BIOdry (version 0.9.1)

frametoLme: LME modeling

Description

LME models are fitted to detrend multilevel ecological data series.

Usage

frametoLme(rd, form = "lmeForm", 
    res.data = TRUE, 
    ...)

Value

groupedData object.

Arguments

rd

data.frame. Multilevel ecological data series.

form

character. Any of two lme formulas: 'lmeForm' and 'tdForm' (see details).

res.data

logical. Save residuals as a multilevel ecological data series. If TRUE then a data frame of name 'fluc' is added to output list.

...

Further arguments to be passed to lme function or to the lme formula in form.

Author

Wilson Lara <[e-mail]> [aut, cre] (<https://orcid.org/0000-0003-3527-1380>), Felipe Bravo <[e-mail]> [aut] (<https://orcid.org/0000-0001-7348-6695>)

Details

This function implements lme function to fit linear mixed-effects models on multilevel ecological data series processed by the modelFrame function. Two kind of model formulas can be fitted: 'lmeForm' and 'tdForm'; these characters implement functions with same names (tdForm and lmeForm). Other lme formulas can be specified by modifying arguments in any of these two functions. After the lme models are fitted, they can be extended by implementing methods in nlme package.

References

Pinheiro J. C., D. M. Bates. 2000. Mixed-effects models in S and S-PLUS. Springer, New York.

Examples

##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)
## }
## Detrending the fluctuations by fitting a (l)td-form model
## with Maximum-likelihood method (ML):
## \donttest{
## pdata <- trwf$'model'$'data'
## rlme <- frametoLme(pdata,
##                    form = 'tdForm',
##                    method = 'ML',
##                    log.t = TRUE)
## summary(rlme$model)
## }
##a plot of the modeled fluctuations:
## \donttest{
## d <- nlme::groupedData(lmeForm(rlme$fluc,lev.rm = 1),data = rlme$fluc)
## plot(d,groups = ~ sample,auto.key = TRUE)
## }
## A model of aridity:
## \donttest{
## cf <- modelFrame(PTclim05,
##                  lv = list('year','year'),
##                  fn = list('moveYr','wlai'),
##                  form = NULL)
## summary(cf)
## }
## An lme model of aridity at 'plot' level:
## \donttest{
## cdata <- cf$'model'$'data'
## rmod <- frametoLme(cdata,form = 'lmeForm')
## summary(rmod$model)
## rk <- nlme::groupedData(lmeForm(rmod$fluc),data=rmod$fluc)
## plot(rk,ylab = 'detrended AI')
## }