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Type 'q()' to quit R. > x <- array(list(121.6 + ,0 + ,118.8 + ,121.6 + ,118.8 + ,0 + ,114.0 + ,118.8 + ,114.0 + ,1 + ,111.5 + ,114.0 + ,111.5 + ,1 + ,97.2 + ,111.5 + ,97.2 + ,1 + ,102.5 + ,97.2 + ,102.5 + ,1 + ,113.4 + ,102.5 + ,113.4 + ,1 + ,109.8 + ,113.4 + ,109.8 + ,1 + ,104.9 + ,109.8 + ,104.9 + ,1 + ,126.1 + ,104.9 + ,126.1 + ,1 + ,80.0 + ,126.1 + ,80.0 + ,1 + ,96.8 + ,80.0 + ,96.8 + ,1 + ,117.2 + ,96.8 + ,117.2 + ,1 + ,112.3 + ,117.2 + ,112.3 + ,1 + ,117.3 + ,112.3 + ,117.3 + ,1 + ,111.1 + ,117.3 + ,111.1 + ,0 + ,102.2 + ,111.1 + ,102.2 + ,0 + ,104.3 + ,102.2 + ,104.3 + ,0 + ,122.9 + ,104.3 + ,122.9 + ,0 + ,107.6 + ,122.9 + ,107.6 + ,0 + ,121.3 + ,107.6 + ,121.3 + ,0 + ,131.5 + ,121.3 + ,131.5 + ,0 + ,89.0 + ,131.5 + ,89.0 + ,0 + ,104.4 + ,89.0 + ,104.4 + ,0 + ,128.9 + ,104.4 + ,128.9 + ,0 + ,135.9 + ,128.9 + ,135.9 + ,0 + ,133.3 + ,135.9 + ,133.3 + ,0 + ,121.3 + ,133.3 + ,121.3 + ,0 + ,120.5 + ,121.3 + ,120.5 + ,0 + ,120.4 + ,120.5 + ,120.4 + ,0 + ,137.9 + ,120.4 + ,137.9 + ,0 + ,126.1 + ,137.9 + ,126.1 + ,0 + ,133.2 + ,126.1 + ,133.2 + ,0 + ,151.1 + ,133.2 + ,151.1 + ,0 + ,105.0 + ,151.1 + ,105.0 + ,0 + ,119.0 + ,105.0 + ,119.0 + ,0 + ,140.4 + ,119.0 + ,140.4 + ,0 + ,156.6 + ,140.4 + ,156.6 + ,0 + ,137.1 + ,156.6 + ,137.1 + ,0 + ,122.7 + ,137.1 + ,122.7 + ,0 + ,125.8 + ,122.7 + ,125.8 + ,0 + ,139.3 + ,125.8 + ,139.3 + ,0 + ,134.9 + ,139.3 + ,134.9 + ,0 + ,149.2 + ,134.9 + ,149.2 + ,1 + ,132.3 + ,149.2 + ,132.3 + ,0 + ,149.0 + ,132.3 + ,149.0 + ,1 + ,117.2 + ,149.0 + ,117.2 + ,1 + ,119.6 + ,117.2 + ,119.6 + ,1 + ,152.0 + ,119.6 + ,152.0 + ,1 + ,149.4 + ,152.0 + ,149.4 + ,1 + ,127.3 + ,149.4 + ,127.3 + ,1 + ,114.1 + ,127.3 + ,114.1 + ,1 + ,102.1 + ,114.1 + ,102.1 + ,1 + ,107.7 + ,102.1 + ,107.7 + ,1 + ,104.4 + ,107.7 + ,104.4 + ,1 + ,102.1 + ,104.4 + ,102.1 + ,1 + ,96.0 + ,102.1 + ,96.0 + ,1 + ,109.3 + ,96.0 + ,109.3 + ,1 + ,90.0 + ,109.3 + ,90.0 + ,1 + ,83.9 + ,90.0) + ,dim=c(4 + ,59) + ,dimnames=list(c('X' + ,'Y' + ,'y(t)' + ,'y(t-1)') + ,1:59)) > y <- array(NA,dim=c(4,59),dimnames=list(c('X','Y','y(t)','y(t-1)'),1:59)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Include Monthly Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > 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 X Y y(t) y(t-1) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 121.6 0 118.8 121.6 1 0 0 0 0 0 0 0 0 0 0 1 2 118.8 0 114.0 118.8 0 1 0 0 0 0 0 0 0 0 0 2 3 114.0 1 111.5 114.0 0 0 1 0 0 0 0 0 0 0 0 3 4 111.5 1 97.2 111.5 0 0 0 1 0 0 0 0 0 0 0 4 5 97.2 1 102.5 97.2 0 0 0 0 1 0 0 0 0 0 0 5 6 102.5 1 113.4 102.5 0 0 0 0 0 1 0 0 0 0 0 6 7 113.4 1 109.8 113.4 0 0 0 0 0 0 1 0 0 0 0 7 8 109.8 1 104.9 109.8 0 0 0 0 0 0 0 1 0 0 0 8 9 104.9 