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(41 + ,38 + ,13 + ,12 + ,14 + ,12 + ,53 + ,32 + ,39 + ,32 + ,16 + ,11 + ,18 + ,11 + ,86 + ,51 + ,30 + ,35 + ,19 + ,15 + ,11 + ,14 + ,66 + ,42 + ,31 + ,33 + ,15 + ,6 + ,12 + ,12 + ,67 + ,41 + ,34 + ,37 + ,14 + ,13 + ,16 + ,21 + ,76 + ,46 + ,35 + ,29 + ,13 + ,10 + ,18 + ,12 + ,78 + ,47 + ,39 + ,31 + ,19 + ,12 + ,14 + ,22 + ,53 + ,37 + ,34 + ,36 + ,15 + ,14 + ,14 + ,11 + ,80 + ,49 + ,36 + ,35 + ,14 + ,12 + ,15 + ,10 + ,74 + ,45 + ,37 + ,38 + ,15 + ,6 + ,15 + ,13 + ,76 + ,47 + ,38 + ,31 + ,16 + ,10 + ,17 + ,10 + ,79 + ,49 + ,36 + ,34 + ,16 + ,12 + ,19 + ,8 + ,54 + ,33 + ,38 + ,35 + ,16 + ,12 + ,10 + ,15 + ,67 + ,42 + ,39 + ,38 + ,16 + ,11 + ,16 + ,14 + ,54 + ,33 + ,33 + ,37 + ,17 + ,15 + ,18 + ,10 + ,87 + ,53 + ,32 + ,33 + ,15 + ,12 + ,14 + ,14 + ,58 + ,36 + ,36 + ,32 + ,15 + ,10 + ,14 + ,14 + ,75 + ,45 + ,38 + ,38 + ,20 + ,12 + ,17 + ,11 + ,88 + ,54 + ,39 + ,38 + ,18 + ,11 + ,14 + ,10 + ,64 + ,41 + ,32 + ,32 + ,16 + ,12 + ,16 + ,13 + ,57 + ,36 + ,32 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,32 + ,27 + ,18 + ,13 + ,16 + ,12 + ,76 + ,47 + ,36 + ,36 + ,13 + ,12 + ,11 + ,13 + ,60 + ,38 + ,32 + ,31 + ,16 + ,12 + ,12 + ,11 + ,75 + ,46 + ,35 + ,32 + ,13 + ,9 + ,9 + ,19 + ,73 + ,46 + ,38 + ,39 + ,10 + ,7 + ,16 + ,12 + ,85 + ,53 + ,42 + ,37 + ,15 + ,13 + ,13 + ,17 + ,79 + ,47 + ,34 + ,38 + ,16 + ,9 + ,16 + ,9 + ,71 + ,41 + ,35 + ,39 + ,16 + ,6 + ,12 + ,12 + ,72 + ,44 + ,35 + ,34 + ,14 + ,8 + ,9 + ,19 + ,69 + ,43 + ,33 + ,31 + ,10 + ,8 + ,13 + ,18 + ,78 + ,51 + ,36 + ,32 + ,17 + ,15 + ,13 + ,15 + ,54 + ,33 + ,32 + ,37 + ,13 + ,6 + ,14 + ,14 + ,69 + ,43 + ,33 + ,36 + ,15 + ,9 + ,19 + ,11 + ,81 + ,53 + ,34 + ,32 + ,16 + ,11 + ,13 + ,9 + ,84 + ,51 + ,32 + ,35 + ,12 + ,8 + ,12 + ,18 + ,84 + ,50 + ,34 + ,36 + ,13 + ,8 + ,13 + ,16 + ,69 + ,46) + ,dim=c(8 + ,162) + ,dimnames=list(c('Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Belonging' + ,'Belonging_Final') + ,1:162)) > y <- array(NA,dim=c(8,162),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:162)) > 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 = '3' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '3' > #'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 Learning Connected Separate Software Happiness Depression Belonging 1 13 41 38 12 14 12 53 2 16 39 32 11 18 11 86 3 19 30 35 15 11 14 66 4 15 31 33 6 12 12 67 5 14 34 37 13 16 21 76 6 13 35 29 10 18 12 78 7 19 39 31 12 14 22 53 8 15 34 36 14 14 11 80 9 14 36 35 12 15 10 74 10 15 37 38 6 15 13 76 11 16 38 31 10 17 10 79 12 16 36 34 12 19 8 54 13 16 38 35 12 10 15 67 14 16 39 38 11 16 14 54 15 17 33 37 15 18 10 87 16 15 32 33 12 14 14 58 17 15 36 32 10 14 14 75 18 20 38 38 12 17 11 88 19 18 39 38 11 14 10 64 20 16 32 32 12 16 13 57 21 16 32 33 11 18 7 66 22 16 31 31 12 11 14 68 23 19 39 38 13 14 12 54 24 16 37 39 11 12 14 56 25 17 39 32 9 17 11 86 26 17 41 32 13 9 9 80 27 16 36 35 10 16 11 76 28 15 33 37 14 14 15 69 29 16 33 33 12 15 14 78 30 14 34 33 10 11 13 67 31 15 31 28 12 16 9 80 32 12 27 32 8 13 15 54 33 14 37 31 10 17 10 71 34 16 34 37 12 15 11 84 35 14 34 30 12 14 13 74 36 7 32 33 7 16 8 71 37 10 29 31 6 9 20 63 38 14 36 33 12 15 12 71 39 16 29 31 10 17 10 76 40 16 35 33 10 13 10 69 41 16 37 32 10 15 9 74 42 14 34 33 12 16 14 75 43 20 38 32 15 16 8 54 44 14 35 33 10 12 14 52 45 14 38 28 10 12 11 69 46 11 37 35 12 11 13 68 47 14 38 39 13 15 9 65 48 15 33 34 11 15 11 75 49 16 36 38 11 17 15 74 50 14 38 32 12 13 11 75 51 16 32 38 14 16 10 72 52 14 32 30 10 14 14 67 53 12 32 33 12 11 18 63 54 16 34 38 13 12 14 62 55 9 32 32 5 12 11 63 56 14 37 32 6 15 12 76 57 16 39 34 12 16 13 74 58 16 29 34 12 15 9 67 59 15 37 36 11 12 10 73 60 16 35 34 10 12 15 70 61 12 30 28 7 8 20 53 62 16 38 34 12 13 12 77 63 16 34 35 14 11 12 77 64 14 31 35 11 14 14 52 65 16 34 31 12 15 13 54 66 17 35 37 13 10 11 80 67 18 36 35 14 11 17 66 68 18 30 27 11 12 12 73 69 12 39 40 12 15 13 63 70 16 35 37 12 15 14 69 71 10 38 36 8 14 13 67 72 14 31 38 11 16 15 54 73 18 34 39 14 15 13 81 74 18 38 41 14 15 10 69 75 16 34 27 12 13 11 84 76 17 39 30 9 12 19 80 77 16 37 37 13 