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irtpwr

This package offers implementation of analytical and sampling-basedpower analyses for the Wald, LR, score, and gradient tests in theframeworks of linear hypotheses and marginal maximum likelihoodestimation. The methods are described in our paper (Zimmer etal. (2022),https://doi.org/10.1007/s11336-022-09883-5).

Installation

You can install the CRAN version using:

install.packages("irtpwr")

Or, you can install the development version of irtpwr fromGitHub with:

# install.packages('devtools')devtools::install_github("flxzimmer/irtpwr",build_vignettes =TRUE)

Toy example

We can load the irtpwr package using:

library(irtpwr)library(mirt)#> Loading required package: stats4#> Loading required package: lattice

We want to know the power and the required sample size for a test ofthe Rasch vs 2PL model. We use the LSAT 7 data set which is included themirt package.

As a first step, we load the data set and fit a 2PL model.

dat<-expand.table(LSAT7)mirtfit<-mirt(dat,1,verbose =FALSE)

The 2PL parameters are then used as parameters for the alternativehypothesis in our hypothesis definition.

hyp<-setup.hypothesis(type ="1PLvs2PL",altpars = mirtfit)

We can now perform the power analysis. We want to determine thesample size necessary for a power of .8 while using an alpha level of.05.

res<-irtpwr(hyp = hyp,power =0.8,alpha =0.05)summary(res)#>#>  Sample sizes for power = 0.8 (alpha = 0.05):#>#>  Statistic    N#>       Wald 1134#>         LR  887#>      Score  932#>   Gradient  778#>#> Method: Analytical

Here we see that the gradient statistic would be the most efficientto test our hypothesis: We would need a sample size of 778 to reach ourdesired power.

We can also plot the power curves to get a more detailed look on therelationships between sample size and power:

plot(res)

Further documentation

Further documentation on how to use the package is found in thepackage vignettes:

• The vignette “demo” gives a short introduction of basicfeatures,
• “power_analysis” gives a more thorough walkthrough of power analysiswith irtpwr,
• “adding_hypotheses” gives a tutorial on setting up customhypotheses,
• “hypothesis_templates” includes some templates for hypothesisobjects that can be altered for your specific use case.

If the vignettes were included during installation (for example usingthe methods above), they can be accessed via:

browsevignettes("irtpwr")