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# gof.trial - Computes chi-square gof test for trial models
#
# Arguments:
# model - ddf model object
# breaks - distance cut points
# nc - number of distance classes
# Value:
# result - lists with observed,expected, chi-square value, df and p-value
gof.trial <- function(model, breaks=NULL, nc=NULL){
width <- model$meta.data$width
left <- model$meta.data$left
xmat <- model$mr$mr$data
data <- eval(model$data)
data <- data[data$observer==1&data$object %in%
as.numeric(names(model$fitted)),]
n <- dim(xmat)[1]
# Set up omega index; 1 - detected by secondary only, 2 - detected by both
xmat$omega <- rep(1,dim(xmat)[1])
xmat$omega[xmat$timesdetected==2] <- 2
# If number of classes for histogram intervals was not set compute
# a reasonable default
if(is.null(nc)){
nc <- round(sqrt(min(length(xmat$distance[xmat$observer==1 &
xmat$detected==1]),
length(xmat$distance[xmat$observer==1 &
xmat$timesdetected==2]))), 0)
}
# Set up default break points unless some are specified
if(is.null(breaks)){
breaks <- left + ((width-left)/nc)*(0:nc)
}else{
nc <- length(breaks)-1
}
# Get predicted values for mr component
xmat$detected <- 1
p1 <- predict(model$mr, xmat, compute=TRUE, integrate=FALSE)$fitted
p.omega <- data.frame(object = rep(1:n, 2),
omega = c(rep(1, n), rep(2, n)),
distance = rep(xmat$distance, 2),
prob = rep(0, 2*n))
p.omega$prob[p.omega$omega==1] <- (1-p1)
p.omega$prob[p.omega$omega==2] <- p1
expected.2 <- by(p.omega$prob,
list(as.factor(p.omega$omega),
cut(p.omega$distance, breaks, include.lowest=TRUE)),
sum, na.rm=TRUE)
# Get predicted values for ds component
expected.1 <- rep(0,nc)
for(j in 1:nc){
expected.1[j] <- sum(predict(model, compute=TRUE,
int.range=matrix(c(breaks[j],breaks[j+1]),
nrow=1))$fitted/
model$fitted, na.rm=TRUE)
}
# Compute observed values of distance bins
observed.count.1 <- table(cut(data$distance, breaks, include.lowest=TRUE))
observed.count.2 <- table(as.factor(xmat$omega),
cut(xmat$distance,breaks, include.lowest=TRUE))
chisq.1 <- sum((observed.count.1-expected.1)^2/expected.1, na.rm=TRUE)
chisq.2 <- sum((observed.count.2-expected.2)^2/expected.2, na.rm=TRUE)
df.1 <- nc-1-length(model$ds$ds$par)
if(df.1<=0){
df.1 <- NA
p.1 <- NA
}else{
p.1 <- 1-pchisq(chisq.1,df.1)
}
df.2 <- nc-length(model$mr$par)
if(df.2<=0){
df.2 <- NA
p.2 <- NA
}else{
p.2 <- 1-pchisq(chisq.2,df.2)
}
return(list(chi1=list(observed=observed.count.1,
expected=expected.1,
chisq=chisq.1,
p=p.1,
df=df.1),
chi2=list(observed=observed.count.2,
expected=expected.2[1:2,],
chisq=chisq.2,
p=p.2,
df=df.2),
pooled.chi=list(chisq=chisq.1+chisq.2,
df=2*nc-length(model$par)-1,
p=1-pchisq(chisq.1+chisq.2,
2*nc-length(model$par)-1))))
}
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