# print.ZOIPM: print.ZOIPM In jucdiaz/ZOIP: ZOIP Distribution, ZOIP Regression, ZOIP Mixed Regression

## Description

print a ZOIP model mixed.

## Usage

 ```1 2``` ```## S3 method for class 'ZOIPM' print(x, ...) ```

## Arguments

 `x` An object of class `ZOIPM`. `...` other arguments.

## Examples

 ``` 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 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47``` ```library(ZOIP) N<-2 ni<-10 set.seed(12345) Ciudad <- rep(1:N, each=ni) Total_mora<-rexp(N*ni,rate=1) set.seed(12345) b0i <- rep(rnorm(n=N,sd=0.5), each=ni) set.seed(12345) b1i <- rep(rnorm(n=N,sd=0.4), each=ni) neta <- (-1.13+b0i)+0.33*Total_mora neta2<-(0.33+b1i)+0.14*Total_mora mu <- 1 / (1 + exp(-neta)) sigma <- 1 / (1 + exp(-neta2)) p0 <- 0.05 p1 <- 0.05 mu[mu==1] <- 0.999 mu[mu==0] <- 0.001 sigma[sigma==1] <- 0.999 sigma[sigma==0] <- 0.001 family<-'R-S' set.seed(12345) Y <- rZOIP(n=length(mu), mu = mu, sigma = sigma ,p0=p0,p1=p1,family=family) data_sim<-data.frame(Y,Total_mora,Ciudad) n.points <- 3 pruning <- TRUE formula.mu=Y~Total_mora formula.sigma=~Total_mora formula.p0=~1 formula.p1=~1 formula.random= ~ 1 | Ciudad link=c('logit','logit','identity','identity') optimizer<-'nlminb' mod<-RMM.ZOIP(formula.mu=formula.mu,formula.sigma=formula.sigma,formula.p0=formula.p0, formula.p1=formula.p1,data=data_sim,formula.random=formula.random,link=link, family=family,optimizer=optimizer,n.points=n.points,pruning=pruning) mod ```

jucdiaz/ZOIP documentation built on Aug. 17, 2018, 2:24 a.m.