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1 1 1 13 12 12 14 14 12 12 53 53 41 41 38 38 1 2 2 16 11 11 18 18 11 11 83 83 39 39 32 32 1 3 3 19 15 15 11 11 14 14 66 66 30 30 35 35 1 4 4 15 6 6 12 12 12 12 67 67 31 31 33 33 1 5 5 14 13 13 16 16 21 21 76 76 34 34 37 37 1 6 6 13 10 10 18 18 12 12 78 78 35 35 29 29 1 7 7 19 12 12 14 14 22 22 53 53 39 39 31 31 1 8 8 15 14 14 14 14 11 11 80 80 34 34 36 36 1 9 9 14 12 12 15 15 10 10 74 74 36 36 35 35 1 10 10 15 9 9 15 15 13 13 76 76 37 37 38 38 1 11 11 16 10 10 17 17 10 10 79 79 38 38 31 31 1 12 12 16 12 12 19 19 8 8 54 54 36 36 34 34 1 13 13 16 12 12 10 10 15 15 67 67 38 38 35 35 1 14 14 16 11 11 16 16 14 14 54 54 39 39 38 38 1 15 15 17 15 15 18 18 10 10 87 87 33 33 37 37 1 16 16 15 12 12 14 14 14 14 58 58 32 32 33 33 1 17 17 15 10 10 14 14 14 14 75 75 36 36 32 32 1 18 18 20 12 12 17 17 11 11 88 88 38 38 38 38 1 19 19 18 11 11 14 14 10 10 64 64 39 39 38 38 1 20 20 16 12 12 16 16 13 13 57 57 32 32 32 32 1 21 21 16 11 11 18 18 9.5 9.5 66 66 32 32 33 33 1 22 22 16 12 12 11 11 14 14 68 68 31 31 31 31 1 23 23 19 13 13 14 14 12 12 54 54 39 39 38 38 1 24 24 16 11 11 12 12 14 14 56 56 37 37 39 39 1 25 25 17 12 12 17 17 11 11 86 86 39 39 32 32 1 26 26 17 13 13 9 9 9 9 80 80 41 41 32 32 1 27 27 16 10 10 16 16 11 11 76 76 36 36 35 35 1 28 28 15 14 14 14 14 15 15 69 69 33 33 37 37 1 29 29 16 12 12 15 15 14 14 78 78 33 33 33 33 1 30 30 14 10 10 11 11 13 13 67 67 34 34 33 33 1 31 31 15 12 12 16 16 9 9 80 80 31 31 31 31 1 32 32 12 8 8 13 13 15 15 54 54 27 27 32 32 1 33 33 14 10 10 17 17 10 10 71 71 37 37 31 31 1 34 34 16 12 12 15 15 11 11 84 84 34 34 37 37 1 35 35 14 12 12 14 14 13 13 74 74 34 34 30 30 1 36 36 10 7 7 16 16 8 8 71 71 32 32 33 33 1 37 37 10 9 9 9 9 20 20 63 63 29 29 31 31 1 38 38 14 12 12 15 15 12 12 71 71 36 36 33 33 1 39 39 16 10 10 17 17 10 10 76 76 29 29 31 31 1 40 40 16 10 10 13 13 10 10 69 69 35 35 33 33 1 41 41 16 10 10 15 15 9 9 74 74 37 37 32 32 1 42 42 14 12 12 16 16 14 14 75 75 34 34 33 33 1 43 43 20 15 15 16 16 8 8 54 54 38 38 32 32 1 44 44 14 10 10 12 12 14 14 52 52 35 35 33 33 1 45 45 14 10 10 15 15 11 11 69 69 38 38 28 28 1 46 46 11 12 12 11 11 13 13 68 68 37 37 35 35 1 47 47 14 13 13 15 15 9 9 65 65 38 38 39 39 1 48 48 15 11 11 15 15 11 11 75 75 33 33 34 34 1 49 49 16 11 11 17 17 15 15 74 74 36 36 38 38 1 50 50 14 12 12 13 13 11 11 75 75 38 38 32 32 1 51 51 16 14 14 16 16 10 10 72 72 32 32 38 38 1 52 52 14 10 10 14 14 14 14 67 67 32 32 30 30 1 53 53 12 12 12 11 11 18 18 63 63 32 32 33 33 1 54 54 16 13 13 12 12 14 14 62 62 34 34 38 38 1 55 55 9 5 5 12 12 11 11 63 63 32 32 32 32 1 56 56 14 6 6 15 15 14.5 14.5 76 76 37 37 35 35 1 57 57 16 12 12 16 16 13 13 74 74 39 39 