Description Usage Arguments Value Examples
This function performs Gibbs sampling from posterior densities of concavex model parameters as well as transformations of parameters.
1 2 3 |
ccvx.mod |
JAGS model file as specified by ccvx_build_jags |
doses |
A vector of dose strengths, with placebo listed first |
mu.hat |
A vector of parameter estimates for each of the doses |
std.err |
A vector of standard errors for each of the doses |
n.chains |
Number of chains used to for Gibbs sampling. Default is 4 |
gibbs.samples |
Number of samples to draw for each chain after burn in. Default is 5000 |
burn.in |
Number of burn in samples to be drawn. Default is 1000 |
sd.ph3 |
The standard deviation of the endpoint being assessed in phase 3. Only needs to be specified when ccvx_fit() computes posterior predictive probabilities (i.e. model-based DDCPs) |
n.per.arm.ph3 |
The number of patients per arm being assessed in hypothetical phase 3 study. Only needs to be specified when ccvx_fit() computes posterior predictive probabilities (i.e. model-based DDCPs) |
A list with the elements
ccvx.mod |
The JAGS code used for Gibbs sampling as generated by ccvx_build_jags or ccvx5_build_jags |
jags.samples |
output containing Gibbs samples from parameter posteriors generated |
coda.samples |
output containing coda samples for MCMC diagnostics |
doses |
A vector of dose strengths, with placebo listed first |
mu.hat |
A vector of parameter estimates for each of the doses |
std.err |
A vector of standard errors for each of the doses |
1 2 3 4 | ccvx.mod <- ccvx_build_jags()
ccvx.samples <- ccvx_fit(ccvx.mod, doses = 0:4, mu.hat = c(1, 20, 50, 60, 65), std.err = rep(10, 5))
names(ccvx.samples$jags.samples)
ccvx_plot_fit(ccvx.samples)
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