glmLimits: glmLimits function
In isglobal-brge/lodregression:

Description Usage Arguments Value Examples

This function allows you to adjust multiple Bayesian GLM models with predictors subjected to a limit of detection (LOD) specified with a BUGS-language description of the prior distribution, and a set of data.

glmLimits(formula, data, LimitVariables, lod = NULL, loq = NULL,
  family = "gaussian", logLimit = FALSE, n.iter = 1000, n.chains = 1,
  alpha.corrected = 0.05, quantiles = unique(c(alpha.corrected/2, 0.025,
  0.975, 1 - (alpha.corrected/2))), quiet = FALSE, progress.bar = "text",
  dic.type = "popt", n.adapt = 1000, ...)

`formula`	an object of class "formula": a symbolic description of the model to be fitted.
`data`	a data.frame containing the variables in the model.
`LimitVariables`	character string of names of the variables subjected to a limit.
`lod`	numeric vector with the same length of LimitVariables containing the lower limit values for the variables with LOD (with the same order).
`loq`	optional numeric vector with the same length of LimitVariables containing the upper limit values for the variables with limits (with the same order)
`family`	character with a description of the error distribution to be used in the model. Possible values are "gaussian" (default) for continuous outcomes or "binomial" for binary outcomes.
`logLimit`	a logical indicating if limit variables would be assumed distributed as log-normal.
`n.iter`	number of iterations of the Markov chain to run to be used in rjags::update(), rjags::coda.samples() and rjags::dic.samples().
`n.chains`	the number of parallel chains for the model to be used in rjags::jags.model().
`alpha.corrected`	significance level to be used for the confident intervals.
`quantiles`	numeric vector containing the percentiles in [0,1] to obtain of the estimated parameters.
`progress.bar`	type of progress bar. Possible values are "text" (default), "gui", and "none" to be used in rjags::update().
`dic.type`	type of penalty to use in rjags::dic.samples().
`n.adapt`	the number of iterations for adaptation to be used in rjags::jags.model(). If n.adapt = 0 then no adaptation takes place.

It returns an object of class "glmLimits" containing a model of class "jags".

glmLimits(formula, data, LimitVariables, lod = NULL, loq = NULL, family = "gaussian", logLimit = FALSE, n.iter = 1000, n.chains = 1, alpha.corrected = 0.05, quantiles = unique(c(alpha.corrected/2, 0.025, 0.975, 1-(alpha.corrected/2))), quiet = FALSE, progress.bar = "text", dic.type = "popt", n.adapt = 1000, ...)