Compare current sigmas and sample sizes with candidate values, by
running variations of estSigmaR
, estN
, and
estSigmaI
on all model components.
iterate(model, ceiling=Inf, p=1, digits.n=0, digits.sigma=2)
fitted scape
model.
largest possible sample size in one year, passed to
estN
.
effective number of parameters estimated in the model, passed
to estSigmaI
.
number of decimal places to use when rounding sample
sizes, or NULL
to suppress rounding.
number of decimal places to use when rounding
sigmas, or NULL
to suppress rounding.
List containing data frames summarizing current sigmas and sample sizes, as well as candidate values. The following abbreviations are used in column names:
candidate sigma, the empirical standard deviation.
candidate sample sizes, the empirical multinomial sample sizes.
vector of candidate values, whose mean equals
sigmahat
or nhat
.
vector of candidate values, whose median equals
sigmahat
or nhat
.
vector of identical candidate values, the mean of
nhat
.
vector of identical candidate values, the median of
nhat
.
getN
, getSigmaI
, getSigmaR
,
estN
, estSigmaI
, and
estSigmaR
extract and estimate sample sizes and sigmas.
iterate
combines all the get*
and est*
functions in one call.
scape-package
gives an overview of the package.
# NOT RUN {
iterate(x.cod)
iterate(x.ling)
iterate(x.oreo)
iterate(x.sbw)
# }
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