Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
0.98 1.34 1.98 1.97 2.62 5.05 8.02 8.47 2.07 1.78 1.25 5.87 1.45 3.91 9.77 4.06 0.98 1.34 1.97 1.98 2.62 5.04 7.98 8.46 2.06 1.77 1.24 5.89 1.46 3.93 9.73 4.12 0.98 1.34 1.98 1.98 2.61 5.02 7.98 8.43 2.06 1.76 1.24 5.88 1.47 3.93 9.74 4.06 0.97 1.34 1.98 1.98 2.61 5.03 7.97 8.41 2.05 1.76 1.24 5.89 1.47 3.93 9.71 4.07 1.04 1.34 1.98 1.98 2.6 5.01 7.96 8.33 2.05 1.75 1.24 5.85 1.47 4.01 9.69 4.05 1.05 1.33 1.97 1.98 2.59 5 7.95 8.26 2.03 1.74 1.23 5.72 1.45 4.07 9.66 4.07 1.07 1.33 1.97 1.98 2.59 5 7.94 8.25 2.02 1.74 1.23 5.69 1.42 4.08 9.65 4.08 1.06 1.33 1.97 1.97 2.59 5 7.91 8.25 2.02 1.73 1.22 5.72 1.42 4.05 9.63 4.07 1.07 1.33 1.97 1.97 2.58 5 7.9 8.25 2.02 1.73 1.23 5.76 1.41 3.96 9.63 4.03 1.03 1.33 1.96 1.97 2.58 4.97 7.9 8.25 2.02 1.73 1.22 5.8 1.41 3.85 9.6 3.97 1.02 1.33 1.96 1.97 2.58 4.97 7.88 8.25 2.01 1.72 1.22 5.87 1.41 3.77 9.59 3.89 1.02 1.33 1.96 1.97 2.57 4.96 7.88 8.25 2 1.72 1.22 5.88 1.4 3.75 9.57 3.91 1.01 1.32 1.95 1.97 2.56 4.93 7.86 8.22 2 1.72 1.21 5.79 1.39 3.71 9.54 3.89 1.01 1.32 1.95 1.96 2.57 4.93 7.86 8.21 2 1.71 1.21 5.83 1.39 3.73 9.54 3.88 1 1.32 1.95 1.96 2.56 4.92 7.86 8.21 2 1.71 1.21 5.8 1.38 3.74 9.53 3.86 1 1.32 1.95 1.96 2.56 4.92 7.84 8.2 2 1.71 1.21 5.66 1.37 3.73 9.52 3.83 1 1.32 1.94 1.96 2.57 4.92 7.79 8.2 1.99 1.72 1.2 5.32 1.3 3.77 9.47 3.77 0.98 1.31 1.93 1.96 2.55 4.91 7.62 8.15 1.99 1.71 1.19 5.3 1.31 3.84 9.38 3.64 0.87 1.3 1.93 1.95 2.53 4.88 7.6 8.1 1.98 1.7 1.18 5.3 1.31 3.75 9.35 3.66 0.82 1.27 1.9 1.92 2.5 4.83 7.55 8.03 1.97 1.69 1.17 5.28 1.32 3.69 9.3 3.62 0.8 1.27 1.9 1.93 2.49 4.82 7.53 8.08 1.97 1.69 1.17 5.3 1.32 3.73 9.27 3.61 0.81 1.27 1.9 1.92 2.48 4.81 7.5 8.04 1.96 1.69 1.17 5.28 1.32 3.73 9.25 3.61 0.81 1.26 1.88 1.9 2.46 4.77 7.4 7.98 1.96 1.67 1.16 5.28 1.29 3.72 9.19 3.59 0.81 1.26 1.88 1.9 2.44 4.74 7.35 7.95 1.95 1.67 1.16 5.25 1.26 3.7 9.15 3.56 0.81 1.25 1.87 1.89 2.43 4.77 7.31 7.88 1.95 1.67 1.16 5.27 1.25 3.69 9.11 3.56 0.81 1.25 1.88 1.89 2.43 4.75 7.35 7.92 1.95 1.67 1.15 5.29 1.24 3.7 9.09 3.55 0.79 1.25 1.87 1.89 2.44 4.76 7.38 7.88 1.95 1.67 1.16 5.26 1.24 3.72 9.07 3.53 0.78 1.25 1.88 1.89 2.43 4.76 7.37 7.95 1.94 