可以使用AIC分数比较多元回归模型,从支持最好的模型到支持最差的模型?
这是我的代码
library(data.table)
Regressions<-
data.table(February)[,
.(Lm = lapply(.SD, function(x) summary(lm(February$PPNA ~ February$Acum1 + x)))),
.SDcols = 80:157]
我们可以基于'AIC'值提取AIC
值和order
library(data.table)
dt <- as.data.table(February)
dt1 <- dt[, .(Lm = lapply(.SD, function(x) lm(February$PPNA ~ February$Acum1 + x))),
.SDcols = 80:157]
dt2 <- dt1[, .(Lm = Lm[order(unlist(lapply(Lm, AIC)))])]
或使用可复制的示例
dt1 <- as.data.table(iris)[, .(Lm = lapply(.SD, function(x)
lm(iris$Petal.Length ~ iris$Species + x)))]
dt2 <- dt1[, .(Lm = Lm[order(unlist(lapply(Lm, AIC)))])]