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#' Stepwise
#'
#' Build a model according to the stepwise procedure (bidirectional) and the
#' given criterion.
#'
#' @inheritParams forward
#'
#' @details Type \code{browseVignettes("bigstep")} for more details.
#'
#' @return An object of class \code{big}.
#'
#' @examples
#' set.seed(1)
#' n <- 30
#' p <- 10
#' X <- matrix(rnorm(n * p), ncol = p)
#' y <- X[, 2] + 2*X[, 3] - X[, 6] + rnorm(n)
#' d <- prepare_data(y, X)
#' stepwise(d)
#' d %>%
#' fast_forward(crit = aic) %>%
#' stepwise(crit = bic)
#'
#' @export
stepwise <- function(data, crit = mbic, ...) {
stopifnot(class(data) == "big")
y <- data$y
fit_fun <- data$fit_fun
Xm <- data$Xm
na <- data$na
maxp <- data$maxp
verb <- data$verbose
metric <- data$metric
n <- length(y)
k <- ncol(Xm)
s <- length(data$stay)
p <- ncol(data$X) + s
data$stepwise <- TRUE
data$model <- colnames(Xm)[-1]
loglik <- loglik(y, Xm, fit_fun, na)
crit_v <- R.utils::doCall(crit, loglik = loglik, n = n, k = k - s, p = p,
Xm = Xm, ...)
data$crit <- crit_v
if (verb) message("Starting stepwise, ", k - 1, " variables, crit = ",
round(crit_v, 2), ", ", metric, " = ",
round(metric(data), 3), ".")
repeat {
model <- data$model
data_f <- forward(data, crit, ...)
data_b <- backward(data, crit, ...)
crit_v_new <- min(data_f$crit, data_b$crit)
if (crit_v_new < crit_v) {
crit_v <- crit_v_new
if (data_f$crit < data_b$crit) {
data <- data_f
if (verb) message("Variable ", setdiff(data$model, model),
" added with crit = ", round(data$crit, 2), ", ", metric, " = ",
round(data$metric_v, 3), ".")
} else {
data <- data_b
if (verb) message("Variable ", setdiff(model, data$model),
" removed with crit = ", round(data$crit, 2), ", ", metric, " = ",
round(data$metric_v, 3), ".")
}
} else break
}
if (verb) message("Done.\n")
data$stepwise <- FALSE
return(invisible(data))
}
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