Type:	Package
Title:	Multilevel Modeling of Dendroclimatical Fluctuations
Version:	0.9.1
Date:	2025-04-03
Maintainer:	Wilson Lara <wilarhen@gmail.com>
Description:	Multilevel ecological data series (MEDS) are sequences of observations ordered according to temporal/spatial hierarchies that are defined by sample designs, with sample variability confined to ecological factors. Dendroclimatic MEDS of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity. Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in modelFrame(), and muleMan() functions.
License:	GPL-3
Depends:	nlme, ecodist
LazyData:	TRUE
NeedsCompilation:	no
Repository:	CRAN
Date/Publication:	2025-04-03 17:50:07 UTC
Packaged:	2025-04-03 17:30:33 UTC; wihe
Author:	Wilson Lara [aut, cre], Felipe Bravo [aut]

Multilevel Modeling of Dendroclimatical Fluctuations

Description

Multilevel ecological data series (MEDS) are sequences of observations ordered according to temporal/spatial hierarchies that are defined by sample designs, with sample variability confined to ecological factors. Dendroclimatic MEDS of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity. Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in modelFrame(), and muleMan() functions.

Details

The DESCRIPTION file:

Index of help topics:

BIOdry-package          Multilevel Modeling of Dendroclimatical
                        Fluctuations
PTclim05                Multilevel data set of monthly cumulative
                        precipitations and monthly average temperatures
Pchron                  Tree-ring width (TRW) chronology
Pradii03                Vector of radii measured on P.pinaster
Prec                    Cumulative precipitations
Prings05                Multilevel data set of P. pinaster
Temp                    Average temperatures
amod                    Allometric scaling.
anova.modelFrame        Compare modelFrame objects
arguSelect              Argument selection
cClass                  Column-class extraction.
frametoLme              LME modeling
levexp                  Vector releveling
lmeForm                 LME formula
mUnits                  Metric system
modelFrame              Dendroclimatic-fluctuations modeling
moveYr                  Seasonal years
muleMan                 Multilevel dendroclimatic correlograms
plot.modelFrame         Plot modelFrame objects
plot.muleMan            Plot muleMan objects
plot.wlai               Plot an wlai object
ringApply               Multilevel apply
rtimes                  Time-units synchronization
scacum                  Cummulative-scaled sums
shiftFrame              MEDS formatting
slitFrame               Multilevel splitting
summary.modelFrame      summarize a modelFrame object
tdForm                  ltd formulas
wlai                    Walter-Lieth aridity index

Maintainer: Wilson Lara <wilarhen@gmail.com>

Author(s)

Wilson Lara <wilarhen@gmail.com>, Felipe Bravo <fbravo@pvs.uva.es>

Multilevel data set of monthly cumulative precipitations and monthly average temperatures

Description

Monthly precipitation sums and average temperatures of two climatic locations.

Usage

data(PTclim05)

Format

A data frame with the following 5 variables.

pr

A numeric vector with the monthly cumulative precipitations (mm month-1)

tm

A numeric vector with the monthly average temperatures (degree C month-1)

month

A numeric vector of months

year

A numeric vector of years

plot

A factor vector of plot codes

Details

Recorded months begin at January and end at December, and observed years span from 1951 to 2005. Factor in data set defines one sample level (plot)

References

Bogino, S., and Bravo, F. (2008). Growth response of Pinus pinaster Ait. to climatic variables in central Spanish forests. Ann. For. Sci. 65, 1-13.

Examples

str(PTclim05)

Tree-ring width (TRW) chronology

Description

TRW chronology for Pinus pinaster forests from two sample plots in northern Spain (Soria) and east-central Spain (Cuenca). The data set contains eight TRW series. To account for source variability, two trees were selected per site, and two samples were extracted from each tree.

Usage

data(Pchron)

Format

data.frame object with eight columns.

Rows

Row names of the data set are time-units labels (years of formation).

