Question

我今天花了一整天来解决这个问题..请帮助我。虽然我在这里只写了一个非常简单的例子，但我的原始数据有很多变量 - 大约2,000个。因此，要运行回归，我需要选择某些变量。我确实需要开发很多模型，所以我应该自动执行这个过程。

我跑步了。
我不知道逐步选择了多少变量。

选择变量后，我会进行滚动回归预测。

 library(car)
 library(zoo)
 # run regression
m <- lm(mpg~., data=mtcars) 

 # run stepwise
s<-step(m, direction="both")

# select variables
variable<- attr(s$terms,"term.labels")
b<-paste(dep,paste(s, collapse="+"),sep = "~")

rollapply(mtcars, width = 2,
          FUN = function(z) coef(lm(b, data = as.data.frame(z))),
          by.column = FALSE, align = "right")

＃这是我开发的自动模型..

models2 <- lapply(1:11, function(x) {
  dep<-names(mtcars)[x]
  ind<-mtcars[-x]
  w<-names(ind)
  indep<-paste(dep,paste(w, collapse="+"),sep = "~")
  m<-lm(indep,data=mtcars)
  s<-step(m, direction="both")
  b<-paste(dep,paste(s, collapse="+"),sep = "~")
  rollapply(mtcars, width = 2,
          FUN = function(z) coef(lm(b, data = as.data.frame(z))),
          by.column = FALSE, align = "right")})

我想从滚动回归计算预测。

但是，设置起来非常困难 data.frame没有关于自变量的预先知识..

There is a similar one here, but in this model independent variables are known already.

Answer 1

您不需要知道自变量！如果您提供包含所有变量的data.frame，predict函数将选择必要的变量。与您链接的帖子类似，您可以这样：

mtcars[,"int"] <- seq(nrow(mtcars)) # add variable used to choose newdata
models2 <- lapply(1:11, function(x) {
  dep <- names(mtcars)[x]
  ind <- mtcars[-x]
  w <- names(ind)
  form <- paste(dep,paste(w, collapse="+"),sep = "~")
  m <- lm(form, data=mtcars)
  s <- step(m, direction="both", trace=0) # model selection (don't print trace)
  b <- formula(s) # This is clearer than your version
  rpl <- rollapply(mtcars, width = 20, # if you use width=2, your model will always be overdetermined
          FUN = function(z) {
            nextD <- max(z[,'int'])+1 # index of row for new data
            fit <- lm(b, data = as.data.frame(z)) # fit the model 
            c(coef=coef(fit), # coefficients
              predicted=predict(fit, newdata=mtcars[nextD,])) # predict using the next row
          },
          by.column = FALSE, align = "right")
  rpl
})

R代码：滚动回归逐步后

1 个答案: