Regrad to this Post, I have created an example to play with linear regression on data.table package as follows:
## rm(list=ls()) # anti-social
library(data.table)
set.seed(1011)
DT = data.table(group=c("b","b","b","a","a","a"),
v1=rnorm(6),v2=rnorm(6), y=rnorm(6))
setkey(DT, group)
ans <- DT[,as.list(coef(lm(y~v1+v2))), by = group]
return,
group (Intercept) v1 v2
1: a 1.374942 -2.151953 -1.355995
2: b -2.292529 3.029726 -9.894993
I am able to obtain the coefficients of the lm
function.
My question is:
How can we directly use predict
to new observations ? If we have the new observations as follows:
new <- data.table(group=c("b","b","b","a","a","a"),v1=rnorm(6),v2=rnorm(6))
I have tried:
setkey(new, group)
DT[,predict(lm(y~v1+v2), new), by = group]
but it returns me strange answers:
group V1
1: a -2.525502
2: a 3.319445
3: a 4.340253
4: a 3.512047
5: a 2.928245
6: a 1.368679
7: b -1.835744
8: b -3.465325
9: b 19.984160
10: b -14.588933
11: b 11.280766
12: b -1.132324
Thank you