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Data X:
178.6 112.7 NA NA NA NA 224.7 122 112.7 NA NA NA 206.7 134.7 122 112.7 NA NA 149.7 109.8 134.7 122 112.7 NA 160.1 130.8 109.8 134.7 122 112.7 154.7 118.7 130.8 109.8 134.7 122 155 104.4 118.7 130.8 109.8 134.7 233.6 87.8 104.4 118.7 130.8 109.8 211.9 134.2 87.8 104.4 118.7 130.8 186.7 143.9 134.2 87.8 104.4 118.7 156.5 140.4 143.9 134.2 87.8 104.4 142 111 140.4 143.9 134.2 87.8 207 126.3 111 140.4 143.9 134.2 232.3 124.4 126.3 111 140.4 143.9 230.4 136.1 124.4 126.3 111 140.4 159.4 118.4 136.1 124.4 126.3 111 158.4 127.4 118.4 136.1 124.4 126.3 164.2 127.9 127.4 118.4 136.1 124.4 179.2 115 127.9 127.4 118.4 136.1 242.9 90.2 115 127.9 127.4 118.4 211.7 131 90.2 115 127.9 127.4 188.6 143.3 131 90.2 115 127.9 151.9 131.5 143.3 131 90.2 115 134.8 98.5 131.5 143.3 131 90.2 218 124.9 98.5 131.5 143.3 131 233.4 122.4 124.9 98.5 131.5 143.3 218.5 128.8 122.4 124.9 98.5 131.5 163.7 125.9 128.8 122.4 124.9 98.5 150.8 120.2 125.9 128.8 122.4 124.9 145.6 120 120.2 125.9 128.8 122.4 190.3 116 120 120.2 125.9 128.8 235.9 89.2 116 120 120.2 125.9 203.7 135.9 89.2 116 120 120.2 185.3 148.7 135.9 89.2 116 120 150.9 128.1 148.7 135.9 89.2 116 136 100.9 128.1 148.7 135.9 89.2 213.9 125.5 100.9 128.1 148.7 135.9 234.1 119.8 125.5 100.9 128.1 148.7 194.8 120.7 119.8 125.5 100.9 128.1 154.2 125 120.7 119.8 125.5 100.9 138.5 109 125 120.7 119.8 125.5 133.7 114.2 109 125 120.7 119.8 186.8 105.6 114.2 109 125 120.7 221.3 80.1 105.6 114.2 109 125 211.7 131.1 80.1 105.6 114.2 109 171.4 136.6 131.1 80.1 105.6 114.2 124.5 119.7 136.6 131.1 80.1 105.6 129.2 102.4 119.7 136.6 131.1 80.1 173.3 114.5 102.4 119.7 136.6 131.1 190.9 112.9 114.5 102.4 119.7 136.6 175 131.8 112.9 114.5 102.4 119.7 113.8 118.7 131.8 112.9 114.5 102.4 98.4 107.1 118.7 131.8 112.9 114.5 116.4 127 107.1 118.7 131.8 112.9 153.9 104.6 127 107.1 118.7 131.8 199.7 85.9 104.6 127 107.1 118.7 168.8 134 85.9 104.6 127 107.1 132.8 127.6 134 85.9 104.6 127 118.8 121.5 127.6 134 85.9 104.6 112.7 104.5 121.5 127.6 134 85.9 150.5 107.3 104.5 121.5 127.6 134 203.5 111.9 107.3 104.5 121.5 127.6 184.3 120.7 111.9 107.3 104.5 121.5 113.5 116.9 120.7 111.9 107.3 104.5 102.4 106.1 116.9 120.7 111.9 107.3 119.3 122.3 106.1 116.9 120.7 111.9 152.4 97.8 122.3 106.1 116.9 120.7 218.5 82.7 97.8 122.3 106.1 116.9 154.6 128.2 82.7 97.8 122.3 106.1 124.9 119 128.2 82.7 97.8 122.3 124 127.4 119 128.2 82.7 97.8 113.8 106 127.4 119 128.2 82.7 162.5 108.7 106 127.4 119 128.2 184.8 