Question

我想对一组变量运行最佳子集回归，然后使用R得到最好的3个变量。我在获得最佳2个变量时遇到问题。我在下面提供了我的代码。

set.seed(10)

a <- 1:100
b <- 1:100
c <- 1:100
d <- 1:100
e <- 1:100
f <- 1:100
g <- 1:100
h <- 1:100

data <- data.frame(a, b, c, d, e, f, g, h)

library(leaps)

# best subsets regression
test <- regsubsets(a ~ b + c + d + e + f + g + h, data=data, nbest=4)
# nbest = 4, is the number of subsets of each size that is reported

# plot a table of models showing variables in each model.
summary(test)

# models are ordered by the selection statistic.
plot(test,scale="r2")

#get the variables that are important to the model
coef(test, 2)

#NOTE: THIS DOESN'T GIVE ME THE 2 BEST VARIABLES. IT ONLY GIVES ME THE BEST VARIABLE AT THE 2ND ITERATION. LOOK AT:
coef(test, 1:2)

非常感谢您的帮助！

最佳，德纳

Answer 1

考虑一个内置mtcars数据集的示例：

test <- regsubsets(mpg ~ ., data = mtcars, nbest = 4)

这是summary(test)的输出：

summary(test)
# Subset selection object
# Call: regsubsets.formula(mpg ~ ., data = mtcars, nbest = 4)
# 10 Variables  (and intercept)
# <..snip..>
# 4 subsets of each size up to 8
# Selection Algorithm: exhaustive
#          cyl disp hp  drat wt  qsec vs  am  gear carb
# 1  ( 1 ) " " " "  " " " "  "*" " "  " " " " " "  " " 
# 1  ( 2 ) "*" " "  " " " "  " " " "  " " " " " "  " " 
# 1  ( 3 ) " " "*"  " " " "  " " " "  " " " " " "  " " 
# 1  ( 4 ) " " " "  "*" " "  " " " "  " " " " " "  " " 
# 2  ( 1 ) "*" " "  " " " "  "*" " "  " " " " " "  " " 
# 2  ( 2 ) " " " "  "*" " "  "*" " "  " " " " " "  " " 
# 2  ( 3 ) " " " "  " " " "  "*" "*"  " " " " " "  " " 
# 2  ( 4 ) " " " "  " " " "  "*" " "  "*" " " " "  " " 
# 3  ( 1 ) " " " "  " " " "  "*" "*"  " " "*" " "  " " 
# <..snip..>

系数集由自变量的数量排列，子集为4（我们用nbest参数表示的）;因此，coef(test, 1:4)将返回具有一个独立变量的模型的系数，coef(test, 5:8)将是具有两个独立变量的系数，依此类推。在每个子集中，最好的＆＃34;模特第一。最好的＆＃34;因此，具有两个自变量的模型将是模型5：

coef(test, 5)
# (Intercept)         cyl          wt 
#   39.686261   -1.507795   -3.190972

从R中的最佳子集回归中获取最佳变量

1 个答案: