sirt: lsem.permutationTest – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

lsem.permutationTest

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)

## 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.
`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. 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.