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Data X:
1 58 1 51 1 57 1 30 1 46 1 51 1 56 1 58 1 44 1 14 1 53 1 42 0 49 1 44 0 62 1 30 1 46 1 50 1 54 1 48 1 55 1 35 1 55 1 41 1 59 1 54 1 55 1 45 1 51 1 47 1 42 1 53 1 53 1 41 1 55 1 55 1 46 1 63 1 43 1 65 1 59 1 39 1 44 0 60 1 57 0 67 0 52 0 52 1 69 1 46 1 46 0 53 1 40 1 70 1 54 1 77 0 45 1 60 0 47 1 50 1 66 1 60 0 41 0 53 0 34 1 51 1 69 1 60 0 45 1 58 1 39 1 51 1 52 1 49 1 63 0 44 1 51 1 52 0 60 0 53 0 53 1 52 1 31 0 51 0 65 0 51 0 49 1 61 0 58 0 62 1 54 0 52 1 72 0 50 1 65 0 53 1 56 1 63 0 62 0 66 0 50 1 45 0 58 1 52 0 53 1 68 0 59 0 58 0 52 1 45 0 58 1 70 1 69 0 71 1 46 0 58 1 39 0 46 0 64 0 67 0 44 1 54 1 41 1 68 1 63 1 57 1 61 1 39 0 69 0 64 0 38 1 59 1 51 0 59 1 51 1 65 0 47 1 50 0 57 1 21 1 47 0 51 1 37 0 67 0 43 1 58 1 51 1 40 0 41 0 58 0 64 1 64 1 58 0 50 0 59 0 55 0 59 0 58 0 41 1 56 1 63 0 77 1 60 0 58 1 64 1 46 0 62 0 60 1 50 1 46 1 44 1 58 0 56 0 43 0 54 0 54 0 56 0 65 0 66 0 62 1 58 0 67 1 25 1 56 0 53 1 56 1 59 1 46 0 49 0 56 0 76 0 33 1 49 1 53 1 58 0 72 0 51 0 42 0 69 0 51 1 54 1 52 1 59 0 51 0 67 0 64 0 58 1 53
Names of X columns:
V4 V5
Column number of first sample
Column number of second sample
Confidence
Alternative
two.sided
two.sided
less
greater
Are observations paired?
unpaired
unpaired
paired
Null Hypothesis
Chart options
Title:
R Code
par1 <- as.numeric(par1) #column number of first sample par2 <- as.numeric(par2) #column number of second sample par3 <- as.numeric(par3) #confidence (= 1 - alpha) if (par5 == 'unpaired') paired <- FALSE else paired <- TRUE par6 <- as.numeric(par6) #H0 z <- t(y) if (par1 == par2) stop('Please, select two different column numbers') if (par1 < 1) stop('Please, select a column number greater than zero for the first sample') if (par2 < 1) stop('Please, select a column number greater than zero for the second sample') if (par1 > length(z[1,])) stop('The column number for the first sample should be smaller') if (par2 > length(z[1,])) stop('The column number for the second sample should be smaller') if (par3 <= 0) stop('The confidence level should be larger than zero') if (par3 >= 1) stop('The confidence level should be smaller than zero') (r.t <- t.test(z[,par1],z[,par2],var.equal=TRUE,alternative=par4,paired=paired,mu=par6,conf.level=par3)) (v.t <- var.test(z[,par1],z[,par2],conf.level=par3)) (r.w <- t.test(z[,par1],z[,par2],var.equal=FALSE,alternative=par4,paired=paired,mu=par6,conf.level=par3)) (w.t <- wilcox.test(z[,par1],z[,par2],alternative=par4,paired=paired,mu=par6,conf.level=par3)) (ks.t <- ks.test(z[,par1],z[,par2],alternative=par4)) m1 <- mean(z[,par1],na.rm=T) m2 <- mean(z[,par2],na.rm=T) mdiff <- m1 - m2 newsam1 <- z[!is.na(z[,par1]),par1] newsam2 <- z[,par2]+mdiff newsam2 <- newsam2[!is.na(newsam2)] (ks1.t <- ks.test(newsam1,newsam2,alternative=par4)) mydf <- data.frame(cbind(z[,par1],z[,par2])) colnames(mydf) <- c('Variable 1','Variable 2') bitmap(file='test1.png') boxplot(mydf, notch=TRUE, ylab='value',main=main) dev.off() bitmap(file='test2.png') qqnorm(z[,par1],main='Normal QQplot - Variable 1') qqline(z[,par1]) dev.off() bitmap(file='test3.png') qqnorm(z[,par2],main='Normal QQplot - Variable 2') qqline(z[,par2]) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Two Sample t-test (',par5,')',sep=''),2,TRUE) a<-table.row.end(a) if(!paired){ a<-table.row.start(a) a<-table.element(a,'Mean of Sample 1',header=TRUE) a<-table.element(a,r.t$estimate[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Mean of Sample 2',header=TRUE) a<-table.element(a,r.t$estimate[[2]]) a<-table.row.end(a) } else { a<-table.row.start(a) a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE) a<-table.element(a,r.t$estimate) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,'t-stat',header=TRUE) a<-table.element(a,r.t$statistic[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'df',header=TRUE) a<-table.element(a,r.t$parameter[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,r.t$p.value) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'H0 