modelFrame | R Documentation |
This function develops recursive evaluation of functions for one-level modeling (FOLM) and LME detrending of dendroclimatic chronologies.
modelFrame(rd, fn = list("rtimes", "scacum", "amod"), lv = list(2, 1, 1), form = "tdForm", ...)
rd |
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fn |
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lv |
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form |
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... |
Further arguments in |
Defaults model fluctuations in
tree-ring width chronologies via recursive
implementation of four FOLM:
rtimes
, scacum
,
amod
, and
frametoLme
. Nevertheless,
other FOLM can be implemented to model
aridity-index fluctuations(see example with
climatic data). Processed chronologies are
detrended with lme
function
and other nlme
methods
. Internal algorithm uses
shiftFrame
arguSelect
and
ringApply
functions. Consequently, arguments that are
not iterated over factor-level labels in the
processed data are specified in 'MoreArgs'
lists (see examples). Arguments in
modelFrame
objects can be updated
with update
function.
Threefold list with fluctuations in fluc
,
groupedData object in model
, and model call in
call
.
Wilson Lara <wilarhen@gmail.com>, Felipe Bravo <fbravo@pvs.uva.es>
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) trwf <- modelFrame(Pchron, to = 'cm', MoreArgs = list(mp = c(2,1, biom_param)), log.t = FALSE, on.time = FALSE) ## Climatic records: data(Temp,envir = environment()) data(Prec,envir = environment()) ## Aridity-index fluctuations: aif <- modelFrame(rd = list(Prec, Temp), fn = list('moveYr','wlai'), lv = list('year','year'), form = 'lmeForm') summary(aif$'model')
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