View source: R/lav_predict_y.R
lavPredictY | R Documentation |
This function can be used to predict the values of (observed) y-variables given the values of (observed) x-variables 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 y-variables. It is for these variables that the function will predict the (model-based) 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 x-variables. 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 (SEM-based) out-of-sample 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) model-implied variance-covariance (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 SEM-based 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) SEM-Based Out-of-Sample 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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