# R code to read individual likelihood statistics
 # May be run in R with a command such as:
# source("piq.il.R",echo=TRUE)
 # It will then compute various statistics, 
 # sort the likelihoods by Mahalanobis Distance, and
 # print them with 1st definition variable
 # If you want output to go to a file uncomment next line
# sink("piq.il.R.ro")
 # ... and remember to issue sink() later to restore R output to screen
 names<-c("Defin_var1", "neg2LnL","Mahalanobis",    "Q" ,"Obs_id","Nvar","Incalculable","Zero","Model"                ,"Weight","LtimesWt")
pi<-matrix(scan("piq.il",multi.line=F),byrow=T,ncol=11)
 dimnames(pi) <- list(NULL,names)
piindlike<-data.frame(pi)
 # NOTE: You must change sd() to stdev() for Splus!
quantfun <- function (x) c(length(x),quantile(x,c(.025,.05,.10,.25,.50,.75,.90,.95,.975)),mean(x),sd(x),mean(x)-1.64485*sd(x),mean(x)+1.64485*sd(x),mean(x)-1.95996*sd(x),mean(x)+1.95996*sd(x),min(x),max(x))
result<- apply(piindlike,2,quantfun)
dimnames(result)[[1]]<-list("N"                        ,"2.5%","5%" ,"10%","25%","50%","75%","90%"                ,"95%","97.5%" ,"mean","sd","mean-1.64sd"                 ,"mean+1.64sd","mean-1.96sd"
 ,"mean+1.96sd"                    ,"min(x)","max(x)")
result
 attach(piindlike)
m<-order(Model,Mahalanobis)
cat ("Sorted By Model and Mahalanobis Distance\n")
cat("Model, First Definition Variable, Mahalanobis Distance")
cbind(Model[m],Defin_var1[m],Mahalanobis[m])