monteBigBAF-class {ssExtra} | R Documentation |
"monteBigBAF"
This class defines a subclass of the virtual "monteDoubleSampling"
for use with Monte Carlo simulations of Big BAF sampling.
Objects can be created by calls to the constructor: monte
by passing a signature object of class
"ssBigBAF"
. Using new("monteBigBAF", ...)
directly is discouraged because of the class' complexity.
In addition to those slots that are defined within the
"monteDoubleSampling"
, the following are
available...
mcSamples
:Object of class "numeric"
: The
number of Monte Carlo replications (samples).
n
:Object of class "numeric"
: A vector of
samples sizes to be drawn from the population (surfaces) and
replicated mcSamples
times.
fpc
:Object of class "numeric"
: Finite
population correction factor.
alpha
:Object of class "numeric"
: The
two-tailed alpha level for normal theory confidence interval
construction.
replace
:Object of class "logical"
:
TRUE
: each Monte Carlo replicate of size n
is drawn
with replacement; FALSE
: samples are drawn withoug
replacement from the surfaces.
ranSeed
:Object of class "numeric"
: The
starting seed for the random number generator.
t.values
:Object of class "numeric"
: Student's
t values for each sample size n
with two-tailed
alpha-level alpha
.
boot
:Object of class "logical"
: TRUE
:
include jackknife and bootstrap estimates; FALSE
: do not
include these.
numBSS
:Object of class "numeric"
: The number
of bootstrap samples to be drawn from each Monte Carlo replicate and
sample size if boot=TRUE
.
means
:Object of class "list"
: A
list ofmcSamples x length(n)
data frames containing
means for volume and basal area, tree VBARs, &c.
vars
:Object of class "list"
: A
list of mcSamples x length(n)
data frames containing the
sample variances.
stDevs
:Object of class "list"
: A
list of mcSamples x length(n)
data frames containing the
sample standard deviations.
varMeans
:Object of class "list"
: A
list of mcSamples x length(n)
data frames containing the
sample variances of the means; e.g., Delta Method, Goodman, &c.
stErrs
:Object of class "list"
: A
list of mcSamples x length(n)
data frames containing the
sample standard errors of the means.
lowerCIs
:Object of class "list"
: A
list of mcSamples x length(n)
data frames containing the
lower confidence points on volume.
upperCIs
:Object of class "list"
: A
list of mcSamples x length(n)
data frames containing the
upper confidence points on volume.
caught
:Object of class "list"
: A
list of mcSamples x length(n)
data frames containing the
percent catch statistics for the above confidence intervals.
otherVarms
:Object of class "list"
: A
list of mcSamples x length(n)
data frames containing other
variances of the means of interest.
n.tvbar
:Object of class "list"
: A list of
mcSamples x length(n)
data frames containing the number of tree VBARS
on each replicate for the count and big BAF samples.
corrs
:Object of class "list"
: A list of
mcSamples x length(n)
data frames containing the
approximate aggregate correlations of various types.
covs
:Object of class "list"
: A list of mcSamples x length(n)
data frames containing the point-based delta method paired covariances. There
are no covariances comparable to those in the corrs
slot stored since their
usefulness is questionable.
gm.all
:Object of class "list"
: The grand
summary means of the above: means, vars, stDevs, varMeans,
stErrs, lowerCIS, upperCIs, caught, otherVarms, corrs
.
sm.all
:Object of class "list"
: The sampling
variances and standard errors of the means
.
mc.samples
:Object of class "list"
: sample size
list of data frames of size length(n) x mcSamples
(for each
n
) holding the actual cell numbers drawn in each Monte Carlo
sample replicate.
Class "monteDoubleSampling"
, directly.
signature(x = "monteBigBAF")
: Generate histograms
signature(object = "monteBigBAF")
: Display a
summary of the object.
signature(object = "monteBigBAF")
: Summary
method for the object.
This describes a long complicated object structure. The constructor is also a long routine and takes time to run. It is best not to do bootstrapping if the number of MC samples is large as the bootstrap code is all in R and is very slow.
Jeffrey H. Gove
The parent (super) class: monteDoubleSampling
showClass("monteBigBAF")