1.67 1.15 5.27 1.23 3.71 9.07 3.55 0.78 1.25 1.87 1.89 2.43 4.75 7.37 7.93 1.94 1.66 1.15 5.21 1.22 3.75 9.07 3.55 0.77 1.25 1.87 1.89 2.44 4.73 7.32 7.95 1.92 1.66 1.15 5.23 1.22 3.76 9.03 3.56 0.78 1.25 1.87 1.89 2.43 4.74 7.24 7.85 1.93 1.65 1.15 5.27 1.21 3.78 8.95 3.53 0.77 1.25 1.87 1.89 2.43 4.74 7.21 7.85 1.93 1.65 1.15 5.26 1.2 3.76 8.95 3.53 0.78 1.25 1.87 1.89 2.43 4.74 7.21 7.85 1.92 1.65 1.15 5.27 1.19 3.77 8.89 3.51 0.79 1.25 1.87 1.89 2.43 4.72 7.19 7.83 1.92 1.65 1.14 5.27 1.19 3.78 8.87 3.53 0.79 1.24 1.87 1.89 2.43 4.71 7.14 7.81 1.91 1.65 1.14 5.3 1.19 3.77 8.84 3.53 0.79 1.25 1.87 1.89 2.42 4.7 7.13 7.85 1.91 1.64 1.14 5.29 1.19 3.79 8.83 3.54 0.79 1.25 1.87 1.89 2.43 4.71 7.12 7.83 1.91 1.64 1.14 5.31 1.19 3.78 8.81 3.56 0.79 1.24 1.87 1.89 2.44 4.72 7.08 7.8 1.9 1.64 1.14 5.32 1.19 3.85 8.74 3.58 0.8 1.24 1.87 1.89 2.44 4.7 7.04 7.81 1.89 1.64 1.14 5.26 1.19 3.8 8.72 3.56 0.8 1.24 1.87 1.89 2.44 4.7 7.04 7.79 1.9 1.64 1.13 5.28 1.18 3.86 8.71 3.55 0.8 1.24 1.87 1.89 2.44 4.7 7.03 7.75 1.88 1.63 1.13 5.27 1.18 3.84 8.7 3.52 0.8 1.24 1.87 1.89 2.44 4.68 7.03 7.77 1.88 1.62 1.13 5.28 1.18 3.82 8.69 3.52 0.81 1.25 1.87 1.89 2.43 4.68 6.99 7.72 1.87 1.62 1.13 5.25 1.18 3.82 8.62 3.49 0.8 1.26 1.88 1.89 2.44 4.67 7 7.68 1.87 1.61 1.13 5.13 1.17 3.8 8.55 3.46 0.82 1.26 1.88 1.9 2.44 4.67 6.97 7.7 1.87 1.62 1.12 5.12 1.16 3.79 8.57 3.46 0.85 1.26 1.87 1.89 2.43 4.67 6.91 7.69 1.86 1.62 1.11 5.12 1.16 3.78 8.54 3.45 0.85 1.26 1.87 1.89 2.42 4.62 6.83 7.64 1.85 1.61 1.1 5.11 1.16 3.8 8.45 3.48 0.86 1.26 1.87 1.89 2.42 4.62 6.8 7.66 1.84 1.61 1.1 5.09 1.15 3.78 8.4 3.48 0.85 1.26 1.87 1.88 2.41 4.61 6.79 7.63 1.83 1.61 1.1 5.05 1.15 3.75 8.37 3.48 0.83 1.26 1.87 1.88 2.41 4.61 6.77 7.64 1.83 1.6 1.1 5.1 1.15 3.77 8.36 3.48 0.81 1.26 1.87 1.88 2.41 4.61 6.78 7.63 1.83 1.59 1.1 5.07 1.15 3.75 8.36 3.46 0.82 1.26 1.87 1.88 2.41 4.61 6.75 7.6 1.82 1.6 1.09 5.09 1.15 3.74 8.35 3.44 0.82 1.25 1.86 1.87 2.38 4.6 6.73 7.58 1.81 1.59 1.09 5.1 1.16 3.71 8.34 3.41 0.78 1.25 1.86 1.87 2.38 4.62 6.68 7.55 1.81 1.58 1.09 5.1 1.14 3.71 8.28 3.4 0.78 1.25 1.85 1.87 2.37 4.61 6.64 7.54 1.8 1.57 1.08 5.07 1.12 3.69 8.24 3.34 0.73 1.24 1.84 1.85 2.35 4.59 6.52 7.49 