lavaSearch2 (version 2.0.3)

skeleton: Pre-computation for the Score

Description

Pre-compute quantities that are necessary to compute the score of a lvm model.

Usage

skeleton(object, X, endogenous, latent, n.cluster, index.Omega)
skeletonDtheta(
  object,
  X,
  endogenous,
  latent,
  missing.pattern,
  unique.pattern,
  name.pattern,
  n.cluster,
  index.Omega
)
skeletonDtheta2(object)

Arguments

object

a lvm object.

X

[matrix] design matrix containing the covariates for each endogeneous and latent variable.

endogenous

[character vector] the name of the endogeneous variables.

latent

[character vector] the name of the latent variables.

Details

When the user specifies names for the coefficients (e.g. Y1[mu:sigma]) or uses constraints (Y1~beta*X1), as.lava=FALSE will use the names specified by the user (e.g. mu, sigma, beta) while as.lava=TRUE will use the name of the first link defining the coefficient.

Examples

if (FALSE) {
skeleton <- lavaSearch2::skeleton
skeleton.lvm <- lavaSearch2::skeleton.lvm
skeleton.lvmfit <- lavaSearch2::skeleton.lvmfit

## without constrain
m <- lvm(Y1~X1+X2+eta,Y2~X3+eta,Y3~eta)
latent(m) <- ~eta

e <- estimate(m, lava::sim(m,1e2))
M.data <- as.matrix(model.frame(e))

skeleton(e$model, as.lava = TRUE,
         name.endogenous = endogenous(e), n.endogenous = 3,
         name.latent = latent(e), 
         update.value = FALSE)
skeleton(e, data = M.data, p = pars(e), as.lava = TRUE,
         name.endogenous = endogenous(e), n.endogenous = 3,
         name.latent = latent(e), 
         update.value = TRUE)

## with constrains
m <- lvm(Y[mu:sigma] ~ beta*X1+X2)
e <- estimate(m, lava::sim(m,1e2))
M.data <- as.matrix(model.frame(e))

skeleton(e$model, as.lava = TRUE,
         name.endogenous = "Y", n.endogenous = 1,
         name.latent = NULL, 
         update.value = FALSE)$skeleton

skeleton(e, data = M.data, p = pars(e), as.lava = FALSE,
         name.endogenous = "Y", n.endogenous = 1,
         name.latent = NULL, 
         update.value = FALSE)$skeleton

}