anova.pgls | R Documentation |

The 'anova' function creates ANOVA tables for a 'pgls' models using sequential sums of squares.

```
## S3 method for class 'pgls'
anova(object, ...)
## S3 method for class 'pglslist'
anova(object, ..., scale = 0, test = "F")
```

`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

`pgls`

```
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)
```

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