R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(24 + ,18 + ,17 + ,25 + ,24 + ,25 + ,18 + ,18 + ,17 + ,25 + ,17 + ,16 + ,18 + ,18 + ,17 + ,18 + ,20 + ,16 + ,18 + ,18 + ,18 + ,16 + ,20 + ,16 + ,18 + ,16 + ,18 + ,16 + ,20 + ,16 + ,20 + ,17 + ,18 + ,16 + ,20 + ,16 + ,23 + ,17 + ,18 + ,16 + ,18 + ,30 + ,23 + ,17 + ,18 + ,17 + ,23 + ,30 + ,23 + ,17 + ,23 + ,18 + ,23 + ,30 + ,23 + ,30 + ,15 + ,18 + ,23 + ,30 + ,23 + ,12 + ,15 + ,18 + ,23 + ,18 + ,21 + ,12 + ,15 + ,18 + ,15 + ,15 + ,21 + ,12 + ,15 + ,12 + ,20 + ,15 + ,21 + ,12 + ,21 + ,31 + ,20 + ,15 + ,21 + ,15 + ,27 + ,31 + ,20 + ,15 + ,20 + ,34 + ,27 + ,31 + ,20 + ,31 + ,21 + ,34 + ,27 + ,31 + ,27 + ,31 + ,21 + ,34 + ,27 + ,34 + ,19 + ,31 + ,21 + ,34 + ,21 + ,16 + ,19 + ,31 + ,21 + ,31 + ,20 + ,16 + ,19 + ,31 + ,19 + ,21 + ,20 + ,16 + ,19 + ,16 + ,22 + ,21 + ,20 + ,16 + ,20 + ,17 + ,22 + ,21 + ,20 + ,21 + ,24 + ,17 + ,22 + ,21 + ,22 + ,25 + ,24 + ,17 + ,22 + ,17 + ,26 + ,25 + ,24 + ,17 + ,24 + ,25 + ,26 + ,25 + ,24 + ,25 + ,17 + ,25 + ,26 + ,25 + 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,19 + ,22 + ,20 + ,15 + ,23 + ,22 + ,23 + ,18 + ,20 + ,15 + ,23 + ,15 + ,23 + ,18 + ,20 + ,15 + ,20 + ,25 + ,23 + ,18 + ,20 + ,18 + ,21 + ,25 + ,23 + ,18 + ,23 + ,24 + ,21 + ,25 + ,23 + ,25 + ,25 + ,24 + ,21 + ,25 + ,21 + ,17 + ,25 + ,24 + ,21 + ,24 + ,13 + ,17 + ,25 + ,24 + ,25 + ,28 + ,13 + ,17 + ,25 + ,17 + ,21 + ,28 + ,13 + ,17 + ,13 + ,25 + ,21 + ,28 + ,13 + ,28 + ,9 + ,25 + ,21 + ,28 + ,21 + ,16 + ,9 + ,25 + ,21 + ,25 + ,19 + ,16 + ,9 + ,25 + ,9 + ,17 + ,19 + ,16 + ,9 + ,16 + ,25 + ,17 + ,19 + ,16 + ,19 + ,20 + ,25 + ,17 + ,19 + ,17 + ,29 + ,20 + ,25 + ,17 + ,25 + ,14 + ,29 + ,20 + ,25 + ,20 + ,22 + ,14 + ,29 + ,20 + ,29 + ,15 + ,22 + ,14 + ,29 + ,14 + ,19 + ,15 + ,22 + ,14 + ,22 + ,20 + ,19 + ,15 + ,22 + ,15 + ,15 + ,20 + ,19 + ,15 + ,19 + ,20 + ,15 + ,20 + ,19 + ,20 + ,18 + ,20 + ,15 + ,20 + ,15 + ,33 + ,18 + ,20 + ,15 + ,20 + ,22 + ,33 + ,18 + ,20 + ,18 + ,16 + ,22 + ,33 + ,18 + ,33 + ,17 + ,16 + ,22 + ,33 + ,22 + ,16 + ,17 + ,16 + ,22 + ,16 + ,21 + ,16 + ,17 + ,16 + ,17 + ,26 + ,21 + ,16 + ,17 + ,16 + ,18 + ,26 + ,21 + ,16 + ,21 + ,18 + ,18 + ,26 + ,21 + ,26 + ,17 + ,18 + ,18 + ,26 + ,18 + ,22 + ,17 + ,18 + ,18 + ,18 + ,30 + ,22 + ,17 + ,18 + ,17 + ,30 + ,30 + ,22 + ,17 + ,22 + ,24 + ,30 + ,30 + ,22 + ,30 + ,21 + ,24 + ,30 + ,30 + ,30 + ,21 + ,21 + ,24 + ,30 + ,24 + ,29 + ,21 + ,21 + ,24 + ,21 + ,31 + ,29 + ,21 + ,21 + ,21 + ,20 + ,31 + ,29 + ,21 + ,29 + ,16 + ,20 + ,31 + ,29 + ,31 + ,22 + ,16 + ,20 + ,31 + ,20 + ,20 + ,22 + ,16 + ,20 + ,16 + ,28 + ,20 + ,22 + ,16 + ,22 + ,38 + ,28 + ,20 + ,22 + ,20 + ,22 + ,38 + ,28 + ,20 + ,28 + ,20 + ,22 + ,38 + ,28 + ,38 + ,17 + ,20 + ,22 + ,38 + ,22 + ,28 + ,17 + ,20 + ,22 + ,20 + ,22 + ,28 + ,17 + ,20 + ,17 + ,31 + ,22 + ,28 + ,17) + ,dim=c(5 + ,156) + ,dimnames=list(c('Concernovermistakes' + ,'Y1' + ,'Y2' + ,'Y3' + ,'Y4') + ,1:156)) > y <- array(NA,dim=c(5,156),dimnames=list(c('Concernovermistakes','Y1','Y2','Y3','Y4'),1:156)) > 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 Concernovermistakes Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 24 18 17 25 24 1 0 0 0 0 0 0 0 0 0 0 1 2 25 18 18 17 25 0 1 0 0 0 0 0 0 0 0 0 2 3 17 16 18 18 17 0 0 1 0 0 0 0 0 0 0 0 3 4 18 20 16 18 18 0 0 0 1 0 0 0 0 0 0 0 4 5 18 16 20 16 18 0 0 0 0 1 0 0 0 0 0 0 5 6 16 18 16 20 16 0 0 0 0 0 1 0 0 0 0 0 6 7 20 17 18 16 20 0 0 0 0 0 0 1 0 0 0 0 7 8 16 23 17 18 16 0 0 0 0 0 0 0 1 0 0 0 8 9 18 30 23 17 18 0 0 0 0 0 0 0 0 1 0 0 9 10 17 23 30 23 17 0 0 0 0 0 0 0 0 0 1 0 10 11 23 18 23 30 23 0 0 0 0 0 0 0 0 0 0 1 11 12 30 15 18 23 30 0 0 0 0 0 0 0 0 0 0 0 12 13 23 12 15 18 23 1 0 0 0 0 0 0 0 0 0 0 13 14 18 21 12 15 18 0 1 0 0 0 0 0 0 0 0 0 14 15 15 15 21 12 15 0 0 1 0 0 0 0 0 0 0 0 15 16 12 20 15 21 12 0 0 0 1 0 0 0 0 0 0 0 16 17 21 31 20 15 21 0 0 0 0 1 0 0 0 0 0 0 17 18 15 27 31 20 15 0 0 0 0 0 1 0 0 0 0 0 18 19 20 34 27 31 20 0 0 0 0 0 0 1 0 0 0 0 19 20 31 21 34 27 31 0 0 0 0 0 0 0 1 0 0 0 20 21 27 31 21 34 27 0 0 0 0 0 0 0 0 1 0 0 21 22 34 19 31 21 34 0 0 0 0 0 0 