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("qvcalc-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('qvcalc')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "indentPrint"
> 
> ### * indentPrint
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: indentPrint
> ### Title: Print with Line Indentation
> ### Aliases: indentPrint
> ### Keywords: IO
> 
> ### ** Examples
> 
> indentPrint("this indented by 10 spaces", indent=10)
          [1] "this indented by 10 spaces"
> 
> 
> 
> cleanEx(); ..nameEx <- "qvcalc"
> 
> ### * qvcalc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: qvcalc
> ### Title: Quasi Variances for Model Coefficients
> ### Aliases: qvcalc summary.qv plot.qv
> ### Keywords: models regression
> 
> ### ** Examples
> 
> ##  Overdispersed Poisson loglinear model for ship damage data
> ##  from McCullagh and Nelder (1989), Sec 6.3.2 
> library(MASS)
> data(ships)
> ships$year <- as.factor(ships$year)
> ships$period <- as.factor(ships$period)
> shipmodel <- glm(formula = incidents ~ type + year + period,
+     family = quasipoisson, 
+     data = ships, subset = (service > 0), offset = log(service))
> shiptype.qvs <- qvcalc(shipmodel, "type")
> summary(shiptype.qvs, digits=4)
Model call:  glm(formula = incidents ~ type + year + period, family = quasipoisson,      data = ships, subset = (service > 0), offset = log(service)) 
Factor name:  type 
      estimate     SE quasiSE quasiVar
    A  0.00000 0.0000  0.2010  0.04039
    B -0.54334 0.2309  0.1127  0.01270
    C -0.68740 0.4279  0.3752  0.14081
    D -0.07596 0.3779  0.3239  0.10490
    E  0.32558 0.3067  0.2322  0.05390
Worst relative errors in SEs of simple contrasts (%):  -0.7 0.9 
Worst relative errors over *all* contrasts (%):  -2.1 1.6 
> plot(shiptype.qvs)
> ## Not run: 
> ##D ##  Example of a negative quasi variance
> ##D ##  Requires the "car" package
> ##D library(car)
> ##D data(Prestige)
> ##D attach(Prestige)
> ##D mymodel <- lm(prestige ~ type + education)
> ##D library(qvcalc)
> ##D type.qvs <- qvcalc(mymodel, "type")
> ##D ##  Warning message: 
> ##D ##  NaNs produced in: sqrt(qv) 
> ##D summary(type.qvs)
> ##D ##  Model call:  lm(formula = prestige ~ type + education) 
> ##D ##  Factor name:  type 
> ##D ##          estimate       SE  quasiSE  quasiVar
> ##D ##    bc    0.000000 0.000000 2.874361  8.261952
> ##D ##    prof  6.142444 4.258961 3.142737  9.876793
> ##D ##    wc   -5.458495 2.690667      NaN -1.022262
> ##D ##  Worst relative errors in SEs of simple contrasts (%):  0 0 
> ##D ##  Worst relative errors over *all* contrasts (%):  0 0
> ##D plot(type.qvs)
> ##D ##  Error in plot.qv(type.qvs) :  No comparison intervals available,
> ##D ##  since one of the quasi variances is negative.  See ?qvcalc for more.
> ## End(Not run) 
> 
> 
> 
> cleanEx(); ..nameEx <- "worstErrors"
> 
> ### * worstErrors
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: worstErrors
> ### Title: Accuracy of a Quasi-variance Approximation
> ### Aliases: worstErrors
> ### Keywords: regression models
> 
> ### ** Examples
> 
> ##  Overdispersed Poisson loglinear model for ship damage data
> ##  from McCullagh and Nelder (1989), Sec 6.3.2
> library(MASS)
> data(ships)
> ships$year <- as.factor(ships$year)
> ships$period <- as.factor(ships$period)
> shipmodel <- glm(formula = incidents ~ type + year + period,
+     family = quasipoisson, 
+     data = ships, subset = (service > 0), offset = log(service))
> shiptype.qvs <- qvcalc(shipmodel, "type")
> summary(shiptype.qvs, digits=4)
Model call:  glm(formula = incidents ~ type + year + period, family = quasipoisson,      data = ships, subset = (service > 0), offset = log(service)) 
Factor name:  type 
      estimate     SE quasiSE quasiVar
    A  0.00000 0.0000  0.2010  0.04039
    B -0.54334 0.2309  0.1127  0.01270
    C -0.68740 0.4279  0.3752  0.14081
    D -0.07596 0.3779  0.3239  0.10490
    E  0.32558 0.3067  0.2322  0.05390
Worst relative errors in SEs of simple contrasts (%):  -0.7 0.9 
Worst relative errors over *all* contrasts (%):  -2.1 1.6 
> worstErrors(shiptype.qvs)
[1] -0.02099427  0.01563661
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  0.47 0.02 0.5 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')