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Data X:
1 -4.813031 0.266482 0.02211 21.033 0.815285 1 -4.075192 0.33559 0.01929 19.085 0.819521 1 -4.443179 0.311173 0.01309 20.651 0.825288 1 -4.117501 0.334147 0.01353 20.644 0.819235 1 -3.747787 0.234513 0.01767 19.649 0.823484 1 -4.242867 0.299111 0.01222 21.378 0.825069 1 -5.634322 0.257682 0.00607 24.886 0.764112 1 -6.167603 0.183721 0.00344 26.892 0.763262 1 -5.498678 0.327769 0.0107 21.812 0.773587 1 -5.011879 0.325996 0.01022 21.862 0.798463 1 -5.24977 0.391002 0.01166 21.118 0.776156 1 -4.960234 0.363566 0.01141 21.414 0.79252 1 -6.547148 0.152813 0.00581 25.703 0.646846 1 -5.660217 0.254989 0.01041 24.889 0.665833 1 -6.105098 0.203653 0.00609 24.922 0.654027 1 -5.340115 0.210185 0.00839 25.175 0.658245 1 -5.44004 0.239764 0.01859 22.333 0.644692 1 -2.93107 0.434326 0.02919 20.376 0.605417 1 -3.949079 0.35787 0.0316 17.28 0.719467 1 -4.554466 0.340176 0.03365 17.153 0.68608 1 -4.095442 0.262564 0.03871 17.536 0.704087 1 -5.18696 0.237622 0.01849 19.493 0.698951 1 -4.330956 0.262384 0.0128 22.468 0.679834 1 -5.248776 0.210279 0.0184 20.422 0.686894 1 -5.557447 0.22089 0.01778 23.831 0.732479 1 -5.571843 0.236853 0.02887 22.066 0.737948 1 -6.18359 0.226278 0.01095 25.908 0.720916 1 -6.27169 0.196102 0.01328 25.119 0.726652 1 -7.120925 0.279789 0.00677 25.97 0.676258 1 -6.635729 0.209866 0.0117 25.678 0.723797 0 -7.3483 0.177551 0.00339 26.775 0.741367 0 -7.682587 0.173319 0.00167 30.94 0.742055 0 -7.067931 0.175181 0.00119 30.775 0.738703 0 -7.695734 0.17854 0.00072 32.684 0.742133 0 -7.964984 0.163519 0.00065 33.047 0.741899 0 -7.777685 0.170183 0.00135 31.732 0.742737 1 -6.149653 0.218037 0.00586 23.216 0.778834 1 -6.006414 0.196371 0.0034 24.951 0.783626 1 -6.452058 0.212294 0.00231 26.738 0.766209 1 -6.006647 0.266892 0.00265 26.31 0.758324 1 -6.647379 0.201095 0.00231 26.822 0.765623 1 -7.044105 0.063412 0.00257 26.453 0.759203 0 -7.31055 0.098648 0.0074 22.736 0.654172 0 -6.793547 0.158266 0.00675 23.145 0.634267 0 -7.057869 0.091608 0.00454 25.368 0.635285 0 -6.99582 0.102083 0.00476 25.032 0.638928 0 -7.156076 0.127642 0.00476 24.602 0.631653 0 -7.31951 0.200873 0.00432 26.805 0.635204 0 -6.439398 0.266392 0.00839 23.162 0.733659 0 -6.482096 0.264967 0.00462 24.971 0.754073 0 -6.650471 0.254498 0.00479 25.135 0.775933 0 -6.689151 0.291954 0.00474 25.03 0.760361 0 -7.072419 0.220434 0.00481 24.692 0.766204 0 -6.836811 0.269866 0.00484 25.429 0.785714 1 -4.649573 0.205558 0.01036 21.028 0.819032 1 -4.333543 0.221727 0.0118 20.767 0.811843 1 -4.438453 0.238298 0.00969 21.422 0.821364 1 -4.60826 0.290024 0.00681 22.817 0.817756 1 -4.476755 0.262633 0.00786 22.603 0.813432 1 -4.609161 0.221711 0.01143 21.66 0.817396 0 -7.040508 0.066994 0.00871 25.554 0.678874 0 -7.293801 0.086372 0.00301 26.138 0.686264 0 -6.966321 0.095882 0.0034 25.856 0.694399 0 -7.24562 0.018689 0.00351 25.964 0.683296 0 -7.496264 0.056844 0.003 26.415 0.673636 0 -7.314237 0.006274 0.0042 24.547 0.681811 1 -5.409423 0.22685 0.02183 19.56 0.720908 1 -5.324574 