34 34 1 58 58 16 12 12 15 15 9 9 67 67 29 29 34 34 1 59 59 15 11 11 12 12 10 10 73 73 37 37 36 36 1 60 60 16 10 10 12 12 15 15 70 70 35 35 34 34 1 61 61 12 7 7 8 8 20 20 53 53 30 30 28 28 1 62 62 16 12 12 13 13 12 12 77 77 38 38 34 34 1 63 63 16 14 14 11 11 12 12 80 80 34 34 35 35 1 64 64 14 11 11 14 14 14 14 52 52 31 31 35 35 1 65 65 16 12 12 15 15 13 13 54 54 34 34 31 31 1 66 66 17 13 13 10 10 11 11 80 80 35 35 37 37 1 67 67 18 14 14 11 11 17 17 66 66 36 36 35 35 1 68 68 18 11 11 12 12 12 12 73 73 30 30 27 27 1 69 69 12 12 12 15 15 13 13 63 63 39 39 40 40 1 70 70 16 12 12 15 15 14 14 69 69 35 35 37 37 1 71 71 10 8 8 14 14 13 13 67 67 38 38 36 36 1 72 72 14 11 11 16 16 15 15 54 54 31 31 38 38 1 73 73 18 14 14 15 15 13 13 81 81 34 34 39 39 1 74 74 18 14 14 15 15 10 10 69 69 38 38 41 41 1 75 75 16 12 12 13 13 11 11 84 84 34 34 27 27 1 76 76 17 9 9 12 12 19 19 80 80 39 39 30 30 1 77 77 16 13 13 17 17 13 13 70 70 37 37 37 37 1 78 78 16 11 11 13 13 17 17 69 69 34 34 31 31 1 79 79 13 12 12 15 15 13 13 77 77 28 28 31 31 1 80 80 16 12 12 13 13 9 9 54 54 37 37 27 27 1 81 81 16 12 12 15 15 11 11 79 79 33 33 36 36 1 82 82 16 12 12 15 15 9 9 71 71 35 35 37 37 1 83 83 15 12 12 16 16 12 12 73 73 37 37 33 33 1 84 84 15 11 11 15 15 12 12 72 72 32 32 34 34 1 85 85 16 10 10 14 14 13 13 77 77 33 33 31 31 1 86 86 14 9 9 15 15 13 13 75 75 38 38 39 39 1 87 87 16 12 12 14 14 12 12 69 69 33 33 34 34 1 88 88 16 12 12 13 13 15 15 54 54 29 29 32 32 1 89 89 15 12 12 7 7 22 22 70 70 33 33 33 33 1 90 90 12 9 9 17 17 13 13 73 73 31 31 36 36 1 91 91 17 15 15 13 13 15 15 54 54 36 36 32 32 1 92 92 16 12 12 15 15 13 13 77 77 35 35 41 41 1 93 93 15 12 12 14 14 15 15 82 82 32 32 28 28 1 94 94 13 12 12 13 13 12.5 12.5 80 80 29 29 30 30 1 95 95 16 10 10 16 16 11 11 80 80 39 39 36 36 1 96 96 16 13 13 12 12 16 16 69 69 37 37 35 35 1 97 97 16 9 9 14 14 11 11 78 78 35 35 31 31 1 98 98 16 12 12 17 17 11 11 81 81 37 37 34 34 1 99 99 14 10 10 15 15 10 10 76 76 32 32 36 36 1 100 100 16 14 14 17 17 10 10 76 76 38 38 36 36 1 101 101 16 11 11 12 12 16 16 73 73 37 37 35 35 1 102 102 20 15 15 16 16 12 12 85 85 36 36 37 37 1 103 103 15 11 11 11 11 11 11 66 66 32 32 28 28 1 104 104 16 11 11 15 15 16 16 79 79 33 33 39 39 1 105 105 13 12 12 9 9 19 19 68 68 40 40 32 32 1 106 106 17 12 12 16 16 11 11 76 76 38 38 35 35 1 107 107 16 12 12 15 15 16 16 71 71 41 41 39 39 1 108 108 16 11 11 10 10 15 15 54 54 36 36 35 35 1 109 109 12 7 7 10 10 24 24 46 46 43 43 42 42 1 110 110 16 12 12 15 15 14 14 85 85 30 30 34 34 1 111 111 16 14 14 11 11 15 15 74 74 31 31 33 33 1 112 112 17 11 11 13 13 11 11 88 88 32 32 41 41 1 113 113 13 11 11 14 14 15 15 38 38 32 32 33 33 1 114 114 12 10 10 18 18 12 12 76 76 