Prediction in structural equation models
Arguments
- object
Model object
- x
optional list of (endogenous) variables to condition on
- y
optional subset of variables to predict
- residual
If true the residuals are predicted
- p
Parameter vector
- data
Data to use in prediction
- path
Path prediction
- quick
If TRUE the conditional mean and variance given covariates are returned (and all other calculations skipped)
- ...
Additional arguments to lower level function
Examples
m <- lvm(list(c(y1,y2,y3)~u,u~x)); latent(m) <- ~u
d <- sim(m,100)
e <- estimate(m,d)
## Conditional mean (and variance as attribute) given covariates
r <- predict(e)
## Best linear unbiased predictor (BLUP)
r <- predict(e,vars(e))
## Conditional mean of y3 giving covariates and y1,y2
r <- predict(e,y3~y1+y2)
## Conditional mean gives covariates and y1
r <- predict(e,~y1)
## Predicted residuals (conditional on all observed variables)
r <- predict(e,vars(e),residual=TRUE)
