R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) 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(9 + ,5 + ,-1 + ,6 + ,24 + ,11 + ,5 + ,-4 + ,6 + ,29 + ,13 + ,9 + ,-6 + ,8 + ,29 + ,12 + ,10 + ,-9 + ,4 + ,25 + ,13 + ,14 + ,-13 + ,8 + ,16 + ,15 + ,19 + ,-13 + ,10 + ,18 + ,13 + ,18 + ,-10 + ,9 + ,13 + ,16 + ,16 + ,-12 + ,12 + ,22 + ,10 + ,8 + ,-9 + ,9 + ,15 + ,14 + ,10 + ,-15 + ,11 + ,20 + ,14 + ,12 + ,-14 + ,11 + ,19 + ,15 + ,13 + ,-18 + ,11 + ,18 + ,13 + ,15 + ,-13 + ,11 + ,13 + ,8 + ,3 + ,-2 + ,11 + ,17 + ,7 + ,2 + ,-1 + ,9 + ,17 + ,3 + ,-2 + ,5 + ,8 + ,13 + ,3 + ,1 + ,8 + ,6 + ,14 + ,4 + ,1 + ,6 + ,7 + ,13 + ,4 + ,-1 + ,7 + ,8 + ,17 + ,0 + ,-6 + ,15 + ,6 + ,17 + ,-4 + ,-13 + ,23 + ,5 + ,15 + ,-14 + ,-25 + ,43 + ,2 + ,9 + ,-18 + ,-26 + ,60 + ,3 + ,10 + ,-8 + ,-9 + ,36 + ,3 + ,9 + ,-1 + ,1 + ,28 + ,7 + ,14 + ,1 + ,3 + ,23 + ,8 + ,18 + ,2 + ,6 + ,23 + ,7 + ,18 + ,0 + ,2 + ,22 + ,7 + ,12 + ,1 + ,5 + ,22 + ,6 + ,16 + ,0 + ,5 + ,24 + ,6 + ,12 + ,-1 + ,0 + ,32 + ,7 + ,19 + ,-3 + ,-5 + ,27 + ,5 + ,13 + ,-3 + ,-4 + ,27 + ,5 + ,12 + ,-3 + ,-2 + ,27 + ,5 + ,13 + ,-4 + ,-1 + ,29 + ,4 + ,11 + ,-8 + ,-8 + ,38 + ,4 + ,10 + ,-9 + ,-16 + ,40 + ,4 + ,16 + ,-13 + ,-19 + ,45 + ,1 + ,12 + ,-18 + ,-28 + ,50 + ,-1 + ,6 + ,-11 + ,-11 + ,43 + ,3 + ,8 + ,-9 + ,-4 + ,44 + ,4 + ,6 + ,-10 + ,-9 + ,44 + ,3 + ,8 + ,-13 + ,-12 + ,49 + ,2 + ,8 + ,-11 + ,-10 + ,42 + ,1 + ,9 + ,-5 + ,-2 + ,36 + ,4 + ,13 + ,-15 + ,-13 + ,57 + ,3 + ,8 + ,-6 + ,0 + ,42 + ,5 + ,11 + ,-6 + ,0 + ,39 + ,6 + ,8 + ,-3 + ,4 + ,33 + ,6 + ,10 + ,-1 + ,7 + ,32 + ,6 + ,15 + ,-3 + ,5 + ,34 + ,6 + ,12 + ,-4 + ,2 + ,37 + ,6 + ,13 + ,-6 + ,-2 + ,38 + ,5 + ,12 + ,0 + ,6 + ,28 + ,6 + ,15 + ,-4 + ,-3 + ,31 + ,5 + ,13 + ,-2 + ,1 + ,28 + ,6 + ,13 + ,-2 + ,0 + ,30 + ,5 + ,16 + ,-6 + ,-7 + ,39 + ,7 + ,14 + ,-7 + ,-6 + ,38 + ,4 + ,12 + ,-6 + ,-4 + ,39 + ,5 + ,15 + ,-6 + ,-4 + ,38 + ,6 + ,14 + ,-3 + ,-2 + ,37 + ,6 + ,19 + ,-2 + ,2 + ,32 + ,5 + ,16 + ,-5 + ,-5 + ,32 + ,3 + ,16 + ,-11 + ,-15 + ,44 + ,2 + ,11 + ,-11 + ,-16 + ,43 + ,3 + ,13 + ,-11 + ,-18 + ,42 + ,3 + ,12 + ,-10 + ,-13 + ,38 + ,2 + ,11 + ,-14 + ,-23 + ,37 + ,0 + ,6 + ,-8 + ,-10 + ,35 + ,4 + ,9 + ,-9 + ,-10 + ,37 + ,4 + ,6 + ,-5 + ,-6 + ,33 + ,5 + ,15 + ,-1 + ,-3 + ,24 + ,6 + ,17 + ,-2 + ,-4 + ,24 + ,6 + ,13 + ,-5 + ,-7 + ,31 + ,5 + ,12 + ,-4 + ,-7 + ,25 + ,5 + ,13 + ,-6 + ,-7 + ,28 + ,3 + ,10 + ,-2 + ,-3 + ,24 + ,5 + ,14 + ,-2 + ,0 + ,25 + ,5 + ,13 + ,-2 + ,-5 + ,16 + ,5 + ,10 + ,-2 + ,-3 + ,17 + ,3 + ,11 + ,2 + ,3 + ,11 + ,6 + ,12 + ,1 + ,2 + ,12 + ,6 + ,7 + ,-8 + ,-7 + ,39 + ,4 + ,11 + ,-1 + ,-1 + ,19 + ,6 + ,9 + ,1 + ,0 + ,14 + ,5 + ,13 + ,-1 + ,-3 + ,15 + ,4 + ,12 + ,2 + ,4 + ,7 + ,5 + ,5 + ,2 + ,2 + ,12 + ,5 + ,13 + ,1 + ,3 + ,12 + ,4 + ,11 + ,-1 + ,0 + ,14 + ,3 + ,8 + ,-2 + ,-10 + ,9 + ,2 + ,8 + ,-2 + ,-10 + ,8 + ,3 + ,8 + ,-1 + ,-9 + ,4 + ,2 + ,8 + ,-8 + ,-22 + ,7 + ,-1 + ,0 + ,-4 + ,-16 + ,3 + ,0 + ,3 + ,-6 + ,-18 + ,5 + ,-2 + ,0 + ,-3 + ,-14 + ,0 + ,1 + ,-1 + ,-3 + ,-12 + ,-2 + ,-2 + ,-1 + ,-7 + ,-17 + ,6 + ,-2 + ,-4 + ,-9 + ,-23 + ,11 + ,-2 + ,1 + ,-11 + ,-28 + ,9 + ,-6 + ,-1 + ,-13 + ,-31 + ,17 + ,-4 + ,0 + ,-11 + ,-21 + ,21 + ,-2 + ,-1 + ,-9 + ,-19 + ,21 + ,0 + ,6 + ,-17 + ,-22 + ,41 + ,-5 + ,0 + ,-22 + ,-22 + ,57 + ,-4 + ,-3 + ,-25 + ,-25 + ,65 + ,-5 + ,-3 + ,-20 + ,-16 + ,68 + ,-1 + ,4 + ,-24 + ,-22 + ,73 + ,-2 + ,1 + ,-24 + ,-21 + ,71 + ,-4 + ,0 + ,-22 + ,-10 + ,71 + ,-1 + ,-4 + ,-19 + ,-7 + ,70 + ,1 + ,-2 + ,-18 + ,-5 + ,69 + ,1 + ,3 + ,-17 + ,-4 + ,65 + ,-2 + ,2 + ,-11 + ,7 + ,57 + ,1 + ,5 + ,-11 + ,6 + ,57 + ,1 + ,6 + ,-12 + ,3 + ,57 + ,3 + ,6 + ,-10 + ,10 + ,55 + ,3 + ,3 + ,-15 + ,0 + ,65 + ,1 + ,4 + ,-15 + ,-2 + ,65 + ,1 + ,7 + ,-15 + ,-1 + ,64 + ,0 + ,5 + ,-13 + ,2 + ,60 + ,2 + ,6 + ,-8 + ,8 + ,43 + ,2 + ,1 + ,-13 + ,-6 + ,47 + ,-1 + ,3 + ,-9 + ,-4 + ,40 + ,1 + ,6 + ,-7 + ,4 + ,31 + ,0 + ,0 + ,-4 + ,7 + ,27 + ,1 + ,3 + ,-4 + ,3 + ,24 + ,1 + ,4 + ,-2 + ,3 + ,23 + ,3 + ,7 + ,0 + ,8 + ,17 + ,2 + ,6 + ,-2 + ,3 + ,16 + ,0 + ,6 + ,-3 + ,-3 + ,15 + ,0 + ,6 + ,1 + ,4 + ,8 + ,3 + ,6 + ,-2 + ,-5 + ,5 + ,-2 + ,2 + ,-1 + ,-1 + ,6 + ,0 + ,2 + ,1 + ,5 + ,5 + ,1 + ,2 + ,-3 + ,0 + ,12 + ,-1 + ,3 + ,-4 + ,-6 + ,8 + ,-2 + ,-1 + ,-9 + ,-13 + ,17 + ,-1 + ,-4 + ,-9 + ,-15 + ,22 + ,-1 + ,4 + ,-7 + ,-8 + ,24 + ,1 + ,5 + ,-14 + ,-20 + ,36 + ,-2 + ,3 + ,-12 + ,-10 + ,31 + ,-5 + ,-1 + ,-16 + ,-22 + ,34 + ,-5 + ,-4) + ,dim=c(5 + ,145) + ,dimnames=list(c('Consumentenvertrouwen' + ,'EcoSituatie' + ,'Werkloosheid' + ,'FinancieleSituatie' + ,'Spaarvermogen') + ,1:145)) > y <- array(NA,dim=c(5,145),dimnames=list(c('Consumentenvertrouwen','EcoSituatie','Werkloosheid','FinancieleSituatie','Spaarvermogen'),1:145)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal 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, 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 Consumentenvertrouwen EcoSituatie Werkloosheid FinancieleSituatie 1 9 5 -1 6 2 11 5 -4 6 3 13 9 -6 8 4 12 10 -9 4 5 13 14 -13 8 6 15 19 -13 10 7 13 18 -10 9 8 16 16 -12 12 9 10 8 -9 9 10 14 10 -15 11 11 14 12 -14 11 12 15 13 -18 11 13 13 15 -13 11 14 8 3 -2 11 15 7 2 -1 9 16 3 -2 5 8 17 3 1 8 6 18 4 1 6 7 19 4 -1 7 8 20 0 -6 15 6 21 -4 -13 23 5 22 -14 -25 43 2 23 -18 -26 60 3 24 -8 -9 36 3 25 -1 1 28 7 26 1 3 23 8 27 2 6 23 7 28 0 2 22 7 29 1 5 22 6 30 0 5 24 6 31 -1 0 32 7 32 -3 -5 27 5 33 -3 -4 27 5 34 -3 -2 27 5 35 -4 -1 29 4 36 -8 -8 38 4 37 -9 -16 40 4 38 -13 -19 45 1 39 -18 -28 50 -1 40 -11 -11 43 3 41 -9 -4 44 4 42 -10 -9 44 3 43 -13 -12 49 2 44 -11 -10 42 1 45 -5 -2 36 4 46 -15 -13 57 3 47 -6 0 42 5 48 -6 0 39 6 49 -3 4 33 6 50 -1 7 32 6 51 -3 5 34 6 52 -4 2 37 6 53 -6 -2 38 5 54 0 6 28 6 55 -4 -3 31 5 56 -2 1 28 6 57 -2 0 30 5 58 -6 -7 39 7 59 -7 -6 38 4 60 -6 -4 39 5 61 -6 -4 38 6 62 -3 -2 37 6 63 -2 2 32 5 64 -5 -5 32 3 65 -11 -15 44 2 66 -11 -16 43 3 67 -11 -18 42 3 68 -10 -13 38 2 69 -14 -23 37 0 70 -8 -10 35 4 71 -9 -10 37 4 72 -5 -6 33 5 73 -1 -3 24 6 74 -2 -4 24 6 75 -5 -7 31 5 76 -4 -7 25 5 77 -6 -7 28 3 78 -2 -3 24 5 79 -2 0 25 5 80 -2 -5 16 5 81 -2 -3 17 3 82 2 3 11 6 83 1 2 12 6 84 -8 -7 39 4 85 -1 -1 19 6 86 1 0 14 5 87 -1 -3 15 4 88 2 4 7 5 89 2 2 12 5 90 1 3 12 4 91 -1 0 14 3 92 -2 -10 9 2 93 -2 -10 8 3 94 -1 -9 4 2 95 -8 -22 7 -1 96 -4 -16 3 0 97 -6 -18 5 -2 98 -3 -14 0 1 99 -3 -12 -2 -2 100 -7 -17 6 -2 101 -9 -23 11 -2 102 -11 -28 9 -6 103 -13 -31 17 -4 104 -11 -21 21 -2 105 -9 -19 21 0 106 -17 -22 41 -5 107 -22 -22 57 -4 108 -25 -25 65 -5 109 -20 -16 68 -1 110 -24 -22 73 -2 111 -24 -21 71 -4 112 -22 -10 71 -1 113 -19 -7 70 1 114 -18 -5 69 1 115 -17 -4 65 -2 116 -11 7 57 1 117 -11 6 57 1 118 -12 3 57 3 119 -10 10 55 3 120 -15 0 65 1 121 -15 -2 65 1 122 -15 -1 64 0 123 -13 2 60 2 124 -8 8 43 2 125 -13 -6 47 -1 126 -9 -4 40 1 127 -7 4 31 0 128 -4 7 27 1 129 -4 3 24 1 130 -2 3 23 3 131 0 8 17 2 132 -2 3 16 0 133 -3 -3 15 0 134 1 4 8 3 135 -2 -5 5 -2 136 -1 -1 6 0 137 1 5 5 1 138 -3 0 12 -1 139 -4 -6 8 -2 140 -9 -13 17 -1 141 -9 -15 22 -1 142 -7 -8 24 1 143 -14 -20 36 -2 144 -12 -10 31 -5 145 -16 -22 34 -5 Spaarvermogen 1 24 2 29 3 29 4 25 5 16 6 18 7 13 8 22 9 15 10 20 11 19 12 18 13 13 14 17 15 17 16 13 17 14 18 13 19 17 20 17 21 15 22 9 23 10 24 9 25 14 26 18 27 18 28 12 29 16 30 12 31 19 32 13 33 12 34 13 35 11 36 10 37 16 38 12 39 6 40 8 41 6 42 8 43 8 44 9 45 13 46 8 47 11 48 8 49 10 50 15 51 12 52 13 53 12 54 15 55 13 56 13 57 16 58 14 59 12 60 15 61 14 62 19 63 16 64 16 65 11 66 13 67 12 68 11 69 6 70 9 71 6 72 15 73 17 74 13 75 12 76 13 77 10 78 14 79 13 80 10 81 11 82 12 83 7 84 11 85 9 86 13 87 12 88 5 89 13 90 11 91 8 92 8 93 8 94 8 95 0 96 3 97 0 98 -1 99 -1 100 -4 101 1 102 -1 103 0 104 -1 105 6 106 0 107 -3 108 -3 109 4 110 1 111 0 112 -4 113 -2 114 3 115 2 116 5 117 6 118 6 119 3 120 4 121 7 122 5 123 6 124 1 125 3 126 6 127 0 128 3 129 4 130 7 131 6 132 6 133 6 134 6 135 2 136 2 137 2 138 3 139 -1 140 -4 141 4 142 5 143 3 144 -1 145 -4 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) EcoSituatie