在R中使用lm()作为公式的快捷方式 [英] Shortcut using lm() in R for formula

问题描述

可以在lm()

m <- matrix(rnorm(100), ncol=5)
lm(m[,1] ~ m[,2:5]

此处与

lm(m[,1] ~ m[,2] + m[,3] + m[,4] + m[,5]

但是在变量不处于同一级别的情况下(至少这是我目前的假设)，此操作不起作用，并且出现错误:

but in the case when variables are not of the same level (at least this is my assumption for now) this does not work and I get the error:

Error in model.frame.default(formula = hm[, 1] ~ hm[, 2:4], drop.unused.levels = TRUE) : 
  invalid type (list) for variable 'hm[, 2:4]'

数据(hm):

     N cor.distance switches  time
1   50   0.04707842        2 0.003
2  100  -0.10769441        2 0.004
3  200  -0.01278359        2 0.004
4  300   0.04229509        5 0.008
5  500  -0.04490092        6 0.010
6 1000   0.01939561        4 0.007

还有一些捷径可以避免写长公式吗?

Is there some shortcut still possible to avoid having to write the long formula?

推荐答案

尝试lm(y ~ ., data)，其中.表示"data中除y之外的所有其他列.

Try lm(y ~ ., data) where . means "every other column in data besides y.

m <- matrix(rnorm(100), ncol =5)
m <- as.data.frame(m)
names(m) <- paste("m", 1:5, sep="")
lm(m1 ~., data=m)

您可以重新分配m以仅包括您作为预测变量的列

You can reassign m to include only the columns you as the predictors

m <- m[ ,2:4]
lm(m1 ~ ., data=m)

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