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Data X:
1 0 0 21 149 86 1.8 12.9 1 0 1 22 139 70 2.1 12.2 1 0 0 22 148 71 2.2 12.8 1 0 1 18 158 108 2.3 7.4 1 0 1 23 128 64 2.1 6.7 1 0 1 12 224 119 2.7 12.6 1 0 0 20 159 97 2.1 14.8 1 0 1 22 105 129 2.4 13.3 1 0 1 21 159 153 2.9 11.1 1 0 1 19 167 78 2.2 8.2 1 0 1 22 165 80 2.1 11.4 1 0 1 15 159 99 2.2 6.4 1 0 1 20 119 68 2.2 10.6 1 0 0 19 176 147 2.7 12.0 1 0 0 18 54 40 1.9 6.3 1 1 0 15 91 57 2.0 11.3 1 0 1 20 163 120 2.5 11.9 1 0 0 21 124 71 2.2 9.3 1 1 1 21 137 84 2.3 9.6 1 0 0 15 121 68 1.9 10.0 1 0 1 16 153 55 2.1 6.4 1 0 1 23 148 137 3.5 13.8 1 0 0 21 221 79 2.1 10.8 1 0 1 18 188 116 2.3 13.8 1 0 1 25 149 101 2.3 11.7 1 0 1 9 244 111 2.2 10.9 1 1 1 30 148 189 3.5 16.1 1 1 0 20 92 66 1.9 13.4 1 0 1 23 150 81 1.9 9.9 1 0 0 16 153 63 1.9 11.5 1 0 0 16 94 69 1.9 8.3 1 0 0 19 156 71 2.1 11.7 1 0 1 25 132 64 2.0 9.0 1 0 1 18 161 143 3.2 9.7 1 0 1 23 105 85 2.3 10.8 1 0 1 21 97 86 2.5 10.3 1 0 0 10 151 55 1.8 10.4 1 1 1 14 131 69 2.4 12.7 1 0 1 22 166 120 2.8 9.3 1 0 0 26 157 96 2.3 11.8 1 0 1 23 111 60 2.0 5.9 1 0 1 23 145 95 2.5 11.4 1 0 1 24 162 100 2.3 13.0 1 0 1 24 163 68 1.8 10.8 1 1 1 18 59 57 1.9 12.3 1 0 0 23 187 105 2.6 11.3 1 0 1 15 109 85 2.0 11.8 1 1 1 19 90 103 2.6 7.9 1 0 0 16 105 57 1.6 12.7 1 1 1 25 83 51 2.2 12.3 1 1 1 23 116 69 2.1 11.6 1 1 1 17 42 41 1.8 6.7 1 0 1 19 148 49 1.8 10.9 1 1 1 21 155 50 1.9 12.1 1 0 1 18 125 93 2.4 13.3 1 0 1 27 116 58 1.9 10.1 1 1 0 21 128 54 2.0 5.7 1 0 1 13 138 74 2.1 14.3 1 1 0 8 49 15 1.7 8.0 1 1 1 29 96 69 1.9 13.3 1 0 1 28 164 107 2.1 9.3 1 0 0 23 162 65 2.4 12.5 1 0 0 21 99 58 1.8 7.6 1 0 1 19 202 107 2.3 15.9 1 0 0 19 186 70 2.1 9.2 1 1 1 20 66 53 2.0 9.1 1 0 0 18 183 136 2.8 11.1 1 0 1 19 214 126 2.0 13.0 1 0 1 17 188 95 2.7 14.5 1 1 0 19 104 69 2.1 12.2 1 0 0 25 177 136 2.9 12.3 1 0 0 19 126 58 2.0 11.4 1 1 0 22 76 59 1.8 8.8 1 1 1 23 99 118 2.6 14.6 1 0 0 14 139 82 2.1 12.6 1 0 1 28 78 50 2.3 NA 1 0 0 16 162 102 2.3 13.0 1 1 1 24 108 65 2.2 12.6 1 0 0 20 159 90 2.0 13.2 1 1 0 12 74 64 2.2 9.9 1 0 1 24 110 83 2.1 7.7 1 1 0 22 96 70 2.1 10.5 1 1 0 12 116 50 1.9 13.4 1 1 0 22 87 77 2.0 10.9 1 1 1 20 97 37 1.7 4.3 1 1 0 10 127 81 2.2 10.3 1 1 1 23 106 101 2.2 11.8 1 1 1 17 80 79 2.3 11.2 1 1 0 22 74 71 2.4 11.4 1 1 0 24 91 60 2.1 8.6 1 1 0 18 133 55 1.9 13.2 1 1 1 21 74 44 1.7 12.6 1 1 1 20 114 40 1.8 5.6 1 1 1 20 140 56 1.5 