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Data X:
56.6 90.4 NA NA NA NA 71.5 127 90.4 NA NA NA 83.3 139.9 127 90.4 NA NA 66.9 113.4 139.9 127 90.4 NA 86.8 137.6 113.4 139.9 127 90.4 74.9 122.7 137.6 113.4 139.9 127 60.9 40.3 122.7 137.6 113.4 139.9 72.1 126.5 40.3 122.7 137.6 113.4 84.3 134.6 126.5 40.3 122.7 137.6 88.6 131.1 134.6 126.5 40.3 122.7 82.2 119.1 131.1 134.6 126.5 40.3 51.8 98.7 119.1 131.1 134.6 126.5 80.9 92.2 98.7 119.1 131.1 134.6 76.7 111.2 92.2 98.7 119.1 131.1 82.6 117.9 111.2 92.2 98.7 119.1 74.6 102.4 117.9 111.2 92.2 98.7 78.6 122.1 102.4 117.9 111.2 92.2 79 122.2 122.1 102.4 117.9 111.2 64.4 45.4 122.2 122.1 102.4 117.9 64 118.6 45.4 122.2 122.1 102.4 77.9 109.8 118.6 45.4 122.2 122.1 83.8 127.6 109.8 118.6 45.4 122.2 74.2 106.3 127.6 109.8 118.6 45.4 51.7 74.6 106.3 127.6 109.8 118.6 79.9 82.7 74.6 106.3 127.6 109.8 74.8 86.5 82.7 74.6 106.3 127.6 78 103.6 86.5 82.7 74.6 106.3 78.4 114 103.6 86.5 82.7 74.6 77.3 112 114 103.6 86.5 82.7 77.9 115.3 112 114 103.6 86.5 72 48.1 115.3 112 114 103.6 66.4 100.5 48.1 115.3 112 114 83.5 120.7 100.5 48.1 115.3 112 85.1 122.7 120.7 100.5 48.1 115.3 74.8 107.6 122.7 120.7 100.5 48.1 56.1 70.8 107.6 122.7 120.7 100.5 75.3 82.5 70.8 107.6 122.7 120.7 75.3 91.6 82.5 70.8 107.6 122.7 75.4 115.4 91.6 82.5 70.8 107.6 76.7 108.7 115.4 91.6 82.5 70.8 72.3 101.4 108.7 115.4 91.6 82.5 78.1 114 101.4 108.7 115.4 91.6 69.4 51.4 114 101.4 108.7 115.4 55 96.2 51.4 114 101.4 108.7 79.9 125.5 96.2 51.4 114 101.4 88.6 120.1 125.5 96.2 51.4 114 72.2 96.9 120.1 125.5 96.2 51.4 59.2 77.3 96.9 120.1 125.5 96.2 77.9 86.6 77.3 96.9 120.1 125.5 77.8 98.2 86.6 77.3 96.9 120.1 90.4 121.7 98.2 86.6 77.3 96.9 87.4 106.8 121.7 98.2 86.6 77.3 82.9 100.6 106.8 121.7 98.2 86.6 97.5 124.5 100.6 106.8 121.7 98.2 75.8 42.7 124.5 100.6 106.8 121.7 74 107 42.7 124.5 100.6 106.8 95.5 123.8 107 42.7 124.5 100.6 95.6 117.3 123.8 107 42.7 124.5 95.8 101.9 117.3 123.8 107 42.7 75.5 86.3 101.9 117.3 123.8 107 89.9 78.7 86.3 101.9 117.3 123.8 91.8 92.2 78.7 86.3 101.9 117.3 97 103.6 92.2 78.7 86.3 101.9 95.7 120.8 103.6 92.2 78.7 86.3 86 105.5 120.8 103.6 92.2 78.7 93.3 127.8 105.5 120.8 103.6 92.2 68.7 36.9 127.8 105.5 120.8 103.6 64.5 112.4 36.9 127.8 105.5 120.8 91 127.5 112.4 36.9 127.8 105.5 84.9 111.5 127.5 112.4 36.9 127.8 97.3 108.7 111.5 127.5 112.4 36.9 70.2 87.3 108.7 111.5 127.5 112.4 100.9 84.6 87.3 108.7 111.5 127.5 99.7 96 84.6 87.3 108.7 111.5 121.3 118.3 96 84.6 87.3 108.7 102.8 107.5 118.3 96 84.6 87.3 111.8 121.5 107.5 118.3 96 84.6 117.6 130.4 121.5 107.5 118.3 96 80.7 41.5 130.4 121.5 107.5 118.3 81.6 116.4 41.5 130.4 121.5 107.5 99.5 130.2 116.4 41.5 130.4 121.5 108.3 121.4 130.2 116.4 41.5 130.4 107.5 120.1 121.4 130.2 116.4 41.5 84.4 88.3 120.1 121.4 130.2 116.4 115.6 97.9 88.3 120.1 121.4 130.2 109.8 109.6 97.9 88.3 120.1 121.4 116.9 126 109.6 97.9 88.3 120.1 106.8 112.7 126 109.6 97.9 88.3 112.9 115.7 112.7 126 109.6 97.9 113.9 128.2 115.7 112.7 126 109.6 94.9 47.9 128.2 115.7 112.7 126 85.1 121.4 47.9 128.2 115.7 112.7 101 123.1 121.4 47.9 128.2 115.7 109.7 137.2 123.1 121.4 47.9 128.2 104.1 119 137.2 123.1 121.4 47.9 76.7 81.5 119 137.2 123.1 121.4 116.5 115.3 81.5 119 137.2 123.1 121.7 124.2 115.3 81.5 119 137.2 117.9 102.9 124.2 115.3 81.5 119 133.3 137.6 102.9 124.2 115.3 81.5 117.8 120.7 137.6 102.9 124.2 115.3 129.8 130.6 120.7 137.6 102.9 124.2 109.1 55.8 130.6 120.7 137.6 102.9 88 110.5 55.8 130.6 120.7 137.6 120.1 134.9 110.5 55.8 130.6 120.7 118.4 125.7 134.9 110.5 