set.species.map {rconifers} | R Documentation |
This function assigns the mapping of user defined species codes to the functional species within the CONIFERS growth model.
set.species.map( sp.map=list(idx,fsp,code,em,msdi,b,m,gwh,gwd) )
sp.map |
a list that contains the following elements:
|
The set.species.map is how the user controls the behavior of
the functional species that is used to project individual plants
forward. The idx *must* be a zero-offset index vector which is
assigned the element SPECIES_RECORD[idx]
in the model. This
function is used to control the mapping between the user species code
and the functional species codes in the growth model.
no value is returned.
Jeff D. Hamann jeff.hamann@forestinformatics.com,
Martin W. Ritchie mritchie@fs.fed.us
Ritchie, M.W. 2008. User's Guide and Help System for CONIFERS: A Simulator for Young Conifer Plantations Version 4.10. See http://www.fs.fed.us/psw/programs/ecology_of_western_forests/projects/conifers/
calc.max.sdi
,
impute
,
plants
,
plots
project
,
rand.seed
,
rconifers
,
sample.data
,
set.species.map
,
set.variant
,
smc
,
summary.sample.data
,
swo
,
thin
library( rconifers ) ## set the variant to the SWO variant set.variant( 0 ) # load the Southwest-Oregon species coefficients into R as a data.frame object data( swo ) # set the species map sp.map <- list(idx=swo$idx, fsp=swo$fsp, code=as.character(swo$code), em=swo$endemic.mort, msdi=swo$max.sdi, b=swo$browse.damage, m=swo$mechanical.damage, gwh=swo$genetic.worth.h, gwd=swo$genetic.worth.d) set.species.map( sp.map ) ## grow the data that was originally swo in the smc variant # load and display CONIFERS example plots data( plots ) print( plots ) # load and display CONIFERS example plants data( plants ) print( plants ) # randomly remove 10 crown.width observations to test # the impute function blanks <- sample( 1:nrow( plants ), 10, replace=FALSE ) plants[blanks,]$crown.width <- NA # create the sample.data list object sample.3 <- list( plots=plots, plants=plants, age=3, x0=0.0 ) class(sample.3) <- "sample.data" # fill in missing values sample.3.imp <- impute( sample.3 ) # print the maximum stand density index for the current settings print( calc.max.sdi( sample.3.imp ) ) # print a summary of the sample print( sample.3.imp ) # now, project the sample forward for 20 years # with all of the options turned off sample.23 <- project( sample.3.imp, 20, control=list(rand.err=0,rand.seed=0,endemic.mort=0,sdi.mort=0)) ## print the projected summaries print( sample.23 ) ## plot the diagnostics from the fit a linear dbh-tht model ## before thinning opar <- par( mfcol=c(2,2 ) ) plot( lm( sample.23$plants$tht ~ sample.23$plants$dbh ) ) par( opar ) ## thin the stand to capture mortality, redistribute growth, ## and possibly generate revenue ## Proportional thin for selected tree species, does not remove shrubs sample.23.t1 <- thin( sample.23, control=list(type=1, target=50.0, target.sp="DF" ) ) print( sample.23.t1 ) ## Proportional thin across all tree species sample.23.t2 <- thin( sample.23, control=list(type=2, target=50.0 ) ) print( sample.23.t2 ) ## Thin from below, by dbh, all species sample.23.t3 <- thin( sample.23, control=list(type=3, target=50.0 ) ) print( sample.23.t3 ) ## Thin from below, by dbh for species "PM" sample.23.t4 <- thin( sample.23, control=list(type=4, target=50.0, target.sp="PM" ) ) print( sample.23.t4 ) ## plot the diagnostics from the fit a linear dbh-tht model ## after proportional thinning opar <- par( mfcol=c(2,2 ) ) plot( lm( sample.23.t2$plants$tht ~ sample.23.t2$plants$dbh ) ) par( opar ) ## print the differences, by species print( sp.sums( sample.23.t4 ) - sp.sums( sample.23 ) )