Probability density function of Gaussian-based model

A probability density function(PDF) of a Gaussian model. It is an underlying routine for plot.gaussmodel to compute the values of PDF. To access parameters and coefficients in an object gmodel of a class gaussmodel, use gmodel$result[k, "mu1"], gmodel$result[k, "sig1"], gmodel$coeffs[[k]] for some index k. This index appears in the leftmost column of estimation table generated by summary(gmodel).

Usage

pdf_gaussmodel(coeff, mu, sig, x)

Arguments

coeff

A coefficient vector in increasing order of degrees; the first element is 0th degree, ..., and last element is the largest degree of coefficients.

mu

A mean of Gaussian distribution.

sig

A standard deviation of Gaussian distribution, which is positive.

x

A numeric input vector.

Value

A numeric vector of PDF of Gaussian-based distribution.

See also

gaussmodel() summary.gaussmodel() estimate.gaussmodel() func.gaussmodel() plot.gaussmodel() cdf_gaussmodel()

Examples

## Create an object of `gaussmodel`
gmodel <- gaussmodel(mix2gauss$n200)
## Estimate with a degree 6, a mean 0, and standard deviations 0.5
gmodel <- estimate(gmodel, 6, 0, 0.5)
#> (6,0,0.5)
#> Status: Normal Termination.
## Input vector
x <- seq(-3, 3, 0.1)
## Output of PDF in above estimation
yv <- pdf_gaussmodel(gmodel$coeffs[[1]], gmodel$result[1, "mu1"],
 gmodel$result[1, "sig1"], x)