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("Rlab-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('Rlab')
Rlab functions and datasets have been loaded.  Use help( Rlab 
) for an overview of this library

Attaching package: 'Rlab'


	The following object(s) are masked from package:stats :

	 dexp dgamma dweibull pexp pgamma pweibull qexp qgamma qweibull rexp rgamma rweibull 


	The following object(s) are masked from package:datasets :

	 precip 

> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "Bernoulli"
> 
> ### * Bernoulli
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Bernoulli
> ### Title: The Bernoulli Distribution
> ### Aliases: Bernoulli dbern pbern qbern rbern
> ### Keywords: distribution
> 
> ### ** Examples
> 
> # Compute P(X=1) for X Bernoulli(0.7)
> dbern(1, 0.7)
[1] 0.7
> 
> 
> 
> cleanEx(); ..nameEx <- "Exponential"
> 
> ### * Exponential
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Exponential
> ### Title: The Exponential Distribution
> ### Aliases: Exponential dexp pexp qexp rexp
> ### Keywords: distribution
> 
> ### ** Examples
> 
> dexp(1) - exp(-1) #-> 0
[1] 0
> 
> 
> 
> cleanEx(); ..nameEx <- "Gamma"
> 
> ### * Gamma
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Gamma
> ### Title: The Gamma Distribution
> ### Aliases: Gamma dgamma pgamma qgamma rgamma
> ### Keywords: distribution
> 
> ### ** Examples
> 
> -log(dgamma(1:4, alpha=1))
[1] 1 2 3 4
> p <- (1:9)/10
> pgamma(qgamma(p,alpha=2), alpha=2)
[1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> 1 - 1/exp(qgamma(p, alpha=1))
[1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
> 
> 
> 
> cleanEx(); ..nameEx <- "US"
> 
> ### * US
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: US
> ### Title: Plot of the US with state boundaries
> ### Aliases: US
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # Draw map in device color # 3 
> US( col=3) 
> 
> 
> 
> cleanEx(); ..nameEx <- "Weibull"
> 
> ### * Weibull
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Weibull
> ### Title: The Weibull Distribution
> ### Aliases: Weibull dweibull pweibull qweibull rweibull
> ### Keywords: distribution
> 
> ### ** Examples
> 
> x <- c(0,rlnorm(50))
> all.equal(dweibull(x, alpha = 1), dexp(x))
[1] TRUE
> all.equal(pweibull(x, alpha = 1, beta = pi), pexp(x, rate = 1/pi))
[1] TRUE
> ## Cumulative hazard H():
> all.equal(pweibull(x, 2.5, pi, lower=FALSE, log=TRUE), -(x/pi)^2.5, tol=1e-15)
[1] TRUE
> all.equal(qweibull(x/11, alpha = 1, beta = pi), qexp(x/11, rate = 1/pi))
[1] TRUE
> 
> 
> 
> cleanEx(); ..nameEx <- "actuator"
> 
> ### * actuator
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: actuator
> ### Title: Actuator force experiment
> ### Aliases: actuator
> ### Keywords: datasets
> 
> ### ** Examples
> 
> bplot(actuator$force,by=actuator$act)
> lplot(actuator$act,actuator$force,actuator$press)
> anova( lm(force ~ (act+press+nozzle+line)^2, data=actuator) )
Analysis of Variance Table

