Dear all,
I am working through a problem at the moment and have got stuck. I have
searched around on the help list for assistance but could not find anything -
but apologies if I have missed something. A dummy example of my problem is
below. I will continue to work on it, but any help would be greatly appreciated.
Thanks in advance for your time.
Best wishes,
Matt
I have a matrix of response variables:
p<-matrix(c(rnorm(120,1),
rnorm(120,1),
rnorm(120,1)),
120,3)
and two matrices of covariates:
g<-matrix(c(rep(1:3, each=40),
rep(3:1, each=40),
rep(1:3, 40)),
120,3)
m<-matrix(c(rep(1:2, 60),
rep(2:1, 60),
rep(1:2, each=60)),
120,3)
For all combinations of the columns of the covariate matrices g and m I want to
run these two models:
test <- function(uniq_m, uniq_g, p = p) {
full <- lm(p ~ factor(uniq_m) * factor(uniq_g))
null <- lm(p ~ factor(uniq_m) + factor(uniq_g))
return(list('f'=full, 'n'=null))
}
So I want to test for an interaction between column 1 of m and column 1 of g,
then column 2 of m and column 1 of g, then column 2 of m and column 2 of
g...and so forth across all possible pairwise interactions. The response
variable is the same each time and is a matrix containing multiple columns.
So far, I can do this for a single combination of columns:
test_1 <- test(m[ ,1], g[ ,1], p)
And I can also run the model over all columns of m and one coloumn of g:
test_2 <- apply(m, 2, function(uniq_m) {
test(uniq_m, g[ ,1], p = p)
})
I can then get the F statistics for each response variable of each model:
sapply(summary(test_2[[1]]$f), function(x) x$fstatistic)
sapply(summary(test_2[[1]]$n), function(x) x$fstatistic)
And I can compare models for each response variable using an F-test:
d1<-colSums(matrix(residuals(test_2[[1]]$n),nrow(g),ncol(p))^2)
d2<-colSums(matrix(residuals(test_2[[2]]$f),nrow(g),ncol(p))^2)
F<-((d1-d2) / (d2/114))
My question is how do I run the lm models over all combinations of columns from
the m and the g matrix, and get the F-statistics? While this is a dummy
example, the real analysis will have a response matrix that is 700 x 8000, and
the covariate matrices will be 700 x 4000 and 700 x 100 so I need something
that is as fast as possible.
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