0 0 0 1 0 22 23 21 16 19 31 21 0 0 0 0 0 0 0 0 0 0 1 23 24 31 20 16 19 31 0 0 0 0 0 0 0 0 0 0 0 24 25 19 21 20 16 19 1 0 0 0 0 0 0 0 0 0 0 25 26 16 22 21 20 16 0 1 0 0 0 0 0 0 0 0 0 26 27 20 17 22 21 20 0 0 1 0 0 0 0 0 0 0 0 27 28 21 24 17 22 21 0 0 0 1 0 0 0 0 0 0 0 28 29 22 25 24 17 22 0 0 0 0 1 0 0 0 0 0 0 29 30 17 26 25 24 17 0 0 0 0 0 1 0 0 0 0 0 30 31 24 25 26 25 24 0 0 0 0 0 0 1 0 0 0 0 31 32 25 17 25 26 25 0 0 0 0 0 0 0 1 0 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124 125 19 20 15 20 19 0 0 0 0 1 0 0 0 0 0 0 125 126 20 18 20 15 20 0 0 0 0 0 1 0 0 0 0 0 126 127 15 33 18 20 15 0 0 0 0 0 0 1 0 0 0 0 127 128 20 22 33 18 20 0 0 0 0 0 0 0 1 0 0 0 128 129 18 16 22 33 18 0 0 0 0 0 0 0 0 1 0 0 129 130 33 17 16 22 33 0 0 0 0 0 0 0 0 0 1 0 130 131 22 16 17 16 22 0 0 0 0 0 0 0 0 0 0 1 131 132 16 21 16 17 16 0 0 0 0 0 0 0 0 0 0 0 132 133 17 26 21 16 17 1 0 0 0 0 0 0 0 0 0 0 133 134 16 18 26 21 16 0 1 0 0 0 0 0 0 0 0 0 134 135 21 18 18 26 21 0 0 1 0 0 0 0 0 0 0 0 135 136 26 17 18 18 26 0 0 0 1 0 0 0 0 0 0 0 136 137 18 22 17 18 18 0 0 0 0 1 0 0 0 0 0 0 137 138 18 30 22 17 18 0 0 0 0 0 1 0 0 0 0 0 138 139 17 30 30 22 17 0 0 0 0 0 0 1 0 0 0 0 139 140 22 24 30 30 22 0 0 0 0 0 0 0 1 0 0 0 140 141 30 21 24 30 30 0 0 0 0 0 0 0 0 1 0 0 141 142 30 21 21 24 30 0 0 0 0 0 0 0 0 0 1 0 142 143 24 29 21 21 24 0 0 0 0 0 0 0 0 0 0 1 143 144 21 31 29 21 21 0 0 0 0 0 0 0 0 0 0 0 144 145 21 20 31 29 21 1 0 0 0 0 0 0 0 0 0 0 145 146 29 16 20 31 29 0 1 0 0 0 0 0 0 0 0 0 146 147 31 22 16 20 31 0 0 1 0 0 0 0 0 0 0 0 147 148 20 20 22 16 20 0 0 0 1 0 0 0 0 0 0 0 148 149 16 28 20 22 16 0 0 0 0 1 0 0 0 0 0 0 149 150 22 38 28 20 22 0 0 0 0 0 1 0 0 0 0 0 150 151 20 22 38 28 20 0 0 0 0 0 0 1 0 0 0 0 151 152 28 20 22 38 28 0 0 0 0 0 0 0 1 0 0 0 152 153 38 17 20 22 38 0 0 0 0 0 0 0 0 1 0 0 153 154 22 28 17 20 22 0 0 0 0 0 0 0 0 0 1 0 154 155 20 22 28 17 20 0 0 0 0 0 0 0 0 0 0 1 155 156 17 31 22 28 17 0 0 0 0 0 0 0 0 0 0 0 156 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Y1 Y2 Y3 Y4 M1 -1.756e-14 1.532e-16 -6.311e-18 -1.773e-16 1.000e+00 5.517e-16 M2 M3 M4 M5 M6 M7 8.539e-16 4.089e-16 5.838e-16 -4.127e-15 3.665e-16 1.397e-16 M8 M9 M10 M11 t 1.970e-16 7.664e-17 2.833e-17 1.648e-16 1.068e-17 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -5.284e-14 -4.967e-16 -2.979e-17 5.477e-16 5.216e-15 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.756e-14 3.385e-15 -5.188e+00 7.36e-07 *** Y1 1.532e-16 7.186e-17 2.133e+00 0.0347 * Y2 -6.311e-18 7.178e-17 -8.800e-02 0.9301 Y3 -1.773e-16 7.155e-17 -2.478e+00 0.0144 * Y4 1.000e+00 7.205e-17 1.388e+16 < 2e-16 *** M1 5.517e-16 1.912e-15 2.890e-01 0.7734 M2 8.539e-16 1.909e-15 4.470e-01 0.6553 M3 4.089e-16 1.924e-15 2.130e-01 0.8320 M4 5.838e-16 1.926e-15 3.030e-01 0.7623 M5 -4.127e-15 1.933e-15 -2.136e+00 0.0345 * M6 3.665e-16 1.920e-15 1.910e-01 0.8489 M7 1.397e-16 1.905e-15 7.300e-02 0.9417 M8 1.970e-16 1.903e-15 1.040e-01 0.9177 M9 7.664e-17 1.881e-15 4.100e-02 0.9675 M10 2.833e-17 1.913e-15 1.500e-02 0.9882 M11 1.648e-16 1.924e-15 8.600e-02 0.9319 t 1.068e-17 8.520e-18 1.254e+00 0.2120 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.769e-15 on 139 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 1.386e+31 on 16 and 139 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,] 0.000000e+00 0.000000e+00 1.000000e+00 [2,] 7.198570e-01 5.602860e-01 2.801430e-01 [3,] 6.688704e-01 6.622592e-01 3.311296e-01 [4,] 1.000000e+00 1.200865e-09 6.004323e-10 [5,] 9.981270e-01 3.745952e-03 1.872976e-03 [6,] 6.245803e-01 7.508393e-01 3.754197e-01 [7,] 3.396990e-03 6.793980e-03 9.966030e-01 [8,] 9.976733e-06 1.995347e-05 9.999900e-01 [9,] 1.815941e-03 3.631882e-03 9.981841e-01 [10,] 1.000000e+00 3.520600e-42 1.760300e-42 [11,] 1.152225e-01 2.304451e-01 8.847775e-01 [12,] 8.945102e-01 2.109797e-01 1.054898e-01 [13,] 4.200935e-07 8.401869e-07 9.999996e-01 [14,] 9.991661e-01 1.667724e-03 8.338622e-04 [15,] 9.039315e-01 1.921370e-01 9.606848e-02 [16,] 3.506658e-14 7.013317e-14 1.000000e+00 [17,] 3.081442e-03 6.162884e-03 9.969186e-01 [18,] 9.999526e-01 9.479466e-05 4.739733e-05 [19,] 1.000000e+00 2.519787e-18 1.259893e-18 [20,] 9.999994e-01 1.261612e-06 6.308062e-07 [21,] 5.403276e-08 1.080655e-07 9.999999e-01 [22,] 1.004682e-50 2.009363e-50 1.000000e+00 [23,] 2.425017e-01 4.850035e-01 7.574983e-01 [24,] 1.000000e+00 2.026898e-31 1.013449e-31 [25,] 1.310803e-07 2.621607e-07 9.999999e-01 [26,] 1.680250e-36 3.360499e-36 1.000000e+00 [27,] 1.000000e+00 4.714018e-47 2.357009e-47 [28,] 1.329967e-12 2.659933e-12 1.000000e+00 [29,] 8.588513e-01 2.822974e-01 1.411487e-01 [30,] 1.000000e+00 1.617904e-15 8.089518e-16 [31,] 4.301769e-01 8.603537e-01 5.698231e-01 [32,] 1.910538e-11 3.821076e-11 1.000000e+00 [33,] 1.000000e+00 7.228454e-10 3.614227e-10 [34,] 7.129521e-01 5.740958e-01 2.870479e-01 [35,] 3.073553e-01 6.147106e-01 6.926447e-01 [36,] 1.000000e+00 2.738675e-16 1.369338e-16 [37,] 1.000000e+00 1.124344e-17 5.621718e-18 [38,] 1.000000e+00 1.526665e-66 7.633325e-67 [39,] 9.986379e-01 2.724189e-03 1.362095e-03 [40,] 2.830769e-04 5.661537e-04 9.997169e-01 [41,] 1.000000e+00 2.187635e-20 1.093818e-20 [42,] 1.027924e-29 2.055849e-29 1.000000e+00 [43,] 1.284376e-11 2.568753e-11 1.000000e+00 [44,] 1.000000e+00 2.371205e-57 1.185602e-57 [45,] 3.546642e-01 7.093284e-01 6.453358e-01 [46,] 1.369503e-02 2.739005e-02 9.863050e-01 [47,] 3.093467e-11 6.186934e-11 1.000000e+00 [48,] 1.000000e+00 1.503109e-08 7.515544e-09 [49,] 1.000000e+00 1.021806e-25 5.109032e-26 [50,] 1.221805e-02 2.443611e-02 9.877819e-01 [51,] 9.811043e-01 3.779137e-02 1.889568e-02 [52,] 3.708829e-12 7.417659e-12 1.000000e+00 [53,] 1.773887e-05 3.547775e-05 9.999823e-01 [54,] 3.422081e-23 6.844161e-23 1.000000e+00 [55,] 1.721987e-15 3.443974e-15 1.000000e+00 [56,] 1.000000e+00 3.772565e-18 1.886282e-18 [57,] 1.059939e-02 2.119879e-02 9.894006e-01 [58,] 3.622209e-03 7.244418e-03 9.963778e-01 [59,] 1.893461e-07 3.786922e-07 9.999998e-01 [60,] 3.840768e-01 7.681536e-01 6.159232e-01 [61,] 3.425756e-02 6.851512e-02 9.657424e-01 [62,] 1.641003e-07 3.282007e-07 9.999998e-01 [63,] 1.000000e+00 3.803630e-15 1.901815e-15 [64,] 9.842636e-01 3.147272e-02 1.573636e-02 [65,] 9.024767e-01 1.950466e-01 9.752331e-02 [66,] 1.528735e-02 3.057470e-02 9.847127e-01 [67,] 1.000000e+00 4.455404e-22 2.227702e-22 [68,] 6.387927e-01 7.224146e-01 3.612073e-01 [69,] 3.951118e-04 7.902235e-04 9.996049e-01 [70,] 4.198539e-01 8.397079e-01 5.801461e-01 [71,] 9.994970e-01 1.005945e-03 5.029723e-04 [72,] 1.000000e+00 4.807839e-21 2.403920e-21 [73,] 7.901207e-02 1.580241e-01 9.209879e-01 [74,] 2.233248e-23 4.466496e-23 1.000000e+00 [75,] 1.823382e-41 3.646764e-41 1.000000e+00 [76,] 1.000000e+00 4.381578e-20 2.190789e-20 [77,] 9.998862e-01 2.275663e-04 1.137831e-04 [78,] 1.000000e+00 2.131899e-36 1.065949e-36 [79,] 9.257684e-01 1.484633e-01 7.423163e-02 [80,] 1.000000e+00 1.497163e-20 7.485813e-21 [81,] 2.416851e-22 4.833702e-22 1.000000e+00 [82,] 1.236168e-06 2.472337e-06 9.999988e-01 [83,] 1.000000e+00 6.752308e-12 3.376154e-12 [84,] 1.000000e+00 1.602212e-19 8.011058e-20 [85,] 1.000000e+00 1.333557e-18 6.667786e-19 [86,] 6.341295e-03 1.268259e-02 9.936587e-01 [87,] 9.999995e-01 9.518043e-07 4.759021e-07 [88,] 3.539033e-03 7.078066e-03 9.964610e-01 [89,] 2.577752e-01 5.155503e-01 7.422248e-01 [90,] 2.370447e-09 4.740893e-09 1.000000e+00 [91,] 1.000000e+00 1.034984e-09 5.174918e-10 [92,] 9.997076e-01 5.847877e-04 2.923939e-04 [93,] 1.706233e-01 3.412465e-01 8.293767e-01 [94,] 1.619037e-01 3.238075e-01 8.380963e-01 [95,] 9.518983e-01 9.620340e-02 4.810170e-02 [96,] 9.999782e-01 4.366222e-05 2.183111e-05 [97,] 9.999999e-01 1.195888e-07 5.979440e-08 [98,] 9.999616e-01 7.671063e-05 3.835532e-05 [99,] 1.000000e+00 1.254490e-10 6.272449e-11 [100,] 9.999994e-01 1.164881e-06 5.824404e-07 [101,] 8.789369e-04 1.757874e-03 9.991211e-01 [102,] 9.495470e-01 1.009060e-01 5.045301e-02 [103,] 9.999997e-01 6.616181e-07 3.308090e-07 [104,] 9.184163e-03 1.836833e-02 9.908158e-01 [105,] 1.000000e+00 3.757663e-10 1.878831e-10 [106,] 9.969595e-01 6.081090e-03 3.040545e-03 [107,] 1.916310e-01 3.832619e-01 8.083690e-01 [108,] 8.982955e-01 2.034090e-01 1.017045e-01 [109,] 9.939199e-01 1.216024e-02 6.080122e-03 [110,] 9.999154e-01 1.692540e-04 8.462699e-05 [111,] 9.988780e-01 2.244003e-03 1.122001e-03 [112,] 9.985833e-01 2.833412e-03 1.416706e-03 [113,] 9.999999e-01 1.983700e-07 9.918501e-08 [114,] 9.982419e-01 3.516247e-03 1.758124e-03 [115,] 9.334150e-01 1.331700e-01 6.658502e-02 [116,] 9.710444e-01 5.791113e-02 2.895556e-02 [117,] 3.559132e-01 7.118264e-01 6.440868e-01 > postscript(file="/var/www/html/rcomp/tmp/1yaad1290857931.