nlme (version 3.1-99)

anova.lme: Compare Likelihoods of Fitted Objects

Description

When only one fitted model object is present, a data frame with the sums of squares, numerator degrees of freedom, denominator degrees of freedom, F-values, and P-values for Wald tests for the terms in the model (when Terms and L are NULL), a combination of model terms (when Terms in not NULL), or linear combinations of the model coefficients (when L is not NULL). Otherwise, when multiple fitted objects are being compared, a data frame with the degrees of freedom, the (restricted) log-likelihood, the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC) of each object is returned. If test=TRUE, whenever two consecutive objects have different number of degrees of freedom, a likelihood ratio statistic, with the associated p-value is included in the returned data frame.

Usage

## S3 method for class 'lme':
anova(object, \dots, test, type, adjustSigma, Terms, L, verbose)
## S3 method for class 'anova.lme':
print(x, verbose, \dots)

Arguments

object

a fitted model object inheriting from class lme, representing a mixed-effects model.

...

other optional fitted model objects inheriting from classes gls, gnls, lm, lme, lmList, nlme, nlsList, or nls.

test

an optional logical value controlling whether likelihood ratio tests should be used to compare the fitted models represented by object and the objects in .... Defaults to TRUE.

type

an optional character string specifying the type of sum of squares to be used in F-tests for the terms in the model. If "sequential", the sequential sum of squares obtained by including the terms in the order they appear in the mode

adjustSigma

an optional logical value. If TRUE and the estimation method used to obtain object was maximum likelihood, the residual standard error is multiplied by $\sqrt{n_{obs}/(n_{obs} - n_{par})}$, converting it to a REML-lik

Terms

an optional integer or character vector specifying which terms in the model should be jointly tested to be zero using a Wald F-test. If given as a character vector, its elements must correspond to term names; else, if given as an integer vector,

L

an optional numeric vector or array specifying linear combinations of the coefficients in the model that should be tested to be zero. If given as an array, its rows define the linear combinations to be tested. If names are assigned to the vector

x

an object inheriting from class anova.lme

verbose

an optional logical value. If TRUE, the calling sequences for each fitted model object are printed with the rest of the output, being omitted if verbose = FALSE. Defaults to FALSE.

Value

• a data frame inheriting from class anova.lme.

References

Pinheiro, J.C., and Bates, D.M. (2000) "Mixed-Effects Models in S and S-PLUS", Springer.

See Also

gls, gnls, nlme, lme, AIC, BIC, print.anova.lme, logLik.lme,

Examples

fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)
anova(fm1)
fm2 <- update(fm1, random = pdDiag(~age))
anova(fm1, fm2)

# Pinheiro and Bates, pp. 251-254
fm1Orth.gls <- gls(distance ~ Sex * I(age - 11), Orthodont,
            correlation = corSymm(form = ~ 1 | Subject),
            weights = varIdent(form = ~ 1 | age))
fm2Orth.gls <- update(fm1Orth.gls,
                corr = corCompSymm(form = ~ 1 | Subject))
# anova.gls
anova(fm1Orth.gls, fm2Orth.gls)
fm3Orth.gls <- update(fm2Orth.gls, weights = NULL)
# anova.gls
anova(fm2Orth.gls, fm3Orth.gls)
fm4Orth.gls <- update(fm3Orth.gls,
                 weights = varIdent(form = ~ 1 | Sex))
# anova.gls
anova(fm3Orth.gls, fm4Orth.gls)
# not in book but needed for the following command
fm3Orth.lme <-
    lme(distance~Sex*I(age-11), data = Orthodont,
        random = ~ I(age-11) | Subject,
        weights = varIdent(form = ~ 1 | Sex))
# anova.lme to compare an "lme" object with a "gls" object 
anova(fm3Orth.lme, fm4Orth.gls, test = FALSE)

# Pinheiro and Bates, pp. 222-225
options(contrasts = c("contr.treatment", "contr.poly"))
fm1BW.lme <- lme(weight ~ Time * Diet, BodyWeight,
                   random = ~ Time)
fm2BW.lme <- update(fm1BW.lme, weights = varPower())
# Test a specific contrast
anova(fm2BW.lme, L = c("Time:Diet2" = 1, "Time:Diet3" = -1))

fm1Theo.lis <- nlsList(
     conc ~ SSfol(Dose, Time, lKe, lKa, lCl), data=Theoph)
fm1Theo.lis

# Pinheiro and Bates, pp. 352-365
fm1Theo.lis <- nlsList(
     conc ~ SSfol(Dose, Time, lKe, lKa, lCl), data=Theoph)
fm1Theo.nlme <- nlme(fm1Theo.lis)
fm2Theo.nlme <- update(fm1Theo.nlme,
   random=pdDiag(lKe+lKa+lCl~1) )
fm3Theo.nlme <- update(fm2Theo.nlme,
   random=pdDiag(lKa+lCl~1) )

# anova comparing 3 models
anova(fm1Theo.nlme, fm3Theo.nlme, fm2Theo.nlme)