R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- array(list(111
+ ,45
+ ,27
+ ,52
+ ,102
+ ,50
+ ,18
+ ,55
+ ,108
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+ ,24
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+ ,29
+ ,50
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+ ,30
+ ,33
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+ ,26
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+ ,70
+ ,28
+ ,25
+ ,88
+ ,78
+ ,19
+ ,30
+ ,111
+ ,44
+ ,28
+ ,60
+ ,103
+ ,48
+ ,33
+ ,40
+ ,107
+ ,52
+ ,32
+ ,40
+ ,118
+ ,40
+ ,28
+ ,50)
+ ,dim=c(4
+ ,88)
+ ,dimnames=list(c('IQ'
+ ,'Add'
+ ,'MumAge'
+ ,'Grade')
+ ,1:88))
>  y <- array(NA,dim=c(4,88),dimnames=list(c('IQ','Add','MumAge','Grade'),1:88))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
> par3 = 'FALSE'
> par2 = '2'
> par1 = '1'
> ylab = 'Y Variable Name'
> xlab = 'X Variable Name'
> main = 'Title Goes Here'
> par3 <- 'FALSE'
> par2 <- '2'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: aston2
> #To cite this work: Ian E. Holliday, 2012, One-Way-Between-Groups ANOVA (v1.0.4) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/Ian.Holliday/rwasp_One%20Factor%20ANOVA.wasp/
> #Source of accompanying publication: 
> #
> 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] "IQ"
> (V2<-dimnames(y)[[1]][cat2])
[1] "Add"
> 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:
Treatment26  Treatment29  Treatment30  Treatment33  Treatment34  Treatment35  
     129.00       127.00       109.00       106.00       119.00       111.00  
Treatment36  Treatment37  Treatment39  Treatment40  Treatment42  Treatment43  
     117.50       118.00       118.00       110.67       105.00       103.50  
Treatment44  Treatment45  Treatment46  Treatment47  Treatment48  Treatment49  
     111.00       115.50       101.33       102.00        95.50        96.80  
Treatment50  Treatment51  Treatment52  Treatment53  Treatment54  Treatment55  
      98.33        90.00       101.00       108.33        95.00        98.50  
Treatment56  Treatment57  Treatment58  Treatment59  Treatment60  Treatment61  
     102.00        85.50        93.50        81.00        92.67        95.00  
Treatment62  Treatment63  Treatment64  Treatment65  Treatment66  Treatment68  
     101.50        89.00        75.00        93.00        91.00        79.00  
Treatment69  Treatment70  Treatment72  Treatment74  Treatment75  Treatment76  
      90.50        96.00        91.00       102.00        98.00        89.50  
Treatment78  Treatment79  
      88.00        82.00  

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

Terms:
                Treatment Residuals
Sum of Squares     892392      4755
Deg. of Freedom        44        44

Residual standard error: 10.39555
Estimated effects are balanced
> (anova.xdf<-anova(lmxdf) )
Analysis of Variance Table

Response: Response
          Df Sum Sq Mean Sq F value    Pr(>F)    
Treatment 44 892392 20281.6  187.68 < 2.2e-16 ***
Residuals 44   4755   108.1                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> 
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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/fisher/rcomp/tmp/181ks1386028079.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/fisher/rcomp/tmp/2f0nh1386028079.tab") 
> postscript(file="/var/fisher/rcomp/tmp/3rc9l1386028079.ps",horizontal=F,onefile=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){
+ 'Tukey Plot'
+ thsd<-TukeyHSD(aov.xdf)
+ postscript(file="/var/fisher/rcomp/tmp/4xht41386028079.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
+ plot(thsd)
+ dev.off()
+ }
> if(intercept==TRUE){
+ 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/fisher/rcomp/tmp/509xb1386028079.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/fisher/rcomp/tmp/6eb241386028079.tab") 
+ }
> library(car)
Loading required package: MASS
Loading required package: nnet
> lt.lmxdf<-leveneTest(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/fisher/rcomp/tmp/7heyb1386028079.tab") 
> 
> try(system("convert tmp/3rc9l1386028079.ps tmp/3rc9l1386028079.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xht41386028079.ps tmp/4xht41386028079.png",intern=TRUE))
convert: unable to open image `tmp/4xht41386028079.ps':  @ error/blob.c/OpenBlob/2587.
convert: missing an image filename `tmp/4xht41386028079.png' @ error/convert.c/ConvertImageCommand/3011.
character(0)
attr(,"status")
[1] 1
Warning message:
running command 'convert tmp/4xht41386028079.ps tmp/4xht41386028079.png' had status 1 
> 
> 
> proc.time()
   user  system elapsed 
  1.761   0.523   2.254