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Data X:
1 119.992 0.06545 0.02971 0.00784 2.301442 0.414783 1 122.4 0.09403 0.04368 0.00968 2.486855 0.458359 1 116.682 0.0827 0.0359 0.0105 2.342259 0.429895 1 116.676 0.08771 0.03772 0.00997 2.405554 0.434969 1 116.014 0.1047 0.04465 0.01284 2.33218 0.417356 1 120.552 0.06985 0.03243 0.00968 2.18756 0.415564 1 120.267 0.02337 0.01351 0.00333 1.854785 0.59604 1 107.332 0.02487 0.01256 0.0029 2.064693 0.63742 1 95.73 0.03218 0.01717 0.00551 2.322511 0.615551 1 95.056 0.04324 0.02444 0.00532 2.432792 0.547037 1 88.333 0.03237 0.01892 0.00505 2.407313 0.611137 1 91.904 0.04272 0.02214 0.0054 2.642476 0.58339 1 136.926 0.01968 0.0114 0.00293 2.041277 0.4606 1 139.173 0.02184 0.01797 0.0039 2.519422 0.430166 1 152.845 0.03191 0.01246 0.00294 2.125618 0.474791 1 142.167 0.02316 0.01359 0.00369 2.205546 0.565924 1 144.188 0.02908 0.02074 0.00544 2.264501 0.56738 1 168.778 0.04322 0.0343 0.00718 3.007463 0.631099 1 153.046 0.07413 0.05767 0.00742 3.10901 0.665318 1 156.405 0.05164 0.0431 0.00768 2.856676 0.649554 1 153.848 0.05 0.04055 0.0084 2.73971 0.660125 1 153.88 0.06062 0.04525 0.0048 2.557536 0.629017 1 167.93 0.06685 0.04246 0.00442 2.916777 0.61906 1 173.917 0.06562 0.03772 0.00476 2.547508 0.537264 1 163.656 0.02214 0.01497 0.00742 2.692176 0.397937 1 104.4 0.05197 0.0378 0.00633 2.846369 0.522746 1 171.041 0.02666 0.01872 0.00455 2.589702 0.418622 1 146.845 0.0265 0.01826 0.00496 2.314209 0.358773 1 155.358 0.02307 0.01661 0.0031 2.241742 0.470478 1 162.568 0.0238 0.01799 0.00502 1.957961 0.427785 0 197.076 0.01689 0.00802 0.00289 1.743867 0.422229 0 199.228 0.01513 0.00762 0.00241 2.103106 0.432439 0 198.383 0.01919 0.00951 0.00212 1.512275 0.465946 0 202.266 0.01407 0.00719 0.0018 1.544609 0.368535 0 203.184 0.01403 0.00726 0.00178 1.423287 0.340068 0 201.464 0.01758 0.00957 0.00198 2.447064 0.344252 1 177.876 0.03463 0.01612 0.00411 2.477082 0.360148 1 176.17 0.02814 0.01491 0.00369 2.536527 0.341435 1 180.198 0.02177 0.0119 0.00284 2.269398 0.403884 1 187.733 0.02488 0.01366 0.00316 2.382544 0.396793 1 186.163 0.02321 0.01233 0.00298 2.374073 0.32648 1 184.055 0.02226 0.01234 0.00258 2.361532 0.306443 0 237.226 0.03104 0.01133 0.00298 2.416838 0.305062 0 241.404 0.03017 0.01251 0.00281 2.256699 0.457702 0 243.439 0.0233 0.01033 0.0021 2.330716 0.438296 0 242.852 0.02542 0.01014 0.00225 2.3658 0.431285 0 245.51 0.02719 0.01149 0.00235 2.392122 0.467489 0 252.455 0.01841 0.0086 0.00185 2.028612 0.610367 0 122.188 0.02566 0.01433 0.00524 2.079922 0.579597 0 122.964 0.02789 0.014 0.00428 2.054419 0.538688 0 124.445 0.03724 0.01685 0.00431 1.840198 0.553134 0 126.344 0.03429 0.01614 0.00448 2.431854 0.507504 0 128.001 0.03969 0.01677 0.00436 1.972297 0.459766 0 129.336 0.04188 0.01947 0.0049 2.223719 0.420383 1 108.807 0.0445 0.02067 0.00761 1.986899 0.536009 1 109.86 0.05368 0.02454 0.00874 2.014606 0.558586 1 110.417 0.06097 0.02802 0.00784 1.92294 0.541781 1 117.274 0.03568 0.01948 0.00752 2.021591 0.530529 1 116.879 0.04183 0.02137 0.00788 1.827012 0.540049 1 114.847 0.05414 0.02519 0.00867 1.831691 0.547975 0 209.144 0.02925 0.01382 0.00282 2.460791 0.341788 0 223.365 0.03039 0.0134 0.00264 2.32156 0.447979 0 222.236 0.02602 0.012 0.00266 2.278687 0.364867 0 228.832 0.02647 0.01179 0.00296 2.498224 0.25657 0 229.401 0.02308 0.01016 0.00205 2.003032 0.27685 0 228.969 0.02827 0.01234 0.00238 2.118596 0.305429 1 