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