9.9 1 1 0 22 95 43 1.9 8.8 1 1 1 19 98 45 1.9 7.7 1 1 0 20 121 32 1.7 9.0 1 1 1 26 126 56 1.9 7.3 1 1 1 23 98 40 1.9 11.4 1 1 1 24 95 34 1.8 13.6 1 1 1 21 110 89 2.4 7.9 1 1 1 21 70 50 1.8 10.7 1 1 0 19 102 56 1.9 10.3 1 1 1 8 86 46 1.8 8.3 1 1 1 17 130 76 2.1 9.6 1 1 1 20 96 64 1.9 14.2 1 1 0 11 102 74 2.2 8.5 1 1 0 8 100 57 2.0 13.5 1 1 0 15 94 45 1.7 4.9 1 1 0 18 52 30 1.7 6.4 1 1 0 18 98 62 1.8 9.6 1 1 0 19 118 51 1.9 11.6 1 1 1 19 99 36 1.8 11.1 0 0 1 23 48 34 1 4.35 0 0 1 22 50 61 1 12.7 0 0 1 21 150 70 4 18.1 0 0 1 25 154 69 4 17.85 0 1 0 30 109 145 3 16.6 0 1 1 17 68 23 2 12.6 0 0 1 27 194 120 4 17.1 0 0 0 23 158 147 4 19.1 0 0 1 23 159 215 4 16.1 0 0 0 18 67 24 2 13.35 0 0 0 18 147 84 4 18.4 0 0 1 23 39 30 1 14.7 0 0 1 19 100 77 3 10.6 0 0 1 15 111 46 3 12.6 0 0 1 20 138 61 4 16.2 0 0 1 16 101 178 3 13.6 0 1 1 24 131 160 4 18.9 0 0 1 25 101 57 3 14.1 0 0 1 25 114 42 3 14.5 0 0 0 19 165 163 4 16.15 0 0 1 19 114 75 3 14.75 0 0 1 16 111 94 3 14.8 0 0 1 19 75 45 2 12.45 0 0 1 19 82 78 2 12.65 0 0 1 23 121 47 3 17.35 0 0 1 21 32 29 1 8.6 0 0 0 22 150 97 4 18.4 0 0 1 19 117 116 3 16.1 0 1 1 20 71 32 2 11.6 0 0 1 20 165 50 4 17.75 0 0 1 3 154 118 4 15.25 0 0 1 23 126 66 4 17.65 0 0 0 23 149 86 4 16.35 0 0 0 20 145 89 4 17.65 0 0 1 15 120 76 3 13.6 0 0 0 16 109 75 3 14.35 0 0 0 7 132 57 4 14.75 0 0 1 24 172 72 4 18.25 0 0 0 17 169 60 4 9.9 0 0 1 24 114 109 3 16 0 0 1 24 156 76 4 18.25 0 0 0 19 172 65 4 16.85 0 1 1 25 68 40 2 14.6 0 1 1 20 89 58 2 13.85 0 0 1 28 167 123 4 18.95 0 0 0 23 113 71 3 15.6 0 1 0 27 115 102 3 14.85 0 1 0 18 78 80 2 11.75 0 1 0 28 118 97 3 18.45 0 1 1 21 87 46 2 15.9 0 0 0 19 173 93 4 17.1 0 0 1 23 2 19 1 16.1 0 1 0 27 162 140 4 19.9 0 1 1 22 49 78 1 10.95 0 1 0 28 122 98 4 18.45 0 1 1 25 96 40 3 15.1 0 1 0 21 100 80 3 15 0 1 0 22 82 76 2 11.35 0 1 1 28 100 79 3 15.95 0 1 0 20 115 87 3 18.1 0 1 1 29 141 95 4 14.6 0 0 1 25 165 49 4 15.4 0 0 1 25 165 49 4 15.4 0 1 1 20 110 80 3 17.6 0 0 1 20 118 86 3 13.35 0 0 0 16 158 69 4 19.1 0 1 1 20 146 79 4 15.35 0 0 0 20 49 52 1 7.6 0 1 0 23 90 120 2 13.4 0 1 0 18 121 69 3 13.9 0 0 1 25 155 94 4 19.1 0 1 0 18 104 72 3 15.25 0 1 1 19 147 43 4 12.9 0 1 0 25 110 87 3 16.1 0 1 0 25 108 52 3 17.35 0 1 0 25 113 71 3 13.15 0 1 0 24 115 61 3 12.15 0 1 1 19 61 51 1 12.6 0 1 1 26 60 50 1 10.35 0 1 1 10 109 67 3 15.4 0 1 1 17 68 30 2 9.6 0 1 0 13 111 70 3 18.2 0 1 0 17 77 52 2 13.6 0 1 1 30 73 75 2 14.85 0 0 0 25 151 87 4 14.75 0 1 0 4 89 69 2 14.1 0 1 0 16 78 72 2 14.9 0 1 0 21 110 79 3 16.25 0 0 1 23 220 121 4 19.25 0 1 1 22 65 43 2 13.6 0 0 0 17 141 58 4 13.6 0 1 0 20 117 57 3 15.65 0 0 1 20 122 50 4 12.75 0 1 0 22 63 69 2 14.6 0 0 1 16 44 64 1 9.85 0 1 1 23 52 38 1 12.65 0 1 0 0 131 90 4 19.2 0 1 1 18 101 96 3 16.6 0 1 1 25 42 49 1 11.2 0 0 1 23 152 56 4 15.25 0 0 0 12 107 102 3 11.9 0 1 0 18 77 40 2 13.2 0 0 0 24 154 100 4 16.35 0 0 1 11 103 67 3 12.4 0 1 1 18 96 78 3 15.85 0 0 1 23 175 55 4 18.15 0 1 1 24 57 59 1 11.15 0 1 0 29 112 96 3 15.65 0 0 0 18 143 86 4 17.75 0 1 0 15 49 38 1 7.65 0 0 1 29 110 43 3 12.35 0 0 1 16 131 23 4 15.6 0 0 0 19 167 77 4 19.3 0 1 0 22 56 48 1 15.2 0 0 0 16 137 26 4 17.1 0 1 1 23 86 91 2 15.6 0 0 1 23 121 94 3 18.4 0 0 0 19 149 62 4 19.05 0 0 0 4 168 74 4 18.55 0 0 0 20 140 114 4 19.1 0 1 1 24 88 52 2 13.1 0 0 1 20 168 64 4 12.85 0 0 1 4 94 31 2 9.5 0 0 1 24 51 38 1 4.5 0 1 0 22 48 27 1 11.85 0 0 1 16 145 105 4 13.6 0 0 1 3 66 64 2 11.7 0 1 1 15 85 62 2 12.4 0 0 0 24 109 65 3 13.35 0 1 0 17 63 58 2 11.4 0 1 1 20 102 76 3 14.9 0 1 0 27 162 140 4 19.9 0 1 1 26 86 68 2 11.2 0 1 1 23 114 80 3 14.6 0 0 0 17 164 71 4 17.6 0 0 1 20 119 76 3 14.05 0 0 0 22 126 63 4 16.1 0 0 1 19 132 46 4 13.35 0 0 1 24 142 53 4 11.85 0 0 0 19 83 74 2 11.95 0 1 1 23 94 70 2 14.75 0 1 0 15 81 78 2 15.15 0 0 1 27 166 56 4 13.2 0 1 0 26 110 100 3 16.85 0 1 1 22 64 51 2 7.85 0 0 0 22 93 52 2 7.7 0 1 0 18 104 102 3 12.6 0 1 1 15 105 78 3 7.85 0 1 1 22 49 78 1 10.95 0 1 0 27 88 55 2 12.35 0 1 1 10 95 98 2 9.95 0 1 1 20 102 76 3 14.9 0 1 0 17 99 73 3 16.65 0 1 1 23 63 47 2 13.4 0 1 0 19 76 45 2 13.95 0 1 0 13 109 83 3 15.7 0 1 1 27 117 60 3 16.85 0 1 1 23 57 48 1 10.95 0 1 0 16 120 50 3 15.35 0 1 1 25 73 56 2 12.2 0 1 0 2 91 77 2 15.1 0 1 0 26 108 91 3 17.75 0 1 1 20 105 76 3 15.2 0 0 0 23 117 68 3 14.6 0 1 0 22 119 74 3 16.65 0 1 1 24 31 29 1 8.1
Names of X columns:
jaar_bin group_bin gender_bin NUMERACYTOT LFM Hour PR TOT
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
grey
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
FALSE
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$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') }
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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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