Nothing
variableOut <- function(model, k = 2, update.it = FALSE, xlab, ylab,
pch, col, lty, ylim, ...) {
if(missing(xlab)) xlab <- 'Excluded variable'
if(missing(ylab)) ylab <- 'AIC'
if(missing(pch)) pch <- 16
if(missing(lty)) lty <- 2
if(inherits(model, "glm")) {
fam = model$family$family
not.admitted <- c('quasi', 'quasibinomial', 'quasipoisson')
if(fam %in% not.admitted) stop("Can't use AIC with quasi-GLM")
}
Terms <- terms(model)
vars <- colnames(attr(Terms, "factors"))
formulae <- Terms ; attributes(formulae) <- NULL
n.scores <- length(vars)
scores <- rep(NA, n.scores)
models <- list()
scoref <- function(mod) {
AIC(mod, k = k)
}
score.full <- scoref(model)
for(i in 1:n.scores) {
new.formulae <- update.formula(formulae,
paste(". ~ . -", vars[i]))
new.model <- update(model, new.formulae)
scores[i] <- scoref(new.model)
models[[i]] <- new.model
}
x <- seq_len(n.scores)
y0 <- rep(score.full, n.scores)
diffs <- y0 - scores
ymax <- max(scores)
ymin <- min(c(scores, score.full))
if(missing(ylim)) ylim <- c(ymin, ymax + .17*(ymax - ymin))
if(missing(col)) col <- ifelse(diffs < 0, 1, 2)
plot(x = x, y = scores, xaxt = "n", xlab = xlab, ylab = ylab,
pch = pch, col = col, ylim = ylim, ...)
axis(side = 1, at = x, labels = vars)
segments(x0 = x, y0 = y0, y1 = scores, col = col, lwd = 2)
abline(h = score.full, lty = lty)
legend("topleft", col = 1, lty = 2, legend = 'Full model',
bty = 'n')
if(update.it) {
scores <- c(scores, score.full)
models[[n.scores + 1]] <- model
mod.out <- models[[which.min(scores)]]
mod.out
}
}
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