Description Usage Arguments Details Value Note Author(s) See Also Examples
The 'anova' function creates ANOVA tables for a 'pgls' models using sequential sums of squares.
1 2 3 4 |
object |
A 'pgls' model object. |
... |
Additional 'pgls' models. |
scale |
A character string specifying the test statistic to be used. Can be one of "F", "Chisq" or "Cp", with partial matching allowed, or NULL for no test. |
test |
numeric. An estimate of the noise variance sigma^2. If zero this will be estimated from the largest model considered. |
The sequential sums of squares are calculated by refitting the model in the order of the terms of the formula and so can take a little time to calculate. Branch length transformations are held at the values of the initial object. The 'logLik.pgls' provides a simple accessor function that allows the use of AIC model comparisons. Note that the generic AIC methods do no checking to ensure that sensible models are being compared.
A table of class 'anova' and 'data.frame' that employs the generic plot methods for 'anova' tables.
The functions build heavily on the generic methods 'anova.lm' and 'anova.lmlist'.
Rob Freckleton, David Orme
1 2 3 4 5 6 7 8 9 10 11 12 | data(shorebird)
shorebird <- comparative.data(shorebird.tree, shorebird.data, Species, vcv=TRUE, vcv.dim=3)
mod1 <- pgls(log(Egg.Mass) ~ log(M.Mass) * log(F.Mass), shorebird)
anova(mod1)
mod2 <- pgls(log(Egg.Mass) ~ log(M.Mass) + log(F.Mass), shorebird)
mod3 <- pgls(log(Egg.Mass) ~ log(M.Mass) , shorebird)
mod4 <- pgls(log(Egg.Mass) ~ 1, shorebird)
anova(mod1, mod2, mod3, mod4)
AIC(mod1, mod2, mod3, mod4)
|
Loading required package: ape
Loading required package: MASS
Loading required package: mvtnorm
Analysis of Variance Table
Sequential SS for pgls: lambda = 1.00, delta = 1.00, kappa = 1.00
Response: log(Egg.Mass)
Df Sum Sq Mean Sq F value Pr(>F)
log(M.Mass) 1 0.42146 0.42146 371.8424 <2e-16 ***
log(F.Mass) 1 0.00114 0.00114 1.0014 0.3206
log(M.Mass):log(F.Mass) 1 0.00004 0.00004 0.0339 0.8545
Residuals 67 0.07594 0.00113
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Variance Table
pgls: lambda = 1.00, delta = 1.00, kappa = 1.00
Model 1: log(Egg.Mass) ~ log(M.Mass) * log(F.Mass)
Model 2: log(Egg.Mass) ~ log(M.Mass) + log(F.Mass)
Model 3: log(Egg.Mass) ~ log(M.Mass)
Model 4: log(Egg.Mass) ~ 1
Res.Df RSS Df Sum of Sq F Pr(>F)
1 67 0.07594
2 68 0.07598 -1 -0.00004 0.0339 0.8545
3 69 0.07711 -1 -0.00114 1.0014 0.3206
4 70 0.49858 -1 -0.42146 371.8424 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
df AIC
mod1 4 -64.96854
mod2 3 -66.93263
mod3 2 -67.87981
mod4 1 62.63955
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