search for: blanketb2

...[,1] [,2] [,3] [1,] 1 0 0 [2,] 0 1 0 [3,] 0 0 1 Absolute Error Tolerance: 1e-04 90 % quantile: 1.8431 Coefficients: Estimate -- 90 % t value Std.Err. p raw p Bonf p adj blanketb1-b0 -2.1333 -Inf 0.8226 -1.3302 1.6038 0.0958 0.2874 0.2412 blanketb2-b0 -7.4667 -Inf -4.5108 -4.6556 1.6038 0.0000 0.0001 0.0001 blanketb3-b0 -1.6667 -Inf -0.0360 -1.8837 0.8848 0.0337 0.1012 0.0924 > ## change lm to aov > aovmod <- aov(minutes ~ blanket, data=recovery, contrasts=list(blanket = + "contr.Dunnett")) > aovmod Cal...

This has a stats question and a R question. I am sure there are many core statisticians here how would know the answer to this simple question. In determining the significant comparisons using the methods in multcomp, the ones that are designated as significant are the ones that do not intersect the zero line. What is the physical meaning of this and why are those considered significant? I can

Dear R-devel list members, I'd like to suggest a more flexible procedure for generating contrast names. I apologise for a relatively long message -- I want my proposal to be clear. I've never liked the current approach. For example, the names generated by contr.treatment paste factor to level names with no separation between the two; contr.sum simply numbers contrasts (I recall an