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Data X:
12.9 68 86 149 96 18 1.8 1.5 12.2 39 70 139 70 31 2.1 2.1 12.8 32 71 148 88 39 2.2 2.1 7.4 62 108 158 114 46 2.3 1.9 6.7 33 64 128 69 31 2.1 1.6 12.6 52 119 224 176 67 2.7 2.1 14.8 62 97 159 114 35 2.1 2.1 13.3 77 129 105 121 52 2.4 2.2 11.1 76 153 159 110 77 2.9 1.5 8.2 41 78 167 158 37 2.2 1.9 11.4 48 80 165 116 32 2.1 2.2 6.4 63 99 159 181 36 2.2 1.6 10.6 30 68 119 77 38 2.2 1.5 12 78 147 176 141 69 2.7 1.9 6.3 19 40 54 35 21 1.9 0.1 11.3 31 57 91 80 26 2 2.2 11.9 66 120 163 152 54 2.5 1.8 9.3 35 71 124 97 36 2.2 1.6 9.6 42 84 137 99 42 2.3 2.2 10 45 68 121 84 23 1.9 2.1 6.4 21 55 153 68 34 2.1 1.9 13.8 25 137 148 101 112 3.5 1.6 10.8 44 79 221 107 35 2.1 1.9 13.8 69 116 188 88 47 2.3 2.2 11.7 54 101 149 112 47 2.3 1.8 10.9 74 111 244 171 37 2.2 2.4 16.1 80 189 148 137 109 3.5 2.4 13.4 42 66 92 77 24 1.9 2.5 9.9 61 81 150 66 20 1.9 1.9 11.5 41 63 153 93 22 1.9 2.1 8.3 46 69 94 105 23 1.9 1.9 11.7 39 71 156 131 32 2.1 2.1 9 34 64 132 102 30 2 1.5 9.7 51 143 161 161 92 3.2 1.9 10.8 42 85 105 120 43 2.3 2.1 10.3 31 86 97 127 55 2.5 1.5 10.4 39 55 151 77 16 1.8 2.1 12.7 20 69 131 108 49 2.4 2.1 9.3 49 120 166 85 71 2.8 1.8 11.8 53 96 157 168 43 2.3 2.4 5.9 31 60 111 48 29 2 2.1 11.4 39 95 145 152 56 2.5 1.9 13 54 100 162 75 46 2.3 2.1 10.8 49 68 163 107 19 1.8 1.9 12.3 34 57 59 62 23 1.9 2.4 11.3 46 105 187 121 59 2.6 2.1 11.8 55 85 109 124 30 2 2.2 7.9 42 103 90 72 61 2.6 2.2 12.7 50 57 105 40 7 1.6 1.8 12.3 13 51 83 58 38 2.2 2.1 11.6 37 69 116 97 32 2.1 2.4 6.7 25 41 42 88 16 1.8 2.2 10.9 30 49 148 126 19 1.8 2.1 12.1 28 50 155 104 22 1.9 1.5 13.3 45 93 125 148 48 2.4 1.9 10.1 35 58 116 146 23 1.9 1.8 5.7 28 54 128 80 26 2 1.8 14.3 41 74 138 97 33 2.1 1.6 8 6 15 49 25 9 1.7 1.2 13.3 45 69 96 99 24 1.9 1.8 9.3 73 107 164 118 34 2.1 1.5 12.5 17 65 162 58 48 2.4 2.1 7.6 40 58 99 63 18 1.8 2.4 15.9 64 107 202 139 43 2.3 2.4 9.2 37 70 186 50 33 2.1 1.5 9.1 25 53 66 60 28 2 1.8 11.1 65 136 183 152 71 2.8 2.1 13 100 126 214 142 26 2 2.2 14.5 28 95 188 94 67 2.7 2.1 12.2 35 69 104 66 34 2.1 1.9 12.3 56 136 177 127 80 2.9 2.1 11.4 29 58 126 67 29 2 1.9 8.8 43 59 76 90 16 1.8 1.6 14.6 59 118 99 75 59 2.6 2.4 12.6 50 82 139 128 32 2.1 1.9 13 59 102 162 146 43 2.3 2.1 12.6 27 65 108 69 38 2.2 1.8 13.2 61 90 159 186 29 2 2.1 9.9 28 64 74 81 36 2.2 2.4 7.7 51 83 110 85 32 2.1 2.1 10.5 35 70 96 54 35 2.1 2.2 13.4 29 50 116 46 21 1.9 2.1 10.9 48 77 87 106 29 2 2.2 4.3 25 37 97 34 12 1.7 1.6 10.3 44 81 127 60 37 2.2 2.4 11.8 64 101 106 95 37 2.2 2.1 11.2 32 79 80 57 47 2.3 1.9 11.4 20 71 74 62 51 2.4 2.4 8.6 28 60 91 36 32 2.1 2.1 13.2 34 55 133 56 21 1.9 1.8 12.6 31 44 74 54 13 1.7 2.1 5.6 26 40 114 64 14 1.8 1.8 9.9 58 56 140 76 -2 1.5 