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

## Description

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

## Usage

 ```1 2``` ```## 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

`BImm`, `summary.BImm`
 ``` 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 27``` ```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 ```