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Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("zicounts-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('zicounts')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "enzinb"
> 
> ### * enzinb
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ezinb
> ### Title: The expected value of the censored zero-inflated negative
> ###   binomial model
> ### Aliases: ezinb
> ### Keywords: models
> 
> ### ** Examples
> 
> ## load the artificial 'teeth' data
>  data(teeth)
>  names(teeth)
[1] "dmft"   "decid"  "age"    "gender"
>  
> ##  fit zero-inflated negative binomial regression model
> zinb.cens <- zicensor(resp = dmft~., upper = decid~.,x =~gender + age,z =~gender + age, data=teeth)
> zinb.cens 

Censored Zero-Inflated Negative Binomial 

Call: 
 Formula = ( dmft , decid ) ~  gender + age | gender + age 


Subscripts:
	-- x: coefficients of censored negative binomial part
	-- z: coefficients of censored zero-inflated part

Coefficients:
(Intercept)x       genderx          agex  (Intercept)z       genderz  
    1.424919     -0.004769     -0.047181     -3.590246      0.284743  
        agez      log(tau)  
    0.407158      2.927695  

Degrees of Freedom: 193 
Deviance:     571.6 	 AIC: 585.6 	 BIC: 608.7 

Number of iterations BFGS is called before convergence: 1 
> 
> ## expected value of the censored zinb model
> ## uncomment to run
> #zinb.e <- ezinb(coeff=zinb.cens$coeff,resp = dmft~., upper = decid~.,
> #       x =~gender + age,z =~gender + age, data=teeth)
> #zinb.e
> 
> 
> 
> cleanEx(); ..nameEx <- "summary.zinb"
> 
> ### * summary.zinb
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.zinb
> ### Title: Printing summary of the (censored) zinb regression model
> ### Aliases: summary.zinb summary.czinb print.zinb print.czinb
> ###   prnt.zinb.int
> ### Keywords: models
> 
> ### ** Examples
> 
> ## See help for ?zicounts or ?zicensor
> 
> 
> 
> cleanEx(); ..nameEx <- "teeth"
> 
> ### * teeth
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: teeth
> ### Title: A simulated data set to demonstrate the zicounts package
> ### Aliases: teeth
> ### Keywords: models
> 
> ### ** Examples
> 
> ## See ?zicouts, zicensor 
> 
> 
> 
> cleanEx(); ..nameEx <- "zicensor"
> 
> ### * zicensor
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: zicensor
> ### Title: Fitting classical and zero-inflated count regression models
> ### Aliases: zicensor
> ### Keywords: models
> 
> ### ** Examples
> 
> ## load the artificial 'teeth' data
>  data(teeth)
>  names(teeth)
[1] "dmft"   "decid"  "age"    "gender"
>   
> ## a) fit a Poisson regression model
> pois.cens1 <- zicensor(parm=c(-2,-2,1),resp = dmft~., upper = decid~.,x =~gender + age, 
+         data=teeth, distr = "Poisson")
> pois.cens1 

Censored Poisson 

Call: 
 Formula = ( dmft , decid ) ~  gender + age 

Coefficients:
(Intercept)x       genderx          agex  
      1.8615       -0.1125       -0.1599  

Degrees of Freedom: 197 
Deviance:     665.6 	 AIC: 671.6 	 BIC: 681.5 

Number of iterations BFGS is called before convergence: 1 
> # fit for  boys only -- using 'sub'
> pois.boys <- zicensor(resp = dmft~., upper = decid~.,x =~age, data=teeth, distr = "Poisson",
+         sub=expression(gender==0))
> pois.boys  

Censored Poisson 

Call: 
 Formula = ( dmft , decid ) ~  age 

Coefficients:
(Intercept)x          agex  
    0.794384     -0.007336  

Degrees of Freedom: 74 
Deviance:     254.8 	 AIC: 258.8 	 BIC: 263.5 

Number of iterations BFGS is called before convergence: 1 
> 
> ## b) fit zero-inflated  Poisson regression model
> zip.cens <- zicensor(resp = dmft~., upper = decid~.,x =~gender + age,z =~gender + age, 
+         data=teeth, distr = "ZIP")
> zip.cens

