DIFlasso: A penalty approach to Differential Item Functioning in Rasch...

In masko-p/DIFlasso: A penalty approach to Differential Item Functioning in Rasch Models

Description

A function to perform DIFlasso, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many covariates and also metric covariates. The method is described is Tutz and Schauberger (2013).

Usage

DIFlasso(Y, X, l.lambda = 20, trace = FALSE)

Arguments

Y	Data frame (one row per person, one column per item) containing response. May only contain 0 or 1.
X	Data frame (one row per person, one column per covariate) containing covariates. Has to be standardized.
l.lambda	Length of the grid of tuning parameters for DIFlasso. Default is 20 different tuning parameters.
trace	Should the trace of the grplasso function be printed?

Details

The method assumes the DIFmodel from Tutz and Schauberger (2013) where a Group Lasso penalty is used for DIF detection. Computation is based on the function grplasso.

Value

theta	Estimated person abilities; one row per person, one column per tuning parameter
beta	Estimated item difficulties; one row per item, one column per tuning parameter
gamma	Estimated item-specific parameters; one row per item and covariate, one column per tuning parameter (first line: first item, first covariate; second line: first item, second covariate and so on)
P	Number of (valid) persons; removed persons are found in removed.persons
I	Number of items
m	Number of covariates
logLik	Log-likelihood
BIC	BIC
AIC	AIC
df	Degrees of freedom
refit.matrix	Design matrix for function refitDIFlasso
lambda	Sequence of tuning parameters used by grplasso
ref.item	Reference item
dif.mat	Estimates of the item-specific parameter estima tes (at BIC-optimal lambda)
dif.items	Which items have been detected to be DIF items (at BIC-optimal lambda)?
names.y	Names of the items
names.x	Names of the covariates
removed.persons	Which persons have been removed because they either solved no item or all items?

Author(s)

Gunther Schauberger
[e-mail]
http://www.statistik.lmu.de/~schauberger/

References

Tutz, Gerhard and Schauberger, Gunther (2013): A Penalty Approach to Differential Item Functioning in Rasch Models, Technical Report 134, Department of Statistics, LMU Munich

See Also

refitDIFlasso, plot.DIFlasso, print.DIFlasso, grplasso

Examples

## Not run: 
data(simul.data)

Y <- simul.data[,1:10]
X <- simul.data[,11:13]

m1 <- DIFlasso(Y = Y, X = X, trace = TRUE) 
print(m1)
plot(m1)

m2 <- refitDIFlasso(m1)
print(m2)
plot(m2)

## End(Not run)

masko-p/DIFlasso documentation built on May 21, 2019, 12:42 p.m.