| iieSimvSRI {assocInd} | R Documentation |
Simulate vSRI with individual identification error
Description
Generate an estimated very simple ratio index under a given rate of missing observations of one individual given that it is present
Usage
iieSimvSRI(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 very simple ratio index and whether the value lies within the 95 percent confidence intervals of the very simple ratio index given the number of samples and under the assumption of no error.
Value
Returns two values: the simulated very simple ratio 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 <- iieSimvSRI(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")