moments2: Compute Key Quantities of a Latent Variable Model
In lavaSearch2: Tools for Model Specification in the Latent Variable Framework

moments2

R Documentation

Compute Key Quantities of a Latent Variable Model

Description

Compute conditional mean, conditional variance, their first and second derivative regarding model parameters, as well as various derivatives of the log-likelihood.

Usage

moments2(
  object,
  param,
  data,
  weights,
  Omega,
  Psi,
  initialize,
  usefit,
  update.dmoment,
  update.d2moment,
  score,
  information,
  hessian,
  vcov,
  dVcov,
  dVcov.robust,
  residuals,
  leverage,
  derivative
)

## S3 method for class 'lvm'
moments2(
  object,
  param = NULL,
  data = NULL,
  weights = NULL,
  Omega = NULL,
  Psi = NULL,
  initialize = TRUE,
  usefit = TRUE,
  update.dmoment = TRUE,
  update.d2moment = TRUE,
  score = TRUE,
  information = TRUE,
  hessian = TRUE,
  vcov = TRUE,
  dVcov = TRUE,
  dVcov.robust = TRUE,
  residuals = TRUE,
  leverage = TRUE,
  derivative = "analytic"
)

## S3 method for class 'lvmfit'
moments2(
  object,
  param = NULL,
  data = NULL,
  weights = NULL,
  Omega = NULL,
  Psi = NULL,
  initialize = TRUE,
  usefit = TRUE,
  update.dmoment = TRUE,
  update.d2moment = TRUE,
  score = TRUE,
  information = TRUE,
  hessian = TRUE,
  vcov = TRUE,
  dVcov = TRUE,
  dVcov.robust = TRUE,
  residuals = TRUE,
  leverage = TRUE,
  derivative = "analytic"
)

Arguments

`object`	a latent variable model.
`param`	[numeric vector] value of the model parameters if different from the estimated ones.
`data`	[data.frame] dataset if different from the one used to fit the model.
`Psi`	[matrix] Average first order bias in the residual variance. Only necessary for computing adjusted residuals.
`initialize`	[logical] Pre-compute quantities dependent on the data but not on the parameters values.
`usefit`	[logical] Compute key quantities based on the parameter values.
`update.dmoment`	[logical] should the first derivative of the moments be computed/updated?
`update.d2moment`	[logical] should the second derivative of the the moments be computed/updated?
`score`	[logical] should the score be output?
`information`	[logical] should the expected information be output?
`hessian`	[logical] should the hessian be output?
`vcov`	[logical] should the variance-covariance matrix based on the expected information be output?
`dVcov`	[logical] should the derivative of the variance-covariance matrix be output?
`dVcov.robust`	[logical] should the derivative of the robust variance-covariance matrix be output?
`...`	[internal] only used by the generic method or by the <- methods.

Details

For lvmfit objects, there are two levels of pre-computation:

a basic one that do no involve the model coefficient (conditionalMoment.lvm).
an advanced one that require the model coefficients (conditionalMoment.lvmfit).

Examples

m <- lvm(Y1~eta,Y2~eta,Y3~eta)
latent(m) <- ~eta

d <- lava::sim(m,1e2)
e <- estimate(m, d)

## basic pre-computation
res1 <- moments2(e, data = d, initialize = TRUE, usefit = FALSE,
                score = TRUE, information = TRUE, hessian = TRUE, vcov = TRUE,
                dVcov = TRUE, dVcov.robust = TRUE, residuals = TRUE, leverage = FALSE,
                derivative = "analytic")
res1$skeleton$param$Sigma

## full pre-computation
res2 <- moments2(e, param = coef(e), data = d, initialize = TRUE, usefit = TRUE,
                score = TRUE, information = TRUE, hessian = TRUE, vcov = TRUE,
                dVcov = TRUE, dVcov.robust = TRUE, residuals = TRUE, leverage = FALSE,
                derivative = "analytic")
res2$moment$Omega

lavaSearch2 documentation built on April 12, 2023, 12:33 p.m.