h2Estimate {multiDimBio}

Estimates the heritability of a binomial trait

Description

Estimates the narrow-sense heritability of a binomial trait and calculates a p value by randomization.

Usage

h2Estimate(data,nreps=1000)

Arguments

data

a (non-empty) numeric matrix with three columns. The first two should contain the trait data (number of occurances of each outcome type) and the third should contain the group ids.

nreps

a (non-empty) numeric value indicating the number of resamples to perform when calculating the emperical p value.

Details

Estimates the narrow-sense heritability of a binomial trait. This function works by fitting two models, one with and one without a random-effect of sire. These models are compared by randomizing the sire ids nreps times and re-fitting the model. For each of the nreps model pairs, a deviance is calculated and a "p value" estimated by comparing that distribution of deviance to the observed. The heritability is approximatly tau2/(tau2+(pi/sqrt(3))^2), where tau2 is the random-effect variance due to sire.

Value

Returns a list. The list contains:

h2

The estimated narrow-sense heritability. The narrow-sense heritability is approximatly tau2/(tau2+(pi/sqrt(3))^2), where tau2 is the random-effect variance due to sire.

pval

The probability that the best-fit model includes an extra variance term for sire (random effect of dad). The value is calculated by comparing the deviances from nreps number of randomized model comparisions.

deviance

The deviance between a null model without a random effect of dad and a model with.

sim

The simulated deviances used in calculating the p value in pval.

obsMod

The glmer model object resulting from the observed data.

Examples

	#non-zero heritability
	ndads <- 18
	mm <- 4
	vv <-  6
	tau2 <- 2.5
	nbins <- 3
	
	mylogit <- function(x) log(x/{1-x})
	ilogit <- function(x) 1/{1+exp(-x)}
	
	swimprob <- ilogit(rnorm(ndads, 0, sqrt(tau2)))
	mytable <- NULL
	for(i in 1:ndads) {
		bincounts <- pmax(1,rnbinom(nbins, mu = mm, size = mm^2/{vv-mm}))
		swim <- rbinom(3, bincounts,swimprob[i])
		set <- bincounts - swim
		theserows <- data.frame(set=set,swim=swim, Dad = i, Bin = 1:nbins)
		mytable <- rbind(mytable, theserows)
	}
	
	est <- h2Estimate(mytable,nreps=10)
	
	print(est$h2)
	
	#zero heritability
	ndads <- 18
	mm <- 4
	vv <-  6
	tau2 <- 0
	nbins <- 3
	
	mylogit <- function(x) log(x/{1-x})
	ilogit <- function(x) 1/{1+exp(-x)}
	
	swimprob <- ilogit(rnorm(ndads, 0, sqrt(tau2)))
	mytable0 <- NULL
	for(i in 1:ndads) {
		bincounts <- pmax(1,rnbinom(nbins, mu = mm, size = mm^2/{vv-mm}))
		swim <- rbinom(3, bincounts,swimprob[i])
		set <- bincounts - swim
		theserows <- data.frame(set=set,swim=swim, Dad = i, Bin = 1:nbins)
		mytable0 <- rbind(mytable0, theserows)
	}
	
	est0 <- h2Estimate(mytable0,nreps=10)
	
	print(est0$h2)