lavaan (version 0.7-2)

lavNames: lavaan Names

Description

Extract variable 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 criteria. For example, "group = 2L" (in a multiple-group analysis) restricts the selection to 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": modeled 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": isolated observed variables (only their intercepts or variances appear in the model syntax)

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

• "th": threshold names (ordinal variables only)

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

• "lv": latent variables

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

• "lv.composite": composites (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")