R : Copyright 2005, The R Foundation for Statistical Computing
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("glmmML-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('glmmML')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "glmmML"
> 
> ### * glmmML
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: glmmML
> ### Title: Generalized Linear Models with random intercept
> ### Aliases: glmmML
> ### Keywords: regression
> 
> ### ** Examples
> 
> id <- factor(rep(1:20, rep(5, 20)))
> y <- rbinom(100, prob = rep(runif(20), rep(5, 20)), size = 1)
> x <- rnorm(100)
> dat <- data.frame(y = y, x = x, id = id)
> glmmML(y ~ x, data = dat, cluster = id)

Call:  glmmML(formula = y ~ x, data = dat, cluster = id) 


              coef se(coef)     z Pr(>|z|)
(Intercept) 0.4878   0.4332 1.126    0.260
x           0.1387   0.2938 0.472    0.637

Standard deviation in mixing distribution:  1.511 
Std. Error:                                 0.4922 

Residual deviance: 124.8 on 97 degrees of freedom 	AIC: 130.8 
> 
> 
> 
> cleanEx(); ..nameEx <- "glmmML.fit"
> 
> ### * glmmML.fit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: glmmML.fit
> ### Title: Generalized Linear Model with random intercept
> ### Aliases: glmmML.fit
> ### Keywords: regression
> 
> ### ** Examples
> 
> x <- cbind(rep(1, 14), rnorm(14))
> y <- rbinom(14, prob = 0.5, size = 1)
> id <- rep(1:7, 2)
> 
> glmmML.fit(x, y, cluster = id, mixed = TRUE, method = 1)
$beta
[1] 0.5353834 0.6468762

$sigma
[1] 2.193987

$loglik
[1] -8.720531

$coef.variance
          [,1]      [,2]
[1,] 1.4174344 0.2655526
[2,] 0.2655526 1.1151869

$sigma.variance
[1] 4.614609

$frail
[1]  0.75920696 -0.22808166 -0.91905638  0.54919344 -1.06570998 -0.02043518
[7]  0.92182042

$residuals
function (object, ...) 
UseMethod("residuals")
<environment: namespace:stats>

$fitted.values
 [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0

$family

Family: binomial 
Link function: logit 


$deviance
[1] 17.44106

$aic
[1] 23.44106

$df.residual
[1] 11

$df.null
[1] 13

$convergence
[1] 1

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "glmmboot"
> 
> ### * glmmboot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: glmmboot
> ### Title: Generalized Linear Models with fixed effects grouping
> ### Aliases: glmmboot
> ### Keywords: regression nonlinear
> 
> ### ** Examples
> 
> id <- factor(rep(1:20, rep(5, 20)))
> y <- rbinom(100, prob = rep(runif(20), rep(5, 20)), size = 1)
> x <- rnorm(100)
> dat <- data.frame(y = y, x = x, id = id)
> res <- glmmboot(y ~ x, cluster = id, data = dat, boot = 5000)
> ##system.time(res.glm <- glm(y ~ x + id, family = binomial))
> 
> 
> 
> cleanEx(); ..nameEx <- "print.glmmML"
> 
> ### * print.glmmML
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.glmmML
> ### Title: Prints a 'glmmML' object.
> ### Aliases: print.glmmML
> ### Keywords: print
> 
> ### ** Examples
> 
> ## The function is currently defined as
> function (x, digits = max(3, getOption("digits") - 3), na.print = "", 
+     ...) 
+ {
+   cat("\nCall: ", deparse(x$call), "\n\n")
+   cat("Coefficients:\n")
+   print.default(format(x$coefficients, digits = digits), print.gap = 2, 
+                 quote = FALSE)
+   cat("Standard errors:\n")
+   print.default(format(x$sd, digits = digits), print.gap = 2, 
+                 quote = FALSE)
+   if(x$mixed){
+     cat("Standard deviation in mixing distribution: ", x$sigma, "\n")
+     cat("Std. Error:                                ", x$sigma.sd, "\n")
+   }
+   cat("\nDegrees of Freedom:", 
+       x$df.residual, "Residual\n")
+   cat("Residual Deviance:",
+       format(signif(x$deviance, digits)),
+       "\tAIC:",
+       format(signif(x$aic, digits)), "\n")
+   invisible(x)
+   }
function (x, digits = max(3, getOption("digits") - 3), na.print = "", 
    ...) 
{
    cat("\nCall: ", deparse(x$call), "\n\n")
    cat("Coefficients:\n")
    print.default(format(x$coefficients, digits = digits), print.gap = 2, 
        quote = FALSE)
    cat("Standard errors:\n")
    print.default(format(x$sd, digits = digits), print.gap = 2, 
        quote = FALSE)
    if (x$mixed) {
        cat("Standard deviation in mixing distribution: ", x$sigma, 
            "\n")
        cat("Std. Error:                                ", x$sigma.sd, 
            "\n")
    }
    cat("\nDegrees of Freedom:", x$df.residual, "Residual\n")
    cat("Residual Deviance:", format(signif(x$deviance, digits)), 
        "\tAIC:", format(signif(x$aic, digits)), "\n")
    invisible(x)
}
> 
> 
> 
> cleanEx(); ..nameEx <- "summary.glmmML"
> 
> ### * summary.glmmML
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.glmmML
> ### Title: Summary of a glmmML object
> ### Aliases: summary.glmmML
> ### Keywords: print
> 
> ### ** Examples
> 
> ##---- Should be DIRECTLY executable !! ----
> ##-- ==>  Define data, use random,
> ##--    or do  help(data=index)  for the standard data sets.
> 
> ## The function is currently defined as
> function(object, ...){
+     print.glmmML(object, ...)
+   }
function (object, ...) 
{
    print.glmmML(object, ...)
}
> 
> 
> 
> cleanEx(); ..nameEx <- "summary.glmmboot"
> 
> ### * summary.glmmboot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.glmmboot
> ### Title: Summary of a glmmboot object
> ### Aliases: summary.glmmboot
> ### Keywords: print
> 
> ### ** Examples
> 
> ##---- Should be DIRECTLY executable !! ----
> ##-- ==>  Define data, use random,
> ##--    or do  help(data=index)  for the standard data sets.
> 
> ## The function is currently defined as
> function(object, ...){
+     print.glmmboot(object, ...)
+   }
function (object, ...) 
{
    print.glmmboot(object, ...)
}
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  8.64 0.03 8.81 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')