View source: R/lav_lavaanList_inspect.R

lavListInspect | R Documentation |

The `lavListInspect()`

and `lavListTech()`

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

```
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)
```

`object` |
An object of class |

`what` |
Character. What needs to be inspected/extracted? See Details for a
full list. Note: the |

`add.labels` |
If |

`add.class` |
If |

`list.by.group` |
Logical. Only used when the output are model matrices.
If |

`drop.list.single.group` |
If |

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:

`"free"`

:A list of model matrices. The non-zero integers represent the free parameters. The numbers themselves correspond to the position of the free parameter in the parameter vector. This determines the order of the model parameters in the output of for example

`coef()`

and`vcov()`

.`"partable"`

:A list of model matrices. The non-zero integers represent both the fixed parameters (for example, factor loadings fixed at 1.0), and the free parameters if we ignore any equality constraints. They correspond with all entries (fixed or free) in the parameter table. See

`parTable`

.`"start"`

:A list of model matrices. The values represent the starting values for all model parameters. Alias:

`"starting.values"`

.

Information about the data (including missing patterns):

`"group"`

:A character string. The group variable in the data.frame (if any).

`"ngroups"`

:Integer. The number of groups.

`"group.label"`

:A character vector. The group labels.

`"level.label"`

:A character vector. The level labels.

`"cluster"`

:A character vector. The cluster variable(s) in the data.frame (if any).

`"nlevels"`

:Integer. The number of levels.

`"ordered"`

:A character vector. The ordered variables.

`"nobs"`

:Integer vector. The number of observations in each group that were used in the analysis (in each dataset).

`"norig"`

:Integer vector. The original number of observations in each group (in each dataset).

`"ntotal"`

:Integer. The total number of observations that were used in the analysis. If there is just a single group, this is the same as the

`"nobs"`

option; if there are multiple groups, this is the sum of the`"nobs"`

numbers for each group (in each dataset).

Model features:

`"meanstructure"`

:Logical.

`TRUE`

if a meanstructure was included in the model.`"categorical"`

:Logical.

`TRUE`

if categorical endogenous variables were part of the model.`"fixed.x"`

:Logical.

`TRUE`

if the exogenous x-covariates are treated as fixed.`"parameterization"`

:Character. Either

`"delta"`

or`"theta"`

.

`"list"`

:The parameter table. The same output as given by

`parTable()`

.`"options"`

:List. The option list.

`"call"`

:List. The call as returned by match.call, coerced to a list.

`lavaanList`

```
# 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")
```

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