summary.gbp: Summarizing Estimation Result

In Rgbp: Hierarchical Modeling and Frequency Method Checking on Overdispersed Gaussian, Poisson, and Binomial Data

Description

summary.gbp prepares a summary of estimation result saved in the object defined as "gbp" class creating "summary.gbp" class

Usage

## S3 method for class 'gbp'
summary(object, ...)

Arguments

object	a resultant object of gbp function.
...	further arguments passed to other methods.

Value

summary.gbp prepares below contents:

main	a table to be displayed by summary(gbp.object). print.summary.gbp.
sec.var	a vector containing an estimation result of the second-level variance component. print.summary.gbp.
reg	a vector composed of a summary of regression fit (if fitted). print.summary.gbp.

Author(s)

Hyungsuk Tak, Joseph Kelly, and Carl Morris

Examples

  data(hospital)

  z <- hospital$d
  n <- hospital$n
  y <- hospital$y
  se <- hospital$se
  
  ###################################################################################
  # We do not have any covariates and do not know a mean of the prior distribution. #
  ###################################################################################

    ###############################################################
    # Gaussian Regression Interactive Multilevel Modeling (GRIMM) #
    ###############################################################

    g <- gbp(y, se, model = "gaussian")
    summary(g)

    ###############################################################
    # Binomial Regression Interactive Multilevel Modeling (BRIMM) #
    ###############################################################

    b <- gbp(z, n, model = "binomial")
    summary(b)

    ##############################################################
    # Poisson Regression Interactive Multilevel Modeling (PRIMM) #
    ##############################################################

    p <- gbp(z, n, mean.PriorDist = 0.03, model = "poisson")
    summary(p)

Rgbp documentation built on Dec. 18, 2019, 1:41 a.m.