cdf_gaussmodel: Cumulative distribution function of Gaussian-based model

In dsdp: Density Estimation with Semidefinite Programming

Cumulative distribution function of Gaussian-based model

Description

A cumulative distribution function(CDF) of Gaussian-based model. 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

cdf_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 CDF of Gaussian-based model.

See Also

gaussmodel() summary.gaussmodel() estimate.gaussmodel() func.gaussmodel() pdf_gaussmodel()

Examples

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

dsdp documentation built on Feb. 16, 2023, 8:36 p.m.