1 126.1 104.9 0 0 0 0 0 0 0 0 1 0 0 9 10 126.1 1 80.0 126.1 0 0 0 0 0 0 0 0 0 1 0 10 11 80.0 1 96.8 80.0 0 0 0 0 0 0 0 0 0 0 1 11 12 96.8 1 117.2 96.8 0 0 0 0 0 0 0 0 0 0 0 12 13 117.2 1 112.3 117.2 1 0 0 0 0 0 0 0 0 0 0 13 14 112.3 1 117.3 112.3 0 1 0 0 0 0 0 0 0 0 0 14 15 117.3 1 111.1 117.3 0 0 1 0 0 0 0 0 0 0 0 15 16 111.1 0 102.2 111.1 0 0 0 1 0 0 0 0 0 0 0 16 17 102.2 0 104.3 102.2 0 0 0 0 1 0 0 0 0 0 0 17 18 104.3 0 122.9 104.3 0 0 0 0 0 1 0 0 0 0 0 18 19 122.9 0 107.6 122.9 0 0 0 0 0 0 1 0 0 0 0 19 20 107.6 0 121.3 107.6 0 0 0 0 0 0 0 1 0 0 0 20 21 121.3 0 131.5 121.3 0 0 0 0 0 0 0 0 1 0 0 21 22 131.5 0 89.0 131.5 0 0 0 0 0 0 0 0 0 1 0 22 23 89.0 0 104.4 89.0 0 0 0 0 0 0 0 0 0 0 1 23 24 104.4 0 128.9 104.4 0 0 0 0 0 0 0 0 0 0 0 24 25 128.9 0 135.9 128.9 1 0 0 0 0 0 0 0 0 0 0 25 26 135.9 0 133.3 135.9 0 1 0 0 0 0 0 0 0 0 0 26 27 133.3 0 121.3 133.3 0 0 1 0 0 0 0 0 0 0 0 27 28 121.3 0 120.5 121.3 0 0 0 1 0 0 0 0 0 0 0 28 29 120.5 0 120.4 120.5 0 0 0 0 1 0 0 0 0 0 0 29 30 120.4 0 137.9 120.4 0 0 0 0 0 1 0 0 0 0 0 30 31 137.9 0 126.1 137.9 0 0 0 0 0 0 1 0 0 0 0 31 32 126.1 0 133.2 126.1 0 0 0 0 0 0 0 1 0 0 0 32 33 133.2 0 151.1 133.2 0 0 0 0 0 0 0 0 1 0 0 33 34 151.1 0 105.0 151.1 0 0 0 0 0 0 0 0 0 1 0 34 35 105.0 0 119.0 105.0 0 0 0 0 0 0 0 0 0 0 1 35 36 119.0 0 140.4 119.0 0 0 0 0 0 0 0 0 0 0 0 36 37 140.4 0 156.6 140.4 1 0 0 0 0 0 0 0 0 0 0 37 38 156.6 0 137.1 156.6 0 1 0 0 0 0 0 0 0 0 0 38 39 137.1 0 122.7 137.1 0 0 1 0 0 0 0 0 0 0 0 39 40 122.7 0 125.8 122.7 0 0 0 1 0 0 0 0 0 0 0 40 41 125.8 0 139.3 125.8 0 0 0 0 1 0 0 0 0 0 0 41 42 139.3 0 134.9 139.3 0 0 0 0 0 1 0 0 0 0 0 42 43 134.9 0 149.2 134.9 0 0 0 0 0 0 1 0 0 0 0 43 44 149.2 1 132.3 149.2 0 0 0 0 0 0 0 1 0 0 0 44 45 132.3 0 149.0 132.3 0 0 0 0 0 0 0 0 1 0 0 45 46 149.0 1 117.2 149.0 0 0 0 0 0 0 0 0 0 1 0 46 47 117.2 1 119.6 117.2 0 0 0 0 0 0 0 0 0 0 1 47 48 119.6 1 152.0 119.6 0 0 0 0 0 0 0 0 0 0 0 48 49 152.0 1 149.4 152.0 1 0 0 0 0 0 0 0 0 0 0 49 50 149.4 1 127.3 149.4 0 1 0 0 0 0 0 0 0 0 0 50 51 127.3 1 114.1 127.3 0 0 1 0 0 0 0 0 0 0 0 51 52 114.1 1 102.1 114.1 0 0 0 1 0 0 0 0 0 0 0 52 53 102.1 1 107.7 102.1 0 0 0 0 1 0 0 0 0 0 0 53 54 107.7 1 104.4 107.7 0 0 0 0 0 1 0 0 0 0 0 54 55 104.4 1 102.1 104.4 0 0 0 0 0 0 1 0 0 0 0 55 56 102.1 1 96.0 102.1 0 0 0 0 0 0 0 1 0 0 0 56 57 96.0 1 109.3 96.0 0 0 0 0 0 0 0 0 1 0 0 57 58 109.3 1 90.0 109.3 0 0 0 0 0 0 0 0 0 1 0 58 59 90.0 1 83.9 90.0 0 0 0 0 0 0 0 0 0 0 1 59 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Y `y(t)` `y(t-1)` M1 M2 2.426e-14 -1.106e-15 1.739e-16 1.000e+00 3.587e-15 1.453e-15 M3 M4 M5 M6 M7 M8 3.863e-15 -4.585e-15 1.426e-15 6.726e-16 1.801e-15 2.488e-15 M9 M10 M11 t 5.592e-17 3.139e-15 9.235e-16 6.167e-17 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2.457e-14 -1.200e-15 7.453e-18 1.261e-15 7.019e-15 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.426e-14 8.042e-15 3.016e+00 0.00429 ** Y -1.106e-15 1.486e-15 -7.440e-01 0.46072 `y(t)` 1.739e-16 8.185e-17 2.125e+00 0.03936 * `y(t-1)` 1.000e+00 8.171e-17 1.224e+16 < 2e-16 *** M1 3.587e-15 3.653e-15 9.820e-01 0.33161 M2 1.453e-15 4.095e-15 3.550e-01 0.72445 M3 3.863e-15 4.081e-15 9.470e-01 0.34911 M4 -4.585e-15 4.006e-15 -1.145e+00 0.25871 M5 1.426e-15 3.550e-15 4.020e-01 0.68996 M6 6.726e-16 3.416e-15 1.970e-01 0.84484 M7 1.801e-15 3.828e-15 4.700e-01 0.64051 M8 2.488e-15 3.709e-15 6.710e-01 0.50598 M9 5.592e-17 3.222e-15 1.700e-02 0.98623 M10 3.139e-15 5.639e-15 5.570e-01 0.58066 M11 9.235e-16 3.630e-15 2.540e-01 0.80040 t 6.167e-17 4.144e-17 1.488e+00 0.14405 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.687e-15 on 43 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 5.178e+31 on 15 and 43 DF, p-value: < 2.2e-16 > 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 + } [,1] [,2] [,3] [1,] 6.914217e-01 6.171566e-01 3.085783e-01 [2,] 2.574500e-02 5.148999e-02 9.742550e-01 [3,] 5.954062e-02 1.190812e-01 9.404594e-01 [4,] 1.012014e-05 2.024028e-05 9.999899e-01 [5,] 2.533064e-04 5.066127e-04 9.997467e-01 [6,] 3.019465e-02 6.038930e-02 9.698054e-01 [7,] 2.849370e-01 5.698739e-01 7.150630e-01 [8,] 9.999918e-01 1.645136e-05 8.225679e-06 [9,] 8.683865e-01 2.632269e-01 1.316135e-01 [10,] 1.154949e-01 2.309898e-01 8.845051e-01 [11,] 9.984905e-01 3.018943e-03 1.509471e-03 [12,] 9.969144e-01 6.171150e-03 3.085575e-03 [13,] 5.976077e-01 8.047846e-01 4.023923e-01 [14,] 2.548815e-02 5.097630e-02 9.745119e-01 [15,] 9.923768e-01 1.524640e-02 7.623202e-03 [16,] 8.974147e-01 2.051706e-01 1.025853e-01 [17,] 2.560796e-06 5.121591e-06 9.999974e-01 [18,] 9.896970e-01 2.060604e-02 1.030302e-02 [19,] 9.607615e-01 7.847696e-02 3.923848e-02 [20,] 1.200536e-02 2.401072e-02 9.879946e-01 [21,] 7.100137e-01 5.799727e-01 2.899863e-01 [22,] 9.328166e-05 1.865633e-04 9.999067e-01 > postscript(file="/var/www/html/rcomp/tmp/1mrod1258623999.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2gn5a1258623999.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3lbiz1258623999.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/458wi1258623999.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/56fbp1258623999.