hi there, i have the following table with two factors A, B each respectively with 3 and 4 levels (unbalanced design)
>S1 samples A B 1 1.3398553 0 0 2 0.8455924 0 0 3 1.0290893 0 0 4 1.2720512 0 0 5 1.2071754 0 0 6 1.1859539 0 0 7 2.7399659 2 3 8 1.2476911 2 3 9 2.6389479 2 2 10 1.6914068 1 2 11 2.2260561 2 1 12 1.2955187 1 1 13 1.6526140 1 3 14 2.3159151 2 3 15 2.3905009 1 2 16 2.9520105 2 2 17 1.9478868 1 1 18 1.9936118 1 1 19 1.3775338 1 3 20 1.9638190 2 2 21 1.4697860 1 2 22 2.2028858 2 3 23 2.4024771 2 1 24 1.9935864 1 1 i fit two different models fit1<-aov(samples~A + B,data=S1,contrasts = list(A = contr.treatment, B = contr.treatment)) fit2<-aov(samples~A,data=S1,contrasts = list(A = contr.treatment)) fit3<-aov(samples~B,data=S1,contrasts = list(B = contr.treatment)) and using >anova(fit1,fit2) Analysis of Variance Table Model 1: samples ~ A + B Model 2: samples ~ A Res.Df RSS Df Sum of Sq F Pr(>F) 1 19 2.74820 2 21 3.14667 -2 -0.39847 1.3774 0.2763 i get B as not significant and >anova(fit1,fit3) Analysis of Variance Table Model 1: samples ~ A + B Model 2: samples ~ B Res.Df RSS Df Sum of Sq F Pr(>F) 1 19 2.7482 2 20 4.2391 -1 -1.4909 10.308 0.004604 ** A as significant. however if i do >anova(fit3) Analysis of Variance Table Response: samples Df Sum Sq Mean Sq F value Pr(>F) B 3 3.7241 1.2414 5.8567 0.004854 ** Residuals 20 4.2391 0.2120 i get B as significant and >anova(fit2) Analysis of Variance Table Response: samples Df Sum Sq Mean Sq F value Pr(>F) A 2 4.8165 2.4083 16.072 5.835e-05 *** Residuals 21 3.1467 0.1498 A as significant. Should i conclude that A is significant and B is not or rather that both factors are significant ? all the best ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html