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Data X:
1 -4.813031 0.266482 119.992 157.302 74.997 1 -4.075192 0.33559 122.4 148.65 113.819 1 -4.443179 0.311173 116.682 131.111 111.555 1 -4.117501 0.334147 116.676 137.871 111.366 1 -3.747787 0.234513 116.014 141.781 110.655 1 -4.242867 0.299111 120.552 131.162 113.787 1 -5.634322 0.257682 120.267 137.244 114.82 1 -6.167603 0.183721 107.332 113.84 104.315 1 -5.498678 0.327769 95.73 132.068 91.754 1 -5.011879 0.325996 95.056 120.103 91.226 1 -5.24977 0.391002 88.333 112.24 84.072 1 -4.960234 0.363566 91.904 115.871 86.292 1 -6.547148 0.152813 136.926 159.866 131.276 1 -5.660217 0.254989 139.173 179.139 76.556 1 -6.105098 0.203653 152.845 163.305 75.836 1 -5.340115 0.210185 142.167 217.455 83.159 1 -5.44004 0.239764 144.188 349.259 82.764 1 -2.93107 0.434326 168.778 232.181 75.603 1 -3.949079 0.35787 153.046 175.829 68.623 1 -4.554466 0.340176 156.405 189.398 142.822 1 -4.095442 0.262564 153.848 165.738 65.782 1 -5.18696 0.237622 153.88 172.86 78.128 1 -4.330956 0.262384 167.93 193.221 79.068 1 -5.248776 0.210279 173.917 192.735 86.18 1 -5.557447 0.22089 163.656 200.841 76.779 1 -5.571843 0.236853 104.4 206.002 77.968 1 -6.18359 0.226278 171.041 208.313 75.501 1 -6.27169 0.196102 146.845 208.701 81.737 1 -7.120925 0.279789 155.358 227.383 80.055 1 -6.635729 0.209866 162.568 198.346 77.63 0 -7.3483 0.177551 197.076 206.896 192.055 0 -7.682587 0.173319 199.228 209.512 192.091 0 -7.067931 0.175181 198.383 215.203 193.104 0 -7.695734 0.17854 202.266 211.604 197.079 0 -7.964984 0.163519 203.184 211.526 196.16 0 -7.777685 0.170183 201.464 210.565 195.708 1 -6.149653 0.218037 177.876 192.921 168.013 1 -6.006414 0.196371 176.17 185.604 163.564 1 -6.452058 0.212294 180.198 201.249 175.456 1 -6.006647 0.266892 187.733 202.324 173.015 1 -6.647379 0.201095 186.163 197.724 177.584 1 -7.044105 0.063412 184.055 196.537 166.977 0 -7.31055 0.098648 237.226 247.326 225.227 0 -6.793547 0.158266 241.404 248.834 232.483 0 -7.057869 0.091608 243.439 250.912 232.435 0 -6.99582 0.102083 242.852 255.034 227.911 0 -7.156076 0.127642 245.51 262.09 231.848 0 -7.31951 0.200873 252.455 261.487 182.786 0 -6.439398 0.266392 122.188 128.611 115.765 0 -6.482096 0.264967 122.964 130.049 114.676 0 -6.650471 0.254498 124.445 135.069 117.495 0 -6.689151 0.291954 126.344 134.231 112.773 0 -7.072419 0.220434 128.001 138.052 122.08 0 -6.836811 0.269866 129.336 139.867 118.604 1 -4.649573 0.205558 108.807 134.656 102.874 1 -4.333543 0.221727 109.86 126.358 104.437 1 -4.438453 0.238298 110.417 131.067 103.37 1 -4.60826 0.290024 117.274 129.916 110.402 1 -4.476755 0.262633 116.879 131.897 108.153 1 -4.609161 0.221711 114.847 271.314 104.68 0 -7.040508 0.066994 209.144 237.494 109.379 0 -7.293801 0.086372 223.365 238.987 98.664 0 -6.966321 0.095882 222.236 231.345 205.495 0 -7.24562 0.018689 228.832 234.619 223.634 0 -7.496264 0.056844 229.401 252.221 221.156 0 -7.314237 0.006274 228.969 239.541 113.201 1 -5.409423 0.22685 140.341 159.774 67.021 1 -5.324574 0.20566 136.969 166.607 66.004 1 -5.86975 0.151814 143.533 162.215 65.809 1 -6.261141 0.120956 148.09 162.824 67.343 1 -5.720868 0.15883 142.729 162.408 65.476 1 -5.207985 0.224852 136.358 176.595 65.75 1 -5.79182 0.329066 120.08 139.71 111.208 1 -5.389129 0.306636 112.014 588.518 107.024 1 -5.31336 0.201861 110.793 128.101 107.316 1 -5.477592 0.315074 110.707 122.611 105.007 1 -5.775966 0.341169 112.876 148.826 106.981 1 -5.391029 0.250572 110.568 125.394 106.821 1 -5.115212 0.249494 95.385 102.145 90.264 1 -4.913885 0.265699 100.77 115.697 85.545 1 -4.441519 0.155097 96.106 108.664 84.51 1 -5.132032 0.210458 95.605 107.715 87.549 1 -5.022288 0.146948 100.96 110.019 95.628 1 -6.025367 0.078202 98.804 102.305 87.804 1 -5.288912 0.343073 176.858 205.56 75.344 1 -5.657899 0.315903 180.978 200.125 155.495 1 -6.366916 0.335753 178.222 202.45 141.047 1 -5.515071 0.299549 176.281 227.381 125.61 1 -5.783272 0.299793 173.898 211.35 74.677 1 -4.379411 0.375531 179.711 225.93 144.878 1 -4.508984 0.389232 166.605 206.008 78.032 1 -6.411497 0.207156 151.955 163.335 147.226 1 -5.952058 0.08784 148.272 164.989 142.299 1 -6.152551 0.17352 152.125 161.469 76.596 1 -6.251425 0.188056 157.821 172.975 68.401 1 -6.247076 0.180528 157.447 163.267 149.605 1 -6.41744 0.194627 159.116 168.913 144.811 1 -4.020042 0.265315 125.036 143.946 116.187 1 -5.159169 0.202146 125.791 140.557 96.206 1 -3.760348 0.242861 126.512 141.756 99.77 1 -3.700544 0.260481 125.641 141.068 116.346 1 -4.20273 0.310163 128.451 150.449 75.632 1 -3.269487 0.270641 139.224 586.567 66.157 1 -6.878393 0.089267 150.258 154.609 75.349 1 -7.111576 0.14478 154.003 160.267 128.621 1 -6.997403 0.210279 149.689 160.368 133.608 1 -6.981201 0.18455 155.078 163.736 144.148 1 -6.600023 0.249172 151.884 157.765 133.751 1 -6.739151 0.160686 151.989 157.339 132.857 1 -5.845099 0.278679 193.03 208.9 80.297 1 -5.25832 0.256454 200.714 223.982 89.686 1 -6.471427 0.184378 208.519 220.315 199.02 1 -4.876336 0.212054 204.664 221.3 189.621 1 -5.96304 0.250283 210.141 232.706 185.258 1 -6.729713 0.181701 206.327 226.355 92.02 1 -4.673241 0.261549 151.872 492.892 69.085 1 -6.051233 0.27328 158.219 442.557 71.948 1 -4.597834 0.372114 170.756 450.247 79.032 1 -4.913137 0.393056 178.285 442.824 82.063 1 -5.517173 0.389295 217.116 233.481 93.978 1 -6.186128 0.279933 128.94 479.697 88.251 1 -4.711007 0.281618 176.824 215.293 83.961 1 -5.418787 0.160267 138.19 203.522 83.34 1 -5.44514 0.142466 182.018 197.173 79.187 1 -5.944191 0.143359 156.239 195.107 79.82 1 -5.594275 0.12795 145.174 198.109 80.637 1 -5.540351 0.087165 138.145 197.238 81.114 1 -5.825257 0.115697 166.888 198.966 79.512 1 -6.890021 0.152941 119.031 127.533 109.216 1 -5.892061 0.195976 120.078 126.632 105.667 1 -6.135296 0.20363 120.289 128.143 100.209 1 -6.112667 0.217013 120.256 125.306 104.773 1 -5.436135 0.254909 119.056 125.213 86.795 1 -6.448134 0.178713 