17 13 70 78 16 34 31 11 13 17 69 79 13 28 31 12 15 13 77 80 16 37 27 12 13 9 54 81 16 33 36 12 15 11 79 82 20 37 38 12 16 10 30 83 16 35 37 12 15 9 71 84 15 37 33 12 16 12 73 85 15 32 34 11 15 12 72 86 16 33 31 10 14 13 77 87 14 38 39 9 15 13 75 88 16 33 34 12 14 12 69 89 16 29 32 12 13 15 54 90 15 33 33 12 7 22 70 91 12 31 36 9 17 13 73 92 17 36 32 15 13 15 54 93 16 35 41 12 15 13 77 94 15 32 28 12 14 15 82 95 13 29 30 12 13 10 80 96 16 39 36 10 16 11 80 97 16 37 35 13 12 16 69 98 16 35 31 9 14 11 78 99 16 37 34 12 17 11 81 100 14 32 36 10 15 10 76 101 16 38 36 14 17 10 76 102 16 37 35 11 12 16 73 103 20 36 37 15 16 12 85 104 15 32 28 11 11 11 66 105 16 33 39 11 15 16 79 106 13 40 32 12 9 19 68 107 17 38 35 12 16 11 76 108 16 41 39 12 15 16 71 109 16 36 35 11 10 15 54 110 12 43 42 7 10 24 46 111 16 30 34 12 15 14 82 112 16 31 33 14 11 15 74 113 17 32 41 11 13 11 88 114 13 32 33 11 14 15 38 115 12 37 34 10 18 12 76 116 18 37 32 13 16 10 86 117 14 33 40 13 14 14 54 118 14 34 40 8 14 13 70 119 13 33 35 11 14 9 69 120 16 38 36 12 14 15 90 121 13 33 37 11 12 15 54 122 16 31 27 13 14 14 76 123 13 38 39 12 15 11 89 124 16 37 38 14 15 8 76 125 15 33 31 13 15 11 73 126 16 31 33 15 13 11 79 127 15 39 32 10 17 8 90 128 17 44 39 11 17 10 74 129 15 33 36 9 19 11 81 130 12 35 33 11 15 13 72 131 16 32 33 10 13 11 71 132 10 28 32 11 9 20 66 133 16 40 37 8 15 10 77 134 12 27 30 11 15 15 65 135 14 37 38 12 15 12 74 136 15 32 29 12 16 14 82 137 13 28 22 9 11 23 54 138 15 34 35 11 14 14 63 139 11 30 35 10 11 16 54 140 12 35 34 8 15 11 64 141 8 31 35 9 13 12 69 142 16 32 34 8 15 10 54 143 15 30 34 9 16 14 84 144 17 30 35 15 14 12 86 145 16 31 23 11 15 12 77 146 10 40 31 8 16 11 89 147 18 32 27 13 16 12 76 148 13 36 36 12 11 13 60 149 16 32 31 12 12 11 75 150 13 35 32 9 9 19 73 151 10 38 39 7 16 12 85 152 15 42 37 13 13 17 79 153 16 34 38 9 16 9 71 154 16 35 39 6 12 12 72 155 14 35 34 8 9 19 69 156 10 33 31 8 13 18 78 157 17 36 32 15 13 15 54 158 13 32 37 6 14 14 69 159 15 33 36 9 19 11 81 160 16 34 32 11 13 9 84 161 12 32 35 8 12 18 84 162 13 34 36 8 13 16 69 Belonging_Final 1 32 2 51 3 42 4 41 5 46 6 47 7 37 8 49 9 45 10 47 11 49 12 33 13 42 14 33 15 53 16 36 17 45 18 54 19 41 20 36 21 41 22 44 23 33 24 37 25 52 26 47 27 43 28 44 29 45 30 44 31 49 32 33 33 43 34 54 35 42 36 44 37 37 38 43 39 46 40 42 41 45 42 44 43 33 44 31 45 42 46 40 47 43 48 46 49 42 50 45 51 44 52 40 53 37 54 46 55 36 56 47 57 45 58 42 59 43 60 43 61 32 62 45 63 45 64 31 65 33 66 49 67 42 68 41 69 38 70 42 71 44 72 33 73 48 74 40 75 50 76 49 77 43 78 44 79 47 80 33 81 46 82 0 83 45 84 43 85 44 86 47 87 45 88 42 89 33 90 43 91 46 92 33 93 46 94 48 95 47 96 47 97 43 98 46 99 48 100 46 101 45 102 45 103 52 104 42 105 47 106 41 107 47 108 43 109 33 110 30 111 49 112 44 113 55 114 11 115 47 116 53 117 33 118 44 119 42 120 55 121 33 122 46 123 54 124 47 125 45 126 47 127 55 128 44 129 53 130 44 131 42 132 40 133 46 134 40 135 46 136 53 137 33 138 42 139 35 140 40 141 41 142 33 143 51 144 53 145 46 146 55 147 47 148 38 149 46 150 46 151 53 152 47 153 41 154 44 155 43 156 51 157 33 158 43 159 53 160 51 161 50 162 46 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Connected Separate Software 5.51005 0.11405 -0.02067 0.54256 Happiness Depression Belonging Belonging_Final 0.05971 -0.07116 0.03576 -0.05223 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -5.9026 -1.0985 0.1985 1.1037 3.9386 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.51005 2.59687 2.122 0.0355 * Connected 0.11405 0.04689 2.432 0.0161 * Separate -0.02067 0.04481 -0.461 0.6453 Software 0.54256 0.06895 7.869 5.9e-13 *** Happiness 0.05971 0.07638 0.782 0.4356 Depression -0.07116 0.05634 -1.263 0.2085 Belonging 0.03576 0.04453 0.803 0.4232 Belonging_Final -0.05223 0.06396 -0.817 0.4154 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.85 on 154 degrees of freedom Multiple R-squared: 0.3567, Adjusted R-squared: 0.3275 F-statistic: 12.2 on 7 and 154 DF, p-value: 2.357e-12 > 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,] 0.39159274 0.78318547 0.60840726 [2,] 0.62549432 0.74901135 0.37450568 [3,] 0.48670911 0.97341822 0.51329089 [4,] 0.46037903 0.92075807 0.53962097 [5,] 0.35164038 0.70328077 0.64835962 [6,] 0.26714594 0.53429188 0.73285406 [7,] 0.21797197 0.43594394 0.78202803 [8,] 0.41923109 0.83846217 0.58076891 [9,] 0.34207776 0.68415551 0.65792224 [10,] 0.26286710 0.52573420 0.73713290 [11,] 0.20332055 0.40664111 0.79667945 [12,] 0.18552385 0.37104770 0.81447615 [13,] 0.37000980 0.74001961 0.62999020 [14,] 0.40633422 0.81266845 0.59366578 [15,] 0.39534637 0.79069273 0.60465363 [16,] 0.35780658 0.71561317 0.64219342 [17,] 0.41748068 0.83496136 0.58251932 [18,] 0.42373791 0.84747582 0.57626209 [19,] 0.40499458 0.80998916 0.59500542 [20,] 0.44697981 0.89395962 0.55302019 [21,] 0.38896039 0.77792078 0.61103961 [22,] 0.34245154 0.68490307 0.65754846 [23,] 0.31917191 0.63834383 0.68082809 [24,] 0.28603986 0.57207972 0.71396014 [25,] 0.24507328 0.49014656 0.75492672 [26,] 0.82914093 0.34171814 0.17085907 [27,] 0.80277845 0.39444309 0.19722155 [28,] 0.79678649 0.40642701 0.20321351 [29,] 0.81823558 0.36352883 0.18176442 [30,] 0.79979375 0.40041250 0.20020625 [31,] 0.76696936 0.46606127 0.23303064 [32,] 0.74189201 0.51621597 0.25810799 [33,] 0.76721351 0.46557298 0.23278649 [34,] 0.72447243 0.55105513 0.27552757 [35,] 0.69584575 0.60830851 0.30415425 [36,] 0.86170785 0.27658431 0.13829215 [37,] 0.90959795 0.18080410 0.09040205 [38,] 0.88662233 0.22675534 0.11337767 [39,] 0.87065954 0.25868093 0.12934046 [40,] 0.86968555 0.26062891 0.13031445 [41,] 0.84142878 0.31714244 0.15857122 [42,] 0.80892453 0.38215094 0.19107547 [43,] 0.82610056 0.34779888 0.17389944 [44,] 0.81311457 0.37377086 0.18688543 [45,] 0.82701302 0.34597397 0.17298698 [46,] 0.80170663 0.39658675 0.19829337 [47,] 0.76747248 0.46505503 0.23252752 [48,] 0.74405610 0.51188780 0.25594390 [49,] 0.70627687 0.58744626 0.29372313 [50,] 0.70259845 0.59480310 0.29740155 [51,] 0.66452297 0.67095405 0.33547703 [52,] 0.62191824 0.75616353 0.37808176 [53,] 0.58218328 0.83563345 0.41781672 [54,] 0.53472523 0.93054954 0.46527477 [55,] 0.49584330 0.99168661 0.50415670 [56,] 0.46667406 0.93334812 0.53332594 [57,] 0.46372115 0.92744229 0.53627885 [58,] 0.61347894 0.77304212 0.38652106 [59,] 0.72935834 0.54128333 0.27064166 [60,] 0.69440388 0.61119224 0.30559612 [61,] 0.80262952 0.39474095 0.19737048 [62,] 0.76821289 0.46357421 0.23178711 [63,] 0.75866737 0.48266525 0.24133263 [64,] 0.73901560 0.52196879 0.26098440 [65,] 0.69957099 0.60085802 0.30042901 [66,] 0.75085640 0.49828719 0.24914360 [67,] 0.71368595 0.57262810 0.28631405 [68,] 0.70049648 0.59900705 0.29950352 [69,] 0.70634683 0.58730635 0.29365317 [70,] 0.66478008 0.67043984 0.33521992 [71,] 0.62547243 0.74905514 0.37452757 [72,] 0.74964020 0.50071961 0.25035980 [73,] 0.71257853 0.57484295 0.28742147 [74,] 0.68280185 0.63439631 0.31719815 [75,] 0.64073672 0.71852655 0.35926328 [76,] 0.63433147 0.73133706 0.36566853 [77,] 0.58950032 0.82099936 0.41049968 [78,] 0.54961569 0.90076863 0.45038431 [79,] 0.53296106 0.93407788 0.46703894 [80,] 0.49894954 0.99789908 0.50105046 [81,] 0.48002794 0.96005588 0.51997206 [82,] 0.44178065 0.88356129 0.55821935 [83,] 0.39973765 0.79947530 0.60026235 [84,] 0.35767207 0.71534414 0.64232793 [85,] 0.37383999 0.74767999 0.62616001 [86,] 0.33894948 0.67789897 0.66105052 [87,] 0.30119431 0.60238862 0.69880569 [88,] 0.29987885 0.59975770 0.70012115 [89,] 0.26002036 0.52004072 0.73997964 [90,] 0.22445621 0.44891242 0.77554379 [91,] 0.20285474 0.40570947 0.79714526 [92,] 0.18812218 0.37624436 0.81187782 [93,] 0.23142939 0.46285879 0.76857061 [94,] 0.19737412 0.39474824 0.80262588 [95,] 0.19107205 0.38214410 0.80892795 [96,] 0.20360493 0.40720987 0.79639507 [97,] 0.18369426 0.36738852 0.81630574 [98,] 0.16340521 0.32681042 0.83659479 [99,] 0.15704365 0.31408729 0.84295635 [100,] 0.14758145 0.29516290 0.85241855 [101,] 0.13003758 0.26007515 0.86996242 [102,] 0.10807782 0.21615563 0.89192218 [103,] 0.12620747 0.25241495 0.87379253 [104,] 0.11611181 0.23222362 0.88388819 [105,] 0.14804638 0.29609276 0.85195362 [106,] 0.14101835 0.28203670 0.85898165 [107,] 0.12219812 0.24439624 