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Data X:
46.8 46.6 39.8 45.5 59.9 47.9 19.9 39.3 28.2 40 47.9 48.3 36.7 45.3 61.6 46.8 19.6 40.3 28.9 41 48.5 51.3 35.4 46.7 62.6 49 19.6 32.4 30 32.6 49.7 48.3 37.1 47.3 62.8 49.6 20.6 32.7 32.8 32.7 48 44.9 37.9 46.5 61.5 48.9 20.1 34.5 33.6 34.6 48.2 43.9 36.8 44.8 60 46.6 19.8 32.4 34 32.3 47.3 43.2 35.8 44.1 58.5 46.1 19.6 33.1 34.3 33.1 46.6 44.6 36.7 43.9 55.7 47.2 19.7 34.9 34.8 34.9 45.6 46.5 34.6 42.3 53.8 45 19.9 34.1 35 34.1 47.7 50.4 33.2 40.9 52.4 43.1 20.1 31.9 34.6 31.7 48.1 50 32.9 39.8 51.2 41.7 20 32.7 34.5 32.6 47.6 47.6 32 38.9 50.1 40.4 20 32.5 34.1 32.4 46.3 45.2 31.6 37.8 49.5 38.7 19.9 27.2 32.9 26.9 46.1 46.5 33.8 38.4 49.2 40.1 19.5 24.3 32.4 23.8 46.7 45.2 34.5 38.3 47.8 40.7 19.5 24 30.2 23.7 47.1 45.4 34.7 38.9 48.6 41.8 18.9 24.7 28.9 24.5 46.7 44.8 34.8 39.4 49.8 41.9 19.2 25.6 29.8 25.3 46.3 46.7 37.2 40.6 51.2 43.3 19.5 30.1 30.1 30.1 45.9 47.4 37.2 40.3 51 42.7 20 32.1 29.5 32.3 46.6 48.9 36.2 40.1 50.9 41.9 20.8 32.3 28.5 32.5 49 51.1 35.9 40.9 52.3 42.6 21.3 31 27.9 31.2 54.1 55.6 37.4 41 53.5 42.1 21.6 32.2 26.2 32.5 59.2 55.5 37.2 39.9 53.1 40.2 21.6 33.2 24.6 33.8 63.8 55 41.3 40.2 53.6 40.4 21.6 35.2 25.1 35.8 62.5 52.6 43.2 40.3 53.4 40.7 21.5 34.2 25.6 34.7 59.5 54.1 42.1 41.6 54.4 42.8 21.6 31 27.1 31.2 56.9 53.9 42.4 41.8 54.7 43 21.7 34.1 27.3 34.6 54.4 54.1 44.6 43.2 56.9 43.6 23.7 37.8 27.4 38.5 54.7 55 46 44.8 59.1 45.1 24.9 40.6 27.3 41.4 53.3 54.4 41.8 45.1 61.4 44.1 25.4 37.5 26.8 38.1 52.9 56.5 41.4 44.3 62.1 42.3 25.3 31.8 26.1 32.2 54.8 59.6 40.7 45.4 63.2 44.2 24.5 32.4 25.9 32.8 52.6 59.1 37.2 45.5 63.1 44.9 23.3 34.6 26.7 35.1 49.1 55.7 38.4 45.6 61.9 45.8 23.4 35.6 27.5 36.1 52.6 54.3 38.7 46.3 61.9 46.7 24.4 37 28.4 37.5 53.6 58.9 39.6 46.6 63.1 46.3 25.2 33.8 30 34 52.7 68.6 37.6 49.2 66.6 49.1 26.2 36.2 31.1 36.5 55.8 71.8 37 50.4 67.4 50.9 26.3 36.6 33.6 36.8 57.9 71.8 38.6 51.7 65.9 53.7 27.3 37.8 38.3 37.8 60.6 76.7 41.2 54.7 67.8 56.8 31.8 39.8 42.9 39.6 61.9 79.9 41.2 57.6 69.1 60.2 35.5 39.7 44.3 39.4 65.5 90.4 41.9 58.2 70 60 38.1 42.8 53 42.1 67.5 92.1 40.9 58 69.9 60.7 35.5 43.4 55.6 42.6 65.5 88.5 41.2 55.6 70.7 56.7 32.5 47.8 58.8 47.1 62.2 81.8 43.1 56.6 71.2 59 31.1 46.3 64.4 45.2 55.5 74.5 42.7 58.2 71 60.9 34.4 48.6 63.3 47.8 52.3 58.7 43.3 57.9 69.4 59.9 37.1 53.1 58.7 52.8 52.5 54.6 44 58.3 70.1 60.6 36.6 52.7 57.4 52.4 50.8 51.8 44.1 60.1 69.4 64 37.9 59 55.3 59.2 50.9 52.1 47.3 61.2 69.7 64.3 41.9 53.9 53.3 54 51.5 52.8 51 60.9 69.3 65.1 39 49.7 53.2 49.5 51.1 51.9 50.9 63.9 71.6 65.5 49.3 