向前逐步回归 [英] forward stepwise regression

问题描述

在 R 逐步向前回归中，我指定了一个最小模型和一组要添加(或不添加)的变量:

In R stepwise forward regression, I specify a minimal model and a set of variables to add (or not to add):

min.model = lm(y ~ 1)
fwd.model = step(min.model, direction='forward', scope=(~ x1 + x2 + x3 + ...))

有没有办法指定使用矩阵/data.frame 中的所有变量，这样我就不必枚举它们?

Is there any way to specify using all variables in a matrix/data.frame, so I don't have to enumerate them?

示例说明我想做什么，但它们不起作用:

Examples to illustrate what I'd like to do, but they don't work:

# 1
fwd.model = step(min.model, direction='forward', scope=(~ ., data=my.data.frame))

# 2
min.model = lm(y ~ 1, data=my.data.frame)
fwd.model = step(min.model, direction='forward', scope=(~ .))

推荐答案

scope expects (quoting the help page ?step)

scope expects (quoting the help page ?step)

单个公式或包含的列表组件上"和下"，都是公式.见有关如何指定公式及其方式的详细信息用过.

either a single formula, or a list containing components ‘upper’ and ‘lower’, both formulae. See the details for how to specify the formulae and how they are used.

您可以提取并使用~"对应的公式.像这样:

You can extract and use the formula corresponding to "~." like this:

> my.data.frame=data.frame(y=rnorm(20),foo=rnorm(20),bar=rnorm(20),baz=rnorm(20))
> min.model = lm(y ~ 1, data=my.data.frame)
> biggest <- formula(lm(y~.,my.data.frame))
> biggest
y ~ foo + bar + baz
> fwd.model = step(min.model, direction='forward', scope=biggest)
Start:  AIC=0.48
y ~ 1

       Df Sum of Sq    RSS      AIC
+ baz   1    2.5178 16.015 -0.44421
<none>              18.533  0.47614
+ foo   1    1.3187 17.214  0.99993
+ bar   1    0.4573 18.075  1.97644

Step:  AIC=-0.44
y ~ baz

       Df Sum of Sq    RSS      AIC
<none>              16.015 -0.44421
+ foo   1   0.41200 15.603  1.03454
+ bar   1   0.20599 15.809  1.29688
> 

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