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("ellipse-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('ellipse')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "ellipse"
> 
> ### * ellipse
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ellipse
> ### Title: Make an ellipse
> ### Aliases: ellipse ellipse.default
> ### Keywords: dplot
> 
> ### ** Examples
> 
> # Plot an ellipse corresponding to a 95% probability region for a
> # bivariate normal distribution with mean 0, unit variances and 
> # correlation 0.8.
> plot(ellipse(0.8), type = 'l')
> 
> 
> 
> cleanEx(); ..nameEx <- "ellipse.arima0"
> 
> ### * ellipse.arima0
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ellipse.arima0
> ### Title: Outline an approximate pairwise confidence region
> ### Aliases: ellipse.arima0
> ### Keywords: dplot ts
> 
> ### ** Examples
> 
> data(USAccDeaths)
> fit <- arima0(USAccDeaths, order = c(0, 1, 1), seasonal = list(order = c(0, 1, 1)))
> # Plot the approximate 95% confidence region for the first two parameters
> # of the model
> plot(ellipse(fit), type = 'l')
> points(fit$coef[1], fit$coef[2])
> 
> 
> 
> cleanEx(); ..nameEx <- "ellipse.glm"
> 
> ### * ellipse.glm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ellipse.glm
> ### Title: Outline an approximate pairwise confidence region
> ### Aliases: ellipse.glm
> ### Keywords: dplot regression
> 
> ### ** Examples
>      
> ## Dobson (1990) Page 93: Randomized Controlled Trial :
> 
>      counts <- c(18,17,15,20,10,20,25,13,12)
>      outcome <- gl(3,1,9)
>      treatment <- gl(3,3)
>      glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())
> 
> # Plot an approximate 95 % confidence region for the two Outcome parameters
> 
>         plot(ellipse(glm.D93, which = c(2,3)), type = 'l')
>         points(glm.D93$coefficients[2], glm.D93$coefficients[3])
> 
> 
> 
> cleanEx(); ..nameEx <- "ellipse.lm"
> 
> ### * ellipse.lm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ellipse.lm
> ### Title: Outline a pairwise confidence region for a linear model fit.
> ### Aliases: ellipse.lm
> ### Keywords: dplot regression
> 
> ### ** Examples
> 
> # Plot the estimate and joint 90% confidence region for the displacement and cylinder
> # count linear coefficients in the mtcars dataset
> data(mtcars)
> fit <- lm(mpg ~ disp + cyl , mtcars)
> plot(ellipse(fit, which = c('disp', 'cyl'), level = 0.90), type = 'l')
> points(fit$coefficients['disp'], fit$coefficients['cyl'])
> 
> 
> 
> cleanEx(); ..nameEx <- "ellipse.nls"
> 
> ### * ellipse.nls
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ellipse.nls
> ### Title: Outline an approximate pairwise confidence region
> ### Aliases: ellipse.nls
> ### Keywords: dplot nonlinear
> 
> ### ** Examples
> 
> # Plot an approximate 95% confidence region for the weight and displacement
> # parameters in the Michaelis Menten model
> data(Puromycin)
> fit <- nls(rate ~ Vm*conc/(K + conc), data = Puromycin, subset = state=="treated", 
+     start = list(K = 0.05, Vm = 200))
> plot(ellipse(fit,which=c('Vm','K')), type = 'l')
> params <- fit$m$getPars()
> points(params['Vm'],params['K'])
> 
> 
> 
> cleanEx(); ..nameEx <- "ellipse.profile"
> 
> ### * ellipse.profile
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ellipse.profile
> ### Title: Pairwise profile sketch
> ### Aliases: ellipse.profile
> ### Keywords: dplot models
> 
> ### ** Examples
> 
> # Plot an approximate 95% confidence region for the Puromycin
> # parameters Vm and K, and overlay the ellipsoidal region
> 
> data(Puromycin)
> Purboth <- nls(formula = rate ~ ((Vm + delV * (state == "treated"))
+   * conc)/(K + conc), data = Puromycin,
+   start = list(Vm = 160, delV = 40, K = 0.05))
> Pur.prof <- profile(Purboth)
