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POusing tapply/dapply etc for t.tests
 

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  1. POusing tapply/dapply etc for t.tests
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    <p>I have a data frame from an interlab study as follows <a href="http://pastebin.com/AD57AYD1" rel="nofollow">http://pastebin.com/AD57AYD1</a></p> <p>Essentially lab=Laboratory, mat=material, fab=strength, thick=thickness</p> <p>I want t.test data to compare each lab for each type of material. </p> <p>I.e., for mat=v, I want to run a t.test to compare lab B against lab S.</p> <p>Similarly for materials c, n and l.</p> <p>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.</p> <pre><code># create a new column with lab/mat factors combined interlab$allfacts&lt;-interaction(interlab$lab,interlab$mat) tv&lt;-with(interlab, t.test(fab[allfacts == "S.v"], fab[allfacts == "B.v"],var.equal=FALSE)) tv tl&lt;-with(interlab, t.test(fab[allfacts == "S.l"], fab[allfacts == "B.l"],var.equal=FALSE)) tl ... etc etc </code></pre> <p>I am sure that I should be able to use one of the plyr functions, perhaps something like this:</p> <pre><code>tapply(interlab$fab, list(interlab$lab,interlab$mat), t.test) </code></pre> <p>but this isn't working out.</p> <p>Any help much appreciated. Pete</p> <p>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 (i.e., it did a t-test of lab B nitrile versus lab S vinyl - which is irrelevant. I called it like this:</p> <pre><code>pairwise.t.test(interlab$fab,interaction(interlab$mat,interlab$lab),paired=FALSE, pool.sd=FALSE) </code></pre> <p>and it gave me</p> <pre><code>&gt; pairwise.t.test(interlab$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 </code></pre> <p>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. </p>
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    1. COYou should at least adjust the p-values for repeated testing. However, a mixed effects model is probably better suited for your problem.
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    2. COOf course. I should have mentioned that I did try the pairwise comparison (see edit above).
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