lavaan (version 0.6-1.1161)

lavPredict: Predict the values of latent variables (and their indicators).

Description

The lavPredict() function can be used to compute (or `predict') estimated values for latent variables, and given these values, the model-implied values for the indicators of these latent variables.

Usage

lavPredict(object, type = "lv", newdata = NULL, method = "EBM",
           se.fit = FALSE, label = TRUE, fsm = FALSE, 
           optim.method = "bfgs")

Arguments

object

An object of class '>lavaan.

type

A character string. If "lv", estimated values for the latent variables in the model are computed. If "ov", model predicted values for the indicators of the latent variables in the model are computed.

newdata

An optional data.frame, containing the same variables as the data.frame used when fitting the model in object.

method

A character string. In the linear case (when the indicators are continuous), the possible options are "regression" or "Bartlett". In the categorical case, the only option (for now) is "EBM" for the Empirical Bayes Modal approach.

se.fit

Not used yet.

label

Logical. If TRUE, the columns are labeled.

fsm

Logical. If TRUE, return the factor score matrix as an attribute. Only for numeric data.

optim.method

Character string. Only used in the categorical case. If "nlminb" (the default in 0.5), the "nlminb()" function is used for the optimization. If "bfgs" or "BFGS" (the default in 0.6), the "optim()" function is used with the BFGS method.

Details

The predict() function calls the lavPredict() function with its default options.

If there are no latent variables in the model, type = "ov" will simply return the values of the observed variables. Note that this function can not be used to `predict' values of dependent variables, given the values of independent values (in the regression sense). In other words, the structural component is completely ignored (for now).

See Also

lavaan

Examples

# NOT RUN {
# fit model
HS.model <- ' visual  =~ x1 + x2 + x3
              textual =~ x4 + x5 + x6
              speed   =~ x7 + x8 + x9 '
     
fit <- cfa(HS.model, data=HolzingerSwineford1939)
head(lavPredict(fit))
head(lavPredict(fit, type = "ov"))
# }