1.9 8.8 23 43 95 98 20 1.9 1.9 7.7 21 45 98 88 24 1.9 2.4 9 21 32 121 35 11 1.7 1.8 7.3 33 56 126 102 23 1.9 1.8 11.4 16 40 98 61 24 1.9 2.1 13.6 20 34 95 80 14 1.8 2.1 7.9 37 89 110 49 52 2.4 2.4 10.7 35 50 70 78 15 1.8 1.9 10.3 33 56 102 90 23 1.9 1.8 8.3 27 46 86 45 19 1.8 1.8 9.6 41 76 130 55 35 2.1 2.2 14.2 40 64 96 96 24 1.9 2.4 8.5 35 74 102 43 39 2.2 1.8 13.5 28 57 100 52 29 2 2.4 4.9 32 45 94 60 13 1.7 1.8 6.4 22 30 52 54 8 1.7 1.9 9.6 44 62 98 51 18 1.8 2.4 11.6 27 51 118 51 24 1.9 2.1 11.1 17 36 99 38 19 1.8 1.9 4.35 12 34 48 41 23 1 2.1 12.7 45 61 50 146 16 1 2.7 18.1 37 70 150 182 33 4 2.1 17.85 37 69 154 192 32 4 2.1 16.6 108 145 109 263 37 3 2.1 12.6 10 23 68 35 14 2 2.1 17.1 68 120 194 439 52 4 2.1 19.1 72 147 158 214 75 4 2.1 16.1 143 215 159 341 72 4 2.1 13.35 9 24 67 58 15 2 2.1 18.4 55 84 147 292 29 4 2.4 14.7 17 30 39 85 13 1 1.95 10.6 37 77 100 200 40 3 2.1 12.6 27 46 111 158 19 3 2.1 16.2 37 61 138 199 24 4 1.95 13.6 58 178 101 297 121 3 2.1 18.9 66 160 131 227 93 4 2.4 14.1 21 57 101 108 36 3 2.1 14.5 19 42 114 86 23 3 2.25 16.15 78 163 165 302 85 4 2.4 14.75 35 75 114 148 41 3 2.25 14.8 48 94 111 178 46 3 2.55 12.45 27 45 75 120 18 2 1.95 12.65 43 78 82 207 35 2 2.4 17.35 30 47 121 157 17 3 2.1 8.6 25 29 32 128 4 1 2.1 18.4 69 97 150 296 28 4 2.4 16.1 72 116 117 323 44 3 2.1 11.6 23 32 71 79 10 2 2.1 17.75 13 50 165 70 38 4 2.25 15.25 61 118 154 146 57 4 2.25 17.65 43 66 126 246 23 4 2.4 16.35 51 86 149 196 36 4 2.1 17.65 67 89 145 199 22 4 2.4 13.6 36 76 120 127 40 3 2.1 14.35 44 75 109 153 31 3 2.1 14.75 45 57 132 299 11 4 2.25 18.25 34 72 172 228 38 4 2.25 9.9 36 60 169 190 24 4 2.4 16 72 109 114 180 37 3 2.25 18.25 39 76 156 212 37 4 2.25 16.85 43 65 172 269 22 4 2.1 14.6 25 40 68 130 15 2 2.1 13.85 56 58 89 179 2 2 2.1 18.95 80 123 167 243 43 4 2.7 15.6 40 71 113 190 31 3 2.1 14.85 73 102 115 299 29 3 2.1 11.75 34 80 78 121 45 2 2.25 18.45 72 97 118 137 25 3 2.7 15.9 42 46 87 305 4 2 2.4 17.1 61 93 173 157 31 4 2.1 16.1 23 19 2 96 -4 1 2.1 19.9 74 140 162 183 66 4 2.4 10.95 16 78 49 52 61 1 1.95 18.45 66 98 122 238 32 4 2.7 15.1 9 40 96 40 31 3 2.1 15 41 80 100 226 39 3 2.25 11.35 57 76 82 190 19 2 2.1 15.95 48 79 100 214 31 3 2.7 18.1 51 87 115 145 36 3 2.1 14.6 53 95 141 119 42 4 2.1 15.4 29 49 165 222 21 4 1.65 15.4 29 49 165 222 21 4 1.65 17.6 55 80 110 159 25 3 2.1 13.35 54 86 118 165 32 3 2.1 19.1 43 69 158 249 26 4 2.1 15.35 51 79 146 125 28 4 2.1 7.6 20 52 49 122 32 1 2.1 13.4 79 120 90 186 41 2 2.4 13.9 39 69 121 148 29 3 2.4 19.1 61 94 155 274 33 4 2.1 15.25 55 72 104 172 17 3 2.25 12.9 30 43 147 84 13 4 2.4 16.1 55 87 110 168 32 3 2.1 17.35 22 52 108 102 30 3 2.1 13.15 37 71 113 106 34 3 2.4 12.15 2 61 115 2 59 3 2.4 12.6 38 51 61 139 13 1 