print.summary.BImm: Print a summary.BImm class model.

In PROreg: Patient Reported Outcomes Regression Analysis

Print a summary.BImm class model.

Description

print.summary.BImm is the summary.BImm specific method fot the generic function print which prints objects returned by modelling functions.

Usage

## S3 method for class 'summary.BImm'
print(x, ...)

Arguments

x	a summary.BImm class model.
...	for extra arguments.

Value

Prints a summary.BImm object.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

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, summary.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)
print(sum.model) # or just sum.model

PROreg documentation built on July 12, 2022, 5:06 p.m.