func.gaussmodel {dsdp} | R Documentation |
Return the evaluation of a vector with Gaussian-based model
Description
Evaluate an input vector x
with Gaussian-based model and
return its vector.
By default, it evaluate with the best model and its density, but
it can designate the model by index and also can evaluate with a cumulative
distribution.
Usage
## S3 method for class 'gaussmodel'
func(model, x, cdf = FALSE, n = 1, scaling = FALSE, ...)
Arguments
model |
|
x |
A numeric vector to be evaluated with a distribution. |
cdf |
A logical scalar whether the evaluation is done with a cumulative
distribution or not. A default value is |
n |
The index indicates the estimates. 1, by default, is the best estimate, and 2 is the 2nd best, etc. |
scaling |
A logical scalar, which indicates whether or not it scales
means and standard deviations in |
... |
Arguments to be passed to or from other methods. |
Value
A numeric vector of the evaluatio of input vector x
with a model.
See Also
gaussmodel()
summary.gaussmodel()
plot.gaussmodel()
estimate.gaussmodel()
pdf_gaussmodel()
cdf_gaussmodel()
Examples
## Create an `gaussmodel` object
gmodel <- gaussmodel(mix2gauss$n200)
## Estimate an model with parameters
gmodel <- estimate(gmodel, deglist=4, mulist=0.15, sdlist=0.73)
## A vector for input
x <- seq(-4, 4, by=0.1)
## Density function
y <- func(gmodel, x)
## Cumulative distribution
y <- func(gmodel, x, cdf=TRUE)