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Type 'q()' to quit R. > x <- array(list(105.7 + ,0 + ,105.7 + ,105.7 + ,111.1 + ,0 + ,111.1 + ,105.7 + ,82.4 + ,0 + ,82.4 + ,111.1 + ,60 + ,0 + ,60 + ,82.4 + ,107.3 + ,0 + ,107.3 + ,60 + ,99.3 + ,0 + ,99.3 + ,107.3 + ,113.5 + ,0 + ,113.5 + ,99.3 + ,108.9 + ,0 + ,108.9 + ,113.5 + ,100.2 + ,0 + ,100.2 + ,108.9 + ,103.9 + ,0 + ,103.9 + ,100.2 + ,138.7 + ,0 + ,138.7 + ,103.9 + ,120.2 + ,0 + ,120.2 + ,138.7 + ,100.2 + ,0 + ,100.2 + ,120.2 + ,143.2 + ,0 + ,143.2 + ,100.2 + ,70.9 + ,0 + ,70.9 + ,143.2 + ,85.2 + ,0 + ,85.2 + ,70.9 + ,133 + ,0 + ,133 + ,85.2 + ,136.6 + ,0 + ,136.6 + ,133 + ,117.9 + ,0 + ,117.9 + ,136.6 + ,106.3 + ,0 + ,106.3 + ,117.9 + ,122.3 + ,0 + ,122.3 + ,106.3 + ,125.5 + ,0 + ,125.5 + ,122.3 + ,148.4 + ,0 + ,148.4 + ,125.5 + ,126.3 + ,0 + ,126.3 + ,148.4 + ,99.6 + ,0 + ,99.6 + ,126.3 + ,140.4 + ,0 + ,140.4 + ,99.6 + ,80.3 + ,0 + ,80.3 + ,140.4 + ,92.6 + ,0 + ,92.6 + ,80.3 + ,138.5 + ,0 + ,138.5 + ,92.6 + ,110.9 + ,0 + ,110.9 + ,138.5 + ,119.6 + ,0 + ,119.6 + ,110.9 + ,105 + ,0 + ,105 + ,119.6 + ,109 + ,0 + ,109 + ,105 + ,129.4 + ,0 + ,129.4 + ,109 + ,148.6 + ,0 + ,148.6 + ,129.4 + ,101.4 + ,0 + ,101.4 + ,148.6 + ,134.8 + ,0 + ,134.8 + ,101.4 + ,143.7 + ,0 + ,143.7 + ,134.8 + ,81.6 + ,0 + ,81.6 + ,143.7 + ,90.3 + ,0 + ,90.3 + ,81.6 + ,141.5 + ,0 + ,141.5 + ,90.3 + ,140.7 + ,0 + ,140.7 + ,141.5 + ,140.2 + ,0 + ,140.2 + ,140.7 + ,100.2 + ,0 + ,100.2 + ,140.2 + ,125.7 + ,0 + ,125.7 + ,100.2 + ,119.6 + ,0 + ,119.6 + ,125.7 + ,134.7 + ,0 + ,134.7 + ,119.6 + ,109 + ,0 + ,109 + ,134.7 + ,116.3 + ,0 + ,116.3 + ,109 + ,146.9 + ,0 + ,146.9 + ,116.3 + ,97.4 + ,0 + ,97.4 + ,146.9 + ,89.4 + ,0 + ,89.4 + ,97.4 + ,132.1 + ,0 + ,132.1 + ,89.4 + ,139.8 + ,0 + ,139.8 + ,132.1 + ,129 + ,0 + ,129 + ,139.8 + ,112.5 + ,0 + ,112.5 + ,129 + ,121.9 + ,1 + ,121.9 + ,112.5 + ,121.7 + ,1 + ,121.7 + ,121.9 + ,123.1 + ,1 + ,123.1 + ,121.7 + ,131.6 + ,1 + ,131.6 + ,123.1 + ,119.3 + ,1 + ,119.3 + ,131.6 + ,132.5 + ,1 + ,132.5 + ,119.3 + ,98.3 + ,1 + ,98.3 + ,132.5 + ,85.1 + ,1 + ,85.1 + ,98.3 + ,131.7 + ,1 + ,131.7 + ,85.1 + ,129.3 + ,1 + ,129.3 + ,131.7 + ,90.7 + ,1 + ,90.7 + ,129.3 + ,78.6 + ,1 + ,78.6 + ,90.7 + ,68.9 + ,1 + ,68.9 + ,78.6 + ,79.1 + ,1 + ,79.1 + ,68.9 + ,83.5 + ,1 + ,83.5 + ,79.1 + ,74.1 + ,1 + ,74.1 + ,83.5 + ,59.7 + ,1 + ,59.7 + ,74.1 + ,93.3 + ,1 + ,93.3 + ,59.7 + ,61.3 + ,1 + ,61.3 + ,93.3 + ,56.6 + ,1 + ,56.6 + ,61.3) + ,dim=c(4 + ,76) + ,dimnames=list(c('Y' + ,'X' + ,'Y1' + ,'Y2') + ,1:76)) > y <- array(NA,dim=c(4,76),dimnames=list(c('Y','X','Y1','Y2'),1:76)) > 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 Y X Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 105.7 0 105.7 105.7 1 0 0 0 0 0 0 0 0 0 0 1 2 111.1 0 111.1 105.7 0 1 0 0 0 0 0 0 0 0 0 2 3 82.4 0 82.4 111.1 0 0 1 0 0 0 0 0 0 0 0 3 4 60.0 0 60.0 82.4 0 0 0 1 0 0 0 0 0 0 0 4 5 107.3 0 107.3 60.0 0 0 0 0 1 0 0 0 0 0 0 5 6 99.3 0 99.3 107.3 0 0 0 0 0 1 0 0 0 0 0 6 7 113.5 0 113.5 99.3 0 0 0 0 0 0 1 0 0 0 0 7 8 108.9 0 108.9 113.5 0 0 0 0 0 0 0 1 0 0 0 8 9 100.2 0 100.2 108.9 0 0 0 0 0 0 0 0 1 0 0 9 10 103.9 0 103.9 100.2 0 0 0 0 0 0 0 0 0 1 0 10 11 138.7 0 138.7 103.9 0 0 0 0 0 0 0 0 0 0 1 11 12 120.2 0 120.2 138.7 0 0 0 0 0 0 0 0 0 0 0 12 13 100.2 0 100.2 120.2 1 0 0 0 0 0 0 0 0 0 0 13 14 143.2 0 143.2 100.2 0 1 0 0 0 0 0 0 0 0 0 14 15 70.9 0 70.9 143.2 0 0 1 0 0 0 0 0 0 0 0 15 16 85.2 0 85.2 70.9 0 0 0 1 0 0 0 0 0 0 0 16 17 133.0 0 133.0 85.2 0 0 0 0 1 0 0 0 0 0 0 17 18 136.6 0 136.6 133.0 0 0 0 0 0 1 0 0 0 0 0 18 19 117.9 0 117.9 136.6 0 0 0 0 0 0 1 0 0 0 0 19 20 106.3 0 106.3 117.9 0 0 0 0 0 0 0 1 0 0 0 20 21 122.3 0 122.3 106.3 0 0 0 0 0 0 0 0 1 0 0 21 22 125.5 0 125.5 122.3 0 0 0 0 0 0 0 0 0 1 0 22 23 148.4 0 148.4 125.5 0 0 0 0 0 0 0 0 0 0 1 23 24 126.3 0 126.3 148.4 0 0 0 0 0 0 0 0 0 0 0 24 25 99.6 0 99.6 126.3 1 0 0 0 0 0 0 0 0 0 0 25 26 140.4 0 140.4 99.6 0 1 0 0 0 0 0 0 0 0 0 26 27 80.3 0 80.3 140.4 0 0 1 0 0 0 0 0 0 0 0 27 28 92.6 0 92.6 80.3 0 0 0 1 0 0 0 0 0 0 0 28 29 138.5 0 138.5 92.6 0 0 0 0 1 0 0 0 0 0 0 29 30 110.9 0 110.9 138.5 0 0 0 0 0 1 0 0 0 0 0 30 31 119.6 0 119.6 110.9 0 0 0 0 0 0 1 0 0 0 0 31 32 105.0 0 105.0 119.6 0 0 0 0 0 0 0 1 0 0 0 32 33 109.0 0 109.0 105.0 0 0 0 0 0 0 0 0 1 0 0 33 34 129.4 0 129.4 109.0 0 0 0 0 0 0 0 0 0 1 0 34 35 148.6 0 148.6 129.4 0 0 0 0 0 0 0 0 0 0 1 35 36 101.4 0 101.4 148.6 0 0 0 0 0 0 0 0 0 0 0 36 37 134.8 0 134.8 101.4 1 0 0 0 0 0 0 0 0 0 0 37 38 143.7 0 143.7 134.8 0 1 0 0 0 0 0 0 0 0 0 38 39 81.6 0 81.6 143.7 0 0 1 0 0 0 0 0 0 0 0 39 40 90.3 0 90.3 81.6 0 0 0 1 0 0 0 0 0 0 0 40 41 141.5 0 141.5 90.3 0 0 0 0 1 0 0 0 0 0 0 41 42 140.7 0 140.7 141.5 0 0 0 0 0 1 0 0 0 0 0 42 43 140.2 0 140.2 140.7 0 0 0 0 0 0 1 0 0 0 0 43 44 100.2 0 100.2 140.2 0 0 0 0 0 0 0 1 0 0 0 44 45 125.7 0 125.7 100.2 0 0 0 0 0 0 0 0 1 0 0 45 46 119.6 0 119.6 125.7 0 0 0 0 0 0 0 0 0 1 0 46 47 134.7 0 134.7 119.6 0 0 0 0 0 0 0 0 0 0 1 47 48 109.0 0 109.0 134.7 0 0 