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/2yaad1290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3yaad1290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4rj9x1290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5rj9x1290857931.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 = 156 Frequency = 1 1 2 3 4 5 3.883367e-15 4.459499e-15 9.242415e-16 4.611255e-15 -5.283882e-14 6 7 8 9 10 1.429031e-15 1.364954e-15 8.626107e-16 5.385162e-16 1.141347e-15 11 12 13 14 15 6.770594e-16 1.442698e-15 9.233770e-16 3.578725e-16 1.606257e-15 16 17 18 19 20 -3.137681e-16 4.581225e-15 1.108191e-15 -3.004122e-16 4.180791e-16 21 22 23 24 25 -6.598701e-16 1.508388e-15 5.748570e-16 1.917255e-16 3.973608e-16 26 27 28 29 30 1.828464e-16 7.586885e-16 -8.255873e-17 4.775565e-15 7.726812e-17 31 32 33 34 35 3.238727e-16 7.185826e-16 -2.656990e-16 2.395643e-16 6.779551e-18 36 37 38 39 40 -4.232750e-16 7.194647e-16 2.210029e-16 -1.083176e-15 -4.262043e-17 41 42 43 44 45 4.034304e-15 1.927339e-16 1.220937e-15 5.426416e-16 -3.174904e-17 46 47 48 49 50 4.496815e-16 6.178842e-17 -4.994473e-17 -2.306361e-16 -4.935578e-16 51 52 53 54 55 4.671314e-16 -5.513919e-16 4.820417e-15 5.628375e-16 4.436823e-16 56 57 58 59 60 4.535804e-16 8.163840e-16 1.040035e-15 1.215758e-15 1.055308e-15 61 62 63 64 65 2.582446e-16 4.111030e-16 -6.232138e-16 -2.495448e-16 5.086338e-15 66 67 68 69 70 2.182746e-16 -1.395376e-16 7.600500e-17 2.078287e-15 -4.248671e-16 71 72 73 74 75 -4.953272e-16 -2.565867e-16 -2.947347e-16 -2.249824e-16 -1.544374e-16 76 77 78 79 80 1.170181e-16 4.594280e-15 -2.783006e-17 4.543358e-16 -3.214323e-16 81 82 83 84 85 2.898931e-16 2.316577e-16 1.059848e-16 2.549468e-16 -2.453134e-17 86 87 88 89 90 -1.088987e-15 1.914534e-16 -1.260396e-15 5.216342e-15 -1.157876e-16 91 92 93 94 95 -7.805202e-16 7.452835e-17 -6.855424e-16 -4.171356e-16 -5.589477e-16 96 97 98 99 100 -2.489561e-16 -1.372216e-15 -1.147235e-15 -2.080576e-17 -6.768189e-16 101 102 103 104 105 4.477658e-15 1.738014e-16 -2.424043e-16 -1.745423e-16 -5.008839e-16 106 107 108 109 110 -3.803133e-16 -2.379044e-16 2.002757e-16 -1.914310e-15 -5.583427e-16 111 112 113 114 115 -1.407109e-15 1.759959e-16 4.030466e-15 -4.823549e-16 6.890653e-16 116 117 118 119 120 -2.400864e-16 1.478931e-17 -5.928851e-16 3.423230e-16 -5.808801e-16 121 122 123 124 125 -1.906785e-16 -4.237660e-16 -5.872895e-16 -3.194023e-16 3.867542e-15 126 127 128 129 130 -1.929656e-16 -1.195218e-15 -1.010998e-16 -3.709714e-16 -3.759114e-16 131 132 133 134 135 -1.033321e-16 6.103602e-16 -1.102749e-15 -1.362362e-16 -3.679598e-16 136 137 138 139 140 -6.259679e-16 3.985246e-15 -1.091127e-15 -9.732642e-16 -7.985964e-16 141 142 143 144 145 -9.528634e-16 -1.280142e-15 -1.084024e-15 -7.674786e-16 -1.051958e-15 146 147 148 149 150 -1.559217e-15 2.962198e-16 -7.818002e-16 3.369437e-15 -1.852071e-15 151 152 153 154 155 -8.654915e-16 -1.510271e-15 -2.702903e-16 -1.139419e-15 -5.050145e-16 156 -1.428194e-15 > postscript(file="/var/www/html/rcomp/tmp/6rj9x1290857931.