ringApply {BIOdry}

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

rd

data.frame. Multileve ecological data series.

lv

numeric position, or character name, of an ecological factor in the processed MEDS.

fn

character name of the function to be evaluated (see details). Default 'scacum' computes scaled-cumulative radii.

...

Further arguments in the function being specified fn argument (see details)

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 <[e-mail]>, Felipe Bravo <[e-mail]>

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)