Bayes_ord {BayesOrdDesign}

Bayesian ordinal regression analysis Estimate the correlation coefficients of treatment variable, with and without the proportional odds assumption

Description

Bayesian ordinal regression based on cumulative likelihood function Estimate the correlation coefficients of treatment variable, with or without the proportional odds assumption

Usage

Bayes_ord(formula, data, structure, U)

Arguments

formula

a formula expression as for regression models, of the form response ~ predictors. The response should be a factor (preferably an ordered factor), which will be interpreted as an ordinal response with levels ordered as in the factor.

data

a data frame in which to interpret the variables occurring in the formula.

structure

the data structure. i.e., structure = "PO" or structure = "NPO".

U

the desirability of each outcome level

Details

This function estimates the coefficients and threshold coefficients. Specifically, the numerical utilities U reflect the desirability of each outcome level. To do this, in our example, we first set U[1] = 100 and U[5] = 0, and then asked physicians to specify numerical values for the intermediate levels, that reflect their desirability relative to the best and worst levels.

Value

Bayes_ord() returns the regression coefficients, including: (1) estimator coefficients (2) thresholds coefficients

Examples

### Example One: PO data structure
fm1 = Bayes_ord(response~treatment, example.data, "PO")

### Example Two: NPO data structure
fm2 = Bayes_ord(response~treatment, example.data, "NPO", U = c(100,80,65,25,10,0))