0.20566 0.02659 19.979 0.729067 1 -5.86975 0.151814 0.04882 20.338 0.731444 1 -6.261141 0.120956 0.02431 21.718 0.727313 1 -5.720868 0.15883 0.02599 20.264 0.730387 1 -5.207985 0.224852 0.03361 18.57 0.733232 1 -5.79182 0.329066 0.00442 25.742 0.762959 1 -5.389129 0.306636 0.00623 24.178 0.789532 1 -5.31336 0.201861 0.00479 25.438 0.815908 1 -5.477592 0.315074 0.00472 25.197 0.807217 1 -5.775966 0.341169 0.00905 23.37 0.789977 1 -5.391029 0.250572 0.0042 25.82 0.81634 1 -5.115212 0.249494 0.01062 21.875 0.779612 1 -4.913885 0.265699 0.0222 19.2 0.790117 1 -4.441519 0.155097 0.01823 19.055 0.770466 1 -5.132032 0.210458 0.01825 19.659 0.778747 1 -5.022288 0.146948 0.01237 20.536 0.787896 1 -6.025367 0.078202 0.00882 22.244 0.772416 1 -5.288912 0.343073 0.0547 13.893 0.729586 1 -5.657899 0.315903 0.02782 16.176 0.727747 1 -6.366916 0.335753 0.03151 15.924 0.712199 1 -5.515071 0.299549 0.04824 13.922 0.740837 1 -5.783272 0.299793 0.04214 14.739 0.743937 1 -4.379411 0.375531 0.07223 11.866 0.745526 1 -4.508984 0.389232 0.08725 11.744 0.733165 1 -6.411497 0.207156 0.01658 19.664 0.71436 1 -5.952058 0.08784 0.01914 18.78 0.734504 1 -6.152551 0.17352 0.01211 20.969 0.69779 1 -6.251425 0.188056 0.0085 22.219 0.71217 1 -6.247076 0.180528 0.01018 21.693 0.705658 1 -6.41744 0.194627 0.00852 22.663 0.693429 1 -4.020042 0.265315 0.08151 15.338 0.714485 1 -5.159169 0.202146 0.10323 15.433 0.690892 1 -3.760348 0.242861 0.16744 12.435 0.674953 1 -3.700544 0.260481 0.31482 8.867 0.656846 1 -4.20273 0.310163 0.11843 15.06 0.643327 1 -3.269487 0.270641 0.2593 10.489 0.641418 1 -6.878393 0.089267 0.00495 26.759 0.722356 1 -7.111576 0.14478 0.00243 28.409 0.691483 1 -6.997403 0.210279 0.00578 27.421 0.719974 1 -6.981201 0.18455 0.00233 29.746 0.67793 1 -6.600023 0.249172 0.00659 26.833 0.700246 1 -6.739151 0.160686 0.00238 29.928 0.676066 1 -5.845099 0.278679 0.00947 21.934 0.740539 1 -5.25832 0.256454 0.00704 23.239 0.727863 1 -6.471427 0.184378 0.0083 22.407 0.712466 1 -4.876336 0.212054 0.01316 21.305 0.722085 1 -5.96304 0.250283 0.0062 23.671 0.722254 1 -6.729713 0.181701 0.01048 21.864 0.715121 1 -4.673241 0.261549 0.06051 23.693 0.662668 1 -6.051233 0.27328 0.01554 26.356 0.653823 1 -4.597834 0.372114 0.01802 25.69 0.676023 1 -4.913137 0.393056 0.00856 25.02 0.655239 1 -5.517173 0.389295 0.00681 24.581 0.58271 1 -6.186128 0.279933 0.0235 24.743 0.68413 1 -4.711007 0.281618 0.01161 27.166 0.656182 1 -5.418787 0.160267 0.01968 18.305 0.74148 1 -5.44514 0.142466 0.01813 18.784 0.732903 1 -5.944191 0.143359 0.0202 19.196 0.728421 1 -5.594275 0.12795 0.01874 18.857 0.735546 1 -5.540351 0.087165 0.01794 18.178 0.738245 1 -5.825257 0.115697 0.01796 18.33 0.736964 1 -6.890021 0.152941 0.01724 26.842 0.699787 1 -5.892061 0.195976 0.00487 26.369 0.718839 1 -6.135296 0.20363 0.0161 23.949 0.724045 1 -6.112667 0.217013 0.01015 26.017 0.735136 1 -5.436135 0.254909 0.00903 23.389 0.721308 1 -6.448134 0.178713 0.00504 25.619 0.723096 1 -5.301321 0.320385 0.03031 17.06 0.744064 1 -5.333619 0.322044 0.02529 17.707 0.706687 