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Data X:
0 1 0 2 0 5 0 2 0 3 0 3 0 4 0 4 0 2 0 2 0 4 0 2 0 4 0 3 0 4 0 4 1 3 3 4 4 4 4 2 2 4 4 2 2 5 5 4 0 4 0 2 0 4 0 2 0 2 0 2 0 2 0 4 1 5 5 3 3 2 2 2 2 2 2 3 3 2 2 4 1 6 6 4 4 5 5 1 1 3 3 2 2 4 4 5 0 7 0 3 0 5 0 1 0 2 0 1 0 4 0 4 1 8 8 3 3 4 4 3 3 3 3 3 3 4 4 3 0 9 0 3 0 3 0 2 0 3 0 2 0 4 0 4 0 10 0 2 0 4 0 1 0 3 0 2 0 2 0 4 1 11 11 4 4 4 4 4 4 3 3 3 3 3 3 4 0 12 0 4 0 2 0 2 0 4 0 2 0 4 0 4 1 13 13 3 3 3 3 3 3 2 2 2 2 3 3 4 1 14 14 3 3 3 3 2 2 2 2 2 2 4 4 2 0 15 0 4 0 4 0 1 0 1 0 3 0 4 0 3 1 16 16 4 4 5 5 1 1 1 1 1 1 4 4 4 0 17 0 3 0 4 0 2 0 3 0 3 0 4 0 3 0 18 0 3 0 2 0 2 0 2 0 2 0 2 0 2 1 19 19 3 3 4 4 2 2 2 2 3 3 4 4 4 0 20 0 4 0 4 0 2 0 3 0 4 0 4 0 3 1 21 21 2 2 4 4 1 1 4 4 2 2 4 4 3 1 22 22 5 5 4 4 2 2 4 4 3 3 3 3 4 0 23 0 4 0 4 0 4 0 3 0 5 0 2 0 3 1 24 24 2 2 4 4 2 2 2 2 2 2 4 4 3 0 25 0 3 0 5 0 2 0 3 0 2 0 2 0 4 0 26 0 4 0 4 0 2 0 4 0 3 0 3 0 4 1 27 27 4 4 4 4 2 2 3 3 2 2 4 4 4 0 28 0 3 0 4 0 2 0 2 0 2 0 3 0 4 1 29 29 4 4 4 4 3 3 1 1 2 2 4 4 4 1 30 30 4 4 4 4 2 2 3 3 2 2 4 4 4 0 31 0 1 0 4 0 1 0 2 0 3 0 4 0 5 1 32 32 4 4 4 4 4 4 4 4 4 4 4 4 4 0 33 0 5 0 2 0 1 0 4 0 1 0 4 0 4 0 34 0 2 0 4 0 2 0 5 0 3 0 4 0 4 1 35 35 4 4 4 4 2 2 2 2 3 3 4 4 3 0 36 0 3 0 5 0 2 0 4 0 2 0 5 0 4 1 37 37 2 2 5 5 2 2 4 4 1 1 4 4 3 1 38 38 4 4 4 4 2 2 2 2 1 1 2 2 4 0 39 0 5 0 3 0 2 0 4 0 2 0 4 0 4 1 40 40 4 4 4 4 2 2 4 4 2 2 4 4 3 0 41 0 4 0 5 0 2 0 2 0 2 0 5 0 5 0 42 0 4 0 4 0 2 0 3 0 1 0 4 0 4 1 43 43 3 3 4 4 2 2 2 2 2 2 2 2 3 0 44 0 4 0 5 0 2 0 4 0 1 0 4 0 3 1 45 45 2 2 4 4 2 2 3 3 2 2 4 4 3 1 46 46 2 2 5 5 1 1 1 1 2 2 4 4 4 0 47 0 4 0 4 0 2 0 2 0 4 0 2 0 4 1 48 48 2 2 4 4 1 1 5 5 2 2 5 5 4 0 49 0 4 0 4 0 2 0 2 0 2 0 4 0 4 0 50 0 4 0 3 0 1 0 4 0 2 0 4 0 4 1 51 51 1 1 4 4 1 1 4 4 1 1 4 4 4 0 52 0 4 0 4 0 2 0 2 0 2 0 4 0 4 1 53 53 2 2 4 4 2 2 2 2 2 2 4 4 5 1 54 54 1 1 2 2 1 1 2 2 1 1 3 3 3 0 55 0 4 0 3 0 5 0 4 0 5 0 5 0 3 1 56 56 3 3 5 5 2 2 3 3 2 2 4 4 5 0 57 0 2 0 4 0 2 0 4 0 2 0 4 0 5 0 58 0 4 0 4 0 1 0 2 0 2 0 4 0 4 1 59 59 3 3 5 5 1 1 3 3 1 1 4 4 4 0 60 0 2 0 3 0 2 0 2 0 3 0 2 0 3 1 61 61 2 2 5 5 2 2 2 2 1 1 4 4 4 1 62 62 3 3 4 4 1 1 3 3 1 1 4 4 4 0 63 0 2 0 5 0 1 0 2 0 2 0 4 0 5 1 64 64 1 1 4 4 2 2 3 3 3 3 4 4 4 0 65 0 3 0 4 0 1 0 2 0 2 0 3 0 4 0 66 0 2 0 5 0 1 0 4 0 2 0 4 0 5 1 67 67 3 3 4 4 2 2 2 2 2 2 2 2 4 0 68 0 3 0 4 0 1 0 5 0 4 0 4 0 3 1 69 69 3 3 5 5 1 1 1 1 1 1 4 4 4 1 70 70 2 2 4 4 2 2 3 3 2 2 4 4 4 0 71 0 3 0 3 0 1 0 2 0 2 0 4 0 4 1 72 72 2 2 4 4 1 1 2 2 2 2 4 4 4 0 73 0 4 0 5 0 3 0 3 0 2 0 4 0 4 0 74 0 4 0 5 0 3 0 4 0 2 0 3 0 4 1 75 75 4 4 5 5 2 2 4 4 1 1 4 4 4 0 76 0 2 0 4 0 2 0 2 0 2 0 4 0 3 1 77 77 3 3 4 4 1 1 3 3 2 2 4 4 4 1 78 78 4 4 5 5 3 3 4 4 2 2 4 4 3 0 79 0 3 0 5 0 2 0 2 0 2 0 4 0 5 1 80 80 4 4 4 4 2 2 2 2 1 1 4 4 4 0 81 0 2 0 5 0 2 0 4 0 4 0 4 0 5 0 82 0 3 0 3 0 2 0 2 0 2 0 2 0 5 1 83 83 3 3 4 4 1 1 4 4 3 3 3 3 4 0 84 0 4 0 4 0 4 0 2 0 2 0 5 0 4 1 85 85 2 2 4 4 1 1 3 3 1 1 3 3 4 1 86 86 4 4 4 4 1 1 4 4 2 2 3 3 4 0 87 0 2 0 4 0 1 0 3 0 2 0 4 0 4 1 88 88 2 2 5 5 1 1 1 1 1 1 4 4 5 0 89 0 4 0 4 0 4 0 3 0 2 0 4 0 4 0 90 0 3 0 4 0 2 0 2 0 1 0 4 0 3 1 91 91 4 4 4 4 2 2 2 2 2 2 4 4 4 0 92 0 2 0 5 0 1 0 1 0 1 0 3 0 3 1 93 93 2 2 3 3 1 1 3 3 2 2 4 4 4 1 94 94 3 3 3 3 1 1 2 2 2 2 4 4 4 0 95 0 3 0 5 0 3 0 3 0 3 0 4 0 4 1 96 96 5 5 5 5 4 4 5 5 4 4 5 5 4 0 97 0 2 0 4 0 4 0 3 0 1 0 4 0 4 0 98 0 3 0 4 0 3 0 4 0 3 0 4 0 3 1 99 99 4 4 4 4 2 2 2 2 1 1 2 2 3 0 100 0 3 0 4 0 2 0 2 0 1 0 3 0 3 1 101 101 4 4 4 4 3 3 3 3 2 2 3 3 3 1 102 102 3 3 4 4 1 1 2 2 1 1 3 3 3 0 103 0 3 0 4 0 3 0 2 0 3 0 4 0 2 1 104 104 2 2 4 4 2 2 2 2 2 2 4 4 3 0 105 0 3 0 5 0 2 0 3 0 2 0 2 0 5 0 106 0 2 0 2 0 2 0 5 0 1 0 3 0 2 1 107 107 3 3 4 4 2 2 2 2 2 2 3 3 2 0 108 0 2 0 2 0 4 0 3 0 2 0 4 0 3 1 109 109 4 4 4 4 3 3 3 3 1 1 4 4 3 1 110 110 2 2 5 5 1 1 1 1 2 2 2 2 3 0 111 0 4 0 3 0 1 0 1 0 2 0 3 0 4 1 112 112 4 4 4 4 2 2 3 3 4 4 4 4 4 0 113 0 1 0 3 