lavaSearch2 (version 1.4)

vcov2: Extract the Variance Covariance Matrix of the Model Parameters

Description

Extract the variance covariance matrix of the model parameters from a Gaussian linear model.

Usage

vcov2(object, ...)
# S3 method for lm
vcov2(object, param = NULL, data = NULL,
  bias.correct = TRUE, ...)
# S3 method for gls
vcov2(object, param = NULL, data = NULL,
  bias.correct = TRUE, ...)
# S3 method for lme
vcov2(object, param = NULL, data = NULL,
  bias.correct = TRUE, ...)
# S3 method for lvmfit
vcov2(object, param = NULL, data = NULL,
  bias.correct = TRUE, ...)
# S3 method for lm2
vcov2(object, param = NULL, data = NULL, ...)
# S3 method for gls2
vcov2(object, param = NULL, data = NULL, ...)
# S3 method for lme2
vcov2(object, param = NULL, data = NULL, ...)
# S3 method for lvmfit2
vcov2(object, param = NULL, data = NULL, ...)

Arguments

object

a linear model or a latent variable model

...

arguments to be passed to sCorrect.

param

[optional] the fitted parameters.

data

[optional] the data set.

bias.correct

[logical] should the standard errors of the coefficients be corrected for small sample bias? Only relevant if the sCorrect function has not yet be applied to the object.

Value

A matrix.

Details

If argument p or data is not null, then the small sample size correction is recomputed to correct the influence function.

See Also

sCorrect to obtain lm2, gls2, lme2, or lvmfit2 objects.

Examples

# NOT RUN {
n <- 5e1
p <- 3
X.name <- paste0("X",1:p)
link.lvm <- paste0("Y~",X.name)
formula.lvm <- as.formula(paste0("Y~",paste0(X.name,collapse="+")))

m <- lvm(formula.lvm)
distribution(m,~Id) <- sequence.lvm(0)
set.seed(10)
d <- lava::sim(m,n)

## linear model
e.lm <- lm(formula.lvm,data=d)
vcov.tempo <- vcov2(e.lm, bias.correct = TRUE)
vcov.tempo[rownames(vcov(e.lm)),colnames(vcov(e.lm))]/vcov(e.lm)

## latent variable model
e.lvm <- estimate(lvm(formula.lvm),data=d)
vcov.tempo <- vcov2(e.lvm, bias.correct = FALSE)
vcov.tempo/vcov(e.lvm)

# }