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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a7825abaacdbdf8ec8a19b6cdd2eac7ec0bcd8fb | 49f984561d088814fbe3a8ac9543dd9deda61759 | /R/plot_path_network2.R | 0723d82b017b41d69c35b9efe12831aba5bc74c6 | [
"MIT"
] | permissive | KWB-R/fakin.path.app | a396a1e142e0c3c3bdac86d13f5337dfb474719b | 3882d91321a5b40a2bda685c1e40f6acb2f503a1 | refs/heads/master | 2022-09-25T04:24:01.362223 | 2020-10-01T14:45:20 | 2020-10-01T14:45:20 | 194,630,996 | 0 | 0 | MIT | 2022-08-19T15:35:58 | 2019-07-01T08:22:38 | R | UTF-8 | R | false | false | 3,472 | r | plot_path_network2.R | # prepare_paths_for_network2 ---------------------------------------------------
prepare_paths_for_network2 <- function(paths)
{
if (inherits(paths, "pathlist")) {
return(paths)
}
# If a path tree is given, flatten the tree into a vector of character
pathlist::pathlist(
paths = if (inherits(paths, "p... |
f3911fd790f88d9516476ed9ad4d0e0bbf040fa1 | d4917fb5c96856ac3f5e64bda6bf0ad2c56a72eb | /OCA/baseline.R | fe7fae00a4b75d5724d83dfba63393cb0cc705d0 | [] | no_license | mariosegal/Consulting | a45a3668f92d9a4a6e4bb04e02e38f1704b4d85f | 3bb424679bae299421b29159b0b7df32e8c9e536 | refs/heads/master | 2016-09-10T20:01:01.789353 | 2015-06-12T18:12:57 | 2015-06-12T18:12:57 | 37,323,705 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 263 | r | baseline.R |
head(complaints)
table(complaints$level_1,useNA='ifany')
table(complaints$level_2,complaints$level_1,useNA='ifany')
table(complaints$level_3[complaints$level_1=='Bank Account or Service' & complaints$level_2=='Checking account'])
dim(complaints)
|
4559cf8a4751422d4912cc519591b51170e443a5 | a6214d7ecd758270d27592c6affe5df3bfd316a2 | /ledgerplots/man/generate.price.table.Rd | c40dab83722e6a0894934cbc9834a26d8b7b8737 | [
"MIT"
] | permissive | RastiGiG/ledger-plots | 3c56fa0a98f0f347ad4a2045742f3d70f76913ff | b8ddb3bf32d51f9ad01ec60cb259fe9815495d38 | refs/heads/master | 2023-03-18T00:09:24.522680 | 2018-11-26T20:38:41 | 2018-11-26T20:38:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 669 | rd | generate.price.table.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ledger-functions.R
\name{generate.price.table}
\alias{generate.price.table}
\title{Generate a latex table with food prices}
\usage{
generate.price.table(FUN, query, ofile = "food-prices.tex",
ledger.options, ledger.path = NULL, conversion =... |
df5ac5b2cd1893cfebff4a447267464e458c8475 | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Miller_And_Freund_S_Probability_And_Statistics_For_Engineers_by_Richard_A._Johnson/CH7/EX7.3/EX7_3.R | e5886d20f614a37af65cfc26a3f98234e7c2efcc | [] | permissive | FOSSEE/R_TBC_Uploads | 1ea929010b46babb1842b3efe0ed34be0deea3c0 | 8ab94daf80307aee399c246682cb79ccf6e9c282 | refs/heads/master | 2023-04-15T04:36:13.331525 | 2023-03-15T18:39:42 | 2023-03-15T18:39:42 | 212,745,783 | 0 | 3 | MIT | 2019-10-04T06:57:33 | 2019-10-04T05:57:19 | null | UTF-8 | R | false | false | 158 | r | EX7_3.R | n=150
sigma=6.2
Z0.05=2.575
E=sigma*Z0.05/sqrt(n)
E
message("Thus, the engineer can assert with probability 0.99 that his error will be at
most 1.30.") |
5c0c26ad15f344e8d38b66ef318a953fa635a3f2 | f9f2dbb4dafe94e4fa9fa9cad75944188c023632 | /data/eda/plot_pr.R | a99aff7f5d51d56a2edd6428c029f7e0beb835ac | [] | no_license | ABlack-git/yolo_object_detection | 2c3c869a3867bb79274bd9ea236ae3f7e955bbab | 069b86c2cc5702e095d59a5e9c99549be8393675 | refs/heads/master | 2021-04-09T14:14:49.303720 | 2019-01-30T12:17:15 | 2019-01-30T12:17:15 | 125,732,815 | 0 | 0 | null | 2018-12-23T17:35:40 | 2018-03-18T14:23:45 | Python | UTF-8 | R | false | false | 4,792 | r | plot_pr.R | library(ggplot2)
library(dplyr)
library(RColorBrewer)
library(scales)
###MODEL 6L#####
#precision
tr_prec_1 <- read.csv('/Users/mac/Desktop/model+6l_valid/train/prec/run_10_11_2018__18-12-tag-train_avg_prec.csv')
tr_prec_2 <- read.csv('/Users/mac/Desktop/model+6l_valid/train/prec/run_10_11_2018__21-10-tag-train_avg_pr... |
72b09b31b38951d423100b92670a0887dd8adc35 | 1a5f9e53317490c0b14a0e81bf5f1e38193c1e5e | /man/english-package.Rd | 57ceac8296a31e5465085ae9183ccfb570976ed8 | [] | no_license | spinkney/english | e3311613090ac4e145166c08db19884745b025a3 | df4447982ba87e29cd4cfb62318e317d1d0390f6 | refs/heads/master | 2021-06-14T15:16:54.107056 | 2017-03-16T07:44:04 | 2017-03-16T07:44:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,598 | rd | english-package.Rd | \name{english-package}
\alias{english-package}
\docType{package}
\title{
English
}
\description{
A simple facility to provide an english language representation of
integer vectors.
}
\details{
\tabular{ll}{
Package: \tab english\cr
Type: \tab Package\cr
Version: \tab 1.1-2\cr
Date: ... |
7a3a8a737fbf7494b12240c727dd34e24b4d975d | a4595b26f0abe6c53242e2e0072f6b8750900f03 | /src/gmy.R | 7c418d67fa76988a2356039100bae0321685b7c2 | [] | no_license | xiaoran831213/DeepG | eedcd159594bcd2248b2e9a746e271b8e25ae666 | de3b2ae9bdfca9cd3feb16f1ca876d0852d0cbc7 | refs/heads/master | 2020-05-22T06:54:28.898259 | 2020-05-15T15:39:47 | 2020-05-15T15:39:47 | 62,501,040 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,021 | r | gmy.R | source('src/dsg.R')
source('src/utl.R')
source('src/hlp.R')
source('src/hwu.R')
source('src/gsm.R')
source('src/tgz.R')
## sim('sim/GXP/01/000011873')
sim <- function(.fn, ssz=NULL, mdl=~g, fam=NULL, r2=.05, pop=NULL, ...)
{
## fetch necessary data & infomation
cat(.fn, ': ', sep='')
dat <- readRDS(.fn)
... |
6e91cdc116a6a9c76fb4abf629a2cc4f37e56e68 | d07115488889d09ff1f5c61b5b469c78fb7473a6 | /man-roxygen/all.R | ba1db6a107de54b335f8a9e7f00c59bed6bce266 | [
"MIT"
] | permissive | arturochian/gistr | 68bd45c9937130d2b17d19ade5f5594942307db0 | a033c72d36ed3c5042e2e4f627730652af1292ea | refs/heads/master | 2021-01-21T18:17:58.513212 | 2014-10-14T15:34:18 | 2014-10-14T15:34:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 114 | r | all.R | #' @param verbose Print informative messages (default TRUE)
#' @param callopts Curl options passed on to httr::GET |
9c601e4a104fd5b4a3f48fdb586acd75439e1223 | e89eb909b5c8920bdb9cb99041632e5d10807f6c | /man/impute.zscore.Rd | 125a12a51d12106665cbeb809d777d5a44443193 | [] | no_license | drveera/metaxcanr | 6ac732b3096f433ecdc52662d5d64aacc1f3f5b6 | 3c62801378f95f834c604b021abe79771798b3da | refs/heads/master | 2021-01-25T12:13:33.402032 | 2019-04-05T09:16:21 | 2019-04-05T09:16:21 | 123,457,984 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 456 | rd | impute.zscore.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/impute.zscore.R
\name{impute.zscore}
\alias{impute.zscore}
\title{impute the z score}
\usage{
impute.zscore(gene.name, gwas, db, snpcov, snpinfo)
}
\arguments{
\item{gene.name}{gene name}
\item{gwas}{gwas data frame}
\item{db}{data frame co... |
620bdeacb4b2fee7330f2e099eb04bd0fd3522d7 | 48ff203020c29a310e0ff766497aa00abca12c52 | /public/archivo/assignments/data/analisis-casencovid.R | b33a9307d4459738c1768c4f4a4b4c9ebd4466d4 | [] | no_license | juancarloscastillo/multivariada | 027686435be0dc780a76c4adf6cfc64534499ce1 | c30ee79f80c5d49487f0d428964c21983edba3b7 | refs/heads/master | 2022-08-02T04:55:54.364039 | 2022-07-20T08:52:49 | 2022-07-20T08:52:49 | 249,482,382 | 4 | 3 | null | 2022-06-29T13:11:35 | 2020-03-23T16:20:23 | HTML | UTF-8 | R | false | false | 2,653 | r | analisis-casencovid.R | ###Codigo Analisis ------
## Practico 5- Regresion simple ----
# Trabajo 1 -----
# 1. Cargar librerías -----
pacman::p_load(dplyr, #Manipulacion de datos
sjmisc, # Tablas
summarytools, #Tablas
sjPlot, # Correlaciones
ggplot2, # Graficos
websh... |
cc2d3dd0ba6dc18fa960254c8f583f136b7f6676 | 977d534f6842f5483c25a3b2a8760ac61e746dd2 | /edgelist.for.supplement.R | f084a65fd13b0671d398e8e4a00aa12ccb439ce0 | [] | no_license | ErinGorsich/Swine-networks | 27efbf4c19142cc50142e110bba0442ab80ab25d | 9b78ec6f55ea323db9cd28a7384112755473dd69 | refs/heads/master | 2021-01-10T17:19:53.801571 | 2020-08-03T11:15:38 | 2020-08-03T11:15:38 | 47,015,959 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,767 | r | edgelist.for.supplement.R | setwd("/Users/u1774615/Dropbox/Swine")
# Read in data
data.cvi<-read.csv("Swine_cvi_final.csv")
data.cvi <- data.cvi[!is.na(data.cvi$NUM_SWINE),]
data.cvi <- data.cvi[data.cvi$NUM_SWINE>0,]
data.cvi <- data.cvi[!is.na(data.cvi$SAMPLE_YEAR2),]
data.cvi <- data.cvi[data.cvi$NUM_SWINE>0,]
data.cvi <-data.cvi[!is.na(da... |
7f3f62563115535273074fc91b0058c2d1aa17e9 | 0ca78ef5a8670fbdab55409eecda579cec2baf68 | /DM/bias_computer.R | 6308347cec8e39f1010a8a0829b03b0e9e67e378 | [] | no_license | zhurui1351/RSTOCK_TRAIL | ab83fdef790778a1e792d08a876522ef13a872e6 | 2396c512c8df81a931ea3ca0c925c151363a2652 | refs/heads/master | 2021-01-23T09:01:36.814253 | 2019-05-17T15:26:19 | 2019-05-17T15:26:19 | 23,482,375 | 9 | 4 | null | null | null | null | UTF-8 | R | false | false | 987 | r | bias_computer.R | getfee = function(contract_usage = 3000,actual_usage = 3500)
{
bias = actual_usage - contract_usage
bias_ratio = (actual_usage - contract_usage) / contract_usage
uplimit = 0.01
up_adjust_serive_fee = 0.75
