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Data X:
13 360 3821 73 0 0 15 390 3850 70 0 0 17 304 3672 72 0 0 19.4 232 3210 78 0 0 24.3 151 3003 80 0 0 18.1 258 3410 78 0 0 20.2 232 3265 79 0 0 18 232 2789 73 0 0 19 232 2634 71 0 0 20 232 2914 75 0 0 21 199 2648 70 0 0 18 199 2774 70 0 0 18 232 2945 73 0 0 19 232 2901 74 0 0 22.5 232 3085 76 0 0 18 258 2962 71 0 0 14 304 3672 73 0 0 15 258 3730 75 0 0 15.5 304 3962 76 0 0 16 258 3632 74 0 0 18 232 3288 71 0 0 14 304 4257 74 0 0 15 304 3892 72 0 0 19 232 3211 75 0 0 17.5 258 3193 76 0 0 16 304 3433 70 0 0 27.4 121 2670 79 0 0 24 107 2430 70 0 1 20 114 2582 73 0 1 23 115 2694 75 0 1 34.3 97 2188 80 0 1 20.3 131 2830 78 0 1 36.4 121 2950 80 0 1 29 98 2219 74 0 1 26 121 2234 70 0 1 21.5 121 2600 77 0 1 17 231 3907 75 0 0 22.4 231 3415 81 0 0 13 350 4100 73 0 0 25 181 2945 82 0 0 13 350 4699 74 0 0 20.6 231 3380 78 0 0 12 455 4951 73 0 0 14 455 3086 70 0 0 16.9 350 4360 79 0 0 13 350 4502 72 0 0 30 111 2155 77 0 0 17.7 231 3445 78 0 0 21 231 3039 75 0 0 20.5 231 3425 77 0 0 26.6 151 2635 81 0 0 15 350 3693 70 0 0 28.4 151 2670 79 0 0 23 350 3900 79 0 0 16.5 350 4380 76 0 0 25 140 2572 76 0 0 16 250 3897 75 0 0 15 350 4440 75 0 0 27 151 2950 82 0 0 13 400 4464 73 0 0 17 305 3840 79 0 0 17.5 305 3880 77 0 0 28 112 2605 82 0 0 34 112 2395 82 0 0 27 112 2640 82 0 0 13 307 4098 72 0 0 17 250 3329 71 0 0 18 307 3504 70 0 0 16 250 3781 74 0 0 17.5 305 4215 76 0 0 29 85 2035 76 0 0 30 98 2155 78 0 0 30.5 98 2051 77 0 0 32.1 98 2120 80 0 0 23.5 173 2725 81 0 0 28 151 2678 80 0 0 28.8 173 2595 79 0 0 17.5 250 3520 77 0 0 11 400 4997 73 0 0 13 350 4274 72 0 0 14 350 4209 71 0 0 14 454 4354 70 0 0 13 350 3988 73 0 0 20.5 200 3155 78 0 0 19.2 267 3605 79 0 0 15 400 3761 70 0 0 15.5 350 4165 77 0 0 19.2 305 3425 78 0 0 15 350 4082 73 0 0 20 262 3221 75 0 0 15 250 3336 74 0 0 18 250 3459 75 0 0 22 250 3353 76 0 0 16 250 3278 73 0 0 20 140 2408 72 0 0 21 140 2401 73 0 0 25 140 2542 74 0 0 22 140 2408 71 0 0 28 140 2264 71 0 0 24.5 98 2164 76 0 0 13 350 4055 76 0 0 10 307 4376 70 0 0 31 119 2720 82 0 0 15.5 400 4325 77 0 0 26 156 2585 82 0 0 17.6 225 3465 81 0 0 18.5 360 3940 79 0 0 13 440 4735 73 0 0 13 400 4422 72 0 0 35 72 1613 71 1 0 23.9 119 2405 78 1 0 32.9 119 2615 81 1 0 31.8 85 2020 79 1 0 40.8 85 2110 80 1 0 37 85 1975 81 1 0 32.7 168 2910 80 1 0 37.2 86 2019 80 1 0 38 91 1995 82 1 0 27.2 119 2300 78 1 0 28 97 2288 72 1 0 37 119 2434 80 1 0 22 108 2379 73 1 0 24 119 2545 75 1 0 32 83 2003 74 1 0 22 146 2815 77 1 0 24.2 146 2930 81 1 0 32 85 1990 76 1 0 31 79 1950 74 1 0 39.4 85 2070 78 1 0 33.5 85 1945 77 1 0 27 97 2130 70 1 0 27 97 2130 71 1 0 29 135 2525 82 0 0 25.8 156 2620 81 0 0 18.6 225 3620 78 0 0 19.1 225 3381 80 0 0 20.6 225 3360 79 0 0 20 225 3651 76 0 0 15 383 3563 70 0 0 36 135 2370 82 0 0 26 98 2255 76 0 0 27.9 156 2800 80 0 0 