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Data X:
12.9 76 21 0 86 149 96 18 68 7.4 119 26 0 62 152 75 7 55 12.2 119 22 0 70 139 70 31 39 12.8 91 22 0 71 148 88 39 32 7.4 138 18 0 108 158 114 46 62 6.7 97 23 0 64 128 69 31 33 12.6 117 12 0 119 224 176 67 52 14.8 125 20 0 97 159 114 35 62 13.3 95 22 0 129 105 121 52 77 11.1 168 21 0 153 159 110 77 76 8.2 109 19 0 78 167 158 37 41 11.4 115 22 0 80 165 116 32 48 6.4 135 15 0 99 159 181 36 63 10.6 130 20 0 68 119 77 38 30 12.0 131 19 0 147 176 141 69 78 6.3 143 18 0 40 54 35 21 19 11.3 134 15 1 57 91 80 26 31 11.9 109 20 0 120 163 152 54 66 9.3 126 21 0 71 124 97 36 35 9.6 117 21 1 84 137 99 42 42 10.0 155 15 0 68 121 84 23 45 6.4 136 16 0 55 153 68 34 21 13.8 132 23 0 137 148 101 112 25 10.8 129 21 0 79 221 107 35 44 13.8 129 18 0 116 188 88 47 69 11.7 131 25 0 101 149 112 47 54 10.9 123 9 0 111 244 171 37 74 16.1 125 30 1 189 148 137 109 80 13.4 112 20 1 66 92 77 24 42 9.9 131 23 0 81 150 66 20 61 11.5 129 16 0 63 153 93 22 41 8.3 99 16 0 69 94 105 23 46 11.7 114 19 0 71 156 131 32 39 6.1 128 25 0 70 146 89 7 63 9.0 130 25 0 64 132 102 30 34 9.7 112 18 0 143 161 161 92 51 10.8 122 23 0 85 105 120 43 42 10.3 115 21 0 86 97 127 55 31 10.4 124 10 0 55 151 77 16 39 12.7 120 14 1 69 131 108 49 20 9.3 119 22 0 120 166 85 71 49 11.8 123 26 0 96 157 168 43 53 5.9 91 23 0 60 111 48 29 31 11.4 107 23 0 95 145 152 56 39 13.0 118 24 0 100 162 75 46 54 10.8 111 24 0 68 163 107 19 49 12.3 95 18 1 57 59 62 23 34 11.3 135 23 0 105 187 121 59 46 11.8 108 15 0 85 109 124 30 55 7.9 130 19 1 103 90 72 61 42 12.7 143 16 0 57 105 40 7 50 12.3 110 25 1 51 83 58 38 13 11.6 131 23 1 69 116 97 32 37 6.7 113 17 1 41 42 88 16 25 10.9 103 19 0 49 148 126 19 30 12.1 140 21 1 50 155 104 22 28 13.3 125 18 0 93 125 148 48 45 10.1 127 27 0 58 116 146 23 35 5.7 116 21 1 54 128 80 26 28 14.3 120 13 0 74 138 97 33 41 8.0 108 8 1 15 49 25 9 6 13.3 111 29 1 69 96 99 24 45 9.3 125 28 0 107 164 118 34 73 12.5 126 23 0 65 162 58 48 17 7.6 116 21 0 58 99 63 18 40 15.9 104 19 0 107 202 139 43 64 9.2 113 19 0 70 186 50 33 37 9.1 106 20 1 53 66 60 28 25 11.1 139 18 0 136 183 152 71 65 13.0 119 19 0 126 214 142 26 100 14.5 112 17 0 95 188 94 67 28 12.2 101 19 1 69 104 66 34 35 12.3 110 25 0 136 177 127 80 56 11.4 115 19 0 58 126 67 29 29 8.8 116 22 1 59 76 90 16 43 14.6 122 23 1 118 99 75 59 59 7.3 111 26 0 110 157 96 58 52 12.6 115 14 0 82 139 128 32 50 NA 112 28 0 50 78 41 47 3 13.0 109 16 0 102 162 146 43 59 12.6 97 24 1 65 108 69 38 27 13.2 132 20 0 90 159 186 29 61 9.9 102 12 1 64 74 81 36 28 7.7 124 24 0 83 110 85 32 51 10.5 115 22 1 70 96 54 35 35 13.4 128 12 1 50 116 46 21 29 10.9 121 22 1 77 87 106 29 48 4.3 130 20 1 37 97 34 12 25 10.3 99 10 1 81 127 60 37 44 11.8 122 23 1 101 106 95 37 64 11.2 126 17 1 79 80 57 47 32 11.4 141 22 1 71 74 62 51 20 8.6 124 24 1 60 91 36 32 28 13.2 127 18 1 55 133 56 21 34 12.6 114 21 1 44 74 54 13 31 5.6 99 20 1 40 114 64 14 26 9.9 137 20 1 56 140 76 -2 58 8.8 97 22 1 43 95 98 20 23 7.7 108 19 1 45 98 88 24 21 9.0 138 20 1 32 121 35 11 21 7.3 123 26 1 