Nothing
# A new function: probabilities for ordered choice
ocProb <- function(w, nam.c, n = 100, digits = 3)
{
# 1. Check inputs
if (!inherits(w, "polr")) {
stop("Need an ordered choice model from 'polr()'.\n")
}
if (w$method != "probit" & w$method != "logistic") {
stop("Need a probit or logit model.\n")
}
if (missing(nam.c)) stop("Need a continous variable name'.\n")
# 2. Abstract data out
lev <- w$lev; J <- length(lev)
x.name <- attr(x = w$terms, which = "term.labels")
x2 <- w$model[, x.name]
if (identical(sort(unique(x2[, nam.c])), c(0, 1)) ||
inherits(x2[, nam.c], what = "factor")) {
stop("nam.c must be a continuous variable.")
}
ww <- paste("~ 1", paste("+", x.name, collapse = " "), collapse = " ")
x <- model.matrix(as.formula(ww), data = x2)[, -1]
b.est <- as.matrix(coef(w)); K <- nrow(b.est)
z <- c(-10^6, w$zeta, 10^6) # expand it with two extreme thresholds
z2 <- matrix(data = z, nrow = n, ncol = length(z), byrow = TRUE)
pfun <- switch(w$method, probit = pnorm, logistic = plogis)
dfun <- switch(w$method, probit = dnorm, logistic = dlogis)
V2 <- vcov(w) # increase covarance matrix by 2 fixed thresholds
V3 <- rbind(cbind(V2, 0, 0), 0, 0)
ind <- c(1:K, nrow(V3)-1, (K+1):(K+J-1), nrow(V3))
V4 <- V3[ind, ]; V5 <- V4[, ind]
# 3. Construct x matrix and compute xb
mm <- matrix(data = colMeans(x), ncol = ncol(x), nrow = n, byrow = TRUE)
colnames(mm) <- colnames(x)
ran <- range(x[, nam.c])
mm[, nam.c] <- seq(from = ran[1], to = ran[2], length.out = n)
xb <- mm %*% b.est
xb2 <- matrix(data = xb, nrow = n, ncol = J, byrow = FALSE) # J copy
# 4. Compute probability by category; vectorized on z2 and xb2
pp <- pfun(z2[, 2:(J+1)] - xb2) - pfun(z2[, 1:J] - xb2)
trend <- cbind(mm[, nam.c], pp)
colnames(trend) <- c(nam.c, paste("p", lev, sep="."))
# 5. Compute the standard errors
se <- matrix(data = 0, nrow = n, ncol = J)
for (i in 1:J) {
z1 <- z[i] - xb; z2 <- z[i+1] - xb
d1 <- diag(c(dfun(z1) - dfun(z2)), n, n) %*% mm
q1 <- - dfun(z1); q2 <- dfun(z2)
dr <- cbind(d1, q1, q2)
V <- V5[c(1:K, K+i, K+i+1), c(1:K, K+i, K+i+1)]
va <- dr %*% V %*% t(dr)
se[, i] <- sqrt(diag(va))
}
colnames(se) <- paste("Pred_SE", lev, sep = ".")
# 6. Report results
t.value <- pp / se
p.value <- 2 * (1 - pt(abs(t.value), n - K))
out <- list()
for (i in 1:J) {
out[[i]] <- round(cbind(predicted_prob = pp[, i], error = se[, i],
t.value = t.value[, i], p.value = p.value[, i]), digits)
}
out[[J+1]] <- round(x = trend, digits = digits)
names(out) <- paste("predicted_prob", c(lev, "all"), sep = ".")
result <- listn(w, nam.c, method=w$method, mean.x=colMeans(x), out, lev)
class(result) <- "ocProb"; return(result)
}
# Example: include "Freq" to have a continuous variable for demo
library(erer); library(MASS); data(housing); str(housing); tail(housing)
reg2 <- polr(formula = Sat ~ Infl + Type + Cont + Freq, data = housing,
Hess = TRUE, method = "probit")
p2 <- ocProb(w = reg2, nam.c = 'Freq', n = 300); p2
plot(p2)
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