54.3 50.9 54.5 51.1 52.7 55.1 65.4 73.6 67.4 49.5 55.9 48.4 56.3 54.3 60.8 60.5 67.4 74.8 70.7 49.3 63.9 51.8 64.6 51.9 60.3 57.5 66.1 74.1 68.6 49.2 64 51.6 64.7 52.4 61.8 57.6 62.5 70.7 65.2 44.6 60.7 52.4 61.2 53.4 66.6 56.9 61.3 67.8 65.6 41.8 67.8 52.9 68.7 56 66.1 54.1 61.7 69.1 65.8 41.5 70.5 52.8 71.5 53.4 60.6 53.3 64.4 69.5 68.9 46.4 76.6 48 78.3 53.8 56.2 51.8 65 69.6 68.5 50.1 76.2 46.3 78.1 53.8 55.2 53.6 65.8 71.4 70.6 46.1 71.8 42 73.6 51.6 55.7 54.8 67.6 70.4 74.1 47.7 67.8 38.6 69.6 54.2 58 57.3 71.3 71.1 81.1 47.2 69.7 41.3 71.5 55.7 57.1 65 75.5 74 85.8 51.9 76.7 45.3 78.7 59.2 56.8 65.3 77.8 75.2 89.3 52.6 74.2 49.9 75.7 59.8 56.1 62.9 78.1 75 89.6 53.7 75.8 53.8 77.1 61.6 55.5 63.5 85 75.1 102.6 54.8 84.3 55.1 86.1 65.8 57 62.1 93.7 78 117.7 55.4 84.9 52.9 86.8 64.2 57 59.3 87.4 80 104.1 55.9 84.4 53.5 86.3 67 56.5 61.6 91.8 82.4 111 56.8 89.4 53.8 91.5 62.8 54.2 61.5 91.9 82.4 112.2 54.1 88.5 52 90.7 65.5 53.9 60.1 90.9 79.7 112 53.2 76.5 48.2 78.2 75.2 58 59.5 94.5 80.6 118.4 53.7 71.4 45.5 73 80.9 64.1 62.7 94.5 80.9 118.4 53.5 72.1 45.7 73.7 83.2 63.4 65.5 96.8 84.1 120.8 53.9 75.8 52.5 77.3 83.7 67.2 63.8 96.6 89.1 116.1 58.4 66.6 52.3 67.5 86.4 72 63.8 98.3 90.6 118 59.3 71.7 54.8 72.7 85.9 72.1 62.7 101.6 90.7 122.7 63.9 75.4 54.7 76.6 80.4 70 62.3 107.7 94.6 132.4 64 80.9 54.9 82.4 81.8 73.9 62.4 108.1 95.4 133.7 61.5 80.7 54.9 82.3 87.5 79.1 64.8 102.6 94.1 124.2 60.5 85 64.2 86.3 83.7 81.1 66.4 103 96.2 123.7 60.8 91.5 66.4 93 87 81.1 65.1 97.3 95.5 113.2 60.3 87.7 69.1 88.8 99.7 90.3 67.4 96.2 94.3 111.5 61.1 95.3 68.3 96.9 101.4 94.2 68.8 98.5 94.2 115.7 61.3 102.4 77.3 103.9 101.9 100.8 68.6 96.2 95.1 111 61 114.2 89.6 115.7 115.7 108.4 71.5 92.7 96.6 103.1 61.4 111.7 93 112.8 123.2 119.6 75 101.3 95.9 107.3 93.6 113.7 96.1 114.7 136.9 128.8 84.3 106.9 94 118.9 94.3 118.8 131.3 118 146.8 127.9 84 112.6 94.9 128.3 97.3 129 125.3 129.3 149.6 123.9 79.1 113.2 95.2 126.5 104.6 136.4 126 137 146.5 125.4 78.8 112.9 95.8 121.7 113.8 155 138.3 156 157 139.5 82.7 112.3 97.9 119.5 113.8 166 163 166.2 147.9 139 85.3 113.2 98.3 121.4 113.1 168.7 182.5 167.8 133.6 117.3 84.5 105 93.7 110.3 107.2 145.5 164.6 144.3 128.7 108.1 80.8 96.7 91.7 101.5 91.6 127.3 148.8 126 100.8 85.4 70.1 79.2 80.1 78.5 79.7 91.5 109.3 90.4 91.8 82.8 68.2 69.4 71.1 66.8 73.7 69 93.5 67.5 89.3 80 68.1 63.1 67.1 55.2 77.4 54 80.2 52.4 96.7 92.6 72.3 60.1 66 55.3 63.9 56.3 84 54.6 91.6 88.2 73.1 58.2 62.9 53.1 64.5 54.2 75.5 52.9 93.3 85.2 71.5 59.2 62.9 55.2 64.1 59.3 62.4 59.1 93.3 96.5 74.1 62.6 63.9 61.2 64.5 63.4 64.2 