HRQoL (version 1.0)

summary.BImm:

Description

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

Usage

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

Arguments

object

a BImm class model.

...

for extra arguments.

Value

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

fixed.coefficients

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

random.coef

predicted random effects of the regression.

sigma.table

a table which inlcudes the estimation and standard errors of the parameters which the variance-covariance matrix of the random effects consists of.

fitted.values

the fitted mean values of the probability parameter of the conditional beta-binomial distribution.

residuals

residuals of the model.

deviance

deviance of the model.

df

degrees of freedom of the model.

nRand

number of random effects.

nComp

number of random components.

nRandComp

number of random components in each random effect of the model.

namesRand

names of the random components.

iter

number of iterations in the estimation method.

nObs

number of observations in the data.

y

dependent response variable in the model.

X

model matrix of the fixed effects.

Z

model matrix of the random effects.

balanced

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

m

number of trials in each binomial observation.

conv

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

Details

summary.BImm summarizes all the relevant information about the estimation of the parameters in a BImm class model. The function performs statistical significance hypothesis about the estimated fixed parameters based on the normal distribution of the estimates.

References

Breslow N. E. & Calyton D. G. (1993): Approximate Inference in Generalized Linear Mixed Models, Journal of the American Statistical Association, 88, 9-25 McCulloch C. E. & Searle S. R. (2001): Generalized, Linear, and Mixed Models, Jhon Wiley & Sons Pawitan Y. (2001): In All Likelihood: Statistical Modelling and Inference Using Likelihood, Oxford University Press

See Also

BImm

Examples

set.seed(5)
# Fixing parameters for the simulation:
nObs <- 1000
m <- 10
beta <- c(1.5,-1.1)
sigma <- 0.8

# Simulating the covariate:
x <- runif(nObs,-5,5)

# Simulating the random effects:
z <- as.factor(rBI(nObs,5,0.5,2))
u <- rnorm(6,0,sigma)

# Getting the linear predictor and probability parameter.
X <- model.matrix(~x)
Z <- model.matrix(~z-1)
eta <- beta[1]+beta[2]*x+crossprod(t(Z),u)
p <- 1/(1+exp(-eta))

# Simulating the response variable
y <- rBI(nObs,m,p)

# Apply the model
model <- BImm(fixed.formula = y~x,random.formula = ~z,m=m)
sum.model <- summary(model)