Run regression in data.table

Question

Please see the fake data set.

library(data.table)
library(MASS)
n=5000
DT = data.table(
      grp=1:n,
      name=as.character(as.hexmode(1:n)), 
      x= sample(c(1:400),n,replace = TRUE)
    )

setkey(DT,grp)

UIDlist <- unique(DT[,grp])
IDnamelist <- paste0("V", 1 : length(UIDlist), sep = "")
test <- DT[, (IDnamelist):=lapply(UIDlist,function(x) grp ==x)][, V5000:= NULL]

I have a data.table, in which there're 4 columns, "grp", "Name", "x", "y". And then I add dummy on each level in "grp". Then I need to run the regression using glm.nb in MASS package.

First I tried this

SumResult <- glm.nb(x ~ factor(uid), data = test) 

But when adding dummies, we must notice that when there're N levels in "grp", we add N-1 dummies. So this method is not appropriate as far as I think.

So I tried this:

SumResult <- glm.nb( x ~ V1 + V2 + V3 + V4 + .....+ V4999  , data = test)

It's stupid to write all of the V1, V2, ... V4999 to do the regression.

Is there code can achieve the purpose?

Thanks

Answer 1

You can try to create your formula object by string manipulation

formula <- as.formula(paste0("x ~ ", paste(names(test)[-(1:3)], collapse = " + ")))
sumresult <- glm.nb(formula, data = test)

You can also use the more readable code of @BrandonBertelsen

glm.nb(x ~ ., data = test[-c(1:3)])