Censored Zero-Inflated Poisson 

Call: 
 Formula = ( dmft , decid ) ~  gender + age | gender + age 

Subscripts:
	-- x: coefficients of censored Poisson part 
	-- z: coefficients of censored zero-inflated part

Coefficients:
(Intercept)x       genderx          agex  (Intercept)z       genderz  
     1.39837       0.01205      -0.05841      -3.16086       0.72597  
        agez  
     0.28842  

Degrees of Freedom: 194 
Deviance:     577.2 	 AIC: 589.2 	 BIC: 609 

Number of iterations BFGS is called before convergence: 1 
> #summary(zip.cens)
> 
> ## c) fit negative binomial regression model
> nb.cens <- zicensor(resp = dmft~., upper = decid~.,x =~gender + age,data=teeth, distr = "NB")
> nb.cens

Censored Negative Binomial 

Call: 
 Formula = ( dmft , decid ) ~  gender + age 

Coefficients:
(Intercept)x       genderx          agex      log(tau)  
      2.0229       -0.1108       -0.1860        0.1673  

Degrees of Freedom: 196 
Deviance:     594.8 	 AIC: 602.8 	 BIC: 616 

Number of iterations BFGS is called before convergence: 1 
> #summary(nb.cens)
> 
> ## d) fit zero-inflated negative binomial regression model
> zinb.cens <- zicensor(resp = dmft~., upper = decid~.,x =~gender + age,z =~gender + age, data=teeth)
> zinb.cens

Censored Zero-Inflated Negative Binomial 

Call: 
 Formula = ( dmft , decid ) ~  gender + age | gender + age 


Subscripts:
	-- x: coefficients of censored negative binomial part
	-- z: coefficients of censored zero-inflated part

Coefficients:
(Intercept)x       genderx          agex  (Intercept)z       genderz  
    1.424919     -0.004769     -0.047181     -3.590246      0.284743  
        agez      log(tau)  
    0.407158      2.927695  

Degrees of Freedom: 193 
Deviance:     571.6 	 AIC: 585.6 	 BIC: 608.7 

Number of iterations BFGS is called before convergence: 1 
> #summary(zinb.cens)
> 
> 
> 
> cleanEx(); ..nameEx <- "zicounts"
> 
> ### * zicounts
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: zicounts
> ### Title: Fitting classical and zero-inflated count regression models
> ### Aliases: zicounts
> ### Keywords: models
> 
> ### ** Examples
> 
> ## load the artificial 'teeth' data
>  data(teeth)
>  names(teeth)
[1] "dmft"   "decid"  "age"    "gender"
>   
> ## a) fit a Poisson regression model
> pois.zc1 <- zicounts(parm=c(-2,-2,1),resp = dmft~.,x =~gender + age, data=teeth, distr = "Poisson")
> pois.zc1 # starting values specified

Poisson 

Call: 
 Formula =  dmft  ~  gender + age 

Coefficients:
(Intercept)x       genderx          agex  
      1.9444       -0.1119       -0.1947  

Degrees of Freedom: 197 
Deviance:     814.9 	 AIC: 820.9 	 BIC: 830.8 

Number of iterations BFGS is called before convergence: 1 
> 
> pois.zc2 <- zicounts(resp = dmft~.,x =~gender + age, data=teeth, distr = "Poisson")
> pois.zc2 # starting values estimated from data

Poisson 

Call: 
 Formula =  dmft  ~  gender + age 

Coefficients:
(Intercept)x       genderx          agex  
      1.9444       -0.1119       -0.1947  

Degrees of Freedom: 197 
Deviance:     814.9 	 AIC: 820.9 	 BIC: 830.8 

Number of iterations BFGS is called before convergence: 1 
> 
> # compare with glm
> pois.glm <- glm(dmft~gender + age, family=poisson,data=teeth)
> pois.glm

Call:  glm(formula = dmft ~ gender + age, family = poisson, data = teeth) 

Coefficients:
(Intercept)       gender          age  
     1.9444      -0.1119      -0.1947  

