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
,
sample.data
,
set.species.map
,
set.variant
,
smc
,
summary.sample.data
,
swo
library( rconifers ) #Example 1 Using SMC ## set the variant to the SMC variant set.species.map(set.variant( 1 )) ## grow the data that was originally swo in the smc variant # load and display CONIFERS example plots data( plots.smc ) print( plots.smc ) # load and display CONIFERS example plants data( plants.smc ) print( plants.smc ) # create the sample.data list object sample.smc.3 <- list( plots=plots.smc, plants=plants.smc, age=3, x0=0.0 ) class(sample.smc.3) <- "sample.data" print( sp.sums( sample.smc.3 ) ) smry<-sp.sums( sample.smc.3 ) dfqmd<-smry["DF","qmd"] #Example 2 Using SWO ## set the variant to the SWO variant set.species.map(set.variant( 0 ) ) ## grow the swo data # load and display CONIFERS example plots data( plots.swo ) print( plots.swo ) # load and display CONIFERS example plants data( plants.swo ) print( plants.swo ) # create the sample.data list object sample.swo.3 <- list( plots=plots.swo, plants=plants.swo, age=3, x0=0.0 ) class(sample.swo.3) <- "sample.data" # print the maximum stand density index for the current settings print( calc.max.sdi( sample.swo.3 ) ) # print a summary of the sample print( sample.swo.3 ) # now, project the sample forward for 20 years # with all of the options turned off sample.swo.23 <- project( sample.swo.3, 20, control=list(rand.err=0,rand.seed=0,endemic.mort=0,sdi.mort=0)) ## print the projected summaries print( sample.swo.23 ) ## thin the stand ## Proportional thin for selected tree species, does not remove shrubs sample.swo.23.t1 <- thin( sample.swo.23, control=list(type=1, target=50.0, target.sp="DF" ) ) print( sample.swo.23.t1 ) ## Proportional thin across all tree species sample.swo.23.t2 <- thin( sample.swo.23, control=list(type=2, target=50.0 ) ) print( sample.swo.23.t2 ) ## Thin from below, by dbh, all species sample.swo.23.t3 <- thin( sample.swo.23, control=list(type=3, target=50.0 ) ) print( sample.swo.23.t3 ) ## Thin from below, by dbh for species "PM" sample.swo.23.t4 <- thin( sample.swo.23, control=list(type=4, target=50.0, target.sp="PM" ) ) print( sample.swo.23.t4 ) ## print the differences, by species print( sp.sums( sample.swo.23.t4 ) - sp.sums( sample.swo.23 ) ) ## generate a plot of the species summaries opar <- par(mfrow=c(2,2)) ##hist( sample.swo.23$plants$dbh, xlim=c(0,50), ylim=c(0,50), main="Diameter Distribution" ) ##hist( sample.swo.23$plants$tht, xlim=c(0,100), ylim=c(0,110), main="Height Distribution" ) hist( sample.swo.23$plants$dbh, main="Diameter Distribution" ) hist( sample.swo.23$plants$tht, main="Height Distribution" ) ba <- sp.sums(sample.swo.23)$ba names(ba) <- rownames( sp.sums(sample.swo.23) ) pie( ba[ba>0], main="Basal Area" ) plot( sample.swo.23$plants$tht ~ sample.swo.23$plants$dbh, ylim=c(0,220), xlim=c(0,40), main="Height vs. Basal Diameter" ) par(opar)