0 0 0 0 0 0 0 0 0 48 49 116.3 0 116.3 109.0 1 0 0 0 0 0 0 0 0 0 0 49 50 146.9 0 146.9 116.3 0 1 0 0 0 0 0 0 0 0 0 50 51 97.4 0 97.4 146.9 0 0 1 0 0 0 0 0 0 0 0 51 52 89.4 0 89.4 97.4 0 0 0 1 0 0 0 0 0 0 0 52 53 132.1 0 132.1 89.4 0 0 0 0 1 0 0 0 0 0 0 53 54 139.8 0 139.8 132.1 0 0 0 0 0 1 0 0 0 0 0 54 55 129.0 0 129.0 139.8 0 0 0 0 0 0 1 0 0 0 0 55 56 112.5 0 112.5 129.0 0 0 0 0 0 0 0 1 0 0 0 56 57 121.9 1 121.9 112.5 0 0 0 0 0 0 0 0 1 0 0 57 58 121.7 1 121.7 121.9 0 0 0 0 0 0 0 0 0 1 0 58 59 123.1 1 123.1 121.7 0 0 0 0 0 0 0 0 0 0 1 59 60 131.6 1 131.6 123.1 0 0 0 0 0 0 0 0 0 0 0 60 61 119.3 1 119.3 131.6 1 0 0 0 0 0 0 0 0 0 0 61 62 132.5 1 132.5 119.3 0 1 0 0 0 0 0 0 0 0 0 62 63 98.3 1 98.3 132.5 0 0 1 0 0 0 0 0 0 0 0 63 64 85.1 1 85.1 98.3 0 0 0 1 0 0 0 0 0 0 0 64 65 131.7 1 131.7 85.1 0 0 0 0 1 0 0 0 0 0 0 65 66 129.3 1 129.3 131.7 0 0 0 0 0 1 0 0 0 0 0 66 67 90.7 1 90.7 129.3 0 0 0 0 0 0 1 0 0 0 0 67 68 78.6 1 78.6 90.7 0 0 0 0 0 0 0 1 0 0 0 68 69 68.9 1 68.9 78.6 0 0 0 0 0 0 0 0 1 0 0 69 70 79.1 1 79.1 68.9 0 0 0 0 0 0 0 0 0 1 0 70 71 83.5 1 83.5 79.1 0 0 0 0 0 0 0 0 0 0 1 71 72 74.1 1 74.1 83.5 0 0 0 0 0 0 0 0 0 0 0 72 73 59.7 1 59.7 74.1 1 0 0 0 0 0 0 0 0 0 0 73 74 93.3 1 93.3 59.7 0 1 0 0 0 0 0 0 0 0 0 74 75 61.3 1 61.3 93.3 0 0 1 0 0 0 0 0 0 0 0 75 76 56.6 1 56.6 61.3 0 0 0 1 0 0 0 0 0 0 0 76 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X Y1 Y2 M1 M2 1.063e-13 -7.404e-15 1.000e+00 -2.495e-16 -7.763e-15 -1.125e-14 M3 M4 M5 M6 M7 M8 2.536e-14 -6.824e-15 -1.558e-14 -4.016e-15 -3.485e-15 -2.228e-15 M9 M10 M11 t -7.773e-15 -7.183e-15 -9.968e-15 -1.862e-16 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -2.349e-14 -4.144e-15 -4.758e-16 3.645e-15 1.101e-13 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.063e-13 1.867e-14 5.692e+00 3.99e-07 *** X -7.404e-15 7.540e-15 -9.820e-01 0.3301 Y1 1.000e+00 1.553e-16 6.439e+15 < 2e-16 *** Y2 -2.495e-16 1.533e-16 -1.628e+00 0.1088 M1 -7.763e-15 9.442e-15 -8.220e-01 0.4142 M2 -1.125e-14 1.090e-14 -1.033e+00 0.3058 M3 2.536e-14 1.014e-14 2.500e+00 0.0152 * M4 -6.824e-15 1.090e-14 -6.260e-01 0.5337 M5 -1.558e-14 1.319e-14 -1.181e+00 0.2421 M6 -4.016e-15 9.658e-15 -4.160e-01 0.6790 M7 -3.485e-15 9.549e-15 -3.650e-01 0.7164 M8 -2.228e-15 9.613e-15 -2.320e-01 0.8175 M9 -7.773e-15 1.021e-14 -7.610e-01 0.4495 M10 -7.183e-15 1.005e-14 -7.150e-01 0.4774 M11 -9.968e-15 1.058e-14 -9.420e-01 0.3500 t -1.862e-16 1.361e-16 -1.368e+00 0.1764 