sp.sums {rconifers} | R Documentation |
This function returns a data.frame
object
that contain common summaries by species for
sample.data
object.
sp.sums( x )
x |
an object of class sample.data . |
The data.frame
object returned from sp.sums contains the
following columns for each species:
This function returns a data.frame
object that
contains species level summary information. It is intended
demonstration only and users are encouraged to examine the
source code and modify. All results include all stems and not
only those above breast height.
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 ) )