Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
119.992 157.302 0.00784 0.00007 0.00554 0.02971 122.4 148.65 0.00968 0.00008 0.00696 0.04368 116.682 131.111 0.0105 0.00009 0.00781 0.0359 116.676 137.871 0.00997 0.00009 0.00698 0.03772 116.014 141.781 0.01284 0.00011 0.00908 0.04465 120.552 131.162 0.00968 0.00008 0.0075 0.03243 120.267 137.244 0.00333 0.00003 0.00202 0.01351 107.332 113.84 0.0029 0.00003 0.00182 0.01256 95.73 132.068 0.00551 0.00006 0.00332 0.01717 95.056 120.103 0.00532 0.00006 0.00332 0.02444 88.333 112.24 0.00505 0.00006 0.0033 0.01892 91.904 115.871 0.0054 0.00006 0.00336 0.02214 136.926 159.866 0.00293 0.00002 0.00153 0.0114 139.173 179.139 0.0039 0.00003 0.00208 0.01797 152.845 163.305 0.00294 0.00002 0.00149 0.01246 142.167 217.455 0.00369 0.00003 0.00203 0.01359 144.188 349.259 0.00544 0.00004 0.00292 0.02074 168.778 232.181 0.00718 0.00004 0.00387 0.0343 153.046 175.829 0.00742 0.00005 0.00432 0.05767 156.405 189.398 0.00768 0.00005 0.00399 0.0431 153.848 165.738 0.0084 0.00005 0.0045 0.04055 153.88 172.86 0.0048 0.00003 0.00267 0.04525 167.93 193.221 0.00442 0.00003 0.00247 0.04246 173.917 192.735 0.00476 0.00003 0.00258 0.03772 163.656 200.841 0.00742 0.00005 0.0039 0.01497 104.4 206.002 0.00633 0.00006 0.00375 0.0378 171.041 208.313 0.00455 0.00003 0.00234 0.01872 146.845 208.701 0.00496 0.00003 0.00275 0.01826 155.358 227.383 0.0031 0.00002 0.00176 0.01661 162.568 198.346 0.00502 0.00003 0.00253 0.01799 197.076 206.896 0.00289 0.00001 0.00168 0.00802 199.228 209.512 0.00241 0.00001 0.00138 0.00762 198.383 215.203 0.00212 0.00001 0.00135 0.00951 202.266 211.604 0.0018 0.000009 0.00107 0.00719 203.184 211.526 0.00178 0.000009 0.00106 0.00726 201.464 210.565 0.00198 0.00001 0.00115 0.00957 177.876 192.921 0.00411 0.00002 0.00241 0.01612 176.17 185.604 0.00369 0.00002 0.00218 0.01491 180.198 201.249 0.00284 0.00002 0.00166 0.0119 187.733 202.324 0.00316 0.00002 0.00182 0.01366 186.163 197.724 0.00298 0.00002 0.00175 0.01233 184.055 196.537 0.00258 0.00001 0.00147 0.01234 237.226 247.326 0.00298 0.00001 0.00182 0.01133 241.404 248.834 0.00281 0.00001 0.00173 0.01251 243.439 250.912 0.0021 0.000009 0.00137 0.01033 242.852 255.034 0.00225 0.000009 0.00139 0.01014 245.51 262.09 0.00235 0.00001 0.00148 0.01149 252.455 261.487 0.00185 0.000007 0.00113 0.0086 122.188 128.611 0.00524 0.00004 0.00203 0.01433 122.964 130.049 0.00428 0.00003 0.00155 0.014 124.445 135.069 0.00431 0.00003 0.00167 0.01685 126.344 134.231 0.00448 0.00004 0.00169 0.01614 128.001 138.052 0.00436 0.00003 0.00166 0.01677 129.336 139.867 0.0049 0.00004 0.00183 0.01947 108.807 134.656 0.00761 0.00007 0.00486 0.02067 109.86 126.358 0.00874 0.00008 0.00539 0.02454 110.417 131.067 0.00784 0.00007 0.00514 0.02802 117.274 129.916 0.00752 0.00006 0.00469 0.01948 116.879 131.897 0.00788 0.00007 0.00493 0.02137 114.847 271.314 0.00867 0.00008 0.0052 0.02519 209.144 237.494 0.00282 0.00001 0.00152 0.01382 223.365 238.987 0.00264 0.00001 0.00151 0.0134 222.236 231.345 0.00266 0.00001 0.00144 0.012 228.832 234.619 0.00296 0.00001 0.00155 0.01179 229.401 252.221 0.00205 0.000009 0.00113 0.01016 228.969 239.541 0.00238 0.00001 