63.3 101 109.1 80.3 65.1 66.2 64.1 65.9 73.3 64.7 73.8 100.4 115.6 80.6 69.4 69.5 70.4 66.8 86.7 71 87.6 86.9 100 81.4 72.3 72.8 73.6 68.7 81.3 73.7 81.8 83.9 101.8 87.4 80.6 78 86.2 70.2 89.6 72.6 90.7 80.3 88.8 89.3 80.4 82.2 83.5 70.6 85.3 68.1 86.3 87.7 90.9 93.2 82.6 86.3 85.9 69.6 92.4 72.3 93.6 92.7 95.7 92.8 85.1 90.3 88.6 69.2 96.8 78.5 98 95.5 97.7 96.8 89.3 92.2 95.2 70.7 93.6 81.9 94.3 92 93.4 100.3 91.8 93.1 99.5 70.7 97.6 97.8 97.6 87.4 89.7 95.6 87.9 92.4 92.2 71 94.2 93.1 94.2 86.8 89.7 89 93.5 96 100.7 72.1 99.9 94.2 100.2 83.7 92.5 87.4 103.7 99.6 106.6 101.7 106.4 101.1 106.7 85 90.9 86.7 96.4 96.6 93.6 103 96 101 95.7 81.7 91.7 92.8 93.1 96.3 87.3 103.1 94.9 99.7 94.6 90.9 95.8 98.6 98 98.7 90.1 116.5 94.8 97.1 94.7 101.5 99.5 100.8 102.1 100.4 97.1 116.9 95.9 91.7 96.2 113.8 101.5 105.5 104.9 102 101.3 117.6 96.2 95 96.3 120.1 110.6 107.8 108.6 107.1 109.5 108.3 103.1 98.9 103.3 122.1 119 113.7 108.6 108.2 109.2 107.7 106.9 109 106.8 132.5 124.5 120.3 110.9 108.6 113 108.8 114.2 121.9 113.7 140 133.4 126.5 116.5 112.9 118.2 117.1 118.2 131.5 117.4 149.4 133.3 134.8 120.2 116.4 123.1 118.3 123.9 128.5 123.6 144.3 127.8 134.5 118.9 114.1 121.5 118.8 137.1 128.4 137.6 154.4 127.3 133.1 124.7 118.1 123.8 135.8 146.2 126.4 147.4 151.4 127.6 128.8 120.5 115 117.8 134.6 136.4 123.1 137.2 145.5 126.3 127.1 119.9 116 116 134.8 133.2 123 133.8 136.8 125.1 129.1 120.5 114.1 119.1 132.7 135.9 123.3 136.7 146.6 124.7 128.4 117.3 113.5 112.1 135.7 127.1 123.6 127.3 145.1 121.9 126.5 113.1 110 105.4 136.2 128.5 124.9 128.7 133.6 111.2 117.1 104 105.1 97 120 126.6 120.4 127 131.4 108.5 114.2 99.2 97.7 95.2 111.3 132.6 114.9 133.7 127.5 105.9 109.1 97.6 94 94.1 111 130.9 113.4 132 130.1 111.9 110.3 100.9 93.8 99.8 113 134.1 117.6 135.1 131.1 116.6 109.2 103.8 96.5 103.9 113.2 141.1 117.4 142.6 132.3 125.6 103.6 103.7 97.7 102.4 115.1 147 109 149.3 128.6 133.7 98.9 101.8 98.2 98 116.1 141.3 105.6 143.5 125.1 132.6 95.9 98 95.4 94.8 109.6 129.6 99 131.4 128.7 132 91.2 93.4 90.4 89.4 107.3 113.3 89.8 114.7 156.1 150.4 98.7 92.3 88.9 89.6 103.4 120.5 90.9 122.3 163.2 155.7 94.5 88.5 86.8 88.2 91.6 131.2 93.6 133.4 159.8 153.4 95.6 92.2 88.1 97 85.7 132.1 91.2 134.6 157.4 141.1 93.8 93 89.6 94.9 92.9 128.3 85.4 130.9
Names of X columns:
Graan Oliezaden suiker Totaal Plantaardig Ijzererts schroot Totaal Steenkool aardolie
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }
Compute
Summary of computational transaction
Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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