# print.BImm: Print a BImm class model. In PROreg: Patient Reported Outcomes Regression Analysis

 print.BImm R Documentation

## Print a BImm class model.

### Description

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

### Usage

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

### Arguments

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

### Value

Prints a 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

`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)
print(model) # or just model
```

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