createBBNH {ssExtra} | R Documentation |
ssBigBAF
” object for “Big BAF”
Sampling Northern Hardwood Simulations
This routine is used for the specific purpose of creating a
“ssBigBAF
” object for a synthetic northern hardwoods
stand. The object created can then be used in Monte Carlo simulation experiments to look
more closely at the mechanics of “Big BAF” sampling.
createBBNH(extents = c(x = 178, y = 178), cellSize = 1, bufferWidth = 18, units = "metric", baf.ct = 4, baf.bb = 10, startSeed = 355, ...)
extents |
The extents of raster cells in |
cellSize |
The cell size in meters (for |
bufferWidth |
The width of the buffer (see
“ |
units |
“metric” or “English”. |
baf.ct |
The basal area factor for the count sample. |
baf.bb |
The basal area factor for the volume sample. |
startSeed |
A seed for the random number generator. See
|
... |
Arguments that are passed on to |
The default values in the arguments to this routine will duplicate the
northern hardwood sampling surfaces for one pair of basal area factors
that are used in Gove et al (2020). A quick look at the R code in
this routine will show the necessary steps to make
“ssBigBAF
” objects for other forest
types. There is also some discussion of this in the package user's
guide vignette.
An object of class “ssBigBAF”, invisibly.
It may take some quick calculations to determine the width of the buffer required to hold the largest inclusion zone. And it may have to be iterated as well. But it is quite simple really.
Jeffrey H. Gove
Gove, J. H., Gregoire,T. G., Ducey, M. J., and Lynch, T. B. 2020. A Note on the Estimation of Variance for Big BAF Sampling. Forest Ecosystems, Submitted.
initTract
,
makePop
,
drawTreePop
,
and the “ssBigBAF
” class.
# # use the defaults... # ## Not run: ssBB.nh = createBBNH() ## End(Not run)