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:
Model 1: HeightFinal ~ Treatment + HeightInitial + DiameterInitial + Frost +
HeightInitial:Frost + DiameterInitial:Frost
Model 2: AbsoluteDiameterDiff ~ Treatment + DiameterInitial + Frost +
HeightInitial:Frost + DiameterInitial:Frost
Res.Df RSS Df Sum of Sq F Pr(>F)
1 22 386.3
2 22 386.3 0 -1.08e-12
This is not the output that I'm used to seeing. Typically, a pvalue would
be provided to suggest if the models differ significantly from one another.
Can anyone explain why there's no pvalue in this situation and whether or
not removing HeightInitial was justified.
Thanks,
Jacob Freel
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