lcDmc {lazy.stat} | R Documentation |

Estimates the Density of the Weighed Linear Combination of a Dirichlet Variables by Simulation.

lcDmc(class, f, table = NULL, alpha0 = 0.5, omitmiss = 1, nsample = 1000, smooth = 0, bandwid = 3, maxiter = 2, density = 2, ndensity = 201, epsz = 1e-09, ..., hdr_prob = 0.95, print = 0, plot = 0, outmcmc = 0, simple = 0) stat_lcDmc(class, f, alpha0, hdr_prob = 0, pdf_info)

`class` |
A vector containing the numeric values of scored multinomial |

`f` |
A vector containing the frequency associated with class |

`table` |
The output of native table function. |

`alpha0` |
prior constant for Dirichlet |

`omitmiss` |
= 1 to remove the classes with f=0 from (class,f) |

`nsample` |
# of Dirichlet random numbers to simulate distribution of mu. |

`smooth` |
= 1 to smooth the density by mative smooth |

`bandwid` |
= length of the running average smooth |

`maxiter` |
= # of repetition for smooth |

`density` |
= 1 to use simple tabulation = 2 to use native density function |

`ndensity` |
# of points to be used to density estimation
by native density. |

`epsz` |
= the value which defines almost zero. |

`...` |
additional parameters to native density function. |

`hdr_prob` |
= probability value for hdr: 0 <= hdr_prob < 1 |

`print` |
= 1 to print the result |

`plot` |
= 1 to plot the density |

`outmcmc` |
= 1 to output mcmc result of mu |

`simple` |
= 1 to produce simple output |

Let (class, f) be the observed frequency distribution of the scored
multinomial R.V.

The likelihood of the multinomial parameter vector p is:

*sum_{k=1}^{ncat} f[k]*log(p[k])*

Given the Dirichlet prior of p with parameter vector alpha0,

the posterior distribution of p is the Dirichlet with `alpha=f+alpha0`

.

The posterior mean of the scored multinomial distribution is defined as:

* mu = sum_{k=1}^{ncat} class[k]*p[k] *

and this function simulates the distribution of mu by sampling from
the posterior Dirichlet distribution.

**
When hpd_prob is given or plot=1,
"hpd" function of "TeachingDemos" package is used.
**

When simple = 1:

A list of sample, param, dom

where

param is the alpha parameter of the post means (freq+prior),

class is the categorie values

nobs is the # of observations: sum(f)

nsample # of mcmc samples

pdf_info a list consisting of:

dtable a matrix consisting of the estimated density (ndensity x 3)

where the density in column 2 is normalized to unit sum.

locz1, locnz, locz2, minval, maxval

When simple = 0:

A list consisting of above and more.

When outmcmc = 1:

A list consisting of above and

sample is the vector of length nsample consisting of mu's,

# Increase nsample when in real use. class=1:3 f=c(1,2,1) alpha0=0.5 res=lcDmc( class, f, alpha0=alpha0, nsample=1000, density=2, ndensity=201 , smooth=0, print=1, plot=0, simple=1 ) res2=stat_lcDmc( class, f, alpha0, pdf_info=res$pdf_info )

[Package *lazy.stat* version 0.1.3 Index]