R: Bootstrapping a Lavaan Model

bootstrapLavaan {lavaan}

R Documentation

Bootstrapping a Lavaan Model

Description

Bootstrap functions to get bootstrap standard errors and bootstrap test statistics

Usage

bootstrapLavaan(object, R = 1000L, type = "ordinary", verbose = FALSE, 
                FUN = "coef", warn = -1L, return.boot = FALSE,
                parallel = c("no", "multicore", "snow"),
                ncpus = 1L, cl = NULL, ...)

bootstrapLRT(h0 = NULL, h1 = NULL, R = 1000L, type="bollen.stine", 
             verbose=FALSE, return.LRT = FALSE, 
             calibrate = FALSE, calibrate.R = 1000L, calibrate.alpha = 0.05, 
             warn = -1L, parallel = c("no", "multicore", "snow"),
             ncpus = 1L, cl = NULL)

Arguments

`object`	An object of class `lavaan`.
`h0`	An object of class `lavaan`. The restricted model.
`h1`	An object of class `lavaan`. The unrestricted model.
`R`	Integer. The number of bootstrap draws.
`type`	If `"ordinary"` or `"nonparametric"`, the usual (naive) bootstrap method is used. If `"bollen.stine"`, the data is first transformed such that the null hypothesis holds exactly in the resampling space. If `"parametric"`, the parametric bootstrap approach is used. Currently, this is only valid for continuous data following a multivariate normal distribution.
`FUN`	A function which when applied to the `lavaan` object returns a vector containing the statistic(s) of interest. The default is `FUN="coef"`, returning the estimated values of the free parameters in the model.
`...`	Other named arguments for `FUN` which are passed unchanged each time it is called.
`verbose`	If `TRUE`, show information for each bootstrap draw.
`warn`	Sets the handling of warning messages. See `options`.
`return.boot`	Not used for now.
`return.LRT`	If `TRUE`, return the LRT values as an attribute to the pvalue.
`parallel`	The type of parallel operation to be used (if any). If missing, the default is `"no"`.
`ncpus`	integer: number of processes to be used in parallel operation: typically one would chose this to the number of available CPUs.
`cl`	An optional parallel or snow cluster for use if `parallel = "snow"`. If not supplied, a cluster on the local machine is created for the duration of the `bootstrapLavaan` or `bootstrapLRT` call.
`calibrate`	If `TRUE`, a double (nested) bootstrap is used to compute an additional set of plugin-values for each bootstrap sample.
`calibrate.R`	Integer. The number of bootstrap draws to be use for the double bootstrap.
`calibrate.alpha`	The significance level to compute the adjusted alpha based on the plugin p-values.

Examples

# fit the Holzinger and Swineford (1939) example
HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '

fit <- cfa(HS.model, data=HolzingerSwineford1939)

# get the test statistic for the original sample
T.orig <- fitMeasures(fit, "chisq")

# bootstrap to get bootstrap test statistics
# we only generate 10 bootstrap sample in this example; in practice
# you may wish to use a much higher number
T.boot <- bootstrapLavaan(fit, R=10, type="bollen.stine",
                          FUN=fitMeasures, fit.measures="chisq")

# compute a bootstrap based p-value
pvalue.boot <- length(which(T.boot > T.orig))/length(T.boot)

[Package lavaan version 0.4-12 Index]