up_b = 0.1
neglimit = -0.02
neg_adjust_serive_fee = 0.75
neg_b = 0.1
weighted_average... |
24fbcd7f10b3a30f75a4c2c6cbaa26d96b767643 | 5d6156ff9113920d9cb1163d540c15548b31f0fd | /Module8/GDP_Year/GDPVisualization.R | e3394a2ccff240efac82c5dcaccc76ef6fde2db1 | [] | no_license | Karagul/PowerBI_With_R | a2b98632bcf4b1db0d72b6891f0cc8e461094887 | 0bf7f30cd5f84261b0b3184c588fb1b91d661a13 | refs/heads/master | 2020-07-23T18:29:18.752803 | 2018-06-17T20:59:46 | 2018-06-17T20:59:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,026 | r | GDPVisualization.R | # Change the theme and center the chart title
library(ggplot2)
library(RColorBrewer)
library(ggthemes)
library(ggrepel)
library(dplyr)
dataset <-
GDPData %>%
mutate(Year = as.numeric(Year)) %>%
filter(Year == 2017 & Stat == "GDP")
if (length(unique(dataset$Year)) == 1 & length(unique(dataset$Stat)) == 1) {
ec.co... |
b27ce9c91b55d1e45fd43b75f0c6ef0996efc0d7 | d33f3de15441549a8dbf1ef9ec3beccd02ec173a | /plot1.R | 833055921589fdbada2182cecfc6a308335de55a | [] | no_license | jvidyad/ExData_Plotting1 | 85addbc82d8d1c79441c2111cdcbd9b36f9b97b5 | 8130d4845787bb9640db3fa4218e4bf570409a32 | refs/heads/master | 2021-01-11T15:20:54.555394 | 2017-01-29T12:26:39 | 2017-01-29T12:26:39 | 80,336,272 | 0 | 0 | null | 2017-01-29T08:34:16 | 2017-01-29T08:34:15 | null | UTF-8 | R | false | false | 552 | r | plot1.R | data_file = "data/household_power_consumption.zip"
target_file = "household_power_consumption.txt"
data <- read.table(unz(data_file, target_file), header=TRUE, sep=";",
na.strings="?",
stringsAsFactors=FALSE)
data <- subset(data, Date=="1/2/2007"|Date=="2/2/2007")
temp <- paste(data$Date, data$Time)
data$Date <... |
804fa0d7cb487f99fada0c8c80f888d61159afd9 | 9faa3cfb92ff2cb58db8739fefffe4fd248bcf48 | /lib/R/sma.R | b8647992b56b3dc9ab9f5a49b5a1396a0e3328ae | [
"MIT"
] | permissive | joshterrell805-historic/StockPrediction | fd135e9b0d6f8033207511c2c5b6b2ba24cf150b | 15795889d21421b2ab59c3a4669c36e0956a888d | refs/heads/master | 2021-01-10T09:20:24.380517 | 2017-07-04T19:00:20 | 2017-07-04T19:00:20 | 49,034,347 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 344 | r | sma.R | # calculate simple moving average of data
# assumes data is at least 'count' long
sma = function(data, column, count) {
return(sapply(1:nrow(data), function(i) {
if (i < count) {
return(NA);
} else {
# left sided
start = i - count + 1;
return(sum(data[start:(start+count-1), column] / c... |
a3184cdea893110fd065f69e63910262ccaa0411 | a38d76df0dd29c5f8494a7750f62acac76062389 | /tests/testthat.R | e9f43fcfaab9f7d89ae7b9b84e2b5d87d1661fb9 | [
"MIT"
] | permissive | explore-n-learn/rtrek | d98a68bb160caff445eaa7af66bede6554060a2a | 8199dfeb26602f9d6910f83621bf00b6a6812717 | refs/heads/master | 2023-05-17T03:15:43.273023 | 2021-05-29T18:55:52 | 2021-05-29T18:55:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 54 | r | testthat.R | library(testthat)
library(rtrek)
test_check("rtrek")
|
69864c139d4c9dcd6bd7fd71323c4067733060ba | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/medicalrisk/examples/charlson_weights_orig.Rd.R | 0c72b9ab346ccc582ef495b88c4a1ccf293bb16e | [] | 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 | 225 | r | charlson_weights_orig.Rd.R | library(medicalrisk)
### Name: charlson_weights_orig
### Title: Map of Charlson comorbidity categories to weights
### Aliases: charlson_weights_orig
### Keywords: datasets
### ** Examples
charlson_weights_orig["aids"]
|
218b3734c95d9851162214f53cdc128c7f5d6564 | fc400478eb86e39e640edf56a50d42e06651a03b | /Droping duplicates exploration.R | 9f59f80f0bc64185c1cdbb155e1cf907f7a065e5 | [] | no_license | d2squared/NCAA-March-Madness-Scrape | 7a606983529b8e1dff27f4c6acaef904fc7dbe39 | 11e51a03dd4508404a121fec558c899201a92fe9 | refs/heads/master | 2022-04-19T06:35:15.404753 | 2020-04-01T03:29:27 | 2020-04-01T03:29:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 569 | r | Droping duplicates exploration.R | villanova1 <- all_info_2002 %>%
filter(Team_1 == "Villanova")
villanova2 <- all_info_2002 %>%
filter(Team_2 == "Villanova")
villanova_all <- rbind(villanova1, villanova2)
test_1 <- villanova_all %>%
filter(Team_1 == "Bucknell")
test_2 <- villanova_all %>%
filter(Team_2 == "Bucknell")
test3 <- rbind(test_1, ... |
1bcc1d29ef8dbf00d659adbc9659ee5b86dd21c7 | 259b21039f27e16d00161a233808283c3a0b99cd | /Neural Network (Concrete data).R | 56d29e8e63bcb990c8669e67fb1e9b068e8c6d18 | [] | no_license | vikasbevoor/Neural-Network-with-R | 96c48b6c00ce980504d0899cab9fe05e0c4fab20 | 9e351a6e67e2b4009d65d8f2199054d6fb85dd04 | refs/heads/main | 2022-12-20T05:11:31.234209 | 2020-10-13T07:49:28 | 2020-10-13T07:49:28 | 303,627,711 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,816 | r | Neural Network (Concrete data).R | concrete <- read.csv("D:/Data science videos/R Codes/Assignments docs/Neural Networks/concrete (4).csv")
View(concrete)
attach(concrete)
install.packages("moments")
library(moments)
library(caTools)
library(caret)
# Data exploration
summary(concrete)
str(concrete)
# Graphical exploration
hist(strength)... |
7296b1d15f5533c95fb9b508b224543651ba0b75 | 931eced08131bf4d96c0722b6d8f90e1f7f38c95 | /Portfolio_Optimization.R | 2d0c5eea91f0ef766c3f283a50364f24b5a9cf53 | [] | no_license | pekova13/SPL_MeanVar_ThreeFund | e82939812ad0597fd3107445719d666c5dbedd6b | 1cae4a940c8a66551fd771cb78e07897b68fccac | refs/heads/master | 2021-06-25T21:07:43.843234 | 2020-12-04T15:56:39 | 2020-12-04T15:56:39 | 176,032,450 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 25,570 | r | Portfolio_Optimization.R | # set the working directory
setwd("C:\\Users/TO/Desktop")
# load the dataset
data=read.csv("ten-roberto-wessels.csv",sep=";",header=TRUE)
# Install all necessary packages
install.packages("ggplot2")
install.packages("dygraphs")
install.packages("tidyverse")
install.packages("lubridate")
install.packages("... |
54fb92702fd636b2789c52f6904383c3142f2e72 | cecced4835b4f960141b85e25eabd8756f1702ea | /man/sc_atac_plot_features_per_cell_ordered.Rd | 3e18556700c45d0a1112718a8a395e8197c3d11e | [] | 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 | true | 547 | rd | sc_atac_plot_features_per_cell_ordered.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sc_atac_subfunctions.R
\name{sc_atac_plot_features_per_cell_ordered}
\alias{sc_atac_plot_features_per_cell_ordered}
\title{Plot showing the number of features per cell in ascending order}
\usage{
sc_atac_plot_features_per_cell_ordered(sce)
}
... |
e60a4837d29ce557301cbb09570a04a9b2112fca | 49961ac17375792c2f0e4a6bab7775becb543a98 | /R/number.elements.R | 554e8f30e9d13f73ce982c2d1593c6912ca2b41b | [] | no_license | cran/InfNet | bfd88e01df0ef46c8bda702326c4957804871ea8 | 23d4738311e84597ed10af4d6a111329b225b081 | refs/heads/master | 2021-01-11T05:29:14.343589 | 2006-07-20T00:00:00 | 2006-07-20T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 88 | r | number.elements.R | "number.elements" <-
function(mat,col, x){
a<-length(mat[mat[,col]==x,col])
return(a)
}
|
226c0e007f2e99989b6a37db451ed31c540212c7 | 5247d313d1637170b6bbc5e367aba46c88725efd | /R/package-doc.R | 4df722e43d0f9f90e061f12d8cb3115eaacc1fa5 | [] | no_license | fentonmartin/twitterreport | dac5c512eea0831d1a84bef8d2f849eab2b12373 | 5ddb467b8650289322ae83e0525b4ff01fba0d1d | refs/heads/master | 2021-08-22T04:25:01.834103 | 2017-11-29T07:47:43 | 2017-11-29T07:47:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 162 | r | package-doc.R | #' twitterreport
#'
#' Out-of-the-Box Analysis and Reporting Tools for Twitter
#'
#' @docType package
#' @name twitterreport
#' @author George G. Vega Yon
NULL
|
8103ee64f1b343c54bab57052ec6da8e5c503374 | 88d9c0d58c72ba565d403a21de37f9169ac282a0 | /data-raw/03_seer-pop-us-standard.R | 3c1ff426000c051091db45ee2a6d8b64bdba03e1 | [
"MIT"
] | permissive | GerkeLab/fcds | 01191bc32e4b73a857ae7ab7ef39e29c6c2713c4 | 7d6cbc89726418629d9c3cd54b10414eb7cab028 | refs/heads/master | 2021-07-08T02:48:46.061128 | 2020-07-30T18:45:01 | 2020-07-30T19:04:25 | 167,439,089 | 3 | 1 | NOASSERTION | 2020-07-30T19:04:26 | 2019-01-24T21:16:41 | R | UTF-8 | R | false | false | 4,455 | r | 03_seer-pop-us-standard.R | library(dplyr)
fcds:::requires_package(c("readr", "stringr", "purrr", "here"), "seer_pop_us-standard.R")
library(readr)
library(stringr)
library(purrr)
# SEER Standard Ages ----
# https://seer.cancer.gov/stdpopulations/
# Standard Populations - 18 Age Groups (0-4, 5-9, 10-14, ..., 85+)
# https://seer.cancer.gov/stdpo... |
120a88a4977d602610529427faf8b44f25c84d5f | 3ab868b8eeef4547e97d511aede2fb21ab924e86 | /man/bayesclust-package.Rd | 70aa3efdde3e10b8384335c8281ba27ee6e9bfb4 | [] | no_license | cran/bayesclust | e8341b359081a89b084ec1e6aef60afc531e1268 | 8338e3782b0e88f24f588337e20fdb72d8b542d6 | refs/heads/master | 2021-01-16T19:20:43.969809 | 2012-05-15T00:00:00 | 2012-05-15T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,814 | rd | bayesclust-package.Rd | \name{bayesclust-package}
\alias{bayesclust-package}
\alias{bayesclust}
\docType{package}
\title{
Testing and Searching for Clusters in A Hierarchical Bayes Model
}
\description{