0.87780188 [108,] 0.10584694 0.21169388 0.89415306 [109,] 0.10860531 0.21721063 0.89139469 [110,] 0.10073982 0.20147965 0.89926018 [111,] 0.08513099 0.17026199 0.91486901 [112,] 0.06771939 0.13543877 0.93228061 [113,] 0.07688771 0.15377542 0.92311229 [114,] 0.06138552 0.12277104 0.93861448 [115,] 0.04937173 0.09874346 0.95062827 [116,] 0.03763532 0.07527063 0.96236468 [117,] 0.02818619 0.05637238 0.97181381 [118,] 0.02220529 0.04441057 0.97779471 [119,] 0.01767669 0.03535338 0.98232331 [120,] 0.02474693 0.04949386 0.97525307 [121,] 0.02052937 0.04105874 0.97947063 [122,] 0.03231400 0.06462800 0.96768600 [123,] 0.03349234 0.06698469 0.96650766 [124,] 0.04501108 0.09002216 0.95498892 [125,] 0.03571833 0.07143666 0.96428167 [126,] 0.02452083 0.04904166 0.97547917 [127,] 0.01644191 0.03288382 0.98355809 [128,] 0.01145134 0.02290268 0.98854866 [129,] 0.01871416 0.03742831 0.98128584 [130,] 0.01567360 0.03134719 0.98432640 [131,] 0.61609964 0.76780072 0.38390036 [132,] 0.55528297 0.88943406 0.44471703 [133,] 0.47194886 0.94389773 0.52805114 [134,] 0.39183591 0.78367181 0.60816409 [135,] 0.30519025 0.61038051 0.69480975 [136,] 0.35044474 0.70088948 0.64955526 [137,] 0.30988329 0.61976659 0.69011671 [138,] 0.72321234 0.55357531 0.27678766 [139,] 0.68231323 0.63537353 0.31768677 [140,] 0.54094773 0.91810455 0.45905227 [141,] 0.74596135 0.50807731 0.25403865 > postscript(file="/var/wessaorg/rcomp/tmp/1jf4j1352117340.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2ui7a1352117340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/354kt1352117340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/40aj21352117340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5d3eo1352117340.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 = 162 Frequency = 1 1 2 3 4 5 6 -3.117601011 0.031409542 2.826208692 3.263872656 -1.452637097 -1.883489323 7 8 9 10 11 12 3.938638684 -1.594411390 -1.883298629 2.566461096 0.801769700 -0.196710791 13 14 15 16 17 18 0.636573306 0.692451452 -0.353645490 -0.021982012 0.448428914 3.871783141 19 20 21 22 23 24 2.587482548 0.802533083 0.758692721 1.290129671 2.584417168 1.317476987 25 26 27 28 29 30 2.228481186 0.118860325 1.037292647 -1.042840712 0.559138014 0.039024333 31 32 33 34 35 36 -0.594186433 -0.184903151 -1.111490636 0.569799207 -1.642024303 -5.902573456 37 38 39 40 41 42 -0.866817165 -1.779490672 1.778844077 1.416075093 0.954616002 -1.559576607 43 44 45 46 47 48 2.085282872 -0.206202870 -0.898546595 -4.591634126 -2.425089799 0.068399177 49 50 51 52 53 54 0.800952617 -2.018586087 -0.490631866 -0.111764506 -2.684778008 0.809386322 55 56 57 58 59 60 -2.517571084 1.371304878 -0.092351762 0.916882834 -0.323702883 1.868732899 61 62 63 64 65 66 0.570716868 0.022385148 -0.466440278 -0.370634890 0.564048311 1.093600353 67 68 69 70 71 72 1.897930322 3.326545364 -3.880914238 0.578839151 -3.449196578 -0.323948286 73 74 75 76 77 78 1.462205539 0.845093066 0.273624586 3.112856240 -0.365942088 1.548836272 79 80 81 82 83 84 -1.842852459 -0.026008619 0.424121949 3.227893717 0.308208809 -1.024776775 85 86 87 88 89 90 0.256431299 1.731711306 -0.223327636 0.662336718 1.416730228 0.787728045 91 92 93 94 95 96 -1.482603395 -0.009346032 0.513187104 -0.285606992 -2.178923984 0.781685537 97 98 99 100 101 102 0.140519351 1.815846911 -0.159901088 -0.340573484 -1.366811481 1.187071465 103 104 105 106 107 108 2.585220688 0.410210238 1.436727372 -2.397171216 0.932990453 0.058877693 109 110 111 112 113 114 1.402035548 -0.311586001 0.987859590 0.102928012 2.451743724 -1.998883265 115 116 117 118 119 120 -2.936748200 1.327114786 -1.547598713 0.982402073 -1.987919366 0.274939017 121 122 123 124 125 126 -1.333886989 0.304154405 -3.023893971 -1.129863105 -1.059438245 -0.865806131 127 128 129 130 131 132 -0.513911570 0.657837937 1.107093822 -3.035236727 1.757879269 -3.395499358 133 134 135 136 137 138 1.817021409 -2.001079808 -1.740798301 -0.194351716 0.640535211 0.468402482 139 140 141 142 143 144 -2.255150678 -1.452049433 -5.453720060 2.810925984 1.588789011 0.364109135 145 146 147 148 149 150 1.068825869 -4.254547241 1.980591286 -2.275292865 0.757020012 -0.116840102 151 152 153 154 155 156 -3.208809380 -1.525645289 1.801977048 3.909544202 1.453398005 -2.594466136 157 158 159 160 161 162 -0.009346032 1.288332196 1.107093822 0.829427294 -0.604839935 0.313163501 > postscript(file="/var/wessaorg/rcomp/tmp/6dso51352117340.