model_negbin_exp {beaver} | R Documentation |
Model settings for a negative binomial distribution assuming
an exponential model on the mean. This function is to be used within a
call to beaver_mcmc()
.
model_negbin_exp(
mu_b1,
sigma_b1,
mu_b2,
sigma_b2,
mu_b3,
sigma_b3,
w_prior = 1
)
mu_b1 , sigma_b1 , mu_b2 , sigma_b2 , mu_b3 , sigma_b3 |
hyperparameters. See the model description below for context. |
w_prior |
the prior weight for the model. |
A list with the model's prior weight and hyperparameter values.
Let y_{ij}
be the j
th subject on dose d_i
.
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 * (1 - exp(-b3 * d_i))
b1 ~ N(`mu_b1`, `sigma_b1`^2)
b2 ~ N(`mu_b2`, `sigma_b2`^2)
b3 ~ N(`mu_b3`, `sigma_b3`^2) (Truncated to be positive)
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}
).
Other models:
beaver_mcmc()
,
model_negbin_emax()
,
model_negbin_indep()
,
model_negbin_linear()
,
model_negbin_loglinear()
,
model_negbin_logquad()
,
model_negbin_quad()
,
model_negbin_sigmoid_emax()