Werkloosheid FinancieleSituatie -0.0003961 0.2502627 -0.2506043 0.2705993 Spaarvermogen 0.2410699 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.72416 -0.23192 0.02927 0.26397 0.58796 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0003961 0.0724527 -0.005 0.996 EcoSituatie 0.2502627 0.0036974 67.686 <2e-16 *** Werkloosheid -0.2506043 0.0013956 -179.565 <2e-16 *** FinancieleSituatie 0.2705993 0.0158933 17.026 <2e-16 *** Spaarvermogen 0.2410699 0.0073192 32.937 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3188 on 140 degrees of freedom Multiple R-squared: 0.9986, Adjusted R-squared: 0.9986 F-statistic: 2.555e+04 on 4 and 140 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,] 0.300115364 0.600230727 0.6998846 [2,] 0.362040176 0.724080352 0.6379598 [3,] 0.234896155 0.469792310 0.7651038 [4,] 0.141685721 0.283371441 0.8583143 [5,] 0.079046917 0.158093833 0.9209531 [6,] 0.051837183 0.103674365 0.9481628 [7,] 0.028667190 0.057334380 0.9713328 [8,] 0.014588619 0.029177238 0.9854114 [9,] 0.008614999 0.017229997 0.9913850 [10,] 0.004119572 0.008239144 0.9958804 [11,] 0.025384558 0.050769117 0.9746154 [12,] 0.015048660 0.030097320 0.9849513 [13,] 0.012262603 0.024525205 0.9877374 [14,] 0.034249397 0.068498795 0.9657506 [15,] 0.097087558 0.194175116 0.9029124 [16,] 0.070002356 0.140004713 0.9299976 [17,] 0.052130889 0.104261778 0.9478691 [18,] 0.038637501 0.077275002 0.9613625 [19,] 0.271284832 0.542569664 0.7287152 [20,] 0.244720622 0.489441243 0.7552794 [21,] 0.198569220 0.397138441 0.8014308 [22,] 0.243430367 0.486860733 0.7565696 [23,] 0.198878162 0.397756324 0.8011218 [24,] 0.241566454 0.483132909 0.7584335 [25,] 0.283156041 0.566312082 0.7168440 [26,] 0.308137851 0.616275701 0.6918621 [27,] 0.351344524 0.702689049 0.6486555 [28,] 0.401244649 0.802489298 0.5987554 [29,] 0.354738122 0.709476244 0.6452619 [30,] 0.304005415 0.608010831 0.6959946 [31,] 0.283483812 0.566967624 0.7165162 [32,] 0.282866662 0.565733324 0.7171333 [33,] 0.295668651 0.591337302 0.7043313 [34,] 0.301011231 0.602022462 0.6989888 [35,] 0.328093420 0.656186840 0.6719066 [36,] 0.354436410 0.708872820 0.6455636 [37,] 0.463308575 0.926617149 0.5366914 [38,] 0.433386902 0.866773803 0.5666131 [39,] 0.443304741 0.886609482 0.5566953 [40,] 0.471294364 0.942588728 0.5287056 [41,] 0.428914205 0.857828411 0.5710858 [42,] 0.389979250 0.779958499 0.6100208 [43,] 0.359642600 0.719285200 0.6403574 [44,] 0.384009324 0.768018648 0.6159907 [45,] 0.343685427 0.687370854 0.6563146 [46,] 0.340440271 0.680880541 0.6595597 [47,] 0.317376290 0.634752580 0.6826237 [48,] 0.275606564 0.551213128 0.7243934 [49,] 0.237262714 0.474525428 0.7627373 [50,] 0.229338253 0.458676506 0.7706617 [51,] 0.217566701 0.435133402 0.7824333 [52,] 0.186031340 0.372062680 0.8139687 [53,] 0.176739591 0.353479182 0.8232604 [54,] 0.230310166 0.460620331 0.7696898 [55,] 0.330911843 0.661823687 0.6690882 [56,] 0.336419691 0.672839382 0.6635803 [57,] 0.370707694 0.741415388 0.6292923 [58,] 0.514075804 0.971848392 0.4859242 [59,] 0.480629601 0.961259202 0.5193704 [60,] 0.521973196 0.956053607 0.4780268 [61,] 0.547597512 0.904804976 0.4524025 [62,] 0.552940643 0.894118713 0.4470594 [63,] 0.509309135 0.981381729 0.4906909 [64,] 0.501451128 0.997097744 0.4985489 [65,] 0.471837608 0.943675217 0.5281624 [66,] 0.446280495 0.892560989 0.5537195 [67,] 0.470449636 0.940899273 0.5295504 [68,] 0.513226280 0.973547440 0.4867737 [69,] 0.529467950 0.941064101 0.4705321 [70,] 0.547758719 0.904482562 0.4522413 [71,] 0.525822405 0.948355190 0.4741776 [72,] 0.494429616 0.988859233 0.5055704 [73,] 0.514591737 0.970816526 0.4854083 [74,] 0.518277138 0.963445725 0.4817229 [75,] 0.552923213 0.894153574 0.4470768 [76,] 0.541635299 0.916729403 0.4583647 [77,] 0.507636774 0.984726452 0.4923632 [78,] 0.513948911 0.972102178 0.4860511 [79,] 0.482325607 0.964651214 0.5176744 [80,] 0.481469267 0.962938533 0.5185307 [81,] 0.473816871 0.947633741 0.5261831 [82,] 0.441183134 0.882366268 0.5588169 [83,] 0.457078982 0.914157964 0.5429210 [84,] 0.416479876 0.832959751 0.5835201 [85,] 0.472155938 0.944311876 0.5278441 [86,] 0.423410543 0.846821086 0.5765895 [87,] 0.378860940 0.757721880 0.6211391 [88,] 0.412087220 0.824174440 0.5879128 [89,] 0.385555911 0.771111822 0.6144441 [90,] 0.405302522 0.810605043 0.5946975 [91,] 0.491284987 0.982569973 0.5087150 [92,] 0.472887990 0.945775980 0.5271120 [93,] 0.452890576 0.905781151 0.5471094 [94,] 0.410275118 0.820550236 0.5897249 [95,] 0.363569772 0.727139544 0.6364302 [96,] 0.321939119 0.643878239 0.6780609 [97,] 0.332420881 0.664841762 0.6675791 [98,] 0.333643056 0.667286112 0.6663569 [99,] 0.288142129 0.576284257 0.7118579 [100,] 0.318639075 0.637278151 0.6813609 [101,] 0.356073167 0.712146335 0.6439268 [102,] 0.388813018 0.777626036 0.6111870 [103,] 0.359944760 0.719889520 0.6400552 [104,] 0.322884464 0.645768928 0.6771155 [105,] 0.378853955 0.757707910 0.6211460 [106,] 0.575795829 0.848408342 0.4242042 [107,] 0.564740232 0.870519535 0.4352598 [108,] 0.578239069 0.843521862 0.4217609 [109,] 0.522276266 0.955447468 0.4777237 [110,] 0.470535637 0.941071274 0.5294644 [111,] 0.607859411 0.784281178 0.3921406 [112,] 0.564906464 0.870187071 0.4350935 [113,] 0.518460573 0.963078854 0.4815394 [114,] 0.451758319 0.903516637 0.5482417 [115,] 0.423916582 0.847833164 0.5760834 [116,] 0.406554488 0.813108976 0.5934455 [117,] 0.336072788 0.672145577 0.6639272 [118,] 0.276579379 0.553158758 0.7234206 [119,] 0.324091150 0.648182300 0.6759089 [120,] 0.302597196 0.605194391 0.6974028 [121,] 0.240352304 0.480704607 0.7596477 [122,] 0.180102344 0.360204689 0.8198977 [123,] 0.419879048 0.839758095 0.5801210 [124,] 0.619456426 0.761087148 0.3805436 [125,] 0.517364823 0.965270355 0.4826352 [126,] 0.446480566 0.892961131 0.5535194 [127,] 0.346463289 0.692926579 0.6535367 [128,] 0.315267729 0.630535458 0.6847323 [129,] 0.285082273 0.570164545 0.7149177 [130,] 0.523293212 0.953413577 0.4767068 > postscript(file="/var/wessaorg/rcomp/tmp/1wnjf1352121631.ps",horizontal=F,onefile=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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2bvzv1352121631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/31adf1352121631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4iax91352121631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/54oj51352121631.ps",horizontal=F,onefile=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 = 145 Frequency = 1 1 2 3 4 5 6 0.089204847 0.132042330 0.088584319 0.133185308 0.216949312 -0.057702579 7 8 9 10 11 12 0.420321909 0.438211724 -0.308586475 -0.059285905 -0.068137094 -0.079747193 13 14 15 16 17 18 -0.121901449 -0.326380987 -0.284315434 -0.544759750 -0.243606301 0.225655700 19 20 21 22 23 24 -0.258093449 -0.460746782 0.048665865 0.322122112 0.320989165 0.293089208 25 26 27 28 29 30 0.497880923 -0.490545015 0.029266111 0.226132085 -0.218336425 0.247151859 31 32 33 34 35 36 0.545211385 0.531121292 0.521928490 -0.219666841 -0.215981827 0.032365977 37 38 39 40 41 42 0.089256859 -0.130855974 0.362148018 -0.211085192 0.499220714 0.538993710 43 44 45 46 47 48 -0.186597277 -0.411823618 0.306371335 -0.202099232 0.521012417 0.221809913 49 50 51 52 53 54 0.234993306 0.028251304 -0.246804899 0.014726297 -0.221949366 0.276096718 55 56 57 58 59 60 0.033013167 0.009550091 0.308410982 0.256630176 0.049700737 -0.194029347 61 62 63 64 65 66 -0.474163022 0.569357687 0.309094209 -0.397868296 0.587959508 -0.165121182 67 68 69 70 71 72 0.325869824 -0.416191859 -0.417621010 0.022148335 0.246566711 -0.197129870 73 74 75 76 77 78 0.043904002 0.258446349 0.275133920 -0.469561934 -0.453340717 0.037712988 79 80 81 82 83 84 -0.221400911 -0.502316547 -0.452109035 -0.510178933 0.196037648 -0.208362319 85 86 87 88 89 90 0.218916232 0.021951523 -0.464986852 0.195229682 0.020217453 -0.477306181 91 92 93 94 95 96 -0.231500414 0.288704335 -0.232499249 -0.214579997 -0.468994732 0.033202719 97 98 99 100 101 102 0.299345035 0.474544702 0.284608409 0.263966284 -0.186785374 0.127856387 103 104 105 106 107 108 0.101210686 0.300872263 -0.428341040 0.133949326 -0.403771020 -0.377549035 109 110 111 112 113 114 0.352013139 0.100420012 0.131217080 -0.469190882 0.506078324 -0.450400967 115 116 117 118 119 120 0.349786712 0.057054793 0.066247595 -0.724162790 -0.254000688 0.054798273 121 122 123 124 125 126 -0.167886033 0.083986010 -0.451487847 -0.007988079 -0.172234869 0.308601195 127 128 129 130 131 132 -0.231920697 0.021064887 0.029232849 0.514220280 0.270949948 -0.187142303 133 134 135 136 137 138 0.063829639 -0.254037384 0.563789974 0.272144936 0.249365087 -0.444962480 139 140 141 142 143 144 0.289075384 -0.251036253 -0.426048483 -0.458947239 -0.154605221 -0.134176539 145 0.343998693 > postscript(file="/var/wessaorg/rcomp/tmp/6yv0b1352121631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 145 Frequency = 1 lag(myerror, k = 1) myerror 0 0.089204847 NA 1 0.132042330 0.089204847 2 0.088584319 0.132042330 3 0.133185308 0.088584319 4 0.216949312 0.133185308 5 -0.057702579 0.216949312 6 0.420321909 -0.057702579 7 0.438211724 0.420321909 8 -0.308586475 0.438211724 9 -0.059285905 -0.308586475 10 -0.068137094 -0.059285905 11 -0.079747193 -0.068137094 12 -0.121901449 -0.079747193 13 -0.326380987 -0.121901449 14 -0.284315434 -0.326380987 15 -0.544759750 -0.284315434 16 -0.243606301 -0.544759750 17 0.225655700 -0.243606301 18 -0.258093449 0.225655700 19 -0.460746782 -0.258093449 20 0.048665865 -0.460746782 21 0.322122112 0.048665865 22 0.320989165 0.322122112 23 0.293089208 0.320989165 24 0.497880923 0.293089208 25 -0.490545015 0.497880923 26 0.029266111 -0.490545015 27 0.226132085 0.029266111 28 -0.218336425 0.226132085 29 0.247151859 -0.218336425 30 0.545211385 0.247151859 31 0.531121292 0.545211385 32 0.521928490 0.531121292 33 -0.219666841 0.521928490 34 -0.215981827 -0.219666841 35 0.032365977 -0.215981827 36 0.089256859 0.032365977 37 -0.130855974 0.089256859 38 0.362148018 -0.130855974 39 -0.211085192 0.362148018 40 0.499220714 -0.211085192 41 0.538993710 0.499220714 42 -0.186597277 0.538993710 43 -0.411823618 -0.186597277 44 0.306371335 -0.411823618 45 -0.202099232 0.306371335 46 0.521012417 -0.202099232 47 0.221809913 0.521012417 48 0.234993306 0.221809913 49 0.028251304 0.234993306 50 -0.246804899 0.028251304 51 0.014726297 -0.246804899 52 -0.221949366 0.014726297 53 0.276096718 -0.221949366 54 0.033013167 0.276096718 55 0.009550091 0.033013167 56 0.308410982 0.009550091 57 0.256630176 0.308410982 58 0.049700737 0.256630176 59 -0.194029347 0.049700737 60 -0.474163022 -0.194029347 61 0.569357687 -0.474163022 62 0.309094209 0.569357687 63 -0.397868296 0.309094209 64 0.587959508 -0.397868296 65 -0.165121182 0.587959508 66 0.325869824 -0.165121182 67 -0.416191859 0.325869824 68 -0.417621010 -0.416191859 69 0.022148335 -0.417621010 70 0.246566711 0.022148335 71 -0.197129870 0.246566711 72 0.043904002 -0.197129870 73 0.258446349 0.043904002 74 0.275133920 0.258446349 75 -0.469561934 0.275133920 76 -0.453340717 -0.469561934 77 0.037712988 -0.453340717 78 -0.221400911 0.037712988 79 -0.502316547 -0.221400911 80 -0.452109035 -0.502316547 81 -0.510178933 -0.452109035 82 0.196037648 -0.510178933 83 -0.208362319 0.196037648 84 0.218916232 -0.208362319 85 0.021951523 0.218916232 86 -0.464986852 0.021951523 87 0.195229682 -0.464986852 88 0.020217453 0.195229682 89 -0.477306181 0.020217453 90 -0.231500414 -0.477306181 91 0.288704335 -0.231500414 92 -0.232499249 0.288704335 93 -0.214579997 -0.232499249 94 -0.468994732 -0.214579997 95 0.033202719 -0.468994732 96 0.299345035 0.033202719 97 0.474544702 0.299345035 98 0.284608409 0.474544702 99 0.263966284 0.284608409 100 -0.186785374 0.263966284 101 0.127856387 -0.186785374 102 0.101210686 