Columns

Column names represent levels in three within-stand ecological factors that are common to most TRW data and represent variability in stand qualities (stand), tree-radial morphology (sample), and tree genetics/phenotypes (tree). The names are dot-separated labels representing the hierarchy of the three ecological factors, where higher ecological-factor levels are defined first and lower levels after. For example, code 'P16106.17.a' is the column name of sample replicate 'a' in tree '17' in plot 'P16106'.

Details

This data set belongs to a more extensive tree-ring chronology which was processed in previous studies (Bogino and Bravo, 2008) by measuring TRW in polished-core samples (5 mm diameter) using the Windendro program and cross-dating the tree-ring chronologies using COFECHA software. Time units for the TRWs are formation years spanning from 1861 to 2005. The cross-dating records suggested that maritime pine chronologies had high SNR: (23-28 in northern Spain, and 38-61 in east-central Spain) and high EPS (0.96 in northern Spain, and 0.98 in east-central Spain).

References

Bogino, S., and Bravo, F. (2008). Growth response of Pinus pinaster Ait. to climatic variables in central Spanish forests. Ann. For. Sci. 65, 1-13.

Examples

 str(Pchron) 

Vector of radii measured on P.pinaster

Description

Vector of radii measured in dominant trees of P.pinaster (2003) growing on the Ebro river basin, Spain

Usage

data(Pradii03)

Format

A numeric vector with names of the vector indicating the measured tree.

Details

This vector contains inside-bark radii, measured in four trees during 2003 (mm). This radial vector was derived from the over-bark diameters at breast height (cm), with allometric estimation of inside-bark diameters from the over-bark diameters. Names of the vector correspond to labels at tree level

Source

Sustainable Forest Management Research Institute, Universidad de Valladolid (UVa)-INIA.

Examples

str(Pradii03)

Cumulative precipitations

Description

Monthly cumulative precipitation (mm).

Usage

data(Prec)

Format

data.frame object with 24 records.

Column

Column names contain plot codes and monthly abbreviations

Details

Recorded months begin at January and end at December, and observed years span from 1951 to 2005. Factor in data set defines one sample level (plot)

References

Bogino, S., and Bravo, F. (2008). Growth response of Pinus pinaster Ait. to climatic variables in central Spanish forests. Ann. For. Sci. 65, 1-13.

Examples

str(Prec)

Multilevel data set of P. pinaster

Description

Radial increments of Pinus pinaster from two sample plots located on Northern and Southern portions of Ebro river basin, Spain.

Usage

data(Prings05)

Format

A data frame with the following 5 variables.

x

A numeric vector with the radial increments in mm year-1

year

A numeric vector with the recorded year

sample

A factor indicating the sample replicate

tree

A factor indicating the tree number

plot

A factor indicating the plot code

Details

This data set contains eight series of tree-ring widths of maritime pine (Pinus pinaster), with recorded years spanning from 1810 to 2005. The cores were sampled from dominant trees of two sites, with sample plots being located on both: northern Spain (plot code: P44005) and center-east portion of the same country (plot code: P16106). Two trees were selected by plot, and two core samples were extracted by tree. Consequently, the sample design defined three levels: sample in tree on plot (plot level), sample in tree (tree level), and sample level.

References

Bogino, S., and Bravo, F. (2008). Growth response of Pinus pinaster Ait. to climatic variables in central Spanish forests. Ann. For. Sci. 65, 1-13.

Examples

str(Prings05)

Average temperatures

Description

Monthly average temperatures (degree C).

Usage

data(Temp)

Format

data.frame object with 24 records.

Column

Column names contain plot codes and monthly abbreviations

Details

Recorded months begin at January and end at December, and observed years span from 1951 to 2005. Factor in data set defines one sample level (plot)

References

Bogino, S., and Bravo, F. (2008). Growth response of Pinus pinaster Ait. to climatic variables in central Spanish forests. Ann. For. Sci. 65, 1-13.

