R: performs permutation test for empirical cutoff thresholds
permute
R Documentation
performs permutation test for empirical cutoff thresholds
Description
performs permutation tests under no-DIF conditions to generate empirical distributions of DIF statistics
Usage
permute(obj, alpha = 0.01, nr = 100)
Arguments
obj
an object returned from lordif
alpha
desired significance level (e.g., .01)
nr
number of replications
Details
The vector of group designations is randomly shuffled nr times to estimate the sampling distribution
of the statistics when the null hypothesis is true.
Returns empirical distributions and thresholds for various statistics and effect size measures.
Value
Returns an object (list) of class "lordif.MC" with the following components:
call
calling expression
chi12
prob associated with the LR Chi-square test comparing Model 1 vs. 2
chi13
prob associated with the LR Chi-square test comparing Model 1 vs. 3
chi23
prob associated with the LR Chi-square test comparing Model 2 vs. 3
pseudo12.CoxSnell
Cox & Snell pseudo R-square change from Model 1 to 2
pseudo13.CoxSnell
Cox & Snell pseudo R-square change from Model 1 to 3
pseudo23.CoxSnell
Cox & Snell pseudo R-square change from Model 2 to 3
pseudo12.Nagelkerke
Nagelkerke pseudo R-square change from Model 1 to 2
pseudo13.Nagelkerke
Nagelkerke pseudo R-square change from Model 1 to 3
pseudo23.Nagelkerke
Nagelkerke pseudo R-square change from Model 2 to 3
pseudo12.McFadden
McFadden pseudo R-square change from Model 1 to 2
pseudo13.McFadden
McFadden pseudo R-square change from Model 1 to 3
pseudo23.McFadden
McFadden pseudo R-square change from Model 2 to 3
beta12
proportional beta change from Model 1 to 2
alpha
significance level
nr
number of replications
cutoff
thresholds for the statistics
Note
nr must be a large integer (e.g., 500) for smooth distributions.
Author(s)
Seung W. Choi <choi.phd@gmail.com>
References
Choi, S. W., Gibbons, L. E., Crane, P. K. (2011). lordif: An R Package for Detecting Differential Item Functioning Using Iterative Hybrid Ordinal Logistic Regression/Item Response Theory and Monte Carlo Simulations. Journal of Statistical Software, 39(8), 1-30. URL http://www.jstatsoft.org/v39/i08/.
See Also
montecarlo, lordif
Examples
##load PROMIS Anxiety sample data (n=766)
## Not run: data(Anxiety)
##age : 0=younger than 65 or 1=65 or older
##run age-related DIF on all 29 items (takes about a minute)
## Not run: age.DIF <- lordif(Anxiety[paste("R",1:29,sep="")],Anxiety$age)
##the following takes several minutes
## Not run: age.DIF.MC <- permute(age.DIF,alpha=0.01,nr=100)