0.127856387 103 0.300872263 0.101210686 104 -0.428341040 0.300872263 105 0.133949326 -0.428341040 106 -0.403771020 0.133949326 107 -0.377549035 -0.403771020 108 0.352013139 -0.377549035 109 0.100420012 0.352013139 110 0.131217080 0.100420012 111 -0.469190882 0.131217080 112 0.506078324 -0.469190882 113 -0.450400967 0.506078324 114 0.349786712 -0.450400967 115 0.057054793 0.349786712 116 0.066247595 0.057054793 117 -0.724162790 0.066247595 118 -0.254000688 -0.724162790 119 0.054798273 -0.254000688 120 -0.167886033 0.054798273 121 0.083986010 -0.167886033 122 -0.451487847 0.083986010 123 -0.007988079 -0.451487847 124 -0.172234869 -0.007988079 125 0.308601195 -0.172234869 126 -0.231920697 0.308601195 127 0.021064887 -0.231920697 128 0.029232849 0.021064887 129 0.514220280 0.029232849 130 0.270949948 0.514220280 131 -0.187142303 0.270949948 132 0.063829639 -0.187142303 133 -0.254037384 0.063829639 134 0.563789974 -0.254037384 135 0.272144936 0.563789974 136 0.249365087 0.272144936 137 -0.444962480 0.249365087 138 0.289075384 -0.444962480 139 -0.251036253 0.289075384 140 -0.426048483 -0.251036253 141 -0.458947239 -0.426048483 142 -0.154605221 -0.458947239 143 -0.134176539 -0.154605221 144 0.343998693 -0.134176539 145 NA 0.343998693 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.132042330 0.089204847 [2,] 0.088584319 0.132042330 [3,] 0.133185308 0.088584319 [4,] 0.216949312 0.133185308 [5,] -0.057702579 0.216949312 [6,] 0.420321909 -0.057702579 [7,] 0.438211724 0.420321909 [8,] -0.308586475 0.438211724 [9,] -0.059285905 -0.308586475 [10,] -0.068137094 -0.059285905 [11,] -0.079747193 -0.068137094 [12,] -0.121901449 -0.079747193 [13,] -0.326380987 -0.121901449 [14,] -0.284315434 -0.326380987 [15,] -0.544759750 -0.284315434 [16,] -0.243606301 -0.544759750 [17,] 0.225655700 -0.243606301 [18,] -0.258093449 0.225655700 [19,] -0.460746782 -0.258093449 [20,] 0.048665865 -0.460746782 [21,] 0.322122112 0.048665865 [22,] 0.320989165 0.322122112 [23,] 0.293089208 0.320989165 [24,] 0.497880923 0.293089208 [25,] -0.490545015 0.497880923 [26,] 0.029266111 -0.490545015 [27,] 0.226132085 0.029266111 [28,] -0.218336425 0.226132085 [29,] 0.247151859 -0.218336425 [30,] 0.545211385 0.247151859 [31,] 0.531121292 0.545211385 [32,] 0.521928490 0.531121292 [33,] -0.219666841 0.521928490 [34,] -0.215981827 -0.219666841 [35,] 0.032365977 -0.215981827 [36,] 0.089256859 0.032365977 [37,] -0.130855974 0.089256859 [38,] 0.362148018 -0.130855974 [39,] -0.211085192 0.362148018 [40,] 0.499220714 -0.211085192 [41,] 0.538993710 0.499220714 [42,] -0.186597277 0.538993710 [43,] -0.411823618 -0.186597277 [44,] 0.306371335 -0.411823618 [45,] -0.202099232 0.306371335 [46,] 0.521012417 -0.202099232 [47,] 0.221809913 0.521012417 [48,] 0.234993306 0.221809913 [49,] 0.028251304 0.234993306 [50,] -0.246804899 0.028251304 [51,] 0.014726297 -0.246804899 [52,] -0.221949366 0.014726297 [53,] 0.276096718 -0.221949366 [54,] 0.033013167 0.276096718 [55,] 0.009550091 0.033013167 [56,] 0.308410982 0.009550091 [57,] 0.256630176 0.308410982 [58,] 0.049700737 0.256630176 [59,] -0.194029347 0.049700737 [60,] -0.474163022 -0.194029347 [61,] 0.569357687 -0.474163022 [62,] 0.309094209 0.569357687 [63,] -0.397868296 0.309094209 [64,] 0.587959508 -0.397868296 [65,] -0.165121182 0.587959508 [66,] 0.325869824 -0.165121182 [67,] -0.416191859 0.325869824 [68,] -0.417621010 -0.416191859 [69,] 0.022148335 -0.417621010 [70,] 0.246566711 0.022148335 [71,] -0.197129870 0.246566711 [72,] 0.043904002 -0.197129870 [73,] 0.258446349 0.043904002 [74,] 0.275133920 0.258446349 [75,] -0.469561934 0.275133920 [76,] -0.453340717 -0.469561934 [77,] 0.037712988 -0.453340717 [78,] -0.221400911 0.037712988 [79,] -0.502316547 -0.221400911 [80,] -0.452109035 -0.502316547 [81,] -0.510178933 -0.452109035 [82,] 0.196037648 -0.510178933 [83,] -0.208362319 0.196037648 [84,] 0.218916232 -0.208362319 [85,] 0.021951523 0.218916232 [86,] -0.464986852 0.021951523 [87,] 0.195229682 -0.464986852 [88,] 0.020217453 0.195229682 [89,] -0.477306181 0.020217453 [90,] -0.231500414 -0.477306181 [91,] 0.288704335 -0.231500414 [92,] -0.232499249 0.288704335 [93,] -0.214579997 -0.232499249 [94,] -0.468994732 -0.214579997 [95,] 0.033202719 -0.468994732 [96,] 0.299345035 0.033202719 [97,] 0.474544702 0.299345035 [98,] 0.284608409 0.474544702 [99,] 0.263966284 0.284608409 [100,] -0.186785374 0.263966284 [101,] 0.127856387 -0.186785374 [102,] 0.101210686 0.127856387 [103,] 0.300872263 0.101210686 [104,] -0.428341040 0.300872263 [105,] 0.133949326 -0.428341040 [106,] -0.403771020 0.133949326 [107,] -0.377549035 -0.403771020 [108,] 0.352013139 -0.377549035 [109,] 0.100420012 0.352013139 [110,] 0.131217080 0.100420012 [111,] -0.469190882 0.131217080 [112,] 0.506078324 -0.469190882 [113,] -0.450400967 0.506078324 [114,] 0.349786712 -0.450400967 [115,] 0.057054793 0.349786712 [116,] 0.066247595 0.057054793 [117,] -0.724162790 0.066247595 [118,] -0.254000688 -0.724162790 [119,] 0.054798273 -0.254000688 [120,] -0.167886033 0.054798273 [121,] 0.083986010 -0.167886033 [122,] -0.451487847 0.083986010 [123,] -0.007988079 -0.451487847 [124,] -0.172234869 -0.007988079 [125,] 0.308601195 -0.172234869 [126,] -0.231920697 0.308601195 [127,] 0.021064887 -0.231920697 [128,] 0.029232849 0.021064887 [129,] 0.514220280 0.029232849 [130,] 0.270949948 0.514220280 [131,] -0.187142303 0.270949948 [132,] 0.063829639 -0.187142303 [133,] -0.254037384 0.063829639 [134,] 0.563789974 -0.254037384 [135,] 0.272144936 0.563789974 [136,] 0.249365087 0.272144936 [137,] -0.444962480 0.249365087 [138,] 0.289075384 -0.444962480 [139,] -0.251036253 0.289075384 [140,] -0.426048483 -0.251036253 [141,] -0.458947239 -0.426048483 [142,] -0.154605221 -0.458947239 [143,] -0.134176539 -0.154605221 [144,] 0.343998693 -0.134176539 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.132042330 0.089204847 2 0.088584319 0.132042330 3 0.133185308 0.088584319 4 0.216949312 0.133185308 5 -0.057702579 0.216949312 6 0.420321909 -0.057702579 7 0.438211724 0.420321909 8 -0.308586475 0.438211724 9 -0.059285905 -0.308586475 10 -0.068137094 -0.059285905 11 -0.079747193 -0.068137094 12 -0.121901449 -0.079747193 13 -0.326380987 -0.121901449 14 -0.284315434 -0.326380987 15 -0.544759750 -0.284315434 16 -0.243606301 -0.544759750 17 0.225655700 -0.243606301 18 -0.258093449 0.225655700 19 -0.460746782 -0.258093449 20 0.048665865 -0.460746782 21 0.322122112 0.048665865 22 0.320989165 0.322122112 23 0.293089208 0.320989165 24 0.497880923 0.293089208 25 -0.490545015 0.497880923 26 0.029266111 -0.490545015 27 0.226132085 0.029266111 28 -0.218336425 0.226132085 29 0.247151859 -0.218336425 30 0.545211385 0.247151859 31 0.531121292 0.545211385 32 0.521928490 0.531121292 33 -0.219666841 0.521928490 34 -0.215981827 -0.219666841 35 0.032365977 -0.215981827 36 0.089256859 0.032365977 37 -0.130855974 0.089256859 38 0.362148018 -0.130855974 39 -0.211085192 0.362148018 40 0.499220714 -0.211085192 41 0.538993710 0.499220714 42 -0.186597277 0.538993710 43 -0.411823618 -0.186597277 44 0.306371335 -0.411823618 45 -0.202099232 0.306371335 46 0.521012417 -0.202099232 47 0.221809913 0.521012417 48 0.234993306 0.221809913 49 0.028251304 0.234993306 50 -0.246804899 0.028251304 51 0.014726297 -0.246804899 52 -0.221949366 0.014726297 53 0.276096718 -0.221949366 54 0.033013167 0.276096718 55 0.009550091 0.033013167 56 0.308410982 0.009550091 57 0.256630176 0.308410982 58 0.049700737 0.256630176 59 -0.194029347 0.049700737 60 -0.474163022 -0.194029347 61 0.569357687 -0.474163022 62 0.309094209 0.569357687 63 -0.397868296 0.309094209 64 0.587959508 -0.397868296 65 -0.165121182 0.587959508 66 0.325869824 -0.165121182 67 -0.416191859 0.325869824 68 -0.417621010 -0.416191859 69 0.022148335 -0.417621010 70 0.246566711 0.022148335 71 -0.197129870 0.246566711 72 0.043904002 -0.197129870 73 