Examples

str(Temp)

Allometric scaling.

Description

Allometric models and parameters are used to scale organic growth.

Usage

amod(x, mp = c(1, 1), 
    fun = y ~ a * (x^b))

Arguments

Details

. Allometric models are useful to scale size-components of organisms such as tree diameters (mp = c(2,1)) and basal areas (mp = c(0.25 * pi,2)). Several parameter groups (c(a1,b1,a2,b2, ..., an,bn)) can be recursively processed. This enables computation of complex organic variables. For example, above-ground tree biomass could be computed from two parameter groups for tree-biomass, and over-bark diameter scaling.

Value

data.frame of the scaled variable (x) and relative increments (csx). These are computed with setdiff function.

Author(s)

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>)

Examples

## Simulating TRW records:
set.seed(1)
trw <- ts(abs(rnorm(12,1,1)),start = 1950)
## Cumulative TRW:
cri <- cumsum(trw)
## tree diameters
td <- amod(cri,mp = c(2,1))
## plot of the tree diameters and the
## relative increments:
plot(ts(td))

Compare modelFrame objects

Description

Models in modelFrame lists are compared with anova.lme method.

Usage

## S3 method for class 'modelFrame'
anova(object, 
    ..., test, type, 
    adjustSigma, Terms, 
    L, verbose)

Arguments

Value

data frame inheriting from class "anova.lme".

Author(s)

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>)

References

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.

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)
## }    
## 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)
## }

Argument selection

Description

Arguments of specific functions are selected from arbitrary numbers and varieties of arguments.

Usage

arguSelect(rd = NULL, 
    fun = c("mapply", 
        "ringApply"), 
    ...)

Arguments

Details

Closures with ellipsis terms use this function to extract and pass arguments to other functions. Arguments in MoreArgs lists are also extracted and stored again as MoreArgs lists.

Value

list of selected arguments.

Author(s)

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>)

Examples

##Multilevel ecological data series of tree-ring widths:
data(Prings05,envir = environment())
## Radial increments measured on 2003:
data(Pradii03,envir = environment())    

## Selection of arguments in some functions:
ar1 <- arguSelect(fun = c('amod'),
                  only.dup = TRUE,
                  mp = c(0.5,1),
                  rf.t = 2003)
str(ar1)

ar2 <- arguSelect(fn = 'amod',
                  only.dup = TRUE,
                  mp = c(0.5,1),
                  rf.t = 2003)
str(ar2)
ar3 <- arguSelect(rd = Prings05,
                  fn = 'amod',
                  only.dup = TRUE,
                  mp = c(0.5,1),
                  rf.t = 2003)
str(ar3)

ar4 <- arguSelect(rd = Prings05,
                  fun = 'scacum',
                  sc.c = Pradii03,
                  MoreArgs = list(only.dup = TRUE,
                                  mp = c(0.5,1),
                                  rf.t = 2003))
str(ar4)

ar5 <- arguSelect(rd = Prings05,
                  fun = 'scacum',
                  ref = Pradii03,
                  rf.t = rep(2003:2011),
                  MoreArgs = list(only.dup = TRUE,
                                  mp = c(0.5,1)))
str(ar5)    

Column-class extraction.

Description

Column names of multilevel data sets are extracted according to three classes: numeric values, integer sequences, and factor levels.

Usage

cClass(rd, cl = "all")

Arguments

Value

character names.

Author(s)

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>)

Examples

##Multilevel data frame of tree-ring widths:
data(Prings05,envir = environment())
## Names of variables in Prings05 data containing numeric classes:
cClass(Prings05, 'numeric') # 'x'
## Names of variables containing time units: 
cClass(Prings05, 'integer') # 'year'
## Names of variables containing factors: 
cClass(Prings05, 'factor') # 'sample', 'tree', 'plot'

LME modeling

Description

LME models are fitted to detrend multilevel ecological data series.

