Nothing
drop2 <- function(object, scope, data, scale = 0, test=c("none", "Chisq"),
trace = FALSE, ...)
{
tl <- attr(terms(object), "term.labels")
if(missing(scope)) scope <- drop.scope(object)
else {
if(!is.character(scope))
scope <- attr(terms(update.formula(object$formula, scope)), "term.labels")
if(!all(match(scope, tl, 0L) > 0L))
stop("scope is not a subset of term labels")
}
ns <- length(scope)
ans <- matrix(nrow = ns + 1L, ncol = 2L,
dimnames = list(c("<none>", scope), c("df", "AIC")))
ans[1, ] <- AIC(object)
n0 <- nobs(object, use.fallback = TRUE)
env <- environment(formula(object))
for(i in seq_len(ns)) {
tt <- scope[i]
if(trace > 1) {
cat("trying -", tt, "\n", sep = "")
flush.console()
}
nfit <- update(object$formula, as.formula(paste("~ . -", tt)),
evaluate = FALSE)
fun_in = object$Function
if(fun_in == "GLMcat"){
object$parallel <- object$parallel[object$parallel != tt]
if(length(object$parallel) == 0) {object$parallel <- NA}
nfit <- .GLMcat(nfit, data, object$ratio, object$cdf, object$parallel,
object$categories_order, object$ref_category,
object$threshold, control_glmcat(object$control$maxit, object$control$epsilon, object$control$beta_init),
object$normalization_s0)
}else{
object$arguments$alternative_specific <- object$arguments$alternative_specific[object$arguments$alternative_specific != tt]
if(length(object$arguments$alternative_specific) == 0) {object$arguments$alternative_specific <- NA}
nfit <- .Discrete_CM(formula = nfit,
data = data,
cdf = object$cdf,
case_id = object$arguments$case_id,
alternatives = object$arguments$alternatives,
alternative_specific = object$arguments$alternative_specific,
# object$categories_order,
intercept = object$arguments$intercept,
reference = object$arguments$reference,
control_glmcat(object$control$maxit,
object$control$epsilon, object$control$beta_init),
normalization = object$normalization_s0)
}
ans[i+1, ] <- AIC(nfit)
nnew <- nobs(nfit, use.fallback = TRUE)
if(all(is.finite(c(n0, nnew))) && nnew != n0)
stop("number of rows in use has changed: remove missing values?")
}
dfs <- ans[1L , 1L] - ans[, 1L]
dfs[1L] <- NA
aod <- data.frame(Df = dfs, AIC = ans[,2])
test <- match.arg(test)
if(test == "Chisq") {
dev <- ans[, 2L] - 2*ans[, 1L]
dev <- dev - dev[1L] ; dev[1L] <- NA
nas <- !is.na(dev)
P <- dev
P[nas] <- safe_pchisq(dev[nas], dfs[nas], lower.tail = FALSE)
aod[, c("LRT", "Pr(>Chi)")] <- list(dev, P)
}
head <- c("Single term deletions", "\nModel:", deparse(formula(object)),
if(scale > 0) paste("\nscale: ", format(scale), "\n"))
class(aod) <- c("anova", "data.frame")
attr(aod, "heading") <- head
aod
}
add2 <- function(object, scope, data, scale = 0, test=c("none", "Chisq"),
trace = FALSE, ...)
{
if(missing(scope) || is.null(scope)) stop("no terms in scope")
if(!is.character(scope))
scope <- add.scope(object, update.formula(object, scope))
if(!length(scope))
stop("no terms in scope for adding to object")
# newform <- update.formula(object,
# paste(". ~ . +", paste(scope, collapse="+")))
# data <- model.frame(update(object, newform)) # remove NAs
# object <- update(object, data = data)
ns <- length(scope)
ans <- matrix(nrow = ns + 1L, ncol = 2L,
dimnames = list(c("<none>", scope), c("df", "AIC")))
ans[1L, ] <- AIC(object)
n0 <- nobs(object, use.fallback = TRUE)
# env <- environment(formula(object))
for(i in seq_len(ns)) {
tt <- scope[i]
if(trace > 1) {
cat("trying +", tt, "\n", sep = "")
flush.console()
}
# nfit <- update(object, as.formula(paste("~ . +", tt)),
# evaluate = FALSE)
# nfit <- eval(nfit, envir=env) # was eval.parent(nfit)
nfit <- update(object$formula, as.formula(paste("~ . +", tt)),
evaluate = FALSE)
# object$parallel <- object$parallel[object$parallel != tt]
fun_in = object$Function
if(fun_in == "GLMcat"){
# object$parallel <- object$parallel[object$parallel != tt]
# if(length(object$parallel) == 0) {object$parallel <- NA}
nfit <- .GLMcat(nfit, data, object$ratio, object$cdf, object$parallel,
object$categories_order, object$ref_category,
object$threshold, control_glmcat(object$control$maxit, object$control$epsilon, object$control$beta_init),
object$normalization_s0)
}else{
# object$arguments$alternative_specific <- object$arguments$alternative_specific[object$arguments$alternative_specific != tt]
# if(length(object$arguments$alternative_specific) == 0) {object$arguments$alternative_specific <- NA}
nfit <- .Discrete_CM(formula = nfit,
data = data,
cdf = object$cdf,
alternative_specific = object$arguments$alternative_specific,
# object$categories_order,
case_id = object$arguments$case_id,
alternatives = object$arguments$alternatives,
reference = object$arguments$reference,
intercept = object$arguments$intercept,
control_glmcat(object$control$maxit,
object$control$epsilon, object$control$beta_init),
normalization = object$normalization_s0)
}
ans[i+1L, ] <- AIC(nfit)
nnew <- nobs(nfit, use.fallback = TRUE)
if(all(is.finite(c(n0, nnew))) && nnew != n0)
stop("number of rows in use has changed: remove missing values?")
