Description Usage Arguments Details Value Author(s) References See Also Examples
This function generates a posterior density sample for a Triangular-Dirichlet model.
1 2 3 | TDPdensity(y,support=3,transform=1,ngrid=1000,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. |
transform |
an integer number giving the type of transformation to be considered, 1=Uniform, 2=Normal,3=Logistic,4=Cauchy. The types 2-4 can be only used when the support is the real line. |
ngrid |
number of grid points where the density estimate is
evaluated. This is only used if dimension of |
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 Triangular-Dirichlet model for density estimation:
yi | G ~ G, i=1,…,n
G | kmax, alpha, G0 ~ TDP(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 number of components in the Mixture of Triangular
distributions, 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.
Note that our representation is different to the Mixture of Triangular distributions proposed by Perron and Mengersen (2001). In this function we consider random weights following a Dirichlet prior and we exploit the underlying DP structure. By so doing, we avoid using Reversible-Jumps algorithms.
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 TDPdensity
representing the Triangular-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 number of components in the Mixture of Triangular distriutions. |
Alejandro Jara <atjara@uc.cl>
Escobar, M.D. and West, M. (1995) Bayesian Density Estimation and Inference Using Mixtures. Journal of the American Statistical Association, 90: 577-588.
Perron, F. and Mengersen, K. (2001) Bayesian Nonparametric Modeling Using Mixtures of Triangular Distributions. Biometrics, 57(2): 518-528.
DPdensity
, PTdensity
, BDPdensity
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 | ## 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=50,
a0=1,
b0=1)
# Fitting the model
fit<-TDPdensity(y=speeds,prior=prior,mcmc=mcmc,state=state,status=TRUE)
plot(fit)
## End(Not run)
|
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