Usage

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

Arguments

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.

Value

groupedData object.

Author(s)

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>)

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')
## }

Vector releveling

Description

Expansion or reduction of a numeric vector by matching its level names with the ecological factors of a multilevel ecological data series.

Usage

levexp(x, levels)

Arguments

Value

numeric vector with expanded/reduced levels.

Author(s)

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>)

Examples

##Multilevel ecological data series of tree-ring widths:
data(Prings05,envir = environment())
## tree radii measured at 2003:
data(Pradii03,envir = environment())    

## Releveling the tree radii
refs <- levexp(Pradii03,Prings05)
refs

LME formula

Description

This function computes LME formulas from multilevel ecological data series (MEDS).

Usage

lmeForm(rd, prim.cov = FALSE, 
    resp = NULL, covar = NULL, 
    lev.rm = NULL)

Arguments

Details

Formulas of the form resp ~ cov | group (see groupedData function) are computed from MEDS. The formulas can be implemented by modelFrame function to detrend MEDS

Value

formula with any of the forms: resp ~ cov | group or ~ cov.

Author(s)

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>)

References

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

Examples

##Multilevel ecological data series of tree-ring widths:
data(Prings05,envir = environment())

## LME formula:
form1 <- lmeForm(Prings05,prim.cov = FALSE)
print(form1)
## removing the sample level from the formula
form2 <- lmeForm(Prings05,lev.rm = 'sample')
form2 <- lmeForm(Prings05,lev.rm = 1)

## groupedData object with the LME formula 
gdata <- groupedData(lmeForm(Prings05,lev.rm = 1),
                     data = Prings05)
plot(gdata,groups = ~ sample)

Metric system

Description

This function control metric units.

Usage

mUnits(x, from = "mm", 
    to = "mm")

Arguments

Details

Characters in from and to arguments have the form 'p_', where 'p' is the metric prefix and '_' is a base unit. Sixteen metric prefixes are supported: atto 'a', femto 'f', pico 'p', nano 'n', micro 'mm', mili 'm', centi 'c', deci 'd', deca 'da', hecto 'h', kilo 'k', mega 'M', giga 'G', tera 'T', peta 'P', and exa 'E'.

Value

numeric vector.

Author(s)

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>)

Examples

## Simulation of TRW data
set.seed(1)
w <- abs(rnorm(12,1,1))
trw <- ts(w,start = 1970)
## transforming metric units of trw vector from milimeters to meters
sr <- mUnits(trw, from = 'mm', to = 'm')
attributes(sr)

Dendroclimatic-fluctuations modeling

Description

This function develops recursive evaluation of functions for one-level modeling (FOLM) and LME detrending of dendroclimatic chronologies.

Usage

modelFrame(rd, fn = list("rtimes", 
    "scacum", "amod"), 
    lv = list(2, 1, 1), 
    form = "tdForm", 
    ...)

Arguments

Details

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.

Value

Threefold list with fluctuations in fluc, groupedData object in model, and model call in call.

Author(s)

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>)

References

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.

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)
## }
    ## Climatic records:
    data(Temp,envir = environment())
    data(Prec,envir = environment())
    ## Aridity-index fluctuations:
    ## \donttest{
    ## aif <- modelFrame(rd = list(Prec, Temp),
    ##                   fn = list('moveYr','wlai'),
    ##                   lv = list('year','year'),
    ##                   form = 'lmeForm')
    ## summary(aif$'model')
    ## }

Seasonal years

Description

Monthly records in time-series replicates (usually of climate) are labeled for the years can begin in a month other than January.

Usage

moveYr(cd, ini.mnt = "Oct")

Arguments

Details

character months as defined in month.abb or month.name.

Value

data.frame object with the months being numeric values and the years beginning at ini.mnt argument.

