Dear all, I am perplexed when trying to get the same results using
pairwise.t.test and t.test.
I'm using examples in the ISwR library,
>attach(red.cell.folate)
I can get the same result for pairwise.t.test and t.test when I set the
variances to be non-equal, but not when they are assumed to be equal. Can
anyone explain the differences, or what I'm doing wrong?
Here's an example where I compare the first two ventilations with
pairwise.t.test and t.test
> pairwise.t.test(folate, ventilation, p.adj="none", pool.sd=F)
Pairwise comparisons using t tests with non-pooled SD
data: folate and ventilation
N2O+O2,24h N2O+O2,op
N2O+O2,op 0.029 -
O2,24h 0.161 0.298
P value adjustment method: none
> t.test(folate[1:8], folate[9:17], var.equal=F)
Welch Two Sample t-test
data: folate[1:8] and folate[9:17]
t = 2.4901, df = 11.579, p-value = 0.02906
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
7.310453 113.050658
sample estimates:
mean of x mean of y
316.6250 256.4444
So 0.029 and 0.02906 are identical but if I do the same with pool.sd and
var.equal = T, I get different results
> pairwise.t.test(folate, ventilation, p.adj="none", pool.sd=T)
Pairwise comparisons using t tests with pooled SD
data: folate and ventilation
N2O+O2,24h N2O+O2,op
N2O+O2,op 0.014 -
O2,24h 0.155 0.408
P value adjustment method: none
> t.test(folate[1:8], folate[9:17], var.equal=T)
Two Sample t-test
data: folate[1:8] and folate[9:17]
t = 2.5582, df = 15, p-value = 0.02184
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
10.03871 110.32240
sample estimates:
mean of x mean of y
316.6250 256.4444
So 0.014 and 0.02184 are not the same.
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