HRQoL (version 1.0)

summary.BIreg:

Description

summary.BIreg si the BIreg specific method for the generic function summary which summarizes objects returned by modelling functions.

Usage

# S3 method for BIreg
summary(object, ...)

Arguments

object

a BIreg class model.

...

for extra arguments.

Value

summary.BIreg returns an object of class "summary.BIreg".

coefficients

a table with all the relevant information about the significance of the regression coefficients of the model. It includes the estimations, the standard errors of the estimations, the test-statistics and the p-values.

phi

the estimated value of the dispersion parameter. If disp FALSE, then the estimated value is 1.

deviance

the deviance of the model.

df

the degrees of freedom of the model.

null.deviance

the deviance for the null model. The null model will include only an intercept.

df.null

the degrees of freedom for the null model.

Goodness.of.fit

p-value of the goodness of fit test.

iter

number of iterations in the IWLS method.

conv

convergence of the methodology. If the algorithm has converged it returns "yes", otherwise "no".

X

the model matrix.

y

the dependent variable in the model.

balanced

if the response variable is balanced it returns "yes", otherwise "no".

m

the number of trials in each observation.

nObs

number of observations.

balanced

if the response binomial variable is balanced it returns "yes", otherwise "no".

Details

summary.BIreg summarizes all the relevant information about the estimation of the parameters in a BIreg class model. The function performs statistical significance hypothesis about the estimated regression parameters based on the normal distribution of the estimates. It also performs a goodness of fit test based on the difference between the calculated deviance of the model and the null deviance or deviance of the null model, which it is suppose to follow a Chi-square distribution with degrees of freedom equal to the difference in degrees of freedom of the models.

References

Pawitan Y. (2001): In All Likelihood: Statistical Modelling and Inference Using Likelihood, Oxford University Press Williams D. A. (1982): Extra-Binomial Variation in Logistic Linear Regression, Journal of the Royal Statistical Society. Series C, 31, 144-148

See Also

BIreg, BIiwls

Examples

  set.seed(1234)
  # We simulate a covariable and construct the outcome variable applying
  # an inverse logit link function on it.
  
  m <- 10                       
  k <-100                       
  covariate <- rnorm(k,2,0.5)  
  beta <- c(-6,4)
  p <- 1/(1+exp(-(beta[1]+beta[2]*covariate)))
  outcome <- rBI(k,m,p)
  
  model <- BIreg(outcome~covariate,m,disp=FALSE)
  sum.model <- summary(model)