value',header=TRUE) a<-table.element(a,r.t$null.value[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Alternative',header=TRUE) a<-table.element(a,r.t$alternative) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'CI Level',header=TRUE) a<-table.element(a,attr(r.t$conf.int,'conf.level')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'CI',header=TRUE) a<-table.element(a,paste('[',r.t$conf.int[1],',',r.t$conf.int[2],']',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'F-test to compare two variances',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'F-stat',header=TRUE) a<-table.element(a,v.t$statistic[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'df',header=TRUE) a<-table.element(a,v.t$parameter[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,v.t$p.value) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'H0 value',header=TRUE) a<-table.element(a,v.t$null.value[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Alternative',header=TRUE) a<-table.element(a,v.t$alternative) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'CI Level',header=TRUE) a<-table.element(a,attr(v.t$conf.int,'conf.level')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'CI',header=TRUE) a<-table.element(a,paste('[',v.t$conf.int[1],',',v.t$conf.int[2],']',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Welch Two Sample t-test (',par5,')',sep=''),2,TRUE) a<-table.row.end(a) if(!paired){ a<-table.row.start(a) a<-table.element(a,'Mean of Sample 1',header=TRUE) a<-table.element(a,r.w$estimate[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Mean of Sample 2',header=TRUE) a<-table.element(a,r.w$estimate[[2]]) a<-table.row.end(a) } else { a<-table.row.start(a) a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE) a<-table.element(a,r.w$estimate) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,'t-stat',header=TRUE) a<-table.element(a,r.w$statistic[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'df',header=TRUE) a<-table.element(a,r.w$parameter[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,r.w$p.value) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'H0 value',header=TRUE) a<-table.element(a,r.w$null.value[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Alternative',header=TRUE) a<-table.element(a,r.w$alternative) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'CI Level',header=TRUE) a<-table.element(a,attr(r.w$conf.int,'conf.level')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'CI',header=TRUE) a<-table.element(a,paste('[',r.w$conf.int[1],',',r.w$conf.int[2],']',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Wicoxon rank sum test with continuity correction (',par5,')',sep=''),2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'W',header=TRUE) a<-table.element(a,w.t$statistic[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,w.t$p.value) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'H0 value',header=TRUE) a<-table.element(a,w.t$null.value[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Alternative',header=TRUE) a<-table.element(a,w.t$alternative) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Kolmogorov-Smirnov Test to compare <i>Distributions</i> of two Samples',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'KS Statistic',header=TRUE) a<-table.element(a,ks.t$statistic[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,ks.t$p.value) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Kolmogorov-Smirnov Test to compare <i>Distributional Shape</i> of two Samples',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'KS Statistic',header=TRUE) a<-table.element(a,ks1.t$statistic[[1]]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=TRUE) a<-table.element(a,ks1.t$p.value) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable2.tab')
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