| print.sample.data {rconifers} | R Documentation |
These functions print a few summary values from a sample.data object.
## S3 method for class 'sample.data':
print( x,
digits = max( 3, getOption("digits") - 1 ), ... )
x |
an object of class sample.data. |
digits |
number of digits to print. |
... |
other arguments. |
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 ) )