113.5 108.7 106 127.4 119 177.3 131.4 113.5 108.7 106 127.4 91.4 111.3 131.4 113.5 108.7 106 85.2 119 111.3 131.4 113.5 108.7 120.9 130.7 119 111.3 131.4 113.5 159.8 104.5 130.7 119 111.3 131.4 200.1 88.9 104.5 130.7 119 111.3 171.8 135.4 88.9 104.5 130.7 119 139.5 140.6 135.4 88.9 104.5 130.7 115.7 138.8 140.6 135.4 88.9 104.5 96.8 107.4 138.8 140.6 135.4 88.9 169.9 120.8 107.4 138.8 140.6 135.4 212.3 124.1 120.8 107.4 138.8 140.6 182.3 139.2 124.1 120.8 107.4 138.8 95.2 119.9 139.2 124.1 120.8 107.4 96.9 121 119.9 139.2 124.1 120.8 100.3 133.7 121 119.9 139.2 124.1 131.3 115.2 133.7 121 119.9 139.2 172.3 96.7 115.2 133.7 121 119.9 130.6 131 96.7 115.2 133.7 121 129.5 147.6 131 96.7 115.2 133.7 96.3 132.9 147.6 131 96.7 115.2 91.4 97.4 132.9 147.6 131 96.7 140.7 123.6 97.4 132.9 147.6 131 160.2 124.9 123.6 97.4 132.9 147.6 158.8 118.6 124.9 123.6 97.4 132.9 193.6 127.6 118.6 124.9 123.6 97.4 80.8 110.2 127.6 118.6 124.9 123.6 102 115.4 110.2 127.6 118.6 124.9 119.5 106.6 115.4 110.2 127.6 118.6 129.6 75.5 106.6 115.4 110.2 127.6 113.8 116.7 75.5 106.6 115.4 110.2 102.5 118 116.7 75.5 106.6 115.4 78.4 98.7 118 116.7 75.5 106.6 95.7 81.5 98.7 118 116.7 75.5 143.7 87 81.5 98.7 118 116.7 149.3 86.8 87 81.5 98.7 118 121.7 96.8 86.8 87 81.5 98.7 81 92.7 96.8 86.8 87 81.5 68.1 82.1 92.7 96.8 86.8 87 92.3 94.1 82.1 92.7 96.8 86.8 107.7 89.7 94.1 82.1 92.7 96.8 114.4 67.5 89.7 94.1 82.1 92.7 98.6 102 67.5 89.7 94.1 82.1 106.7 103.2 102 67.5 89.7 94.1 73.9 95.6 103.2 102 67.5 89.7 85.9 83 95.6 103.2 102 67.5 118.4 87.2 83 95.6 103.2 102 144.2 94 87.2 83 95.6 103.2 118.4 107.7 94 87.2 83 95.6 82.6 103.3 107.7 94 87.2 83 68 94.8 103.3 107.7 94 87.2 99.8 112.7 94.8 103.3 107.7 94 93.4 96.8 112.7 94.8 103.3 107.7 107.9 75.9 96.8 112.7 94.8 103.3 101.1 116.7 75.9 96.8 112.7 94.8 100.4 111.4 116.7 75.9 96.8 112.7 76.7 108.6 111.4 116.7 75.9 96.8 89.1 90.9 108.6 111.4 116.7 75.9 105.3 92.6 90.9 108.6 111.4 116.7 124.8 95.7 92.6 90.9 108.6 111.4 111.9 116.7 95.7 92.6 90.9 108.6 89 95.4 116.7 95.7 92.6 90.9 88.6 105.1 95.4 116.7 95.7 92.6 84.5 99.7 105.1 95.4 116.7 95.7 91.1 89.8 99.7 105.1 95.4 116.7 118.1 74 89.8 99.7 105.1 95.4 103.6 108 74 89.8 99.7 105.1 92.6 102.1 108 74 89.8 99.7 70.2 100.2 102.1 108 74 89.8 70.2 83.2 100.2 102.1 108 74 114.3 87.9 83.2 100.2 102.1 108 125.3 93.3 87.9 83.2 100.2 102.1 98.9 98.5 93.3 87.9 83.2 100.2 65.4 84.5 98.5 93.3 87.9 83.2 66 89.3 84.5 98.5 93.3 87.9 71.2 94.2 89.3 84.5 98.5 93.3 84.6 83.5 94.2 89.3 84.5 98.5 102.6 67.5 83.5 94.2 