R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for a HTML browser interface to help.
Type 'q()' to quit R.

> ### * <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("polycor-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('polycor')
Loading required package: mvtnorm
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "hetcor"
> 
> ### * hetcor
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: hetcor
> ### Title: Heterogeneous Correlation Matrix
> ### Aliases: hetcor hetcor.default hetcor.data.frame print.hetcor
> ###   as.matrix.hetcor
> ### Keywords: models
> 
> ### ** Examples
> 
> R <- matrix(0, 4, 4)
> R[upper.tri(R)] <- runif(6)
> diag(R) <- 1
> R <- cov2cor(t(R) %*% R)
> round(R, 4)  # population correlations
       [,1]   [,2]   [,3]   [,4]
[1,] 1.0000 0.2566 0.3073 0.5555
[2,] 0.2566 1.0000 0.5360 0.2618
[3,] 0.3073 0.5360 1.0000 0.6828
[4,] 0.5555 0.2618 0.6828 1.0000
> data <- rmvnorm(1000, rep(0, 4), R)
> round(cor(data), 4)   # sample correlations
       [,1]   [,2]   [,3]   [,4]
[1,] 1.0000 0.2762 0.3585 0.5849
[2,] 0.2762 1.0000 0.5610 0.2877
[3,] 0.3585 0.5610 1.0000 0.6969
[4,] 0.5849 0.2877 0.6969 1.0000
> x1 <- data[,1]
> x2 <- data[,2]
> y1 <- cut(data[,3], c(-Inf, .75, Inf))
> y2 <- cut(data[,4], c(-Inf, -1, .5, 1.5, Inf))
> data <- data.frame(x1, x2, y1, y2)
> hetcor(data)  # Pearson, polychoric, and polyserial correlations, 2-step est.

Two-Step Estimates

Correlations/Type of Correlation:
       x1      x2         y1         y2
x1      1 Pearson Polyserial Polyserial
x2 0.2762       1 Polyserial Polyserial
y1 0.3223  0.5577          1 Polychoric
y2  0.569  0.3062     0.6394          1

Standard Errors:
        x1      x2      y1
x1                        
x2 0.02923                
y1 0.04002 0.03143        
y2 0.02181 0.03021 0.03115

n = 1000 

P-values for Tests of Bivariate Normality:
        x1     x2     y1
x1                      
x2  0.9026              
y1 0.06288 0.8612       
y2  0.7008 0.8767 0.2098
> hetcor(x1, x2, y1, y2, ML=TRUE) # Pearson, polychoric, polyserial correlations, ML est.

Maximum-Likelihood Estimates

Correlations/Type of Correlation:
       x1      x2         y1         y2
x1      1 Pearson Polyserial Polyserial
x2 0.2762       1 Polyserial Polyserial
y1 0.3221  0.5575          1 Polychoric
y2 0.5696  0.3067     0.6394          1

Standard Errors:
        x1      x2     y1
x1                       
x2 0.02923               
y1 0.04008 0.03152       
y2 0.02241 0.03056 0.0323

n = 1000 

P-values for Tests of Bivariate Normality:
        x1     x2     y1
x1                      
x2  0.9026              
y1 0.05947  0.863       
y2  0.7036 0.8861 0.2112
> 
> 
> 
> cleanEx(); ..nameEx <- "polychor"
> 
> ### * polychor
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: polychor
> ### Title: Polychoric Correlation
> ### Aliases: polychor
> ### Keywords: models
> 
> ### ** Examples
> 
> data <- rmvnorm(1000, c(0, 0), matrix(c(1, .5, .5, 1), 2, 2))
> x <- data[,1]
> y <- data[,2]
> cor(x, y)  # sample correlation
[1] 0.50479
> x <- cut(x, c(-Inf, .75, Inf))
> y <- cut(y, c(-Inf, -1, .5, 1.5, Inf))
> polychor(x, y)  # 2-step estimate
[1] 0.4401632
> polychor(x, y, ML=TRUE, std.err=TRUE)  # ML estimate

Polychoric Correlation, ML est. = 0.4401 (0.04075)
Test of bivariate normality: Chisquare = 0.5727, df = 2, p = 0.751

  Row Thresholds
  Threshold Std.Err.
1    0.7513  0.04399


  Column Thresholds
  Threshold Std.Err.
1   -0.9572  0.04695
2    0.4829  0.04132
3    1.4930  0.06070
> 
> 
> 
> cleanEx(); ..nameEx <- "polyserial"
> 
> ### * polyserial
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: polyserial
> ### Title: Polyserial Correlation
> ### Aliases: polyserial
> ### Keywords: models
> 
> ### ** Examples
> 
> data <- rmvnorm(1000, c(0, 0), matrix(c(1, .5, .5, 1), 2, 2))
> x <- data[,1]
> y <- data[,2]
> cor(x, y)  # sample correlation
[1] 0.50479
> y <- cut(y, c(-Inf, -1, .5, 1.5, Inf))
> polyserial(x, y)  # 2-step estimate
[1] 0.484016
> polyserial(x, y, ML=TRUE, std.err=TRUE) # ML estimate

Polyserial Correlation, ML est. = 0.4854 (0.02554)
Test of bivariate normality: Chisquare = 6.185, df = 11, p = 0.8607

                 1       2       3
Threshold -0.95390 0.48070 1.48800
Std.Err.   0.04416 0.03809 0.05816
> 
> 
> 
> cleanEx(); ..nameEx <- "print.polycor"
> 
> ### * print.polycor
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.polycor
> ### Title: Print Method for polycor Objects
> ### Aliases: print.polycor
> ### Keywords: print methods
> 
> ### ** Examples
> 
> data <- rmvnorm(1000, c(0, 0), matrix(c(1, .5, .5, 1), 2, 2))
> x <- data[,1]
> y <- data[,2]
> cor(x, y)  # sample correlation
[1] 0.50479
> x <- cut(x, c(-Inf, .75, Inf))
> y <- cut(y, c(-Inf, -1, .5, 1.5, Inf))
> polychor(x, y, ML=TRUE, std.err=TRUE)  # polychoric correlation, ML estimate

Polychoric Correlation, ML est. = 0.4401 (0.04075)
Test of bivariate normality: Chisquare = 0.5727, df = 2, p = 0.751

  Row Thresholds
  Threshold Std.Err.
1    0.7513  0.04399


  Column Thresholds
  Threshold Std.Err.
1   -0.9572  0.04695
2    0.4829  0.04132
3    1.4930  0.06070
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  8.93 0.45 9.43 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')