plotgrad: Plot the Gradient Function

In nspmix: Nonparametric and Semiparametric Mixture Estimation

Description

Function plotgrad plots the gradient function or its first derivative of a nonparametric mixture.

Usage

plotgrad(
  x,
  mix,
  beta,
  len = 500,
  order = 0,
  col = "blue",
  col2 = "red",
  add = FALSE,
  main = paste0("Class: ", class(x)),
  xlab = expression(theta),
  ylab = paste0("Gradient (order = ", order, ")"),
  cex = 1,
  pch = 1,
  lwd = 1,
  xlim,
  ylim,
  ...
)

Arguments

x	a data object of a mixture model class.
mix	an object of class 'disc', for a discrete mixing distribution.
beta	the structural parameter.
len	number of points used to plot the smooth curve.
order	the order of the derivative of the gradient function to be plotted. If 0, it is the gradient function itself.
col	color for the curve.
col2	color for the support points.
add	if FALSE, create a new plot; if TRUE, add the curve and points to the current one.
main, xlab, ylab, cex, pch, lwd, xlim, ylim	arguments for graphical parameters (see par).
...	arguments passed on to function plot.

Details

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

The support points are shown on the horizontal line of gradient 0. The vertical lines going downwards at the support points are proportional to the mixing proportions at these points.

Author(s)

Yong Wang <[e-mail]>

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

See Also

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

Examples

## Poisson mixture
x = rnppois(200, disc(c(1,4), c(0.7,0.3)))
r = cnm(x)
plotgrad(x, r$mix)

## 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
plotgrad(x, r$mix, r$beta)

nspmix documentation built on Oct. 23, 2020, 6:46 p.m.