Rdistance optimization.Returns a list of optimization controls used in
Rdistance and provides a way to change them if needed.
RdistanceControls(
optimizer = "nlminb",
evalMax = 2000,
maxIters = 1000,
likeTol = 1e-08,
coefTol = 1.5e-08,
hessEps = 1e-08,
requireUnits = TRUE,
maxBSFailPropForWarning = 0.2,
contrasts = NULL
)A list containing named components for each of the controls. This list has the same components as this function has input parameters.
A string specifying the optimizer
to use. Results
vary between optimizers, so switching algorithms sometimes
makes a poorly behaved distance function converge. The valid
values are "optim" which uses optim::optim,
and "nlminb" which uses stats:nlminb. The authors
have had better luck with "nlminb" than "optim" and "nlminb"
runs noticeably faster. Problems with solutions near parameter
boundaries may require use of "optim".
The maximum number of objective function evaluations allowed.
The maximum number of optimization iterations allowed.
The maximum change in the likelihood (the objective) between iterations that is tolerated during optimization. If the likelihood changes by less than this amount, optimization stops and a solution is declared.
The maximum change in the model coefficients between iterations that is tolerated during optimization. If the sum of squared coefficient differences changes by less than this amount between iterations, optimization stops and a solution is declared.
A vector of parameter distances used during
computation of numeric second derivatives. Should have length
1 or the number of parameters in the model. See function
secondDeriv.
A logical specifying whether measurement
units are required on distances and areas. If TRUE,
measurement units are required on off-transect and radial
distances in the input data frame. Likewise, measurement
units are required on transect length and study area size.
Assign units with statement like units(detectionDf$dist) <- "m"
or units(df$transectDf) <- "km". Measurement units do not
need to be the same. All units are converted appropriately during
internal computations. Rdistance recognizes
units listed in units::valid_udunits().
The proportion of bootstrap iterations that can fail without a warning. If the proportion of bootstrap iterations that did not converge exceeds this parameter, a warning about the validity of CI's is issued in the print method for abundance objects.
A list, whose entries are values (numeric matrices, functions or character strings naming functions) to be used as replacement values for the default contrasts function and whose names are the names of columns of data containing factors.
There are several ways to change the contrasts used for factors
in Rdistance because all methods used in linear models (lm)
work. To summarize contrast methods in R, if this parameter is NULL, Rdistance uses
the global contrasts specified in
options(). To change the global contrasts, use a statement
like options(contrasts = c(unordered = "contr.SAS",
ordered = "contr.poly")).
One can also set contrasts for a factor using contrasts(a)
(e.g., contrasts(a) <- "contr.sum")
Lastly, one can set this parameter to a
list that explicitely states the non-global contrasts to use for
which factors in
the Rdistance model. For example, list(a = "contr.helmert")
will use Helmert contrasts for a
and the global contrast option for all other factors. The
built-in R contrast functions are "contr.treatment", "contr.helmert",
"contr.SAS", "contr.sum", and "contr.poly".
# increase number of iterations
RdistanceControls(maxIters=2000)
# change optimizer and decrease tolerance
RdistanceControls(optimizer="optim", likeTol=1e-6)
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