ChCh <jmo101 <at> student.canterbury.ac.nz> writes: > > > Hello all, > > I've become confused by the output produced by a call to > anova(model1,model2). First a brief background. My model used to predict > final tree height is summarised here: > > Df Sum Sq Mean Sq F value Pr(>F) > Treatment 2 748.35 374.17 21.3096 7.123e-06 *** > HeightInitial 1 0.31 0.31 0.0178 0.89519 > DiameterInitial 1 0.52 0.52 0.0298 0.86460 > Frost 1 38.29 38.29 2.1807 0.15392 > HeightInitial:Frost 1 85.83 85.83 4.8882 0.03774 * > DiameterInitial:Frost 1 97.90 97.90 5.5754 0.02749 * > Residuals 22 386.30 17.56 > --- > > Based on this, I should not remove either of the interaction terms, so I > turned my attention to the main factors. Based on p-values, I removed > HeightInitial and used a call to anova(model1,model2) to see if this > resulted in a weaker model. Here is the output: ...
Though should never remove main factors in the presence of interactions. Check Bill Venables penitential sermon which quite on top of google when you enter "exegeses". http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.pdf The usual way to simplify models in R is based on the AIC, not on p-values. See stepAIC and the chapter in MASS. Dieter ______________________________________________ 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.
