nspmix (version 2.0-0)

plot.nspmix: Plots a function for an object of class nspmix

Description

Plots a function for the object of class nspmix, currently either using the plot function of the class or plotting the gradient curve (or its first derivative)

data must belong to a mixture family, as specified by its class.

Class nspmix is an object returned by function cnm, cnmms, cnmpl or cnmap.

Usage

# S3 method for nspmix
plot(x, data, type = c("probability", "gradient"), ...)
# S3 method for nspmix
plot(x, data, type=c("probability","gradient"), ...)

Arguments

x

an object of a mixture model class

data

a data set from the mixture model

type

the type of function to be plotted: the probability model of the mixture family (probability), or the gradient function (gradient).

...

arguments passed on to the plot function called.

Author

Yong Wang <[e-mail]>

Details

Function plot.nspmix plots either the mixture model, if the family of the mixture provides an implementation of the generic plot function, or the gradient function.

data must belong to a mixture family, as specified by its class.

References

Wang, Y. (2007). On fast computation of the non-parametric maximum likelihood estimate of a mixing distribution. Journal of the Royal Statistical Society, Ser. B, 69, 185-198.

Wang, Y. (2010). Maximum likelihood computation for fitting semiparametric mixture models. Statistics and Computing, 20, 75-86

Wang, Y. (2007). On fast computation of the non-parametric maximum likelihood estimate of a mixing distribution. Journal of the Royal Statistical Society, Ser. B, 69, 185-198.

Wang, Y. (2010). Maximum likelihood computation for fitting semiparametric mixture models. Statistics and Computing, 20, 75-86

See Also

plot.nspmix, nnls, cnm, cnmms, npnorm, nppois.

nnls, cnm, cnmms, cnmpl, cnmap, npnorm, nppois.

Examples

## Poisson mixture
x = rnppois(200, disc(c(1,4), c(0.7,0.3)))
plot(cnm(x), x)

## Normal mixture
x = rnpnorm(200, disc(c(0,4), c(0.3,0.7)), sd=1)
r = cnm(x, init=list(beta=0.5))   # sd = 0.5
plot(r, x)
plot(r, x, type="g")
plot(r, x, type="g", order=1)


## Poisson mixture
x = rnppois(200, disc(c(1,4), c(0.7,0.3)))
r = cnm(x)
plot(r, x, "p")
plot(r, x, "g")

## Normal mixture
x = rnpnorm(200, mix=disc(c(0,4), c(0.3,0.7)), sd=1)
r = cnm(x, init=list(beta=0.5))   # sd = 0.5
plot(r, x, "p")
plot(r, x, "g")