118.747 123.723 109.836 1 -5.301321 0.320385 106.516 112.777 93.105 1 -5.333619 0.322044 110.453 127.611 105.554 1 -4.378916 0.300067 113.4 133.344 107.816 1 -4.654894 0.304107 113.166 130.27 100.673 1 -5.634576 0.306014 112.239 126.609 104.095 1 -5.866357 0.23307 116.15 131.731 109.815 1 -4.796845 0.397749 170.368 268.796 79.543 1 -5.410336 0.288917 208.083 253.792 91.802 1 -5.585259 0.310746 198.458 219.29 148.691 1 -5.898673 0.213353 202.805 231.508 86.232 1 -6.132663 0.220617 202.544 241.35 164.168 1 -5.456811 0.345238 223.361 263.872 87.638 1 -3.297668 0.414758 169.774 191.759 151.451 1 -4.276605 0.355736 183.52 216.814 161.34 1 -3.377325 0.335357 188.62 216.302 165.982 1 -4.892495 0.262281 202.632 565.74 177.258 1 -4.484303 0.340256 186.695 211.961 149.442 1 -2.434031 0.450493 192.818 224.429 168.793 1 -2.839756 0.356224 198.116 233.099 174.478 1 -4.865194 0.246404 121.345 139.644 98.25 1 -4.239028 0.175691 119.1 128.442 88.833 1 -3.583722 0.207914 117.87 127.349 95.654 1 -5.4351 0.230532 122.336 142.369 94.794 1 -3.444478 0.303214 117.963 134.209 100.757 1 -5.070096 0.280091 126.144 154.284 97.543 1 -5.498456 0.234196 127.93 138.752 112.173 1 -5.185987 0.259229 114.238 124.393 77.022 1 -5.283009 0.226528 115.322 135.738 107.802 1 -5.529833 0.24275 114.554 126.778 91.121 1 -5.617124 0.184896 112.15 131.669 97.527 1 -2.929379 0.396746 102.273 142.83 85.902 0 -6.816086 0.17227 236.2 244.663 102.137 0 -7.018057 0.176316 237.323 243.709 229.256 0 -7.517934 0.160414 260.105 264.919 237.303 0 -5.736781 0.164529 197.569 217.627 90.794 0 -7.169701 0.073298 240.301 245.135 219.783 0 -7.3045 0.171088 244.99 272.21 239.17 0 -6.323531 0.218885 112.547 133.374 105.715 0 -6.085567 0.192375 110.739 113.597 100.139 0 -5.943501 0.19215 113.715 116.443 96.913 0 -6.012559 0.229298 117.004 144.466 99.923 0 -5.966779 0.197938 115.38 123.109 108.634 0 -6.016891 0.109256 116.388 129.038 108.97 1 -6.486822 0.197919 151.737 190.204 129.859 1 -6.311987 0.182459 148.79 158.359 138.99 1 -5.711205 0.240875 148.143 155.982 135.041 1 -6.261446 0.183218 150.44 163.441 144.736 1 -5.704053 0.216204 148.462 161.078 141.998 1 -6.27717 0.109397 149.818 163.417 144.786 0 -5.61907 0.191576 117.226 123.925 106.656 0 -5.198864 0.206768 116.848 217.552 99.503 0 -5.592584 0.133917 116.286 177.291 96.983 0 -6.431119 0.15331 116.556 592.03 86.228 0 -6.359018 0.116636 116.342 581.289 94.246 0 -6.710219 0.149694 114.563 119.167 86.647 0 -6.934474 0.15989 201.774 262.707 78.228 0 -6.538586 0.121952 174.188 230.978 94.261 0 -6.195325 0.129303 209.516 253.017 89.488 0 -6.787197 0.158453 174.688 240.005 74.287 0 -6.744577 0.207454 198.764 396.961 74.904 0 -5.724056 0.190667 214.289 260.277 77.973
Names of X columns:
status spread1 spread2 MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz)
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
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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