This package contains a suite of functions that allow
the user to carry out the following hypothesis test on genetic data:
\tabular{l}{
... |
0d92f2a46316ad80c5eca91ce8970e3090e2c927 | b3fe239c87958a522c3e96de2486d545476e3b27 | /4.1Network_analysis.R | 313c7d530dcfec4f1bdd0c1062ac0904e44b7042 | [] | no_license | Zefeng-Wu/Arabidopsis_Functional_Network | db055c96a88bb5c72e338a972c01c3a303549a1c | 18ca3769e4b4dcadd647404c9a1fdf764320a513 | refs/heads/master | 2022-11-16T04:33:07.665397 | 2020-06-11T11:49:41 | 2020-06-11T11:49:41 | 220,196,926 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 182,061 | r | 4.1Network_analysis.R | #! /usr/bin/R
set.seed(1000)
options(digits = 5)
df2network<-function(df){
data<-read.table(df,header = TRUE,stringsAsFactors = FALSE,sep="\t")
library(igraph)
g<-graph.data.frame(d = data, directed = FALSE)
g<-simplify(g,remove.multiple = TRUE,remove.loops = TRUE)
return(g)
}
#### read network data with d... |
e40b1870951f8e66e9e1a159bf09a41fb889487f | d8ed284412c99f0ca03491b6b8a65e2ae1ae3964 | /R/FrEDI.R | 8b2b48340b7b3ff63bf31a293f43c7c58282ec24 | [
"MIT"
] | permissive | jwillwerth/FrEDI | e3cd3bef0ee6d05cdc7c5f8518728bb35413fab7 | 1d698e41fe4e70f4e6d21eb2958702d0c77d6d01 | refs/heads/main | 2023-08-15T23:47:54.396764 | 2021-10-15T17:17:06 | 2021-10-15T17:17:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,357 | r | FrEDI.R | ### This file documents the R temperature binning package
#' README
#' FrEDI: The Framework for Evaluating Damages and Impacts
#'
#' [FrEDI] is an R package being developed by the U.S. Environmental Protection Agency (EPA). The functions and data provided by this package can be used to estimate climate change impacts f... |
ab9cb711d8c3268a9251cba1b126748f57c8e131 | c773007a1faad874a93f2597092c18077b9252d7 | /server.R | 048117b43d3e41cde2b7e02f92930bef294353e6 | [] | no_license | arunsri91/Chattalyzer | fa00952c63fd6be9ba4094123ae70f9428ef64a7 | 3bd62675b5c4dc099f1a63f05df02c8cd747903c | refs/heads/master | 2020-03-18T21:34:41.121670 | 2018-05-29T11:56:14 | 2018-05-29T11:56:14 | 135,287,585 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 32,162 | r | server.R |
# This is the server logic for a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#
library(shiny)
shinyServer(function(input, output) {
output$plot <- renderPlot({
# input$file1 will be NULL initially. After the user ... |
429c76c6d5c6e6fd9acdcc1d6af0edb51c00bf41 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/HPbayes/examples/mp8.ll.Rd.R | 70f95d98c8eecd053393d086fe66bcad0fabbf7c | [] | 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 | 593 | r | mp8.ll.Rd.R | library(HPbayes)
### Name: mp8.ll
### Title: Binomial likelihood for a set of Heligman-Pollard Parameters
### Aliases: mp8.ll
### Keywords: misc
### ** Examples
##A set of parameters##
theta <- c(0.06008, 0.31087, 0.34431, 0.00698,
1.98569, 26.71071, 0.00022, 1.08800)
##Deaths and persons at risk##
lx <-... |
73bcf7845972f7c28a82fc9de7cea1f6a7095e8d | 142ac9941ab626c0523ede00777bee280d95c2f0 | /shinyKmeans2/ui.R | e10c59973503358cd3457df8ecf0796929a5c67d | [
"MIT"
] | permissive | caleblareau/shinyTeach | 66e7c86f3fdf6425754ee7645e427cf711b96328 | 0ffef423431298087bfd30b668b670fd59eb50c2 | refs/heads/master | 2021-01-12T11:55:16.787504 | 2017-05-21T18:02:31 | 2017-05-21T18:02:31 | 69,316,315 | 6 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,654 | r | ui.R | source("extRa/startup.R")
shinyUI(
navbarPage(
HTML("<img src='harvard-logo.png'/>"),
tabPanel("Visualize",
fluidPage(
pageWithSidebar(
headerPanel('ADVANCED'),
sidebarPanel(
selectI... |
aa5783368ad8ba2ce800850e2610592f10b38f78 | 897f0581bfc3403318f56072f7af1163b8189733 | /Thaps/ttest.R | 7a0849b88e76d39b63cd25122d06a1c41a555d9e | [] | no_license | jashworth-UTS/ja-scripts | 2985891e628bae59b1f4b8696739cbf63b5a2dc2 | ac837ac0fee63c27b3b8ac4d9a5022810fb31976 | refs/heads/master | 2021-05-28T18:39:20.272694 | 2015-02-04T02:35:17 | 2015-02-04T02:35:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,303 | r | ttest.R | # Justin Ashworth
# Institute for Systems Biology
# 2011
ratiocolregex = 'Day'
do.ttest =
function(ratiosfile='probe.ratios.mean.tsv')
{
d = read.delim(ratiosfile)
ratiocols = grep(ratiocolregex,names(d),value=T)
ratios = as.matrix( d[ , ratiocols ] )
colnames(ratios) = ratiocols
day1 = grep('Day1',colnames(rat... |
46f1520717bb38412b7b2883e616b556afce9bd2 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/IDE/examples/constant_basis.Rd.R | e4ec92ca3984b1d8710fa102e69dbc885c741449 | [] | 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 | 168 | r | constant_basis.Rd.R | library(IDE)
### Name: constant_basis
### Title: Create a single, constant basis function
### Aliases: constant_basis
### ** Examples
basis1 <- constant_basis()
|
3362b7d1a8deb35951775bed4d6a1092a7837d17 | 1a1ff0d19b5cd9c2fb6d3965f8a4771c306b4e45 | /demographics.R | 99f74a1d8e9a008b1f63f0f328cf483fa739da64 | [] | no_license | zkpt-org/intervention | bab4d011292a34d74769f9ae8b41e819ef3f2cdc | af600d5549a089892bc5eb880d2c5e0ebcbb4ec3 | refs/heads/master | 2020-05-21T00:36:06.168394 | 2013-07-10T20:42:49 | 2013-07-10T20:42:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,198 | r | demographics.R | #Add columns for pre-existing conditions
user_data['Diabetes']<-0
user_data['HighBloodPressure']<-0
user_data['HighCholesterol']<-0
user_data['HeartDisease']<-0
user_data['MetabolicSyndrome']<-0
user_data['Other']<-0
#Extract pre-existing conditions
hiblood=sqldf("SELECT UserId FROM condition WHERE Key = 'QUESTION_CHR... |
2f10c8e41d76b2b1277bf805e1761bbfef3a244c | dc284fe45eea59ade9e1a75095af6285be51af3c | /C/plot_sing.R | ac29016b63e41b9dee08f975193b3e5c565c586e | [] | no_license | gui11aume/analytic_combinatorics_seeding | c6d47ecd4a7df4428e0d0a24a578bc276bb01004 | f6dabd7cf074f7069bcf12bca996fe35530b19f0 | refs/heads/master | 2020-05-26T00:29:23.183443 | 2017-10-18T16:09:52 | 2017-10-18T16:09:52 | 84,981,428 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 572 | r | plot_sing.R | d = 17
p = .1
q = 1-p
Q = function(z) 1-p*z*(1-(q*z)^d)/(1-q*z)
pdf("Q.pdf", width=5, height=5)
z = seq(-1.5, 1.5, 0.01)
plot(z, Q(z), type="l", ylim=c(-2,2), lwd=2, plot.first=grid())
abline(h=0, col="grey50")
dev.off()
mat = matrix(NA, nrow=512, ncol=512)
for (x in 1:512) {
for (y in 1:512) {
z = 1.5 * (x-256) /... |
4948a1101e6ba58e037cabae185976f75d5314a8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/bayesDP/examples/bdpnormal.Rd.R | 17d8537ebcbef88bfede16deebf7e377bb06d851 | [] | 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 | 736 | r | bdpnormal.Rd.R | library(bayesDP)
### Name: bdpnormal
### Title: Bayesian Discount Prior: Gaussian mean values
### Aliases: bdpnormal bdpnormal,ANY-method
### ** Examples
# One-arm trial (OPC) example
fit <- bdpnormal(mu_t = 30, sigma_t = 10, N_t = 50,
mu0_t = 32, sigma0_t = 10, N0_t = 50,
metho... |
a71988b220d9426747cfdd5b9b2505059a2b1c2d | 10fbd1788ed37fd0c61403f40e8233853bc00cfc | /man/QAW_eff.Rd | 36c5e505b66418728152a60db21b77d67bf3f727 | [] | no_license | lmmontoya/SL.ODTR | 50dafaa45376dc1f7da74816585ce77047e03a02 | 9ffe0a3021f7c248f59f038a2f82fab135887da2 | refs/heads/master | 2023-03-06T12:48:47.929749 | 2023-02-20T22:14:59 | 2023-02-20T22:14:59 | 214,280,056 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 365 | rd | QAW_eff.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/1DGPfunctions.R
\name{QAW_eff}
\alias{QAW_eff}
\title{Simulate with eff}
\usage{
QAW_eff(A, W)
}
\arguments{
\item{A}{Vector of treatment}
\item{W}{Data frame of observed baseline covariates}
}
\value{
conditional mean of Y given A and W
}
\... |
2f99420dfb746bc49c24768d6a001e4e4e417018 | 2d1655d6f0cf1d00b9cd470a95e06245311e4c89 | /Problem_2.R | 9658060d4fa6798d521eb9bdc61101fab8c7426b | [] | no_license | feb-uni-sofia/homework-1-r-basics-elidakacheva | 626644402f4ac0081d4e6a35ffc8dfea32b4119d | 1c1fcc0fa3ecc5bd7a9aec9f1c5670a68e777959 | refs/heads/master | 2021-09-11T01:09:33.274081 | 2018-03-31T20:57:38 | 2018-03-31T20:57:38 | 126,045,589 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 955 | r | Problem_2.R | # Problem 2
# a)
xmin <- c(23.0, 20.5, 28.2, 20.3, 22.4, 17.2, 18.2)
xmax <- c(25.0, 22.8, 31.2, 27.3, 28.4, 20.2, 24.1)
# b)
xmax - xmin
# c)
avgxmin <- mean(xmin)
avgxmax <- mean(xmax)
# d)
xmin[xmin < avgxmin]
# e)
xmin[xmax > avgxmax]
# f)
dates <- c('03Mon18', '04Tue18', '05Wed18', '06Thu... |
d7b7ab490d8ca7696c59ad76e908ff434d58cfe2 | 6f4432677678937ade732bbbae9829a4113a5e96 | /man/costfunct.Rd | 6f8e06cc8e8e7b7ca7922331373e91689b37ad07 | [
"MIT"
] | permissive | tan92327/nempack2 | 5584aa3f23645c402c8d76cd94893bc99937b6e2 | 03e8d6d271e7a3d969f4f52829a7cd1690a0ae03 | refs/heads/main | 2023-01-18T16:29:11.435739 | 2020-11-25T01:04:22 | 2020-11-25T01:04:22 | 315,790,800 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 477 | rd | costfunct.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/costfunct.R
\name{costfunct}
\alias{costfunct}
\title{Cost function implementing C2 in the binary segmentation article}
\usage{
costfunct(data.vec)
}
\arguments{
\item{data.vec}{Input data vector}
