Is it possible to use the lm () function with a matrix at all? Or maybe the right question is: "Is it possible to dynamically create formulas in R?"
I create a function whose output is a matrix, and the number of columns in the matrix is not fixed = it depends on the user inputs. I want to fit an OLS model using the data in a matrix. - The first column represents the dependent variable. - The other columns are independent variables.
Using a function lmrequires a formula that involves knowing the number of explanatory variables, which is not my case!
Is there any solution besides evaluating the equation manually using the OLS formula?
Playable example:
> # When user 1 uses the function, he obtains m1
> m1 <- replicate(5, rnorm(50))
> colnames(m1) <- c("dep", paste0("ind", 1:(ncol(m1)-1)))
> head(m1)
dep ind1 ind2 ind3 ind4
[1,] 0.5848705 0.3602760 -0.95493403 -1.7278030 -0.1914170
[2,] 1.7167604 -0.1035825 0.31026183 -1.5071415 -1.2748600
[3,] -0.1326187 -0.5669026 0.01819749 0.8346880 -0.6304498
[4,] -0.7381232 0.4612792 -0.36132404 -0.1183131 -0.7446985
[5,] 0.9919123 -1.3228248 -0.44728270 0.6571244 -0.4895385
[6,] -0.8010111 0.8307584 -0.16106804 0.3069870 -0.3834583
>
> # When user 2 uses the function, he obtains m2
> m2 <- replicate(6, rnorm(50))
> colnames(m2) <- c("dep", paste0("ind", 1:(ncol(m2)-1)))
> head(m2)
dep ind1 ind2 ind3 ind4 ind5
[1,] 1.2936031 -0.8060085 0.5020699 -1.699123234 1.0205626 1.0787888
[2,] 1.2357370 0.5973699 -1.2134283 -0.928040354 -0.3037920 -0.1251678
[3,] 0.5292583 0.1063213 -1.3036526 0.395886937 -0.1280863 1.1423532
[4,] 0.9234484 -0.4505604 1.2796922 0.424705893 -0.5547274 -0.3794037
[5,] -0.8016376 1.1362677 -1.1935238 -0.004460092 -1.4449704 -0.3739311
[6,] 0.4385867 0.5671138 0.4493617 -2.277925642 -0.8626944 -0.6880523
User 1 will evaluate the linear model with:
lm(dep ~ ind1 + ind2 + ind3 + ind4, data = m1)
2 :
lm(dep ~ ind1 + ind2 + ind3 + ind4 + ind5, data = m1)
, - ?