1.79 1.56 1.08 5.06 1.11 3.65 8.16 3.34 0.68 1.24 1.83 1.84 2.33 4.58 6.44 7.45 1.78 1.56 1.07 5.05 1.09 3.56 8.09 3.34 0.65 1.23 1.82 1.83 2.33 4.54 6.37 7.31 1.77 1.54 1.06 4.95 1.07 3.44 8.04 3.3 0.62 1.2 1.78 1.79 2.254 4.46 6.11 7.23 1.75 1.52 1.04 4.94 1.07 3.39 7.84 3.27 0.6 1.18 1.75 1.76 2.22 4.43 5.98 7.12 1.73 1.5 1.03 4.94 1.07 3.38 7.73 3.26 0.6 1.17 1.74 1.75 2.212 4.4 5.94 7.09 1.72 1.49 1.03 4.95 1.06 3.38 7.7 3.28 0.59 1.18 1.74 1.75 2.2 4.39 5.94 7.06 1.71 1.49 1.03 4.96 1.07 3.37 7.68 3.3 0.6 1.17 1.74 1.75 2.2 4.39 5.93 7.06 1.71 1.49 1.03 4.95 1.06 3.35 7.68 3.29 0.6 1.17 1.73 1.74 2.2 4.39 5.92 7.05 1.71 1.49 1.03 4.97 1.06 3.31 7.66 3.29 0.6 1.17 1.73 1.74 2.19 4.4 5.91 7.02 1.71 1.47 1.03 4.9 1.06 3.25 7.66 3.25 0.59 1.17 1.73 1.73 2.2 4.38 5.89 6.99 1.7 1.47 1.03 4.9 1.06 3.22 7.62 3.26 0.58 1.16 1.71 1.72 2.16 4.33 5.82 6.92 1.68 1.47 1.01 4.68 1.03 3.25 7.57 3.26 0.56 1.14 1.7 1.71 2.17 4.32 5.77 6.92 1.68 1.46 1.02 4.63 1.03 3.21 7.5 3.24 0.55 1.14 1.7 1.7 2.16 4.32 5.76 6.88 1.67 1.46 1.02 4.62 1.02 3.2 7.49 3.24 0.54 1.13 1.69 1.7 2.15 4.28 5.73 6.88 1.66 1.45 1.01 4.6 1.01 3.17 7.46 3.25 0.55 1.13 1.68 1.69 2.14 4.26 5.72 6.86 1.65 1.44 1 4.64 1.02 3.17 7.42 3.21 0.55 1.12 1.68 1.68 2.13 4.26 5.7 6.87 1.64 1.44 1 4.64 1.02 3.18 7.38 3.2 0.54 1.12 1.68 1.68 2.134 4.26 5.68 6.81 1.63 1.43 1 4.65 1.02 3.19 7.37 3.21 0.54 1.12 1.68 1.68 2.132 4.25 5.68 6.81 1.63 1.43 1 4.65 1.01 3.17 7.36 3.23 0.54 1.12 1.67 1.68 2.13 4.27 5.69 6.81 1.63 1.43 1 4.63 1.02 3.16 7.36 3.2 0.53 1.12 1.66 1.67 2.117 4.25 5.65 6.77 1.63 1.43 0.99 4.65 1.01 3.14 7.33 3.2 0.53 1.11 1.65 1.66 2.11 4.26 5.66 6.75 1.62 1.43 0.99 4.67 1.01 3.13 7.32 3.19 0.53 1.11 1.65 1.66 2.104 4.26 5.64 6.69 1.62 1.42 0.99 4.64 0.99 3.1 7.3 3.16 0.53 1.1 1.65 1.65 2.1 4.24 5.61 6.69 1.61 1.41 0.98 4.64 0.98 3.11 7.27 3.11
Names of X columns:
Flour_1kg Speciality_bread_400g Speciality_bread_800g Brown_bread_800g Multigrain_bread_800g Currant_1kg Roll_1kg Rice_tart_1kg Mocha_tart Fruit_tart Eclair Biscuits_1kg Penny_wafer_200g Spekulatius_1kg Garibaldi biscuit_1kg
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') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation