| iieSimHWI {assocInd} | R Documentation |
Simulate HWI with individual identification error
Description
Generate an estimated half-weight index under a given rate of missing observations of one individual given that it is present
Usage
iieSimHWI(aAB, e, n)
Arguments
aAB |
The real association rate between individuals A and B |
e |
The probability of failing to observe an individual given it is present in a group |
n |
The number of sampling periods (number of observations of the dyad) |
Details
A simple function that simulates data for a given rate of identification error and real association strength. 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
# Create a range of errors
e <- seq(0,0.8,0.01)
# Replicate N times
replicates <- 100 # small number used to save computation time
# Create a blank storage matrices
assocStrength <- matrix(NA,nrow=replicates,ncol=length(e))
inCIs <- matrix(NA,nrow=replicates,ncol=length(e))
# Loop through repeating N times for each error value
for (i in 1:length(e)) {
for (j in 1:replicates) {
out <- iieSimHWI(aAB,e[i],20)
assocStrength[j,i] <- out[1]
inCIs[j,i] <- out[2]
}
}
# Plot the results
par(mfrow=c(1,2))
plot(e,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(e,CIs[1,],e,CIs[2,],len=0.1,code=3,angle=90)
abline(h=0.5,col="red")
plot(e,colMeans(inCIs, na.rm=TRUE), pch=20, ylim=c(0,1), ylab="Percent of times in CIs")
abline(h=0.95, col="red")