140.341 0.0549 0.02428 0.00817 2.359973 0.460139 1 136.969 0.04914 0.02603 0.00923 2.291558 0.498133 1 143.533 0.09455 0.03392 0.01101 2.118496 0.513237 1 148.09 0.1007 0.03635 0.00762 2.137075 0.487407 1 142.729 0.05605 0.02949 0.00831 2.277927 0.489345 1 136.358 0.08247 0.03736 0.00971 2.642276 0.543299 1 120.08 0.02921 0.01345 0.00405 2.205024 0.495954 1 112.014 0.0412 0.01956 0.00533 1.928708 0.509127 1 110.793 0.04295 0.01831 0.00494 2.225815 0.437031 1 110.707 0.03851 0.01715 0.00516 1.862092 0.463514 1 112.876 0.07238 0.02704 0.005 2.007923 0.489538 1 110.568 0.03852 0.01636 0.00462 1.777901 0.429484 1 95.385 0.05408 0.02455 0.00608 2.017753 0.644954 1 100.77 0.0532 0.02139 0.01038 2.398422 0.594387 1 96.106 0.06799 0.02876 0.00694 2.645959 0.544805 1 95.605 0.05377 0.0219 0.00702 2.232576 0.576084 1 100.96 0.04114 0.01751 0.00606 2.428306 0.55461 1 98.804 0.03831 0.01552 0.00432 2.053601 0.576644 1 176.858 0.08037 0.0351 0.00747 3.099301 0.556494 1 180.978 0.06321 0.02877 0.00406 3.098256 0.583574 1 178.222 0.06219 0.02784 0.00321 2.654271 0.598714 1 176.281 0.11012 0.04683 0.0052 3.13655 0.602874 1 173.898 0.11363 0.04802 0.00448 3.007096 0.599371 1 179.711 0.06892 0.03455 0.00709 3.671155 0.590951 1 166.605 0.10949 0.05114 0.00742 3.317586 0.65341 1 151.955 0.13262 0.0569 0.00419 2.344876 0.501037 1 148.272 0.0715 0.03051 0.00459 2.344336 0.454444 1 152.125 0.10024 0.04398 0.00382 2.080121 0.447456 1 157.821 0.06185 0.02764 0.00358 2.143851 0.50238 1 157.447 0.05439 0.02571 0.00369 2.344348 0.447285 1 159.116 0.05417 0.02809 0.00342 2.473239 0.366329 1 125.036 0.06406 0.03088 0.0128 2.671825 0.629574 1 125.791 0.07625 0.03908 0.01378 2.441612 0.57101 1 126.512 0.10833 0.05783 0.01936 2.634633 0.638545 1 125.641 0.16074 0.06196 0.03316 2.991063 0.671299 1 128.451 0.09669 0.05174 0.01551 2.638279 0.639808 1 139.224 0.16654 0.06023 0.03011 2.690917 0.596362 1 150.258 0.01567 0.01009 0.00248 2.004055 0.296888 1 154.003 0.01406 0.00871 0.00183 2.065477 0.263654 1 149.689 0.01979 0.01059 0.00257 1.994387 0.365488 1 155.078 0.01567 0.00928 0.00168 2.129924 0.334171 1 151.884 0.01898 0.01267 0.00258 2.499148 0.393563 1 151.989 0.01364 0.00993 0.00174 2.296873 0.311369 1 193.03 0.05312 0.02084 0.00766 2.608749 0.497554 1 200.714 0.03576 0.01852 0.00621 2.550961 0.436084 1 208.519 0.02855 0.01307 0.00609 2.502336 0.338097 1 204.664 0.03831 0.01767 0.00841 2.376749 0.498877 1 210.141 0.02583 0.01301 0.00534 2.489191 0.441097 1 206.327 0.0332 0.01604 0.00495 2.938114 0.331508 1 151.872 0.02389 0.01271 0.00856 2.702355 0.407701 1 158.219 0.01818 0.01312 0.00476 2.640798 0.450798 1 170.756 0.0227 0.01652 0.00555 2.975889 0.486738 1 178.285 0.01851 0.01151 0.00462 2.816781 0.470422 1 217.116 0.02038 0.01075 0.00404 2.925862 0.462516 1 128.94 0.02548 0.01734 0.00581 2.68624 0.487756 1 176.824 0.01603 0.01104 0.0046 2.655744 0.400088 1 138.19 0.07761 0.0322 0.00704 2.090438 0.538016 1 182.018 0.04115 0.01931 0.00842 2.174306 0.589956 1 156.239 0.03867 0.0172 0.00694 1.929715 0.618663 1 145.174 0.03706 0.01944 0.00733 1.765957 0.637518 1 138.145 0.04451 0.02259 0.00544 1.821297 0.623209 1 166.888 0.04641 0.02301 0.00638 1.996146 0.585169 1 119.031 0.01614 0.00811 0.0044 2.328513 0.457541 1 120.078 0.01428 0.00903 0.0027 2.108873 0.491345 1 120.289 0.0211 0.01194 0.00492 2.539724 0.46716 1 120.256 0.02164 0.0131 0.00407 2.527742 0.468621 1 119.056 0.01898 0.00915 0.00346 2.51632 