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 = 156 Frequency = 1 lag(myerror, k = 1) myerror 0 3.883367e-15 NA 1 4.459499e-15 3.883367e-15 2 9.242415e-16 4.459499e-15 3 4.611255e-15 9.242415e-16 4 -5.283882e-14 4.611255e-15 5 1.429031e-15 -5.283882e-14 6 1.364954e-15 1.429031e-15 7 8.626107e-16 1.364954e-15 8 5.385162e-16 8.626107e-16 9 1.141347e-15 5.385162e-16 10 6.770594e-16 1.141347e-15 11 1.442698e-15 6.770594e-16 12 9.233770e-16 1.442698e-15 13 3.578725e-16 9.233770e-16 14 1.606257e-15 3.578725e-16 15 -3.137681e-16 1.606257e-15 16 4.581225e-15 -3.137681e-16 17 1.108191e-15 4.581225e-15 18 -3.004122e-16 1.108191e-15 19 4.180791e-16 -3.004122e-16 20 -6.598701e-16 4.180791e-16 21 1.508388e-15 -6.598701e-16 22 5.748570e-16 1.508388e-15 23 1.917255e-16 5.748570e-16 24 3.973608e-16 1.917255e-16 25 1.828464e-16 3.973608e-16 26 7.586885e-16 1.828464e-16 27 -8.255873e-17 7.586885e-16 28 4.775565e-15 -8.255873e-17 29 7.726812e-17 4.775565e-15 30 3.238727e-16 7.726812e-17 31 7.185826e-16 3.238727e-16 32 -2.656990e-16 7.185826e-16 33 2.395643e-16 -2.656990e-16 34 6.779551e-18 2.395643e-16 35 -4.232750e-16 6.779551e-18 36 7.194647e-16 -4.232750e-16 37 2.210029e-16 7.194647e-16 38 -1.083176e-15 2.210029e-16 39 -4.262043e-17 -1.083176e-15 40 4.034304e-15 -4.262043e-17 41 1.927339e-16 4.034304e-15 42 1.220937e-15 1.927339e-16 43 5.426416e-16 1.220937e-15 44 -3.174904e-17 5.426416e-16 45 4.496815e-16 -3.174904e-17 46 6.178842e-17 4.496815e-16 47 -4.994473e-17 6.178842e-17 48 -2.306361e-16 -4.994473e-17 49 -4.935578e-16 -2.306361e-16 50 4.671314e-16 -4.935578e-16 51 -5.513919e-16 4.671314e-16 52 4.820417e-15 -5.513919e-16 53 5.628375e-16 4.820417e-15 54 4.436823e-16 5.628375e-16 55 4.535804e-16 4.436823e-16 56 8.163840e-16 4.535804e-16 57 1.040035e-15 8.163840e-16 58 1.215758e-15 1.040035e-15 59 1.055308e-15 1.215758e-15 60 2.582446e-16 1.055308e-15 61 4.111030e-16 2.582446e-16 62 -6.232138e-16 4.111030e-16 63 -2.495448e-16 -6.232138e-16 64 5.086338e-15 -2.495448e-16 65 2.182746e-16 5.086338e-15 66 -1.395376e-16 2.182746e-16 67 7.600500e-17 -1.395376e-16 68 2.078287e-15 7.600500e-17 69 -4.248671e-16 2.078287e-15 70 -4.953272e-16 -4.248671e-16 71 -2.565867e-16 -4.953272e-16 72 -2.947347e-16 -2.565867e-16 73 -2.249824e-16 -2.947347e-16 74 -1.544374e-16 -2.249824e-16 75 1.170181e-16 -1.544374e-16 76 4.594280e-15 1.170181e-16 77 -2.783006e-17 4.594280e-15 78 4.543358e-16 -2.783006e-17 79 -3.214323e-16 4.543358e-16 80 2.898931e-16 -3.214323e-16 81 2.316577e-16 2.898931e-16 82 1.059848e-16 2.316577e-16 83 2.549468e-16 1.059848e-16 84 -2.453134e-17 2.549468e-16 85 -1.088987e-15 -2.453134e-17 86 1.914534e-16 -1.088987e-15 87 -1.260396e-15 1.914534e-16 88 5.216342e-15 -1.260396e-15 89 -1.157876e-16 5.216342e-15 90 -7.805202e-16 -1.157876e-16 91 7.452835e-17 -7.805202e-16 92 -6.855424e-16 7.452835e-17 93 -4.171356e-16 -6.855424e-16 94 -5.589477e-16 -4.171356e-16 95 -2.489561e-16 -5.589477e-16 96 -1.372216e-15 -2.489561e-16 97 -1.147235e-15 -1.372216e-15 98 -2.080576e-17 -1.147235e-15 99 -6.768189e-16 -2.080576e-17 100 4.477658e-15 -6.768189e-16 101 1.738014e-16 4.477658e-15 102 -2.424043e-16 1.738014e-16 103 -1.745423e-16 -2.424043e-16 104 -5.008839e-16 -1.745423e-16 105 -3.803133e-16 -5.008839e-16 106 -2.379044e-16 -3.803133e-16 107 2.002757e-16 -2.379044e-16 108 -1.914310e-15 2.002757e-16 109 -5.583427e-16 -1.914310e-15 110 -1.407109e-15 -5.583427e-16 111 1.759959e-16 -1.407109e-15 112 4.030466e-15 1.759959e-16 113 -4.823549e-16 4.030466e-15 114 6.890653e-16 -4.823549e-16 115 -2.400864e-16 6.890653e-16 116 1.478931e-17 -2.400864e-16 117 -5.928851e-16 1.478931e-17 118 3.423230e-16 -5.928851e-16 119 -5.808801e-16 3.423230e-16 120 -1.906785e-16 -5.808801e-16 121 -4.237660e-16 -1.906785e-16 122 -5.872895e-16 -4.237660e-16 123 -3.194023e-16 -5.872895e-16 124 3.867542e-15 -3.194023e-16 125 -1.929656e-16 3.867542e-15 126 -1.195218e-15 -1.929656e-16 127 -1.010998e-16 -1.195218e-15 128 -3.709714e-16 -1.010998e-16 129 -3.759114e-16 -3.709714e-16 130 -1.033321e-16 -3.759114e-16 131 6.103602e-16 -1.033321e-16 132 -1.102749e-15 6.103602e-16 133 -1.362362e-16 -1.102749e-15 134 -3.679598e-16 -1.362362e-16 135 -6.259679e-16 -3.679598e-16 136 3.985246e-15 -6.259679e-16 137 -1.091127e-15 3.985246e-15 138 -9.732642e-16 -1.091127e-15 139 -7.985964e-16 -9.732642e-16 140 -9.528634e-16 -7.985964e-16 141 -1.280142e-15 -9.528634e-16 142 -1.084024e-15 -1.280142e-15 143 -7.674786e-16 -1.084024e-15 144 -1.051958e-15 -7.674786e-16 145 -1.559217e-15 -1.051958e-15 146 2.962198e-16 -1.559217e-15 147 -7.818002e-16 2.962198e-16 148 3.369437e-15 -7.818002e-16 149 -1.852071e-15 3.369437e-15 150 -8.654915e-16 -1.852071e-15 151 -1.510271e-15 -8.654915e-16 152 -2.702903e-16 -1.510271e-15 153 -1.139419e-15 -2.702903e-16 154 -5.050145e-16 -1.139419e-15 155 -1.428194e-15 -5.050145e-16 156 NA -1.428194e-15 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 4.459499e-15 3.883367e-15 [2,] 9.242415e-16 4.459499e-15 [3,] 4.611255e-15 9.242415e-16 [4,] -5.283882e-14 4.611255e-15 [5,] 1.429031e-15 -5.283882e-14 [6,] 1.364954e-15 1.429031e-15 [7,] 8.626107e-16 1.364954e-15 [8,] 5.385162e-16 8.626107e-16 [9,] 1.141347e-15 5.385162e-16 [10,] 6.770594e-16 1.141347e-15 [11,] 1.442698e-15 6.770594e-16 [12,] 9.233770e-16 1.442698e-15 [13,] 3.578725e-16 9.233770e-16 [14,] 1.606257e-15 3.578725e-16 [15,] -3.137681e-16 1.606257e-15 [16,] 4.581225e-15 -3.137681e-16 [17,] 1.108191e-15 4.581225e-15 [18,] -3.004122e-16 1.108191e-15 [19,] 4.180791e-16 -3.004122e-16 [20,] -6.598701e-16 4.180791e-16 [21,] 1.508388e-15 -6.598701e-16 [22,] 5.748570e-16 1.508388e-15 [23,] 1.917255e-16 5.748570e-16 [24,] 3.973608e-16 1.917255e-16 [25,] 1.828464e-16 3.973608e-16 [26,] 7.586885e-16 1.828464e-16 [27,] -8.255873e-17 7.586885e-16 [28,] 4.775565e-15 -8.255873e-17 [29,] 7.726812e-17 4.775565e-15 [30,] 3.238727e-16 7.726812e-17 [31,] 7.185826e-16 3.238727e-16 [32,] -2.656990e-16 7.185826e-16 [33,] 2.395643e-16 -2.656990e-16 [34,] 6.779551e-18 2.395643e-16 [35,] -4.232750e-16 6.779551e-18 [36,] 7.194647e-16 -4.232750e-16 [37,] 2.210029e-16 7.194647e-16 [38,] -1.083176e-15 2.210029e-16 [39,] -4.262043e-17 -1.083176e-15 [40,] 4.034304e-15 -4.262043e-17 [41,] 1.927339e-16 4.034304e-15 [42,] 1.220937e-15 1.927339e-16 [43,] 5.426416e-16 1.220937e-15 [44,] -3.174904e-17 5.426416e-16 [45,] 4.496815e-16 -3.174904e-17 [46,] 6.178842e-17 4.496815e-16 [47,] -4.994473e-17 6.178842e-17 [48,] -2.306361e-16 -4.994473e-17 [49,] -4.935578e-16 -2.306361e-16 [50,] 4.671314e-16 -4.935578e-16 [51,] -5.513919e-16 4.671314e-16 [52,] 4.820417e-15 -5.513919e-16 [53,] 5.628375e-16 4.820417e-15 [54,] 4.436823e-16 5.628375e-16 [55,] 4.535804e-16 4.436823e-16 [56,] 8.163840e-16 4.535804e-16 [57,] 1.040035e-15 8.163840e-16 [58,] 1.215758e-15 1.040035e-15 [59,] 1.055308e-15 1.215758e-15 [60,] 2.582446e-16 1.055308e-15 [61,] 4.111030e-16 2.582446e-16 [62,] -6.232138e-16 4.111030e-16 [63,] -2.495448e-16 -6.232138e-16 [64,] 5.086338e-15 -2.495448e-16 [65,] 2.182746e-16 5.086338e-15 [66,] -1.395376e-16 2.182746e-16 [67,] 7.600500e-17 -1.395376e-16 [68,] 2.078287e-15 7.600500e-17 [69,] -4.248671e-16 2.078287e-15 [70,] -4.953272e-16 -4.248671e-16 [71,] -2.565867e-16 -4.953272e-16 [72,] -2.947347e-16 -2.565867e-16 [73,] -2.249824e-16 -2.947347e-16 [74,] -1.544374e-16 -2.249824e-16 [75,] 1.170181e-16 -1.544374e-16 [76,] 4.594280e-15 1.170181e-16 [77,] -2.783006e-17 4.594280e-15 [78,] 4.543358e-16 -2.783006e-17 [79,] -3.214323e-16 4.543358e-16 [80,] 2.898931e-16 -3.214323e-16 [81,] 2.316577e-16 2.898931e-16 [82,] 1.059848e-16 2.316577e-16 [83,] 2.549468e-16 1.059848e-16 [84,] -2.453134e-17 2.549468e-16 [85,] -1.088987e-15 -2.453134e-17 [86,] 1.914534e-16 -1.088987e-15 [87,] -1.260396e-15 1.914534e-16 [88,] 5.216342e-15 -1.260396e-15 [89,] -1.157876e-16 5.216342e-15 [90,] -7.805202e-16 -1.157876e-16 [91,] 7.452835e-17 -7.805202e-16 [92,] -6.855424e-16 7.452835e-17 [93,] -4.171356e-16 -6.855424e-16 [94,] -5.589477e-16 -4.171356e-16 [95,] -2.489561e-16 -5.589477e-16 [96,] -1.372216e-15 -2.489561e-16 [97,] -1.147235e-15 -1.372216e-15 [98,] -2.080576e-17 -1.147235e-15 [99,] -6.768189e-16 -2.080576e-17 [100,] 4.477658e-15 -6.768189e-16 [101,] 1.738014e-16 4.477658e-15 [102,] -2.424043e-16 1.738014e-16 [103,] -1.745423e-16 -2.424043e-16 [104,] -5.008839e-16 -1.745423e-16 [105,] -3.803133e-16 -5.008839e-16 [106,] -2.379044e-16 -3.803133e-16 [107,] 2.002757e-16 -2.379044e-16 [108,] -1.914310e-15 2.002757e-16 [109,] -5.583427e-16 -1.914310e-15 [110,] -1.407109e-15 -5.583427e-16 [111,] 1.759959e-16 -1.407109e-15 [112,] 4.030466e-15 1.759959e-16 [113,] -4.823549e-16 4.030466e-15 [114,] 6.890653e-16 -4.823549e-16 [115,] -2.400864e-16 6.890653e-16 [116,] 1.478931e-17 -2.400864e-16 [117,] -5.928851e-16 1.478931e-17 [118,] 3.423230e-16 -5.928851e-16 [119,] -5.808801e-16 3.423230e-16 [120,] -1.906785e-16 -5.808801e-16 [121,] -4.237660e-16 -1.906785e-16 [122,] -5.872895e-16 -4.237660e-16 [123,] -3.194023e-16 -5.872895e-16 [124,] 3.867542e-15 -3.194023e-16 [125,] -1.929656e-16 3.867542e-15 [126,] -1.195218e-15 -1.929656e-16 [127,] -1.010998e-16 -1.195218e-15 [128,] -3.709714e-16 -1.010998e-16 [129,] -3.759114e-16 -3.709714e-16 [130,] -1.033321e-16 -3.759114e-16 [131,] 6.103602e-16 -1.033321e-16 [132,] -1.102749e-15 6.103602e-16 [133,] -1.362362e-16 -1.102749e-15 [134,] -3.679598e-16 -1.362362e-16 [135,] -6.259679e-16 -3.679598e-16 [136,] 3.985246e-15 -6.259679e-16 [137,] -1.091127e-15 3.985246e-15 [138,] -9.732642e-16 -1.091127e-15 [139,] -7.985964e-16 -9.732642e-16 [140,] -9.528634e-16 -7.985964e-16 [141,] -1.280142e-15 -9.528634e-16 [142,] -1.084024e-15 -1.280142e-15 [143,] -7.674786e-16 -1.084024e-15 [144,] -1.051958e-15 -7.674786e-16 [145,] -1.559217e-15 -1.051958e-15 [146,] 2.962198e-16 -1.559217e-15 [147,] -7.818002e-16 2.962198e-16 [148,] 3.369437e-15 -7.818002e-16 [149,] -1.852071e-15 3.369437e-15 [150,] -8.654915e-16 -1.852071e-15 [151,] -1.510271e-15 -8.654915e-16 [152,] -2.702903e-16 -1.510271e-15 [153,] -1.139419e-15 -2.702903e-16 [154,] -5.050145e-16 -1.139419e-15 [155,] -1.428194e-15 -5.050145e-16 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 4.459499e-15 3.883367e-15 2 9.242415e-16 4.459499e-15 3 4.611255e-15 9.242415e-16 4 -5.283882e-14 4.611255e-15 5 1.429031e-15 -5.283882e-14 6 1.364954e-15 1.429031e-15 7 8.626107e-16 1.364954e-15 8 5.385162e-16 8.626107e-16 9 1.141347e-15 5.385162e-16 10 6.770594e-16 1.141347e-15 11 1.442698e-15 6.770594e-16 12 9.233770e-16 1.442698e-15 13 3.578725e-16 9.233770e-16 14 1.606257e-15 3.578725e-16 15 -3.137681e-16 1.606257e-15 16 4.581225e-15 -3.137681e-16 17 1.108191e-15 4.581225e-15 18 -3.004122e-16 1.108191e-15 19 4.180791e-16 -3.004122e-16 20 -6.598701e-16 4.180791e-16 21 1.508388e-15 -6.598701e-16 22 5.748570e-16 1.508388e-15 23 1.917255e-16 5.748570e-16 24 3.973608e-16 1.917255e-16 25 1.828464e-16 3.973608e-16 26 7.586885e-16 1.828464e-16 27 -8.255873e-17 7.586885e-16 28 4.775565e-15 -8.255873e-17 29 7.726812e-17 4.775565e-15 30 3.238727e-16 7.726812e-17 31 7.185826e-16 3.238727e-16 32 -2.656990e-16 7.185826e-16 33 2.395643e-16 -2.656990e-16 34 6.779551e-18 2.395643e-16 35 -4.232750e-16 6.779551e-18 36 7.194647e-16 -4.232750e-16 37 2.210029e-16 7.194647e-16 38 -1.083176e-15 2.210029e-16 39 -4.262043e-17 -1.083176e-15 40 4.034304e-15 -4.262043e-17 41 1.927339e-16 4.034304e-15 42 1.220937e-15 1.927339e-16 43 5.426416e-16 1.220937e-15 44 -3.174904e-17 5.426416e-16 45 4.496815e-16 -3.174904e-17 46 6.178842e-17 4.496815e-16 47 -4.994473e-17 6.178842e-17 48 -2.306361e-16 -4.994473e-17 49 -4.935578e-16 -2.306361e-16 50 4.671314e-16 -4.935578e-16 51 -5.513919e-16 4.671314e-16 52 4.820417e-15 -5.513919e-16 53 5.628375e-16 4.820417e-15 54 4.436823e-16 5.628375e-16 55 4.535804e-16 4.436823e-16 56 8.163840e-16 4.535804e-16 57 1.040035e-15 8.163840e-16 58 1.215758e-15 1.040035e-15 59 1.055308e-15 1.215758e-15 60 2.582446e-16 1.055308e-15 61 4.111030e-16 2.582446e-16 62 -6.232138e-16 4.111030e-16 63 -2.495448e-16 -6.232138e-16 64 5.086338e-15 -2.495448e-16 65 2.182746e-16 5.086338e-15 66 -1.395376e-16 2.182746e-16 67 7.600500e-17 -1.395376e-16 68 2.078287e-15 7.600500e-17 69 -4.248671e-16 2.078287e-15 70 -4.953272e-16 -4.248671e-16 71 -2.565867e-16 -4.953272e-16 72 -2.947347e-16 -2.565867e-16 73 -2.249824e-16 -2.947347e-16 74 -1.544374e-16 -2.249824e-16 75 1.170181e-16 -1.544374e-16 76 4.594280e-15 1.170181e-16 77 -2.783006e-17 4.594280e-15 78 4.543358e-16 -2.783006e-17 79 -3.214323e-16 4.543358e-16 80 2.898931e-16 -3.214323e-16 81 2.316577e-16 2.898931e-16 