因此，我正在尝试对 tweedie 分布进行逐步回归。但是，如果族是 tweedie，则 glm() 将 AIC 作为 NA 返回，这会破坏 stepAIC 命令。我尝试编辑命令的代码以将 extractAIC 更改为 AICtweedie，但我不断收到此错误：Error in [.data.frame(aod, , nc) : undefined columns selected
stepAIC.tweedie = function (object, scope, scale = 0, direction = c("both", "backward",
"forward"), trace = 1, keep = NULL, steps = 1000, use.start = FALSE,
k = 2, ...)
{
mydeviance <- function(x, ...) {
dev <- deviance(x)
if (!is.null(dev))
dev
else extractAIC(x, k = 0)[2L]
}
cut.string <- function(string) {
if (length(string) > 1L)
string[-1L] <- paste("\n", string[-1L], sep = "")
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, abs(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 <- if (usingCp)
data.frame(Step = change, Df = ddf, Deviance = dd,
`Resid. Df` = rdf, `Resid. Dev` = rd, Cp = AIC,
check.names = FALSE)
else data.frame(Step = change, Df = ddf, Deviance = dd,
`Resid. Df` = rdf, `Resid. Dev` = rd, AIC = AIC,
check.names = FALSE)
attr(aod, "heading") <- heading
class(aod) <- c("Anova", "data.frame")
fit$anova <- aod
fit
}
Terms <- terms(object)
object$formula <- Terms
if (inherits(object, "lme"))
object$call$fixed <- Terms
else if (inherits(object, "gls"))
object$call$model <- Terms
else object$call$formula <- Terms
if (use.start)
warning("'use.start' cannot be used with R's version of 'glm'")
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
edf <- extractAIC(fit, scale, k = k, ...)
edf <- edf[1L]
bAIC <- AICtweedie(fit, k=k)
if (is.na(bAIC))
stop("AIC is not defined for this model, so 'stepAIC' cannot proceed")
if (bAIC == -Inf)
stop("AIC is -infinity for this model, so 'stepAIC' cannot proceed")
nm <- 1
Terms <- terms(fit)
if (trace) {
cat("Start: AIC=", format(round(bAIC, 2)), "\n", cut.string(deparse(formula(fit))),
"\n\n", sep = "")
utils::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")
if (!is.null(sp <- attr(Terms, "specials")) && !is.null(st <- sp$strata))
ffac <- ffac[-st, ]
scope <- factor.scope(ffac, list(add = fadd, drop = fdrop))
aod <- NULL
change <- NULL
if (backward && length(scope$drop)) {
aod <- dropterm(fit, scope$drop, scale = scale,
trace = max(0, trace - 1), k = k, ...)
rn <- row.names(aod)
row.names(aod) <- c(rn[1L], paste("-", rn[-1L],
sep = " "))
if (any(aod$Df == 0, na.rm = TRUE)) {
zdf <- aod$Df == 0 & !is.na(aod$Df)
nc <- match(c("Cp", "AIC"), names(aod))
nc <- nc[!is.na(nc)][1L]
ch <- abs(aod[zdf, nc] - aod[1, nc]) > 0.01
if (any(is.finite(ch) & ch)) {
warning("0 df terms are changing AIC")
zdf <- zdf[!ch]
}
if (length(zdf) > 0L)
change <- rev(rownames(aod)[zdf])[1L]
}
}
if (is.null(change)) {
if (forward && length(scope$add)) {
aodf <- addterm(fit, scope$add, scale = scale,
trace = max(0, trace - 1), k = k, ...)
rn <- row.names(aodf)
row.names(aodf) <- c(rn[1L], paste("+", rn[-1L],
sep = " "))
aod <- if (is.null(aod))
aodf
else rbind(aod, aodf[-1, , drop = FALSE])
}
attr(aod, "heading") <- NULL
if (is.null(aod) || ncol(aod) == 0)
break
nzdf <- if (!is.null(aod$Df))
aod$Df != 0 | is.na(aod$Df)
aod <- aod[nzdf, ]
if (is.null(aod) || ncol(aod) == 0)
break
nc <- match(c("Cp", "AIC"), names(aod))
nc <- nc[!is.na(nc)][1L]
o <- order(aod[, nc])
if (trace) {
print(aod[o, ])
utils::flush.console()
}
if (o[1L] == 1)
break
change <- rownames(aod)[o[1L]]
}
usingCp <- match("Cp", names(aod), 0) > 0
fit <- update(fit, paste("~ .", change), evaluate = FALSE)
fit <- eval.parent(fit)
nnew <- nobs(fit, use.fallback = TRUE)
if (all(is.finite(c(n, nnew))) && nnew != n)
stop("number of rows in use has changed: remove missing values?")
Terms <- terms(fit)
edf <- extractAIC(fit, scale, k = k, ...)
edf <- edf[1L]
bAIC <- AICtweedie(fit, k=k)
if (trace) {
cat("\nStep: AIC=", format(round(bAIC, 2)), "\n",
cut.string(deparse(formula(fit))), "\n\n", sep = "")
utils::flush.console()
}
if (bAIC >= AIC + 1e-07)
break
nm <- nm + 1
models[[nm]] <- list(deviance = mydeviance(fit), df.resid = n -
edf, change = change, AIC = bAIC)
if (!is.null(keep))
keep.list[[nm]] <- keep(fit, bAIC)
}
if (!is.null(keep))
fit$keep <- re.arrange(keep.list[seq(nm)])
step.results(models = models[seq(nm)], fit, object, usingCp)
}