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("pcurve-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('pcurve')
Loading required package: mgcv
This is mgcv 1.3-1 
Loading required package: vegan
Loading required package: MASS
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "pca"
> 
> ### * pca
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pca
> ### Title: Principal Component Analysis
> ### Aliases: pca
> ### Keywords: multivariate
> 
> ### ** Examples
> 
>          data(soilspec)
>          species <- sqrt(soilspec[,2:9])
>          specpca <- pca(species)
>          eqscplot(specpca$pcs[,1], specpca$pcs[,2], type = "n",
+               xlab = "Principal component 1",
+               ylab = "Principal component 2") 
>          text(specpca$pcs[,1], specpca$pcs[,2], 
+               soilspec$site) 
>          mtext(paste("Grassland communities in 45 sites"))
> 
> 
> 
> cleanEx(); ..nameEx <- "pcurve"
> 
> ### * pcurve
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pcurve
> ### Title: Principal Curve Analysis
> ### Aliases: pcurve
> ### Keywords: multivariate smooth loess hplot
> 
> ### ** Examples
> 
> #a simulated dataset with 4 response variables (taxa 1-4),
> #n=100.  The response curve is Gaussian and noise is Poisson.
>     data(sim4var)
>     sim4fit <-  pcurve(sim4var, plot.init = FALSE, use.loc = TRUE)

Estimating starting configuration using : CA

You have specified use.loc = TRUE:
To progress to next plot,
left-mouse-click on current plot...
GCV DFs : Penalty =  1
PC B-spline fit DF =  6.86 
Iter  1  --- % dist^2 expl :  90.46   Length :  385.69 
Iter  2  --- % dist^2 expl :  92.52   Length :  433.48 
Iter  3  --- % dist^2 expl :  93.07   Length :  457.79 
Iter  4  --- % dist^2 expl :  93.17   Length :  459.7 
Iter  5  --- % dist^2 expl :  93.22   Length :  460.46 
Iter  6  --- % dist^2 expl :  93.26   Length :  460.99 
Iter  7  --- % dist^2 expl :  93.28   Length :  461.4 
Iter  8  --- % dist^2 expl :  93.28   Length :  461.54 
Iter  9  --- % dist^2 expl :  93.29   Length :  461.68 

% d^2 expl =  93.29 : s^2 =  136.682 : Aprx. dfs =  365.7 

> 
> #Limestone grassland community example worked by De'ath (1999a),
> #from data in Gittins (1985)
>     data(soilspec)
>     species <- sqrt(soilspec[,2:9])
>     envvar <- soilspec[,10:12]
> #indirect gradient analysis
>     spec.fit <- pcurve(species, start = "mds.bc", plot.init = FALSE,
+                        use.loc = TRUE)

Estimating starting configuration using : MDS.BC
Using extended distances 
Too long or NA distances: 0 out of 990 (0.0%)
Stepping across 990 dissimilarities...

You have specified use.loc = TRUE:
To progress to next plot,
left-mouse-click on current plot...
GCV DFs : Penalty =  1
PC B-spline fit DF =  5.08 
Iter  1  --- % dist^2 expl :  67.27   Length :  24.73 
Iter  2  --- % dist^2 expl :  74.22   Length :  27.83 
Iter  3  --- % dist^2 expl :  76.28   Length :  30.89 
Iter  4  --- % dist^2 expl :  76.23   Length :  30.99 
Iter  5  --- % dist^2 expl :  76.19   Length :  31.02 
Iter  6  --- % dist^2 expl :  76.15   Length :  31.02 
Iter  7  --- % dist^2 expl :  76.12   Length :  31.01 
Iter  8  --- % dist^2 expl :  76.1   Length :  31 

% d^2 expl =  76.1 : s^2 =  1.488 : Aprx. dfs =  274.3 

> #direct gradient analysis
>     soilspec.fit <- pcurve(species, xcan = envvar, 
+                            start = "mds.bc", plot.init = FALSE,  
+                            fits = TRUE, prnt.fits = TRUE,
+                            use.loc = TRUE)

Estimating starting configuration using : MDS.BC
Using extended distances 
Too long or NA distances: 0 out of 990 (0.0%)
Stepping across 990 dissimilarities...

You have specified use.loc = TRUE:
To progress to next plot,
left-mouse-click on current plot...
GCV DFs : Penalty =  1
PC B-spline fit DF =  5.08 
Fitting covariates: %var explained >   75.66 
Iter  1  --- % dist^2 expl :  67.22   Length :  21.24 
Fitting covariates: %var explained >   78.42 
Iter  2  --- % dist^2 expl :  68.02   Length :  21.6 
Fitting covariates: %var explained >   76.98 
Iter  3  --- % dist^2 expl :  68.08   Length :  21.64 
Fitting covariates: %var explained >   76.86 
Iter  4  --- % dist^2 expl :  68.08   Length :  21.64 

% d^2 expl =  68.08 : s^2 =  1.987 : Aprx. dfs =  274.3 

  PCA % var Cum PCA %var Cum PC Lengths
1     51.88        51.88          50.78
2     20.52        72.40          91.44
3     10.16        82.56          93.85
4      8.59        91.15          97.22
5      3.44        94.59          98.03
6      2.60        97.19          98.91

 % R2 for smooths : 
  sp1   sp2   sp3   sp4   sp5   sp6   sp7   sp8 
91.55 92.17 66.90 72.90 23.52 47.47 36.85 89.28 

 % Prob (F) for smooths : 
   sp1    sp2    sp3    sp4    sp5    sp6    sp7    sp8 
0.0000 0.0000 0.0961 0.0344 0.9077 0.4898 0.7150 0.0000 
Coefficients:
            Estimate Std. Error t value  Pr(>|t|)
(Intercept)  34.4281   2.2718    15.1545   0.0000
depth        -2.0696   0.3365    -6.1498   0.0000
p           -12.6063   1.7334    -7.2724   0.0000
k             1.4822   1.2441     1.1914   0.2404
Multiple R-Squared: 0.7686 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  2.63 0.06 2.81 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')