Response: force
             Df  Sum Sq Mean Sq   F value    Pr(>F)    
act           1 0.01358 0.01358   34.9902  0.001967 ** 
press         1 0.55048 0.55048 1418.7390 2.485e-07 ***
nozzle        1 0.00095 0.00095    2.4577  0.177734    
line          1 0.00095 0.00095    2.4538  0.178019    
act:press     1 0.00603 0.00603   15.5408  0.010936 *  
act:nozzle    1 0.00010 0.00010    0.2466  0.640529    
act:line      1 0.00039 0.00039    1.0009  0.363024    
press:nozzle  1 0.00001 0.00001    0.0229  0.885684    
press:line    1 0.00004 0.00004    0.1011  0.763334    
nozzle:line   1 0.00010 0.00010    0.2613  0.631011    
Residuals     5 0.00194 0.00039                        
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> 
> 
> 
> cleanEx(); ..nameEx <- "airplane"
> 
> ### * airplane
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: airplane
> ### Title: Distances flown by paper airplanes in an experiment with four
> ###   treatments.
> ### Aliases: airplane
> ### Keywords: datasets
> 
> ### ** Examples
> 
> # Make side by side boxplots of the four treatments:
> 
> bplot(airplane$distance,airplane$treatment)
> 
> 
> 
> cleanEx(); ..nameEx <- "bplot"
> 
> ### * bplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: bplot
> ### Title: boxplot
> ### Aliases: bplot
> ### Keywords: hplot
> 
> ### ** Examples
> 
> #
> set.seed(123)
> temp<- matrix( rnorm(12*8), ncol=12)
> pos<- c(1:6,9:14)
> bplot(temp)
> #
> bplot( temp, pos=pos, labels=paste( "D",1:12), horizontal=TRUE)
> #
> bplot( temp, pos=pos, label.cex=0, horizontal=TRUE)
> # add an axis
> axis( 2)
> 
> 
> 
> cleanEx(); ..nameEx <- "bplot.xy"
> 
> ### * bplot.xy
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: bplot.xy
> ### Title: Boxplots for conditional distribution
> ### Aliases: bplot.xy
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # bivariate normal corr= .6
> set.seed( 123)
> x<-rnorm( 1000)
> y<- .6*x +  sqrt( 1- .6**2)*rnorm( 1000)
> #
> #
> bplot.xy( x,y, breaks=seq( -3, 3,,15) ,xlim =c(-4,4), ylim =c(-4,4))
> points( x,y, pch=".", col=3)
> 
> 
> 
> cleanEx(); ..nameEx <- "capac"
> 
> ### * capac
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: capac
> ### Title: Capacitance of different shaped capacitors.
> ### Aliases: capac
> ### Keywords: datasets
> 
> ### ** Examples
> 
> # Make a means plot of capacitance by shape and area.
> mplot(capac$capac,capac$shape,capac$area,both=TRUE)
plot window will lay out plots in a 2 by 1 matrix 
> 
> 
> 
> cleanEx(); ..nameEx <- "cavendish"
> 
> ### * cavendish
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cavendish
> ### Title: Cavendish's 1798 determinations of the density of the earth
> ### Aliases: cavendish
> ### Keywords: datasets
> 
> ### ** Examples
> 
> plot(cavendish)
> 
> 
> 
> cleanEx(); ..nameEx <- "count"
> 
> ### * count
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: count
> ### Title: Counts elements which meet specified conditions
> ### Aliases: count
> ### Keywords: univar
> 
> ### ** Examples
> 
> 
> set.seed(1)
> x <- rnorm(100)
> 
> # Count the number of times the values in x are greater then 0
> count( x>0 )
[1] 54
> 
> # Count the number of times the values in x are within the 95
> count( (x>-1.96) & (x<1.96) )
[1] 95
> # Or could have used
> count( abs(x)<1.96 )
[1] 95
> 
> # Count the number of times the values in x are the same as the first element
> count( x==x[1] )
[1] 1
> 
> 
> 
> cleanEx(); ..nameEx <- "drill"
> 
> ### * drill
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: drill
> ### Title: Drill testing results
> ### Aliases: drill
> ### Keywords: datasets
> 
> ### ** Examples
> 
> lplot(drill$brand,drill$price/drill$holes,
+ main='Price per Hole for Drill Bits',ylab='Price per Hole')
> 
> 
> 
> cleanEx(); ..nameEx <- "hplot"
> 
> ### * hplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: hplot
> ### Title: Histogram allowing forced number of bins
> ### Aliases: hplot
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # Create and graph some Normal data
> set.seed(100)
> set.panel(3,1)
plot window will lay out plots in a 3 by 1 matrix 
> z<- rnorm(100)
> hplot(z, nclass=5, main="Standard Normal", xlim=c(-10,10), ylim=c(0,.4))
> z<- rnorm(100, sd=2)
> hplot(z, nclass=10, main="Std Dev of 2", xlim=c(-10,10), ylim=c(0,.4))
> z<- rnorm(100, sd=3)
> hplot(z, nclass=15, main="Std Dev of 3", xlim=c(-10,10), ylim=c(0,.4))
> 
> 
> 
> cleanEx(); ..nameEx <- "jet"
> 
> ### * jet
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: jet
> ### Title: Jet (actuator) force experiment
> ### Aliases: jet
> ### Keywords: datasets
> 
> ### ** Examples
> 
> bplot(jet$force,by=jet$act)
> mplot(jet$force,jet$act,jet$press,both=TRUE)
plot window will lay out plots in a 2 by 1 matrix 
> anova( lm(force ~ act+press+act:press, data=jet) )
Analysis of Variance Table

