# Simulate HWI under combined group and individual location errors with unequal observation probabilities Generate an estimated half weight index under conditions where both group and individual location errors are present, but where individual location errors are not equal for all individuals ```r ceSimErrorsUnequalHWI(aAB, w, psi, Emean, Esd, n) ``` ## Arguments - `aAB`: The real association rate between individuals A and B - `w`: The group location error term - `psi`: The relative importance of the group location error to the individual location error - `Emean`: The mean probability of missing one individual given that it is not with the other - `Esd`: The standard deviation of the individual location error distribution - `n`: The number of sampling periods ## 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. E (Epsilon) is probability of missing one individual given that it is not with the other. psi represents the relative importance of the group location error to the individual identification error. The function allows a distribution of individual location error probabilities to be given using Emean and Esd. 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. ## Returns Returns two values: the simulated half weight index and whether or not it falls within the 95 percent confidence intervals (1 = yes, 0 = no) ## References Hoppitt, W. & Farine, D.R. (in prep) Association indices for quantifying social relationships: how to deal with missing observations of individuals or groups. ## Author(s) William Hoppitt Damien Farine ## Examples ```r # Set a real association index aAB <- 0.5 # Set w range w <- seq(-1,1,0.1) # Set errors parameters psi <- 0.5 Emean <- 0.5 Esd <- 0.05 # 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 <- ceSimErrorsUnequalHWI(aAB,w[i],psi,Emean,Esd,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") ```

ceSimErrorsUnequalHWI function