aster (version 1.3-5)

anova.asterOrReaster: Analysis of Deviance for Reaster Model Fits

Description

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

Usage

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

Value

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

Arguments

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.

Warning

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.

Details

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.

See Also

aster, reaster, anova.

Examples

### 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
if (FALSE) {
### 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)
}