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# Purpose : Goodness of fit statistics for categorical varibles
# Maintainer : Brendan Malone (brendan.malone@sydney.edu.au);
# Contributions :
# Status : working
# Note :
#
goofcat<- function(observed = NULL, predicted = NULL, conf.mat, imp=FALSE){
if (imp==TRUE){
if(class(conf.mat)!="matrix"){
stop("Entered data is NOT a matrix")}
if(nrow(conf.mat)!= ncol(conf.mat)) {
stop("Entered data is NOT a confusion matrix")}
else {OA<- ceiling(sum(diag(conf.mat))/sum(colSums(conf.mat)) * 100)
PA<- ceiling(diag(conf.mat)/colSums(conf.mat) * 100)
UA<- ceiling(diag(conf.mat)/rowSums(conf.mat) * 100)
PE_mat <- matrix(NA, ncol = 1, nrow = length(rowSums(conf.mat)))
for (i in 1:length(rowSums(conf.mat))) {
PE_mat[i, 1] <- (rowSums(conf.mat)[i]/sum(colSums(conf.mat))) * (colSums(conf.mat)[i]/sum(colSums(conf.mat)))}
KS <- (sum(diag(conf.mat))/sum(colSums(conf.mat)) - sum(PE_mat))/(1 - sum(PE_mat))}}
if (imp==FALSE) {obsMat<- table(observed,observed)
df<- data.frame(observed, predicted)
names(df)<- c("observed", "predicted")
#make a confusion matrix
cfuM<- function(df,obsMat){
c.Mat<- as.matrix(obsMat)
snames1<- c(colnames(c.Mat))
for (i in 1:nrow(c.Mat)){
for (j in 1:nrow(c.Mat)){
c.Mat[j,i]<- nrow(subset(df, df$observed ==snames1[i] & df$predicted ==snames1[j]))}}
fmat<- matrix(NA, nrow=nrow(c.Mat), ncol=ncol(c.Mat))
rownames(fmat)<- rownames(c.Mat)
colnames(fmat)<- colnames(c.Mat)
for (i in 1:nrow(c.Mat)){
fmat[i,]<- c(c.Mat[i,])}
return(fmat)}
conf.mat<- cfuM(df, obsMat)
OA<- ceiling(sum(diag(conf.mat))/sum(colSums(conf.mat)) * 100)
PA<- ceiling(diag(conf.mat)/colSums(conf.mat) * 100)
UA<- ceiling(diag(conf.mat)/rowSums(conf.mat) * 100)
PE_mat <- matrix(NA, ncol = 1, nrow = length(rowSums(conf.mat)))
for (i in 1:length(rowSums(conf.mat))) {
PE_mat[i, 1] <- (rowSums(conf.mat)[i]/sum(colSums(conf.mat))) * (colSums(conf.mat)[i]/sum(colSums(conf.mat)))}
KS <- (sum(diag(conf.mat))/sum(colSums(conf.mat)) - sum(PE_mat))/(1 - sum(PE_mat))}
retval<- list(conf.mat,OA, PA, UA, KS)
names(retval)<- c("confusion_matrix", "overall_accuracy", "producers_accuracy", "users_accuracy", "kappa")
return(retval)}
# end script
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