56 126 102 23 33 11.4 125 23 1 40 98 61 24 16 13.6 106 24 1 34 95 80 14 20 7.9 138 21 1 89 110 49 52 37 10.7 127 21 1 50 70 78 15 35 10.3 142 19 1 56 102 90 23 33 8.3 94 8 1 46 86 45 19 27 9.6 133 17 1 76 130 55 35 41 14.2 119 20 1 64 96 96 24 40 8.5 114 11 1 74 102 43 39 35 13.5 114 8 1 57 100 52 29 28 4.9 123 15 1 45 94 60 13 32 6.4 126 18 1 30 52 54 8 22 9.6 118 18 1 62 98 51 18 44 11.6 132 19 1 51 118 51 24 27 11.1 127 19 1 36 99 38 19 17 4.35 103 23 0 34 48 41 23 12 12.7 72 22 0 61 50 146 16 45 18.1 113 21 0 70 150 182 33 37 17.85 125 25 0 69 154 192 32 37 16.6 109 30 1 145 109 263 37 108 12.6 111 17 1 23 68 35 14 10 17.1 105 27 0 120 194 439 52 68 19.1 122 23 0 147 158 214 75 72 16.1 127 23 0 215 159 341 72 143 13.35 117 18 0 24 67 58 15 9 18.4 91 18 0 84 147 292 29 55 14.7 143 23 0 30 39 85 13 17 10.6 116 19 0 77 100 200 40 37 12.6 123 15 0 46 111 158 19 27 16.2 101 20 0 61 138 199 24 37 13.6 119 16 0 178 101 297 121 58 18.9 143 24 1 160 131 227 93 66 14.1 114 25 0 57 101 108 36 21 14.5 108 25 0 42 114 86 23 19 16.15 128 19 0 163 165 302 85 78 14.75 122 19 0 75 114 148 41 35 14.8 133 16 0 94 111 178 46 48 12.45 94 19 0 45 75 120 18 27 12.65 128 19 0 78 82 207 35 43 17.35 108 23 0 47 121 157 17 30 8.6 125 21 0 29 32 128 4 25 18.4 130 22 0 97 150 296 28 69 16.1 112 19 0 116 117 323 44 72 11.6 89 20 1 32 71 79 10 23 17.75 117 20 0 50 165 70 38 13 15.25 142 3 0 118 154 146 57 61 17.65 128 23 0 66 126 246 23 43 15.6 114 14 0 48 138 145 26 22 16.35 123 23 0 86 149 196 36 51 17.65 89 20 0 89 145 199 22 67 13.6 125 15 0 76 120 127 40 36 11.7 110 13 0 39 138 91 18 21 14.35 112 16 0 75 109 153 31 44 14.75 109 7 0 57 132 299 11 45 18.25 108 24 0 72 172 228 38 34 9.9 139 17 0 60 169 190 24 36 16 116 24 0 109 114 180 37 72 18.25 116 24 0 76 156 212 37 39 16.85 88 19 0 65 172 269 22 43 14.6 128 25 1 40 68 130 15 25 13.85 125 20 1 58 89 179 2 56 18.95 134 28 0 123 167 243 43 80 15.6 126 23 0 71 113 190 31 40 14.85 121 27 1 102 115 299 29 73 11.75 106 18 1 80 78 121 45 34 18.45 109 28 1 97 118 137 25 72 15.9 127 21 1 46 87 305 4 42 17.1 101 19 0 93 173 157 31 61 16.1 120 23 0 19 2 96 -4 23 19.9 99 27 1 140 162 183 66 74 10.95 116 22 1 78 49 52 61 16 18.45 125 28 1 98 122 238 32 66 15.1 121 25 1 40 96 40 31 9 15 127 21 1 80 100 226 39 41 11.35 129 22 1 76 82 190 19 57 15.95 155 28 1 79 100 214 31 48 18.1 113 20 1 87 115 145 36 51 14.6 125 29 1 95 141 119 42 53 15.4 114 25 0 49 165 222 21 29 15.4 116 25 0 49 165 222 21 29 17.6 127 20 1 80 110 159 25 55 13.35 102 20 0 86 118 165 32 54 19.1 87 16 0 69 158 249 26 43 15.35 110 20 1 79 146 125 28 51 7.6 115 20 0 52 49 122 32 20 13.4 108 23 1 120 90 186 41 79 13.9 97 18 1 69 121 148 29 39 19.1 119 25 0 94 155 274 33 61 15.25 130 18 1 72 104 172 17 55 12.9 97 19 1 43 147 84 13 30 16.1 120 25 1 87 110 168 32 55 17.35 125 25 1 52 108 102 30 22 13.15 131 25 1 71 113 106 34 37 12.15 129 24 1 61 115 2 59 2 12.6 125 19 1 51 61 139 13 38 10.35 108 26 1 50 60 95 23 27 15.4 142 10 1 67 109 130 10 56 9.6 117 17 1 30 68 72 5 25 18.2 130 13 1 70 111 141 31 39 13.6 93 17 1 52 77 113 