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Data X:
21 12.9 72 149 96 86 13 12 22 12.2 115 139 70 70 8 8 22 12.8 86 148 88 71 14 11 18 7.4 134 158 114 108 16 13 23 6.7 93 128 69 64 14 11 12 12.6 108 224 176 119 13 10 20 14.8 117 159 114 97 15 7 22 13.3 84 105 121 129 13 10 21 11.1 164 159 110 153 20 15 19 8.2 105 167 158 78 17 12 22 11.4 109 165 116 80 15 12 15 6.4 131 159 181 99 16 10 20 10.6 122 119 77 68 12 10 19 12.0 127 176 141 147 17 14 18 6.3 139 54 35 40 11 6 15 11.3 123 91 80 57 16 12 20 11.9 105 163 152 120 16 14 21 9.3 122 124 97 71 15 11 21 9.6 111 137 99 84 13 8 15 10.0 149 121 84 68 14 12 16 6.4 132 153 68 55 19 15 23 13.8 124 148 101 137 16 13 21 10.8 124 221 107 79 17 11 18 13.8 125 188 88 116 10 12 25 11.7 122 149 112 101 15 7 9 10.9 119 244 171 111 14 11 30 16.1 118 148 137 189 14 7 20 13.4 102 92 77 66 16 12 23 9.9 127 150 66 81 15 12 16 11.5 125 153 93 63 17 13 16 8.3 92 94 105 69 14 9 19 11.7 102 156 131 71 16 11 25 9.0 123 132 102 64 15 12 18 9.7 107 161 161 143 16 15 23 10.8 114 105 120 85 16 12 21 10.3 110 97 127 86 10 6 10 10.4 120 151 77 55 8 5 14 12.7 111 131 108 69 17 13 22 9.3 112 166 85 120 14 11 26 11.8 119 157 168 96 10 6 23 5.9 87 111 48 60 14 12 23 11.4 103 145 152 95 12 10 24 13.0 114 162 75 100 16 6 24 10.8 107 163 107 68 16 12 18 12.3 88 59 62 57 16 11 23 11.3 131 187 121 105 8 6 15 11.8 101 109 124 85 16 12 19 7.9 126 90 72 103 15 12 16 12.7 139 105 40 57 8 8 25 12.3 106 83 58 51 13 10 23 11.6 127 116 97 69 14 11 17 6.7 105 42 88 41 13 7 19 10.9 99 148 126 49 16 12 21 12.1 136 155 104 50 19 13 18 13.3 121 125 148 93 19 14 27 10.1 123 116 146 58 14 12 21 5.7 109 128 80 54 15 6 13 14.3 108 138 97 74 13 14 8 8.0 104 49 25 15 10 10 29 13.3 107 96 99 69 16 12 28 9.3 121 164 118 107 15 11 23 12.5 121 162 58 65 11 10 21 7.6 101 99 63 58 9 7 19 15.9 99 202 139 107 16 12 19 9.2 103 186 50 70 12 7 20 9.1 97 66 60 53 12 12 18 11.1 131 183 152 136 14 12 19 13.0 115 214 142 126 14 10 17 14.5 107 188 94 95 13 10 19 12.2 97 104 66 69 15 12 25 12.3 101 177 127 136 17 12 19 11.4 111 126 67 58 14 12 22 8.8 106 76 90 59 11 8 23 14.6 118 99 75 118 9 10 14 12.6 111 139 128 82 7 5 16 13.0 102 162 146 102 15 10 24 12.6 92 108 69 65 12 12 20 13.2 128 159 186 90 15 11 12 9.9 98 74 81 64 14 9 24 7.7 120 110 85 83 16 12 22 10.5 111 96 54 70 14 11 12 13.4 124 116 46 50 13 10 22 10.9 117 87 106 77 16 12 20 4.3 124 97 34 37 13 10 10 10.3 89 127 60 81 16 9 23 11.8 115 106 95 101 16 11 17 11.2 122 80 57 79 16 12 22 11.4 137 74 62 71 10 7 24 8.6 117 91 36 60 12 11 18 13.2 123 133 56 55 12 12 21 12.6 106 74 54 44 12 6 20 5.6 88 114 64 40 12 9 20 9.9 131 140 76 56 19 15 22 8.8 83 95 98 43 14 10 19 7.7 103 98 88 45 13 11 20 9.0 134 121 35 32 16 12 26 7.3 115 126 102 56 15 12 23 11.4 116 98 61 40 12 12 24 13.6 102 95 80 34 8 11 21 7.9 134 110 49 89 10 9 21 10.7 122 70 78 50 16 11 19 10.3 138 102 90 56 16 12 8 8.3 89 86 45 46 10 12 17 9.6 129 130 55 76 18 14 20 14.2 115 96 96 64 12 8 11 8.5 107 102 43 74 16 10 8 13.5 104 100 52 57 10 9 15 4.9 119 94 60 45 14 10 18 6.4 121 52 54 30 12 9 18 9.6 114 98 51 62 11 10 19 11.6 128 118 51 51 15 12 19 11.1 123 99 38 36 7 11 23 4.4 97 48 41 34 16 9 22 12.7 68 50 146 61 16 11 21 18.1 105 150 182 70 16 12 25 17.9 