817 | /man/vanke1127.Rd | f26a4e9df448cab61f9c64c7fdb980db3770c3db | [] | no_license | dgrtwo/animation | 37daaf114583e548c2e569fa1f3b12183b12c15f | c7a3155a3544d80b94b5378f6ace8811774632de | refs/heads/master | 2021-01-09T05:25:47.116366 | 2016-02-14T02:57:17 | 2016-02-14T02:57:17 | 51,805,168 | 2 | 1 | null | 2016-02-16T03:29:58 | 2016-02-16T03:29:56 | R | UTF-8 | R | false | false | 1,539 | rd | vanke1127.Rd | % Please edit documentation in R/animation-package.R
\docType{data}
\name{vanke1127}
\alias{vanke1127}
\title{Stock prices of Vanke Co., Ltd on 2009/11/27}
\format{A data frame with 2831 observations on the following 2 variables.
\describe{ \item{time}{POSIXt: the time corresponding to stock prices}
\item{price}{a ... |
6040a2643dd02d4a92658559d407a7d687ec73a5 | 9f72ac41c3e173a0873bfd85b396a4a3e64fc975 | /R_Scripts/Investigation_5_8_regression.R | 9a88495fa8c32039f8f45836baafc90f010090b6 | [] | no_license | gmtanner-cord/MATH205 | 278735ab544989b101a2a60d643b0db17fcb828b | 194faf9a3903b0edc823cd97c22c4a5a743c43d3 | refs/heads/master | 2021-06-26T00:25:32.832588 | 2021-04-07T20:27:37 | 2021-04-07T20:27:37 | 224,021,522 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 619 | r | Investigation_5_8_regression.R | # Investigation 5.8
# Load data
HeightFoot = read.delim("http://www.rossmanchance.com/iscam2/data/HeightFoot.txt")
# Plot data
plot(HeightFoot$height~HeightFoot$foot)
# Correlation
cor(HeightFoot$height,HeightFoot$foot) # r
cor(HeightFoot$height,HeightFoot$foot)^2 # r-squared
# Regression Model
?lm
lm... |
2e6b7707423a41b611b6edaff78f1e6ab621e70c | 420cac816c739b8f6a3581c1628d706f7d398beb | /R/RefSigmaW.r | af83d21d8386e8a69f13c1b1bdbed79a159e4f8e | [] | no_license | cran/RobustAFT | d80a89efb8ffcc80b604d5959893210aab0ae31b | 357b7400ae0a4d0be157b6a46970eb04d8b9ea51 | refs/heads/master | 2023-08-31T10:42:53.415730 | 2023-08-21T16:40:02 | 2023-08-21T17:30:23 | 17,693,388 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 354 | r | RefSigmaW.r | RefSigmaW <- function(sigma,Beta,X,y,delta,tol=0.0001,maxit=100,nitmon)
{
# Fixed point algorithm for scale
nit <- 1
repeat{
sigmao <- sigma
sigma <- ( RefAve2W(sigmao,Beta,X,y,delta)*sigmao^2/0.5 )^0.5; d <- sigma-sigmao
if (nit==maxit | abs(d)<tol) break
if(nitmon) cat(nit,sigma,"\n")
nit <- nit+... |
6b9d8c49c6ca17ead22076ff55d05ffac0432e12 | 2ca2e5579439d507467b3e45f8538148f9676b86 | /getclean_data/course_project/run_analysis.R | 7853b8db2bcd3fcd598e0bb464777860bfd43ce2 | [] | no_license | carterfawson/datasciencecoursera | c00a5d85a50b219b884e915e05d65cc765ab0906 | b383e360085281a0d5e10b88ae2017a1d9a06682 | refs/heads/master | 2021-01-17T09:29:36.094144 | 2016-04-18T04:39:34 | 2016-04-18T04:39:34 | 38,410,152 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,025 | r | run_analysis.R | setwd("~/datasciencecoursera/getclean_data/course_project/")
library(dplyr)
#Load in activities
activities <- read.table("UCI HAR Dataset/activity_labels.txt")
#Load in features
features_data <- read.table("UCI HAR Dataset/features.txt")
# Grab desired features
features <- grep('mean[^A-Z]+|.*std.*', features_dat... |
62ebd867ca205c32a3b074493e14520e201cf56c | 46e23dffc271469c88b3fc72c4731852a6ec46ec | /man/Rdenominator.Rd | 195da20a8464a14169bbbaf99ac652627d1d0581 | [] | no_license | cran/tolBasis | 11cac2f8dbef7e3613467d73d3c100f264b925eb | 3c8ecd25cb93d05be9cbda6dd23cfaee59ea3556 | refs/heads/master | 2021-01-10T13:15:09.394899 | 2015-11-05T13:38:39 | 2015-11-05T13:38:39 | 48,090,484 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 463 | rd | Rdenominator.Rd | \name{Rdenominator}
\alias{Rdenominator}
\title{Ratio Denominator}
\description{
Denominator of a Ratio object
}
\usage{
Rdenominator(r)
}
\arguments{
\item{r}{
a Ratio object
}
}
\value{
Returns the Polyn object corresponding to the denominator of the Ratio.
}
\seealso{
See also the function \code{... |
46a161dc77068fd963cc525b1eec15cc0b660b88 | 7bd83dcebeeb58ef6df4d8e99d67f0bf20d1a980 | /R/nexoc-sample.R | 6ddc5a750aac87508d3a53eb565ccb45e0744186 | [
"MIT"
] | permissive | biokcb/coxen | b0558be6383712cda0f9e9ca148d45d3a911a535 | 70a557df97fef5d75ac19c1916ad6603afe4f08f | refs/heads/master | 2020-03-31T06:39:03.699867 | 2019-02-16T23:20:12 | 2019-02-16T23:20:12 | 151,989,904 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,304 | r | nexoc-sample.R | ### Example script on how to use the NEXOC function... which I guess is really just a coxen pipeline function, but whatever
#set working directory
setwd("/Users/Kristen/Desktop/COXEN")
#input directory where the coxen set is located
input.dir <- "./Laval"
#input directory where the independent test set is located
in... |
d73e43c42146e30a2b2429f0d2b257ef437dd26a | 95aaf5a86ead034a389aaa457643556424ff6173 | /R/incrementor.R | 52d533a74296977adc72c61cca322f68804bdbae | [
"MIT"
] | permissive | schloerke/sortableR | 5d5bd176f2d716ca0d70f89da03a24aad7b70cdd | f9e47b90d7052906669045e14f6c994b360add3e | refs/heads/master | 2020-05-31T11:10:03.904115 | 2019-05-18T05:23:24 | 2019-05-18T05:23:24 | 190,256,120 | 0 | 0 | null | 2019-06-04T18:17:53 | 2019-06-04T18:17:52 | null | UTF-8 | R | false | false | 183 | r | incrementor.R | incrementor <- function(prefix = "increment_"){
i <- 0
function(){
i <<- i + 1
paste0(prefix, i)
}
}
incrementSortableItemlist <- incrementor("sortable_itemlist_id_")
|
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