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.628e-14 on 60 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 1.108e+31 on 15 and 60 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,] 9.324997e-03 1.864999e-02 9.906750e-01 [2,] 1.572409e-02 3.144818e-02 9.842759e-01 [3,] 9.060778e-01 1.878444e-01 9.392219e-02 [4,] 9.622950e-01 7.541007e-02 3.770504e-02 [5,] 7.878055e-01 4.243889e-01 2.121945e-01 [6,] 1.000000e+00 1.144729e-11 5.723643e-12 [7,] 3.895995e-01 7.791989e-01 6.104005e-01 [8,] 9.552514e-01 8.949714e-02 4.474857e-02 [9,] 2.193911e-05 4.387821e-05 9.999781e-01 [10,] 9.999981e-01 3.861923e-06 1.930961e-06 [11,] 5.791348e-03 1.158270e-02 9.942087e-01 [12,] 9.996448e-01 7.104741e-04 3.552371e-04 [13,] 4.210949e-02 8.421897e-02 9.578905e-01 [14,] 9.844197e-01 3.116052e-02 1.558026e-02 [15,] 1.000000e+00 4.189078e-10 2.094539e-10 [16,] 5.404045e-01 9.191910e-01 4.595955e-01 [17,] 7.039109e-05 1.407822e-04 9.999296e-01 [18,] 9.975832e-01 4.833536e-03 2.416768e-03 [19,] 1.000000e+00 3.457167e-10 1.728583e-10 [20,] 2.624889e-07 5.249779e-07 9.999997e-01 [21,] 9.999998e-01 4.032879e-07 2.016439e-07 [22,] 9.999999e-01 1.784350e-07 8.921748e-08 [23,] 9.319199e-01 1.361602e-01 6.808008e-02 [24,] 1.000000e+00 7.527765e-13 3.763882e-13 [25,] 1.501062e-12 3.002124e-12 1.000000e+00 [26,] 7.145344e-16 1.429069e-15 1.000000e+00 [27,] 1.314728e-01 2.629456e-01 8.685272e-01 [28,] 2.683894e-12 5.367787e-12 1.000000e+00 [29,] 1.188053e-04 2.376105e-04 9.998812e-01 [30,] 5.460942e-01 9.078116e-01 4.539058e-01 [31,] 9.338007e-01 1.323987e-01 6.619933e-02 [32,] 1.000000e+00 0.000000e+00 0.000000e+00 [33,] 9.958957e-01 8.208511e-03 4.104255e-03 [34,] 3.299155e-02 6.598311e-02 9.670084e-01 [35,] 2.120169e-02 4.240339e-02 9.787983e-01 [36,] 9.840567e-01 3.188666e-02 1.594333e-02 [37,] 9.999525e-01 9.508382e-05 4.754191e-05 [38,] 2.114456e-02 4.228912e-02 9.788554e-01 [39,] 6.753245e-01 6.493510e-01 3.246755e-01 > postscript(file="/var/www/html/rcomp/tmp/1t0xt1258742129.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2biur1258742129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3p5961258742129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4k2dw1258742129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/526gz1258742129.