Predictions of conditinoal mean and variance and calculation of jacobian with respect to parameter vector.

predictlvm(object, formula, p = coef(object), data = model.frame(object), ...)

## Arguments

object

Model object

formula

Formula specifying which variables to predict and which to condition on

p

Parameter vector

data

Data.frame

...

Additional arguments to lower level functions

predict.lvm

## Examples

m <- lvm(c(x1,x2,x3)~u1,u1~z,
c(y1,y2,y3)~u2,u2~u1+z)
latent(m) <- ~u1+u2
d <- simulate(m,10,"u2,u2"=2,"u1,u1"=0.5,seed=123)
e <- estimate(m,d)

## Conditional mean given covariates
predictlvm(e,c(x1,x2)~1)$mean #> x1 x2 #> [1,] -0.17634038 0.001097242 #> [2,] 0.22409175 0.370702775 #> [3,] -0.64578819 -0.432210919 #> [4,] 2.17930239 2.175394823 #> [5,] 1.38879089 1.445739518 #> [6,] -0.52874258 -0.324175875 #> [7,] 0.06371187 0.222669470 #> [8,] 0.01125438 0.174250335 #> [9,] 1.03672161 1.120773697 #> [10,] 0.32654483 0.465268679 ## Conditional variance of u1,y1 given x1,x2 predictlvm(e,c(u1,y1)~x1+x2)$var
#>            u1         y1
#> u1 0.17501758 0.09920436
#> y1 0.09920436 0.89761525