2.1 10.35 27 50 60 95 23 1 2.1 15.4 56 67 109 130 10 3 2.4 9.6 25 30 68 72 5 2 2.1 18.2 39 70 111 141 31 3 2.7 13.6 33 52 77 113 19 2 2.1 14.85 43 75 73 206 32 2 2.1 14.75 57 87 151 268 30 4 2.25 14.1 43 69 89 175 25 2 2.1 14.9 23 72 78 77 48 2 2.4 16.25 44 79 110 125 35 3 2.25 19.25 54 121 220 255 67 4 2.25 13.6 28 43 65 111 15 2 2.1 13.6 36 58 141 132 22 4 2.1 15.65 39 57 117 211 18 3 2.4 12.75 16 50 122 92 33 4 2.25 14.6 23 69 63 76 46 2 2.1 9.85 40 64 44 171 24 1 2.1 12.65 24 38 52 83 14 1 1.65 19.2 78 90 131 266 12 4 2.7 16.6 57 96 101 186 38 3 2.1 11.2 37 49 42 50 12 1 1.95 15.25 27 56 152 117 28 4 2.25 11.9 61 102 107 219 41 3 2.4 13.2 27 40 77 246 12 2 1.95 16.35 69 100 154 279 31 4 2.1 12.4 34 67 103 148 33 3 2.4 15.85 44 78 96 137 34 3 2.1 18.15 34 55 175 181 21 4 2.4 11.15 39 59 57 98 20 1 2.4 15.65 51 96 112 226 44 3 2.4 17.75 34 86 143 234 52 4 2.25 7.65 31 38 49 138 7 1 2.4 12.35 13 43 110 85 29 3 2.1 15.6 12 23 131 66 11 4 2.1 19.3 51 77 167 236 26 4 1.8 15.2 24 48 56 106 24 1 2.7 17.1 19 26 137 135 7 4 2.1 15.6 30 91 86 122 60 2 2.1 18.4 81 94 121 218 13 3 2.4 19.05 42 62 149 199 20 4 2.55 18.55 22 74 168 112 52 4 2.55 19.1 85 114 140 278 28 4 2.1 13.1 27 52 88 94 25 2 2.1 12.85 25 64 168 113 39 4 2.1 9.5 22 31 94 84 9 2 2.25 4.5 19 38 51 86 19 1 2.25 11.85 14 27 48 62 13 1 2.1 13.6 45 105 145 222 60 4 2.1 11.7 45 64 66 167 19 2 1.95 12.4 28 62 85 82 34 2 2.4 13.35 51 65 109 207 14 3 2.1 11.4 41 58 63 184 17 2 2.4 14.9 31 76 102 83 45 3 2.4 19.9 74 140 162 183 66 4 2.4 11.2 19 68 86 89 48 2 1.95 14.6 51 80 114 225 29 3 2.1 17.6 73 71 164 237 -2 4 2.1 14.05 24 76 119 102 51 3 2.55 16.1 61 63 126 221 2 4 2.1 13.35 23 46 132 128 24 4 2.1 11.85 14 53 142 91 40 4 2.1 11.95 54 74 83 198 20 2 1.95 14.75 51 70 94 204 19 2 2.25 15.15 62 78 81 158 16 2 2.4 13.2 36 56 166 138 20 4 1.95 16.85 59 100 110 226 40 3 2.1 7.85 24 51 64 44 27 2 2.1 7.7 26 52 93 196 25 2 1.95 12.6 54 102 104 83 49 3 2.1 7.85 39 78 105 79 39 3 2.1 10.95 16 78 49 52 61 1 1.95 12.35 36 55 88 105 19 2 2.1 9.95 31 98 95 116 67 2 1.95 14.9 31 76 102 83 45 3 2.4 16.65 42 73 99 196 30 3 2.4 13.4 39 47 63 153 8 2 2.4 13.95 25 45 76 157 19 2 1.95 15.7 31 83 109 75 52 3 2.7 16.85 38 60 117 106 22 3 2.1 10.95 31 48 57 58 17 1 1.95 15.35 17 50 120 75 33 3 2.1 12.2 22 56 73 74 34 2 1.95 15.1 55 77 91 185 22 2 2.1 17.75 62 91 108 265 30 3 2.25 15.2 51 76 105 131 25 3 2.7 14.6 30 68 117 139 38 3 2.1 16.65 49 74 119 196 26 3 2.4 8.1 16 29 31 78 13 1 1.35
Names of X columns:
TOT CH Hours LFM Blogs PRH PR PA
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$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
0 seconds
R Server
Big Analytics Cloud Computing Center
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