Response: force
          Df  Sum Sq Mean Sq  F value    Pr(>F)    
act        1 0.01357 0.01357   36.360 5.938e-05 ***
press      1 0.55045 0.55045 1475.279 6.245e-14 ***
act:press  1 0.00603 0.00603   16.170  0.001696 ** 
Residuals 12 0.00448 0.00037                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> 
> 
> 
> cleanEx(); ..nameEx <- "lplot"
> 
> ### * lplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lplot
> ### Title: Label plot
> ### Aliases: lplot
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # Create some Normal data
> set.seed(123)
> temp<- data.frame(matrix(rnorm(12*8), ncol=12))
> pos<- c(1:6,9:14)
> lplot(temp)
> 
> # Now see some labels
> lplot(temp, labels=paste("Y",1:12), tcex=.5)
> 
> # Create a data set with two factors (age and gender)
> race<-data.frame(c(1.02,.99,1.11,1.30,1.09,1.26,1.21,1.19,1.30,1.45,1.34,1.49),
+                  c('M','M','M','M','M','M','F','F','F','F','F','F'),
+                  c('under 50','under 50','under 50','over 50','over 50','over 50',
+                    'under 50','under 50','under 50','over 50','over 50','over 50'))
> names(race)<-c("time","gender","age")
> 
> # Plot the data to see the factors
> lplot(race$gender, race$time, race$age)
> 
> 
> 
> cleanEx(); ..nameEx <- "ls.rlab"
> 
> ### * ls.rlab
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ls.rlab
> ### Title: List of objects in Rlab
> ### Aliases: ls.rlab ls.summary.rlab
> ### Keywords: list
> 
> ### ** Examples
> 
> 
> # list all Rlab datasets and their sizes
> ls.summary.rlab()
        Dataset      Class    Mode Dimension
1     SpGravity data.frame    list   40 x 40
2        US.dat       list    list         3
3      actuator data.frame    list   16 x 16
4      airplane data.frame    list   24 x 24
5          alka data.frame    list     8 x 8
6         bread data.frame    list   48 x 48
7        bread2 data.frame    list     6 x 6
8        cancer data.frame    list   25 x 25
9         capac data.frame    list   15 x 15
10    cavendish    numeric numeric        29
11      climate data.frame    list   50 x 50
12      college data.frame    list   15 x 15
13        draft data.frame    list   12 x 12
14        drill data.frame    list   14 x 14
15       earthq data.frame    list 148 x 148
16     etruscan data.frame    list 154 x 154
17    framerate data.frame    list   36 x 36
18          ftp data.frame    list     6 x 6
19      ftptime    numeric numeric        40
20         golf data.frame    list 195 x 195
21     insulate data.frame    list   56 x 56
22          jet data.frame    list   16 x 16
23       magnet data.frame    list     6 x 6
24     marathon data.frame    list   21 x 21
25     metalcut data.frame    list   18 x 18
26    michelson data.frame    list 100 x 100
27      monarch data.frame    list   72 x 72
28         ncsu data.frame    list   92 x 92
29          oil data.frame    list     8 x 8
30        ozone data.frame    list 518 x 518
31         ping data.frame    list   12 x 12
32      popcorn data.frame    list   16 x 16
33       precip data.frame    list   45 x 45
34        quake data.frame    list 496 x 496
35 raleigh.snow data.frame    list   30 x 30
36 raleigh.temp data.frame    list 480 x 480
37   randomdata    numeric numeric       200
38         salt data.frame    list   24 x 24
39        solar data.frame    list   18 x 18
40       us.age data.frame    list   10 x 10
41       us.pop data.frame    list 100 x 100
42    viscosity data.frame    list   26 x 26
43        vocab data.frame    list   10 x 10
44      webhost data.frame    list   15 x 15
45         wire data.frame    list   27 x 27
46    world.dat       list    list         2
47         yarn data.frame    list   60 x 60
48      yarnred data.frame    list     6 x 6
> 
> # list all Rlab functions
> ls.rlab("functions")
 [1] "US"               "bplot"            "bplot.obj"        "bplot.old"       
 [5] "bplot.xy"         "cat.to.list"      "count"            "dbern"           
 [9] "describe"         "describe.bplot"   "dexp"             "dgamma"          
[13] "draw.bplot"       "draw.bplot.obj"   "dtri"             "dweibull"        
[17] "ex"               "hplot"            "lplot"            "ls.rlab"         
[21] "ls.summary.rlab"  "make.surface"     "means"            "means.2way"      
[25] "message2"         "mfit"             "mfit.2way"        "mklevel"         
[29] "mplot"            "nplot"            "pbern"            "pexp"            
[33] "pgamma"           "ptri"             "pweibull"         "qbern"           
[37] "qexp"             "qgamma"           "qtri"             "qweibull"        
[41] "random.walk"      "rbern"            "read.data"        "rexp"            
[45] "rgamma"           "rweibull"         "set.panel"        "simulate.reg"    
[49] "simulate.samples" "simulate.sums"    "stats"            "stats.bplot"     
[53] "view"             "world"            "write.data"       "xline"           
[57] "yline"           
>  
> 
> 
> cleanEx(); ..nameEx <- "make.surface"
> 
> ### * make.surface
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: make.surface
> ### Title: Creates a surface
> ### Aliases: make.surface
> ### Keywords: manip
> 
> ### ** Examples
> 
> 
> # Generate a 2-dim grid
> seq(-1,1,length=50)->xgrid
> seq(-1.5,.8,length=50)->ygrid
> 
> # Create a surface for x^2-y^2
> make.surface(xgrid,ygrid,"xgrid**2-ygrid**2")->look
xgrid ygrid
Evaluating the formula on the grid formed from x and y
> 
> # Plot it various ways
> image(look)
> contour(look)
> persp(look,theta=45)
> 
> 
> 
> cleanEx(); ..nameEx <- "means"
> 
> ### * means
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: means
> ### Title: Computes one-way and two-way means tables
> ### Aliases: means
> ### Keywords: univar
> 
> ### ** Examples
> 
> 
> # Create a data set with two factors (age and gender)
> race<-data.frame(c(1.02,.99,1.11,1.30,1.09,1.26,1.21,1.19,1.30,1.45,1.34,1.49),
+                  c('M','M','M','M','M','M','F','F','F','F','F','F'),
+                  c('under 50','under 50','under 50','over 50','over 50','over 50',
+                    'under 50','under 50','under 50','over 50','over 50','over 50'))
> names(race)<-c("time","gender","age")
> 
> # Show mean times broken by age, gender and age & gender
> means(race$time, race$age, race$gender)
 
