lcxJAGS: Estimate LCx for a toxin

Description Usage Arguments Details Value See Also

Description

Bayesian estimates of LCx from survival data in the presence of additional stressors and non-ignorable control mortality

Usage

1
2
3
4
lcxJAGS(formula, concentration, group, data, start = NULL,
  link = c("probit", "logit"), lethal = 50, common.background = FALSE,
  n.adapt = 500, n.chains = 4, alpha.mu = 0, alpha.tau = 1e-04,
  beta.mu = 0, beta.tau = 1e-04, gamma.mu = 0, gamma.tau = 1e-04)

Arguments

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 start=NULL these parameters are determined by lcx.initialize.

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 jags.model.

n.chains

Parameter passed to jags.model.

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.

Details

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.

Value

Returns an object inheriting from class jags which can be used to generate dependent samples from the posterior distribution of the parameters

See Also

lcx


abilouhill/LC50 documentation built on May 10, 2019, 4:10 a.m.