28 90 2125 74 0 0 28 98 2164 72 0 0 25 97.5 2126 72 0 0 35.7 98 1915 79 0 0 33.5 98 2075 77 0 0 16 318 4190 76 0 0 15 318 3777 73 0 0 14 318 4457 74 0 0 13 318 3755 76 0 0 11 318 4382 70 0 0 15 318 3399 73 0 0 19.4 318 3735 78 0 0 17.5 318 4080 78 0 0 12 383 4955 71 0 0 15.5 318 4140 77 0 0 30.9 105 2230 78 0 0 32 135 2295 82 0 0 18.2 318 3830 79 0 0 26 98 2265 73 0 1 26 116 2246 74 0 1 30 88 2065 71 0 1 24 90 2108 74 0 1 29 68 1867 73 0 1 28 107 2464 76 0 1 37.3 91 2130 79 0 1 31 79 2000 74 0 1 12 400 4906 73 0 0 13 400 4746 71 0 0 15.5 351 4054 79 0 0 29.9 98 2380 81 0 0 34.4 98 2045 81 0 0 13 302 3870 76 0 0 10 360 4615 70 0 0 26.4 140 2870 80 0 0 20.2 200 2965 78 0 0 25.1 140 2720 78 0 0 22.3 140 2890 79 0 0 24 140 2865 82 0 0 36.1 98 1800 78 0 0 18.1 302 3205 78 0 0 14 351 4129 72 0 0 14 351 4154 71 0 0 15 429 4341 70 0 0 14 302 4042 73 0 0 14.5 351 4215 76 0 0 16 302 4141 74 0 0 13 302 4294 72 0 0 14 302 4638 74 0 0 18.5 250 3525 77 0 0 18 250 3574 76 0 0 20.2 200 3060 81 0 0 22 232 2835 82 0 0 13 351 4363 73 0 0 14 351 4657 75 0 0 17.6 302 3725 79 0 0 15 250 3158 75 0 0 18 250 3021 73 0 0 21 200 2587 70 0 0 24 200 3012 76 0 0 18 250 3139 71 0 0 27 140 2790 82 0 0 13 302 3169 75 0 0 25.5 140 2755 77 0 0 18 171 2984 75 0 0 19 122 2310 73 0 0 23 140 2639 75 0 0 26 122 2451 74 0 0 26.5 140 2565 76 0 0 22 122 2395 72 0 0 21 122 2226 72 0 0 28 120 2625 82 0 0 16 351 4335 77 0 0 17 302 3449 70 0 0 19 250 3302 71 0 0 9 304 4732 70 0 0 32.4 107 2290 80 1 0 36 107 2205 82 1 0 31.5 98 2045 77 1 0 29.5 98 2135 78 1 0 24 120 2489 74 1 0 33 91 1795 76 1 0 38 91 1965 82 1 0 32 91 1965 82 1 0 35.1 81 1760 81 1 0 44.6 91 1850 80 1 0 33 91 1795 75 1 0 36.1 91 1800 78 1 0 33.7 107 2210 81 1 0 34.1 86 1975 79 1 0 18 70 2124 73 1 0 31.3 120 2542 80 1 0 31.6 120 2635 81 1 0 46.6 86 2110 80 1 0 34.1 91 1985 81 1 0 31 91 1970 82 1 0 37 91 2025 82 1 0 32.8 78 1985 78 1 0 21.5 80 2720 77 1 0 23.7 70 2420 80 1 0 19 70 2330 72 1 0 25.4 183 3530 79 0 1 30 146 3250 80 0 1 16.5 168 3820 76 0 1 23 122 2220 71 0 0 21 155 2472 73 0 0 15 302 4295 77 0 0 16.5 351 3955 79 0 0 36 98 2125 82 0 0 11 429 4633 72 0 0 12 429 4952 73 0 0 15 250 3432 75 0 0 20.2 302 3570 78 0 0 20.8 200 3070 78 0 0 19.8 200 2990 79 0 0 36 120 2160 82 1 0 38 262 3015 82 0 0 26.6 350 3725 81 0 0 19.9 260 3365 78 0 0 23.9 260 3420 79 0 0 17 260 4060 77 0 0 12 350 4456 72 0 0 11 350 3664 73 0 0 26.8 173 2700 79 0 0 23.8 151 2855 78 0 0 12 350 4499 73 0 0 25 116 2220 76 0 1 28 116 2123 71 0 1 24 116 2158 73 0 1 26 97 2300 74 0 1 30 79 2074 71 0 1 19 120 3270 76 0 1 23 120 2957 75 0 1 25 110 2672 70 0 1 27.2 141 3190 79 0 1 21 120 2979 72 0 1 28.1 141 3230 81 0 1 16.2 163 3410 78 0 1 14 340 3609 70 0 0 25.5 122 2300 77 0 0 39 86 1875 81 0 0 26 91 1955 71 0 0 13 360 4654 73 0 0 20 198 3102 74 0 0 22 198 2833 70 0 0 23 198 2904 73 0 0 18 225 3785 75 