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 = 162 Frequency = 1 lag(myerror, k = 1) myerror 0 -3.117601011 NA 1 0.031409542 -3.117601011 2 2.826208692 0.031409542 3 3.263872656 2.826208692 4 -1.452637097 3.263872656 5 -1.883489323 -1.452637097 6 3.938638684 -1.883489323 7 -1.594411390 3.938638684 8 -1.883298629 -1.594411390 9 2.566461096 -1.883298629 10 0.801769700 2.566461096 11 -0.196710791 0.801769700 12 0.636573306 -0.196710791 13 0.692451452 0.636573306 14 -0.353645490 0.692451452 15 -0.021982012 -0.353645490 16 0.448428914 -0.021982012 17 3.871783141 0.448428914 18 2.587482548 3.871783141 19 0.802533083 2.587482548 20 0.758692721 0.802533083 21 1.290129671 0.758692721 22 2.584417168 1.290129671 23 1.317476987 2.584417168 24 2.228481186 1.317476987 25 0.118860325 2.228481186 26 1.037292647 0.118860325 27 -1.042840712 1.037292647 28 0.559138014 -1.042840712 29 0.039024333 0.559138014 30 -0.594186433 0.039024333 31 -0.184903151 -0.594186433 32 -1.111490636 -0.184903151 33 0.569799207 -1.111490636 34 -1.642024303 0.569799207 35 -5.902573456 -1.642024303 36 -0.866817165 -5.902573456 37 -1.779490672 -0.866817165 38 1.778844077 -1.779490672 39 1.416075093 1.778844077 40 0.954616002 1.416075093 41 -1.559576607 0.954616002 42 2.085282872 -1.559576607 43 -0.206202870 2.085282872 44 -0.898546595 -0.206202870 45 -4.591634126 -0.898546595 46 -2.425089799 -4.591634126 47 0.068399177 -2.425089799 48 0.800952617 0.068399177 49 -2.018586087 0.800952617 50 -0.490631866 -2.018586087 51 -0.111764506 -0.490631866 52 -2.684778008 -0.111764506 53 0.809386322 -2.684778008 54 -2.517571084 0.809386322 55 1.371304878 -2.517571084 56 -0.092351762 1.371304878 57 0.916882834 -0.092351762 58 -0.323702883 0.916882834 59 1.868732899 -0.323702883 60 0.570716868 1.868732899 61 0.022385148 0.570716868 62 -0.466440278 0.022385148 63 -0.370634890 -0.466440278 64 0.564048311 -0.370634890 65 1.093600353 0.564048311 66 1.897930322 1.093600353 67 3.326545364 1.897930322 68 -3.880914238 3.326545364 69 0.578839151 -3.880914238 70 -3.449196578 0.578839151 71 -0.323948286 -3.449196578 72 1.462205539 -0.323948286 73 0.845093066 1.462205539 74 0.273624586 0.845093066 75 3.112856240 0.273624586 76 -0.365942088 3.112856240 77 1.548836272 -0.365942088 78 -1.842852459 1.548836272 79 -0.026008619 -1.842852459 80 0.424121949 -0.026008619 81 3.227893717 0.424121949 82 0.308208809 3.227893717 83 -1.024776775 0.308208809 84 0.256431299 -1.024776775 85 1.731711306 0.256431299 86 -0.223327636 1.731711306 87 0.662336718 -0.223327636 88 1.416730228 0.662336718 89 0.787728045 1.416730228 90 -1.482603395 0.787728045 91 -0.009346032 -1.482603395 92 0.513187104 -0.009346032 93 -0.285606992 0.513187104 94 -2.178923984 -0.285606992 95 0.781685537 -2.178923984 96 0.140519351 0.781685537 97 1.815846911 0.140519351 98 -0.159901088 1.815846911 99 -0.340573484 -0.159901088 100 -1.366811481 -0.340573484 101 1.187071465 -1.366811481 102 2.585220688 1.187071465 103 0.410210238 2.585220688 104 1.436727372 0.410210238 105 -2.397171216 1.436727372 106 0.932990453 -2.397171216 107 0.058877693 0.932990453 108 1.402035548 0.058877693 109 -0.311586001 1.402035548 110 0.987859590 -0.311586001 111 0.102928012 0.987859590 112 2.451743724 0.102928012 113 -1.998883265 2.451743724 114 -2.936748200 -1.998883265 115 1.327114786 -2.936748200 116 -1.547598713 1.327114786 117 0.982402073 -1.547598713 118 -1.987919366 0.982402073 119 0.274939017 -1.987919366 120 -1.333886989 0.274939017 121 0.304154405 -1.333886989 122 -3.023893971 0.304154405 123 -1.129863105 -3.023893971 124 -1.059438245 -1.129863105 125 -0.865806131 -1.059438245 126 -0.513911570 -0.865806131 127 0.657837937 -0.513911570 128 1.107093822 0.657837937 129 -3.035236727 1.107093822 130 1.757879269 -3.035236727 131 -3.395499358 1.757879269 