0.0014 0.01234 140.341 159.774 0.00817 0.00006 0.0044 0.02428 136.969 166.607 0.00923 0.00007 0.00463 0.02603 143.533 162.215 0.01101 0.00008 0.00467 0.03392 148.09 162.824 0.00762 0.00005 0.00354 0.03635 142.729 162.408 0.00831 0.00006 0.00419 0.02949 136.358 176.595 0.00971 0.00007 0.00478 0.03736 120.08 139.71 0.00405 0.00003 0.0022 0.01345 112.014 588.518 0.00533 0.00005 0.00329 0.01956 110.793 128.101 0.00494 0.00004 0.00283 0.01831 110.707 122.611 0.00516 0.00005 0.00289 0.01715 112.876 148.826 0.005 0.00004 0.00289 0.02704 110.568 125.394 0.00462 0.00004 0.0028 0.01636 95.385 102.145 0.00608 0.00006 0.00332 0.02455 100.77 115.697 0.01038 0.0001 0.00576 0.02139 96.106 108.664 0.00694 0.00007 0.00415 0.02876 95.605 107.715 0.00702 0.00007 0.00371 0.0219 100.96 110.019 0.00606 0.00006 0.00348 0.01751 98.804 102.305 0.00432 0.00004 0.00258 0.01552 176.858 205.56 0.00747 0.00004 0.0042 0.0351 180.978 200.125 0.00406 0.00002 0.00244 0.02877 178.222 202.45 0.00321 0.00002 0.00194 0.02784 176.281 227.381 0.0052 0.00003 0.00312 0.04683 173.898 211.35 0.00448 0.00003 0.00254 0.04802 179.711 225.93 0.00709 0.00004 0.00419 0.03455 166.605 206.008 0.00742 0.00004 0.00453 0.05114 151.955 163.335 0.00419 0.00003 0.00227 0.0569 148.272 164.989 0.00459 0.00003 0.00256 0.03051 152.125 161.469 0.00382 0.00003 0.00226 0.04398 157.821 172.975 0.00358 0.00002 0.00196 0.02764 157.447 163.267 0.00369 0.00002 0.00197 0.02571 159.116 168.913 0.00342 0.00002 0.00184 0.02809 125.036 143.946 0.0128 0.0001 0.00623 0.03088 125.791 140.557 0.01378 0.00011 0.00655 0.03908 126.512 141.756 0.01936 0.00015 0.0099 0.05783 125.641 141.068 0.03316 0.00026 0.01522 0.06196 128.451 150.449 0.01551 0.00012 0.00909 0.05174 139.224 586.567 0.03011 0.00022 0.01628 0.06023 150.258 154.609 0.00248 0.00002 0.00136 0.01009 154.003 160.267 0.00183 0.00001 0.001 0.00871 149.689 160.368 0.00257 0.00002 0.00134 0.01059 155.078 163.736 0.00168 0.00001 0.00092 0.00928 151.884 157.765 0.00258 0.00002 0.00122 0.01267 151.989 157.339 0.00174 0.00001 0.00096 0.00993 193.03 208.9 0.00766 0.00004 0.00389 0.02084 200.714 223.982 0.00621 0.00003 0.00337 0.01852 208.519 220.315 0.00609 0.00003 0.00339 0.01307 204.664 221.3 0.00841 0.00004 0.00485 0.01767 210.141 232.706 0.00534 0.00003 0.0028 0.01301 206.327 226.355 0.00495 0.00002 0.00246 0.01604 151.872 492.892 0.00856 0.00006 0.00385 0.01271 158.219 442.557 0.00476 0.00003 0.00207 0.01312 170.756 450.247 0.00555 0.00003 0.00261 0.01652 178.285 442.824 0.00462 0.00003 0.00194 0.01151 217.116 233.481 0.00404 0.00002 0.00128 0.01075 128.94 479.697 0.00581 0.00005 0.00314 0.01734 176.824 215.293 0.0046 0.00003 0.00221 0.01104 138.19 203.522 0.00704 0.00005 0.00398 0.0322 182.018 197.173 0.00842 0.00005 0.00449 0.01931 156.239 195.107 0.00694 0.00004 0.00395 0.0172 145.174 198.109 0.00733 0.00005 0.00422 0.01944 138.145 197.238 0.00544 0.00004 0.00327 0.02259 166.888 198.966 0.00638 0.00004 0.00351 0.02301 119.031 127.533 0.0044 0.00004 0.00192 0.00811 120.078 126.632 0.0027 0.00002 0.00135 0.00903 120.289 128.143 0.00492 0.00004 0.00238 0.01194 120.256 125.306 0.00407 0.00003 0.00205 0.0131 119.056 125.213 0.00346 0.00003 