0 0 14 318 4237 73 0 0 14 318 4096 71 0 0 14 440 4312 70 0 0 15 318 4135 72 0 0 16 318 4498 75 0 0 34.2 105 2200 79 0 0 34.7 105 2215 81 0 0 38 105 2125 82 0 0 34.5 105 2150 79 0 0 27.2 135 2490 81 0 0 30 135 2385 81 0 0 23.2 156 2745 78 0 0 18 318 3436 70 0 0 16 225 3439 71 0 0 14 318 4077 72 0 0 18 225 3613 74 0 0 18 225 3121 73 0 0 22 225 3233 76 0 0 19 225 3264 75 0 0 20.5 225 3430 78 0 0 19 225 3630 77 0 0 13 318 3940 76 0 0 23 140 2592 75 0 0 14 400 4385 72 0 0 14 455 4425 70 0 0 16 400 4668 75 0 0 14 400 4464 71 0 0 19 250 3282 71 0 0 16 400 4278 73 0 0 16 400 4220 77 0 0 31 112 2575 82 0 0 21.5 231 3245 79 0 0 27 151 2735 82 0 0 33.5 151 2556 79 0 0 19.2 231 3535 78 0 0 13 400 5140 71 0 0 24.5 151 2740 77 0 0 18.5 250 3645 76 0 0 26 96 2189 72 0 1 27 101 2202 76 0 1 36 79 1825 77 0 1 25 104 2375 70 0 1 21.6 121 2795 78 0 1 24 121 2660 73 0 1 25 121 2671 75 0 1 26 108 2391 74 1 0 32.3 97 2065 81 1 0 30 97 1985 77 1 0 33.8 97 2145 80 1 0 20 97 2279 73 1 0 32 144 2665 82 1 0 21.1 134 2515 78 1 0 28 97 2155 76 1 0 29 97 2171 75 1 0 32.2 108 2265 80 1 0 32.4 108 2350 81 1 0 34 108 2245 82 1 0 31 71 1773 71 1 0 32 71 1836 74 1 0 27 97 2100 72 1 0 26 97 2265 77 1 0 38.1 89 1968 80 1 0 24 134 2702 75 1 0 25 113 2228 71 1 0 27.5 134 2560 78 1 0 31 76 1649 74 1 0 24 113 2278 72 1 0 29.8 134 2711 80 1 0 24 113 2372 70 1 0 25.4 168 2900 81 1 0 19 156 2930 76 1 0 20 156 2807 73 1 0 39.1 79 1755 81 1 0 37.7 89 2050 81 1 0 23 120 2506 72 1 0 35 122 2500 80 0 1 29.8 89 1845 80 0 1 26 97 1835 70 0 1 22 121 2511 72 0 1 25 90 2223 75 0 1 26 79 1963 74 0 1 30.5 97 2190 77 0 1 33 105 2190 81 0 1 27 97 1834 71 0 1 29 90 1937 75 0 1 29.5 97 1825 76 0 1 29 97 1940 77 0 1 43.1 90 1985 78 0 1 36 105 1980 82 0 1 31.5 89 1990 78 0 1 26 97 1950 73 0 1 23 97 2254 72 0 1 19 121 2868 73 0 1 18 121 2933 72 0 1 22 121 2945 75 0 1 20 130 3150 76 0 1 17 163 3140 78 0 1 30.7 145 3160 81 0 1 43.4 90 2335 80 0 1 44 97 2130 82 0 1 29 90 1937 76 0 1 41.5 98 2144 80 0 1 44.3 90 2085 80 0 1 31.9 89 1925 79 0 1
Names of X columns:
MPG Displ Weight Year OriginJapan OriginEurope
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) 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, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) 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,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) 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,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) 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,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) 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,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) 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 Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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