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Data X:
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189 8.590909091 6.47826087 27 1 134 243 8.379310345 2.481481481 28 1 171 111 5.842105263 5.7 24 1 237 172 6.142857143 4.9375 27 1 242 80 3.80952381 7.117647059 17 1 59 256 5.953488372 2.269230769 23 1 279 208 6.117647059 4.810344828 31 1 216 146 11.23076923 3.927272727 21 1 152 100 3.225806452 3.454545455 38 1 266 159 7.227272727 7.189189189 3 1 20 241 5.127659574 0.8 21 1 250 132 5.5 5.208333333 17 1 75 222 8.222222222 2.205882353 28 1 68 316 7.523809524 2.193548387 55 1 403 81 9 6.2 14 1 89 170 6.071428571 2.282051282 19 1 186 267 5.134615385 5.8125 52 1 289 174 6.692307692 7.41025641 20 1 56 243 7.59375 2 6 1 89 304 6.755555556 2.119047619 24 1 141 121 3.78125 3.43902439 34 1 196 283 7.648648649 5.157894737 31 1 249 162 7.363636364 6.552631579 10 1 68 144 4.965517241 3.777777778 37 1 158 302 9.4375 4.647058824 54 1 307 453 12.24324324 6.673913043 49 1 266 386 9.19047619 6.487804878 49 1 325 209 11 7.065217391 34 1 159 257 9.518518519 3.613636364 35 1 96 296 6.727272727 3.096774194 38 1 293 138 7.263157895 5.528301887 27 1 214 340 5.964912281 6.6875 36 1 202 304 7.794871795 4.29787234 44 1 326 230 10.45454545 6.791666667 41 1 304 279 6.2 5.846153846 40 1 272 234 8.666666667 5.666666667 14 1 59 271 7.742857143 1.966666667 14 1 192 245 5 4.923076923 20 1 190 244 8.714285714 3.454545455 24 1 135 506 6.746666667 3.375 28 1 94 274 5.956521739 3.133333333 48 1 332 275 12.5 5.928571429 27 1 289 158 6.869565217 6.720930233 14 1 180 98 3.769230769 5.625 7 1 101 131 4.09375 3.482758621 16 1 158 176 7.04 2.872727273 41 1 165 329 8.024390244 4.230769231 38 1 165 220 6.666666667 4.024390244 42 1 217 258 8.6 4.822222222 22 1 72 367 8.534883721 2.88 15 1 226 166 4.368421053 5.380952381 52 1 158 362 9.282051282 4.647058824 32 1 255 88 3.384615385 7.96875 33 1 171 381 9.525 4.071428571 9 1 197 110 4.230769231 4.020408163 20 1 124 129 7.166666667 2.88372093 56 1 297 163 8.578947368 5.823529412 7 1 -26 229 6.361111111 -1 27 1 130 273 7.8 2.765957447 31 1 200 261 10.03846154 4.347826087 31 1 204 109 3.75862069 4.857142857 21 1 268 58 5.8 5.36 45 1 244 232 6.823529412 5.422222222 52 1 193 235 10.2173913 5.216216216 42 1 276 235 6.911764706 5.872340426 38 1 86 300 11.53846154 3.071428571 48 1 171 407 16.28 4.5 52 1 250 315 9.84375 5.208333333 0 1 82 127 3.432432432 2.733333333 7 1 73 95 2.878787879 2.517241379 56 1 195 208 8.32 5.571428571 56 1 310 302 8.162162162 6.458333333 13 1 69 181 5.323529412 2.3 65 1 361 200 10 6.811320755 16 1 70 137 4.28125 3.181818182 7 1 146 109 4.739130435 6.347826087 30 1 141 237 4.9375 4.40625 40 1 174 213 10.65 4.461538462 34 1 190 285 7.5 5.277777778 24 1 73 311 7.068181818 2.147058824 38 1 152 280 8.235294118 4.903225806 28 1 319 175 4.375 6.787234043 14 1 86 46 2.875 2.965517241 20 1 156 159 6.625 3.627906977 21 1 261 140 23.33333333 5.019230769 42 1 143 359 10.55882353 3.666666667 0 1 125 23 1.4375 2.840909091 50 1 73 406 9.441860465 2.517241379 58 1 415 129 4.448275862 9.431818182 23 1 130 183 7.32 4.333333333 8 1 88 135 4.5 3.034482759 14 1 80 190 5.428571429 2.5 30 1 333 203 5.486486486 5.644067797 27 1 149 332 5.824561404 7.095238095 19 1 127 320 8.421052632 3.735294118 23 1 213 202 8.08 5.605263158 49 1 323 195 7.8 7.340909091 21 1 78 278 7.513513514 2.294117647 35 