132 1.817021409 -3.395499358 133 -2.001079808 1.817021409 134 -1.740798301 -2.001079808 135 -0.194351716 -1.740798301 136 0.640535211 -0.194351716 137 0.468402482 0.640535211 138 -2.255150678 0.468402482 139 -1.452049433 -2.255150678 140 -5.453720060 -1.452049433 141 2.810925984 -5.453720060 142 1.588789011 2.810925984 143 0.364109135 1.588789011 144 1.068825869 0.364109135 145 -4.254547241 1.068825869 146 1.980591286 -4.254547241 147 -2.275292865 1.980591286 148 0.757020012 -2.275292865 149 -0.116840102 0.757020012 150 -3.208809380 -0.116840102 151 -1.525645289 -3.208809380 152 1.801977048 -1.525645289 153 3.909544202 1.801977048 154 1.453398005 3.909544202 155 -2.594466136 1.453398005 156 -0.009346032 -2.594466136 157 1.288332196 -0.009346032 158 1.107093822 1.288332196 159 0.829427294 1.107093822 160 -0.604839935 0.829427294 161 0.313163501 -0.604839935 162 NA 0.313163501 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.031409542 -3.117601011 [2,] 2.826208692 0.031409542 [3,] 3.263872656 2.826208692 [4,] -1.452637097 3.263872656 [5,] -1.883489323 -1.452637097 [6,] 3.938638684 -1.883489323 [7,] -1.594411390 3.938638684 [8,] -1.883298629 -1.594411390 [9,] 2.566461096 -1.883298629 [10,] 0.801769700 2.566461096 [11,] -0.196710791 0.801769700 [12,] 0.636573306 -0.196710791 [13,] 0.692451452 0.636573306 [14,] -0.353645490 0.692451452 [15,] -0.021982012 -0.353645490 [16,] 0.448428914 -0.021982012 [17,] 3.871783141 0.448428914 [18,] 2.587482548 3.871783141 [19,] 0.802533083 2.587482548 [20,] 0.758692721 0.802533083 [21,] 1.290129671 0.758692721 [22,] 2.584417168 1.290129671 [23,] 1.317476987 2.584417168 [24,] 2.228481186 1.317476987 [25,] 0.118860325 2.228481186 [26,] 1.037292647 0.118860325 [27,] -1.042840712 1.037292647 [28,] 0.559138014 -1.042840712 [29,] 0.039024333 0.559138014 [30,] -0.594186433 0.039024333 [31,] -0.184903151 -0.594186433 [32,] -1.111490636 -0.184903151 [33,] 0.569799207 -1.111490636 [34,] -1.642024303 0.569799207 [35,] -5.902573456 -1.642024303 [36,] -0.866817165 -5.902573456 [37,] -1.779490672 -0.866817165 [38,] 1.778844077 -1.779490672 [39,] 1.416075093 1.778844077 [40,] 0.954616002 1.416075093 [41,] -1.559576607 0.954616002 [42,] 2.085282872 -1.559576607 [43,] -0.206202870 2.085282872 [44,] -0.898546595 -0.206202870 [45,] -4.591634126 -0.898546595 [46,] -2.425089799 -4.591634126 [47,] 0.068399177 -2.425089799 [48,] 0.800952617 0.068399177 [49,] -2.018586087 0.800952617 [50,] -0.490631866 -2.018586087 [51,] -0.111764506 -0.490631866 [52,] -2.684778008 -0.111764506 [53,] 0.809386322 -2.684778008 [54,] -2.517571084 0.809386322 [55,] 1.371304878 -2.517571084 [56,] -0.092351762 1.371304878 [57,] 0.916882834 -0.092351762 [58,] -0.323702883 0.916882834 [59,] 1.868732899 -0.323702883 [60,] 0.570716868 1.868732899 [61,] 0.022385148 0.570716868 [62,] -0.466440278 0.022385148 [63,] -0.370634890 -0.466440278 [64,] 0.564048311 -0.370634890 [65,] 1.093600353 0.564048311 [66,] 1.897930322 1.093600353 [67,] 3.326545364 1.897930322 [68,] -3.880914238 3.326545364 [69,] 0.578839151 -3.880914238 [70,] -3.449196578 0.578839151 [71,] -0.323948286 -3.449196578 [72,] 1.462205539 -0.323948286 [73,] 0.845093066 1.462205539 [74,] 0.273624586 0.845093066 [75,] 3.112856240 0.273624586 [76,] -0.365942088 3.112856240 [77,] 1.548836272 -0.365942088 [78,] -1.842852459 1.548836272 [79,] -0.026008619 -1.842852459 [80,] 0.424121949 -0.026008619 [81,] 3.227893717 0.424121949 [82,] 0.308208809 3.227893717 [83,] -1.024776775 0.308208809 [84,] 0.256431299 -1.024776775 [85,] 1.731711306 0.256431299 [86,] -0.223327636 1.731711306 [87,] 0.662336718 -0.223327636 [88,] 1.416730228 0.662336718 [89,] 0.787728045 1.416730228 [90,] -1.482603395 0.787728045 [91,] -0.009346032 -1.482603395 [92,] 0.513187104 -0.009346032 [93,] -0.285606992 0.513187104 [94,] -2.178923984 -0.285606992 [95,] 0.781685537 -2.178923984 [96,] 0.140519351 0.781685537 [97,] 1.815846911 0.140519351 [98,] -0.159901088 1.815846911 [99,] -0.340573484 -0.159901088 [100,] -1.366811481 -0.340573484 [101,] 1.187071465 -1.366811481 [102,] 2.585220688 1.187071465 [103,] 0.410210238 