Hello Everyone, I was wondering if anyone could interpret the raw p-values calculated using mt.teststat() (this function is part of the multtest package). I am using the following lines: ## To Calculate two-sided unequal variance t test values ## teststat <- mt.teststat(compSet, label, test="t", nonpara="n") rawp <- 2 * (1-pnorm(abs(teststat))) I am keeping track of the rawp values between all possible comparisons of samples. However, I have not been able to completely decipher the values that are coming out of this combination. Example: WT = 58.286, 48.842, 51.108 KO = 7.486, 6.373, 7.899 The p value calculated using "test_t.test(x[k,],xr[k,],paired=F,var.equal=F)" is 0.003219981, which is the same value that is calculated using the t.test within Excel. Mt.teststat() produces a value of 0.00000000 (basically 0, I'm not sure if it is lack of precision?). Does anyone understand why the pvalue generated from mt.teststat() is so much smaller than the traditional t.test() and what this means? Thank you, Richard Park Computational Data Analyzer Joslin Diabetes Center

On Tue, 8 Apr 2003, Park, Richard wrote: > I was wondering if anyone could interpret the raw p-values calculated > using mt.teststat() (this function is part of the multtest package). I > am using the following lines: > > teststat <- mt.teststat(compSet, label, test="t", nonpara="n") > rawp <- 2 * (1-pnorm(abs(teststat))) Hi Richard, mt.teststat is fine. You need to use the "pt" function and not "pnorm" if you want to calculate the p-value of a t-test from the t-statistic. Also, in order to compare it to the result of ctest::t.test, there you'll have to use var.equal=TRUE. Best regards Wolfgang