Description Usage Arguments Details Value Author(s) References See Also Examples
Calls the case1ph
, case2ph
, or case2probit
function to fit the corresponding model.
Give point estimates and credible intervals for regression coefficients and estimation and plot of survival functions.
1 2 3 4 5 6 7 8 9 |
L |
a column vector of left-points of observed time intervals. |
R |
a column vector of right-points of observed time intervals. Use NA to denote infinity. |
model |
a character string specifying the type of model. Possible values are "case1ph", "case2ph", "case2po", and "case2probit". |
status |
a vector of censoring indicators. If |
xcov |
a matrix of covariates, each column corresponds to one covariate. |
x_user |
a vector of covariate values, default is NULL. Need to specify for survival estimation. |
order |
degree of I-splines ( |
sig0 |
standard deviation of normal prior for each regression coefficient |
coef_range |
specify support domain of target density for |
m0 |
mean of normal prior for |
v0 |
precision of normal prior for |
a_eta |
shape parameter of Gamma prior for |
b_eta |
rate parameter of Gamma prior for |
knots |
a sequence of points to define I-splines. Default is a sequence of time points from min to max with length=10. |
grids |
a sequence of points where survival function is to be estimated. Defalult is a sequence of time points from min to max with length=100. |
conf.int |
level for a two-sided credible interval on coefficient estimate(s). Default is 0.95. |
niter |
total number of iterations of MCMC chains. Default is 5000. |
burnin |
number of iterations to discard at the beginning of an MCMC run. Default is 1000. |
thin |
specify thinning of MCMC draws. Default is 1. |
seed |
a use-specified random seed. Default is NULL. |
formula |
a symbolic description of the model to be fit. |
data |
a data frame containing the variables in the model. |
... |
values passed to other functions. |
For "case1ph", "case1po", and "case2ph" models, function arms
is used to sample regression coefficient beta_r
, and coef_range
specifies the support of the indFunc
in arms
. The baseline cumulative hazard in "case1ph"and "case2ph" models and the baseline odds function in "case1po" are modeled by a linear combination of I-splines:
sum_{l=1}^{k}(gamma_l*b_l)
.
For "case2probit" model, baseline function is modeled by a linear combination of I-splines:
gamma_0+sum_{l=1}^{k}(gamma_l*b_l)
.
For "case2probit" model, regression coefficient vector beta
is sampled from a multivariate normal distribution.
For more information, please see reference.
an object of class ICBayes
containing the following elements:
coef |
a vector of regression coefficient estimates |
coef_ssd |
a vector of sample standard deviations of regression coefficient estimates |
coef_ci |
credible intervals for regression coefficients |
LPML |
log pseudo marginal likelihood for model selection, the larger the better |
grids |
the sequance of points where baseline survival functions is estimated |
h0_m |
estimated baseline hazard at |
h_m |
a |
h_ci |
credible intervals for hazard function at |
S0_m |
estimated baseline survival probabilities at |
S_m |
a |
S_ci |
credible intervals for survival probablities at |
mcmc_beta |
a |
mcmc_surv |
a |
Chun Pan
Cai, B., Lin, X., and Wang, L. (2011). Bayesian proportional hazards model for current status data with monotone splines. Computational Statistics and Data Analysis, 55 2644-2651.
Lin, X. and Wang, L. (2009). A semiparametric probit model for case 2 interval-censored failure time data. Statistics in Medicine, 29 972-981.
Lin, X. and Wang, L. (2011). Bayesian proportional odds model for analyzing current status data: univariate, clustered, and multivariate. Communication in Statistics-Simulation and Computation, 40 1171-1181.
Lin, X., Cai, B., Wang, L., and Zhang, Z. (submitted). Bayesian proportional hazards model for general interval-censored data.
case1ph
, case1po
, case2ph
, case2probit
1 2 3 4 5 6 7 8 9 10 11 | # To save time in checking package, niter is set to only 500 iterations.
# formula form
data(bcdata)
bcdata<-data.frame(bcdata) # must be a data frame
try<-ICBayes(Surv(L,R,type='interval2')~x1,data=bcdata,
model='case2ph',status=bcdata[,3],x_user=c(0,1),knots=seq(0.1,60.1,length=10),
grids=seq(0.1,60.1,by=1),coef.int=0.95,niter=500,burnin=100,seed=20161224)
# general form
try2<-ICBayes(model='case2ph',L=bcdata[,1],R=bcdata[,2],status=bcdata[,3],
xcov=bcdata[,4],x_user=c(0,1),knots=seq(0.1,60.1,length=10),
grids=seq(0.1,60.1,by=1),coef.int=0.95,niter=500,burnin=100,seed=20161224)
|
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