2.585220688 [104,] 1.436727372 0.410210238 [105,] -2.397171216 1.436727372 [106,] 0.932990453 -2.397171216 [107,] 0.058877693 0.932990453 [108,] 1.402035548 0.058877693 [109,] -0.311586001 1.402035548 [110,] 0.987859590 -0.311586001 [111,] 0.102928012 0.987859590 [112,] 2.451743724 0.102928012 [113,] -1.998883265 2.451743724 [114,] -2.936748200 -1.998883265 [115,] 1.327114786 -2.936748200 [116,] -1.547598713 1.327114786 [117,] 0.982402073 -1.547598713 [118,] -1.987919366 0.982402073 [119,] 0.274939017 -1.987919366 [120,] -1.333886989 0.274939017 [121,] 0.304154405 -1.333886989 [122,] -3.023893971 0.304154405 [123,] -1.129863105 -3.023893971 [124,] -1.059438245 -1.129863105 [125,] -0.865806131 -1.059438245 [126,] -0.513911570 -0.865806131 [127,] 0.657837937 -0.513911570 [128,] 1.107093822 0.657837937 [129,] -3.035236727 1.107093822 [130,] 1.757879269 -3.035236727 [131,] -3.395499358 1.757879269 [132,] 1.817021409 -3.395499358 [133,] -2.001079808 1.817021409 [134,] -1.740798301 -2.001079808 [135,] -0.194351716 -1.740798301 [136,] 0.640535211 -0.194351716 [137,] 0.468402482 0.640535211 [138,] -2.255150678 0.468402482 [139,] -1.452049433 -2.255150678 [140,] -5.453720060 -1.452049433 [141,] 2.810925984 -5.453720060 [142,] 1.588789011 2.810925984 [143,] 0.364109135 1.588789011 [144,] 1.068825869 0.364109135 [145,] -4.254547241 1.068825869 [146,] 1.980591286 -4.254547241 [147,] -2.275292865 1.980591286 [148,] 0.757020012 -2.275292865 [149,] -0.116840102 0.757020012 [150,] -3.208809380 -0.116840102 [151,] -1.525645289 -3.208809380 [152,] 1.801977048 -1.525645289 [153,] 3.909544202 1.801977048 [154,] 1.453398005 3.909544202 [155,] -2.594466136 1.453398005 [156,] -0.009346032 -2.594466136 [157,] 1.288332196 -0.009346032 [158,] 1.107093822 1.288332196 [159,] 0.829427294 1.107093822 [160,] -0.604839935 0.829427294 [161,] 0.313163501 -0.604839935 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.031409542 -3.117601011 2 2.826208692 0.031409542 3 3.263872656 2.826208692 4 -1.452637097 3.263872656 5 -1.883489323 -1.452637097 6 3.938638684 -1.883489323 7 -1.594411390 3.938638684 8 -1.883298629 -1.594411390 9 2.566461096 -1.883298629 10 0.801769700 2.566461096 11 -0.196710791 0.801769700 12 0.636573306 -0.196710791 13 0.692451452 0.636573306 14 -0.353645490 0.692451452 15 -0.021982012 -0.353645490 16 0.448428914 -0.021982012 17 3.871783141 0.448428914 18 2.587482548 3.871783141 19 0.802533083 2.587482548 20 0.758692721 0.802533083 21 1.290129671 0.758692721 22 2.584417168 1.290129671 23 1.317476987 2.584417168 24 2.228481186 1.317476987 25 0.118860325 2.228481186 26 1.037292647 0.118860325 27 -1.042840712 1.037292647 28 0.559138014 -1.042840712 29 0.039024333 0.559138014 30 -0.594186433 0.039024333 31 -0.184903151 -0.594186433 32 -1.111490636 -0.184903151 33 0.569799207 -1.111490636 34 -1.642024303 0.569799207 35 -5.902573456 -1.642024303 36 -0.866817165 -5.902573456 37 -1.779490672 -0.866817165 38 1.778844077 -1.779490672 39 1.416075093 1.778844077 40 0.954616002 1.416075093 41 -1.559576607 0.954616002 42 2.085282872 -1.559576607 43 -0.206202870 2.085282872 44 -0.898546595 -0.206202870 45 -4.591634126 -0.898546595 46 -2.425089799 -4.591634126 47 0.068399177 -2.425089799 48 0.800952617 0.068399177 49 -2.018586087 0.800952617 50 -0.490631866 -2.018586087 51 -0.111764506 -0.490631866 52 -2.684778008 -0.111764506 53 0.809386322 -2.684778008 54 -2.517571084 0.809386322 55 1.371304878 -2.517571084 56 -0.092351762 1.371304878 57 0.916882834 -0.092351762 58 -0.323702883 0.916882834 59 1.868732899 -0.323702883 60 0.570716868 1.868732899 61 0.022385148 0.570716868 62 -0.466440278 0.022385148 63 -0.370634890 -0.466440278 64 0.564048311 -0.370634890 65 1.093600353 0.564048311 66 1.897930322 1.093600353 67 3.326545364 1.897930322 68 -3.880914238 3.326545364 69 0.578839151 -3.880914238 70 -3.449196578 0.578839151 71 -0.323948286 -3.449196578 72 1.462205539 -0.323948286 73 0.845093066 1.462205539 74 0.273624586 0.845093066 75 3.112856240 0.273624586 76 -0.365942088 3.112856240 77 1.548836272 -0.365942088 78 -1.842852459 1.548836272 79 -0.026008619 -1.842852459 80 0.424121949 -0.026008619 81 3.227893717 0.424121949 82 0.308208809 3.227893717 83 -1.024776775 0.308208809 84 0.256431299 -1.024776775 85 1.731711306 0.256431299 86 -0.223327636 1.731711306 87 0.662336718 -0.223327636 88 1.416730228 0.662336718 89 0.787728045 1.416730228 90 -1.482603395 0.787728045 91 -0.009346032 -1.482603395 92 0.513187104 -0.009346032 93 -0.285606992 0.513187104 94 -2.178923984 -0.285606992 95 0.781685537 -2.178923984 96 0.140519351 0.781685537 97 1.815846911 0.140519351 98 -0.159901088 1.815846911 99 -0.340573484 -0.159901088 100 -1.366811481 -0.340573484 101 1.187071465 -1.366811481 102 2.585220688 1.187071465 103 0.410210238 2.585220688 104 1.436727372 0.410210238 105 -2.397171216 1.436727372 106 0.932990453 -2.397171216 107 0.058877693 0.932990453 108 1.402035548 0.058877693 109 -0.311586001 1.402035548 110 0.987859590 -0.311586001 111 0.102928012 0.987859590 112 2.451743724 0.102928012 113 -1.998883265 2.451743724 114 -2.936748200 -1.998883265 115 1.327114786 -2.936748200 116 -1.547598713 1.327114786 117 0.982402073 -1.547598713 118 -1.987919366 0.982402073 119 0.274939017 -1.987919366 120 -1.333886989 0.274939017 121 0.304154405 -1.333886989 122 -3.023893971 0.304154405 123 -1.129863105 -3.023893971 124 -1.059438245 -1.129863105 125 -0.865806131 -1.059438245 126 -0.513911570 -0.865806131 127 0.657837937 -0.513911570 128 1.107093822 0.657837937 129 -3.035236727 1.107093822 130 1.757879269 -3.035236727 131 -3.395499358 1.757879269 132 1.817021409 -3.395499358 133 -2.001079808 1.817021409 134 -1.740798301 -2.001079808 135 -0.194351716 -1.740798301 136 0.640535211 -0.194351716 137 0.468402482 0.640535211 138 -2.255150678 0.468402482 139 -1.452049433 -2.255150678 140 -5.453720060 -1.452049433 141 2.810925984 -5.453720060 142 1.588789011 2.810925984 143 0.364109135 1.588789011 144 1.068825869 0.364109135 145 -4.254547241 1.068825869 146 1.980591286 -4.254547241 147 -2.275292865 1.980591286 148 0.757020012 -2.275292865 149 -0.116840102 0.757020012 150 -3.208809380 -0.116840102 151 -1.525645289 -3.208809380 152 1.801977048 -1.525645289 153 3.909544202 1.801977048 154 1.453398005 3.909544202 155 -2.594466136 1.453398005 156 -0.009346032 -2.594466136 157 1.288332196 -0.009346032 158 1.107093822 1.288332196 159 0.829427294 1.107093822 160 -0.604839935 0.829427294 161 0.313163501 -0.604839935 > 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/7ulzs1352117340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/84grb1352117340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9xl6m1352117340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10q9el1352117340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11ysze1352117340.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/12m4p11352117340.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/13aucv1352117340.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/14gwyz1352117340.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/15q0ch1352117340.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/16oxkp1352117340.tab") + } > > try(system("convert tmp/1jf4j1352117340.ps tmp/1jf4j1352117340.png",intern=TRUE)) character(0) > try(system("convert tmp/2ui7a1352117340.ps tmp/2ui7a1352117340.png",intern=TRUE)) character(0) > try(system("convert tmp/354kt1352117340.ps tmp/354kt1352117340.png",intern=TRUE)) character(0) > try(system("convert tmp/40aj21352117340.ps tmp/40aj21352117340.png",intern=TRUE)) character(0) > try(system("convert tmp/5d3eo1352117340.ps tmp/5d3eo1352117340.png",intern=TRUE)) character(0) > try(system("convert tmp/6dso51352117340.ps tmp/6dso51352117340.png",intern=TRUE)) character(0) > try(system("convert tmp/7ulzs1352117340.ps tmp/7ulzs1352117340.png",intern=TRUE)) character(0) > try(system("convert tmp/84grb1352117340.ps tmp/84grb1352117340.png",intern=TRUE)) character(0) > try(system("convert tmp/9xl6m1352117340.ps tmp/9xl6m1352117340.png",intern=TRUE)) character(0) > try(system("convert tmp/10q9el1352117340.ps tmp/10q9el1352117340.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.581 0.913 9.499