82 1.059848e-16 2.316577e-16 83 2.549468e-16 1.059848e-16 84 -2.453134e-17 2.549468e-16 85 -1.088987e-15 -2.453134e-17 86 1.914534e-16 -1.088987e-15 87 -1.260396e-15 1.914534e-16 88 5.216342e-15 -1.260396e-15 89 -1.157876e-16 5.216342e-15 90 -7.805202e-16 -1.157876e-16 91 7.452835e-17 -7.805202e-16 92 -6.855424e-16 7.452835e-17 93 -4.171356e-16 -6.855424e-16 94 -5.589477e-16 -4.171356e-16 95 -2.489561e-16 -5.589477e-16 96 -1.372216e-15 -2.489561e-16 97 -1.147235e-15 -1.372216e-15 98 -2.080576e-17 -1.147235e-15 99 -6.768189e-16 -2.080576e-17 100 4.477658e-15 -6.768189e-16 101 1.738014e-16 4.477658e-15 102 -2.424043e-16 1.738014e-16 103 -1.745423e-16 -2.424043e-16 104 -5.008839e-16 -1.745423e-16 105 -3.803133e-16 -5.008839e-16 106 -2.379044e-16 -3.803133e-16 107 2.002757e-16 -2.379044e-16 108 -1.914310e-15 2.002757e-16 109 -5.583427e-16 -1.914310e-15 110 -1.407109e-15 -5.583427e-16 111 1.759959e-16 -1.407109e-15 112 4.030466e-15 1.759959e-16 113 -4.823549e-16 4.030466e-15 114 6.890653e-16 -4.823549e-16 115 -2.400864e-16 6.890653e-16 116 1.478931e-17 -2.400864e-16 117 -5.928851e-16 1.478931e-17 118 3.423230e-16 -5.928851e-16 119 -5.808801e-16 3.423230e-16 120 -1.906785e-16 -5.808801e-16 121 -4.237660e-16 -1.906785e-16 122 -5.872895e-16 -4.237660e-16 123 -3.194023e-16 -5.872895e-16 124 3.867542e-15 -3.194023e-16 125 -1.929656e-16 3.867542e-15 126 -1.195218e-15 -1.929656e-16 127 -1.010998e-16 -1.195218e-15 128 -3.709714e-16 -1.010998e-16 129 -3.759114e-16 -3.709714e-16 130 -1.033321e-16 -3.759114e-16 131 6.103602e-16 -1.033321e-16 132 -1.102749e-15 6.103602e-16 133 -1.362362e-16 -1.102749e-15 134 -3.679598e-16 -1.362362e-16 135 -6.259679e-16 -3.679598e-16 136 3.985246e-15 -6.259679e-16 137 -1.091127e-15 3.985246e-15 138 -9.732642e-16 -1.091127e-15 139 -7.985964e-16 -9.732642e-16 140 -9.528634e-16 -7.985964e-16 141 -1.280142e-15 -9.528634e-16 142 -1.084024e-15 -1.280142e-15 143 -7.674786e-16 -1.084024e-15 144 -1.051958e-15 -7.674786e-16 145 -1.559217e-15 -1.051958e-15 146 2.962198e-16 -1.559217e-15 147 -7.818002e-16 2.962198e-16 148 3.369437e-15 -7.818002e-16 149 -1.852071e-15 3.369437e-15 150 -8.654915e-16 -1.852071e-15 151 -1.510271e-15 -8.654915e-16 152 -2.702903e-16 -1.510271e-15 153 -1.139419e-15 -2.702903e-16 154 -5.050145e-16 -1.139419e-15 155 -1.428194e-15 -5.050145e-16 > 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/71s801290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8c2p31290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9c2p31290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10c2p31290857931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/119b5c1290857931.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/12j3mf1290857931.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/138mjr1290857931.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/141d1c1290857931.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/154dhi1290857931.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/16inx91290857931.tab") + } > > try(system("convert tmp/1yaad1290857931.ps tmp/1yaad1290857931.png",intern=TRUE)) character(0) > try(system("convert tmp/2yaad1290857931.ps tmp/2yaad1290857931.png",intern=TRUE)) character(0) > try(system("convert tmp/3yaad1290857931.ps tmp/3yaad1290857931.png",intern=TRUE)) character(0) > try(system("convert tmp/4rj9x1290857931.ps tmp/4rj9x1290857931.png",intern=TRUE)) character(0) > try(system("convert tmp/5rj9x1290857931.ps tmp/5rj9x1290857931.png",intern=TRUE)) character(0) > try(system("convert tmp/6rj9x1290857931.ps tmp/6rj9x1290857931.png",intern=TRUE)) character(0) > try(system("convert tmp/71s801290857931.ps tmp/71s801290857931.png",intern=TRUE)) character(0) > try(system("convert tmp/8c2p31290857931.ps tmp/8c2p31290857931.png",intern=TRUE)) character(0) > try(system("convert tmp/9c2p31290857931.ps tmp/9c2p31290857931.png",intern=TRUE)) character(0) > try(system("convert tmp/10c2p31290857931.ps tmp/10c2p31290857931.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.212 1.855 11.992