Mean of Y variable race$time by X variable race$age
 
         over 50 under 50
N          6.000    6.000
mean       1.322    1.137
Std.Dev.   0.144    0.119
******************************
Mean of Y variable race$time by X variable race$gender
 
             F     M
N        6.000 6.000
mean     1.330 1.128
Std.Dev. 0.122 0.126
******************************
Note: in the following, if sample sizes are not equal
row and col means will not be the average of cell means.

Mean of Y variable race$time by X variables race$age and race$gender
             F     M Row Mean
over 50  1.427 1.217    1.322
under 50 1.233 1.040    1.137
Col Mean 1.330 1.128    1.229
> 
> 
> 
> cleanEx(); ..nameEx <- "mfit"
> 
> ### * mfit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mfit
> ### Title: Computes main and interaction fitted effects
> ### Aliases: mfit
> ### Keywords: univar
> 
> ### ** Examples
> 
> 
> # Create a data set with two factors (age and gender)
> race<-data.frame(c(1.02,.99,1.11,1.30,1.09,1.26,1.21,1.19,1.30,1.45,1.34,1.49),
+                  c('M','M','M','M','M','M','F','F','F','F','F','F'),
+                  c('under 50','under 50','under 50','over 50','over 50','over 50',
+                    'under 50','under 50','under 50','over 50','over 50','over 50'))
> names(race)<-c("time","gender","age")
> 
> # Show fitted effects for age, gender and age & gender
> means(race$time, race$age, race$gender)
 
Mean of Y variable race$time by X variable race$age
 
         over 50 under 50
N          6.000    6.000
mean       1.322    1.137
Std.Dev.   0.144    0.119
******************************
Mean of Y variable race$time by X variable race$gender
 
             F     M
N        6.000 6.000
mean     1.330 1.128
Std.Dev. 0.122 0.126
******************************
Note: in the following, if sample sizes are not equal
row and col means will not be the average of cell means.