}
\value{
The sum of squares of the data vect... |
c03e40a511e09e9c97aed7fb457fda3b43891a9a | 1004816de8f435d930167ec03e7196f3d033db1f | /Rpkg/R/KMeans.R | 00ed6f2a996b87cede57e03bfd9f0acba50b054d | [
"Apache-2.0"
] | permissive | zheng-da/FlashX | 04e0eedb894f8504a51f0d88a398d766909b2ad5 | 3ddbd8e517d2141d6c2a4a5f712f6d8660bc25c6 | refs/heads/release | 2021-01-21T09:38:24.454071 | 2016-12-28T08:51:44 | 2016-12-28T08:51:44 | 19,077,386 | 22 | 11 | null | null | null | null | UTF-8 | R | false | false | 1,878 | r | KMeans.R | fm.KMeans <- function(data, K, debug=FALSE)
{
data <- fm.conv.layout(data, TRUE)
data <- fm.materialize(data)
n <- dim(data)[1]
m <- dim(data)[2]
agg.sum <- fm.create.agg.op(fm.bo.add, fm.bo.add, "sum")
agg.which.min <- fm.create.agg.op(fm.bo.which.min, NULL, "which.min")
cal.centers <- function(data, parts) {
... |
634e43aa1cbb068e783336d6e499acdc21bcac42 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/yuima/examples/ybook.Rd.R | e42c0d37805a4715a1ce224f476b5f37c5a5685c | [] | 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 | 138 | r | ybook.Rd.R | library(yuima)
### Name: ybook
### Title: R code for the Yuima Book
### Aliases: ybook
### Keywords: misc
### ** Examples
ybook(1)
|
308d2abf70c5087b23c6c83aed0204501277bfde | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/SSN/examples/putSSNdata.frame.Rd.R | 19d490b7647aa2dd36b701844e25a234b774174d | [] | 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 | 794 | r | putSSNdata.frame.Rd.R | library(SSN)
### Name: putSSNdata.frame
### Title: putSSNdata.frame
### Aliases: putSSNdata.frame
### ** Examples
library(SSN)
#for examples, copy MiddleFork04.ssn directory to R's temporary directory
copyLSN2temp()
# NOT RUN
# Create a SpatialStreamNetork object that also contains prediction sites
#mf04 <- import... |
2a39597fa2b5ba81c44b1114052fdf90927e453b | e5b8abb99aa8d5e0d6f6679039a777585d982ca5 | /R/rcbdCheck.R | 7157bb4132cc29994ac45a3aba6a9938690c626e | [] | no_license | Prof-ThiagoOliveira/planExp | 88ae76a045bb2d59b2d45f7bbe77cc602cc9caf6 | a65d2efca012eedfaddb56766ac7c5e03ac44c8b | refs/heads/master | 2023-04-10T08:19:51.778213 | 2020-07-02T07:37:04 | 2020-07-02T07:37:04 | 273,422,811 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,273 | r | rcbdCheck.R | #######################################################################
# #
# Package: planExp #
# #
# File: rcbdCheck.R ... |
6962e27fc49295912eac4767db2a30cdd1ab29f7 | c521edcb2e192a222407de84c82f649c431e4c4f | /affairs.R | 69502d38a5ed600361e0ab933a8c47ace82a5f12 | [] | no_license | arunailani/DATA-SCIENCE-ASSIGNMENTS | 5d7e2cb97701689116868b4eac1b643623be515c | e04517ba14ce690d038aa7ee0ac819a57c009b12 | refs/heads/master | 2022-09-12T04:49:19.735641 | 2020-05-15T13:11:18 | 2020-05-15T13:11:18 | 264,187,158 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 896 | r | affairs.R | affairs = read.csv(file.choose())
View(affairs)
attach(affairs)
detach(affairs)
summary(affairs)
str(affairs)
factor(affairs$affairs)
#since the affairs is dependent column so making its value to 0 and 1 for building the confusion matrix
affairs$affairs = ifelse(affairs$affairs > 0, 1, 0)
#model building
... |
2007f4401a91ab868c6be39989ee96adcb605982 | 18df2d7c536789ad87d82d13145d46a6b262671f | /quake_plot_app.R | 41c74c1403e76c5acc0c7d84c3a6bd6ff2e54b4e | [] | no_license | putt-ad/quakes_shiny | ca96932f68a9ae96bbedfb22ac2b5c25a97df6f9 | 4e851a5710a667e260b3c7737d7492071f45d0ea | refs/heads/master | 2020-08-31T21:10:04.284552 | 2019-11-01T14:33:09 | 2019-11-01T14:33:09 | 218,786,963 | 0 | 2 | null | 2019-11-01T14:33:10 | 2019-10-31T14:36:26 | R | UTF-8 | R | false | false | 3,350 | r | quake_plot_app.R |
########
# shiny quakes app
# A. Putt
# GEOL590 | 2019.10.31
########
library(shiny)
library(tidyverse)
library(ggplot2)
#quakes dataset is a list of earth quake site location lat and long, station location, depth, magnitude (from richter scale)
# We'll limit the range of selectable carats to teh actual range of e... |
831661474faad92a7a667cfc54f0b74b0ecdc2b3 | 59debac2c846d50901fbcf74d8487237cdf8d77f | /man/itcadd.Rd | 3daf0d9f91716d144b79654347b0d96c5e1ec0ee | [] | no_license | trooper197/tccox | bcc306c1b8e9d1fddad3d1fd48f4ae248957220a | c3da04d5e0a363671340cc75e03aca621eb2644e | refs/heads/master | 2021-08-23T09:17:45.528914 | 2017-12-01T20:23:45 | 2017-12-01T20:23:45 | 112,348,548 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,226 | rd | itcadd.Rd | \name{itcadd}
\alias{itcadd}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Determines potential ITC intervals according to expected event criteria or as fixed
}
\description{
Either speciifies the ITC interval endpoints as fixed or else determines candidates based on expected event criteria... |
5323335f47a2b3c9795470f04943d1e4c5dc7bae | 2b9d185f6992e663a9e1eb4890c5e1a23e78e778 | /docs2/lib/myown-1.0/R/scriptTest.R | 7468809ba85b19d414d9ab1edf3e5845aaf45037 | [
"MIT"
] | permissive | Juju1OO1/usr_lobby | 5930ae17b47fd85f936d61887d48b9d9aef34498 | eb4f45f3bc0273bb68dc2e8dc75f92411ecb00e5 | refs/heads/main | 2023-06-05T06:56:19.789997 | 2021-06-23T12:18:40 | 2021-06-23T12:18:40 | 364,794,159 | 0 | 0 | MIT | 2021-06-23T12:18:41 | 2021-05-06T05:24:26 | JavaScript | UTF-8 | R | false | false | 851 | r | scriptTest.R | card_reveal <- function(){
tags$div(class="card",
tags$div(class="card-image waves-effect waves-block waves-light",
tags$img(class="activator", src="https://materializecss.github.io/materialize/images/office.jpg")
),
tags$div(class="card-content",
... |
dd8f1b4cf5200b41012cf87465a762a9f8c7393f | 1bb4a3b57f8de59e66a325a70d4b30c82b3da0db | /plotVI.R | 0a02174e7e921dfa13903f0823848c1f04be119d | [] | no_license | pegah-hafiz/CDSS-infertility | 2b49fce95262e8eaa48411c0bce19df7b8387327 | f9b29321f49006cafa945a28b48a91e58af92426 | refs/heads/main | 2023-08-30T18:53:16.685612 | 2021-11-06T15:10:37 | 2021-11-06T15:10:37 | 415,905,845 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 550 | r | plotVI.R | plotVI <- function(button){
library(randomForest)
#set.seed(2)
filename <- "VI.pdf"
f.address <- ""
main.data<-read.csv(f.address, header=T)
ivf.model <- randomForest(result ~ ., data=main.data, ntree=500,
keep.forest=T, importance=TRUE,
... |
238abe0fd2380451d5181ae359b23f262f8877b8 | 33a76f0df4c925f21d3d2b986c1181bd8a866c84 | /Practice/NeuralPrac.R | 7124135b1f1423de4d1ac130a69efda577f5c01d | [] | no_license | sjngjang/SJ_MaC_P | 6a555d1cce7ba4ef306238595a61539ce4af5a73 | f7e1d1dc26ea70f062ed6d6319184ef9767d7bbf | refs/heads/master | 2021-01-09T05:48:16.090882 | 2017-02-03T09:46:11 | 2017-02-03T09:46:11 | 80,811,282 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 926 | r | NeuralPrac.R | getwd()
prob<- read.csv("Practice/problem.csv", header=T, stringsAsFactors = F)
head(prob)
#---------- 1ver to normalize
for (i in 1:30) {
#change variables to 0 to 1
prob[i]<-prob[i]*(1/5)
}
head(prob)
#------ 2ver to normalize
#normalization function
normalize<-function(x){
return ((x-min(x))/diff(range(x)))... |
b1aee03b9c7b6eea9fcb9c600f92419cb617675a | fbca0cb26d06e18dd5ff84233b6378633a4340fd | /R/scales.R | b44a151be42d30cb9b7b983e63633683dc7d673f | [] | no_license | bvancil/paletti | 894381b0d90b869e7278f905d61aca44ff3c4ae8 | a557e7684e908135c1cc9b711164ce4a9c41afb6 | refs/heads/master | 2022-11-03T06:34:57.247058 | 2020-06-23T14:17:44 | 2020-06-23T14:17:44 | 274,422,324 | 0 | 0 | null | 2020-06-23T14:07:25 | 2020-06-23T14:07:24 | null | UTF-8 | R | false | false | 6,780 | r | scales.R | #' Create the _pal function for ramped colors
#'
#' This wil create a function, that makes the palette with ramped colours.
#'
#' @param hex_object Either a character vector with hex code, or a list that
#' contains the character vectors with hex codes. List elements should be
#' named.
#'
#' @return A function that wi... |
92aea6149c8dc8b0e13a24a94aceb13bf52c4082 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/PReMiuM/man/heatDissMat.Rd | 59655f5d11064eb409db68d03625aa5e8323f6d9 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,670 | rd | heatDissMat.Rd | \name{heatDissMat}
\alias{heatDissMat}
\title{Plot the heatmap of the dissimilarity matrix}
\description{Function to plot the heatmap of the dissimilarity matrix}
\usage{
heatDissMat(dissimObj, main=NULL, xlab=NULL, ylab=NULL)
}
\arguments{
\item{dissimObj}{An object of class dissimObj.}
\item{main}{The usual plot opti... |
cf4a394db78efd80715f7def271f65c3f6adf059 | 28f0bd2591c656d5fd2b7cc71b0490d50873aae4 | /man/jobRemove.Rd | dc8ef24a09611363977a17cb0cbb45942ff277df | [] | no_license | JiaxiangBU/rstudioapi | 6c05581a4fb03be25fd31d147a18387141da9784 | e49222ab99dee37e7c7e33827f13dbea47c07693 | refs/heads/master | 2020-04-14T00:32:19.794396 | 2018-12-19T17:55:20 | 2018-12-19T17:55:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 276 | rd | jobRemove.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/jobs.R
\name{jobRemove}
\alias{jobRemove}
\title{Remove a Job}
\usage{
jobRemove(job)
}
\arguments{
\item{job}{The ID of the job to remove.}
}
\description{
Remove a job from RStudio's Jobs pane.
