model_negbin_indep {beaver}

Negative Binomial Independent Dose Response

Description

Model settings for a negative binomial distribution with an independent mean for each dose. This function is to be used within a call to beaver_mcmc().

Usage

model_negbin_indep(mu_b1, sigma_b1, mu_b2, sigma_b2, w_prior = 1)

Arguments

mu_b1, sigma_b1, mu_b2, sigma_b2

hyperparameters. See the model description below for context.

w_prior

the prior weight for the model.

Value

A list with the model's prior weight and hyperparameter values.

Negative Binomial Independent

Let y_{ij} be the jth subject on the kth dose. The model is

y_{ij} ~ NB(p_i, r_i)

p_i ~ Uniform(0, 1)

r_{ij} = (\mu_{ij} * p_i) / (1 - p_i)

log(\mu_{ij}) = x_{ij} * b1 + b2_k

b1 ~ N(`mu_b1`, `sigma_b1`^2)

b2_k ~ N(`mu_b2`, `sigma_b2`^2)

The model is parameterized in terms of the mean of the negative binomial distribution and the usual probability parameter p. The prior on the mean is an exponential model, and the prior on p at each dose is Uniform(0, 1). The model can adjust for baseline covariates, (

x_{ij}

).

See Also

Other models: beaver_mcmc(), model_negbin_emax(), model_negbin_exp(), model_negbin_linear(), model_negbin_loglinear(), model_negbin_logquad(), model_negbin_quad(), model_negbin_sigmoid_emax()