anova.lme
Compare Likelihoods of Fitted Objects
Description
When only one fitted model object is present, a data frame with the 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, ..., test, type, adjustSigma, Terms, L, verbose) ## S3 method for class 'anova.lme' print(x, verbose, ...)
Arguments
object | an object inheriting from class |
... | other optional fitted model objects inheriting from classes |
test | an optional logical value controlling whether likelihood ratio tests should be used to compare the fitted models represented by |
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 |
adjustSigma | an optional logical value. If |
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, its elements must correspond to the order in which terms are included in the model. This argument is only used when a single fitted object is passed to the function. Default is |
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 elements (array columns), they must correspond to coefficients names and will be used to map the linear combination(s) to the coefficients; else, if no names are available, the vector elements (array columns) are assumed in the same order as the coefficients appear in the model. This argument is only used when a single fitted object is passed to the function. Default is |
x | an object inheriting from class |
verbose | an optional logical value. If |
Value
a data frame inheriting from class "anova.lme"
.
Note
Likelihood comparisons are not meaningful for objects fit using restricted maximum likelihood and with different fixed effects.
Author(s)
José Pinheiro and Douglas Bates [email protected]
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 examples: anova(fm1Orth.gls, fm2Orth.gls) fm3Orth.gls <- update(fm2Orth.gls, weights = NULL) anova(fm2Orth.gls, fm3Orth.gls) fm4Orth.gls <- update(fm3Orth.gls, weights = varIdent(form = ~ 1 | Sex)) 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)) # Compare an "lme" object with a "gls" object (test would be non-sensical!) anova(fm3Orth.lme, fm4Orth.gls, test = FALSE) ## Pinheiro and Bates, pp. 222-225 ------------------------------------------ op <- 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)) ## Pinheiro and Bates, pp. 352-365 ------------------------------------------ fm1Theo.lis <- nlsList( conc ~ SSfol(Dose, Time, lKe, lKa, lCl), data=Theoph) fm1Theo.lis 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) ) # Comparing the 3 nlme models anova(fm1Theo.nlme, fm3Theo.nlme, fm2Theo.nlme) options(op) # (set back to previous state)
Copyright (©) 1999–2012 R Foundation for Statistical Computing.
Licensed under the GNU General Public License.