1 -4.378916 0.300067 0.02278 19.013 0.708144 1 -4.654894 0.304107 0.0369 16.747 0.708617 1 -5.634576 0.306014 0.02629 17.366 0.701404 1 -5.866357 0.23307 0.01827 18.801 0.696049 1 -4.796845 0.397749 0.02485 18.54 0.685057 1 -5.410336 0.288917 0.04238 15.648 0.665945 1 -5.585259 0.310746 0.01728 18.702 0.661735 1 -5.898673 0.213353 0.0201 18.687 0.632631 1 -6.132663 0.220617 0.01049 20.68 0.630409 1 -5.456811 0.345238 0.01493 20.366 0.574282 1 -3.297668 0.414758 0.0753 12.359 0.793509 1 -4.276605 0.355736 0.06057 14.367 0.768974 1 -3.377325 0.335357 0.08069 12.298 0.764036 1 -4.892495 0.262281 0.07889 14.989 0.775708 1 -4.484303 0.340256 0.10952 12.529 0.762726 1 -2.434031 0.450493 0.21713 8.441 0.76832 1 -2.839756 0.356224 0.16265 9.449 0.754449 1 -4.865194 0.246404 0.04179 21.52 0.670475 1 -4.239028 0.175691 0.04611 21.824 0.659333 1 -3.583722 0.207914 0.02631 22.431 0.652025 1 -5.4351 0.230532 0.03191 22.953 0.623731 1 -3.444478 0.303214 0.10748 19.075 0.646786 1 -5.070096 0.280091 0.03828 21.534 0.627337 1 -5.498456 0.234196 0.02663 19.651 0.675865 1 -5.185987 0.259229 0.02073 20.437 0.694571 1 -5.283009 0.226528 0.0281 19.388 0.684373 1 -5.529833 0.24275 0.02707 18.954 0.719576 1 -5.617124 0.184896 0.01435 21.219 0.673086 1 -2.929379 0.396746 0.03882 18.447 0.674562 0 -6.816086 0.17227 0.0062 24.078 0.628232 0 -7.018057 0.176316 0.00533 24.679 0.62671 0 -7.517934 0.160414 0.0091 21.083 0.628058 0 -5.736781 0.164529 0.01337 19.269 0.725216 0 -7.169701 0.073298 0.00965 21.02 0.646167 0 -7.3045 0.171088 0.01049 21.528 0.646818 0 -6.323531 0.218885 0.00435 26.436 0.7567 0 -6.085567 0.192375 0.0043 26.55 0.776158 0 -5.943501 0.19215 0.00478 26.547 0.7667 0 -6.012559 0.229298 0.0059 25.445 0.756482 0 -5.966779 0.197938 0.00401 26.005 0.761255 0 -6.016891 0.109256 0.00415 26.143 0.763242 1 -6.486822 0.197919 0.0057 24.151 0.745957 1 -6.311987 0.182459 0.00488 24.412 0.762508 1 -5.711205 0.240875 0.0054 23.683 0.778349 1 -6.261446 0.183218 0.00611 23.133 0.75932 1 -5.704053 0.216204 0.00639 22.866 0.768845 1 -6.27717 0.109397 0.00595 23.008 0.75718 0 -5.61907 0.191576 0.00955 23.079 0.669565 0 -5.198864 0.206768 0.01179 22.085 0.656516 0 -5.592584 0.133917 0.00737 24.199 0.654331 0 -6.431119 0.15331 0.01397 23.958 0.667654 0 -6.359018 0.116636 0.0068 25.023 0.663884 0 -6.710219 0.149694 0.00703 24.775 0.659132 0 -6.934474 0.15989 0.04441 19.368 0.683761 0 -6.538586 0.121952 0.02764 19.517 0.657899 0 -6.195325 0.129303 0.0181 19.147 0.683244 0 -6.787197 0.158453 0.10715 17.883 0.655683 0 -6.744577 0.207454 0.07223 19.02 0.643956 0 -5.724056 0.190667 0.04398 21.209 0.664357
Names of X columns:
status spread1 spread2 NHR HNR DFA
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
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
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') }
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Raw Output
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Big Analytics Cloud Computing Center
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