R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(111
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+ ,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 = 'TRUE'
> par2 = '4'
> par1 = '1'
> ylab = 'Y Variable Name'
> xlab = 'X Variable Name'
> main = 'Title Goes Here'
> cat1 <- as.numeric(par1) #
> cat2<- as.numeric(par2) #
> intercept<-as.logical(par3)
> x <- t(x)
> xdf<-data.frame(t(y))
> (V1<-dimnames(y)[[1]][cat1])
[1] "IQ"
> (V2<-dimnames(y)[[1]][cat2])
[1] "Grade"
> xdf <- data.frame(xdf[[cat1]], xdf[[cat2]])
> names(xdf)<-c('Y', 'X')
> if(intercept == FALSE) (lmxdf<-lm(Y~ X - 1, data = xdf) ) else (lmxdf<-lm(Y~ X, data = xdf) )

Call:
lm(formula = Y ~ X, data = xdf)

Coefficients:
(Intercept)            X  
    81.2967       0.3796  

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

Terms:
                        X Residuals
Sum of Squares   3852.596 10291.847
Deg. of Freedom         1        86

Residual standard error: 10.9395 
Estimated effects may be unbalanced
> (anova.xdf<-anova(lmxdf) )
Analysis of Variance Table

Response: Y
          Df  Sum Sq Mean Sq F value    Pr(>F)    
X          1  3852.6  3852.6  32.193 1.843e-07 ***
Residuals 86 10291.8   119.7                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
> 
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
> 
> a<-table.start()
> nc <- ncol(sumlmxdf$'coefficients')
> nr <- nrow(sumlmxdf$'coefficients')
> a<-table.row.start(a)
> a<-table.element(a,'Linear Regression Model', nc+1,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, lmxdf$call['formula'],nc+1)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'coefficients:',1,TRUE)
> a<-table.element(a, ' ',nc,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, ' ',1,TRUE)
> for(i in 1 : nc){
+ a<-table.element(a, dimnames(sumlmxdf$'coefficients')[[2]][i],1,TRUE)
+ }#end header
> a<-table.row.end(a)
> for(i in 1: nr){
+ a<-table.element(a,dimnames(sumlmxdf$'coefficients')[[1]][i] ,1,TRUE)
+ for(j in 1 : nc){
+ a<-table.element(a, round(sumlmxdf$coefficients[i, j], digits=3), 1 ,FALSE)
+ }# end cols
+ a<-table.row.end(a)
+ } #end rows
> a<-table.row.start(a)
> a<-table.element(a, '- - - ',1,TRUE)
> a<-table.element(a, ' ',nc,FALSE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Std. Err. ',1,TRUE)
> a<-table.element(a, paste(round(sumlmxdf$'sigma', digits=3), ' on ', sumlmxdf$'df'[2], 'df') ,nc, FALSE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R-sq. ',1,TRUE)
> a<-table.element(a, round(sumlmxdf$'r.squared', digits=3) ,nc, FALSE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-sq. ',1,TRUE)
> a<-table.element(a, round(sumlmxdf$'adj.r.squared', digits=3) ,nc, FALSE)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/16kmy1296325928.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, ' ',1,TRUE)
> a<-table.element(a, 'Df',1,TRUE)
> a<-table.element(a, 'Sum Sq',1,TRUE)
> a<-table.element(a, 'Mean Sq',1,TRUE)
> a<-table.element(a, 'F value',1,TRUE)
> a<-table.element(a, 'Pr(>F)',1,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, V2,1,TRUE)
> a<-table.element(a, anova.xdf$Df[1])
> a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3))
> a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3))
> a<-table.element(a, round(anova.xdf$'F value'[1], digits=3))
> a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residuals',1,TRUE)
> a<-table.element(a, anova.xdf$Df[2])
> a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3))
> a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3))
> a<-table.element(a, ' ')
> a<-table.element(a, ' ')
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/2tkb51296325928.tab") 
> postscript(file="/var/www/html/freestat/rcomp/tmp/3hqjp1296325928.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(Y~ X, data=xdf, xlab=V2, ylab=V1, main='Regression Solution')
> if(intercept == TRUE) abline(coef(lmxdf), col='red')
> if(intercept == FALSE) abline(0.0, coef(lmxdf), col='red')
> dev.off()
null device 
          1 
> library(car)
> postscript(file="/var/www/html/freestat/rcomp/tmp/45g851296325928.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> qq.plot(resid(lmxdf), main='QQplot of Residuals of Fit')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/freestat/rcomp/tmp/5hm9w1296325928.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(xdf$X, resid(lmxdf), main='Scatterplot of Residuals of Model Fit')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/freestat/rcomp/tmp/6o3w01296325928.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot.lm(lmxdf, which=4)
> dev.off()
null device 
          1 
> 
> try(system("convert tmp/3hqjp1296325928.ps tmp/3hqjp1296325928.png",intern=TRUE))
character(0)
> try(system("convert tmp/45g851296325928.ps tmp/45g851296325928.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hm9w1296325928.ps tmp/5hm9w1296325928.png",intern=TRUE))
character(0)
> try(system("convert tmp/6o3w01296325928.ps tmp/6o3w01296325928.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.390   0.864   1.628