120 154 192 69 16 12 30 16.6 105 109 263 145 12 7 17 12.6 94 68 35 23 15 12 27 17.1 101 194 439 120 14 12 23 19.1 118 158 214 147 15 12 23 16.1 119 159 341 215 16 10 18 13.4 113 67 58 24 13 15 18 18.4 84 147 292 84 10 10 23 14.7 139 39 85 30 17 15 19 10.6 112 100 200 77 15 10 15 12.6 118 111 158 46 18 15 20 16.2 94 138 199 61 16 9 16 13.6 115 101 297 178 20 15 24 18.9 139 131 227 160 16 12 25 14.1 107 101 108 57 17 13 25 14.5 97 114 86 42 16 12 19 16.2 121 165 302 163 15 12 19 14.8 118 114 148 75 13 8 16 14.8 129 111 178 94 16 9 19 12.5 90 75 120 45 16 15 19 12.7 124 82 207 78 16 12 23 17.4 104 121 157 47 17 12 21 8.6 121 32 128 29 20 15 22 18.4 124 150 296 97 14 11 19 16.1 104 117 323 116 17 12 20 11.6 66 71 79 32 6 6 20 17.8 113 165 70 50 16 14 3 15.3 134 154 146 118 15 12 23 17.7 122 126 246 66 16 12 23 16.4 119 149 196 86 16 12 20 17.7 82 145 199 89 14 11 15 13.6 121 120 127 76 16 12 16 14.4 108 109 153 75 16 12 7 14.8 105 132 299 57 16 12 24 18.3 98 172 228 72 14 12 17 9.9 133 169 190 60 14 8 24 16.0 111 114 180 109 16 8 24 18.3 111 156 212 76 16 12 19 16.9 84 172 269 65 15 12 25 14.6 124 68 130 40 16 11 20 13.9 120 89 179 58 16 10 28 19.0 129 167 243 123 18 11 23 15.6 121 113 190 71 15 12 27 14.9 116 115 299 102 16 13 18 11.8 102 78 121 80 16 12 28 18.5 103 118 137 97 16 12 21 15.9 123 87 305 46 17 10 19 17.1 97 173 157 93 14 10 23 16.1 116 2 96 19 18 11 27 19.9 90 162 183 140 9 8 22 11.0 98 49 52 78 15 12 28 18.5 119 122 238 98 14 9 25 15.1 116 96 40 40 15 12 21 15.0 123 100 226 80 13 9 22 11.4 118 82 190 76 16 11 28 16.0 151 100 214 79 20 15 20 18.1 103 115 145 87 14 8 29 14.6 119 141 119 95 12 8 25 15.4 106 165 222 49 15 11 25 15.4 108 165 222 49 15 11 20 17.6 121 110 159 80 15 11 20 13.4 94 118 165 86 16 13 16 19.1 83 158 249 69 11 7 20 15.4 106 146 125 79 16 12 20 7.6 106 49 122 52 7 8 23 13.4 99 90 186 120 11 8 18 13.9 92 121 148 69 9 4 25 19.1 115 155 274 94 15 11 18 15.3 126 104 172 72 16 10 19 12.9 82 147 84 43 14 7 25 16.1 110 110 168 87 15 12 25 17.4 116 108 102 52 13 11 25 13.2 124 113 106 71 13 9 24 12.2 120 115 2 61 12 10 19 12.6 119 61 139 51 16 8 26 10.4 104 60 95 50 14 8 10 15.4 135 109 130 67 16 11 17 9.6 113 68 72 30 14 12 13 18.2 123 111 141 70 15 10 17 13.6 89 77 113 52 10 10 30 14.9 82 73 206 75 16 12 25 14.8 116 151 268 87 14 8 4 14.1 101 89 175 69 16 11 16 14.9 107 78 77 72 12 8 21 16.3 126 110 125 79 16 10 23 19.3 38 220 255 121 16 14 22 13.6 109 65 111 43 15 9 17 13.6 122 141 132 58 14 9 20 15.7 110 117 211 57 16 10 20 12.8 125 122 92 50 11 13 22 14.6 108 63 76 69 15 12 16 9.9 122 44 171 64 18 13 23 12.7 74 52 83 38 13 8 0 19.2 114 131 266 90 7 3 18 16.6 118 101 186 96 7 8 25 11.2 114 42 50 49 17 12 23 15.3 123 152 117 56 18 11 12 11.9 130 107 219 102 15 9 18 13.2 129 77 246 40 8 12 24 16.4 127 154 279 100 13 12 11 12.4 92 103 148 67 13 12 18 15.9 90 96 137 78 15 10 23 18.2 125 175 181 55 18 13 24 11.2 108 57 98 59 16 9 29 15.7 112 112 226 96 14 12 18 17.8 132 143 234 86 15 11 15 7.7 130 49 138 38 19 14 29 12.4 107 110 85 43 16 11 16 15.6 112 131 66 23 12 9 19 19.3 115 167 236 77 16 12 22 15.2 93 56 106 48 11 8 16 17.1 118 137 135 26 16 15 23 15.6 128 86 122 91 15 12 23 18.4 132 121 218 94 19 14 19 19.1 113 149 199 62 15 12 