An R package implementing response time trimming methods
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README.md

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")

You can also install the latest developmental version of trimr. Please note, though, that this version is undergoing testing and potentially has unidentified bugs. (If you do use this version and note a bug, please log it as an issue). To install the developmental version, you will first need to install the devtools package and install trimr directly from GitHub by using the following commands:

# install devtools
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.