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Data X:
14.15 0 13.95 0 13.96 0 13.99 0 14.08 1 14.03 0 13.93 1 13.95 2 13.94 3 14.01 1 13.98 1 13.84 0 14.16 0 13.92 0 13.97 0 14.00 0 14.00 0 13.87 0 13.94 0 13.98 3 13.95 7 14.01 2 13.96 1 13.88 0 13.76 0 13.79 0 13.97 0 13.84 0 13.94 0 13.97 0 13.92 0 13.87 4 13.90 3 13.85 6 13.70 0 13.87 0 13.74 0 13.64 0 13.83 0 13.88 0 14.01 1 13.98 0 14.02 0 14.10 3 14.11 4 14.14 2 14.04 0 14.19 0 14.17 0 14.14 1 13.94 0 14.09 0 14.06 0 14.07 0 14.07 0 14.07 2 14.05 2 13.99 2 13.85 0 13.95 0 14.13 0 14.20 0 14.20 0 14.18 0 14.13 1 14.07 0 14.06 0 14.07 3 14.10 3 14.10 4 14.00 1 14.16 1 13.85 0 13.97 0 13.91 0 13.90 0 13.83 0 13.90 1 13.79 1 13.88 2 13.94 4 13.95 1 14.08 1 13.87 1 14.16 0 13.90 0 13.74 0 13.82 0 13.82 0 13.88 0 13.90 1 14.04 4 13.92 5 13.96 2 13.80 0 13.74 0 13.84 0 13.71 0 13.78 0 13.78 0 13.76 0 13.81 1 13.87 1 13.76 1 13.82 3 13.82 1 13.83 0 13.91 0 13.92 0 14.00 0 13.99 0 14.01 0 14.09 0 14.13 2 14.03 0 14.10 1 14.05 3 14.02 1 14.11 0 14.21 0 14.38 0 14.23 0 14.10 0 14.04 0 14.12 0 14.00 1 14.11 0 14.03 4 14.03 4 14.04 1 14.06 0 14.10 0 14.11 0 14.12 0 14.24 0 14.17 0 14.08 1 14.07 2 14.09 1 14.02 1 14.01 3 13.98 2 13.92 0 14.03 0 14.01 0 14.19 0 13.73 0 13.92 0 13.94 0 14.03 1 14.04 2 14.03 2 14.07 2 14.04 0 13.93 0 14.17 0 14.06 0 14.20 0 14.16 0 14.11 0 14.16 0 14.13 0 14.01 1 14.05 0 14.04 6 14.10 2 14.05 3 14.02 1 14.11 0 14.21 0 14.38 0 14.23 0 14.10 0 14.04 0 14.12 0 14.00 1 14.11 0 14.03 4 14.03 4 14.04 1 14.06 0 14.10 0 14.11 0 14.12 0 14.24 0 14.17 0 14.08 1 14.07 2 14.09 1 14.02 1 14.01 3 13.98 2 13.92 0 14.03 0 14.01 0 14.19 0 13.73 0 13.92 0 13.94 0 14.03 1 14.04 2 14.03 2 14.07 2 14.04 0 13.93 0 14.17 0 14.06 0 14.20 0 14.16 0 14.11 0 14.16 0 14.13 0 14.01 1 14.05 0 14.04 6 14.03 2 14.04 2 13.90 0 14.09 0 14.16 0 14.09 0 14.08 0 13.95 0 14.01 0 14.00 0 13.99 2 14.00 2 14.02 1 14.06 0 14.02 0 13.97 0 14.19 0 13.97 0 13.98 0 14.03 0 14.04 0 14.13 1 14.22 1 14.21 5 14.15 2 14.17 0 14.03 0 14.02 0 13.91 0 13.81 0 13.78 0 13.83 0 13.96 1 13.90 1 14.10 1 13.99 0 13.90 0 13.88 0 13.89 0 14.03 0 14.19 0 14.16 0 14.10 0 14.03 1 14.06 5 14.07 6 14.11 5 14.17 1 14.23 0 14.11 0 14.25 0 14.03 0 14.07 0 13.99 1 14.01 0 13.98 2 13.93 2 14.06 3 13.98 2 14.00 0 13.86 0 13.98 0 13.80 0 13.80 0 13.89 0 13.88 0 13.78 0 13.89 1 13.93 4 13.95 6 13.92 1 13.96 1 13.91 0 13.76 0 13.79 0 13.99 0 13.99 0 13.99 1 14.04 1 14.01 0 14.13 2 14.01 2 14.07 0 14.04 1 14.18 0 14.26 0 14.31 0 14.26 0 14.20 0 14.18 0 14.14 0 14.08 2 14.00 2 14.04 2 14.08 2 14.00 0 13.94 0 13.83 0 13.75 0 13.92 0 13.91 0 13.91 0 13.90 1 13.95 1 14.02 4 13.89 4 13.89 1 13.89 0 13.87 0 14.03 0 13.96 0 14.06 0 13.98 0 14.08 0 13.95 1 13.95 1 13.84 2 13.94 3 13.88 1 13.83 0 13.80 1 13.92 0 13.90 0 13.73 0 13.87 0 13.76 1 13.86 0 13.90 1 13.85 6 13.90 2 13.75 1 13.87 0 13.97 0 13.97 0 14.14 0 14.18 0 14.17 0 14.20 0 14.17 0 14.15 