4 18.6 125 168 112 74 14 9 20 19.1 108 140 278 114 14 9 24 13.1 143 88 94 52 17 13 20 12.9 119 168 113 64 16 13 4 9.5 117 94 84 31 20 15 24 4.5 113 51 86 38 16 11 22 11.9 121 48 62 27 9 7 16 13.6 127 145 222 105 13 10 3 11.7 59 66 167 64 15 11 15 12.4 129 85 82 62 19 14 24 13.4 136 109 207 65 16 14 17 11.4 130 63 184 58 17 13 20 14.9 117 102 83 76 16 12 27 19.9 89 162 183 140 9 8 26 11.2 99 86 89 68 11 13 23 14.6 131 114 225 80 14 9 17 17.6 96 164 237 71 19 12 20 14.1 87 119 102 76 13 13 22 16.1 126 126 221 63 14 11 19 13.4 126 132 128 46 15 11 24 11.9 124 142 91 53 15 13 19 12.0 115 83 198 74 14 12 23 14.8 110 94 204 70 16 12 15 15.2 63 81 158 78 17 10 27 13.2 90 166 138 56 12 9 26 16.9 121 110 226 100 15 10 22 7.9 126 64 44 51 17 13 22 7.7 140 93 196 52 15 13 18 12.6 116 104 83 102 10 9 15 7.9 92 105 79 78 16 11 22 11.0 87 49 52 78 15 12 27 12.4 124 88 105 55 11 8 10 10.0 81 95 116 98 16 12 20 14.9 116 102 83 76 16 12 17 16.7 113 99 196 73 16 12 23 13.4 101 63 153 47 14 9 19 14.0 83 76 157 45 14 12 13 15.7 152 109 75 83 16 12 27 16.9 129 117 106 60 16 11 23 11.0 127 57 58 48 18 12 16 15.4 129 120 75 50 14 6 25 12.2 118 73 74 56 20 7 2 15.1 120 91 185 77 15 10 26 17.8 123 108 265 91 16 12 20 15.2 121 105 131 76 16 10 23 14.6 94 117 139 68 16 12 22 16.7 120 119 196 74 12 9 24 8.1 95 31 78 29 8 3
Names of X columns:
Numeracy Examen Motivatie LFM B H Confstat Confsoft
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
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
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2
3
4
5
6
7
8
9
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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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6)) a<-table.element(a, signif(mysum$coefficients[i,2],6)) a<-table.element(a, signif(mysum$coefficients[i,3],4)) a<-table.element(a, signif(mysum$coefficients[i,4],6)) a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, signif(sqrt(mysum$r.squared),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, signif(mysum$r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, signif(mysum$adj.r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[1],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) 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, signif(mysum$sigma,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, signif(sum(myerror*myerror),6)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,signif(x[i],6)) a<-table.element(a,signif(x[i]-mysum$resid[i],6)) a<-table.element(a,signif(mysum$resid[i],6)) 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,signif(gqarr[mypoint-kp3+1,1],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) 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,signif(numsignificant1,6)) a<-table.element(a,signif(numsignificant1/numgqtests,6)) 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,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) 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,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) 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
1 seconds
R Server
Big Analytics Cloud Computing Center
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