lavaan (version 0.5-23.1086)

lavListInspect: Inspect or extract information from a lavaanList object

Description

The lavListInspect() and lavListTech() functions can be used to inspect/extract information that is stored inside (or can be computed from) a lavaanList object.

Usage

lavListInspect(object, what = "free", add.labels = TRUE,  add.class = TRUE, list.by.group = TRUE, drop.list.single.group = TRUE)
lavListTech(object, what = "free", add.labels = FALSE,  add.class = FALSE, list.by.group = FALSE, drop.list.single.group = FALSE)

Arguments

object

An object of class lavaanList.

what

Character. What needs to be inspected/extracted? See Details for a full list. Note: the what argument is not case-sensitive (everything is converted to lower case.)

add.labels

If TRUE, variable names are added to the vectors and/or matrices.

add.class

If TRUE, vectors are given the `lavaan.vector' class; matrices are given the `lavaan.matrix' class, and symmetric matrices are given the `lavaan.matrix.symmetric' class. This only affects the way they are printed on the screen.

list.by.group

Logical. Only used when the output are model matrices. If TRUE, the model matrices are nested within groups. If FALSE, a flattened list is returned containing all model matrices, with repeated names for multiple groups.

drop.list.single.group

If FALSE, the results are returned as a list, where each element corresponds to a group (even if there is only a single group.) If TRUE, the list will be unlisted if there is only a single group.

Details

The lavListInspect() and lavListTech() functions only differ in the way they return the results. The lavListInspect() function will prettify the output by default, while the lavListTech() will not attempt to prettify the output by default.

Below is a list of possible values for the what argument, organized in several sections:

Model matrices:

Information about the data (including missing patterns):

Model features:

See Also

lavaanList

Examples

# fit model
HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '
     
# a data generating function
generateData <- function() simulateData(HS.model, sample.nobs = 100)
     
set.seed(1234)
fit <- semList(HS.model, dataFunction = generateData, ndat = 5,
               store.slots = "partable")

# extract information
lavListInspect(fit, "free")
lavListTech(fit, "free")