0 14.10 1 14.04 3 14.01 2 14.15 0 14.03 0 14.04 1 14.05 0 14.12 0 14.09 0 13.98 0 13.94 0 14.04 1 13.86 4 14.03 3 13.99 3 14.08 0 14.01 0 14.04 0 13.90 0 14.09 0 14.04 0 13.97 0 14.08 1 13.99 2 14.11 3 14.16 2 14.18 1 14.18 0 14.38 0 14.18 0 14.22 0 14.13 0 14.20 0 14.25 0 14.14 0 14.15 1 14.13 2 14.10 5 14.09 1 14.23 2 14.11 0 14.40 0 14.30 0 14.37 0 14.24 0 14.14 1 14.17 1 14.19 0 14.24 3 14.11 4 14.07 1 14.15 2 14.28 0 14.03 0 14.06 0 13.94 0 14.05 0 14.12 0 14.00 2 14.12 0 13.99 1 14.04 3 14.05 0 14.06 0 14.33 0 14.45 0 14.39 0 14.39 0 14.23 0 14.25 0 14.15 0 14.12 0 14.26 2 14.28 2 14.12 0 14.29 0 14.12 0 14.22 0 14.09 0 14.17 0 14.01 0 14.22 0 13.98 0 14.12 0 14.09 4 14.11 6 14.05 1 13.96 1 13.81 1 14.09 0 13.87 0 14.10 0 14.08 0 14.09 0 14.08 0 13.95 2 14.08 3 14.00 3 14.05 2 13.98 1 14.04 0 14.24 0 14.28 0 14.23 0 14.16 0 14.11 0 14.07 2 14.07 0 14.08 1 14.02 2 14.08 0 14.01 1 14.08 0 14.23 0 14.39 0 14.13 0 14.21 0 14.21 0 14.26 0 14.36 0 14.18 3 14.34 3 14.26 1 14.22 0 14.46 0 14.51 0 14.32 0 14.44 0 14.35 0 14.30 0 14.32 0 14.24 0 14.27 4 14.26 6 14.26 1 14.05 1 14.22 0 14.11 0 14.25 0 14.26 0 14.16 0 14.07 0 14.06 1 14.22 3 14.24 3 14.25 2 14.23 1 14.14 1 14.29 0 14.33 0 14.34 0 14.65 0 14.43 0 14.32 0 14.31 0 14.34 3 14.28 5 14.23 2 14.40 4 14.45 0 14.39 0 14.35 0 14.43 0 14.29 0 14.41 0 14.31 0 14.42 1 14.43 0 14.30 1 14.36 3 14.22 3 14.16 0 14.20 0 14.38 0 14.37 0 14.34 0 14.19 1 14.22 0 14.15 0 14.00 0 14.01 1 13.94 4 14.00 1 13.93 0 14.13 0 14.28 0 14.26 0 14.30 0 14.18 0 14.18 0 14.10 1 14.09 0 14.03 4 14.02 3 14.16 0 14.00 0 14.14 0 14.28 0 13.94 0 14.25 0 14.26 0 14.22 0 14.29 1 14.20 0 14.19 2 14.25 2 14.38 0 14.37 2 14.29 0 14.44 0 14.70 0 14.44 0 14.34 0 14.11 0 14.33 1 14.46 5 14.37 7 14.24 3 14.42 4 14.37 0 14.26 0 14.23 0 14.43 0 14.25 0 14.20 0 14.21 0 14.18 1 14.30 2 14.32 4 14.16 2 14.15 3 14.28 1 14.31 0 14.27 0 14.31 0 14.46 0 14.33 0 14.31 0 14.43 1 14.28 3 14.36 0 14.45 1 14.50 2 14.55 0 14.53 0 14.55 0 14.83 0 14.56 0 14.58 0 14.59 0 14.59 0 14.67 1 14.60 4 14.43 6 14.42 1 14.40 1 14.51 0 14.45 0 14.64 0 14.27 0 14.28 0 14.23 0 14.28 1 14.26 0 14.27 4 14.25 3 14.30 3 14.32 1 14.37 0 14.21 0 14.49 0 14.46 0 14.50 0 14.30 0 14.31 0 14.28 0 14.37 4 14.29 7 14.21 4 14.21 0 14.19 0 14.38 0 14.40 0 14.56 0 14.42 0 14.47 0 14.45 1 14.46 1 14.45 3 14.45 5 14.43 5 14.68 2 14.47 0 14.74 0 14.75 0 14.81 0 14.54 0 14.51 0 14.43 0 14.53 1 14.43 3 14.46 8 14.48 0 14.51 0 14.33 0
Names of X columns:
Temperatuur Orkanen
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Include Monthly Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
Seasonal Differences (s=12)
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 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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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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