}
|
4cd51b8505be5c7b96130a3e035198b0803fbbe4 | 7474309f742266ed7a7e120a1888e8f5e4f36aba | /project-2/KNN_R_Project.R | 20a56905dab1b8d0809071e7c1b368cca5026262 | [] | no_license | hcmora/knn-classification-exercises | e9ad4854d495a44ffa3a401060732e73273620b1 | 0fc436fe4b3fdb3fd09d3a72609447e176852809 | refs/heads/master | 2020-04-27T14:52:32.000416 | 2019-03-11T21:49:54 | 2019-03-11T21:49:54 | 174,423,694 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,704 | r | KNN_R_Project.R | setwd('R Studio/KNN Project')
project = read.csv('KNN_Project_Data',header = TRUE)
head(project,1)
# Still haven't worked out how to make the pairs plot differentiate on the target class, for some reason
# it only colors the first argument
# pairs(project, main = "Project Database", pch=21,bg = c("green3","blu... |
88d3246ef8be3fb496373a75c414548642ddc9ac | 4885d9e77e11d5b63d0b1bfaf08f33934b60d770 | /R/plot.R | 6178e7c68039552d14c40016e16c1ded92b6460d | [
"Apache-2.0"
] | permissive | r-spatial/sftime | 7db4f44fe8f425df631867b7b8bd35a902f0015b | 423f39e1e9d7225392e2b3241e8b7926b7b74f15 | refs/heads/main | 2023-07-06T03:45:35.136467 | 2023-06-28T08:34:00 | 2023-06-28T08:34:00 | 212,058,235 | 45 | 4 | Apache-2.0 | 2023-06-28T07:32:40 | 2019-10-01T09:29:51 | R | UTF-8 | R | false | false | 2,594 | r | plot.R | #' Plots an \code{sftime} object
#'
#' \code{plot.sftime}
#'
#' @aliases plot
#' @param x The \code{\link[=st_sftime]{sftime}} object to be plotted.
#' @param y A character value; The variable name to be plotted; if missing, the
#' first variable is plotted.
#' @param ... Additional arguments; Passed on to \code{\li... |
56764126274ca741a7faa9f3ec21347fc5317968 | 9a7a84f823afcece5ff704b5778581f4e8d3d189 | /Individual_Functions/create_sim_repped.R | 51cdc30a38218c1be8f9d9d59755d2b43864df87 | [] | no_license | multach87/FreqInBayes0.1 | 9068f877cc8cec049734f4d32c2f635749d3625d | f5155280616e60c87db955e5ed34ccfc3c6bda7e | refs/heads/master | 2020-09-25T02:59:05.538917 | 2020-01-04T17:31:01 | 2020-01-04T17:31:01 | 225,902,525 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 830 | r | create_sim_repped.R | create_sim_repped <- function(sim.structure , num.conds = nrow(sim.structure) ,
num.pars , num.iters) {
sim.structure.repped <- as.data.frame(matrix(ncol = (num.pars + 2) ,
nrow = (num.conds * num.iters)))
colnames(sim.s... |
ca658a83918d27533fc1d582f80c3e39491bbe18 | 7a1fab30064fe5debe1499295c270453610396db | /R/vars.R | 22c7bf790e0e22601f10af660f320044cfbd431f | [
"Apache-2.0"
] | permissive | dkgaraujo/brazilianbanks | 195ef670ebecb2484d3dffdd3d4635678dd2dd72 | 708dd27ec7ab060965fcfb6c8802c0f02cccdd38 | refs/heads/main | 2023-05-23T12:31:42.738173 | 2023-02-23T17:12:19 | 2023-02-23T17:12:19 | 373,839,928 | 7 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,749 | r | vars.R | # Copyright 2022 Douglas Kiarelly Godoy de Araujo
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed... |
a8d7879fabc54848fa215816f2ae10aff764d8b4 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/GD/examples/riskmean.Rd.R | 80a145a6459499c31548f2525eee5d7cc59a66bf | [] | 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 | 351 | r | riskmean.Rd.R | library(GD)
### Name: riskmean
### Title: Geographical detectors: risk means in risk detector.
### Aliases: riskmean print.riskmean plot.riskmean
### ** Examples
rm1 <- riskmean(NDVIchange ~ Climatezone + Mining, data = ndvi_40)
rm1
plot(rm1)
## No test:
data <- ndvi_40[,1:3]
rm2 <- riskmean(NDVIchange ~ ., data =... |
0987c4bded8d7f6736e3256e865e67896ce1264e | e43e0d9b80fb33919ec9b253b5ebdb0dda7e12f8 | /R/prepare_data.R | 2de593aac57d314f2f724d5b796bf345238b2165 | [] | no_license | jmoss13148/sandpr | ce1034e864e73155fd41f3193eb5f213f0e66ade | 6e4bd6ca262e13f2fcf76133d353040056f4313e | refs/heads/master | 2021-08-23T21:22:20.999081 | 2017-12-06T16:09:41 | 2017-12-06T16:09:41 | 110,973,284 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,243 | r | prepare_data.R | ##' @description Prepare data for neural network model
##'
##' @import dplyr
##' @import tidyr
##'
##' @return a dataframe with the relavent columns
##'
##' @export
prepare_data = function(x, y) {
## Remove variables we don't want
x = x %>% select(-X, -For.Year)
## Coerce to correct classes
... |
9dbf8c62591b05b666899ea37312feaa823a4f78 | bb6e0f698c434945a622b5b605a025ae92dc7729 | /ntc-presentation/Helper Basic Plots.R | 73ba0e7ff43382d811a5c2198beb29282fe964b5 | [
"CC0-1.0"
] | permissive | mvparrot/vis-serve | 933f2f211b817b00177cd7b53b89f324ed882c0e | f2c307837d4d89054073864dedc8b352613c7920 | refs/heads/master | 2021-01-17T10:19:33.786055 | 2016-09-29T11:09:43 | 2016-09-29T11:09:43 | 56,582,980 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,035 | r | Helper Basic Plots.R | #================================================
#--- Basic Plots
#--- Alwin Wang
#================================================
#--- Packages Required
# require(ggplot2)
# require(plotly)
#--- Outline of the court
court_trace <- data.frame(x = c(-11.89, -11.89, 0, 0, 0, 11.89, 11.89, -11.89, -11.89, 11.89, 11.89,... |
c79783ec16450625689f2a03b2de104e8896205a | 3d96222bc3bb07f94c074794aab6a7a79e0fdb40 | /man/kh_clr.Rd | c5fd9773e6828f5f206a71cc71e94f8db50df3b1 | [] | no_license | k-hench/kh3d | 2868a9e2d367515cada65d809e61da1b34f21fbe | 570611316ff1495e5114525f8b2b6eac944d3ba9 | refs/heads/master | 2020-06-15T10:03:53.306677 | 2019-07-05T12:48:21 | 2019-07-05T12:48:21 | 195,268,389 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 365 | rd | kh_clr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kh3d_basic.R
\docType{data}
\name{kh_clr}
\alias{kh_clr}
\title{kh color vector}
\format{An object of class \code{character} of length 2.}
\usage{
kh_clr
}
\description{
\code{kh_clr} is a combination of colors I like.
}
\details{
A vector co... |
9720bf58461a130648b50f0cc0a07eddb4101962 | 4f769a3a6bb0a81892754a91618881d3c4ded208 | /Week4/best.R | 6f5c2701c3cb2aece867a936013d95b0e266a82c | [] | no_license | frmont/CourseraRProgramming | 24a5ea1cbacaaec818888cb1cd52920759b19f5e | 0975be0f1c4c52b357fe3684550571218638eeda | refs/heads/master | 2022-11-25T04:55:29.835236 | 2020-08-02T16:26:19 | 2020-08-02T16:26:19 | 262,094,350 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,638 | r | best.R | setwd("~/Desktop/GoogleDrive/R_Programming/CourseraRProgramming/Week4/rprog_data_ProgAssignment3-data")
best <-function(state, outcome) {
data <- read.csv("outcome-of-care-measures.csv", colClasses = "character") ## Read outcome data
data1 <- as.data.frame(cbind(data[, 2], # extracting particular outcomes: hosp... |
265f31e7c6ef853f0b10291304337bdb86324d30 | 43c75ab90ac98e0a15b354d06660d8ebbc5b9175 | /Archive/windowplots.tmp.R | 6e443b5ac257f3ab1b483474d2e8f0819c93b9ff | [] | no_license | pnandak/eRNA_project | c061a00f25c05289d3a7846598025cc9275cc28a | 7ca1f033050444e777a7b135cdcc0d51d3ab7d28 | refs/heads/master | 2020-04-03T13:47:49.910471 | 2014-03-12T10:08:57 | 2014-03-12T10:08:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,757 | r | windowplots.tmp.R | setwd('~/Harnett/TSS_CAGE_myfolder/')
rm(list=ls())
source('src/tss_cage_functions.R')
library(ggplot2)
#Load ROCR library for roc plots
library(ROCR,lib.loc='~/Harnett/R')
#define the number of rpgc and pvalue thresholds we'll try
rpgc.step.num<-100
pval.step.num<-20
#load the window table as 'z'
load('windowtable.r... |
fe7b1533ca9f8a57f72f959c306146a954b41ef5 | 3224a5f179537503f9c7244ac5c09837aa3eb2cc | /man/minnaert.Rd | b2476a9bcd51728950afef4080bcdbfd6a4e92b0 | [] | no_license | cran/landsat | b9510fc49610a5124691c3b2f223f590a7c563ec | b05d50fe6995d1ee05895fe4dbb5a28facff2c4c | refs/heads/master | 2023-08-31T07:33:21.957052 | 2023-08-24T22:20:06 | 2023-08-24T23:30:43 | 17,696,975 | 9 | 4 | null | null | null | null | UTF-8 | R | false | false | 2,786 | rd | minnaert.Rd | \name{minnaert}
\alias{minnaert}
\title{
Whole-image and pixel-based Minnaert topographic correction of remote sensing data.
}
\description{
Adds several modified Minnaert corrections to the capabilities of topocorr().
