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#' Stepwise AIC backward elimination
#'
#' @description
#' Build regression model from a set of candidate predictor variables by
#' removing predictors based on akaike information criterion, in a stepwise
#' manner until there is no variable left to remove any more.
#'
#' @param model An object of class \code{glm}; the model should include all
#' candidate predictor variables.
#' @param progress Logical; if \code{TRUE}, will display variable selection progress.
#' @param details Logical; if \code{TRUE}, will print the regression result at
#' each step.
#' @param ... Other arguments.
#' @param x An object of class \code{blr_step_aic_backward}.
#' @param text_size size of the text in the plot.
#' @param print_plot logical; if \code{TRUE}, prints the plot else returns a plot object.
#'
#' @return \code{blr_step_aic_backward} returns an object of class
#' \code{"blr_step_aic_backward"}. An object of class
#' \code{"blr_step_aic_backward"} is a list containing the following components:
#'
#' \item{model}{model with the least AIC; an object of class \code{glm}}
#' \item{candidates}{candidate predictor variables}
#' \item{steps}{total number of steps}
#' \item{predictors}{variables removed from the model}
#' \item{aics}{akaike information criteria}
#' \item{bics}{bayesian information criteria}
#' \item{devs}{deviances}
#'
#' @references
#' Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
#'
#' @examples
#' \dontrun{
#' model <- glm(honcomp ~ female + read + science + math + prog + socst,
#' data = hsb2, family = binomial(link = 'logit'))
#'
#' # elimination summary
#' blr_step_aic_backward(model)
#'
#' # print details of each step
#' blr_step_aic_backward(model, details = TRUE)
#'
#' # plot
#' plot(blr_step_aic_backward(model))
#'
#' # final model
#' k <- blr_step_aic_backward(model)
#' k$model
#'
#' }
#'
#' @family variable selection procedures
#'
#' @export
#'
blr_step_aic_backward <- function(model, ...) UseMethod("blr_step_aic_backward")
#' @export
#' @rdname blr_step_aic_backward
#'
blr_step_aic_backward.default <- function(model, progress = FALSE, details = FALSE, ...) {
if (details) {
progress <- TRUE
}
blr_check_model(model)
blr_check_logic(details)
blr_check_npredictors(model, 3)
response <- names(model$model)[1]
l <- model$model
nam <- coeff_names(model)
preds <- nam
aic_f <- model_aic(model)
mi <- glm(
paste(response, "~", paste(preds, collapse = " + ")),
data = l, family = binomial(link = "logit")
)
laic <- aic_f
lbic <- model_bic(mi)
ldev <- model_deviance(mi)
if (progress) {
cat(format("Backward Elimination Method", justify = "left", width = 27), "\n")
cat(rep("-", 27), sep = "", "\n\n")
cat(format("Candidate Terms:", justify = "left", width = 16), "\n\n")
for (i in seq_len(length(nam))) {
cat(paste(i, ".", nam[i]), "\n")
}
cat("\n")
}
if (details) {
cat(" Step 0: AIC =", aic_f, "\n", paste(response, "~", paste(preds, collapse = " + "), "\n\n"))
}
ilp <- length(preds)
end <- FALSE
step <- 0
rpred <- c()
aics <- c()
bics <- c()
devs <- c()
for (i in seq_len(ilp)) {
predictors <- preds[-i]
m <- glm(
paste(response, "~", paste(predictors, collapse = " + ")),
data = l, family = binomial(link = "logit")
)
aics[i] <- model_aic(m)
bics[i] <- model_bic(m)
devs[i] <- model_deviance(m)
}
da <- data.frame(predictors = preds, aics = aics, bics = bics, devs = devs)
da2 <- da[order(da[['aics']]), ]
if (details) {
w1 <- max(nchar("Predictor"), nchar(predictors))
w2 <- 2
w3 <- max(nchar("AIC"), nchar(format(round(aics, 3), nsmall = 3)))
w4 <- max(nchar("BIC"), nchar(format(round(bics, 3), nsmall = 3)))
w5 <- max(nchar("Deviance"), nchar(format(round(devs, 3), nsmall = 3)))
w <- sum(w1, w2, w3, w4, w5, 16)
ln <- length(aics)
cat(rep("-", w), sep = "", "\n")
cat(
fl("Variable", w1), fs(), fc("DF", w2), fs(), fc("AIC", w3), fs(),
fc("BIC", w4), fs(), fc("Deviance", w5), "\n"
)
cat(rep("-", w), sep = "", "\n")
for (i in seq_len(ln)) {
cat(
fl(da2[i, 1], w1), fs(), fc(1, w2), fs(),
fg(format(round(da2[i, 2], 3), nsmall = 3), w3), fs(),
fg(format(round(da2[i, 3], 3), nsmall = 3), w4), fs(),