55.8 130.6 89.7 105 125.7 134.9 110.5 55.8 71.4 82.6 105 125.7 134.9 110.5 75.9 90.8 82.6 105 125.7 134.9 75.2 107.2 90.8 82.6 105 125.7 79.2 124.9 107.2 90.8 82.6 105 70.8 108.7 124.9 107.2 90.8 82.6 73.7 108.5 108.7 124.9 107.2 90.8 79.4 124.5 108.5 108.7 124.9 107.2 68.5 52.1 124.5 108.5 108.7 124.9 66.5 106.8 52.1 124.5 108.5 108.7 93 129.8 106.8 52.1 124.5 108.5 91.9 129.2 129.8 106.8 52.1 124.5 86.1 95.5 129.2 129.8 106.8 52.1 66.2 75.1 95.5 129.2 129.8 106.8 90.4 77.7 75.1 95.5 129.2 129.8 92.4 86.3 77.7 75.1 95.5 129.2 108.8 130.3 86.3 77.7 75.1 95.5 103.6 110.4 130.3 86.3 77.7 75.1 103 100 110.4 130.3 86.3 77.7 117.1 127.2 100 110.4 130.3 86.3 91.9 46.7 127.2 100 110.4 130.3 80.3 109.9 46.7 127.2 100 110.4 111.6 127.7 109.9 46.7 127.2 100 106.6 122.2 127.7 109.9 46.7 127.2 107 100.9 122.2 127.7 109.9 46.7 87.3 60.7 100.9 122.2 127.7 109.9 104.5 86.7 60.7 100.9 122.2 127.7 102.8 112.3 86.7 60.7 100.9 122.2 116.2 134.2 112.3 86.7 60.7 100.9 103.4 105 134.2 112.3 86.7 60.7 112.8 126.5 105 134.2 112.3 86.7 103 114.5 126.5 105 134.2 112.3 85.5 43.6 114.5 126.5 105 134.2 83.2 112.4 43.6 114.5 126.5 105 106.4 129.4 112.4 43.6 114.5 126.5 98.2 116.2 129.4 112.4 43.6 114.5 100.5 115.9 116.2 129.4 112.4 43.6 75.5 85.6 115.9 116.2 129.4 112.4 101.3 92.5 85.6 115.9 116.2 129.4 105.2 91.2 92.5 85.6 115.9 116.2 112.7 128.8 91.2 92.5 85.6 115.9 95.7 103.6 128.8 91.2 92.5 85.6 99.3 113.8 103.6 128.8 91.2 92.5 103 120.9 113.8 103.6 128.8 91.2 88.4 52.5 120.9 113.8 103.6 128.8 78.5 112.8 52.5 120.9 113.8 103.6 97 115.8 112.8 52.5 120.9 113.8 106.4 123.4 115.8 112.8 52.5 120.9 94.7 112.1 123.4 115.8 112.8 52.5 73.7 71.9 112.1 123.4 115.8 112.8 101.5 76.6 71.9 112.1 123.4 115.8 100.5 91.2 76.6 71.9 112.1 123.4 102.1 105.4 91.2 76.6 71.9 112.1 101.4 107.8 105.4 91.2 76.6 71.9 98.6 105.9 107.8 105.4 91.2 76.6 104.7 114.5 105.9 107.8 105.4 91.2 87.6 54.4 114.5 105.9 107.8 105.4 76 97.2 54.4 114.5 105.9 107.8 102.9 116.9 97.2 54.4 114.5 105.9 107.8 121.5 116.9 97.2 54.4 114.5 96 101.2 121.5 116.9 97.2 54.4 69.6 81.6 101.2 121.5 116.9 97.2 105.4 100.4 81.6 101.2 121.5 116.9 100.5 101 100.4 81.6 101.2 121.5 100.4 110.6 101 100.4 81.6 101.2 101.8 100 110.6 101 100.4 81.6 94.9 98.7 100 110.6 101 100.4 100.5 106.2 98.7 100 110.6 101 89.4 51.8 106.2 98.7 100 110.6 75.9 89.8 51.8 106.2 98.7 100 109.1 116.3 89.8 51.8 106.2 98.7 107.4 118.3 116.3 89.8 51.8 106.2 86.6 94.3 118.3 116.3 89.8 51.8 75.7 71.7 94.3 118.3 116.3 89.8 105.3 90.8 71.7 94.3 118.3 116.3 104.4 93.6 90.8 71.7 94.3 118.3 119.5 112.3 93.6 90.8 71.7 94.3 111.6 97 112.3 93.6 90.8 71.7 105.7 90.2 97 112.3 93.6 90.8 122.3 114.6 90.2 97 112.3 93.6 97.7 50.9 114.6 90.2 97 112.3 82.4 94.3 50.9 114.6 90.2 97 113.4 112.2 94.3 50.9 114.6 90.2 113.8 114 112.2 94.3 50.9 114.6 103.1 88.4 114 112.2 94.3 50.9 82.2 67.7 88.4 114 112.2 94.3 104.5 87.6 67.7 88.4 114 112.2 104.8 96.3 87.6 67.7 88.4 114 110.7 97 96.3 87.6 67.7 88.4 110.6 105.8 97 96.3 87.6 67.7 103.9 95.2 105.8 97 96.3 87.6 111.9 119.6 95.2 105.8 97 96.3 82.8 45.4 119.6 95.2 105.8 97 81.4 98.6 45.4 119.6 95.2 105.8 108.3 112.7 98.6 45.4 119.6 95.2 103.9 101.3 112.7 98.6 45.4 119.6 105.3 84.7 101.3 112.7 98.6 45.4 86 78 84.7 101.3 112.7 98.6 109.9 73.6 78 84.7 101.3 112.7 103.9 96.3 73.6 78 84.7 101.3 120.5 113.8 96.3 73.6 78 84.7 102.6 85 113.8 96.3 73.6 78 110.7 103.5 85 113.8 96.3 73.6 116.8 106.4 103.5 85 113.8 96.3 86.7 44.3 106.4 103.5 85 113.8 90.1 95.9 44.3 106.4 103.5 85
Names of X columns:
BM Build0 Build1 Build2 Build3 Build4

