R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- array(list(0.175856683,"Silk_Cut",-0.447651543,"Silk_Cut",1.189631467,"Silk_Cut",0.630458094,"Silk_Cut",0.799144649,"Silk_Cut",-1.092828626,"Silk_Cut",-1.238732879,"Silk_Cut",1.16725143,"Silk_Cut",-0.113810912,"Gold_Blend",1.679383017,"Gold_Blend",2.436619868,"Gold_Blend",0.303840895,"Gold_Blend",2.087609483,"Gold_Blend",1.505986604,"Gold_Blend",0.514409262,"Gold_Blend",1.446292098,"Gold_Blend",-0.234668772,"Camel",1.873012882,"Camel",-0.229861698,"Camel",-0.421339844,"Camel",2.09408266,"Camel",-0.206760875,"Camel",-1.821729039,"Camel",2.229756407,"Camel",3.066981458,"Benson",3.212035062,"Benson",3.575401654,"Benson",3.804450001,"Benson",3.311658761,"Benson",3.814650799,"Benson",3.948269992,"Benson",3.141274587,"Benson"),dim=c(2,32),dimnames=list(c('Rating','Fags'),1:32))
>  y <- array(NA,dim=c(2,32),dimnames=list(c('Rating','Fags'),1:32))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
There were 32 warnings (use warnings() to see them)
> par3 = 'FALSE'
> par2 = '2'
> par1 = '1'
> ylab = 'Y Variable Name'
> xlab = 'X Variable Name'
> main = 'Title Goes Here'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Dr. Ian E. Holliday
> #To cite this work: Ian E. Holliday, 2009, STARS Bullying Study (v1.0.1) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/Ian.Holliday/rwasp_STARS_Bullying_Study_alt.wasp/
> #Source of accompanying publication: 
> #Technical description: 
> cat1 <- as.numeric(par1) #
> cat2<- as.numeric(par2) #
> intercept<-as.logical(par3)
> x <- t(x)
> x1<-as.numeric(x[,cat1])
> f1<-as.character(x[,cat2])
> xdf<-data.frame(x1,f1)
> (V1<-dimnames(y)[[1]][cat1])
[1] "Rating"
> (V2<-dimnames(y)[[1]][cat2])
[1] "Fags"
> names(xdf)<-c('Response', 'Treatment')
> if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment - 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment, data = xdf) )

Call:
lm(formula = Response ~ Treatment - 1, data = xdf)

Coefficients:
    TreatmentBenson       TreatmentCamel  TreatmentGold_Blend  
             3.4843               0.4103               1.2325  
  TreatmentSilk_Cut  
             0.1479  

> (aov.xdf<-aov(lmxdf) )
Call:
   aov(formula = lmxdf)

Terms:
                Treatment Residuals
Sum of Squares  110.80010  28.28698
Deg. of Freedom         4        28

Residual standard error: 1.005112 
Estimated effects are balanced
> (anova.xdf<-anova(lmxdf) )
Analysis of Variance Table

Response: Response
          Df  Sum Sq Mean Sq F value    Pr(>F)    
Treatment  4 110.800  27.700  27.419 2.517e-09 ***
Residuals 28  28.287   1.010                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> 
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'ANOVA Model', length(lmxdf$coefficients)+1,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, lmxdf$call['formula'],length(lmxdf$coefficients)+1,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'means',,TRUE)
> for(i in 1:length(lmxdf$coefficients)){
+ a<-table.element(a, round(lmxdf$coefficients[i], digits=3),,FALSE)
+ }
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/1u0711259351469.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'ANOVA Statistics', 5+1,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, ' ',,TRUE)
> a<-table.element(a, 'Df',,FALSE)
> a<-table.element(a, 'Sum Sq',,FALSE)
> a<-table.element(a, 'Mean Sq',,FALSE)
> a<-table.element(a, 'F value',,FALSE)
> a<-table.element(a, 'Pr(>F)',,FALSE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Treatment',,TRUE)
> a<-table.element(a, anova.xdf$Df[1],,FALSE)
> a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3),,FALSE)
> a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3),,FALSE)
> a<-table.element(a, round(anova.xdf$'F value'[1], digits=3),,FALSE)
> a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3),,FALSE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residuals',,TRUE)
> a<-table.element(a, anova.xdf$Df[2],,FALSE)
> a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3),,FALSE)
> a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3),,FALSE)
> a<-table.element(a, ' ',,FALSE)
> a<-table.element(a, ' ',,FALSE)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/2dn1i1259351469.tab") 
> postscript(file="/var/www/html/rcomp/tmp/30b2f1259351469.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> boxplot(Response ~ Treatment, data=xdf, xlab=V2, ylab=V1)
> dev.off()
null device 
          1 
> 
> system("convert tmp/30b2f1259351469.ps tmp/30b2f1259351469.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.390   0.151   0.458