我需要使用ggplot在步骤函数中绘制每个回归步骤的BIC值。我不知道如何使用ggplot绘制每个步骤的BIC值。
form_model <- formula(lm(price~sqft_living+sqft_lot+waterfront+sqft_above+sqft_basement+years_since_renovations+age_of_house+grade_int+bed_int+bath_int+floors_dummy+view_dummy+condition_dummy+basement_dummy+renovated_dummy+weekend_dummy))
mod <- lm(price~1)
n <- (nrow(House_Regr))
forwardBIC <- step(mod,form_model,direction = "forward", k=log(n) )
这是我正在使用的模型。
Start: AIC=181611.1
price ~ 1
Df Sum of Sq RSS AIC
+ sqft_living 1 5.5908e+16 6.9104e+16 178111
+ grade_int 1 4.2600e+16 8.2413e+16 179154
+ sqft_above 1 3.8988e+16 8.6024e+16 179407
+ view_dummy 1 1.5755e+16 1.0926e+17 180822
+ sqft_basement 1 1.1560e+16 1.1345e+17 181045
+ bed_int 1 1.0586e+16 1.1443e+17 181096
+ floors_dummy 1 8.6756e+15 1.1634e+17 181194
+ waterfront 1 8.1097e+15 1.1690e+17 181223
+ basement_dummy 1 3.8336e+15 1.2118e+17 181435
+ bath_int 1 2.1104e+15 1.2290e+17 181519
+ renovated_dummy 1 1.3665e+15 1.2365e+17 181555
+ years_since_renovations 1 8.6785e+14 1.2414e+17 181579
+ sqft_lot 1 8.2901e+14 1.2418e+17 181580
+ condition_dummy 1 6.4654e+14 1.2437e+17 181589
<none> 1.2501e+17 181611
+ age_of_house 1 1.7600e+14 1.2484e+17 181611
+ weekend_dummy 1 9.3267e+11 1.2501e+17 181620
Step: AIC=178111
price ~ sqft_living
Df Sum of Sq RSS AIC
+ view_dummy 1 4.7046e+15 6.4399e+16 177702
+ age_of_house 1 4.5059e+15 6.4598e+16 177721
+ waterfront 1 4.3957e+15 6.4708e+16 177731
+ grade_int 1 3.1890e+15 6.5915e+16 177840
+ years_since_renovations 1 3.0576e+15 6.6046e+16 177852
+ bed_int 1 1.7778e+15 6.7326e+16 177965
+ bath_int 1 1.7527e+15 6.7351e+16 177968
+ renovated_dummy 1 7.2312e+14 6.8381e+16 178057
+ basement_dummy 1 3.1144e+14 6.8793e+16 178093
+ sqft_above 1 1.6922e+14 6.8935e+16 178105
+ sqft_basement 1 1.6922e+14 6.8935e+16 178105
+ sqft_lot 1 1.2746e+14 6.8977e+16 178109
<none> 6.9104e+16 178111
+ condition_dummy 1 3.6244e+13 6.9068e+16 178117
+ floors_dummy 1 1.0259e+13 6.9094e+16 178119
+ weekend_dummy 1 5.9534e+12 6.9098e+16 178119
这里是回归的一小部分输出。我需要使用ggplot绘制每个步骤的BIC值。我的想法是仅提取每个步骤的BIC值，然后使用ggplot对其进行绘制，但是正如我所说的，我不知道如何完成此操作，或者对于ggplot而言是否需要提取BIC。
我将如何在ggplot上为回归的每个步骤绘制BIC？
我通常不建议这样做，所以如果有使用实函数的答案，那就去做。在其中调用了一个函数：extractAIC，它存储结果，然后打印这些表。您可以通过在控制台中输入step函数来获得它。快速扫描显示，在此函数内的变量aod中，它存储了每次迭代打印的表。
一种不明智的方法是在此函数内创建一个列表，每次更改时用表更新列表，然后将其添加到响应中（通常的方法）或将其分配给全局环境（不好的方法）。由于我对阶跃函数的响应类一无所知，因此我选择了错误的方法。完整功能在这里。您可以搜索# (!) addition标志以查看将其添加到的位置。
AIC列包含BIC值。您可以在k调用中更改step值时看到它的变化
希望这对您有用，我正在使用step函数中的示例
step2 <- function (object, scope, scale = 0, direction = c("both", "backward",
"forward"), trace = 1, keep = NULL, steps = 1000, k = 2,
...)
{
# (!) addition
aod.all <- list()
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] <- 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$anova <- aod
fit
}
Terms <- terms(object)
object$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 <- extractAIC(fit, scale, k = k, ...)
edf <- bAIC[1L]
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$drop)) {
aod <- drop1(fit, scope$drop, scale = scale, trace = trace,
k = k, ...)
rn <- row.names(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 <- add1(fit, scope$add, scale = scale, trace = trace,
k = k, ...)
rn <- row.names(aodf)
row.names(aodf) <- c(rn[1L], paste("+", rn[-1L]))
aod <- if (is.null(aod))
aodf
else rbind(aod, aodf[-1, , drop = FALSE])
}
attr(aod, "heading") <- NULL
nzdf <- if (!is.null(aod$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])
# (!) addition
aod.all <- c(aod.all, list(aod))
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)
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 <- extractAIC(fit, scale, k = k, ...)
edf <- bAIC[1L]
bAIC <- bAIC[2L]
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)])
# (!) addition
assign("aod.all", aod.all, envir = .GlobalEnv)
step.results(models = models[seq(nm)], fit, object, usingCp)
}
lm1 <- lm(Fertility ~ ., data = swiss)
slm1 <- step2(lm1)
aod.all