1 260 96 8 6.5 34 1 242 189 14.53846154 5.377777778 35 1 8 433 8.490196078 0.347826087 10 1 110 154 3.85 2.894736842 21 1 73 255 7.5 1.921052632 23 1 138 336 8.195121951 4.181818182 20 1 229 140 7.368421053 4.490196078 28 1 208 102 6 4 24 1 189 142 5.259259259 6.096774194 20 1 137 296 6.577777778 3.605263158 31 1 151 368 8.975609756 3.511627907 45 1 240 203 10.15 4.363636364 49 1 176 370 9.25 4.093023256 52 1 218 337 9.361111111 4.954545455 45 1 403 64 2.782608696 8.395833333 26 1 160 287 7.175 3.720930233 3 1 87 116 4.296296296 3.222222222 35 1 175 408 10.2 5 52 1 308 366 9.631578947 6.039215686 30 1 201 280 8.484848485 5.153846154 34 1 308 206 5.567567568 5.923076923 7 1 194 82 4.555555556 4.731707317 55 1 188 248 9.185185185 4.947368421 24 1 -2 407 7.980392157 -0.086956522 20 1 262 171 4.5 6.55 63 1 195 288 13.09090909 4.642857143 20 1 153 88 3.259259259 3.477272727 21 1 112 338 9.657142857 4.869565217 35 1 290 248 9.92 6.304347826 28 1 119 315 7.682926829 4.76 24 1 92 199 7.653846154 2.486486486 38 1 199 309 9.967741935 4.627906977 16 1 68 317 6.215686275 3.777777778 28 1 154 148 4.484848485 3.347826087 30 1 213 202 7.769230769 4.733333333 7 1 152 140 6.086956522 3.534883721 13 1 57 307 6.531914894 2.28 14 1 78 241 5.87804878 2.516129032 27 1 307 126 4.846153846 5.032786885 40 1 235 175 6.730769231 3.983050847 20 1 216 108 4.153846154 4.32 28 1 206 243 5.4 4.790697674 31 1 38 287 6.833333333 1.583333333 32 1 181 221 7.366666667 4.763157895 41 1 186 344 10.42424242 3.957446809 17 1 129 76 2.620689655 4.161290323 53 1 331 192 7.68 6.365384615 28 1 230 172 8.19047619 5 55 1 489 53 7.571428571 8.732142857 14 1 100 215 6.142857143 2.857142857 13 1 91 169 7.347826087 2.459459459 21 1 53 223 6.027027027 2.944444444 34 1 187 343 8.365853659 4.56097561 36 1 150 336 5.793103448 4.166666667 31 1 216 182 5.515151515 4.8 2 1 128 134 3.435897436 3.2 31 1 320 88 7.333333333 6.037735849 23 1 132 325 7.222222222 4.551724138 19 1 244 83 3.952380952 5.191489362 48 1 138 542 10.62745098 6.272727273 27 1 161 160 5.517241379 4.128205128 28 1 240 116 7.25 4.444444444 27 1 148 254 6.047619048 4.352941176 47 1 377 135 15 6.732142857 38 1 153 269 12.80952381 3.558139535 24 1 98 206 6.64516129 2 24 1 233 106 4.818181818 5.825 14 1 107 152 5.428571429 2.815789474 29 1 233 204 7.846153846 4.017241379 42 1 64 239 9.192307692 1.488372093 17 1 395 84 3 5.984848485 55 1 343 192 12 7.456521739 17 1 103 207 5.307692308 2.641025641 45 1 111 410 7.884615385 3.083333333 7 1 135 68 3.578947368 3.068181818 13 1 52 216 8 2.166666667 27 1 214 151 5.592592593 3.821428571 27 1 233 203 14.5 5.065217391 41 1 262 155 11.92307692 5.137254902 45 1 319 289 6.880952381 5.696428571 47 1 229 458 12.05263158 6.361111111 16 1 23 208 7.172413793 1.15 31 1 303 124 4.769230769 6.183673469 37 1 319 189 8.590909091 4.623188406 35 1 126 71 4.4375 2.680851064 21 1 131 237 5.266666667 4.517241379 35 1 290 307 9.029411765 6.444444444 32 1 150 285 9.827586207 4.545454545 27 1 116 246 7.235294118 4 7 1 -5 38 1.80952381 -0.166666667 45 1 282 331 12.25925926 6.87804878 7 1 77 202 6.516129032 2.40625 37 1 326 174 9.157894737 5.258064516 16 1 171 218 6.055555556 4.170731707 55 1 254 344 9.052631579 5.906976744 44 1 373 163 4.075 8.108695652 31 1 22 353 9.051282051 0.733333333 44 1 417 173 9.611111111 5.873239437 21 1 158 223 7.433333333 4.051282051 27 1 29 370 