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(3,0,6,88,8,94,8,90,7,73,5,68,7,80,8,86,9,86,9,91,3,79,9,96,7,92,9,72,8,96,6,70,7,86,8,87,9,88,7,79,6,90,8,95,7,85,7,0,8,90,9,115,9,84,7,79,4,94,7,97,7,86,9,111,7,87,9,98,10,87,5,68,6,88,9,82,9,111,8,75,6,94,6,95,5,80,8,95,8,68,5,94,6,88,9,84,8,0,4,101,8,98,9,78,7,109,7,102,6,81,9,97,9,75,8,97,4,0,6,101,10,101,8,95,7,95,7,0,8,95,3,90,8,107,10,92,7,86,5,70,10,95,5,96,8,91,9,87,6,92,9,97,8,102,5,91,8,68,3,88,7,97,8,90,10,101,9,94,10,101,9,109,8,100,8,103,8,94,9,97,4,85,6,75,7,77,4,87,9,78,7,108,8,97,0,105,8,106,7,107,7,95,9,107,8,115,8,101,9,85,9,90,10,115,7,95,8,97,5,112,9,97,8,77,7,90,8,94,8,103,7,77,6,98,7,90,7,111,6,77,6,88,7,75,9,92,6,78,10,106,4,80,8,87,7,92,10,0,0,111,5,86,9,85,8,90,9,101,8,94,8,86,9,86,8,90,9,75,7,86,6,91,8,97,6,91,5,70,3,98,6,96,8,95,7,100,8,95,6,97,9,97,9,92,10,115,7,88,5,87,8,100,9,98,8,102,8,0,4,96),dim=c(2,160),dimnames=list(c('maternalwarmth','yr7iq'),1:160))
>  y <- array(NA,dim=c(2,160),dimnames=list(c('maternalwarmth','yr7iq'),1:160))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
> par3 = 'TRUE'
> par2 = '2'
> par1 = '1'
> ylab = 'year 7 IQ'
> xlab = 'maternal warmth'
> main = 'maternalwarmth and verbal IQ yr7'
> #'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] "maternalwarmth"
> (V2<-dimnames(y)[[1]][cat2])
[1] "yr7iq"
> 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, data = xdf)

Coefficients:
 (Intercept)  Treatment100  Treatment101  Treatment102  Treatment103  
     6.71429       0.95238       1.42857       0.95238       1.28571  
Treatment105  Treatment106  Treatment107  Treatment108  Treatment109  
    -6.71429       2.28571       1.28571       0.28571       1.28571  
Treatment111  Treatment112  Treatment115   Treatment68   Treatment70  
    -0.46429      -1.71429       2.53571      -0.21429      -1.38095  
 Treatment72   Treatment73   Treatment75   Treatment77   Treatment78  
     2.28571       0.28571       1.08571       0.28571       1.28571  
 Treatment79   Treatment80   Treatment81   Treatment82   Treatment84  
    -1.04762      -1.38095      -0.71429       2.28571       2.28571  
 Treatment85   Treatment86   Treatment87   Treatment88   Treatment90  
     0.53571       0.73016       0.57143      -0.57143       0.48571  
 Treatment91   Treatment92   Treatment94   Treatment95   Treatment96  
     0.08571       1.28571       0.28571       1.01299      -0.31429  
 Treatment97   Treatment98  
     1.36905       0.28571  

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

Terms:
                Treatment Residuals
Sum of Squares   174.0995  382.8755
Deg. of Freedom        36       123

Residual standard error: 1.764315 
Estimated effects may be unbalanced
> (anova.xdf<-anova(lmxdf) )
Analysis of Variance Table

Response: Response
           Df Sum Sq Mean Sq F value  Pr(>F)  
Treatment  36 174.10    4.84  1.5536 0.04006 *
Residuals 123 382.88    3.11                  
---
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$coefficients)+1,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, paste(V1, ' ~ ', V2), length(lmxdf$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/1apzj1260834343.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, V2,,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/2wb941260834343.tab") 
> postscript(file="/var/www/html/rcomp/tmp/3k5561260834343.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 
> if(intercept==TRUE){
+ thsd<-TukeyHSD(aov.xdf)
+ postscript(file="/var/www/html/rcomp/tmp/4gp5d1260834343.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
+ plot(thsd)
+ dev.off()
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Tukey Honest Significant Difference Comparisons', 5,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a, ' ', 1, TRUE)
+ for(i in 1:4){
+ a<-table.element(a,colnames(thsd[[1]])[i], 1, TRUE)
+ }
+ a<-table.row.end(a)
+ for(i in 1:length(rownames(thsd[[1]]))){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(thsd[[1]])[i], 1, TRUE)
+ for(j in 1:4){
+ a<-table.element(a,round(thsd[[1]][i,j], digits=3), 1, FALSE)
+ }
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/5hvyf1260834343.tab") 
+ }
> if(intercept==FALSE){
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'TukeyHSD Message', 1,TRUE)
+ a<-table.row.end(a)
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Must Include Intercept to use Tukey Test ', 1, FALSE)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/6iuhz1260834343.tab") 
+ }
> library(car)
> lt.lmxdf<-levene.test(lmxdf)
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Levenes Test for Homogeneity of Variance', 4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,' ', 1, TRUE)
> for (i in 1:3){
+ a<-table.element(a,names(lt.lmxdf)[i], 1, FALSE)
+ }
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Group', 1, TRUE)
> for (i in 1:3){
+ a<-table.element(a,round(lt.lmxdf[[i]][1], digits=3), 1, FALSE)
+ }
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,' ', 1, TRUE)
> a<-table.element(a,lt.lmxdf[[1]][2], 1, FALSE)
> a<-table.element(a,' ', 1, FALSE)
> a<-table.element(a,' ', 1, FALSE)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/7z51v1260834343.tab") 
> 
> try(system("convert tmp/3k5561260834343.ps tmp/3k5561260834343.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gp5d1260834343.ps tmp/4gp5d1260834343.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  7.811   0.441   8.166