View source: R/lav_predict_y.R
lavPredictY  R Documentation 
This function can be used to predict the values of (observed) yvariables given the values of (observed) xvariables in a structural equation model.
lavPredictY(object, newdata = NULL,
ynames = lavNames(object, "ov.y"),
xnames = lavNames(object, "ov.x"),
method = "conditional.mean",
label = TRUE, assemble = TRUE, force.zero.mean = FALSE)
object 
An object of class 
newdata 
An optional data.frame, containing the same variables as
the data.frame that was used when fitting the model in 
ynames 
The names of the observed variables that should be treated as the yvariables. It is for these variables that the function will predict the (modelbased) values for each observation. Can also be a list to allow for a separate set of variable names per group (or block). 
xnames 
The names of the observed variables that should be treated as the xvariables. Can also be a list to allow for a separate set of variable names per group (or block). 
method 
A character string. The only available option for now is

label 
Logical. If TRUE, the columns of the output are labeled. 
assemble 
Logical. If TRUE, the predictions of the separate multiple groups in the output are reassembled again to form a single data.frame with a group column, having the same dimensions as the original (or newdata) dataset. 
force.zero.mean 
Logical. Only relevant if there is no mean structure.
If 
This function can be used for (SEMbased) outofsample predictions of
outcome (y) variables, given the values of predictor (x) variables. This
is in contrast to the lavPredict()
function which (historically)
only ‘predicts’ the (factor) scores for latent variables, ignoring the
structural part of the model.
When method = "conditional.mean"
, predictions (for y given x)
are based on the (joint y and x) modelimplied variancecovariance (Sigma)
matrix and mean vector (Mu), and the standard expression for the
conditional mean of a multivariate normal distribution. Note that if the
model is saturated (and hence df = 0), the SEMbased predictions are identical
to ordinary least squares predictions.
de Rooij, M., Karch, J.D., Fokkema, M., Bakk, Z., Pratiwi, B.C, and Kelderman, H. (2022) SEMBased OutofSample Predictions, Structural Equation Modeling: A Multidisciplinary Journal. DOI:10.1080/10705511.2022.2061494
lavPredict
to compute scores for latent variables.
model < '
# latent variable definitions
ind60 =~ x1 + x2 + x3
dem60 =~ y1 + a*y2 + b*y3 + c*y4
dem65 =~ y5 + a*y6 + b*y7 + c*y8
# regressions
dem60 ~ ind60
dem65 ~ ind60 + dem60
# residual correlations
y1 ~~ y5
y2 ~~ y4 + y6
y3 ~~ y7
y4 ~~ y8
y6 ~~ y8
'
fit < sem(model, data = PoliticalDemocracy)
lavPredictY(fit, ynames = c("y5", "y6", "y7", "y8"),
xnames = c("x1", "x2", "x3", "y1", "y2", "y3", "y4"))
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