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Data X:
0.815402 0.591772 0.586795 0.806701 0.764876 0.442682 0.413728 0.629126 0.462717 0.897289 0.931093 0.656383 1.04604 0.680635 0.553083 0.755128 0.733062 0.777507 0.800234 0.626868 0.601043 0.676524 0.608236 0.821496 0.628641 0.689699 0.631185 0.75707 0.789816 0.709535 0.684615 0.707059 0.777683 0.607249 0.655898 0.772858 0.762992 0.87721 0.73791 0.773947 0.857709 0.716612 0.735852 0.822517 0.537905 0.698957 0.887378 0.813575 0.725381 0.713147 0.616444 0.356151 0.688407 0.655163 0.583601 0.61734 0.645463 0.573014 0.701581 0.590261 0.741326 0.685607 0.647584 0.722673 0.803082 0.703267 0.674896 0.707468 0.735212 0.694542 0.740589 0.733794 0.774312 0.845874 0.799514 0.85674
Data Y:
309.528 206.39 177.346 258.007 236.255 164.124 145.562 208.19 135.982 325.414 362.083 220.626 438.995 243.908 170.871 253.939 265.279 274.123 267.944 252.335 227.8 239.333 209.089 304.367 189.765 246.378 201.524 292.973 278.276 263.268 213.64 259.621 255.813 213.479 244.207 263.449 324.646 264.598 194.609 220.232 273.889 258.964 255.457 258.949 195.902 291.995 291.867 273.314 222.094 259.031 208.947 164.406 236.442 210.816 238.243 142.692 228.91 172.09 233.642 193.08 246.657 235.303 224.761 240.969 254.253 219.735 242.859 232.824 238.785 287.846 297.121 269.624 280.478 276.454 231.495 294.735
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R Code
library(psychometric) x <- x[!is.na(y)] y <- y[!is.na(y)] y <- y[!is.na(x)] x <- x[!is.na(x)] bitmap(file='test1.png') histx <- hist(x, plot=FALSE) histy <- hist(y, plot=FALSE) maxcounts <- max(c(histx$counts, histx$counts)) xrange <- c(min(x),max(x)) yrange <- c(min(y),max(y)) nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), TRUE) par(mar=c(4,4,1,1)) plot(x, y, xlim=xrange, ylim=yrange, xlab=xlab, ylab=ylab, sub=main) par(mar=c(0,4,1,1)) barplot(histx$counts, axes=FALSE, ylim=c(0, maxcounts), space=0) par(mar=c(4,0,1,1)) barplot(histy$counts, axes=FALSE, xlim=c(0, maxcounts), space=0, horiz=TRUE) dev.off() lx = length(x) makebiased = (lx-1)/lx varx = var(x)*makebiased vary = var(y)*makebiased corxy <- cor.test(x,y,method='pearson', na.rm = T) cxy <- as.matrix(corxy$estimate)[1,1] load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Pearson Product Moment Correlation - Ungrouped Data',3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Statistic',1,TRUE) a<-table.element(a,'Variable X',1,TRUE) a<-table.element(a,'Variable Y',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Mean',header=TRUE) a<-table.element(a,mean(x)) a<-table.element(a,mean(y)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Biased Variance',header=TRUE) a<-table.element(a,varx) a<-table.element(a,vary) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Biased Standard Deviation',header=TRUE) a<-table.element(a,sqrt(varx)) a<-table.element(a,sqrt(vary)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Covariance',header=TRUE) a<-table.element(a,cov(x,y),2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Correlation',header=TRUE) a<-table.element(a,cxy,2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Determination',header=TRUE) a<-table.element(a,cxy*cxy,2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'T-Test',header=TRUE) a<-table.element(a,as.matrix(corxy$statistic)[1,1],2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value (2 sided)',header=TRUE) a<-table.element(a,(p2 <- as.matrix(corxy$p.value)[1,1]),2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value (1 sided)',header=TRUE) a<-table.element(a,p2/2,2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'95% CI of Correlation',header=TRUE) a<-table.element(a,paste('[',CIr(r=cxy, n = lx, level = .95)[1],', ', CIr(r=cxy, n = lx, level = .95)[2],']',sep=''),2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Degrees of Freedom',header=TRUE) a<-table.element(a,lx-2,2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of Observations',header=TRUE) a<-table.element(a,lx,2) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') library(moments) library(nortest) jarque.x <- jarque.test(x) jarque.y <- jarque.test(y) if(lx>7) { ad.x <- ad.test(x) ad.y <- ad.test(y) } a<-table.start() a<-table.row.start(a) a<-table.element(a,'Normality Tests',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('jarque.x'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('jarque.y'),'</pre>',sep='')) a<-table.row.end(a) if(lx>7) { a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('ad.x'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('ad.y'),'</pre>',sep='')) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab') library(car) bitmap(file='test2.png') qqPlot(x,main='QQplot of variable x') dev.off() bitmap(file='test3.png') qqPlot(y,main='QQplot of variable y') dev.off()
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