anova.modelFrame | R Documentation |
Models in modelFrame
lists are compared with
anova.lme
method.
## S3 method for class 'modelFrame'
anova(object,
..., test, type,
adjustSigma, Terms,
L, verbose)
object |
an |
... |
other optional fitted model objects inheriting from
classes "modelFrame", "lme", "lm", among other (see
|
test |
optional character string specifying the |
type |
optional character string specifying the |
adjustSigma |
If TRUE and the estimation method used to obtain
|
Terms |
optional integer or character vector specifying which
terms in the model should be jointly tested to be zero
using a Wald F-test (see |
L |
optional numeric vector or array specifying linear
combinations of the coefficients in the model that should be
tested to be zero (see |
verbose |
optional logical value. If TRUE, the calling
sequences for each fitted model |
data frame inheriting from class "anova.lme".
Wilson Lara <wilarhen@gmail.com> [aut, cre] (<https://orcid.org/0000-0003-3527-1380>), Felipe Bravo <fbravo@pvs.uva.es> [aut] (<https://orcid.org/0000-0001-7348-6695>)
Lara W., F. Bravo, D. Maguire. 2013. Modeling patterns between drought and tree biomass growth from dendrochronological data: A multilevel approach. Agric. For. Meteorol., 178-179:140-151.
##TRW chronology (mm) and inside-bark radii
data(Pchron,envir = environment())
## Parameters of allometric model to compute Diameter at Breast
## Height over bark (DBH, cm) from diameter inside bark (dib, cm)
## and Total Tree Biomass (TTB, kg tree -1 ) from DBH (Lara
## et. al. 2013):
biom_param <- c(2.87, 0.85, 0.05, 2.5)
## Modeling tree-biomass fluctuations while accounting for
## within-plot source variability (see defaults in "modelFrame"
## function)
## \donttest{
## trwf <- modelFrame(Pchron,
## to = 'cm',
## MoreArgs = list(mp = c(2,1, biom_param)),
## log.t = FALSE,
## on.time = FALSE)
## }
## Fitting a single linear regression of the "tdForm" formula
## without random effects to the tree-biomass data:
## \donttest{
## trwfl <- lm(log(x) ~ log(csx) + year,
## data = trwf$'model'$'data')
## }
## Comparing model likelihoods with anova method:
## \donttest{
## anova(trwf, trwfl)
## }
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