Predict the values of latent variables, observed variables, and missing variables.

The purpose of the blavPredict() function is to compute various types of model predictions, conditioned on observed data. This differs somewhat from lavPredict() in lavaan.

Usage

blavPredict(object, newdata = NULL, type = "lv", level = 1L)

Arguments

object

An object of class blavaan.

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" or "yhat", predicted means for the observed variables in the model are computed. If "ypred" or "ydist", predicted values for the observed variables (including residual noise) are computed. If "ymis" or "ovmis", model predicted values ("imputations") for the missing data are computed. See details for further information.

level

For type = "lv", used to specify whether one desires the level 1 latent variables or level 2 latent variables.

Details

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

Below, we provide more information about each type option. Most options only work for target="stan", and "number of samples" is defined as the number of posterior samples across all chains.

type="lv": The posterior distribution of latent variables conditioned on observed variables. Returns a list with "number of samples" entries, where each entry is a matrix where rows are observations and columns are latent variables.

type="yhat": The posterior expected value of observed variables conditioned on the sampled latent variables. Returns a list with "number of samples" entries, where each entry is a matrix where rows are observations and columns are observed variables.

type="ypred": The posterior predictive distribution of observed variables conditioned on the sampled latent variables (including residual variability). Returns a list with "number of samples" entries, where each entry is a data frame where rows are observations and columns are observed variables.

type="ymis": The posterior predictive distribution of missing values conditioned on observed variables. Returns a matrix with "number of samples" rows and "number of missing variables" columns.

See also

Users may also wish to generate the posterior predictive distribution of observed data, not conditioned on the latent variables. This would often be viewed as data from new clusters (people) that were not observed in the original dataset. For that, see sampleData().

Examples

if (FALSE) { # \dontrun{
data(HolzingerSwineford1939, package = "lavaan")

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

fit <- bcfa(HS.model, data = HolzingerSwineford1939, save.lvs = TRUE)
lapply(blavPredict(fit)[1:2], head) # first 6 rows of first 10 posterior samples
head(blavPredict(fit, type = "yhat")[[1]]) # top of first posterior sample

## multigroup models return a list of factor scores (one per group)
mgfit <- bcfa(HS.model, data = HolzingerSwineford1939, group = "school",
              group.equal = c("loadings","intercepts"), save.lvs = TRUE)

lapply(blavPredict(fit)[1:2], head)
head(blavPredict(fit, type = "ypred")[[1]])
} # }