plot.monotonicity.class: Plot monotonicity.class objects

In mokken: Conducts Mokken Scale Analysis

Plot monotonicity.class objects

Description

S3 Method to plot objects of class monotonicity.class. Graphic display of the checks of monotonicity. One graph for each item plotting the estimated item step response functions and/or item response function, plus confidence envelopes (Van der Ark, 2012).

Usage

## S3 method for class 'monotonicity.class'
plot(x, items = all.items, curves = "both", ci = TRUE, 
              alpha = .05, color = "black", transparancy = 20, ask = TRUE, ...)

Arguments

x	Object of class monotonicity.class produced by check.monotonicity.
items	vector containing the numbers of the items for which the results are depicted graphically. Default the results for all items are depicted.
curves	"ISRF": Item step response functions (ISRFs) are depicted; "IRF": item response functions (IRFs) are depicted; "both": Both ISRFs and IRFs are depicted. Default is "both"
ci	Boolean. If TRUE (default), then confidence envelops are plotted around IRFs and ISRFs.
alpha	Type of plotted (1 - alpha) confidence intervals. By default 95-percent confidence intervals are depicted
color	Color of the plotted curves and confidence envelops. Default is black.
transparancy	Transparancy of the confidence intervals. Higher values result in more opaque colors for the confidence intervals.
ask	Boolean. If TRUE (default), then par("ask"=TRUE); i.e., a hard return between subsequent plots is required. If FALSE, then par("ask"=FALSE).
...	Optional graphical parameters will be ignored

Details

For details of the method, see Molenaar and Sijtsma (2000) and Sijtsma and Molenaar (2002). For details of the confidence envelopes, see Van der Ark (2012) For the implementation in R, see Van der Ark (2007). For curves=="both", both plots are plotted simultaneously using layout(matrix(c(1,2)1,2)). For ask=="FALSE", the default graphic device in R may only display the last graph.

Value

Returns a graph.

Author(s)

L. A. van der Ark [e-mail]

References

Koopman, L., Zijlstra, B. J. H., & Van der Ark, L. A. (2023a). Assumptions and Properties of Two-Level Nonparametric Item Response Theory Models. Manuscript submitted for publication.

Koopman, L., Zijlstra, B. J. H., & Van der Ark, L. A. (2023b). Evaluating Model Fit in Two-Level Mokken Scale Analysis. Manuscript submitted for publication.

Molenaar, I.W., & Sijtsma, K. (2000) User's Manual MSP5 for Windows [Software manual]. IEC ProGAMMA.

Sijtsma, K., & Molenaar, I. W. (2002) Introduction to nonparametric item response theory. Sage.

Van der Ark, L. A. (2007). Mokken scale analysis in R. Journal of Statistical Software. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v020.i11")}

Van der Ark, L. A. (2014). Visualizing uncertainty of estimated response functions in nonparametric item response theory. In R. E. Millsap, L. A. van der Ark, D. Bolt, & C. M. Woods (Eds.), New developments in quantitative psychology (pp. 59-68). New York: Springer. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-1-4614-9348-8_5")}

See Also

check.monotonicity, summary.monotonicity.class

Examples

data(acl)
Communality <- acl[,1:10]
monotonicity.list <- check.monotonicity(Communality)
plot(monotonicity.list)
summary(monotonicity.list)

# Compute two-level fit statistics (Koopman et al., 2023a, 2023b)
data("autonomySupport")
dat <- autonomySupport[, -1]
groups <- autonomySupport[, 1]
autonomyMM <- check.monotonicity(dat, level.two.var = groups)
summary(autonomyMM)
plot(autonomyMM)

mokken documentation built on July 9, 2023, 7:24 p.m.