}
\usage{
minnaert(x, slope, aspect, sunelev, sunazimuth, na.value = NA, GRASS.aspect=FALSE,
IL.e... |
b1bb28cd0e63480b1dd39e27a8bc4634482815d4 | 11b2940a026c615b307452a7106bdc9f7866868e | /R/plot.R | f8a9e0e4a0c618eee72f827640ac54551c0b1e66 | [
"MIT"
] | permissive | sahirbhatnagar/ggmix | 6cc770baf21d5711961ac65f0b76338dc4a18325 | eb1c8a71ddc8f7e450dfbab68e7c562eac0ed487 | refs/heads/master | 2021-06-04T04:57:24.322156 | 2021-04-14T00:13:54 | 2021-04-14T00:13:54 | 90,279,895 | 12 | 12 | NOASSERTION | 2021-04-14T00:13:55 | 2017-05-04T15:32:36 | HTML | UTF-8 | R | false | false | 9,087 | r | plot.R | #' Plot the Generalised Information Criteria curve produced by \code{gic}
#'
#' @description Plots the Generalised Information Criteria curve, as a function
#' of the lambda values used
#' @param x fitted linear mixed model object of class \code{ggmix_gic} from the
#' \code{\link{gic}} function
#' @param sign.lambd... |
53c6cd5c86c90234761471213e8d06556985d594 | 032a64be429d21fa4bbd04bfae163d7236122406 | /Analyzing Baseball Data With R/Book - First Edition/Chapter 2 - Introduction to R/ch2_exercise5.R | dcbc450e333c722319c5d6ff6ab4e500e087d5f0 | [] | no_license | connormoffatt/Baseball | 5be37b7e4af62c14d5d25f5a86a9bbd589691d6f | 83a414091dc1be322e076df087cbb725e1ff6a19 | refs/heads/master | 2020-04-12T03:28:42.937403 | 2019-01-06T09:23:10 | 2019-01-06T09:23:10 | 161,848,190 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,743 | r | ch2_exercise5.R | rm(list=ls())
# (a)
# Read the Lahman "pitching.csv" data file into R into a dataframe Pitching
chapter_path = "C:/Users/conno/Documents/GitHub/Baseball/Analyzing Baseball Data With R/Book - First Edition/Chapter 2 - Introduction to R"
setwd(chapter_path)
Pitching <- read.csv("pitching.csv")
# (b)
# The following fun... |
826a76d566c609e97a063151be68d77b818f2e42 | ff9118e3811dca7f1dd06007a5c96c32d73a68b9 | /test2.R | 0b984aea18141a55f1c89d62c6787fb84f504574 | [] | no_license | JINSUKJEONG/rstudio-p | 9b3decc9b47b6eb549c5af5c485151f0041787f1 | 7da2f32f6ea3708702eeafb0ccc150a6d189a550 | refs/heads/master | 2023-07-02T10:28:42.302228 | 2021-08-12T13:00:44 | 2021-08-12T13:00:44 | 394,989,349 | 0 | 0 | null | null | null | null | UHC | R | false | false | 317 | r | test2.R | print("hello")
# about probability
# https://statkclee.github.io/r-algorithm/r-probability-exercise.html
# pnorm function. calculation of probability
pnorm(1.9, mean=1.7, sd = 0.1)
1-pnorm(1.6, mean = 1.7, sd=0.1)
# 난수 함수 rnorm
# 확률밀도함수 dnorm
# 누적분포함수 pnorm
# 분위수함수 qnorm |
c00ce6fcd6b544978bb135e261c0073a28988470 | ecf284f4e4ab63fb7b4949e31ed698dfdb1cbad1 | /R/plan.R | bcb09b36aa2a3ab217e8660ed2276d0502fbc57e | [] | no_license | jasonvii/perpignan | fe96267f3de0ecb0494c249d6baed15bc9975ad4 | b0b91b5894685a922bf76b192af396931dede97d | refs/heads/master | 2020-04-25T10:07:45.178537 | 2019-02-26T11:48:01 | 2019-02-26T11:48:01 | 172,698,563 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 566 | r | plan.R | plan <- drake::drake_plan(
data = read.csv('data/gapminder-FiveYearData.csv', stringsAsFactors = FALSE),
data_1982 = data[data$year == 1982, ],
model = lme4::lmer(log(lifeExp) ~ log(gdpPercap) + (1 + log(gdpPercap) | year), data = data),
out = coef(model),
folder = dir.create('outp... |
04d0e55bb344f2d3c7117dd2c76b20d12a46e2d0 | afdab8f2dbb6d68e0c4bf475efb5b1aed4b9dca3 | /fs.boundary_full.R | 76ca340bf0c49cc7fb5c8e5e685521ab63ed6d20 | [] | no_license | EdDonlon/Geospatial-Functional-Data-Analysis | 1404d66ad5fa76eca8c34847ae2970bec4134783 | a6701acf504c7ede13b81c19c41938ceac50682e | refs/heads/master | 2021-09-21T00:57:45.209964 | 2018-08-18T09:48:26 | 2018-08-18T09:48:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,820 | r | fs.boundary_full.R | fs.boundary_full<- function (r0 = 0.1, r = 0.5, l = 3, n.theta = 20)
{
rr <- r + (r - r0)
theta <- seq(pi, pi/2, length = n.theta)
x <- rr * cos(theta)
y <- rr * sin(theta)
theta <- seq(pi/2, -pi/2, length = 2 * n.theta)
x <- c(x, (r - r0) * cos(theta) + l)
y <- c(y, (r - r0) * sin(theta) + r)
theta <-... |
3daabdcb6758cc5ed16b1ae791d30284649dac4c | f741757039a32ef1d02253e8efdb7f5e32360f6f | /R/RcppExports.R | 0503d7b0647c5c623a5ab5b664c8e1478e40d2d9 | [] | no_license | dkahle/bayesRates | c3c29649e3dd963ddbf166978de568a57ecdeae9 | 7a0ede7bec8447d600cbbc4d7f1c437175318861 | refs/heads/master | 2020-08-04T23:25:17.597835 | 2019-02-02T02:38:18 | 2019-02-02T02:38:18 | 29,612,284 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 687 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
sampleAlphaPoissonCpp <- function(t, a1, b1, a2, b2, a, b, pi0, pi1, c) {
.Call('_bayesRates_sampleAlphaPoissonCpp', PACKAGE = 'bayesRates', t, a1, b1, a2, b2, a, b, pi0, pi1, c)
}
sampleP... |
f393abfa222c3d9fe8dbf71945f6a759fd576e33 | ad6fd375c9f30c81728aca17c7ccd3be2912d098 | /code/doublefinder.R | 59d182056cbc39be6f1044f50083b16dfecbb58a | [] | no_license | zerostwo/scRNA_tutorial | 6447bd47b1adb4ce9e745e2020c24a627557db37 | 06046da6610194a25e759760a0a2f10638a52350 | refs/heads/main | 2023-06-06T08:07:45.236151 | 2021-06-28T06:09:42 | 2021-06-28T06:09:42 | 380,460,029 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,887 | r | doublefinder.R | #### Information ----
# Title : Double cells prediction
# File : doublefinder.R
# Author : Songqi Duan
# Contact : songqi.duan@outlook.com
# License : Copyright (C) by Songqi Duan
# Created : 2021/06/24 16:06:40
# Updated : none
#### 加载包 ----
library(DoubletFinder)
library(tidyverse)
library(Seurat... |
b089892368d6da2d1f47547aa23160c690b9f5e3 | 23808da268a9b1117119c8a4e1e0eb42550ca057 | /man/multiplot.Rd | 9acf29d84a670187fde57dcaa5e825d5b1206128 | [] | no_license | mdlincoln/multiplot | 721f2e94cf220916bbf8abc3d06d42a15ccaf21c | ce1d38f528141c2e351f7589e20c629e0538d66d | refs/heads/master | 2020-06-05T05:07:20.727611 | 2015-04-29T15:16:26 | 2015-04-29T15:16:26 | 34,801,299 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 861 | rd | multiplot.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/multiplot.R
\name{multiplot}
\alias{multiplot}
\title{Multiple Plots}
\usage{
multiplot(..., plotlist = NULL, file, cols = 1, layout = NULL)
}
\arguments{
\item{...}{ggplot objects can be passed in}
\item{plotlist}{As a list of ggplo... |
0244149a2d8546b5115864ec23e5ab801b25ea94 | d7f550a841d2a846f5470d5f5dd591c98c2b155e | /Lesson 5-2.R | fef0b5898adfb67e82b7cb6569c7f25804eb75cd | [] | no_license | stevezxyu/R-Language | e8effcc411ae24b3819c75bce4e3706fa030b87b | e8557b452ddaecf71f0b07db4aaed06cb3a6f8f1 | refs/heads/master | 2021-09-23T12:15:38.460386 | 2017-12-01T13:56:30 | 2017-12-01T13:56:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 803 | r | Lesson 5-2.R | #在一個畫布上畫多個圖形, 使用 gridExtra 套件來幫忙
install.packages("gridExtra")
library(gridExtra)
gg1 <- ggplot(cars, aes(x = speed, y = dist)) + geom_point()
gg2 <- ggplot(iris, aes(x = Petal.Length, y = Petal.Width, colour = Species)) + geom_point()
grid.arrange(gg1, gg2, nrow = 2)
gg1 <- ggplot(mpg, aes(x= class))
gg1 ... |
fc7717e5b121e40d4d05e317bdc62005073bd4c3 | ea2e37abc55ab78978a96b8d7ef96ef1c8f2cd97 | /R/BasinData.R | ea4c3d161015b062a1b437babd839908d70e97b1 | [] | no_license | kongdd/airGR | d2559ffaaaf500ef79dcf23318f06061abdeab23 | a72a592cc54bef69e5969a4aca27eec2d950cf56 | refs/heads/master | 2020-07-11T04:09:05.445644 | 2018-10-10T09:20:03 | 2018-10-10T09:20:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,673 | r | BasinData.R | #' @name BasinInfo
#' @docType data
#' @title Data sample: characteristics of a fictional catchment (L0123001, L0123002 or L0123003)
#' @description
#' R-object containing the code, station's name, area and hypsometric curve of the catchment.
#' @encoding UTF-8
#' @format
#' List named 'BasinInfo' containing
#'... |
6cf16084217902c73607df770eeb34c37a4fb612 | 01952802d02506e5741019650079870fc70da227 | /plot3.R | dbf8230f6de486a6d83e23988256b4193d3e4d0c | [] | no_license | Nadhir10/ExData_Plotting1 | dff1eec8e3f7483d142799ffb38bb9dcc188b59f | e2c0e5bf1b03005b476b99733e0dcba1d3088ae3 | refs/heads/master | 2021-01-21T10:52:48.219077 | 2017-05-19T10:19:56 | 2017-05-19T10:19:56 | 91,711,750 | 0 | 0 | null | 2017-05-18T15:50:18 | 2017-05-18T15:50:17 | null | UTF-8 | R | false | false | 930 | r | plot3.R | ## Exploratory Data Analysis
## Week 1 : peer review assignment
## Plot 3
## read and label data
## the text file should be in your working space
x<-read.table("household_power_consumption.txt",sep=";" ,skip=66637, nrows=2880, na.strings="?")
label<-read.table("household_power_consumption.txt",header= TRUE, sep=";", ... |
52bfc16d719913ad99a0b13dee03cb4dba208496 | 33a7b35ba0b5f86f7bd64b06155784c31e47b2c5 | /rmd2md.R | be298f882a211489807ff81020ba7596ae0fb69d | [] | no_license | bici-sancta/dincerti.github.io | 02900dc552a478d8676bacb3fdea2efd913aa4f9 | 5059614400d49154628fbc06ae7abbb8fbbb8e86 | refs/heads/master | 2020-04-08T14:34:31.183660 | 2018-11-18T18:14:58 | 2018-11-18T18:14:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 616 | r | rmd2md.R | library("knitr")
opts_chunk$set(fig.path = "figs/", fig.width = 8, fig.height = 5, fig.align = 'center')
opts_knit$set(base.url = "/", root.dir = getwd())
knit("_rmd-posts/twopart.Rmd", "_posts/2015-09-11-twopart.md")
knit("_rmd-posts/markov_cohort.Rmd", "_posts/2015-10-15-markov_cohort.md")
knit("_rmd-posts/bayesian_m... |
3432a667f720bf5f33760ac5aba9866e5442fe70 | b44a40ea5eb1ff8b88c7ce630cb7cfa81b109c77 | /2020-10-08 Error handling in R/compute_change_table.R | 3bc9b5d2dff1b5d150f23389c9651e13da432caf | [] | no_license | DataS-DHSC/coffee-and-coding | 929c5f5be0163c3d5608b4a17e4d80ddb357ebb2 | 3ce5961b67210c71522488d62f404dac1c27f097 | refs/heads/master | 2023-07-10T10:23:29.394206 | 2023-06-29T12:06:18 | 2023-06-29T12:06:18 | 244,601,849 | 18 | 8 | null | 2023-06-29T12:06:20 | 2020-03-03T10:03:42 | HTML | UTF-8 | R | false | false | 428 | r | compute_change_table.R | compute_change_table <- function(input_data, group_col, time_col, comparison_col, start_time) {
input_data$time_marker <- ifelse(input_data[,time_col] == start_time, -1, 1)
input_data$comparison_marker <- input_data[,comparison_col]
input_data$group_marker <- input_data[,group_col]
return(input_data %>%
... |
6b175c1f5d4bbbf326a6411154cf86035246d9cf | 6ffe510908d9f7df2357e2fd6dc692df70233afb | /src/ve_init.r | 4b1202ad9742d984a0096dfb97022e369c378bd4 | [] | no_license | willbutler42/VertEgg-R | ba2282ccaec2ea222f85330da56e166756d839ac | 53a5204eb66c45cb78064e91cc3b7d116df0bf5b | refs/heads/master | 2020-03-28T00:20:39.522476 | 2020-01-16T20:54:26 | 2020-01-16T20:54:26 | 147,400,584 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,098 | r | ve_init.r | ####################################################################################
#################### Runner for VertEgg R package ##################################
####################################################################################
##############################################
## R scripts... |
fcbcced956178ab9f49543bdbeb5605289a77d84 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/OptimalTiming/examples/SimCml.Rd.R | 5bc1187d09d71e3612d1b3175d82ba8fce1b680b | [] | 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 | 139 | r | SimCml.Rd.R | library(OptimalTiming)
### Name: SimCml
### Title: Simulated data for CML patients
### Aliases: SimCml
### ** Examples
data(SimCml)
|
4f3ab79dd1bb6533900c217f972d7a1f80e45f12 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Renvlp/examples/testcoef.genv.Rd.R | aad2b182d9ed3c79d7939459e6bb28460a9fbfa8 | [] | 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 | 447 | r | testcoef.genv.Rd.R | library(Renvlp)
### Name: testcoef.genv
### Title: Hypothesis test of the coefficients of the groupwise envelope
### model
### Aliases: testcoef.genv
### ** Examples
data(fiberpaper)
X <- fiberpaper[ , c(5, 7)]
Y <- fiberpaper[ , 1:3]
Z <- as.numeric(fiberpaper[ , 6] > mean(fiberpaper[ , 6]))
u <- u.genv(X, Y, Z)... |
8c1dd24478dd9334f19ab7f89528e55ab16ce926 | 51b40311e652db301aedf34723ee848203764503 | /tfTarget/man/searchTFBS.Rd | 9c0dfe3926256005d5c484769988f89561440324 | [] | no_license | Danko-Lab/tfTarget | 5298fb32e852863fa5940014dd5a7a5e9dea6e25 | e4b1994a1c8fce89791f4c8a5776721a09ceaa27 | refs/heads/master | 2022-09-12T01:07:02.458145 | 2022-08-27T15:29:13 | 2022-08-27T15:29:13 | 135,333,078 | 6 | 2 | null | 2020-01-17T03:31:50 | 2018-05-29T17:50:52 | R | UTF-8 | R | false | false | 1,248 | rd | searchTFBS.Rd | \name{searchTFBS}
\alias{searchTFBS}
\title{
Search TFBS
}
\description{
Search TFBS
}
\usage{
searchTFBS(tfTar,
tfs,
file.twoBit,
pval.cutoff.up = 0.01,
pval.cutoff.down = 0.1,
half.size = 150,
mTH = 7,
min.size = 150,
run.repeats = 2,
ncores = 1)
}
\arg... |
57bb2ed13e95a61d66c91942884f61fce4c1d89d | cf7cf9948dc3021d7ee287110f50d1abf470bcf0 | /DAMM model script.R | 1c92b79dfbb16b4623461c27b7980086c0a81678 | [] | no_license | colinaverill/DAMM-model | 2819913143eca0b28e93a46ec710bad654024d40 | fab42d60144ba998a256d327416648f7b824ec35 | refs/heads/master | 2020-05-07T08:59:23.825746 | 2015-02-25T19:09:14 | 2015-02-25T19:09:14 | 31,327,093 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,445 | r | DAMM model script.R | ##coding the DAMM model###
##Davidson et al. 2012. Global Change Biology. 18: 371-384.