Mean of Y variable race$time by X variables race$age and race$gender
             F     M Row Mean
over 50  1.427 1.217    1.322
under 50 1.233 1.040    1.137
Col Mean 1.330 1.128    1.229
> 
> 
> 
> cleanEx(); ..nameEx <- "michelson"
> 
> ### * michelson
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: michelson
> ### Title: Results from Michelson's determination of the velocity of light
> ###   in air.
> ### Aliases: michelson
> ### Keywords: datasets
> 
> ### ** Examples
> 
> lplot(michelson$velocity,michelson$set)
> bplot(michelson$velocity,michelson$set)
> 
> 
> 
> cleanEx(); ..nameEx <- "mplot"
> 
> ### * mplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mplot
> ### Title: Plots factor means
> ### Aliases: mplot
> ### Keywords: hplot
> 
> ### ** Examples
> 
> 
> # Create a data set with three factors (age, gender and number of water breaks)
> race<-data.frame(c(1.02,.99,1.11,1.30,1.09,1.26,1.21,1.19,1.30,1.45,1.34,1.49),
+                  c('M','M','M','M','M','M','F','F','F','F','F','F'),
+                  c('under 50','under 50','under 50','over 50','over 50','over 50',
+                    'under 50','under 50','under 50','over 50','over 50','over 50'),
+                  c(1,0,2,2,0,1,2,1,0,2,1,0))
> names(race)<-c("time","gender","age","water")
> 
> # Show mean times broken by age, gender and age & gender
> mplot(race$time, race$age, race$gender)
plot window will lay out plots in a 1 by 1 matrix 
> 
> # Show 2 plots, with age and then gender along the x-axis
> mplot(race$time, race$age, race$gender, both=TRUE)
plot window will lay out plots in a 2 by 1 matrix 
> 
> # Now also consider water breaks
> mplot(race$time, race$age, race$gender, race$water, both=TRUE)
plot window will lay out plots in a 3 by 2 matrix 
> 
> # Print the means for the above plots
> means(race$time, race$age, race$gender, race$water)
 
Mean of Y variable race$time by X variable race$age
 
         over 50 under 50
N          6.000    6.000
mean       1.322    1.137
Std.Dev.   0.144    0.119
******************************
Mean of Y variable race$time by X variable race$gender
 
             F     M
N        6.000 6.000
mean     1.330 1.128
Std.Dev. 0.122 0.126
******************************
Mean of Y variable race$time by X variable race$water
 
             0     1     2
N        4.000 4.000 4.000
mean     1.218 1.203 1.268
Std.Dev. 0.223 0.136 0.144
******************************
Note: in the following, if sample sizes are not equal
row and col means will not be the average of cell means.

Mean of Y Variable race$time by X variables race$age and race$gender
 
             F     M Row Mean
over 50  1.427 1.217    1.322
under 50 1.233 1.040    1.137
Col Mean 1.330 1.128    1.229
******************************
Mean of Y Variable race$time by X variables race$age and race$water
 
             0     1     2 Row Mean
over 50  1.290 1.300 1.375    1.322
under 50 1.145 1.105 1.160    1.137
Col Mean 1.218 1.203 1.268    1.229
******************************
Mean of Y Variable race$time by X variables race$gender and race$water
 
