An Implementation of Common Response Time Trimming Methods

Provides various commonly-used response time trimming methods, including the recursive / moving-criterion methods reported by Van Selst and Jolicoeur (1994). By passing trimming functions raw data files, the package will return trimmed data ready for inferential testing.


trimr: Response Time Trimming in R

For a detailed overview of how to use trimr, please see the vignettes.

Installation

A stable release of trimr is available on CRAN. To install this, use:

install.packages("trimr")

To install the latest version of trimr (i.e., the development version of next release), install devtools, and install directly from GitHub by using:

install.packages("devtools")
 
# install trimr from GitHub
devools::install_github("JimGrange/trimr")

Overview

trimr is an R package that implements most commonly-used response time trimming methods, allowing the user to go from a raw data file to a finalised data file ready for inferential statistical analysis.

The trimming functions available in trimr fall broadly into three families:

  1. Absolute Value Criterion
  2. Standard Deviation Criterion
  3. Recursive / Moving Criterion

The latter implements the methods first suggsted by Van Selst & Jolicoeur (1994).

Example

In the example below, we go from a data frame containing data from 32 participants (in total, 20,518 trials) to a trimmed data set showing the mean trimmed RT for each experimental condition & participant using the modified recursive trimming procedure of Van Selst & Jolicoeur (1994):

# load trimr's library
library(trimr)
 
# load the example data that ships with trimr
data(exampleData)
 
# look at the top of the example raw data
head(exampleData)
#>   participant condition   rt accuracy
#> 1           1    Switch 1660        1
#> 2           1    Switch  913        1
#> 3           1    Repeat 2312        1
#> 4           1    Repeat  754        1
#> 5           1    Switch 3394        1
#> 6           1    Repeat  930        1
 
# perform the trimming
trimmedData <- modifiedRecursive(data = exampleData, minRT = 150, digits = 0)
 
# look at the trimmedData
trimmedData
#>    participant Switch Repeat
#> 1            1    792    691
#> 2            2   1036    927
#> 3            3    958    716
#> 4            4   1000    712
#> 5            5   1107    827
#> 6            6   1309   1049
#> 7            7    929    777
#> 8            8    976    865
#> 9            9    848    635
#> 10          10    735    619
#> 11          11   1008    900
#> 12          12    846    587
#> 13          13    823    688
#> 14          14    965    726
#> 15          15   1089    760
#> 16          16    845    645
#> 17          17    677    587
#> 18          18    845    718
#> 19          19    637    566
#> 20          20    934    671
#> 21          21    730    625
#> 22          22   1119    813
#> 23          23    752    627
#> 24          24    584    565
#> 25          25    576    581
#> 26          26    709    613
#> 27          27    729    688
#> 28          28    687    623
#> 29          29    528    536
#> 30          30    690    627
#> 31          31    921    859
#> 32          32    604    592

Installation Instructions

To install the package from GitHub, you need the devools package:

install.packages("devtools")
library(devtools)

Then trimr can be directly installed:

devtools::install_github("JimGrange/trimr")

References

Van Selst, M., & Jolicoeur, P. (1994). A solution to the effect of sample size on outlier elimination. Quarterly Journal of Experimental Psychology, 47 (A), 631–650.

News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("trimr")

1.0.1 by James Grange, 4 years ago


www.jimgrange.com


Browse source code at https://github.com/cran/trimr


Authors: James Grange [aut, cre]


Documentation:   PDF Manual  


GPL-3 license


Imports stats

Suggests knitr


See at CRAN