89.3 84.5 91.8 89.4 67.5 83.5 94.2 89.3 97.4 102.4 89.4 67.5 83.5 94.2 64.1 92 102.4 89.4 67.5 83.5 62.3 65.9 92 102.4 89.4 67.5 96.2 85.3 65.9 92 102.4 89.4 104.9 87 85.3 65.9 92 102.4 90.3 91.8 87 85.3 65.9 92 65.2 88.5 91.8 87 85.3 65.9 57.8 89.1 88.5 91.8 87 85.3 70.5 89.8 89.1 88.5 91.8 87 93.2 88.9 89.8 89.1 88.5 91.8 74.2 64 88.9 89.8 89.1 88.5 91.1 93.2 64 88.9 89.8 89.1 85 100.1 93.2 64 88.9 89.8 58.9 89.3 100.1 93.2 64 88.9 68.3 68.1 89.3 100.1 93.2 64 98.1 94.3 68.1 89.3 100.1 93.2 110.5 93.3 94.3 68.1 89.3 100.1 77.6 98.1 93.3 94.3 68.1 89.3 55.1 96.8 98.1 93.3 94.3 68.1 49.8 87.8 96.8 98.1 93.3 94.3 58.5 95.6 87.8 96.8 98.1 93.3 86.5 95.7 95.6 87.8 96.8 98.1 88.8 64.4 95.7 95.6 87.8 96.8 94 108.1 64.4 95.7 95.6 87.8 65 109.6 108.1 64.4 95.7 95.6 52.2 90.9 109.6 108.1 64.4 95.7 70.9 75.6 90.9 109.6 108.1 64.4 88.4 93.5 75.6 90.9 109.6 108.1 107.8 98.1 93.5 75.6 90.9 109.6 75.2 104.5 98.1 93.5 75.6 90.9 58 102.7 104.5 98.1 93.5 75.6 58.3 89.6 102.7 104.5 98.1 93.5 71.6 108.8 89.6 102.7 104.5 98.1 72.4 95.4 108.8 89.6 102.7 104.5 119.8 70.1 95.4 108.8 89.6 102.7 83.4 104.6 70.1 95.4 108.8 89.6 60.6 105.5 104.6 70.1 95.4 108.8 47.1 96.8 105.5 104.6 70.1 95.4 65.5 79.4 96.8 105.5 104.6 70.1 76.1 92.3 79.4 96.8 105.5 104.6 115.2 96.8 92.3 79.4 96.8 105.5 73.5 103 96.8 92.3 79.4 96.8 50.7 99.5 103 96.8 92.3 79.4 53.5 91 99.5 103 96.8 92.3 66.7 103.4 91 99.5 103 96.8 84.5 82 103.4 91 99.5 103 96.4 70.1 82 103.4 91 99.5 63.6 98.1 70.1 82 103.4 91 40.4 95.7 98.1 70.1 82 103.4 56.3 98 95.7 98.1 70.1 82 58.4 77.3 98 95.7 98.1 70.1 103 89.8 77.3 98 95.7 98.1 104.5 91.6 89.8 77.3 98 95.7 84.9 106.5 91.6 89.8 77.3 98 50.8 87.5 106.5 91.6 89.8 77.3 57.9 99.5 87.5 106.5 91.6 89.8 56.9 104.4 99.5 87.5 106.5 91.6 82.8 84.5 104.4 99.5 87.5 106.5 96 68.3 84.5 104.4 99.5 87.5
Names of X columns:
Wearingapparel Textiles0 Textiles1 Textiles2 Textiles3 Textiles4
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
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
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-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep=''))) for (i in 1:(n-par5*par6)) { for (j in 1:par5) { x2[i,j] <- x[i+par5*par6-j*par6,par1] } } x <- cbind(x[(par5*par6+1):n,], x2) n <- n - par5*par6 } 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(myerror*myerror),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')
Compute
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
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