On Sat, 2007-12-08 at 22:51 +0100, Irene Mantzouni wrote: > Hi all! > > I am fitting a (mixed) model with a factor (F) and continuous response and > predictor: > y~F+F:x > > (How) can I check the significance of the model at each factor level (i.e. > the model could be significant only at one of the levels)? > > Thank you!
Irene If I understand your doubt is necessary only use a summary command. Example set.seed(123) x<-rnorm(300,sd=2) F<-sample(rep(letters[1:3],100)) y<-rnorm(300,mean=2,sd=1.5) model<-lm(y~F+F:x) summary(model) (..) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.118128 0.151543 13.977 <2e-16 *** Fb -0.052866 0.213729 -0.247 0.805 Fc -0.229110 0.213726 -1.072 0.285 Fa:x 0.036987 0.074549 0.496 0.620 Fb:x 0.059439 0.083823 0.709 0.479 Fc:x 0.003769 0.082373 0.046 0.964 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (...) In this case only the Intercept is significant -- Bernardo Rangel Tura, M.D,MPH,Ph.D National Institute of Cardiology Brazil ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.