0.258446349 0.043904002 74 0.275133920 0.258446349 75 -0.469561934 0.275133920 76 -0.453340717 -0.469561934 77 0.037712988 -0.453340717 78 -0.221400911 0.037712988 79 -0.502316547 -0.221400911 80 -0.452109035 -0.502316547 81 -0.510178933 -0.452109035 82 0.196037648 -0.510178933 83 -0.208362319 0.196037648 84 0.218916232 -0.208362319 85 0.021951523 0.218916232 86 -0.464986852 0.021951523 87 0.195229682 -0.464986852 88 0.020217453 0.195229682 89 -0.477306181 0.020217453 90 -0.231500414 -0.477306181 91 0.288704335 -0.231500414 92 -0.232499249 0.288704335 93 -0.214579997 -0.232499249 94 -0.468994732 -0.214579997 95 0.033202719 -0.468994732 96 0.299345035 0.033202719 97 0.474544702 0.299345035 98 0.284608409 0.474544702 99 0.263966284 0.284608409 100 -0.186785374 0.263966284 101 0.127856387 -0.186785374 102 0.101210686 0.127856387 103 0.300872263 0.101210686 104 -0.428341040 0.300872263 105 0.133949326 -0.428341040 106 -0.403771020 0.133949326 107 -0.377549035 -0.403771020 108 0.352013139 -0.377549035 109 0.100420012 0.352013139 110 0.131217080 0.100420012 111 -0.469190882 0.131217080 112 0.506078324 -0.469190882 113 -0.450400967 0.506078324 114 0.349786712 -0.450400967 115 0.057054793 0.349786712 116 0.066247595 0.057054793 117 -0.724162790 0.066247595 118 -0.254000688 -0.724162790 119 0.054798273 -0.254000688 120 -0.167886033 0.054798273 121 0.083986010 -0.167886033 122 -0.451487847 0.083986010 123 -0.007988079 -0.451487847 124 -0.172234869 -0.007988079 125 0.308601195 -0.172234869 126 -0.231920697 0.308601195 127 0.021064887 -0.231920697 128 0.029232849 0.021064887 129 0.514220280 0.029232849 130 0.270949948 0.514220280 131 -0.187142303 0.270949948 132 0.063829639 -0.187142303 133 -0.254037384 0.063829639 134 0.563789974 -0.254037384 135 0.272144936 0.563789974 136 0.249365087 0.272144936 137 -0.444962480 0.249365087 138 0.289075384 -0.444962480 139 -0.251036253 0.289075384 140 -0.426048483 -0.251036253 141 -0.458947239 -0.426048483 142 -0.154605221 -0.458947239 143 -0.134176539 -0.154605221 144 0.343998693 -0.134176539 > 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/wessaorg/rcomp/tmp/7f2a01352121631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8ecux1352121631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9e9zn1352121631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10caxb1352121631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/wessaorg/rcomp/tmp/11v3fn1352121631.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/wessaorg/rcomp/tmp/12ogtf1352121631.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/wessaorg/rcomp/tmp/13p1yz1352121632.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/wessaorg/rcomp/tmp/14cm631352121632.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/wessaorg/rcomp/tmp/15zdix1352121632.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/wessaorg/rcomp/tmp/16tfhx1352121632.tab") + } > > try(system("convert tmp/1wnjf1352121631.ps tmp/1wnjf1352121631.png",intern=TRUE)) character(0) > try(system("convert tmp/2bvzv1352121631.ps tmp/2bvzv1352121631.png",intern=TRUE)) character(0) > try(system("convert tmp/31adf1352121631.ps tmp/31adf1352121631.png",intern=TRUE)) character(0) > try(system("convert tmp/4iax91352121631.ps tmp/4iax91352121631.png",intern=TRUE)) character(0) > try(system("convert tmp/54oj51352121631.ps tmp/54oj51352121631.png",intern=TRUE)) character(0) > try(system("convert tmp/6yv0b1352121631.ps tmp/6yv0b1352121631.png",intern=TRUE)) character(0) > try(system("convert tmp/7f2a01352121631.ps tmp/7f2a01352121631.png",intern=TRUE)) character(0) > try(system("convert tmp/8ecux1352121631.ps tmp/8ecux1352121631.png",intern=TRUE)) character(0) > try(system("convert tmp/9e9zn1352121631.ps tmp/9e9zn1352121631.png",intern=TRUE)) character(0) > try(system("convert tmp/10caxb1352121631.ps tmp/10caxb1352121631.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 9.205 1.260 10.447