# ypbp: Fits the Yang and Prentice using Bernstein polynomials to... In YPBP: Yang and Prentice Model with Baseline Distribution Modeled by Bernstein Polynomials

## Description

Fits the Yang and Prentice model with either the baseline hazard hazard or the baseline odds modeled via Bernstein polynomials.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```ypbp( formula, data, degree = NULL, tau = NULL, approach = c("mle", "bayes"), baseline = c("hazard", "odds"), hessian = TRUE, hyper_parms = list(h1_gamma = 0, h2_gamma = 4, mu_psi = 0, sigma_psi = 4, mu_phi = 0, sigma_phi = 4, mu_beta = 0, sigma_beta = 4), ... ) ```

## Arguments

 `formula` an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. `data` an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which ypbp is called. `degree` number of intervals of the PE distribution. If NULL, default value (square root of n) is used. `tau` the maximum time of follow-up. If NULL, tau = max(time), where time is the vector of observed survival times. `approach` approach to be used to fit the model (mle: maximum likelihood; bayes: Bayesian approach). `baseline` baseline function to be modeled. `hessian` logical; If TRUE (default), the hessian matrix is returned when approach="mle". `hyper_parms` a list containing the hyper-parameters of the prior distributions (when approach = "bayes"). If not specified, default values are used. `...` Arguments passed to either 'rstan::optimizing' or 'rstan::sampling' .

## Value

ypbp returns an object of class "ypbp" containing the fitted model.

## Examples

 ```1 2 3 4 5 6 7``` ```library(YPBP) mle1 <- ypbp(Surv(time, status)~trt, data=gastric, baseline = "hazard") mle2 <- ypbp(Surv(time, status)~trt, data=gastric, baseline = "odds") bayes1 <- ypbp(Surv(time, status)~trt, data=gastric, baseline = "hazard", approach = "bayes", chains = 2, iter = 500) bayes2 <- ypbp(Surv(time, status)~trt, data=gastric, baseline = "odds", approach = "bayes", chains = 2, iter = 500) ```

YPBP documentation built on July 1, 2020, 10:19 p.m.