Author(s)

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>)

Examples

## Climatic records of monthly precipitation sums and monthly
## average temperatures
data(PTclim05,envir = environment())

## Making the year 1955 in plot 'P16106' to begin on 'April'
cl1 <- slitFrame(PTclim05,c('year','plot'))[[1]]
cl2 <- moveYr(cl1,ini.mnt = 'Mar')
head(cl2)

## a simple vector of years
yr <- rep(2005,12)
names(yr) <- month.abb[1:12]
moveYr(yr)

Multilevel dendroclimatic correlograms

Description

Multivariate correlograms between TRW fluctuations and climatic fluctuations.

Usage

muleMan(rd, cd, rd.var = NULL, 
    cd.var = NULL, ...)

Arguments

Details

Function mgram is implemented to compare the dendroclimatic fluctuations. Models being compared should have common higher-level factors (see example).

Value

data.frame object of multivariate correlations.

Author(s)

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>)

References

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.

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)
## }
## Climatic Records:
data(Temp,envir = environment())
data(Prec,envir = environment())
## Aridity-index fluctuations:
## /donttest{
##     aif <- modelFrame(rd = list(Prec, Temp),
##                       fn = list('moveYr','wlai'),
##                       lv = list('year','year'),
##                       form = 'lmeForm')
## }
##Multivariate comparison:
## /donttest{
## mcomp <- muleMan(trwf,
##                     aif,
##                  nperm = 10^3)
## str(mcomp)
## }

Plot modelFrame objects

Description

Diagnostic Trellis plot for fluctuations in modelFrame objects are obtained.

Usage

## S3 method for class 'modelFrame'
plot(x, ...)

Arguments

Author(s)

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>)

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)
##     plot(trwf, grid = FALSE)
## }

Plot muleMan objects

Description

Diagnostic Trellis plot for fluctuations in muleMan objects are obtained.

Usage

## S3 method for class 'muleMan'
plot(x, ...)

Arguments

Author(s)

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>)

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)
## }
## Climatic Records:
data(Temp,envir = environment())
data(Prec,envir = environment())
## Aridity-index fluctuations:
## \donttest{
##     aif <- modelFrame(rd = list(Prec, Temp),
##                       fn = list('moveYr','wlai'),
##                       lv = list('year','year'),
##                       form = 'lmeForm')
## }
##Multivariate comparison:
## \donttest{
## mcomp <- muleMan(trwf,
##                     aif,
##                  nperm = 10^3)

## plot(mcomp, grid = FALSE)
## }

Plot an wlai object

Description

A Walter-Lieth climate diagram is produced.

Usage

## S3 method for class 'wlai'
plot(x, ...)

Arguments

Details

Areas between temperature and precipitation lines when precipitation exceeds temperature (moist seasons) are plotted in gray color, and areas where temperature exceeds precipitation (dry seasons) are plotted in black color. Monthly cumulative precipitations over 100 mm are scaled such that 1 degree C of average temperature is equal to 5 mm of precipitation.

Value

A plot of the Walter-Lieth diagram.

Author(s)

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>)

References

Manrique E., A. Fernandez-Cancio. 2000. Extreme climatic events in dendroclimatic reconstructions from Spain. Clim. Chang., 44: 123-138.

Examples

##random simulation of climatic records
set.seed(1)
pr <- rnorm(12,1,1)
tm <- rnorm(12,0,1)
cld <- data.frame(pr,tm)
##labels of months from october to september
rownames(cld) <- month.abb[c(10:12,1:9)]
rownames(cld) <- c(10:12,1:9)
##computation of the aridity index and climate diagram
AI <- wlai(cld)
plot.wlai(AI)

Multilevel apply

Description

Wrapper of Map to apply functions on multilevel data series and preserve factor-level structure in the outputs.

Usage

ringApply(rd, lv = 1, 
    fn = "scacum", ...)

Arguments

Details

Other functions such as rtimes, scacum, amod, or wlai can be implemented. Function arguments should be formulated as suggested in mapply, with constant arguments being stored in a MoreArgs list. This function is implemented by modelFrame for recursive modeling of MEDS.

