summary.DIFtree {DIFtree} | R Documentation |
The function takes an object of class "DIFtree"
and returns an useful summary
with an overiew of all executed splits during the estimation procedure.
## S3 method for class 'DIFtree'
summary(object, ...)
## S3 method for class 'summary.DIFtree'
print(x, ...)
object |
Object of class |
... |
Further arguments passed to or from other methods |
x |
Object of class |
Object of class "summary.DIFtree"
.
An object of class "summary.DIFtree"
is a list containing the following components:
stats |
Useful overview of detected DIF items, responsible variables and executed splits |
nosplits |
Total number of executed splits during the estimation procedure |
Moritz Berger <moritz.berger@imbie.uni-bonn.de>
http://www.imbie.uni-bonn.de/personen/dr-moritz-berger/
Berger, Moritz and Tutz, Gerhard (2016): Detection of Uniform and Non-Uniform Differential Item Functioning by Item Focussed Trees, Journal of Educational and Behavioral Statistics 41(6), 559-592.
Bollmann, Stella, Berger, Moritz & Tutz, Gerhard (2018): Item-Focussed Trees for the Detection of Differential Item Functioning in Partial Credit Models, Educational and Psychological Measurement 78(5), 781-804.
Tutz, Gerhard and Berger, Moritz (2016): Item focussed Trees for the Identification of Items in Differential Item Functioning, Psychometrika 81(3), 727-750.
DIFtree
, plot.DIFtree
, predict.DIFtree
data(data_sim_Rasch)
Y <- data_sim_Rasch[,1]
X <- data_sim_Rasch[,-1]
## Not run:
mod <- DIFtree(Y=Y,X=X,model="Logistic",type="udif",alpha=0.05,nperm=1000,trace=TRUE)
summary(mod)
## End(Not run)