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 = 76 Frequency = 1 1 2 3 4 5 -1.875012e-14 -7.881863e-15 1.101002e-13 1.262888e-15 -6.750433e-15 6 7 8 9 10 -5.364582e-15 -1.034757e-14 -6.744674e-15 -1.457890e-15 -3.799914e-15 11 12 13 14 15 -6.271586e-15 -4.088973e-15 2.096485e-15 -5.835708e-15 -2.348720e-14 16 17 18 19 20 -7.778927e-15 -6.196480e-15 -5.327296e-15 1.919561e-17 -2.855378e-15 21 22 23 24 25 -3.720518e-15 -1.227682e-15 -3.801606e-15 -4.686525e-16 6.391209e-15 26 27 28 29 30 -1.238008e-15 -1.617742e-14 -2.735710e-15 5.306387e-17 4.470405e-15 31 32 33 34 35 -4.351981e-15 -8.065020e-16 1.244842e-15 -4.829266e-16 -7.737900e-16 36 37 38 39 40 4.624181e-15 -4.310980e-15 6.816556e-15 -1.428659e-14 -1.665456e-15 41 42 43 44 45 2.573653e-15 4.687538e-15 1.038871e-15 7.322778e-15 -2.049064e-15 46 47 48 49 50 5.831926e-15 4.385765e-15 3.674728e-15 3.545225e-15 1.563155e-15 51 52 53 54 55 -1.477678e-14 5.318600e-15 5.914791e-15 1.831683e-15 5.995568e-15 56 57 58 59 60 5.048998e-15 3.634726e-15 2.203455e-15 3.867349e-15 -2.535626e-15 61 62 63 64 65 7.916008e-15 7.335086e-15 -2.219327e-14 3.277878e-15 4.405405e-15 66 67 68 69 70 -2.977482e-16 7.645920e-15 -1.965223e-15 2.347904e-15 -2.524859e-15 71 72 73 74 75 2.593868e-15 -1.205657e-15 3.112176e-15 -7.592177e-16 -1.917890e-14 76 2.320727e-15 > postscript(file="/var/www/html/rcomp/tmp/60j8w1258742129.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 = 76 Frequency = 1 lag(myerror, k = 1) myerror 0 -1.875012e-14 NA 1 -7.881863e-15 -1.875012e-14 2 1.101002e-13 -7.881863e-15 3 1.262888e-15 1.101002e-13 4 -6.750433e-15 1.262888e-15 5 -5.364582e-15 -6.750433e-15 6 -1.034757e-14 -5.364582e-15 7 -6.744674e-15 -1.034757e-14 8 -1.457890e-15 -6.744674e-15 9 -3.799914e-15 -1.457890e-15 10 -6.271586e-15 -3.799914e-15 11 -4.088973e-15 -6.271586e-15 12 2.096485e-15 -4.088973e-15 13 -5.835708e-15 2.096485e-15 14 -2.348720e-14 -5.835708e-15 15 -7.778927e-15 -2.348720e-14 16 -6.196480e-15 -7.778927e-15 17 -5.327296e-15 -6.196480e-15 18 1.919561e-17 -5.327296e-15 19 -2.855378e-15 1.919561e-17 20 -3.720518e-15 -2.855378e-15 21 -1.227682e-15 -3.720518e-15 22 -3.801606e-15 -1.227682e-15 23 -4.686525e-16 -3.801606e-15 24 6.391209e-15 -4.686525e-16 25 -1.238008e-15 6.391209e-15 26 -1.617742e-14 -1.238008e-15 27 -2.735710e-15 -1.617742e-14 28 5.306387e-17 -2.735710e-15 29 4.470405e-15 5.306387e-17 30 -4.351981e-15 4.470405e-15 31 -8.065020e-16 -4.351981e-15 32 1.244842e-15 -8.065020e-16 33 -4.829266e-16 1.244842e-15 34 -7.737900e-16 -4.829266e-16 35 4.624181e-15 -7.737900e-16 36 -4.310980e-15 4.624181e-15 37 6.816556e-15 -4.310980e-15 38 -1.428659e-14 6.816556e-15 39 -1.665456e-15 -1.428659e-14 40 2.573653e-15 -1.665456e-15 41 4.687538e-15 2.573653e-15 42 1.038871e-15 4.687538e-15 43 7.322778e-15 1.038871e-15 44 -2.049064e-15 7.322778e-15 45 5.831926e-15 -2.049064e-15 46 4.385765e-15 5.831926e-15 47 3.674728e-15 4.385765e-15 48 3.545225e-15 3.674728e-15 49 1.563155e-15 3.545225e-15 50 -1.477678e-14 1.563155e-15 51 5.318600e-15 -1.477678e-14 52 5.914791e-15 5.318600e-15 53 1.831683e-15 5.914791e-15 54 5.995568e-15 1.831683e-15 55 5.048998e-15 5.995568e-15 56 3.634726e-15 5.048998e-15 57 2.203455e-15 3.634726e-15 58 3.867349e-15 2.203455e-15 59 -2.535626e-15 3.867349e-15 60 7.916008e-15 -2.535626e-15 61 7.335086e-15 7.916008e-15 62 -2.219327e-14 7.335086e-15 63 3.277878e-15 -2.219327e-14 64 4.405405e-15 3.277878e-15 65 -2.977482e-16 4.405405e-15 66 7.645920e-15 -2.977482e-16 67 -1.965223e-15 7.645920e-15 68 2.347904e-15 -1.965223e-15 69 -2.524859e-15 2.347904e-15 70 2.593868e-15 -2.524859e-15 71 -1.205657e-15 2.593868e-15 72 3.112176e-15 -1.205657e-15 73 -7.592177e-16 3.112176e-15 74 -1.917890e-14 -7.592177e-16 75 2.320727e-15 -1.917890e-14 76 NA 2.320727e-15 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -7.881863e-15 -1.875012e-14 [2,] 1.101002e-13 -7.881863e-15 [3,] 1.262888e-15 1.101002e-13 [4,] -6.750433e-15 1.262888e-15 [5,] -5.364582e-15 -6.750433e-15 [6,] -1.034757e-14 -5.364582e-15 [7,] -6.744674e-15 -1.034757e-14 [8,] -1.457890e-15 -6.744674e-15 [9,] -3.799914e-15 -1.457890e-15 [10,] -6.271586e-15 -3.799914e-15 [11,] -4.088973e-15 -6.271586e-15 [12,] 2.096485e-15 -4.088973e-15 [13,] -5.835708e-15 2.096485e-15 [14,] -2.348720e-14 -5.835708e-15 [15,] -7.778927e-15 -2.348720e-14 [16,] -6.196480e-15 -7.778927e-15 [17,] -5.327296e-15 -6.196480e-15 [18,] 1.919561e-17 -5.327296e-15 [19,] -2.855378e-15 1.919561e-17 [20,] -3.720518e-15 -2.855378e-15 [21,] -1.227682e-15 -3.720518e-15 [22,] -3.801606e-15 -1.227682e-15 [23,] -4.686525e-16 -3.801606e-15 [24,] 6.391209e-15 -4.686525e-16 [25,] -1.238008e-15 6.391209e-15 [26,] -1.617742e-14 -1.238008e-15 [27,] -2.735710e-15 -1.617742e-14 [28,] 5.306387e-17 -2.735710e-15 [29,] 4.470405e-15 5.306387e-17 [30,] -4.351981e-15 4.470405e-15 [31,] -8.065020e-16 -4.351981e-15 [32,] 1.244842e-15 -8.065020e-16 [33,] -4.829266e-16 1.244842e-15 [34,] -7.737900e-16 -4.829266e-16 [35,] 4.624181e-15 -7.737900e-16 [36,] -4.310980e-15 4.624181e-15 [37,] 6.816556e-15 -4.310980e-15 [38,] -1.428659e-14 6.816556e-15 [39,] -1.665456e-15 -1.428659e-14 [40,] 2.573653e-15 -1.665456e-15 [41,] 4.687538e-15 2.573653e-15 [42,] 1.038871e-15 4.687538e-15 [43,] 7.322778e-15 1.038871e-15 [44,] -2.049064e-15 7.322778e-15 [45,] 5.831926e-15 -2.049064e-15 [46,] 4.385765e-15 5.831926e-15 [47,] 3.674728e-15 4.385765e-15 [48,] 3.545225e-15 3.674728e-15 [49,] 1.563155e-15 3.545225e-15 [50,] -1.477678e-14 1.563155e-15 [51,] 5.318600e-15 -1.477678e-14 [52,] 5.914791e-15 5.318600e-15 [53,] 1.831683e-15 5.914791e-15 [54,] 5.995568e-15 1.831683e-15 [55,] 5.048998e-15 5.995568e-15 [56,] 3.634726e-15 5.048998e-15 [57,] 2.203455e-15 3.634726e-15 [58,] 3.867349e-15 2.203455e-15 [59,] -2.535626e-15 3.867349e-15 [60,] 7.916008e-15 -2.535626e-15 [61,] 7.335086e-15 7.916008e-15 [62,] -2.219327e-14 7.335086e-15 [63,] 3.277878e-15 -2.219327e-14 [64,] 4.405405e-15 3.277878e-15 [65,] -2.977482e-16 4.405405e-15 [66,] 7.645920e-15 -2.977482e-16 [67,] -1.965223e-15 7.645920e-15 [68,] 2.347904e-15 -1.965223e-15 [69,] -2.524859e-15 2.347904e-15 [70,] 2.593868e-15 -2.524859e-15 [71,] -1.205657e-15 2.593868e-15 [72,] 3.112176e-15 -1.205657e-15 [73,] -7.592177e-16 3.112176e-15 [74,] -1.917890e-14 -7.592177e-16 [75,] 2.320727e-15 -1.917890e-14 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -7.881863e-15 -1.875012e-14 2 1.101002e-13 -7.881863e-15 3 1.262888e-15 1.101002e-13 4 -6.750433e-15 1.262888e-15 5 -5.364582e-15 -6.750433e-15 6 -1.034757e-14 -5.364582e-15 7 -6.744674e-15 -1.034757e-14 8 -1.457890e-15 -6.744674e-15 9 -3.799914e-15 -1.457890e-15 10 -6.271586e-15 -3.799914e-15 11 -4.088973e-15 -6.271586e-15 12 2.096485e-15 -4.088973e-15 13 -5.835708e-15 2.096485e-15 14 -2.348720e-14 -5.835708e-15 15 -7.778927e-15 -2.348720e-14 16 -6.196480e-15 -7.778927e-15 17 -5.327296e-15 -6.196480e-15 18 1.919561e-17 -5.327296e-15 19 -2.855378e-15 1.919561e-17 20 -3.720518e-15 -2.855378e-15 21 -1.227682e-15 -3.720518e-15 22 -3.801606e-15 -1.227682e-15 23 -4.686525e-16 -3.801606e-15 24 6.391209e-15 -4.686525e-16 25 -1.238008e-15 6.391209e-15 26 -1.617742e-14 -1.238008e-15 27 -2.735710e-15 -1.617742e-14 28 5.306387e-17 -2.735710e-15 29 4.470405e-15 5.306387e-17 30 -4.351981e-15 4.470405e-15 31 -8.065020e-16 -4.351981e-15 32 1.244842e-15 -8.065020e-16 33 -4.829266e-16 1.244842e-15 34 -7.737900e-16 -4.829266e-16 35 4.624181e-15 -7.737900e-16 36 -4.310980e-15 4.624181e-15 37 6.816556e-15 -4.310980e-15 38 -1.428659e-14 6.816556e-15 39 -1.665456e-15 -1.428659e-14 40 2.573653e-15 -1.665456e-15 41 4.687538e-15 