gap (version 1.1-16)

h2: Heritability estimation according to twin correlations

Description

Heritability and variance estimation according to twin pair correlations.

Usage

h2(mzDat=NULL,dzDat=NULL,rmz=NULL,rdz=NULL,nmz=NULL,ndz=NULL,selV=NULL)

Arguments

mzDat

a data frame for monzygotic twins (MZ)

dzDat

a data frame for dizygotic twins (DZ)

rmz

correlation for MZ twins

rdz

correlation for DZ twins

nmz

sample size for MZ twins

ndz

sample size for DZ twins

selV

names of variables for twin and cotwin

Value

The returned value is a matrix containing heritability and their variance estimations for "h2","c2","e2","vh","vc","ve".

References

Keeping ES. Introduction to Statistical Inference, Dover Pulications, Inc. 1995

Details

The example section shows how to obtain bootstrap 95%CI.

Examples

## Not run: 
# 
# ACE_CI <- function(mzData,dzData,n.sim=5,selV=NULL,verbose=TRUE)
# {
# ACEr_twinData <- h2(mzDat=mzData,dzDat=dzData,selV=selV)
# print(ACEr_twinData)
# 
# nmz <- dim(mzData)[1]
# ndz <- dim(dzData)[1]
# a <- ar <- vector()
# set.seed(12345)
# for(i in 1:n.sim)
# {
#   cat("\rRunning # ",i,"/", n.sim,"\r",sep="")
#   sampled_mz <- sample(1:nmz, replace=TRUE)
#   sampled_dz <- sample(1:ndz, replace=TRUE)
#   mzDat <- mzData[sampled_mz,]
#   dzDat <- dzData[sampled_dz,]
#   ACEr_i <- h2(mzDat=mzDat,dzDat=dzDat,selV=selV)
#   if(verbose) print(ACEr_i)
#   ar <- rbind(ar,ACEr_i)
# }
# cat("\n\nheritability according to correlations\n\n")
# ar <- as.data.frame(ar)
# m <- mean(ar,na.rm=TRUE)
# s <- sd(ar,na.rm=TRUE)
# allr <- data.frame(mean=m,sd=s,lcl=m-1.96*s,ucl=m+1.96*s)
# print(allr)
# }
# 
# selVars <- c('bmi1','bmi2')
# 
# library(mvtnorm)
# n.sim <- 500
# cat ("\nThe first study\n\n")
# mzm <- as.data.frame(rmvnorm(195, c(22.75,22.75),
#                      matrix(2.66^2*c(1, 0.67, 0.67, 1), 2)))
# dzm <- as.data.frame(rmvnorm(130, c(23.44,23.44),
#                      matrix(2.75^2*c(1, 0.32, 0.32, 1), 2)))
# mzw <- as.data.frame(rmvnorm(384, c(21.44,21.44),
#                      matrix(3.08^2*c(1, 0.72, 0.72, 1), 2)))
# dzw <- as.data.frame(rmvnorm(243, c(21.72,21.72),
#                      matrix(3.12^2*c(1, 0.33, 0.33, 1), 2)))
# names(mzm) <- names(dzm) <- names(mzw) <- names(dzw) <- c("bmi1","bmi2")
# ACE_CI(mzm,dzm,n.sim,selV=selVars,verbose=FALSE)
# ACE_CI(mzw,dzw,n.sim,selV=selVars,verbose=FALSE)
# 
# ## End(Not run)