modelinf.fun {BayesianPlatformDesignTimeTrend}

modelinf.fun

Description

This function summarize the input parameters describing the model for analysis and transfer them into a list

Usage

modelinf.fun(
  model = "tlr",
  ibb.inf = list(pi.star = 0.5, pess = 2, betabinomialmodel = ibetabinomial.post),
  tlr.inf = list(beta0_prior_mu = 0, beta1_prior_mu = 0, beta0_prior_sigma = 2.5,
    beta1_prior_sigma = 2.5, beta0_df = 7, beta1_df = 7, reg.inf = "main", variable.inf =
    "Fixeffect")
)

Arguments

model

The statistical model. ibb: betabinomial model / tlr: logistic model

ibb.inf

The list of information for betabinomial model including: betabinomialmodel: The betabinomial model, pi.star: prior response rate, pess: prior effective sample size

tlr.inf

The list of information for logistic model including: The mean (mu), variance (sigma), degree of freedom (df) of the intercept and the main effect of the linear terms in logistic model. reg.inf: The type of linear function in logistic model. variable.inf: Fixeffect/Mixeffect. Indicating whether a mix effect model is used (for time trend effect modelling)

Value

A list of model information including model, ibb.inf and tlr.inf

Author(s)

Ziyan Wang

Examples

modelinf.fun(model = "tlr",
  ibb.inf = list(pi.star = 0.5,
                 pess = 2,
                 betabinomialmodel = ibetabinomial.post),
  tlr.inf = list(beta0_prior_mu = 0,
                 beta1_prior_mu = 0,
                 beta0_prior_sigma = 2.5,
                 beta1_prior_sigma = 2.5,
                 beta0_df = 7,
                 beta1_df = 7,
                 reg.inf =  "main",
                 variable.inf = "Fixeffect"
                 ))