"scaling.fits" <-
function(model, data, test="logit", digits = 2,rowwise=TRUE) {
model <- as.matrix(model)
data <- as.matrix(data)
if (test=="choice") {
model <- as.vector(model)
if (min(model) <= 0 ) model <- model -min(model)
prob = model/(model %+% t(model))
} else {
pdif <- model %+%-t(model)
if (test=="logit") {
prob <- 1/(1 + exp(-pdif)) }
else {if (test=="normal") {
prob <- pnorm(pdif) }} }
if (rowwise) {prob= 1- prob}
error <- data - prob
sum.error2 <- sum(error^2, na.rm = TRUE)
sum.data2 <- sum(data^2, na.rm = TRUE)
gof <- 1 - sum.error2/sum.data2
fit <- list(GF = gof, original = sum.data2, resid = sum.error2,
residual = round(error,
digits))
return(fit) }
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