Value

data.frame object preserving initial factor-level columns.

Author(s)

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>)

References

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.

Examples

##Multilevel ecological data series (MEDS) of tree-ring widths:
data(Prings05,envir = environment())
## Radial increments measured on 2003:
data(Pradii03,envir = environment())    
## MEDS of monthly precipitation sums and average temperatures:
data(PTclim05,envir = environment())

##Tree-level scaling of years of formation
##with 'rtimes' function:
dfm1 <- ringApply(Prings05,
                  lv = 2,
                  fn = 'rtimes')
str(dfm1)
##Relative time-units from year 1 to year 9:
subset(dfm1,time%in%c(1:9,NA))

## Sample-level scaling of TRW chronologies around reference radii
## which were measured at 2003:
dfm2 <- ringApply(dfm1,
                  lv = 'sample',
                  sc.c = Pradii03,
                  rf.t = 2003,
                  fn = 'scacum')
str(dfm2)    
##Sample-level modeling of basal areas (mm2) via allometric
##scaling:
dfm3 <- ringApply(dfm2,
                  lv = 'sample',
                  fn = 'amod',
                  MoreArgs = list(mp = c(2,1,0.25 * pi,2)))
str(dfm3)

## Seasonal years from 'October' to 'September':
cl1 <- ringApply(PTclim05,
                 lv = 'year',
                 fn = 'moveYr')
tail(cl1,15)

##Year-level aridity indexes: 
wl <- ringApply(cl1,
                lv = 'year',
                fn = 'wlai')
str(wl)

## Plot of aridity-index fluctuations:
d <- groupedData(lmeForm(wl),wl)
plot(d)        

Time-units synchronization

Description

Unique observations in time-series replicates are excluded

Usage

rtimes(x, only.dup = TRUE)

Arguments

Value

data.frame object with the initial vector and its time units.

Author(s)

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>)

Examples

## row names of a vector
fy <- function(y,span){(y - span):y}
x <- c(fy(2005,5),fy(2007,10)) 
## (not run) Simulating the vector
r <- abs(rnorm(length(x)))
names(r) <- x
## (not run) computing the synchronized times:
rtimes(r,only.dup = TRUE)        
## (not run) Extracting only duplicated times:
na.omit(rtimes(r,only.dup = TRUE))

Cummulative-scaled sums

Description

This function computes cummulative and scaled sums of time-series replicates.

Usage

scacum(x, sc.c = NA, 
    rf.t = NA)

Arguments

Details

Cummulative sums of time-series replicates (e.g. tree-ring widths) are scaled around reference values (e.g. tree radii).

Value

data frame with the original vector, and its scaled-cummulative sums.

Author(s)

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>)

Examples

x <- c(0.79,0.32,0.53,0.43,0.18)
names(x) <- 1948:1952
scacum(x,sc.c = 4,rf.t = 1951)

##If sc.c = NA then cummulative values are scaled arround
##max(cumsum(x)):
max(cumsum(x))
scacum(x,NA,1951)

MEDS formatting

Description

dendroclimatic chronologies (trw, and climatic data) are formatted into multilevel ecological data series. SI units of continuous variables in the data can be transformed.

Usage

shiftFrame(rd, f.nm = NULL, 
    x.nm = names(rd)[1L], 
    t.nm = "year", ...)

Arguments

Details

Row names of dendroclimatic data frames are time units (e.g. years). Column names are dot-separated labels representing the hierarchy of ecological or time-units factors, where the higher levels are defined first and the lower levels after. For example, code 'P16106.17' is the column name of core 'a' in tree '17' in plot 'P16106'. Labels containing monthly abbreviations are also formatted.

Value

When rd argument is a dendroclimatic chronology (see details) then the output is a groupedData object, and viceversa.

