Settings used to estimate Emax model
Usage
emax_nls_options(
optim_method = "port",
optim_control = NULL,
quiet = FALSE,
weights = NULL,
na.action = options("na.action")
)Arguments
- optim_method
Character string specifying the algorithm used to solve the nonlinear least squares optimization problem. Supported pptions are "gauss", "port", and "levenberg". See details.
- optim_control
A list of arguments used to control the behavior of the optimization algorithm. Allowed values differ depending on which algorithm is used
- quiet
When
quiet=TRUE, messages are suppressed- weights
Numeric vector providing the weights for observations. When specified, weighted least squares is used
- na.action
How should missing values in the data be handled
At present there are three supported values for
optim_method:"gauss": Estimate parameters using the Gauss-Newton algorithm. This is equivalent to the using "default" option in
nls()"port": Estimate parameters using bounded optimization with the "nl2sol" algorithm from from the the Port library. Equivalent to "port" in
nls()"levenberg": Estimate parameters using the Levenberg-Marquardt algorithm. This is equivalent to using
nlsLM()from the "minpack.lm" package.
Note that the Golub-Pereyra algorithm for partially linear least-squares (i.e. the "plinear" option in
nls()) is not currently supported for Emax regression. Informal testing suggests it does not perform well for these models, and rarely converges.