6.607142857 1.115384615 36 1 285 149 9.933333333 5.377358491 23 1 222 310 7.380952381 5.285714286 49 1 645 111 6.166666667 7.678571429 51 1 189 380 9.047619048 5.4 23 1 177 217 7.482758621 3.847826087 27 1 307 227 8.730769231 6.673913043 10 1 99 106 2.864864865 3.193548387 45 1 226 194 6.466666667 4.264150943 37 1 312 218 6.8125 5.379310345 24 1 16 397 8.270833333 0.695652174 38 1 154 286 9.225806452 4.4 16 1 48 239 7.242424242 1.263157895 7 1 118 155 6.2 4.37037037 44 1 285 211 6.806451613 5.277777778 48 1 207 347 9.914285714 5.594594595 45 1 254 315 12.11538462 6.047619048 47 1 174 371 7.893617021 3.70212766 24 1 166 161 7 4.048780488 42 1 102 418 8.038461538 3.1875 40 1 145 405 8.617021277 3.815789474
Names of X columns:
Score HomeAdv RushYards PassYards YardsPerPass YardsPerRush
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
par5 <- '0' par4 <- '0' 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 mywarning <- '' par1 <- as.numeric(par1) if(is.na(par1)) { par1 <- 1 mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.' } if (par4=='') par4 <- 0 par4 <- as.numeric(par4) if (par5=='') par5 <- 0 par5 <- as.numeric(par5) x <- na.omit(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'){ (n <- n -1) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par3 == 'Seasonal Differences (s=12)'){ (n <- n - 12) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+12,j] - x[i,j] } } x <- x2 } if (par3 == 'First and Seasonal Differences (s=12)'){ (n <- n -1) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 (n <- n - 12) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+12,j] - x[i,j] } } x <- x2 } if(par4 > 0) { x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep=''))) for (i in 1:(n-par4)) { for (j in 1:par4) { x2[i,j] <- x[i+par4-j,par1] } } x <- cbind(x[(par4+1):n,], x2) n <- n - par4 } if(par5 > 0) { x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep=''))) for (i in 1:(n-par5*12)) { for (j in 1:par5) { x2[i,j] <- x[i+par5*12-j*12,par1] } } x <- cbind(x[(par5*12+1):n,], x2) n <- n - par5*12 } 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[n,])) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x (k <- length(x[n,])) head(x) 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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, mywarning) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+')) a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+')) a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' ')) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$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$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' ')) 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,formatC(signif(mysum$sigma,6),format='g',flag=' ')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') if(n < 200) { a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' ')) 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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' ')) 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,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' ')) 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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Summary of computational transaction
Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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