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lavaan Names

lavNames.Rd

Extract variables names from a fitted lavaan object.

Usage

lavNames(object, type = "ov", ...)

Arguments

object

An object of class lavaan.

type

Character. The type of variables whose names should be extracted. See details for a complete list.

...

Additional selection variables. For example "group = 2L" (in a multiple-group analysis) only considers the variables included in the model for the second group.

Details

The order of the variable names, as returned by lav_object_vnames determines the order in which the variables are listed in the parameter table, and therefore also in the summary output.

The following variable types are available:

  • "ov": observed variables

  • "ov.x": (pure) exogenous observed variables (no mediators)

  • "ov.nox": non-exogenous observed variables

  • "ov.model": modelled observed variables (joint vs conditional)

  • "ov.y": (pure) endogenous variables (dependent only) (no mediators)

  • "ov.num": numeric observed variables

  • "ov.ord": ordinal observed variables

  • "ov.ind": observed indicators of latent variables

  • "ov.orphan": lonely observed variables (only intercepts/variancesappear in the model syntax)

  • "ov.interaction": interaction terms (defined by the colon operator)

  • "th": threshold names ordinal variables only

  • "th.mean": threshold names ordinal + numeric variables (if any)

  • "lv": latent variables

  • "lv.regular": latent variables (defined by =~ only)

  • "lv.formative": latent variables (defined by <~ only)

  • "lv.x": (pure) exogenous variables

  • "lv.y": (pure) endogenous variables

  • "lv.nox": non-exogenous latent variables

  • "lv.nonnormal": latent variables with non-normal indicators

  • "lv.interaction": interaction terms at the latent level

  • "eqs.y": variables that appear as dependent variables in a regression formula (but not indicators of latent variables)

  • "eqs.x": variables that appear as independent variables in a regression formula

See also

lavParTable, parTable

Examples

HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '

fit <- cfa(HS.model, data=HolzingerSwineford1939)
lavNames(fit, "ov")
#> [1] "x1" "x2" "x3" "x4" "x5" "x6" "x7" "x8" "x9"