Sample Range: (leave blank to include all observations)
From:
To:

Column Number of Endogenous Series (?)


Fixed Seasonal Effects


Type of Equation


Degree of Predetermination (lagged endogenous variables)


Degree of Seasonal Predetermination


Seasonality


Chart options



R Code
par6 <- '12' par5 <- '1' par4 <- '10' par3 <- 'First and Seasonal Differences (s)' par2 <- 'Do not include Seasonal Dummies' par1 <- '1' library(lattice) library(lmtest) library(car) library(MASS) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test mywarning <- '' par6 <- as.numeric(par6) if(is.na(par6)) { par6 <- 12 mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.' } 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 (!is.numeric(par4)) par4 <- 0 if (par5=='') par5 <- 0 par5 <- as.numeric(par5) if (!is.numeric(par5)) par5 <- 0 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)'){ (n <- n - par6) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+par6,j] - x[i,j] } } x <- x2 } if (par3 == 'First and Seasonal Differences (s)'){ (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 - par6) x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep=''))) for (i in 1:n) { for (j in 1:k) { x2[i,j] <- x[i+par6,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-par5par6,par5), dimnames=list(1:(n-par5par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep=''))) for (i in 1:(n-par5par6)) { for (j in 1:par5) { x2[i,j] <- x[i+par5par6-jpar6,par1] } } x <- cbind(x[(par5par6+1):n,], x2) n <- n - par5par6 } if (par2 == 'Include Seasonal Dummies'){ x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep =''))) for (i in 1:(par6-1)){ x2[seq(i,n,par6),i] <- 1 } x <- cbind(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[n,])) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } print(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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') sresid <- studres(mylm) hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals') xfit<-seq(min(sresid),max(sresid),length=40) yfit<-dnorm(xfit) lines(xfit, yfit) grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqPlot(mylm, main='QQ Plot') grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) print(z) plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$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,'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(myerrormyerror),6),format='g',flag=' ')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') myr <- as.numeric(mysum$resid) myr a <-table.start() a <- table.row.start(a) a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE) a <- table.row.end(a) a <- table.row.start(a) a <- table.element(a,'Description',1,TRUE) a <- table.element(a,'Link',1,TRUE) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Histogram',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'QQ Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Kernel Density Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Spectral Analysis',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a <- table.row.start(a) a <- table.element(a,'Summary Statistics',1,header=TRUE) a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1) a <- table.row.end(a) a<-table.end(a) table.save(a,file='mytable7.tab') if(n < 200) { a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' ')) a<-table.element(a,formatC(signif(x[i]-mysum$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') } } a<-table.start() a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_fitted <- resettest(mylm,power=2:3,type='fitted') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_fitted'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_regressors <- resettest(mylm,power=2:3,type='regressor') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_regressors'),'</pre>',sep='')) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp') a<-table.element(a,paste('<pre>',RC.texteval('reset_test_principal_components'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable8.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) vif <- vif(mylm) a<-table.element(a,paste('<pre>',RC.texteval('vif'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable9.tab')

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center