}
dfs <- ans[, 1L] - ans[1L, 1L]
dfs[1L] <- NA
aod <- data.frame(Df = dfs, AIC = ans[, 2L])
test <- match.arg(test)
if(test == "Chisq") {
dev <- ans[, 2L] - 2*ans[, 1L]
dev <- dev[1L] - dev; dev[1L] <- NA
nas <- !is.na(dev)
P <- dev
P[nas] <- safe_pchisq(dev[nas], dfs[nas], lower.tail=FALSE)
aod[, c("LRT", "Pr(>Chi)")] <- list(dev, P)
}
head <- c("Single term additions", "\nModel:", deparse(formula(object)),
if(scale > 0) paste("\nscale: ", format(scale), "\n"))
class(aod) <- c("anova", "data.frame")
attr(aod, "heading") <- head
aod
}
#' Stepwise for a \code{glmcat} model object
#' @description Stepwise for a \code{glmcat} model object based on the AIC.
#' @param object an fitted object of class \code{glmcat}.
#' @param scope defines the range of models examined in the stepwise search (same as in the step function of the stats package). This should be either a single formula, or a list containing components upper and lower, both formulae.
#' @param scale the scaling parameter (if applicable).
#' @param direction the mode of the stepwise search.
#' @param trace to print the process information.
#' @param keep a logical value indicating whether to keep the models from all steps.
#' @param steps the maximum number of steps.
#' @param k additional arguments (if needed).
#' @rdname step
#' @method step glmcat
#' @exportS3Method
step.glmcat <-
function(object, scope, scale, direction = c("both", "backward", "forward"), trace = 1, keep = FALSE, steps = 1000, k, ...) {
# function body
data <- object$data
mydeviance <- function(x, ...) {
dev <- deviance(x)
if (!is.null(dev))
dev
else AIC(x)
}
cut.string <- function(string) {
if (length(string) > 1L)
string[-1L] <- paste0("\n", string[-1L])
string
}
# re.arrange <- function(keep) {
# namr <- names(k1 <- keep[[1L]])
# namc <- names(keep)
# nc <- length(keep)
# nr <- length(k1)
# array(unlist(keep, recursive = FALSE), c(nr, nc), list(namr,
# namc))
# }
step.results <- function(models, fit, object, usingCp = FALSE) {
change <- sapply(models, "[[", "change")
rd <- sapply(models, "[[", "deviance")
dd <- c(NA, abs(diff(rd)))
rdf <- sapply(models, "[[", "df.resid")
ddf <- c(NA, diff(rdf))
AIC <- sapply(models, "[[", "AIC")
heading <- c("Stepwise Model Path \nAnalysis of Deviance Table",
"\nInitial Model:", deparse(formula(object)), "\nFinal Model:",
deparse(formula(fit)), "\n")
aod <- data.frame(Step = I(change), Df = ddf, Deviance = dd,
`Resid. Df` = rdf, `Resid. Dev` = rd, AIC = AIC,
check.names = FALSE)
if (usingCp) {
cn <- colnames(aod)
cn[cn == "AIC"] <- "Cp"
colnames(aod) <- cn
}
attr(aod, "heading") <- heading
fit$anova <- aod
fit
}
object$terms <- terms(formula(object$formula), data = data)
Terms <- terms(object)
object$call$formula <- object$formula <- Terms
md <- missing(direction)
direction <- match.arg(direction)
backward <- direction == "both" | direction == "backward"
forward <- direction == "both" | direction == "forward"
if (missing(scope)) {
fdrop <- numeric()
fadd <- attr(Terms, "factors")
if (md)
forward <- FALSE
}
else {
if (is.list(scope)) {
fdrop <- if (!is.null(fdrop <- scope$lower))
attr(terms(update.formula(object, fdrop)), "factors")
else numeric()
fadd <- if (!is.null(fadd <- scope$upper))
attr(terms(update.formula(object, fadd)), "factors")
}
else {
fadd <- if (!is.null(fadd <- scope))
attr(terms(update.formula(object, scope)), "factors")
fdrop <- numeric()
}
}
models <- vector("list", steps)
# if (!is.null(keep))
# keep.list <- vector("list", steps)
n <- nobs(object, use.fallback = TRUE)
fit <- object
bAIC <- AIC(fit)
edf <- length(attr(fit$terms,"term.labels"))
# bAIC <- bAIC[2L]
if (is.na(bAIC))
stop("AIC is not defined for this model, so 'step' cannot proceed")
if (bAIC == -Inf)
stop("AIC is -infinity for this model, so 'step' cannot proceed")
nm <- 1
if (trace) {