19 33 14.85 97 30 1 75 73 206 32 43 14.75 120 25 0 87 151 268 30 57 14.1 110 4 1 69 89 175 25 43 14.9 111 16 1 72 78 77 48 23 16.25 130 21 1 79 110 125 35 44 19.25 66 23 0 121 220 255 67 54 13.6 113 22 1 43 65 111 15 28 13.6 126 17 0 58 141 132 22 36 15.65 114 20 1 57 117 211 18 39 12.75 130 20 0 50 122 92 33 16 14.6 112 22 1 69 63 76 46 23 9.85 126 16 0 64 44 171 24 40 12.65 86 23 1 38 52 83 14 24 11.9 122 16 1 53 62 119 23 29 19.2 118 0 1 90 131 266 12 78 16.6 124 18 1 96 101 186 38 57 11.2 120 25 1 49 42 50 12 37 15.25 128 23 0 56 152 117 28 27 11.9 134 12 0 102 107 219 41 61 13.2 133 18 1 40 77 246 12 27 16.35 131 24 0 100 154 279 31 69 12.4 102 11 0 67 103 148 33 34 15.85 97 18 1 78 96 137 34 44 14.35 93 14 0 62 154 130 41 21 18.15 129 23 0 55 175 181 21 34 11.15 115 24 1 59 57 98 20 39 15.65 116 29 1 96 112 226 44 51 17.75 136 18 0 86 143 234 52 34 7.65 142 15 1 38 49 138 7 31 12.35 112 29 0 43 110 85 29 13 15.6 120 16 0 23 131 66 11 12 19.3 121 19 0 77 167 236 26 51 15.2 110 22 1 48 56 106 24 24 17.1 122 16 0 26 137 135 7 19 15.6 133 23 1 91 86 122 60 30 18.4 136 23 0 94 121 218 13 81 19.05 118 19 0 62 149 199 20 42 18.55 130 4 0 74 168 112 52 22 19.1 114 20 0 114 140 278 28 85 13.1 147 24 1 52 88 94 25 27 12.85 123 20 0 64 168 113 39 25 9.5 121 4 0 31 94 84 9 22 4.5 119 24 0 38 51 86 19 19 11.85 129 22 1 27 48 62 13 14 13.6 137 16 0 105 145 222 60 45 11.7 63 3 0 64 66 167 19 45 12.4 134 15 1 62 85 82 34 28 13.35 140 24 0 65 109 207 14 51 11.4 134 17 1 58 63 184 17 41 14.9 121 20 1 76 102 83 45 31 19.9 105 27 1 140 162 183 66 74 17.75 114 23 0 48 128 85 24 24 11.2 106 26 1 68 86 89 48 19 14.6 135 23 1 80 114 225 29 51 17.6 100 17 0 71 164 237 -2 73 14.05 101 20 0 76 119 102 51 24 16.1 131 22 0 63 126 221 2 61 13.35 131 19 0 46 132 128 24 23 11.85 129 24 0 53 142 91 40 14 11.95 120 19 0 74 83 198 20 54 14.75 117 23 1 70 94 204 19 51 15.15 82 15 1 78 81 158 16 62 13.2 106 27 0 56 166 138 20 36 16.85 125 26 1 100 110 226 40 59 7.85 130 22 1 51 64 44 27 24 7.7 147 22 0 52 93 196 25 26 12.6 125 18 1 102 104 83 49 54 7.85 97 15 1 78 105 79 39 39 10.95 101 22 1 78 49 52 61 16 12.35 128 27 1 55 88 105 19 36 9.95 97 10 1 98 95 116 67 31 14.9 126 20 1 76 102 83 45 31 16.65 118 17 1 73 99 196 30 42 13.4 107 23 1 47 63 153 8 39 13.95 87 19 1 45 76 157 19 25 15.7 156 13 1 83 109 75 52 31 16.85 133 27 1 60 117 106 22 38 10.95 132 23 1 48 57 58 17 31 15.35 133 16 1 50 120 75 33 17 12.2 122 25 1 56 73 74 34 22 15.1 125 2 1 77 91 185 22 55 17.75 127 26 1 91 108 265 30 62 15.2 125 20 1 76 105 131 25 51 14.6 99 23 0 68 117 139 38 30 16.65 128 22 1 74 119 196 26 49 8.1 110 24 1 29 31 78 13 16
Names of X columns:
TOT.SCORE TOT.AMS NUMERACY BA_of_SCH H zinvolle_teksten B PRH CH
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
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
par3 <- 'No Linear Trend' par2 <- 'Do not include Seasonal Dummies' par1 <- '1' 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
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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