Description Usage Arguments Details Value Note See Also Examples
Estimate the effective sigma (magnitude of observation noise) for a survey or commercial abundance index, based on the empirical standard deviation.
1 2 
model 
fitted 
what 
which effective sigma to estimate: 
series 
vector of strings indicating which gears or surveys to analyze (all by default). 
init 
initial sigma, determining the relative pattern of the effective sigmas between years. 
FUN 
function to use when scaling a vector of sigmas. 
p 
effective number of parameters estimated in the model. 
digits 
number of decimal places to use when rounding, or

The init
sigmas set a fixed pattern for the relative sigmas
between years. For example, if there are two years of abundance index
data and the initial sigmas are 0.1 in year 1 and 0.2 in year 2, the
effective sigma will be two times greater in year 2 than in year 1,
although both will be scaled up or down depending on how closely the
model fits the abundance index. The value of init
can be one of
the following:
NULL
means read the initial sigmas from the existing
CV
column (default).
means read the initial sigmas from the CV
column
in that model (object of class scape
).
means those are the initial sigmas (same length as the number of years).
FALSE
or 1
means use one effective sigma (sigmahat) across all years.
The idea behind FUN=mean
is to guarantee that regardless of the
value of init
, the mean effective sigma will always be the
same. Other functions can be used to a similar effect, such as
FUN=median
.
Numeric vector of effective sigmas (one value if init=1
), or a
list of such vectors when analyzing multiple series.
This function uses the empirical standard deviation to estimate an effective sigma, which may be appropriate as likelihood weights for abundance index data. The better the model fits the data, the smaller the effective sigma.
estSigmaI
can be used iteratively, along with
estN
and estSigmaR
to assign likelihood
weights that are indicated by the model fit to the data. Sigmas and
sample sizes are then adjusted between model runs, until they
converge. The iterate
function facilitates this procedure.
If rss is the residual sum of squares in log space, n is the number of abundance index data points, and p is the effective number of parameters estimated in the model, then the estimated effective sigma is:
sigmahat = sqrt(rss/(np))
There is no simple way to calculate p for statistical catchatage models. The default value of 1 is likely to underestimate the true magnitude of observation noise.
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.
plotIndex
shows what is behind the sigma estimation.
scapepackage
gives an overview of the package.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  ## Exploring candidate sigmas:
getSigmaI(x.cod) # sigma used in assessment 0.20
estSigmaI(x.cod) # model fit implies 0.17
plotIndex(x.cod) # model fit
estSigmaI(x.cod, p=8) # eight estimated parameters implies 0.22
getSigmaI(x.sbw) # sigma used in assessment
estSigmaI(x.sbw) # model fit implies smaller sigma
estSigmaI(x.sbw, init=1) # could use 0.17 in all years
## Same mean, regardless of init:
mean(estSigmaI(x.sbw, digits=NULL))
mean(estSigmaI(x.sbw, digits=NULL, init=1))
## Same median, regardless of init:
median(estSigmaI(x.sbw, FUN=median, digits=NULL))
median(estSigmaI(x.sbw, FUN=median, digits=NULL, init=1))
## Multiple series:
getSigmaI(x.oreo, "c") # sigma used in assessment
getSigmaI(x.oreo, "c", digits=2) # rounded
estSigmaI(x.oreo, "c") # model fit implies smaller sigma
estSigmaI(x.oreo, "c", init=1) # could use 0.19 in all years
estSigmaI(x.oreo, "c", init=1, digits=3) # series 2 slightly worse fit
# estSigmaI(x.oreo, "c", init=1, p=11) # more parameters than datapoints
getSigmaI(x.oreo, "c", series="Series 21") # get one series
estSigmaI(x.oreo, "c", series="Series 21") # estimate one series

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