gleSimHWI {assocInd}R Documentation

Simulate HWI with group location error

Description

Generate an estimated simple ratio index under a given rate of missing observations of groups that are present

Usage

gleSimHWI(aAB, w, pMissA, pMissB, n)

Arguments

aAB

The real association rate between individuals A and B

w

The group location error term

pMissA

Probability of missing group A

pMissB

Probability of missing group A

n

The number of sampling periods (number of observations of the dyad)

Details

A simple function that simulates data for a given probability of missing groups and real association strength. The w term represents the likelihood of failing to observe a group containing a and b compared to failing to observe both groups containing a and b if the two individuals are apart. The function returns the simulated half weight index and whether the value lies within the 95 percent confidence intervals of the half weight index given the number of samples and under the assumption of no error.

Value

Returns two values: the simulated half weight index and whether or not it falls within the 95 percent confidence intervals (1 = yes, 0 = no)

Author(s)

William Hoppitt <W.J.E.Hoppitt@leeds.ac.uk> Damien Farine <dfarine@orn.mpg.de>

References

Hoppitt, W. & Farine, D.R. (in prep) Association indices for quantifying social relationships: how to deal with missing observations of individuals or groups.

Examples


	# Set a real association index
	aAB <- 0.5
	
	# Set w range
	w <- seq(-1,1,0.1)

	# Set observation errors
	pMissA <- 0.7
	pMissB <- 0.7
	
	# Replicate N times
	replicates <- 100  # small number used to save computation time
	
	# Create a blank storage matrices
	assocStrength <- matrix(NA,nrow=replicates,ncol=length(w))
	inCIs <- matrix(NA,nrow=replicates,ncol=length(w))
	
	# Loop through repeating N times for each error value
	for (i in 1:length(w)) { 
		for (j in 1:replicates) {
			out <- gleSimHWI(aAB,w[i],pMissA,pMissB,20)
			assocStrength[j,i] <- out[1]
			inCIs[j,i] <- out[2]
		}
	}
	
	# Plot the results
	par(mfrow=c(1,2))
	plot(w,colMeans(assocStrength, na.rm=TRUE), pch=20, ylim=c(0,1), ylab="Simulated HWI")
	CIs <- apply(assocStrength,2,quantile,c(0.025,0.975),na.rm=TRUE)
	arrows(w,CIs[1,],w,CIs[2,],len=0.1,code=3,angle=90)
	abline(h=0.5,col="red")
	
	plot(w,colMeans(inCIs, na.rm=TRUE), pch=20, ylim=c(0,1), ylab="Percent of times in CIs")
	abline(h=0.95, col="red")


[Package assocInd version 1.0.1 Index]