It is possible to use a shortcut for formula in lm()
m <- matrix(rnorm(100), ncol=5)
lm(m[,1] ~ m[,2:5]
here it would be the same as
lm(m[,1] ~ m[,2] + m[,3] + m[,4] + m[,5]
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]'
Data (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
Is there some shortcut still possible to avoid having to write the long formula?
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)
You can reassign m
to include only the columns you as the predictors
m <- m[ ,2:4]
lm(m1 ~ ., data=m)
There is another one shortcut for the cases when a dependent variable is in the first column:
data <- data.frame(y = rnorm(10), x1 = rnorm(10), x2 = rnorm(10))
lm(data)
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