ssCovMODWT: Generate Objects of Class "'ssCovMODWT'"
In ssWavelets: Wavelet Functionality for Package 'sampSurf'

Description Usage Arguments Details Value Note Author(s) See Also Examples

This is the generic function for class “ssCovMODWT”. Please see the associated method in ssCovMODWT-methods for more details.

1	ssCovMODWT(ssMODWT.a, ssMODWT.b, ...)

`ssMODWT.a`	First signature object. Because there is only one extant method, this argument name is derived from the class “`ssMODWT`,” to which it applies.
`ssMODWT.b`	Second signature object. Again, this argument name is derived from the class “`ssMODWT`,” to which it applies.
`...`	See the method(s) for details.

Only one method is available for this generic function as noted above. Please see the link above for other arguments that are possible in the constructor.

A valid object of class “ssCovMODWT.”

The example below creates an object of class “ssCovMODWT” in such a way that the two sampling methods used are comparable. For example, the same tract and population of standing trees is used for both sampSurf runs. Both the horizontal point and critical height methods use the same angle gauge, though this is not necessary, but nice for illustration. In addition, to make sure the inclusion zones for critical height sampling match those for horizontal point sampling, notice that the reference height is requested to be at DBH rather than ground level (the default), though again this is not strictly necessary, depending on the intent of the simulations. Finally, the ssMODWT decompositions share the same total decomposition level J. The points above that should be routinely followed in all comparisons are to use (i) the same tract, (ii) the same population of trees and (iii) the same decomposition level. Note that it is perfectly acceptable to compare two different basal area factors in HPS, for example, or two different plot sizes for fixed area plot sampling. The results of such comparisons might surprise you.

Jeffrey H. Gove

“ssMODWT”, “sampSurf”.

#
# creates a sampSurf object with horizontal point sampling BAF 5,
# then creates a J_0 = 4-level MODWT decomposition object...
#
tr = Tract(c(x = 64, y = 64), cellSize = 1) #square tract ~0.5ha
btr = bufferedTract(10, tr)
ag5m = angleGauge(5)
strees = standingTrees(10, btr, dbhs=c(15,25), topDiam=c(0,0), startSeed = 123)
strees.hps = standingTreeIZs(strees, 'horizontalPointIZ', angleGauge = ag5m)
ss.hps = sampSurf(strees.hps, btr)
modwt.hps = ssMODWT(ss.hps, J = 4)
#
# creates a sampSurf object with critical height sampling BAF 5,
# then creates a J_0 = 4-level MODWT decomposition object...
#
strees.chs = standingTreeIZs(strees, 'criticalHeightIZ', angleGauge = ag5m,
                             referenceHeight='dbh')
ss.chs = sampSurf(strees.chs, btr)
modwt.chs = ssMODWT(ss.chs, J = 4)
#
# now both HPS and CHS are to the same reference height with the 
# same population on the same tract, so we can decompose 
# the covariance...
#
modwt.cov = ssCovMODWT(modwt.hps, modwt.chs)
#
# take a look at the level j=3 surface...
#
## Not run: 
plotMODWT2D(modwt.cov, level=3, type='var')

## End(Not run)