getVarCov2: Residual Variance-Covariance Matrix With Small Sample...

In lavaSearch2: Tools for Model Specification in the Latent Variable Framework

Residual Variance-Covariance Matrix With Small Sample Correction.

Description

Reconstruct the residual variance-covariance matrix from a latent variable model. It is similar to nlme::getVarCov but with small sample correction.

Usage

getVarCov2(object, ...)

## S3 method for class 'lvmfit'
getVarCov2(object, ssc = lava.options()$ssc, ...)

## S3 method for class 'lvmfit2'
getVarCov2(object, ...)

Arguments

object	a lvmfit or lvmfit2 object (i.e. output of lava::estimate or lavaSearch2::estimate2).
...	additional argument passed to estimate2 when using a lvmfit object.
ssc	[character] method used to correct the small sample bias of the variance coefficients: no correction (code"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.

Details

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

Value

A matrix with as many rows and column as the number of endogenous variables

Examples

#### simulate data ####
set.seed(10)
n <- 101

Y1 <- rnorm(n, mean = 0)
Y2 <- rnorm(n, mean = 0.3)
Id <- findInterval(runif(n), seq(0.1,1,0.1))
data.df <- rbind(data.frame(Y=Y1,G="1",Id = Id),
           data.frame(Y=Y2,G="2",Id = Id)
           )

#### latent variable models ####
library(lava)
e.lvm <- estimate(lvm(Y ~ G), data = data.df)
getVarCov2(e.lvm)

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