0 1 0 4 0 3 0 4 0 3 0 114 0 5 0 4 0 3 0 5 0 2 0 5 0 2 1 115 115 2 2 4 4 2 2 3 3 5 5 3 3 3 0 116 0 3 0 4 0 2 0 3 0 1 0 3 0 4 1 117 117 4 4 2 2 2 2 3 3 2 2 4 4 2 1 118 118 1 1 1 1 1 1 2 2 1 1 3 3 4 0 119 0 5 0 4 0 3 0 3 0 2 0 3 0 4 1 120 120 3 3 3 3 1 1 2 2 1 1 2 2 2 0 121 0 3 0 4 0 1 0 3 0 1 0 4 0 3 1 122 122 3 3 3 3 2 2 2 2 2 2 3 3 3 1 123 123 3 3 3 3 3 3 4 4 2 2 4 4 3 0 124 0 2 0 5 0 2 0 2 0 2 0 5 0 4 1 125 125 2 2 4 4 1 1 2 2 3 3 4 4 4 0 126 0 4 0 3 0 2 0 4 0 2 0 3 0 4 0 127 0 4 0 4 0 1 0 4 0 1 0 3 0 3 1 128 128 3 3 4 4 2 2 3 3 2 2 3 3 4 0 129 0 3 0 4 0 1 0 3 0 2 0 3 0 4 1 130 130 3 3 4 4 2 2 3 3 3 3 4 4 4 1 131 131 4 4 3 3 3 3 4 4 2 2 4 4 2 0 132 0 3 0 4 0 2 0 2 0 2 0 3 0 4 1 133 133 4 4 4 4 1 1 1 1 2 2 2 2 5 0 134 0 4 0 4 0 1 0 3 0 1 0 3 0 4 0 135 0 2 0 4 0 2 0 2 0 2 0 2 0 4 1 136 136 4 4 4 4 2 2 3 3 2 2 4 4 4 0 137 0 2 0 3 0 1 0 2 0 2 0 4 0 3 1 138 138 4 4 4 4 2 2 2 2 3 3 4 4 1 1 139 139 3 3 4 4 3 3 3 3 1 1 4 4 4 0 140 0 3 0 2 0 4 0 2 0 3 0 4 0 3 1 141 141 2 2 2 2 2 2 4 4 4 4 4 4 3 0 142 0 2 0 4 0 4 0 4 0 2 0 5 0 3 0 143 0 5 0 2 0 5 0 2 0 5 0 3 0 1 1 144 144 2 2 4 4 1 1 2 2 1 1 4 4 4 0 145 0 4 0 3 0 3 0 3 0 2 0 4 0 5 1 146 146 3 3 4 4 2 2 4 4 3 3 4 4 4 1 147 147 3 3 3 3 2 2 4 4 2 2 5 5 3 0 148 0 3 0 2 0 2 0 4 0 2 0 3 0 4 1 149 149 3 3 2 2 1 1 1 1 3 3 2 2 3 0 150 0 4 0 4 0 4 0 4 0 2 0 4 0 4 0 151 0 4 0 3 0 2 0 4 0 1 0 3 0 4 1 152 152 4 4 4 4 2 2 3 3 2 2 4 4 4 0 153 0 4 0 4 0 3 0 1 0 1 0 5 0 5 1 154 154 4 4 2 2 1 1 2 2 2 2 3 3 2 1 155 155 5 5 5 5 4 4 2 2 3 3 3 3 3 0 156 0 3 0 4 0 2 0 2 0 2 0 3 0 3 1 157 157 3 3 4 4 2 2 3 3 2 2 5 5 4 0 158 0 4 0 4 0 4 0 3 0 2 0 4 0 4 0 159 0 4 0 3 0 4 0 3 0 4 0 2 0 3
Names of X columns:
pop t pop_t standards standards_t organization organization_t punished punished_t secondrate secondrate_t mistakes mistakes_t competent competent_t neat
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 Output
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