#Vmax parameters
#a.Sx, Ea and Km.S have been calibrated to the field data presented in Davidson et al. 2012.
R <- 0.008314472 #universal gas constant kJ / K / mol
a.Sx <- 5.38*10^10 #pre-exponential factor of Vmax, mg C / cm3 /... |
815430a6dfd2da481e0453ec8199c11f3bfa28d7 | 996ade89e9fc1a460fa1ec90f24f548c14d1d241 | /tests/testthat/test_head2tailratio.R | 178efdc3add53d45e425defcc6ff5fa357cfc647 | [
"MIT"
] | permissive | anspiess/PCRedux | 323788e8da02781363a2c6414ec81a660e1bf645 | 9472df4f9d2f5284aa995e5167abb44d7e7a287a | refs/heads/master | 2021-08-22T21:18:28.342931 | 2017-12-01T09:34:16 | 2017-12-01T09:34:16 | 112,632,885 | 0 | 0 | null | 2017-11-30T16:17:38 | 2017-11-30T16:17:37 | null | UTF-8 | R | false | false | 505 | r | test_head2tailratio.R | library(PCRedux)
context("head2tailratio")
test_that("head2tailratio gives the correct dimensions and properties", {
library(qpcR)
res <- head2tailratio(y=competimer[, 2], normalize=FALSE, slope_normalizer=TRUE)
res_normalized <- head2tailratio(y=competimer[, 2], normalize=TRUE, slope_normalizer=TRUE)
... |
ed343d3ee40f911bfe8bd5cf6ba44d1327400538 | d38ab28cf6ee680b5a82f37e7841d31617750da4 | /Examples/ImageIOSelection/ImageIOSelection.R | ada69ddeec48cbcce7b3179de94046d9106c6351 | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | SimpleITK/SimpleITK | cdd9f417acc7f7fe20b006a75dc483d6bb6d9b20 | cfb40ba1149ba9f186793ccdd206f7179c8ba7a3 | refs/heads/master | 2023-09-01T15:01:04.024343 | 2023-08-31T19:09:36 | 2023-08-31T19:09:36 | 1,069,177 | 764 | 216 | Apache-2.0 | 2023-09-13T17:48:23 | 2010-11-10T18:56:04 | SWIG | UTF-8 | R | false | false | 1,804 | r | ImageIOSelection.R | #=========================================================================
#
# Copyright NumFOCUS
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/license... |
2cc49a31ce136593f012b35d16987876f812e2ab | 46b8efea7116a3808a2009faad5ed5b90238ec04 | /R/stylesim-package.r | 4a2e85bdd60165277a102c92b275fbccc5f6407c | [] | no_license | erge324/stylesim | 205e49855323280f16d3c2dc8f604f3827ee1719 | bd2ee33771ac5c7c480e34e71231f5504a1e488c | refs/heads/master | 2022-02-25T08:20:50.295444 | 2016-03-04T10:57:00 | 2016-03-04T10:57:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 632 | r | stylesim-package.r | #' Simulate (and Analyse) Data Distorted by Response Styles
#'
#' The most important function is \code{\link{sim_style_data}}, which allows to
#' simulate data distorted by response styles.
#'
#' @references
#' Plieninger, H. (in press). Mountain or molehill: A simulation study on the impact of reponse styles. \emph{... |
51f7882103429f4de6dfa434876aae8ab01661af | 701192bbbb772d4448ce469a398ebb7f50047ca1 | /ISM.Recon_Diagnostics.R | efff87e9de2c2ffcd13c419d2fcb025b0c05569b | [] | no_license | ecgill/paleoclimate_reconstructor | ccda80323ae9fac243d7d74586da4ee7ef9ea10d | 5d39bdcabff9cfe666d5cf1f36ea1d929434fb2e | refs/heads/master | 2021-05-06T06:46:21.527754 | 2018-05-14T02:20:44 | 2018-05-14T02:20:44 | 113,892,855 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,331 | r | ISM.Recon_Diagnostics.R | rm(list=ls())
library(RColorBrewer)
library(fields)
myPalette1 <- colorRampPalette(rev(brewer.pal(9, "RdBu")), space="Lab")
myPalette2 <- colorRampPalette(rev(brewer.pal(9, "Greens")), space="Lab")
myPalette3 <- colorRampPalette(rev(brewer.pal(9, "YlOrRd")), space="Lab")
myPalette4 <- colorRampPalette(rev(brewer.pal(... |
430e7149bbeb4c9eb36ed010c43e34146901b63a | 7114d68d53dd95e1ee2a96052ccd395da024b52e | /r/man/lift.Rd | 5ae5c4cf8c4ba553c4ca1f6d54f91dd7eb6fa72c | [
"Apache-2.0"
] | permissive | shishehchi/cdh-datascientist-tools | b813af52f3aa34f855a9726a3082c0125cbfcd7f | 129cbab2179f43c077c86a880d4c809a542fbd46 | refs/heads/master | 2023-08-23T22:19:24.009605 | 2021-11-15T21:36:41 | 2021-11-15T21:36:41 | 225,455,891 | 0 | 0 | Apache-2.0 | 2021-11-15T21:38:23 | 2019-12-02T19:49:06 | Jupyter Notebook | UTF-8 | R | false | true | 606 | rd | lift.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cdh_utils.R
\name{lift}
\alias{lift}
\title{Calculates lift from counts of positives and negatives.}
\usage{
lift(pos, neg)
}
\arguments{
\item{pos}{Vector with counts of the positive responses}
\item{neg}{Vector with counts of the negative ... |
19afafb63c00496c59cc0d66f8484c3e420f6848 | 50df53284f5b28bd2a7462ba3f7de82b0a4009e5 | /man/libproj_version.Rd | 90786a2190575aaac743a2377376346c4b4cbdba | [
"MIT"
] | permissive | minghao2016/libproj | 335ef8bf5abc60a04f29269567cf6c5d421265d6 | 45d70fb2fdc0ef21c25c5d5fe47afd9320e45d2a | refs/heads/master | 2022-12-06T17:41:25.139257 | 2020-08-26T15:38:54 | 2020-08-26T15:38:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,370 | rd | libproj_version.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/libproj-package.R
\name{libproj_version}
\alias{libproj_version}
\alias{libproj_has_libtiff}
\alias{libproj_has_libcurl}
\alias{libproj_temp_dir}
\alias{with_libproj_configuration}
\alias{libproj_configuration}
\alias{libproj_configure}
\titl... |
1fd5feb9faa7c94ad5a6f65e2b44d63817cfef57 | ddb833152a50f7c070e3da8842dca4b11c1a9c16 | /R/control.rds.estimates.R | a57217f4121e19b7b53ba1877dd32cd5f3c4db08 | [] | no_license | Edouard-Legoupil/RDS | 31f48a91d81254cda645b55ebf1b63a0ea87f4dc | 9fc932ab45c76fc2232e2cd91e9839592a66ed7a | refs/heads/master | 2023-02-23T17:43:12.623395 | 2021-01-26T20:25:40 | 2021-01-26T20:25:40 | 312,030,910 | 0 | 1 | null | 2021-01-26T20:18:01 | 2020-11-11T16:44:41 | R | UTF-8 | R | false | false | 4,930 | r | control.rds.estimates.R | utils::globalVariables(c(".control.rds.estimates"))
#' Auxiliary for Controlling RDS.bootstrap.intervals
#'
#' Auxiliary function as user interface for fine-tuning RDS.bootstrap.intervals algorithm,
#' which computes interval estimates for via bootstrapping.
#'
#' This function is only used within a call to the \code{\... |
e8e69748ea1b0c72bae7a658860a122ddfd11fe1 | 7eab7f927bf12c6b9cb1bb4ab72fd4d5db63c0dc | /lesson2/RE-1.R | 7458438039b3e1b470e784dc750465f7e2139712 | [] | no_license | elect000/p-recog | 63a804f820c573bf6e9548b2f884d7098005abcb | 61b790682a2355e471345f63b3a55b615101f0a7 | refs/heads/master | 2020-03-31T23:59:08.847289 | 2018-12-18T19:06:49 | 2018-12-18T19:06:49 | 152,677,012 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,904 | r | RE-1.R | # R 3.5.1 で実行確認
# import libraries
library(nnet)
library(MASS)
# レポート課題1.1
# 1)
# 隠れ素子の数を一つづつ増やし、10回の学習で10回とも正しく識別出来るようになった隠れ素子の数を求めなさい。
# 2)
# 隠れ素子の数によって誤識別率の平均がどのように変化するのかをグラフで示しなさい。
# 3)
# 隠れ素子が1個の場合に得られた学習結果について、結合係数の大きさの分布を示しなさい。
# 4)
# 10回とも正しく識別できた場合の学習結果について、結合係数の大きさの分布を示し、隠れ素子が1個の場合と比較検討しなさい。
h... |
24e5f2099008c6d5518fd836ee632637adf38513 | 92d54f598099f13f7150d8a6fbf39d14e7371ff4 | /R/dbDataType_PqConnection.R | 1d9de51c90c4d1d830d45ff695d2cd14b2ee22b2 | [
"MIT"
] | permissive | r-dbi/RPostgres | 3c44d9eabe682e866411b44095a4671cbad275af | 58a052b20f046c95723c332a0bb06fdb9ed362c4 | refs/heads/main | 2023-08-18T09:48:04.523198 | 2023-07-11T02:17:42 | 2023-07-11T02:17:42 | 28,823,976 | 230 | 66 | NOASSERTION | 2023-08-31T08:20:25 | 2015-01-05T17:43:02 | R | UTF-8 | R | false | false | 342 | r | dbDataType_PqConnection.R | # dbSendQuery()
# dbSendStatement()
# dbDataType()
#' @rdname dbDataType
#' @usage NULL
dbDataType_PqConnection <- function(dbObj, obj, ...) {
if (is.data.frame(obj)) return(vapply(obj, dbDataType, "", dbObj = dbObj))
get_data_type(obj)
}
#' @rdname dbDataType
#' @export
setMethod("dbDataType", "PqConnection", dbD... |
66ab83a2c912c191e93abdd3f62551f6c2c38ebe | 0762056267ee05a56d6c45ecc6605430e0d4209a | /plot1.R | 9c44ba8dbb60f5d38dbac306324f63b2a1258492 | [] | no_license | amrutaghare/Coursera-Exploratory-Data-Analysis-Week-1-Assignment | 15a6779104a42e5cd542e0dc3af0e09d0769df52 | 13d9a67ed4221c3b87dda6e5577619c70773bbff | refs/heads/master | 2021-04-26T23:12:25.587692 | 2018-03-05T16:39:07 | 2018-03-05T16:39:07 | 123,947,574 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 659 | r | plot1.R | ##Setting directory##
setwd("C:/Users/amrutag542/Documents/Study Material/Coursera/Exploratory Data Analysis/Week 1/Assignment 1")
##Reading data##
data<-read.csv("household_power_consumption.txt", header=T, sep=';', na.strings="?", stringsAsFactors=F,dec=".")
