lordif (version 0.1-9)

montecarlo: performs Monte Carlo simulations for empirical cutoff thresholds

Description

performs Monte Carlo simulations under no-DIF conditions to generate empirical cutoff thresholds

Usage

montecarlo(obj, alpha = 0.01, nr = 100)

Arguments

obj

an object returned from lordif

alpha

desired significance level (e.g., .01)

nr

number of replications

Value

• Returns a 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

Details

Simulated datasets are generated under no-DIF conditions and have the same dimensions as the empirical dataset. Group differences (impact) in theta estimates are preserved in the simulated datasets. Returns empirical thresholds for various statistics and effect size measures.

References

Crane, P. K., Gibbons, L. E., Jolley, L., and van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques: DIF detect and difwithpar. Medical Care, 44(11 Suppl 3), S115-S123.

See Also

lordif

Examples

##load PROMIS Anxiety sample data (n=766)
  data(Anxiety)
##age : 0=younger than 65 or 1=65 or older
##run age-related DIF on all 29 items (takes about a minute)
  age.DIF <- lordif(Anxiety[paste("R",1:29,sep="")],Anxiety$age) 
##the following takes several minutes
  age.DIF.MC <- montecarlo(age.DIF,alpha=0.01,nr=100)