Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
0.46 0.73 0.73 0.52 0.78 0.83 0.73 NA 0.93 0.88 0.75 0.78 0.82 0.56 0.79 0.8 0.89 0.48 NA 0.59 0.65 0.73 0.69 0.75 NA 0.85 0.78 0.39 0.39 0.64 0.55 0.5 0.91 NA 0.37 0.39 0.83 0.72 0.72 0.5 0.57 0.42 0.76 NA 0.82 0.77 0.85 0.87 0.92 0.46 0.72 0.71 0.73 0.69 0.66 0.58 0.39 0.85 0.43 0.72 0.88 0.89 NA NA 0.67 0.44 0.75 0.91 0.57 0.86 0.74 NA 0.62 0.41 0.42 0.63 0.48 0.61 0.82 0.6 0.68 0.76 0.65 0.91 0.89 0.87 0.72 0.89 0.75 0.78 0.54 NA 0.89 0.82 0.65 0.56 0.81 0.76 0.48 0.42 0.74 0.83 0.89 0.74 0.51 0.43 0.77 0.41 NA 0.5 0.77 0.75 0.68 0.71 0.8 NA 0.62 0.41 0.53 0.62 NA 0.54 0.92 NA 0.91 0.63 0.34 0.5 0.94 0.79 0.53 0.77 0.5 0.67 0.73 0.66 0.84 0.83 0.85 NA 0.79 0.79 0.48 0.74 0.73 0.72 0.7 0.55 0.83 0.46 0.76 0.4 0.91 0.84 0.88 0.5 NA 0.66 0.87 0.75 0.71 0.53 0.9 0.93 0.62 0.62 0.51 0.72 0.6 0.47 0.72 0.77 0.72 0.76 0.68 0.48 0.74 0.9 0.83 0.91 0.79 0.67 0.763846 0.66 NA 0.5 0.58 0.49
Data Y:
0.18 0.87 1.14 0.2 NA 1.08 0.89 NA 4.85 4.14 1.25 4.46 6.19 0.26 3.28 2.57 4.43 0.51 NA 0.63 0.67 1.74 2.36 0.91 NA 3.24 2.08 0.12 0.04 NA NA 0.19 5 3.56 0.08 0.01 2.04 2.32 0.67 0.25 0.47 0.07 1.37 0.26 2.21 1.23 2.94 3.42 2.6 NA 1.47 0.86 1.08 1.02 0.84 3.17 0.03 NA 0.07 1.06 NA 2.71 1.58 2.39 0.43 0.21 0.83 3.28 0.43 2.58 NA 2.61 0.7 0.16 0.09 1.25 0.15 0.6 1.9 0.61 0.64 1.72 1.36 3.22 4.59 2.77 1.09 3.69 1.09 4.59 0.2 0.68 4.17 6.89 0.95 0.09 1.66 2.52 0.51 0.14 2.33 2.15 12.65 2.06 0.07 0.07 2.1 0.1 1.73 0.55 1.99 1.74 1.03 2.09 2.13 NA 0.67 0.17 0.09 1.02 NA 0.16 3.23 1.78 2.84 0.45 0.1 0.21 NA 5.8 0.38 1.44 0.35 0.97 0.67 0.34 2.64 2.15 9.57 3.27 1.46 3.87 0.07 3.34 1.56 NA 0.96 0.37 4.21 0.3 1.66 0.07 5.91 2.82 4.27 0 0.07 2.34 2.22 0.52 3.01 0.67 3.88 4.26 0.81 0.13 0.17 1.54 0.06 0.31 0.88 6.89 1.11 1.92 4.13 0.08 1.92 3.14 6.37 5.9 0.98 1.41 2.13 0.79 NA 0.42 0.24 0.53
Chart options
Title:
Label y-axis:
Label x-axis:
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()
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation