CODEWITHSUNDEEP
rstatistics
JohnK
JohnK

Reputation: 1039

Access variables of a regression model in R

I am working with a linear model, say

y<-rnorm(20)
x1<-rgamma(20,2,1)
x2<-rpois(20,3)

fit<-lm(y~x1*x2)
summary(fit)

and I was wondering, is there a way to access the regression variables through lm? One option would be to simply use

fit$model

and what you get is 

             y        x1 x2
1   1.52366782 1.1741392  4
2  -0.23640711 0.8780224  2
3   0.90728028 3.2192029  3
4  -0.07964816 2.5476499  3
5  -0.50999322 1.8515032  2
6   0.08854942 0.2892199  1
7   0.19708785 1.1865428  3
8   0.09641827 0.5808471  1
9  -0.28815596 1.3589239  1
10 -0.45784790 3.7514056  2
11 -0.39785151 0.8648221  0
12 -0.17503763 0.8153766  3
13  1.44095562 0.1933351  1
14 -0.86787846 2.7348324  5
15  0.30369142 0.7547339  7
16 -0.76884945 2.1558952  1
17 -0.81620973 1.2373447  3
18 -0.40978079 1.2046777  2
19 -0.23160424 1.8455335  2
20  2.90504457 0.9968425  2

and then the response is displayed along with the covariates. The problem is that the interaction x1:x2 is not there and it is often an important part of model building.

The problem arises in the context of variable selection when stepwise procedures reduce a huge dataset into a few variables and their interactions. I would like to build the covariate matrix for this model with no reference to the dataset.

Thank you in advance.

Upvotes: 3

Views: 1050

Answers (1)

user20650
user20650

Reputation: 25914

Thanks to Roland

You can get the model string by accessing the summary attributes (have a look at str(s))

s <- summary(fit)
mymod <- paste(attr(s$terms, "term.labels"), collapse=" + ")
mymod
[1] "x1 + x2 + x1:x2"

However, you can get the data by passing the model fit to model.matrix

model.matrix(fit)

There will be a bit of extra work if you have factor variables.

Upvotes: 3

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