> plot(ellipse(Pur.prof, which = c('Vm', 'K')), type = 'l')
> lines(ellipse(Purboth, which = c('Vm', 'K')), lty = 2)
> params <- Purboth$m$getPars()
> points(params['Vm'],params['K'])
> 
> 
> 
> cleanEx(); ..nameEx <- "ellipse.profile.glm"
> 
> ### * ellipse.profile.glm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ellipse.profile.glm
> ### Title: Pairwise profile sketch for GLM profiles
> ### Aliases: ellipse.profile.glm
> ### Keywords: dplot models
> 
> ### ** Examples
> 
> ## MASS has a pairs.profile function that conflicts with ours, so
> ## do a little trickery here
>          noMASS <- is.na(match('package:MASS', search()))
>          if (noMASS) require(MASS)
Loading required package: MASS
[1] TRUE
> 
> ## Dobson (1990) Page 93: Randomized Controlled Trial :
> 
>      counts <- c(18,17,15,20,10,20,25,13,12)
>      outcome <- gl(3,1,9)
>      treatment <- gl(3,3)
>      glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())
> 
> ##  Plot an approximate 95% confidence region for the two outcome variables
>      prof.D93 <- profile(glm.D93)
>      plot(ellipse(prof.D93, which = 2:3), type = 'l')
>      lines(ellipse(glm.D93, which = 2:3), lty = 2)
>      params <- glm.D93$coefficients
>      points(params[2],params[3])
>      
> ##  Clean up our trickery
>          if (noMASS) detach('package:MASS')
>          
> 
> 
> 
> cleanEx(); ..nameEx <- "ellipse.profile.nls"
> 
> ### * ellipse.profile.nls
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ellipse.profile.nls
> ### Title: Pairwise profile sketch
> ### Aliases: ellipse.profile.nls
> ### Keywords: dplot models
> 
> ### ** Examples
> 
> # Plot an approximate 95% confidence region for the Puromycin
> # parameters Vm and K, and overlay the ellipsoidal region
> data(Puromycin)
> Purboth <- nls(formula = rate ~ ((Vm + delV * (state == "treated"))
+   * conc)/(K + conc), data = Puromycin,
+   start = list(Vm = 160, delV = 40, K = 0.05))
> Pur.prof <- profile(Purboth)
> plot(ellipse(Pur.prof, which = c('Vm', 'K')), type = 'l')
> lines(ellipse(Purboth, which = c('Vm', 'K')), lty = 2)
> params <- Purboth$m$getPars()
> points(params['Vm'],params['K'])
> 
> 
> 
> cleanEx(); ..nameEx <- "pairs.profile"
> 
> ### * pairs.profile
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pairs.profile
> ### Title: Profile pairs
> ### Aliases: pairs.profile
> ### Keywords: dplot regression nonlinear
> 
> ### ** Examples
> 
>  # Plot everything for the Puromycin data
>  data(Puromycin)
>  Purboth <- nls(formula = rate ~ ((Vm + delV * (state == "treated"))
+    * conc)/(K + conc), data = Puromycin,
+    start = list(Vm = 160, delV = 40, K = 0.05))
>  Pur.prof <- profile(Purboth)
>  pairs(Pur.prof, plot.ellipse = TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "plotcorr"
> 
> ### * plotcorr
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plotcorr
> ### Title: Plot correlation matrix ellipses
> ### Aliases: plotcorr
> ### Keywords: hplot
> 
> ### ** Examples
> 
> save.par <- par(ask = interactive())
> 
> # Plot the correlation matrix for the mtcars data full model fit 
> data(mtcars)
> fit <- lm(mpg ~ ., mtcars)
> plotcorr(summary(fit, correlation = TRUE)$correlation)
> 
> # Plot a second figure with numbers in place of the
> # ellipses
> plotcorr(summary(fit, correlation = TRUE)$correlation, numbers = TRUE)
> 
> # Colour the ellipses to emphasize the differences
> corr.mtcars <- cor(mtcars)
> ord <- order(corr.mtcars[1,])
> xc <- corr.mtcars[ord, ord]
> plotcorr(xc, col=cm.colors(11)[5*xc + 6])
> 
> plotcorr(xc, col=cm.colors(11)[5*xc + 6], type = "upper")
> plotcorr(xc, col=cm.colors(11)[5*xc + 6], type = "lower", diag = TRUE)
> par(save.par)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  4.4 0.08 4.5 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')