cat("Start: AIC=", format(round(bAIC, 2)), "\n", cut.string(deparse(formula(fit))),
"\n\n", sep = "")
flush.console()
}
models[[nm]] <- list(deviance = mydeviance(fit), df.resid = n -
edf, change = "", AIC = bAIC)
# if (!is.null(keep))
# keep.list[[nm]] <- keep(fit, bAIC)
usingCp <- FALSE
while (steps > 0) {
steps <- steps - 1
AIC <- bAIC
ffac <- attr(Terms, "factors")
scope <- factor.scope(ffac, list(add = fadd, drop = fdrop))
aod <- NULL
change <- NULL
if (backward && length(scope$drop)) {
aod <- drop2(fit, scope$drop, data, scale = 0, trace = trace)
rn <- row.names(aod)
# print(aod)
row.names(aod) <- c(rn[1L], paste("-", rn[-1L]))
if (any(aod$Df == 0, na.rm = TRUE)) {
zdf <- aod$Df == 0 & !is.na(aod$Df)
change <- rev(rownames(aod)[zdf])[1L]
}
}
if (is.null(change)) {
if (forward && length(scope$add)) {
aodf <- add2(fit, scope$add, data, scale = 0,
trace = trace)
rn <- row.names(aodf)
row.names(aodf) <- c(rn[1L], paste("+", rn[-1L]))
aod <- if (is.null(aod))
aodf
else rbind(aod, aodf[-1, , drop = FALSE])
}
attr(aod, "heading") <- NULL
nzdf <- if (!is.null(aod$Df))
aod$Df != 0 | is.na(aod$Df)
aod <- aod[nzdf, ]
if (is.null(aod) || ncol(aod) == 0)
break
nc <- match(c("Cp", "AIC"), names(aod))
nc <- nc[!is.na(nc)][1L]
o <- order(aod[, nc])
if (trace)
print(aod[o, ])
if (o[1L] == 1)
break
change <- rownames(aod)[o[1L]]
}
usingCp <- match("Cp", names(aod), 0L) > 0L
# fit <- update(fit, paste("~ .", change), evaluate = FALSE)
# fit <- eval.parent(fit)
form1 <- update(fit$formula, as.formula(paste("~ .", change)),
evaluate = FALSE)
object$parallel <- object$parallel[object$parallel != str_trim(sub("-","", change))]
if(length(object$parallel) == 0) {object$parallel <- NA}
# fit <- GLMcat(form1, data, object$ratio, object$cdf, object$parallel,
# object$categories_order, object$ref_category,
# object$threshold, control_glmcat(object$control$maxit, object$control$epsilon, object$control$beta_init),
# object$normalization_s0)
fun_in = object$Function
if(fun_in == "GLMcat"){
fit <- .GLMcat(form1, data, object$ratio, object$cdf, object$parallel,
object$categories_order, object$ref_category,
object$threshold, control_glmcat(object$control$maxit, object$control$epsilon, object$control$beta_init),
object$normalization_s0)
}else{
# object$arguments$alternative_specific <- object$arguments$alternative_specific[object$arguments$alternative_specific != tt]
# if(length(object$arguments$alternative_specific) == 0) {object$arguments$alternative_specific <- NA}
fit <- .Discrete_CM(formula = form1,
data = data,
cdf = object$cdf,
alternative_specific = object$arguments$alternative_specific,
# object$categories_order,
case_id = object$arguments$case_id,
alternatives = object$arguments$alternatives,
reference = object$arguments$reference,
intercept = object$arguments$intercept,
control_glmcat(object$control$maxit,
object$control$epsilon, object$control$beta_init),
normalization = object$normalization_s0)
}
fit$terms <- terms(formula(fit$formula), data = fit$data)
nnew <- nobs(fit, use.fallback = TRUE)
if (all(is.finite(c(n, nnew))) && nnew != n)
stop("number of rows in use has changed: remove missing values?")
Terms <- terms(fit)
bAIC <- AIC(fit)
edf <- length(attr(fit$terms,"term.labels"))
if (trace) {
cat("\nStep: AIC=", format(round(bAIC, 2)), "\n",
cut.string(deparse(formula(fit))), "\n\n", sep = "")
flush.console()
}
if (bAIC >= AIC + 1e-07)
break
nm <- nm + 1
models[[nm]] <- list(deviance = mydeviance(fit), df.resid = n -
edf, change = change, AIC = bAIC)
# if (!is.null(keep))
# keep.list[[nm]] <- keep(fit, bAIC)
}
# if (!is.null(keep))
# fit$keep <- re.arrange(keep.list[seq(nm)])
results <- step.results(models = models[seq(nm)], fit, object, usingCp)
class(results) <- c("step.glmcat")
return(results)
}
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