Fitting Proportional Odds Models

Function to fit proportional odds (PO) models.

Usage

poreg(formula, data, baseline = "weibull", dist = NULL, init = 0, ...)

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: 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 function is called.
baseline: the chosen baseline distribution; options currently available are: exponential, weibull, lognormal, loglogistic and Birnbaum-Saunders (fatigue) distributions.
dist: alternative way to specify the baseline distribution (for compatibility with the survreg function); default is NULL.
init: initial values specification (default value is 0); see the detailed documentation for init in optimizing.
...: further arguments passed to other methods.

Value

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

Examples

# \donttest{
library(survstan)
fit <- poreg(Surv(futime, fustat) ~ ecog.ps + rx, data = ovarian, baseline = "weibull")
summary(fit)
#> Call:
#> poreg(formula = Surv(futime, fustat) ~ ecog.ps + rx, data = ovarian, 
#>     baseline = "weibull")
#> 
#> Proportional odds model fit with weibull baseline distribution: 
#> 
#> Regression coefficients:
#>         Estimate Std. Error z value Pr(>|z|)
#> ecog.ps  0.81190    0.71810  1.1306   0.2582
#> rx      -0.69296    0.71685 -0.9667   0.3337
#> 
#> Baseline parameters:
#>         Estimate Std. Error       2.5%     97.5%
#> alpha    1.18318    0.36436    0.64703    2.1636
#> gamma 1340.62000 1086.20339  273.93102 6561.0022
#> --- 
#> loglik = -97.03918   AIC = 202.0784 
# }