0.470972 1 118.747 0.01471 0.00903 0.00331 2.034827 0.482296 1 106.516 0.0805 0.03651 0.00589 2.375138 0.637814 1 110.453 0.06688 0.03316 0.00494 2.631793 0.653427 1 113.4 0.07154 0.0437 0.00451 2.445502 0.6479 1 113.166 0.08689 0.04134 0.00502 2.672362 0.625362 1 112.239 0.09211 0.04451 0.00472 2.419253 0.640945 1 116.15 0.04543 0.0277 0.00381 2.445646 0.624811 1 170.368 0.05139 0.02824 0.00571 2.963799 0.677131 1 208.083 0.12047 0.04464 0.00757 2.665133 0.606344 1 198.458 0.06165 0.0253 0.00376 2.465528 0.606273 1 202.805 0.0335 0.01506 0.0037 2.470746 0.536102 1 202.544 0.04426 0.02006 0.00254 2.576563 0.49748 1 223.361 0.04137 0.01909 0.00352 2.840556 0.566849 1 169.774 0.11411 0.08808 0.01568 3.413649 0.56161 1 183.52 0.08595 0.06359 0.01466 3.142364 0.478024 1 188.62 0.10422 0.06824 0.01719 3.274865 0.55287 1 202.632 0.10546 0.0646 0.01627 2.910213 0.427627 1 186.695 0.08096 0.06259 0.01872 2.958815 0.507826 1 192.818 0.16942 0.13778 0.03107 3.079221 0.625866 1 198.116 0.12851 0.08318 0.02714 3.184027 0.584164 1 121.345 0.04019 0.02056 0.00684 2.01353 0.566867 1 119.1 0.04451 0.02018 0.00692 2.45113 0.65168 1 117.87 0.04977 0.02402 0.00647 2.439597 0.6283 1 122.336 0.03615 0.01771 0.00727 2.699645 0.611679 1 117.963 0.0783 0.02916 0.01813 2.964568 0.630547 1 126.144 0.04499 0.02157 0.00975 2.8923 0.635015 1 127.93 0.04079 0.03105 0.00605 2.103014 0.654945 1 114.238 0.04736 0.04114 0.00581 2.151121 0.653139 1 115.322 0.04933 0.02931 0.00619 2.442906 0.577802 1 114.554 0.05592 0.03091 0.00651 2.408689 0.685151 1 112.15 0.02902 0.01363 0.00519 1.871871 0.557045 1 102.273 0.04736 0.02073 0.00907 2.560422 0.671378 0 236.2 0.04231 0.01621 0.00277 2.235197 0.469928 0 237.323 0.02089 0.00882 0.00303 1.852402 0.384868 0 260.105 0.03557 0.01367 0.00339 1.881767 0.440988 0 197.569 0.03836 0.01439 0.00803 2.88245 0.372222 0 240.301 0.03529 0.01344 0.00517 2.266432 0.371837 0 244.99 0.03253 0.01255 0.00451 2.095237 0.522812 0 112.547 0.01992 0.0114 0.00355 2.193412 0.413295 0 110.739 0.02261 0.01285 0.00356 1.889002 0.36909 0 113.715 0.02245 0.01148 0.00349 1.852542 0.380253 0 117.004 0.02643 0.01318 0.00353 1.872946 0.387482 0 115.38 0.02436 0.01133 0.00332 1.974857 0.405991 0 116.388 0.02623 0.01331 0.00346 2.004719 0.361232 1 151.737 0.02184 0.0123 0.00314 2.449763 0.39661 1 148.79 0.02518 0.01309 0.00309 2.251553 0.402591 1 148.143 0.02175 0.01263 0.00392 2.845109 0.398499 1 150.44 0.03964 0.02148 0.00396 2.264226 0.352396 1 148.462 0.02849 0.01559 0.00397 2.679185 0.408598 1 149.818 0.03464 0.01666 0.00336 2.209021 0.329577 0 117.226 0.02592 0.01949 0.00417 2.027228 0.603515 0 116.848 0.02429 0.01756 0.00531 2.120412 0.663842 0 116.286 0.02001 0.01691 0.00314 2.058658 0.598515 0 116.556 0.0246 0.01491 0.00496 2.161936 0.566424 0 116.342 0.01892 0.01144 0.00267 2.152083 0.528485 0 114.563 0.01672 0.01095 0.00327 1.91399 0.555303 0 201.774 0.04363 0.01758 0.00694 2.316346 0.508479 0 174.188 0.07008 0.02745 0.00459 2.657476 0.448439 0 209.516 0.04812 0.01879 0.00564 2.784312 0.431674 0 174.688 0.03804 0.01667 0.0136 2.679772 0.407567 0 198.764 0.03794 0.01588 0.0074 2.138608 0.451221 0 214.289 0.03078 0.01373 0.00567 2.555477 0.462803
Names of X columns:
status MDVP:Fo(Hz) Shimmer:DDA MDVP:APQ MDVP:Jitter(%) D2 RPDE
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
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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