bayesHistogram.help: Helping function for Bayesian smoothing of (bi)-variate...

Description Usage Arguments Value Value for bayesHistogram.design Value for bayesHistogram.priorInit Author(s)

Description

These functions are not to be called by ordinary users.

These are just sub-parts of ‘bayesHistogram’ function to make it more readable for the programmer.

Usage

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bayesHistogram.design(y1, y2)

bayesHistogram.checkStore(store)

bayesHistogram.priorInit(prior, init, mcmc.par, design)

bayesHistogram.writeHeaders(dir, design, prior.init, store)

Arguments

y1

response for the first dimension. This should be a~survival object created by Surv

y2

response object for the second dimension (if bivariate density is to be smoothed). This should be a~survival object created by Surv. Further, it must be consistent with y1, i.e. it has to have the same number of rows.

store

a~list with appropriate components

prior

a~list as required by prior argument of function bayesHistogram

init

a~list as required by init argument of function bayesHistogram

mcmc.par

a~list as required by mcmc.par argument of function bayesHistogram

design

a~list with the design information as returned by the function bayesHistogram.design. The list is assumed to have the following components:

y.left

...

y.right

...

status

...

dim

...

dir

string giving a~directory where to store sampled files

prior.init

an~object as returned by bayesHistogram.priorInit

Value

Some lists.

Value for bayesHistogram.design

A~list with the following components:

y.left

vector or matrix with either observed, right or left censored observations or with the lower limits of interval censored observations. It is a vector if dim == 1 and it is a matrix with 2 rows and n columns if dim == 2, where n is a~sample size. In that case, the first row of the matrix are responses for the first dimension and the second row of the matrix are responses for the second dimension.

y.right

vector or matrix with entries equal to 1 for observed, right or left censored observations and entries equal to the upper limits of interval censored observations. The structure is the same as that of y.left.

status

a~vector or matrix with censoring indicators (1 = exactly observed, 0 = right censored, 2 = left censored, 3 = interval censored). The structure is the same as that of y.left.

dim

dimension of the response, i.e. 1 (univariate smoothing) or 2 (bivariate smoothing)

Value for bayesHistogram.priorInit

A~list with the following components:

Gparmi

integer parameters for the G-spline constructor in the C++ code

Gparmd

double parameters for the G-spline constructor in the C++ code

iter

index of the nullth iteration

y

vector of initial values for the response, sorted as y[1,1],y[1,2], …, y[n,1],y[n,2] in the case of bivariate response with sample size equal to n

r

initial component labels (vector of size n) taking values from 1 to the total length of the G-spline

specification

specification of the G-spline model (1 or 2), see bayesHistogram for more detail

and the following attributes:

init
prior
mcmc.par

Author(s)

Arno<c5><a1>t Kom<c3><a1>rek arnost.komarek[AT]mff.cuni.cz


bayesSurv documentation built on May 2, 2019, 3:26 a.m.