2.573653e-15 42 1.038871e-15 4.687538e-15 43 7.322778e-15 1.038871e-15 44 -2.049064e-15 7.322778e-15 45 5.831926e-15 -2.049064e-15 46 4.385765e-15 5.831926e-15 47 3.674728e-15 4.385765e-15 48 3.545225e-15 3.674728e-15 49 1.563155e-15 3.545225e-15 50 -1.477678e-14 1.563155e-15 51 5.318600e-15 -1.477678e-14 52 5.914791e-15 5.318600e-15 53 1.831683e-15 5.914791e-15 54 5.995568e-15 1.831683e-15 55 5.048998e-15 5.995568e-15 56 3.634726e-15 5.048998e-15 57 2.203455e-15 3.634726e-15 58 3.867349e-15 2.203455e-15 59 -2.535626e-15 3.867349e-15 60 7.916008e-15 -2.535626e-15 61 7.335086e-15 7.916008e-15 62 -2.219327e-14 7.335086e-15 63 3.277878e-15 -2.219327e-14 64 4.405405e-15 3.277878e-15 65 -2.977482e-16 4.405405e-15 66 7.645920e-15 -2.977482e-16 67 -1.965223e-15 7.645920e-15 68 2.347904e-15 -1.965223e-15 69 -2.524859e-15 2.347904e-15 70 2.593868e-15 -2.524859e-15 71 -1.205657e-15 2.593868e-15 72 3.112176e-15 -1.205657e-15 73 -7.592177e-16 3.112176e-15 74 -1.917890e-14 -7.592177e-16 75 2.320727e-15 -1.917890e-14 > 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/7p5yl1258742129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8ljn01258742129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9p8x31258742129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10k7dj1258742129.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11qglo1258742129.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/126ttd1258742129.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/13dug51258742129.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/14uiku1258742129.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/15h19o1258742129.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/16z09t1258742129.tab") + } > > system("convert tmp/1t0xt1258742129.ps tmp/1t0xt1258742129.png") > system("convert tmp/2biur1258742129.ps tmp/2biur1258742129.png") > system("convert tmp/3p5961258742129.ps tmp/3p5961258742129.png") > system("convert tmp/4k2dw1258742129.ps tmp/4k2dw1258742129.png") > system("convert tmp/526gz1258742129.ps tmp/526gz1258742129.png") > system("convert tmp/60j8w1258742129.ps tmp/60j8w1258742129.png") > system("convert tmp/7p5yl1258742129.ps tmp/7p5yl1258742129.png") > system("convert tmp/8ljn01258742129.ps tmp/8ljn01258742129.png") > system("convert tmp/9p8x31258742129.ps tmp/9p8x31258742129.png") > system("convert tmp/10k7dj1258742129.ps tmp/10k7dj1258742129.png") > > > proc.time() user system elapsed 2.600 1.573 3.029