Degrees of Freedom: 199 Total (i.e. Null);  197 Residual
Null Deviance:	    505.2 
Residual Deviance: 502.3 	AIC: 820.9 
> 
> # fit for boys only -- using 'sub'
> pois.girl <- zicounts(resp = dmft~.,x =~age, data=teeth, distr = "Poisson",sub=expression(gender==0))
> pois.girl # starting values estimated from data

Poisson 

Call: 
 Formula =  dmft  ~  age 

Coefficients:
(Intercept)x          agex  
     1.03854      -0.06515  

Degrees of Freedom: 74 
Deviance:     317.4 	 AIC: 321.4 	 BIC: 326 

Number of iterations BFGS is called before convergence: 1 
> 
> # Poisson with offset
> pois.zc3 <- zicounts(resp = dmft~.,x =~gender + age, offset= expression(log(decid + 1)), data=teeth, distr = "Poisson")
> pois.zc3

Poisson 

Call: 
 Formula =  dmft  ~  gender + age (with offest) 

Coefficients:
(Intercept)x       genderx          agex  
     0.06165      -0.05201      -0.10235  

Degrees of Freedom: 197 
Deviance:     501.6 	 AIC: 507.6 	 BIC: 517.5 

Number of iterations BFGS is called before convergence: 1 
> 
> # compare with glm
> pois.glm2 <- glm(dmft~gender + age, offset= log(decid + 1), family=poisson,data=teeth)
> pois.glm2  

Call:  glm(formula = dmft ~ gender + age, family = poisson, data = teeth,      offset = log(decid + 1)) 

Coefficients:
(Intercept)       gender          age  
    0.06165     -0.05201     -0.10235  

Degrees of Freedom: 199 Total (i.e. Null);  197 Residual
Null Deviance:	    189.7 
Residual Deviance: 189 	AIC: 507.6 
> 
> ## b) fit zero-inflated  Poisson regression model
> zip.zc <- zicounts(resp = dmft~.,x =~gender + age,z =~gender + age, data=teeth, distr = "ZIP")
> zip.zc

Zero-Inflated Poisson 

Call: 
 Formula =  dmft  ~  gender + age | gender + age 

Subscripts:
	-- x: coefficients of Poisson part
	-- z: coefficients of zero-inflated part

Coefficients:
(Intercept)x       genderx          agex  (Intercept)z       genderz  
    1.133932     -0.039455     -0.004717     -3.519678      0.171604  
        agez  
    0.446568  

Degrees of Freedom: 194 
Deviance:     668.4 	 AIC: 680.4 	 BIC: 700.2 

Number of iterations BFGS is called before convergence: 1 
> #summary(zip.zc)
> 
> ## c) fit negative binomial regression model
> nb.zc <- zicounts(resp = dmft~.,x =~gender + age,data=teeth, distr = "NB")
> nb.zc

Negative Binomial 

Call: 
 Formula =  dmft  ~  gender + age 

Coefficients:
(Intercept)x       genderx          agex      log(tau)  
      2.0640       -0.1172       -0.2113       -0.2556  

Degrees of Freedom: 196 
Deviance:     704.4 	 AIC: 712.4 	 BIC: 725.6 

Number of iterations BFGS is called before convergence: 1 
> #summary(nb.zc)
> 
> ## d) fit zero-inflated negative binomial regression model
> zinb.zc <- zicounts(resp = dmft~.,x =~gender + age,z =~gender + age, data=teeth)
> zinb.zc

Zero-Inflated Negative Binomial 

Call: 
 Formula =  dmft  ~  gender + age | gender + age 


Subscripts:
	-- x: coefficients of of negative binomial part
	-- z: coefficients of zero-inflated part

Coefficients:
(Intercept)x       genderx          agex  (Intercept)z       genderz  
    1.125896     -0.040113     -0.004892     -3.584960      0.172345  
        agez      log(tau)  
    0.452557      3.421015  

Degrees of Freedom: 193 
Deviance:     668 	 AIC: 682 	 BIC: 705.1 

Number of iterations BFGS is called before convergence: 1 
> #summary(zinb.zc)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  1.25 0.04 1.3 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')