anova.asterOrReaster: Analysis of Deviance for Reaster Model Fits
In aster: Aster Models

Description Usage Arguments Details Value Warning See Also Examples

Compute an analysis of deviance table for two or more aster model fits with or without random effects.

## S3 method for class 'asterOrReaster'
anova(object, ...,
    tolerance = .Machine$double.eps ^ 0.75)
anovaAsterOrReasterList(objectlist, tolerance = .Machine$double.eps ^ 0.75)

`object, ...`	objects of class `"asterOrReaster"`, typically the result of a call to `aster` or `reaster`.
`objectlist`	list of objects of class `"asterOrReaster"`.
`tolerance`	tolerance for comparing nesting of model matrices.

Constructs a table having a row for the degrees of freedom and deviance for each model. For all but the first model, the change in degrees of freedom and deviance is also given, as is the corresponding asymptotic P-value.

For objects of class "reaster", the quantity called deviance is only approximate. See references on help for reaster.

When objects of class "reaster" are among those supplied, degrees of freedom for fixed effects and degrees of freedom for variance components are reported separately, because tests for fixed effects are effectively two-tailed and tests for variance components are effectively one-tailed.

In case models being compared differ by one variance component, the reference distribution is half a chi-square with the fixed effect degrees of freedom (difference of number of fixed effects in the two models) and half a chi-square with one more degrees of freedom.

In case models being compared differ by two or more variance components, we do not know how to how to do the test. The reference distribution is a mixture of chi-squares but the mixing weights are difficult to calculate. An error is given in this case.

An object of class "anova" inheriting from class "data.frame".

The comparison between two or more models by anova or anovaAsterOrReasterList will only be valid if they are (1) fitted to the same dataset, (2) models are nested, (3) have the same dependence graph and exponential families. Some of this is currently checked. Some warnings are given.

aster, reaster, anova.

### see package vignette for explanation ###
library(aster)
data(echinacea)
vars <- c("ld02", "ld03", "ld04", "fl02", "fl03", "fl04",
    "hdct02", "hdct03", "hdct04")
redata <- reshape(echinacea, varying = list(vars), direction = "long",
    timevar = "varb", times = as.factor(vars), v.names = "resp")
redata <- data.frame(redata, root = 1)
pred <- c(0, 1, 2, 1, 2, 3, 4, 5, 6)
fam <- c(1, 1, 1, 1, 1, 1, 3, 3, 3)
hdct <- grepl("hdct", as.character(redata$varb))
redata <- data.frame(redata, hdct = as.integer(hdct))
level <- gsub("[0-9]", "", as.character(redata$varb))
redata <- data.frame(redata, level = as.factor(level))
aout1 <- aster(resp ~ varb + hdct : (nsloc + ewloc + pop),
    pred, fam, varb, id, root, data = redata)
aout2 <- aster(resp ~ varb + level : (nsloc + ewloc) + hdct : pop,
    pred, fam, varb, id, root, data = redata)
aout3 <- aster(resp ~ varb + level : (nsloc + ewloc + pop),
    pred, fam, varb, id, root, data = redata)
anova(aout1, aout2, aout3)

# now random effects models and models without random effects mixed
## Not run: 
### CRAN policy says examples must take < 5 sec.
### This doesn't (on their computers).
data(radish)
pred <- c(0,1,2)
fam <- c(1,3,2)
rout2 <- reaster(resp ~ varb + fit : (Site * Region),
    list(block = ~ 0 + fit : Block, pop = ~ 0 + fit : Pop),
    pred, fam, varb, id, root, data = radish)
rout1 <- reaster(resp ~ varb + fit : (Site * Region),
    list(block = ~ 0 + fit : Block),
    pred, fam, varb, id, root, data = radish)
rout0 <- aster(resp ~ varb + fit : (Site * Region),
    pred, fam, varb, id, root, data = radish)
anova(rout0, rout1, rout2)

## End(Not run)

Loading required package: trust
Analysis of Deviance Table

Model 1: resp ~ varb + hdct:(nsloc + ewloc + pop)
Model 2: resp ~ varb + level:(nsloc + ewloc) + hdct:pop
Model 3: resp ~ varb + level:(nsloc + ewloc + pop)
  Model Df Model Dev Df Deviance P(>|Chi|)    
1       17   -2746.7                          
2       21   -2712.5  4   34.203 6.772e-07 ***
3       33   -2674.7 12   37.838 0.0001632 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Deviance Table

Model 1: resp ~ varb + fit:(Site * Region)
Model 2: resp ~ varb + fit:(Site * Region), ~0 + fit:Block
Model 3: resp ~ varb + fit:(Site * Region), ~0 + fit:Block, ~0 + fit:Pop
  Mod Df Fix Mod Df Rand Mod Dev Df Fix Df Rand Deviance   P-value
1          6           0 1167586                                  
2          6           1 1175913      0       1   8327.4  0.00e+00
3          6           2 1176704      0       1    791.5 1.94e-174