fg(format(round(da2[i, 4], 3), nsmall = 3), w5), "\n"
)
}
cat(rep("-", w), sep = "", "\n\n")
}
if (progress) {
cat("\n")
if (!details) {
cat("Variables Removed:", "\n\n")
}
}
while (!end) {
minc <- which(aics == min(aics))
if (aics[minc] < aic_f) {
rpred <- c(rpred, preds[minc])
preds <- preds[-minc]
ilp <- length(preds)
step <- step + 1
aic_f <- aics[minc]
mi <- glm(
paste(response, "~", paste(preds, collapse = " + ")),
data = l, family = binomial(link = "logit")
)
laic <- c(laic, aic_f)
lbic <- c(lbic, model_bic(mi))
ldev <- c(ldev, model_deviance(mi))
aics <- c()
bics <- c()
devs <- c()
if (progress) {
cat(paste("x", rev(rpred)[1], "\n"))
}
for (i in seq_len(ilp)) {
predictors <- preds[-i]
m <- glm(
paste(response, "~", paste(predictors, collapse = " + ")),
data = l, family = binomial(link = "logit")
)
aics[i] <- model_aic(m)
bics[i] <- model_bic(m)
devs[i] <- model_deviance(m)
}
if (details) {
cat("\n\n", " Step", step, ": AIC =", aic_f, "\n", paste(response, "~", paste(preds, collapse = " + "), "\n\n"))
da <- data.frame(
predictors = preds, aics = aics, bics = bics,
devs = devs
)
da2 <- da[order(da[['aics']]), ]
w1 <- max(nchar("Predictor"), nchar(predictors))
w2 <- 2
w3 <- max(nchar("AIC"), nchar(format(round(aics, 3), nsmall = 3)))
w4 <- max(nchar("BIC"), nchar(format(round(bics, 3), nsmall = 3)))
w5 <- max(nchar("Deviance"), nchar(format(round(devs, 3), nsmall = 3)))
w <- sum(w1, w2, w3, w4, w5, 16)
ln <- length(aics)
cat(rep("-", w), sep = "", "\n")
cat(
fl("Variable", w1), fs(), fc("DF", w2), fs(), fc("AIC", w3), fs(),
fc("BIC", w4), fs(), fc("Deviance", w5), "\n"
)
cat(rep("-", w), sep = "", "\n")
for (i in seq_len(ln)) {
cat(
fl(da2[i, 1], w1), fs(), fc(1, w2), fs(),
fg(format(round(da2[i, 2], 3), nsmall = 3), w3), fs(),
fg(format(round(da2[i, 3], 3), nsmall = 3), w4), fs(),
fg(format(round(da2[i, 4], 3), nsmall = 3), w5), "\n"
)
}
cat(rep("-", w), sep = "", "\n\n")
}
} else {
end <- TRUE
if (details) {
cat("No more variables to be removed.")
}
}
}
if (details) {
cat("\n\n")
cat("Variables Removed:", "\n\n")
for (i in seq_len(length(rpred))) {
cat(paste("-", rpred[i]), "\n")
}
}
if (progress) {
cat("\n\n")
cat("Final Model Output", "\n")
cat(rep("-", 18), sep = "", "\n\n")
fi <- blr_regress(
paste(response, "~", paste(preds, collapse = " + ")),
data = l
)
print(fi)
}
final_model <- glm(paste(response, "~", paste(preds, collapse = " + ")),
data = l, family = binomial(link = 'logit'))
vars <- c("Full Model", rpred)
step_result <- data.frame(variable = vars,
aic = laic,
bic = lbic,
deviance = ldev)
out <- list(
candidates = nam,
steps = step,
predictors = rpred,
result = step_result,
model = final_model
)
class(out) <- "blr_step_aic_backward"
return(out)
}
#' @export
#'
print.blr_step_aic_backward <- function(x, ...) {
if (x$steps > 0) {
print_backward_elimination(x)
} else {
print("No variables have been removed from the model.")
}
}
#' @rdname blr_step_aic_backward
#' @export
#'
plot.blr_step_aic_backward <- function(x, text_size = 3, print_plot = TRUE, ...) {
steps <- NULL
aics <- NULL
tx <- NULL
a <- NULL
b <- NULL
y <- c(0, seq_len(x$steps))
xloc <- y - 0.1
yloc <- x$result$aic - 0.2
xmin <- min(y) - 0.4
xmax <- max(y) + 1
ymin <- min(x$result$aic) - 1
ymax <- max(x$result$aic) + 1
predictors <- c("Full Model", x$predictors)
d2 <- data.frame(x = xloc, y = yloc, tx = predictors)
d <- data.frame(a = y, b = x$result$aic)
p <-
ggplot(d, aes(x = a, y = b)) + geom_line(color = "blue") +
geom_point(color = "blue", shape = 1, size = 2) + xlim(c(xmin, xmax)) +
ylim(c(ymin, ymax)) + xlab("Step") + ylab("AIC") +
ggtitle("Stepwise AIC Backward Elimination") +
geom_text(data = d2, aes(x = x, y = y, label = tx),
size = text_size, hjust = 0, nudge_x = 0.1)
if (print_plot) {
print(p)
}
invisible(p)
}
#' Coefficient names
#'
#' Returns the names of the coefficients including
#' interaction variables.
#'
#' @param model An object of class \code{lm}.
#'
#' @noRd
#'
coeff_names <- function(model) {
colnames(attr(model$terms, which = "factors"))
}
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