This package contains a number of functions that serve two goals: first, make R more accessible to people migrating from SPSS by adding a number of functions that behave roughly like their SPSS equivalents; and second, make a number of slightly more advanced functions more user friendly to relatively novice users.
|License:||GPL (>= 2)|
The package contains a variety of functions designed to make life easier. These functions are geared towards researchers in psychology.
The package implements many solutions provided by people all over the world, most from Stack Exchange (both from Cross Validated and Stack Overflow). I credit these authors in the help pages of those functions and in the Author(s) section of this page. If you wrote a function included here, and you want me to take it out, feel free to contact me of course (also, see http://meta.stackoverflow.com/questions/319171/i-would-like-to-use-a-function-written-by-a-stack-overflow-member-in-an-r-packag).
Author: Gjalt-Jorn Peters (Open University of the Netherlands, Greater Good, and Maastricht University).
Contributors: Peter Verboon (
convert.omegasq.to.cohensf, Open University of the Netherlands), Amy Chan (
ggPie), Jeff Baggett (
posthocTGH, University of Wisconsin - La Crosse), Daniel McNeish (
scaleStructure, University of North Carolina), Nick Sabbe (
curfnfinder, Arteveldehogeschool), Douglas Bonett (
pwr.confIntR, UC Santa Cruz, United States), Murray Moinester (
pwr.confIntR, Tel Aviv University, Israel), Stefan Gruijters (
erDataSeq, Maastricht University).
A number of functions in this package use code fragments that were used without explicit communicating with the author (because I've been unable to find contact details of the authors, or because I haven't gotten around to contacting them yet). The authors of these fragments are John Fox (
car code in
ggqq), Floo0 (
ggqq), Jason Aizkalns (
ggBoxplot), Luke Tierney (in
pwr.cohensdCI, its alias
In addition, the function
escapeRegEx from package
Hmisc is included and used internally to avoid importing that entire package just for that function. This function was written by Charles Dupont (Department of Biostatistics, Vanderbilt University). The help page was also taken from that package.
Maintainer: Gjalt-Jorn Peters <firstname.lastname@example.org>
Peters, G.-J. Y. (2014). The alpha and the omega of scale reliability and validity: why and how to abandon Cronbach's alpha and the route towards more comprehensive assessment of scale quality. European Health Psychologist, 16(2), 56-69.
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### Create simple dataset dat <- PlantGrowth[1:20,]; ### Remove third level from group factor dat$group <- factor(dat$group); ### Examine normality normalityAssessment(dat$weight); ### Compute mean difference and show it meanDiff(dat$weight ~ dat$group, plot=TRUE); ### Show the t-test didacticPlot(meanDiff(dat$weight ~ dat$group)$t, statistic='t', df1=meanDiff(dat$weight ~ dat$group)$df); ### Load data from simulated dataset testRetestSimData (which ### satisfies essential tau-equivalence). data(testRetestSimData); ### Select some items in the first measurement exampleData <- testRetestSimData[2:6]; ## Not run: ### Show reliabilities scaleStructure(dat=exampleData, ci=FALSE, omega.psych=FALSE, poly=FALSE); ## End(Not run) ### Create a dichotomous variable exampleData$group <- cut(exampleData$t0_item2, 2); ### Show item distributions and means meansDiamondPlot(exampleData); ### Show a dlvPlot dlvPlot(exampleData, x="group", y="t0_item1"); ### show a dlvPlot with less participants, showing the confidence ### interval and standard error bars better dlvPlot(exampleData[1:30, ], x="group", y="t0_item1");
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