Description Usage Arguments Details Value Author(s) References See Also Examples
This function generates a posterior density sample for a Bernstein-Dirichlet model.
1 2 3 | BDPdensity(y,support=3,ngrid=1000,grid=NULL,prior,mcmc,state,status,
data=sys.frame(sys.parent()),na.action=na.fail)
|
y |
a vector giving the data from which the density estimate is to be computed. |
support |
an integer number giving the support of the random density, 1=[0,1], 2=(0, +Inf], and 3=(-In,+Inf). Depending on this, the data is transformed to lie in the [0,1] interval. |
ngrid |
number of grid points where the density estimate is
evaluated. This is only used if dimension of |
grid |
vector of grid points where the density estimate is evaluated. The default value is NULL and the grid is chosen according to the range of the data. |
prior |
a list giving the prior information. The list includes the following
parameter: |
mcmc |
a list giving the MCMC parameters. The list must include
the following integers: |
state |
a list giving the current value of the parameters. This list is used if the current analysis is the continuation of a previous analysis. |
status |
a logical variable indicating whether this run is new ( |
data |
data frame. |
na.action |
a function that indicates what should happen when the data
contain |
This generic function fits a Bernstein-Dirichlet model for density estimation (Petrone, 1999a, 1999b; Petrone and Waserman, 2002):
yi | G ~ G, i=1,…,n
G | kmax, alpha, G0 ~ BDP(kmax, alpha G0)
where, yi is the transformed data to lie in [0,1], kmax
is the upper limit of the discrete uniform prior for the degree of the Bernstein
polynomial, alpha is the total mass parameter of the Dirichlet process component,
and G0 is the centering distribution of the DP. The centering distribution corresponds
to a G0=Beta(a0,b0) distribution.
The precision or total mass parameter, α, of the DP
prior
can be considered as random, having a gamma
distribution, Gamma(a0,b0),
or fixed at some particular value. When alpha is random the method described by
Escobar and West (1995) is used. To let alpha to be fixed at a particular
value, set a0 to NULL in the prior specification.
An object of class BDPdensity
representing the Bernstein-Dirichlet
model fit. Generic functions such as print
, summary
, and plot
have methods to
show the results of the fit. The results include the degree of the polynomial k
, alpha
, and the
number of clusters.
The MCMC samples of the parameters and the errors in the model are stored in the object
thetasave
and randsave
, respectively. Both objects are included in the
list save.state
and are matrices which can be analyzed directly by functions
provided by the coda package.
The list state
in the output object contains the current value of the parameters
necessary to restart the analysis. If you want to specify different starting values
to run multiple chains set status=TRUE
and create the list state based on
this starting values. In this case the list state
must include the following objects:
ncluster |
an integer giving the number of clusters. |
yclus |
a real vector giving the |
ss |
an interger vector defining to which of the |
alpha |
giving the value of the precision parameter. |
k |
giving the degree of the Bernstein polynomial. |
Alejandro Jara <atjara@uc.cl>
Fernando Quintana <quintana@mat.puc.cl>
Escobar, M.D. and West, M. (1995) Bayesian Density Estimation and Inference Using Mixtures. Journal of the American Statistical Association, 90: 577-588.
Petrone, S. (1999a) Random Bernstein Polynomials. Scandinavian Journal of Statistics, 26: 373-393.
Petrone, S. (1999b) Bayesian density estimation using Bernstein polynomials. The Canadian Journal of Statistics, 27: 105-126.
Petrone, S. and Waserman, L. (2002) Consistency of Bernstein polynomial posterior. Journal of the Royal Statistical Society, Series B, 64: 79-100.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ## Not run:
# Data
data(galaxy)
galaxy<-data.frame(galaxy,speeds=galaxy$speed/1000)
attach(galaxy)
# Initial state
state <- NULL
# MCMC parameters
nburn<-1000
nsave<-10000
nskip<-10
ndisplay<-100
mcmc <- list(nburn=nburn,nsave=nsave,nskip=nskip,ndisplay=ndisplay)
# Prior
prior<-list(aa0=2.01,
ab0=0.01,
kmax=1000,
a0=1,
b0=1)
# Fitting the model
fit <- BDPdensity(y=speeds,prior=prior,mcmc=mcmc,
state=state,status=TRUE)
plot(fit)
## End(Not run)
|
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