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 59 Frequency = 1 1 2 3 4 5 5.159897e-15 -2.470485e-15 5.971014e-15 -2.456672e-14 2.633961e-15 6 7 8 9 10 1.317326e-15 2.484069e-15 1.725957e-15 6.003645e-16 2.276340e-15 11 12 13 14 15 -7.880706e-16 1.557244e-15 5.897194e-16 1.634250e-15 5.333171e-17 16 17 18 19 20 6.702890e-15 2.804885e-16 -1.325182e-15 5.013121e-16 -2.423143e-15 21 22 23 24 25 1.205351e-15 5.071287e-16 7.452983e-18 -4.802453e-16 -2.298894e-15 26 27 28 29 30 6.394638e-16 -1.458212e-15 5.930714e-15 -1.825114e-16 -9.248457e-16 31 32 33 34 35 -2.829442e-16 -1.553629e-15 3.737164e-16 -3.383000e-15 3.938220e-16 36 37 38 39 40 -5.353571e-17 -3.264706e-15 -2.224292e-16 -2.969563e-15 4.913848e-15 41 42 43 44 45 -1.731525e-15 6.641196e-16 -1.904874e-15 3.826328e-15 -3.504784e-16 46 47 48 49 50 1.674668e-15 6.241473e-16 -1.023463e-15 -1.860161e-16 4.192011e-16 51 52 53 54 55 -1.596570e-15 7.019270e-15 -1.000413e-15 2.685824e-16 -7.975630e-16 56 57 58 59 -1.575513e-15 -1.828954e-15 -1.075137e-15 -2.373517e-16 > postscript(file="/var/www/html/rcomp/tmp/6ppjc1258623999.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 59 Frequency = 1 lag(myerror, k = 1) myerror 0 5.159897e-15 NA 1 -2.470485e-15 5.159897e-15 2 5.971014e-15 -2.470485e-15 3 -2.456672e-14 5.971014e-15 4 2.633961e-15 -2.456672e-14 5 1.317326e-15 2.633961e-15 6 2.484069e-15 1.317326e-15 7 1.725957e-15 2.484069e-15 8 6.003645e-16 1.725957e-15 9 2.276340e-15 6.003645e-16 10 -7.880706e-16 2.276340e-15 11 1.557244e-15 -7.880706e-16 12 5.897194e-16 1.557244e-15 13 1.634250e-15 5.897194e-16 14 5.333171e-17 1.634250e-15 15 6.702890e-15 5.333171e-17 16 2.804885e-16 6.702890e-15 17 -1.325182e-15 2.804885e-16 18 5.013121e-16 -1.325182e-15 19 -2.423143e-15 5.013121e-16 20 1.205351e-15 -2.423143e-15 21 5.071287e-16 1.205351e-15 22 7.452983e-18 5.071287e-16 23 -4.802453e-16 7.452983e-18 24 -2.298894e-15 -4.802453e-16 25 6.394638e-16 -2.298894e-15 26 -1.458212e-15 6.394638e-16 27 5.930714e-15 -1.458212e-15 28 -1.825114e-16 5.930714e-15 29 -9.248457e-16 -1.825114e-16 30 -2.829442e-16 -9.248457e-16 31 -1.553629e-15 -2.829442e-16 32 3.737164e-16 -1.553629e-15 33 -3.383000e-15 3.737164e-16 34 3.938220e-16 -3.383000e-15 35 -5.353571e-17 3.938220e-16 36 -3.264706e-15 -5.353571e-17 37 -2.224292e-16 -3.264706e-15 38 -2.969563e-15 -2.224292e-16 39 4.913848e-15 -2.969563e-15 40 -1.731525e-15 4.913848e-15 41 6.641196e-16 -1.731525e-15 42 -1.904874e-15 6.641196e-16 43 3.826328e-15 -1.904874e-15 44 -3.504784e-16 3.826328e-15 45 1.674668e-15 -3.504784e-16 46 6.241473e-16 1.674668e-15 47 -1.023463e-15 6.241473e-16 48 -1.860161e-16 -1.023463e-15 49 4.192011e-16 -1.860161e-16 50 -1.596570e-15 4.192011e-16 51 7.019270e-15 -1.596570e-15 52 -1.000413e-15 7.019270e-15 53 2.685824e-16 -1.000413e-15 54 -7.975630e-16 2.685824e-16 55 -1.575513e-15 -7.975630e-16 56 -1.828954e-15 -1.575513e-15 57 -1.075137e-15 -1.828954e-15 58 -2.373517e-16 -1.075137e-15 59 NA -2.373517e-16 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -2.470485e-15 5.159897e-15 [2,] 5.971014e-15 -2.470485e-15 [3,] -2.456672e-14 5.971014e-15 [4,] 2.633961e-15 -2.456672e-14 [5,] 1.317326e-15 2.633961e-15 [6,] 2.484069e-15 1.317326e-15 [7,] 1.725957e-15 2.484069e-15 [8,] 6.003645e-16 1.725957e-15 [9,] 2.276340e-15 6.003645e-16 [10,] -7.880706e-16 2.276340e-15 [11,] 1.557244e-15 -7.880706e-16 [12,] 5.897194e-16 1.557244e-15 [13,] 1.634250e-15 5.897194e-16 [14,] 5.333171e-17 1.634250e-15 [15,] 6.702890e-15 5.333171e-17 [16,] 2.804885e-16 6.702890e-15 [17,] -1.325182e-15 2.804885e-16 [18,] 5.013121e-16 -1.325182e-15 [19,] -2.423143e-15 5.013121e-16 [20,] 1.205351e-15 -2.423143e-15 [21,] 5.071287e-16 1.205351e-15 [22,] 7.452983e-18 5.071287e-16 [23,] -4.802453e-16 7.452983e-18 [24,] -2.298894e-15 -4.802453e-16 [25,] 6.394638e-16 -2.298894e-15 [26,] -1.458212e-15 6.394638e-16 [27,] 5.930714e-15 -1.458212e-15 [28,] -1.825114e-16 5.930714e-15 [29,] -9.248457e-16 -1.825114e-16 [30,] -2.829442e-16 -9.248457e-16 [31,] -1.553629e-15 -2.829442e-16 [32,] 3.737164e-16 -1.553629e-15 [33,] -3.383000e-15 3.737164e-16 [34,] 3.938220e-16 -3.383000e-15 [35,] -5.353571e-17 3.938220e-16 [36,] -3.264706e-15 -5.353571e-17 [37,] -2.224292e-16 -3.264706e-15 [38,] -2.969563e-15 -2.224292e-16 [39,] 4.913848e-15 -2.969563e-15 [40,] -1.731525e-15 4.913848e-15 [41,] 6.641196e-16 -1.731525e-15 [42,] -1.904874e-15 6.641196e-16 [43,] 3.826328e-15 -1.904874e-15 [44,] -3.504784e-16 3.826328e-15 [45,] 1.674668e-15 -3.504784e-16 [46,] 6.241473e-16 1.674668e-15 [47,] -1.023463e-15 6.241473e-16 [48,] -1.860161e-16 -1.023463e-15 [49,] 4.192011e-16 -1.860161e-16 [50,] -1.596570e-15 4.192011e-16 [51,] 7.019270e-15 -1.596570e-15 [52,] -1.000413e-15 7.019270e-15 [53,] 2.685824e-16 -1.000413e-15 [54,] -7.975630e-16 2.685824e-16 [55,] -1.575513e-15 -7.975630e-16 [56,] -1.828954e-15 -1.575513e-15 [57,] -1.075137e-15 -1.828954e-15 [58,] -2.373517e-16 -1.075137e-15 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -2.470485e-15 5.159897e-15 2 5.971014e-15 -2.470485e-15 3 -2.456672e-14 5.971014e-15 4 2.633961e-15 -2.456672e-14 5 1.317326e-15 2.633961e-15 6 2.484069e-15 1.317326e-15 7 1.725957e-15 2.484069e-15 8 6.003645e-16 1.725957e-15 9 2.276340e-15 6.003645e-16 10 -7.880706e-16 2.276340e-15 11 1.557244e-15 -7.880706e-16 12 5.897194e-16 1.557244e-15 13 1.634250e-15 5.897194e-16 14 5.333171e-17 1.634250e-15 15 6.702890e-15 5.333171e-17 16 2.804885e-16 6.702890e-15 17 -1.325182e-15 2.804885e-16 18 5.013121e-16 -1.325182e-15 19 -2.423143e-15 5.013121e-16 20 1.205351e-15 -2.423143e-15 21 5.071287e-16 1.205351e-15 22 7.452983e-18 5.071287e-16 23 -4.802453e-16 7.452983e-18 24 -2.298894e-15 -4.802453e-16 25 6.394638e-16 -2.298894e-15 26 -1.458212e-15 6.394638e-16 27 5.930714e-15 -1.458212e-15 28 -1.825114e-16 5.930714e-15 29 -9.248457e-16 -1.825114e-16 30 -2.829442e-16 -9.248457e-16 31 -1.553629e-15 -2.829442e-16 32 3.737164e-16 -1.553629e-15 33 -3.383000e-15 3.737164e-16 34 3.938220e-16 -3.383000e-15 35 -5.353571e-17 3.938220e-16 36 -3.264706e-15 -5.353571e-17 37 -2.224292e-16 -3.264706e-15 38 -2.969563e-15 -2.224292e-16 39 4.913848e-15 -2.969563e-15 40 -1.731525e-15 4.913848e-15 41 6.641196e-16 -1.731525e-15 42 -1.904874e-15 6.641196e-16 43 3.826328e-15 -1.904874e-15 44 -3.504784e-16 3.826328e-15 45 1.674668e-15 -3.504784e-16 46 6.241473e-16 1.674668e-15 47 -1.023463e-15 6.241473e-16 48 -1.860161e-16 -1.023463e-15 49 4.192011e-16 -1.860161e-16 50 -1.596570e-15 4.192011e-16 51 7.019270e-15 -1.596570e-15 52 -1.000413e-15 7.019270e-15 53 2.685824e-16 -1.000413e-15 54 -7.975630e-16 2.685824e-16 55 -1.575513e-15 -7.975630e-16 56 -1.828954e-15 -1.575513e-15 57 -1.075137e-15 -1.828954e-15 58 -2.373517e-16 -1.075137e-15 > 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() null device 1 > postscript(file="/var/www/html/rcomp/tmp/7lmax1258623999.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/813wz1258623999.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9a55q1258623999.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10sdjs1258623999.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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="/var/www/html/rcomp/tmp/11024t1258623999.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
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="/var/www/html/rcomp/tmp/126v0p1258623999.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="/var/www/html/rcomp/tmp/13od4n1258623999.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
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
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="/var/www/html/rcomp/tmp/14f6eq1258624000.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="/var/www/html/rcomp/tmp/154dkr1258624000.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="/var/www/html/rcomp/tmp/16upoy1258624000.tab") + } > > system("convert tmp/1mrod1258623999.ps tmp/1mrod1258623999.png") > system("convert tmp/2gn5a1258623999.ps tmp/2gn5a1258623999.png") > system("convert tmp/3lbiz1258623999.ps tmp/3lbiz1258623999.png") > system("convert tmp/458wi1258623999.ps tmp/458wi1258623999.png") > system("convert tmp/56fbp1258623999.ps tmp/56fbp1258623999.png") > system("convert tmp/6ppjc1258623999.ps tmp/6ppjc1258623999.png") > system("convert tmp/7lmax1258623999.ps tmp/7lmax1258623999.png") > system("convert tmp/813wz1258623999.ps tmp/813wz1258623999.png") > system("convert tmp/9a55q1258623999.ps tmp/9a55q1258623999.png") > system("convert tmp/10sdjs1258623999.ps tmp/10sdjs1258623999.png") > > > proc.time() user system elapsed 2.415 1.593 3.536