lavaan: lavPredict – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

lavPredict

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

Description

The main purpose of the lavPredict() function is to compute (or ‘predict’) estimated values for the latent variables in the model (‘factor scores’). NOTE: the goal of this function is NOT to predict future values of dependent variables as in the regression framework!

Usage

lavPredict(object, newdata = NULL, type = "lv", method = "EBM",
           se = "none", acov = "none", label = TRUE, fsm = FALSE, 
           append.data = FALSE, assemble = FALSE,
           level = 1L, optim.method = "bfgs", ETA = NULL)

Arguments

`object`	An object of class `lavaan`.
`newdata`	An optional data.frame, containing the same variables as the data.frame used when fitting the model in object.
`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. If `"yhat"`, the estimated value for the observed indicators, given user-specified values for the latent variables provided by de `ETA` argument. If `"fy"`, densities (or probabilities) for each observed indicator, given user-specified values for the latent variables provided by de `ETA` argument.
`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 two options are `"EBM"` for the Empirical Bayes Modal approach, and `"ML"` for the maximum likelihood approach.
`se`	Character. If `"none"`, no standard errors are computed. If `"standard"`, naive standard errors are computed (assuming the parameters of the measurement model are known). The standard errors are returned as an attribute. Currently only available for complete continuous data.
`acov`	Similar to the `"se"` argument, but optionally returns the full sampling covariance matrix of factor scores as an attribute. Currently only available for complete continuous data.
`label`	Logical. If TRUE, the columns are labeled.
`fsm`	Logical. If TRUE, return the factor score matrix as an attribute. Only for numeric data.
`append.data`	Logical. Only used when `type = "lv"`. If TRUE, the original data (or the data provided in the newdata argument) is appended to the factor scores.
`assemble`	Logical. If TRUE, the separate multiple groups as reassembled again to form a single data.frame with a group column, having the same dimensions are the original dataset.
`level`	Integer. Only used in a multilevel SEM. If `level = 1`, only factor scores for latent variable defined at the first (within) level are computed; if `level = 2`, only factor scores for latent variables defined at the second (between) level are computed.
`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.
`ETA`	An optional matrix or list, containing latent variable values for each observation. Used for computations when `type = "ov"`.

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).

Examples

data(HolzingerSwineford1939)

## 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"))


## ------------------------------------------
## merge factor scores to original data.frame
## ------------------------------------------

idx <- lavInspect(fit, "case.idx")
fscores <- lavPredict(fit)
## loop over factors
for (fs in colnames(fscores)) {
  HolzingerSwineford1939[idx, fs] <- fscores[ , fs]
}
head(HolzingerSwineford1939)


## multigroup models return a list of factor scores (one per group)
data(HolzingerSwineford1939)
mgfit <- update(fit, group = "school", group.equal = c("loadings","intercepts"))

idx <- lavInspect(mgfit, "case.idx") # list: 1 vector per group
fscores <- lavPredict(mgfit)         # list: 1 matrix per group
## loop over groups and factors
for (g in seq_along(fscores)) {
  for (fs in colnames(fscores[[g]])) {
    HolzingerSwineford1939[ idx[[g]], fs] <- fscores[[g]][ , fs]
  }
}
head(HolzingerSwineford1939)

## -------------------------------------
## Use factor scores in susequent models
## -------------------------------------

## see Examples in semTools package: ?plausibleValues

lavaan

Latent Variable Analysis

v0.6-10

GPL (>= 2)

Authors

Yves Rosseel [aut, cre] (<https://orcid.org/0000-0002-4129-4477>), Terrence D. Jorgensen [aut] (<https://orcid.org/0000-0001-5111-6773>), Nicholas Rockwood [aut] (<https://orcid.org/0000-0001-5931-183X>), Daniel Oberski [ctb], Jarrett Byrnes [ctb], Leonard Vanbrabant [ctb], Victoria Savalei [ctb], Ed Merkle [ctb], Michael Hallquist [ctb], Mijke Rhemtulla [ctb], Myrsini Katsikatsou [ctb], Mariska Barendse [ctb], Florian Scharf [ctb], Han Du [ctb]

Initial release