| gleSimGLECI {assocInd} | R Documentation |
Simulate GLECI with group location error
Description
Generate an estimated group location error corrected index under a given rate of missing observations of groups that are present
Usage
gleSimGLECI(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 group location error corrected index and whether the value lies within the 95 percent confidence intervals of the group location error corrected index given the number of samples and under the assumption of no error.
Value
Returns two values: the simulated group location error corrected 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 <- gleSimGLECI(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 GLECI")
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")