lsem.permutationTest: Permutation Test for a Local Structural Equation Model

In alexanderrobitzsch/sirt: Supplementary Item Response Theory Models

Permutation Test for a Local Structural Equation Model

Description

Performs a permutation test for testing the hypothesis that model parameter are independent of a moderator variable (see Hildebrandt, Wilhelm, & Robitzsch, 2009; Hildebrandt, Luedtke, Robitzsch, Sommer, & Wilhelm, 2016).

Usage

lsem.permutationTest(lsem.object, B=1000, residualize=TRUE, verbose=TRUE,
     n.core=1, cl.type="PSOCK")

## S3 method for class 'lsem.permutationTest'
summary(object, file=NULL, digits=3, ...)

## S3 method for class 'lsem.permutationTest'
plot(x, type="global", stattype="SD",
    parindex=NULL, sig_add=TRUE, sig_level=0.05, sig_pch=17, nonsig_pch=2,
    sig_cex=1, sig_lab="p value",  stat_lab="Test statistic",
    moderator_lab=NULL, digits=3, title=NULL, parlabels=NULL,
    ask=TRUE, ...)

Arguments

lsem.object	Fitted object of class lsem with lsem.estimate
B	Number of permutation samples
residualize	Optional logical indicating whether residualization of the moderator should be performed for each permutation sample.
verbose	Optional logical printing information about computation progress.
n.core	A scalar indicating the number of cores that should be used.
cl.type	The cluster type. Default value is "PSOCK". Posix machines (Linux, Mac) generally benefit from much faster cluster computation if type is set to type="FORK".
object	Object of class lsem
file	A file name in which the summary output will be written.
digits	Number of digits.
...	Further arguments to be passed.
x	Object of class lsem
type	Type of the statistic to be plotted. If type="global", a global test will be displayed. If type="pointwise" for each value at the focal point (defined in moderator.grid) are calculated.
stattype	Type of test statistics. Can be MAD (mean absolute deviation), SD (standard deviation) or lin_slo (linear slope).
parindex	Vector of indices of selected parameters.
sig_add	Logical indicating whether significance values (p values) should be displayed.
sig_level	Significance level.
sig_pch	Point symbol for significant values.
nonsig_pch	Point symbol for non-significant values.
sig_cex	Point size for graphic displaying p values
sig_lab	Label for significance value (p value).
stat_lab	Label of y axis for graphic with pointwise test statistic
moderator_lab	Label of the moderator.
title	Title of the plot. Can be a vector.
parlabels	Labels of the parameters. Can be a vector.
ask	A logical which asks for changing the graphic for each parameter.

Value

List with following entries

teststat	Data frame with global test statistics. The statistics are SD, MAD and lin_slo with their corresponding p values.
parameters_pointwise_test	Data frame with pointwise test statistics.
parameters	Original parameters.
parameters	Parameters in permutation samples.
parameters_summary	Original parameter summary.
parameters_summary_M	Mean of each parameter in permutation sample.
parameters_summary_SD	Standard deviation (SD) statistic in permutation slope.
parameters_summary_MAD	Mean absolute deviation (MAD) statistic in permutation sample.
parameters_summary_MAD	Linear slope parameter in permutation sample.
nonconverged_rate	Percentage of permuted dataset in which a LSEM model did not converge

Author(s)

Alexander Robitzsch, Oliver Luedtke, Andrea Hildebrandt

References

Hildebrandt, A., Luedtke, O., Robitzsch, A., Sommer, C., & Wilhelm, O. (2016). Exploring factor model parameters across continuous variables with local structural equation models. Multivariate Behavioral Research, 51(2-3), 257-278. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00273171.2016.1142856")}

Hildebrandt, A., Wilhelm, O., & Robitzsch, A. (2009). Complementary and competing factor analytic approaches for the investigation of measurement invariance. Review of Psychology, 16, 87-102.

See Also

For Examples see lsem.estimate.

alexanderrobitzsch/sirt documentation built on Dec. 1, 2024, 2:18 a.m.