hello, after an anova I use pairwise.t.test(), it gives only p.value and I want the t.stat. I try to get these by computing the Welch approximation of the degree of freedom and using the qt(p.value,df) function but when I test this method with t.test results (the function gives p.value and t.test), I doesn't find the same t.stat. I also use the simtest(x~y,type="Tukey") function and it gives me only negative t.stat, resulting in a weard distribution... thank you in advance for all the attention you 'll give to my problems. Julie BERTRAND
multiple comparison test
2 messages · Julie BERTRAND, Peter Dalgaard
Julie BERTRAND <juliebertrand2001 at yahoo.fr> writes:
hello, after an anova I use pairwise.t.test(), it gives only p.value and I want the t.stat. I try to get these by computing the Welch approximation of the degree of freedom and using the qt(p.value,df) function but when I test this method with t.test results (the function gives p.value and t.test), I doesn't find the same t.stat. I also use the simtest(x~y,type="Tukey") function and it gives me only negative t.stat, resulting in a weard distribution... thank you in advance for all the attention you 'll give to my problems. Julie BERTRAND
Notice that the p-values in pairwise.t.test() are adjusted for multiple comparisons and use a pooled SD. You're not going to get anything similar to the t.test output unless you set pool.sd=FALSE and p.adjust.method="none". You probably also want to make the tests one-sided, or halve the p-value. It could well be easier just to modify the function to give you the result that you desire...
O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907