lavaSearch2 (version 2.0.3)

information2: Expected Information With Small Sample Correction.

Description

Extract the expected information matrix from a latent variable model. Similar to lava::information but with small sample correction.

Usage

information2(object, as.lava, ssc, ...)
# S3 method for lvmfit
information2(object, as.lava = TRUE, ssc = lava.options()$ssc, ...)
# S3 method for lvmfit2
information2(object, as.lava = TRUE, ...)
# S3 method for lvmfit2
information(x, ...)

Value

A matrix with as many rows and columns as the number of coefficients.

Arguments

object, x

a lvmfit or lvmfit2 object (i.e. output of lava::estimate or lavaSearch2::estimate2).

as.lava

[logical] if TRUE, uses the same names as when using stats::coef.

ssc

[character] method used to correct the small sample bias of the variance coefficients: no correction ("none"/FALSE/NA), correct the first order bias in the residual variance ("residual"), or correct the first order bias in the estimated coefficients "cox"). Only relevant when using a lvmfit object.

...

additional argument passed to estimate2 when using a lvmfit object.

Details

When argument object is a lvmfit object, the method first calls estimate2 and then extract the information matrix.

See Also

estimate2 to obtain lvmfit2 objects.

Examples

#### simulate data ####
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 models ####
e.lm <- lm(formula.lvm,data=d)

#### latent variable models ####
e.lvm <- estimate(lvm(formula.lvm),data=d)
information(e.lvm)
information2(e.lvm)
information2(e.lvm)[1:4,1:4] -  solve(vcov(e.lm))