plot.GPCMlasso {GPCMlasso}

Plot function for GPCMlasso

Description

Plot function for a GPCMlasso object. Plots show coefficient paths of DIF (or DSF) parameters along (a transformation of) the tuning parameter lambda. One plot per item is created, every single parameter corresponding to this item is depicted by a single path. The optimal model is highlighted with a red dashed line.

Usage

## S3 method for class 'GPCMlasso'
plot(x, select = c("BIC", "AIC", "cAIC", "cv"),
log.lambda = TRUE, items_per_page = 1, items = "all", 
columns = NULL, ask_new = TRUE, lambda.lines = TRUE,
equal_range = TRUE, ...)

Arguments

x

GPCMlasso object

select

Specifies which criterion to use for the optimal model, we recommend the default value "BIC". If cross-validation was performed, automatically the optimal model according to cross-validation is used. The chosen optimal model is highlighted with a red dashed line.

log.lambda

A logical value indicating whether lambda or a log-transformation of lambda should be used as x-axis in the plots.

items_per_page

By default, each plot/item is put on a separate page. For example, items_per_page=4 would put four plots/items on one page.

items

By default, all items are plotted. If items=c(1,3), only the first and the third item are plotted.

columns

Specifies the number of columns to use when several plots are on one page. Only relevant if items_per_page>1.

ask_new

If TRUE, the user is asked to confirm before the next item is plotted.

lambda.lines

A logical value indicating whether a thin gray line plotted for each value from the vector of tuning parameters from object

equal_range

A logical value indicating whether for each plot equal limits on the y-axis shall be used.

...

Further plot arguments.

Author(s)

Gunther Schauberger
[e-mail]

References

Schauberger, Gunther and Mair, Patrick (2019): A Regularization Approach for the Detection of Differential Item Functioning in Generalized Partial Credit Models, Behavior Research Methods, https://link.springer.com/article/10.3758/s13428-019-01224-2

See Also

GPCMlasso

Examples

data(tenseness_small)

## formula for simple model without covariates
form.0 <- as.formula(paste("cbind(",paste(colnames(tenseness_small)[1:5],collapse=","),")~0"))

######
## fit simple RSM where loglikelihood and score function are evaluated parallel on 2 cores
rsm.0 <- GPCMlasso(form.0, tenseness_small, model = "RSM", 
control= ctrl_GPCMlasso(cores=2))
rsm.0

## Not run: 
## formula for model with covariates (and DIF detection)
form <- as.formula(paste("cbind(",paste(colnames(tenseness_small)[1:5],collapse=","),")~."))

######
## fit GPCM model with 10 different tuning parameters
gpcm <- GPCMlasso(form, tenseness_small, model = "GPCM", 
                  control = ctrl_GPCMlasso(l.lambda = 10))
gpcm
plot(gpcm)
pred.gpcm <- predict(gpcm)
trait.gpcm <- trait.posterior(gpcm)

######
## fit RSM, detect differential step functioning (DSF)
rsm.DSF <- GPCMlasso(form, tenseness_small, model = "RSM", DSF = TRUE, 
                     control = ctrl_GPCMlasso(l.lambda = 10))
rsm.DSF
plot(rsm.DSF)

## create binary data set
tenseness_small_binary <- tenseness_small
tenseness_small_binary[,1:5][tenseness_small[,1:5]>1] <- 2

######
## fit and cross-validate Rasch model
set.seed(1860)
rm.cv <- GPCMlasso(form, tenseness_small_binary, model = "RM", cv = TRUE, 
                   control = ctrl_GPCMlasso(l.lambda = 10))
rm.cv
plot(rm.cv)

## End(Not run)