| set.variant {rconifers} | R Documentation |
Assigns the variant for the CONIFERS forest growth model.
set.variant( var=0 )
var |
is the CONIFERS variant. The only possible
values for the current at the current time are 0
(SWO_CONIFERS) and 1 (SMC_CONIFERS). |
The set.variant function only accepts an argument of zero (0)
or one (1). The default variant is the (SWO\_CONIFERS)
variant.
none.
Jeff D. Hamann jeff.hamann@forestinformatics.com,
Martin W. Ritchie mritchie@fs.fed.us
Ritchie, M. and J. Hamann. 2006. Modeling dynamics of competing vegetation in young conifer plantations of northern California and southern Oregon, USA. Canadian Journal of Forest Research 36(10): 2523-2532.
Ritchie, M. and J. Hamann. 2008. Individual-tree height-, diameter- and crown-width increment equations for young Douglas-fir plantations. New Forests 35(2):173-186.
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/
Vaughn, Nicholas. 2007. An individual-tree model to predict the annual growth of young stands of Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) in the Pacific northwest. M.S. Thesis, University of Washington. 91p.
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 ) )