Author(s)

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>)

Examples

##tree-ring widths formated as a \code{\link[nlme]{groupedData}} object:
data(Prings05,envir = environment())

## Formatting the \code{\link[nlme]{groupedData}} object into a ring-data frame:
pwide <- shiftFrame(Prings05, from = 'mm', to = 'mmm')
str(pwide)
## Formatting the ring-data frame into a groupedData object, and
## changing SI units from micrometers to milimeters:
plong <- shiftFrame(pwide,from = 'mmm', to = 'mm')
plot(plong)

Multilevel splitting

Description

This function splits a Multilevel data frame into factor levels.

Usage

slitFrame(rd, lv = cClass(rd, 
    "factor"))

Arguments

Value

list of data.frame objects.

Author(s)

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>)

Examples

##Ring data frame:
##Multilevel data frame of tree-ring widths:
data(Prings05, envir = environment())
data(PTclim05, envir = environment())
## split multilevel data into its second factor-level column:
spl <- slitFrame(Prings05)
str(spl)
## split the data into the factor-level: 'year':
spl <- slitFrame(Prings05,'year')
str(spl)
spl <- slitFrame(PTclim05,'year')
str(spl)

summarize a modelFrame object

Description

A summary of a modelFrame object is obtained.

Usage

## S3 method for class 'modelFrame'
summary(object, 
    ...)

Arguments

Author(s)

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>)

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 = TRUE,
##                    on.time = TRUE)
## summary(trwf)
## }

ltd formulas

Description

This function formulates linear time-decline formulas (ltd) from categorical variables in multilevel ecological data series.

Usage

tdForm(rd, prim.cov = FALSE, 
    on.time = TRUE, log.t = FALSE, 
    lev.rm = NULL)

Arguments

Details

the ltd formulas belong to following general equation: log (x) = log (csx) + f(time); where the relative organic growth (x) is explained by the cumulative organic growth (csx) plus a function of time f(time); with f(time) being either the time or a logarithmic transformation the time. The ltd can be implemented by modelFrame function to subtract trends in organic MEDS

Value

formula with the forms: 'resp ~ cov | group' or '~ cov'.

Author(s)

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>)

References

Zeide B. 1993. Analysis of Growth Equations. For. Sci., 39: 594-616.

Examples

## an ltd formula:
lev <- c('plot','tree')
tdeq <- tdForm(lev,log.t = TRUE)
tdeq
## (not run) only primary covariate:
tdeq1 <- tdForm(lev,prim.cov = TRUE)
tdeq1
##Multilevel data frame of tree-ring widths:
data(Prings05,envir = environment())
## removing two levels: 'plot' and 'tree' from the formula
tdea2 <- tdForm(Prings05, lev.rm = c('plot','tree'))
tdea2 <- tdForm(Prings05, lev.rm = 2:3)

Walter-Lieth aridity index

Description

Computing the annual aridity index from Walter-Lieth climate diagrams

Usage

wlai(cd, sqt = TRUE)

Arguments

Details

Areas between temperature and precipitation lines when precipitation exceeds temperature are calculated as indicators of moist seasons, and areas where temperature exceeds precipitation are calculated as indicator of dry season. The aridity index is defined as the quotient between the areas of dry and wet seasons. Precipitations over 100 mm are scaled such that 1 degree C is equal to 5 mm.

Value

numeric aridity index and plot of the Walter-Lieth diagram.

Author(s)

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>)

References

Manrique E., A. Fernandez-Cancio. 2000. Extreme climatic events in dendroclimatic reconstructions from Spain. Clim. Chang., 44: 123-138.

Examples

##random simulation of climatic records
set.seed(1)
pr <- rnorm(12,1,1)
tm <- rnorm(12,0,1)
cld <- data.frame(pr,tm)
##labels of months from october to september
rownames(cld) <- month.abb[c(10:12,1:9)]
rownames(cld) <- c(10:12,1:9)
##computation of the aridity index and climate diagram
AI <- wlai(cld)
AI