project {rconifers} | R Documentation |
Projects a CONIFERS sample.data object.
project( x,years=1,control=list(rand.err=0, rand.seed=0, endemic.mort=0, sdi.mort=0) )
x |
a sample.data object. |
years |
number of years to project the sample.data |
control |
A list of control parameters. See *Details* |
The project function returns a sample.data object that contains the projected plant list. The plot data are simply copied from the input sample.data object, and the x0 member is updated if the sample has been projected past the point where stand growth becomes limited by competition.
By default this function projects the sample.data one year, with the default control parameters. The defaults project the sample with a random error component added to the height increment, with endemic mortality turned on and the stand density index induced mortality turned on as well.
The 'control' argument is a list that can supply any of the following components:
returns a projected sample.data object.
Jeff D. Hamann jeff.hamann@forestinformatics.com,
Martin W. Ritchie mritchie@fs.fed.us
Hann, D.W. and C.H. Wang. 1990. Mortality equations for individual trees in southwest Oregon. Oregon State University, Forest Research Laboratory, Corvallis, Oregon. Research Bulletin 67. 17p.
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 ) )