researchfunctions

The goal of researchfunctions is to allow me to keep all my functions for my project in one place.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("dakthomps00/researchfunctions")
#> Downloading GitHub repo dakthomps00/researchfunctions@HEAD
#>          checking for file 'C:\Users\Dakota\AppData\Local\Temp\Rtmpo3nZyL\remotes765c12565ba1\dakthomps00-researchfunctions-82ae48c/DESCRIPTION' ...  v  checking for file 'C:\Users\Dakota\AppData\Local\Temp\Rtmpo3nZyL\remotes765c12565ba1\dakthomps00-researchfunctions-82ae48c/DESCRIPTION'
#>       -  preparing 'researchfunctions':
#>    checking DESCRIPTION meta-information ...     checking DESCRIPTION meta-information ...   v  checking DESCRIPTION meta-information
#>       -  checking for LF line-endings in source and make files and shell scripts
#>   -  checking for empty or unneeded directories
#>       -  building 'researchfunctions_0.0.0.9000.tar.gz'
#>      
#> 

Example

This is a basic example which shows you how to solve a common problem:

Say we want to get confidence intervals of effect sizes for random normally distributed data sets we would do this

library(researchfunctions)

cohen <- replicate(10000, cohensd_OG(75,0,1,1))
mad <- replicate(10000, deltamad_OG(75,0,1,1))

confidint(cohen, 75)
#> $Low
#> [1] 0.9702434
#> 
#> $High
#> [1] 1.037725
confidint(mad,75)
#> $Low
#> [1] 0.9769869
#> 
#> $High
#> [1] 1.066084

This is another basic example which shows you how to solve a common problem:

Say we want to get confidence intervals of effect sizes for random normally distributed data sets that are contaminated with another normally distributed data set, we would do this

library(researchfunctions)

cohen <- replicate(10000, cohensd_A(75,0.8,0.2,0,1,1.5,1,1,0.5))
mad <- replicate(10000, deltamad_A(75,0.8,0.2,0,1,1.5,1,1,0.5))

cohen1 <- t(cohen)
mad1 <- t(mad)

confidint(cohen1[,1], 75)
#> $Low
#> [1] 1.081323
#> 
#> $High
#> [1] 1.121612
confidint(mad1[,1],75)
#> $Low
#> [1] 1.079024
#> 
#> $High
#> [1] 1.120095

This is another basic example which shows you how to solve a common problem:

Say we want to get confidence intervals of effect sizes for random normally distributed data sets that are contaminated with a uniform data set, we would do this

library(researchfunctions)

cohen <- replicate(10000, cohensd_B(75,0.8,0.2,0,1,1.5,1,1,2))
mad <- replicate(10000, deltamad_B(75,0.8,0.2,0,1,1.5,1,1,2))

cohen1 <- t(cohen)
mad1 <- t(mad)

confidint(cohen1[,1], 75)
#> $Low
#> [1] 1.129039
#> 
#> $High
#> [1] 1.16921
confidint(mad1[,1],75)
#> $Low
#> [1] 1.130294
#> 
#> $High
#> [1] 1.170551