lavPredictY: Predict the values of y-variables given the values of x-variables

Description

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.

Usage

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)

Arguments

object: An object of class lavaan.
newdata: An optional data.frame, containing the same variables as the data.frame that was used when fitting the model in object. This data.frame should also include the y-variables (although their values will be ignored). Note that if no meanstructure was used in the original fit, we will use the saturated sample means of the original fit as substitutes for the model-implied means. Alternatively, refit the model using meanstructure = TRUE.
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 "conditional.mean". See Details.
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 TRUE, the (model-implied) mean vector is set to the zero vector. If FALSE, the (model-implied) mean vector is set to the (unrestricted) sample mean vector.

Details

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.

References

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

Examples

Run this code

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"))