使用tapply / dapply等进行t.tests

Question

I have a data frame from an interlab study as follows http://pastebin.com/AD57AYD1

Essentially lab=Laboratory, mat=material, fab=strength, thick=thickness

I want t.test data to compare each lab for each type of material.

Ie, for mat=v, I want to run a t.test to compare lab B against lab S.

Similarly for materials c, n and l.

Due to my inability to work out how to do this efficiently, I create a column using the intaraction function (I know there must be easier ways!) and then run t.tests for each combination I am interested in.

# create a new column with lab/mat factors combined
interlab$allfacts<-interaction(interlab$lab,interlab$mat)

tv<-with(interlab, t.test(fab[allfacts == "S.v"],
        fab[allfacts == "B.v"],var.equal=FALSE))
tv

tl<-with(interlab, t.test(fab[allfacts == "S.l"],
        fab[allfacts == "B.l"],var.equal=FALSE))
tl

... etc etc

I am sure that I should be able to use one of the plyr functions, perhaps something like this:

tapply(interlab$fab, list(interlab$lab,interlab$mat), t.test)

but this isn't working out.

Any help much appreciated. Pete

EDIT: Further to the comment below, I had also looked at the pairwise.t.test function in this respect, but it did too many comparisons (ie, it did a t-test of lab B nitrile versus lab S vinyl - which is irrelevant. I called it like this:

pairwise.t.test(interlab$fab,interaction(interlab$mat,interlab$lab),paired=FALSE, pool.sd=FALSE)

and it gave me

> pairwise.t.test(interlab$fab,interaction(interlab$mat,interlab$lab),paired=FALSE, pool.sd=FALSE)

Pairwise comparisons using t tests with non-pooled SD 

    data:  interlab$fab and interaction(interlab$mat, interlab$lab) 

    c.B     l.B     n.B     v.B     c.S     l.S     n.S    
    l.B 0.54484 -       -       -       -       -       -      
    n.B 3.8e-07 1.9e-06 -       -       -       -       -      
    v.B 0.93881 0.22393 3.6e-07 -       -       -       -      
    c.S 0.00576 0.93881 1.2e-05 0.00026 -       -       -      
    l.S 0.00067 0.48601 2.5e-05 4.6e-05 0.89883 -       -      
    n.S 4.3e-12 2.2e-10 0.92366 5.4e-12 6.7e-10 7.7e-10 -      
    v.S 0.93881 0.93881 1.9e-06 0.31885 0.01217 0.00169 1.3e-10

    P value adjustment method: holm 

I understand the problem with undertaking multiple comparisons and having to adjust the significance criteria. I just couldn't get the pairwise call to only do the tests I was interested in.

Answer 1

there is a solution using the package plyr

FUN <- function(x) {
 mat <- matrix(numeric(), 1, 2)
 colnames(mat) <- tail(colnames(x),2)
 for (i in 1:ncol(mat)) {
    mat[1,i] <- t.test(x[,"thick"]~x[,"lab"])$p.value
    mat[1,i] <- t.test(x[,"fab"] ~ x[,"lab"])$p.value
  }
  mat  
}
# if you are only interested by fab :
 FUN <- function(x) {
   t.test(x[,"fab"] ~ x[,"lab"])$p.value  
  }

ddply(tab, .(mat), FUN)

HTH