Multidimensional logistic regression in r?

Question

How to perform multivariate (several dependent variables) logical regression in R?

I know that you are doing this for linear regression, and it works

form <-cbind(A,B,C,D)~shopping_pt+price
mlm.model.1 <- lm(form, data = train)

But when I try to do the following (see below) for logistic regression, this will not work.

model.logistic <- glm(form, family=binomial(link=logit), data=train)

Thank you for your help.

To add, it looks like my code for this with the linear models above might be wrong. I am trying to understand what is stated in this document, which some may find useful.

ftp://ftp.cis.upenn.edu/pub/datamining/public_html/ReadingGroup/papers/multiResponse.pdf

Answer 1

, lm(cbind(A,B,C,D)~shopping_pt+price) . :

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> set.seed(0)
> x1 <- runif(10)
> x2 <- runif(10)
> y1 <- 2*x1 + 3*x2 + rnorm(10)
> y2 <- 4*x1 + 5*x2 + rnorm(10)
> mm <- lm(cbind(y1,y2)~x1+x2)
> m1 <- lm(y1~x1+x2)
> m2 <- lm(y2~x1+x2)
# If we look at mm, m1 and m2, we see that models are identical
# If we predict new data, they give the same estimates
> x1_ <- runif(10)
> x2_ <- runif(10)
> predict(mm, newdata=list(x1=x1_, x2=x2_))
          y1       y2
1  2.9714571 5.965774
2  2.7153855 5.327974
3  2.5101344 5.434516
4  1.3702441 3.853450
5  0.9447582 3.376867
6  2.3809256 5.051257
7  2.5782102 5.544434
8  3.1514895 6.156506
9  2.4421892 5.061288
10 1.6712042 4.470486
> predict(m1, newdata=list(x1=x1_, x2=x2_))
        1         2         3         4         5         6         7         8         9        10 
2.9714571 2.7153855 2.5101344 1.3702441 0.9447582 2.3809256 2.5782102 3.1514895 2.4421892 1.6712042 
> predict(m2, newdata=list(x1=x1_, x2=x2_))
       1        2        3        4        5        6        7        8        9       10 
5.965774 5.327974 5.434516 3.853450 3.376867 5.051257 5.544434 6.156506 5.061288 4.470486

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