             0     1     2 Row Mean
F        1.395 1.265 1.330    1.330
M        1.040 1.140 1.205    1.128
Col Mean 1.218 1.203 1.268    1.229
******************************
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "stats"
> 
> ### * stats
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: stats
> ### Title: Calculate summary statistics
> ### Aliases: stats
> ### Keywords: univar
> 
> ### ** Examples
> 
> #Statistics for 8 normal random samples: 
> zork<- matrix( rnorm(200), ncol=8) 
> stats(zork) 
                     [,1]        [,2]       [,3]        [,4]       [,5]
N              25.0000000 25.00000000 25.0000000 25.00000000 25.0000000
mean            0.1686652  0.03223135  0.1654091  0.06924384  0.1047730
Std.Dev.        0.9501080  0.70628065  1.1051952  0.83071749  0.7834642
min            -2.2146999 -1.47075238 -1.8049586 -1.52356680 -0.9109216
Q1             -0.3053884 -0.39428995 -0.7099464 -0.54252003 -0.4616447
median          0.3898432 -0.05931340  0.1532533  0.07434132 -0.1795565
Q3              0.7821363  0.55666320  0.6107264  0.59394619  0.4941883
max             1.5952808  1.35867955  2.4016178  1.58683345  1.7672873
missing values  0.0000000  0.00000000  0.0000000  0.00000000  0.0000000
                      [,6]        [,7]        [,8]
N              25.00000000 25.00000000 25.00000000
mean           -0.40974386  0.08186525  0.07187332
Std.Dev.        0.94926150  1.04846008  0.98889447
min            -1.91435943 -1.48746031 -1.46725003
Q1             -1.11592011 -0.61924305 -0.76608200
median         -0.46353040 -0.07715294 -0.03472603
Q3              0.01739562  0.45699881  0.83037317
max             2.08716655  2.30797840  2.07524501
missing values  0.00000000  0.00000000  0.00000000
> 
> zork<- rnorm( 200)
> id<- sample( 1:8, 200, replace=TRUE)
> stats( zork, by=id)
                        7          1           8           6           3
N              20.0000000 31.0000000 30.00000000 25.00000000 27.00000000
mean           -0.0870513  0.2785115 -0.08431315 -0.08174414 -0.08016774
Std.Dev.        0.9223204  0.9243022  1.09643708  0.91234158  1.30152812
min            -1.1864586 -1.8697888 -2.88892067 -1.36329126 -2.59232767
Q1             -0.8699710 -0.1324568 -0.86771313 -0.45413691 -0.46497113
median         -0.2668835  0.2441649  0.03369048 -0.25567071  0.17048947
Q3              0.4847286  0.9530747  0.77929008  0.37996269  0.77178164
max             1.8874745  1.8031419  1.58658843  2.49766159  1.97133739
missing values  0.0000000  0.0000000  0.00000000  0.00000000  0.00000000
                         4            5          2
N              24.00000000 16.000000000 27.0000000
mean           -0.14069755  0.118826273  0.3499169
Std.Dev.        1.00722745  0.964964832  0.8439739
min            -2.26488936 -2.129360648 -1.1676623
Q1             -0.64123101 -0.352820471 -0.3492914
median         -0.02606136 -0.001606084  0.3747244
Q3              0.63153533  0.835102364  0.7510879
max             1.31400217  1.577891795  2.6491669
missing values  0.00000000  0.000000000  0.0000000
> 
> 
> 
> cleanEx(); ..nameEx <- "world"
> 
> ### * world
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: world
> ### Title: Plot of the world
> ### Aliases: world
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # Draw map in device color number 2 
> world( col=2 )
> # add the US
> US( add=TRUE )
> 
> ## Western Europe (*which* big island is missing ?)
> ## with a (light color!) coordinate grid:
> 
> world(xlim=c(-10,18),ylim=c(36,60), xaxt = "s", yaxt = "s")
> grid()
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "xline"
> 
> ### * xline
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: xline
> ### Title: Draw a vertical line
> ### Aliases: xline
> ### Keywords: aplot
> 
> ### ** Examples
> 
> 
> plot( 1:10)
> xline( 6.5, col=2)
>  
> world( col=3) 
> yline( seq( -80,80,10),col=4, lty=2)
> xline( seq( -180,180,10),col=4,lty=2)
> yline( 0, lwd=2, col=4)
> 
> 
> 
> cleanEx(); ..nameEx <- "yline"
> 
> ### * yline
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: yline
> ### Title: Draw horizontal lines
> ### Aliases: yline
> ### Keywords: aplot
> 
> ### ** Examples
> 
> world( col=3)
> yline( seq( -80,80,10),col=4, lty=2)
> xline( seq( -180,180,10),col=4,lty=2)
> yline( 0, lwd=2, col=4)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  1.57 0.14 1.7 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')