Description Usage Arguments Details Value See Also
Bayesian estimates of LCx from survival data in the presence of additional stressors and non-ignorable control mortality
1 2 3 4 |
formula |
A formula relating log LCx to covariates describing the additional stressors. |
concentration |
The name of variable that is the concentration of the toxin. |
group |
A factor distinguishing treatment groups for the additional stressors. |
data |
Dataframe containing the variables in the model. |
start |
Starting values used to initialize the model. If
|
link |
The link function for survival fractions. |
lethal |
The level of lethality (ie "x") to be estimated. |
common.background |
Should a common background survival be estimated for each treatment group? |
n.adapt |
Parameter passed to |
n.chains |
Parameter passed to |
alpha.mu |
Prior mean for alpha. |
alpha.tau |
Prior precision for alpha. |
beta.mu |
Either a single prior mean for all beta parameters, or a vector of prior means, one for each parameter. |
beta.tau |
Either a single prior precision for all beta parameters, or a vector of prior precisions, one for each parameter. |
gamma.mu |
Prior mean for gamma. |
gamma.tau |
Prior precision for gamma. |
This function is an analog of lcx
that produces an
object of class jags
which can be used to draw samples from
the posterior using update
and coda.samples
from
rjags.
The model assumes half Normal priors for alpha
and Normal
priors for beta
and gamma
. For alpha
and
gamma
, a single prior mean and precision is assumed for all
groups, for beta
individual prior means and precisions can
be specified.
Returns an object inheriting from class jags
which
can be used to generate dependent samples from the posterior
distribution of the parameters
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