Description Usage Arguments Value Author(s) References See Also Examples
print.BImm
is the BImm specific method fot the generic function print which prints objects returned by modelling functions.
1 2 |
x |
a BImm class model. |
... |
for extra arguments. |
Prints a BImm object.
J. Najera-Zuloaga
D.-J. Lee
I. Arostegui
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | 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)
print(model) # or just model
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.