Estimates Gaussian-based model gaussmodel
among
parameter vectors, deglist
, mulist
, sdlist
.
Then it sorts the results by AIC.
Usage
# S3 method for gaussmodel
estimate(
model,
deglist = deglist,
mulist = mulist,
sdlist = sdlist,
scaling = FALSE,
recompute = FALSE,
stepsize = NULL,
verbose = FALSE,
...
)
Arguments
- model
An object of a
gaussmodel
class.- deglist
A vector of degrees of polynomials. The element should be positive even numbers.
- mulist
A vector of means for Gaussian-based models.
- sdlist
A vector of standard deviations for Gaussian-based models. The element should be larger than 0.
- scaling
A logical scalar, which indicates whether or not it scales means and standard deviations in
mulist
andsdlist
. The default value isFALSE
.- recompute
If
TRUE
, recomputes the results for better estimation and accuracy. Parameters whose accuracies had been already attained sufficiently, namely around 1.0e-6, are not included in candidates for recomputing.- stepsize
A vector in descending order whose values are between 0 and 1. If a small step size is supplied, it can attain successful estimates, but it might take more iterations.
- verbose
If
TRUE
, it shows the detailed message of SDP solver.- ...
Arguments to be passed to or from other methods.
Value
A gaussmodel
object including the estimates.
Those estimates are stored in model$result
with
data.frame
format and model$coeffs
in list
format.
Examples
## Create an `gaussmodel` object
gmodel <- gaussmodel(mix2gauss$n200)
## Estimate a model with parameters
gmodel <- estimate(gmodel, deglist=c(2, 4), mulist=c(0.0, 0.2),
sdlist=c(0.75, 1.0))
#> (2,0,0.75)
#> Status: Normal Termination.
#> (2,0,1)
#> Status: Normal Termination.
#> (2,0.2,0.75)
#> Status: Normal Termination.
#> (2,0.2,1)
#> Status: Normal Termination.
#> (4,0,0.75)
#> Status: Normal Termination.
#> (4,0,1)
#> Status: Normal Termination.
#> (4,0.2,0.75)
#> Status: Normal Termination.
#> (4,0.2,1)
#> Status: Normal Termination.