##Subsetting data##
data1 <-subset(data, Date %in% c("... |
4c585b2769a09ef48cda90b6a45db78b97ed05c4 | 5a9fd2f7bf916a36f4accf37ea4dadea0577fd9b | /01_readData_2020.R | 19cf6b95fd015a1f8d69d15f0785674b3ffa46bd | [] | no_license | hnagaty/airbnb | fdebd73be2eead36aba915bc24659f320a8053e3 | bf1c7209a97a07247f36a5d17a6e084dadec992f | refs/heads/main | 2023-06-04T23:49:45.877888 | 2021-06-16T08:07:43 | 2021-06-16T08:07:43 | 377,419,633 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,758 | r | 01_readData_2020.R | # a redo of reading & basic data munging
# a redo on Jul-2020, during the COVID-19 lockdown
# this is basic exploration of files
# the actual reading is included in the script 03b_features.R
# this file is superceded by 01b_readData_2020.R
library(tidyverse)
library(anytime)
dataPath <- "/home/hnagaty/dataNAS/airbn... |
65edccb37cabe8534fc131856df6cbb303e2f28c | b65269b268c9c672ce750ffcbbd578eb2ba09d70 | /man/no_tab_linter.Rd | fd87c6e35923f427035b2387abef1a658995bfea | [
"MIT"
] | permissive | dpprdan/lintr | 8b2d2f92de81d033d23d713ea9497e1ce9aa82ea | 67da292a1c4c81368aa1f0e82b1013f5ae86df4d | refs/heads/master | 2023-01-22T17:55:23.234948 | 2023-01-17T16:33:53 | 2023-01-17T16:33:53 | 242,086,839 | 0 | 0 | NOASSERTION | 2020-02-21T08:09:52 | 2020-02-21T08:09:51 | null | UTF-8 | R | false | true | 858 | rd | no_tab_linter.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/no_tab_linter.R
\name{no_tab_linter}
\alias{no_tab_linter}
\title{No tab linter}
\usage{
no_tab_linter()
}
\description{
Check that only spaces are used for indentation, not tabs. Much ink has been
spilled on this topic, and we encourage you ... |
5fb929d7c49870c361c705b7ade8fee41335b46d | a76e6b446f784d30e8e0eb761b816d92cf056934 | /man/gmedian.Rd | 5572d60c23b572195c6b981341c06944f4b5895f | [] | no_license | tilltnet/ratingScaleSummary | 55879033b905cc5370f10d82cb44e040eb94c680 | c792c882eec2cef0cd0e8983518cf5f48bde6085 | refs/heads/master | 2021-01-11T19:48:45.545975 | 2017-01-19T22:59:24 | 2017-01-19T22:59:24 | 79,402,288 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,368 | rd | gmedian.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gmedian.r
\name{gmedian}
\alias{gmedian}
\alias{gmedian.factor}
\alias{gmedian.numeric}
\title{Estimates Median of a grouped frequency distribution.}
\usage{
gmedian(x, percentile = 0.5, scale_interval = 1, w = NULL)
\method{gmedian}{factor}... |
89951181d5cdb353b99dedf852a5aecbeab63dd3 | 10867272feb49d6de65b0ff8ee29c07994268e2a | /Q14.R | fdb231b41bf04d0bf1904747517fb5ff4bcd4807 | [] | no_license | umairhanif00/R-Assignment-2 | 8d47e93176c5187c43857741bb3ed3ec793efd5a | 36d78223433f50c57fb2724270144e4ee307c3d5 | refs/heads/master | 2021-01-23T04:39:44.261903 | 2017-03-27T11:33:47 | 2017-03-27T11:33:47 | 86,233,545 | 0 | 0 | null | 2017-03-26T13:12:26 | 2017-03-26T13:12:26 | null | UTF-8 | R | false | false | 162 | r | Q14.R | #Question 14
dataf$TotalCharges<-as.numeric(as.character(dataf$TotalCharges))
sum_of_charges <- sum(dataf$TotalCharges, na.rm = TRUE)
print(sum_of_charges)
|
cc7c5f5387cbe0c8ff06220d1f8eff3c5d1136e6 | b8a6b9459a67085abe2b68dd90b97703eabe1a1f | /man/apply_combinations.Rd | 7f6335effabac0b89dde6a84f7c4170ed725eba6 | [] | no_license | robjohnnoble/demonanalysis | 5fd33509bcaa0f7c31ea57a90ffffb5070511a15 | 8ed218bf0b4162f85922a2c398e39dce8de7f90f | refs/heads/master | 2022-11-10T11:48:20.158836 | 2020-06-29T15:12:50 | 2020-06-29T15:12:50 | 126,359,266 | 3 | 1 | null | null | null | null | UTF-8 | R | false | true | 625 | rd | apply_combinations.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/demonanalysis.R
\name{apply_combinations}
\alias{apply_combinations}
\title{Apply a function to every combination of some sequences}
\usage{
apply_combinations(vec, fn, ...)
}
\arguments{
\item{vec}{vector of final values of the sequences (in... |
f02fced37b3ef6ea9021035ba3af6fcfc211c08f | f5b0206714a952321940c5f78378ebe095d1c5a6 | /1-Analysis-of-Missouri-Sex-Offender-Registry-Data/Missouri-Sex-Offenders.R | a6bd9019f29f461010e586d0516e168d4d07c436 | [] | no_license | EarlGlynn/MO-offenders-near-daycares | 6175062b05b8a145fd39471272fb8335cc533963 | 546ad402fcff07042124d775c8a768ca4e0a0e1f | refs/heads/master | 2021-01-21T22:26:49.099685 | 2015-01-03T19:58:34 | 2015-01-03T19:58:34 | 28,752,306 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,927 | r | Missouri-Sex-Offenders.R | # Process Missouri Sex Offender Registry (MSOR) data transferred from bottom of page
# http://www.mshp.dps.mo.gov/MSHPWeb/PatrolDivisions/CRID/SOR/SORPage.html and
# prepare for geocoding the list.
#
# Each line in the Excel file is an offense committed by an offender. There are
# multiple lines of offenses for some o... |
fa63ece3e953d6ce8f8ded3027930cb97ead4b92 | 63704a6472534c2f1be52f998f6640d0f85f9e77 | /man/sumStatsPar.Rd | 164f4fcd3e353acb9fdeafe695fa5b0f28c349be | [] | no_license | annavesely/sumSome | ebc9f1685918f62cddae78eaaf6502cdbcee4feb | f11ef3b60890c46ae92617d3d64ce0c0c6b875b6 | refs/heads/master | 2023-06-08T06:39:00.733593 | 2023-06-01T10:14:03 | 2023-06-01T10:14:03 | 324,800,427 | 0 | 1 | null | 2022-08-18T13:18:07 | 2020-12-27T16:19:16 | R | UTF-8 | R | false | true | 2,588 | rd | sumStatsPar.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sumStatsPar.R
\name{sumStatsPar}
\alias{sumStatsPar}
\title{True Discovery Guarantee for Generic Statistics - Parametric}
\usage{
sumStatsPar(g, S = NULL, alpha = 0.05, cvs)
}
\arguments{
\item{g}{numeric vector of statistics.}
\item{S}{vect... |
238f937da33915f5910c21cdb4808d306cc49987 | 2c1f7e1f84c5580c15b26ee3d1b87805e49c177c | /man/makeFoldsGLMcv.Rd | 3079c49b6e7e8922fdb5c5392cc165a615813525 | [] | no_license | cran/porridge | d3ad06287f6fafd4d9f4ec9cd42f1eb10bf40a9e | c29cdbf217c365866ae02800769bd5939657db07 | refs/heads/master | 2022-05-29T01:25:25.412977 | 2022-05-20T12:20:02 | 2022-05-20T12:20:02 | 218,986,466 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,360 | rd | makeFoldsGLMcv.Rd | \name{makeFoldsGLMcv}
\alias{makeFoldsGLMcv}
\title{
Generate folds for cross-validation of generalized linear models.
}
\description{
Function that evaluates the targeted ridge estimator of the regression parameter of generalized linear models.
}
\usage{
makeFoldsGLMcv(fold, Y, stratified=TRUE, model="linear")
}
\arg... |
a129043cd07764826de6995524c4199e6a0dcbdf | 6d045a743a3d49ab9377c1a3b16d9b62ebadf401 | /man/createLocalRepos.Rd | 8d6bba569f64b20dd5b440406f5df1cc171445ed | [] | no_license | rtaph/irutils | 6388c9cf48d10b2edb2223f180566012d08b3cbe | cb980c84c0ef8132d1d8e789e97634bd8f56af33 | refs/heads/master | 2021-01-21T08:29:16.551007 | 2013-01-14T13:07:55 | 2013-01-14T13:07:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 466 | rd | createLocalRepos.Rd | \name{createLocalRepos}
\alias{createLocalRepos}
\title{This package will download all packages for the given type to \code{local.repos}
from the given repository.}
\usage{
createLocalRepos(local.repos,
repos = "http://cran.r-project.org", type = "source")
}
\description{
This package will download all packages... |
9f4e857a359ac21d468bab5356f34831100ab9fd | fa05945647038b74b196b9cc8027576576d413e5 | /0604.R | fa407b580f802c9b2643654ee6b1e227ea234c1c | [] | no_license | saeheeeom/RforKoreanJsonData | 15ccacb35b93da0f25cb7319933dbb52b103cd44 | 02e1043ce7b990712b0d04bdb8bc8e72ebc0c67a | refs/heads/main | 2023-06-06T05:55:53.322141 | 2021-06-24T11:12:59 | 2021-06-24T11:12:59 | 379,896,353 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,732 | r | 0604.R | # 0604 수업
# anova test: 두 개 이상의 그룹을 비교할 때 많이 사용
# 연속적인 값 비교할 때 (이산적X)
# aov()안에 넣어서 결과 도출
library(tidyverse)
library(jsonlite)
gss_cat
aov(tvhours ~ race, data=gss_cat )#보고자 하는 결과변수(종속변수), 결과변수에 영향을 주는 설명변수(독립변수)
aov(tvhours ~ race, data=gss_cat) %>% summary() # f값이 크면 클수록 설명변수의 영향이 뚜렷하게 나타남
aov(tvhours ~ ... |
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