0.0017 0.00915 118.747 123.723 0.00331 0.00003 0.00171 0.00903 106.516 112.777 0.00589 0.00006 0.00319 0.03651 110.453 127.611 0.00494 0.00004 0.00315 0.03316 113.4 133.344 0.00451 0.00004 0.00283 0.0437 113.166 130.27 0.00502 0.00004 0.00312 0.04134 112.239 126.609 0.00472 0.00004 0.0029 0.04451 116.15 131.731 0.00381 0.00003 0.00232 0.0277 170.368 268.796 0.00571 0.00003 0.00269 0.02824 208.083 253.792 0.00757 0.00004 0.00428 0.04464 198.458 219.29 0.00376 0.00002 0.00215 0.0253 202.805 231.508 0.0037 0.00002 0.00211 0.01506 202.544 241.35 0.00254 0.00001 0.00133 0.02006 223.361 263.872 0.00352 0.00002 0.00188 0.01909 169.774 191.759 0.01568 0.00009 0.00946 0.08808 183.52 216.814 0.01466 0.00008 0.00819 0.06359 188.62 216.302 0.01719 0.00009 0.01027 0.06824 202.632 565.74 0.01627 0.00008 0.00963 0.0646 186.695 211.961 0.01872 0.0001 0.01154 0.06259 192.818 224.429 0.03107 0.00016 0.01958 0.13778 198.116 233.099 0.02714 0.00014 0.01699 0.08318 121.345 139.644 0.00684 0.00006 0.00332 0.02056 119.1 128.442 0.00692 0.00006 0.003 0.02018 117.87 127.349 0.00647 0.00005 0.003 0.02402 122.336 142.369 0.00727 0.00006 0.00339 0.01771 117.963 134.209 0.01813 0.00015 0.00718 0.02916 126.144 154.284 0.00975 0.00008 0.00454 0.02157 127.93 138.752 0.00605 0.00005 0.00318 0.03105 114.238 124.393 0.00581 0.00005 0.00316 0.04114 115.322 135.738 0.00619 0.00005 0.00329 0.02931 114.554 126.778 0.00651 0.00006 0.0034 0.03091 112.15 131.669 0.00519 0.00005 0.00284 0.01363 102.273 142.83 0.00907 0.00009 0.00461 0.02073 236.2 244.663 0.00277 0.00001 0.00153 0.01621 237.323 243.709 0.00303 0.00001 0.00159 0.00882 260.105 264.919 0.00339 0.00001 0.00186 0.01367 197.569 217.627 0.00803 0.00004 0.00448 0.01439 240.301 245.135 0.00517 0.00002 0.00283 0.01344 244.99 272.21 0.00451 0.00002 0.00237 0.01255 112.547 133.374 0.00355 0.00003 0.0019 0.0114 110.739 113.597 0.00356 0.00003 0.002 0.01285 113.715 116.443 0.00349 0.00003 0.00203 0.01148 117.004 144.466 0.00353 0.00003 0.00218 0.01318 115.38 123.109 0.00332 0.00003 0.00199 0.01133 116.388 129.038 0.00346 0.00003 0.00213 0.01331 151.737 190.204 0.00314 0.00002 0.00162 0.0123 148.79 158.359 0.00309 0.00002 0.00186 0.01309 148.143 155.982 0.00392 0.00003 0.00231 0.01263 150.44 163.441 0.00396 0.00003 0.00233 0.02148 148.462 161.078 0.00397 0.00003 0.00235 0.01559 149.818 163.417 0.00336 0.00002 0.00198 0.01666 117.226 123.925 0.00417 0.00004 0.0027 0.01949 116.848 217.552 0.00531 0.00005 0.00346 0.01756 116.286 177.291 0.00314 0.00003 0.00192 0.01691 116.556 592.03 0.00496 0.00004 0.00263 0.01491 116.342 581.289 0.00267 0.00002 0.00148 0.01144 114.563 119.167 0.00327 0.00003 0.00184 0.01095 201.774 262.707 0.00694 0.00003 0.00396 0.01758 174.188 230.978 0.00459 0.00003 0.00259 0.02745 209.516 253.017 0.00564 0.00003 0.00292 0.01879 174.688 240.005 0.0136 0.00008 0.00564 0.01667 198.764 396.961 0.0074 0.00004 0.0039 0.01588 214.289 260.277 0.00567 0.00003 0.00317 0.01373
Names of X columns:
MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Jitter(%) MDVP:Jitter(Abs) MDVP:PPQ MDVP:APQ
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
Click here to blog (archive) this computation