37 37 34 34 1 115 115 18 13 13 16 16 10 10 86 86 37 37 32 32 1 116 116 14 13 13 14 14 14 14 54 54 33 33 40 40 1 117 117 14 8 8 14 14 13 13 67 67 34 34 40 40 1 118 118 13 11 11 14 14 9 9 69 69 33 33 35 35 1 119 119 16 12 12 14 14 15 15 90 90 38 38 36 36 1 120 120 13 11 11 12 12 15 15 54 54 33 33 37 37 1 121 121 16 13 13 14 14 14 14 76 76 31 31 27 27 1 122 122 13 12 12 15 15 11 11 89 89 38 38 39 39 1 123 123 16 14 14 15 15 8 8 76 76 37 37 38 38 1 124 124 15 13 13 15 15 11 11 73 73 36 36 31 31 1 125 125 16 15 15 13 13 11 11 79 79 31 31 33 33 1 126 126 15 10 10 17 17 8 8 90 90 39 39 32 32 1 127 127 17 11 11 17 17 10 10 74 74 44 44 39 39 1 128 128 15 9 9 19 19 11 11 81 81 33 33 36 36 1 129 129 12 11 11 15 15 13 13 72 72 35 35 33 33 1 130 130 16 10 10 13 13 11 11 71 71 32 32 33 33 1 131 131 10 11 11 9 9 20 20 66 66 28 28 32 32 1 132 132 16 8 8 15 15 10 10 77 77 40 40 37 37 1 133 133 12 11 11 15 15 15 15 65 65 27 27 30 30 1 134 134 14 12 12 15 15 12 12 74 74 37 37 38 38 1 135 135 15 12 12 16 16 14 14 85 85 32 32 29 29 1 136 136 13 9 9 11 11 23 23 54 54 28 28 22 22 1 137 137 15 11 11 14 14 14 14 63 63 34 34 35 35 1 138 138 11 10 10 11 11 16 16 54 54 30 30 35 35 1 139 139 12 8 8 15 15 11 11 64 64 35 35 34 34 1 140 140 11 9 9 13 13 12 12 69 69 31 31 35 35 1 141 141 16 8 8 15 15 10 10 54 54 32 32 34 34 1 142 142 15 9 9 16 16 14 14 84 84 30 30 37 37 1 143 143 17 15 15 14 14 12 12 86 86 30 30 35 35 1 144 144 16 11 11 15 15 12 12 77 77 31 31 23 23 1 145 145 10 8 8 16 16 11 11 89 89 40 40 31 31 1 146 146 18 13 13 16 16 12 12 76 76 32 32 27 27 1 147 147 13 12 12 11 11 13 13 60 60 36 36 36 36 1 148 148 16 12 12 12 12 11 11 75 75 32 32 31 31 1 149 149 13 9 9 9 9 19 19 73 73 35 35 32 32 1 150 150 10 7 7 16 16 12 12 85 85 38 38 39 39 1 151 151 15 13 13 13 13 17 17 79 79 42 42 37 37 1 152 152 16 9 9 16 16 9 9 71 71 34 34 38 38 1 153 153 16 6 6 12 12 12 12 72 72 35 35 39 39 1 154 154 14 8 8 9 9 19 19 69 69 38 38 34 34 1 155 155 10 8 8 13 13 18 18 78 78 33 33 31 31 1 156 156 17 15 15 13 13 15 15 54 54 36 36 32 32 1 157 157 13 6 6 14 14 14 14 69 69 32 32 37 37 1 158 158 15 9 9 19 19 11 11 81 81 33 33 36 36 1 159 159 16 11 11 13 13 9 9 84 84 34 34 32 32 1 160 160 12 8 8 12 12 18 18 84 84 32 32 38 38 1 161 161 13 8 8 13 13 16 16 69 69 34 34 36 36 0 162 0 13 10 0 10 0 24 0 66 0 27 0 26 0
Names of X columns:
Pop t Pop_t Learning Software Software_p Happiness Happiness_p Depression Depression_p Belonging Belonging_p Connected Connected_p Separate Separate_p
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
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R Server
Big Analytics Cloud Computing Center
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