sp.sums {rconifers}R Documentation

A summary of a CONIFERS sample.data object by species

Description

This function returns a data.frame object that contain common summaries by species for sample.data object.

Usage

   sp.sums( x )

Arguments

x an object of class sample.data.

Format

The data.frame object returned from sp.sums contains the following columns for each species:

expf
The total plants per acre.
tht
The mean total height.
ba
The total basal area for all plants.
qmd
The quadratic mean diameter for all trees. See Details.

Details

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.

Author(s)

Jeff D. Hamann jeff.hamann@forestinformatics.com,
Martin W. Ritchie mritchie@fs.fed.us

References

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/

See Also

calc.max.sdi, impute, plants, plots project, rand.seed, rconifers, sample.data, set.species.map, set.variant, smc, summary.sample.data, swo, thin

Examples

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 ) )


[Package rconifers version 0.0-9 Index]