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(19 + ,39 + ,31 + ,12 + ,14 + ,22 + ,53 + ,37 + ,15 + ,34 + ,36 + ,14 + ,14 + ,11 + ,80 + ,49 + ,14 + ,36 + ,35 + ,12 + ,15 + ,10 + ,74 + ,45 + ,15 + ,37 + ,38 + ,6 + ,15 + ,13 + ,76 + ,47 + ,16 + ,38 + ,31 + ,10 + ,17 + ,10 + ,79 + ,49 + ,16 + ,36 + ,34 + ,12 + ,19 + ,8 + ,54 + ,33 + ,16 + ,38 + ,35 + ,12 + ,10 + ,15 + ,67 + ,42 + ,16 + ,39 + ,38 + ,11 + ,16 + ,14 + ,54 + ,33 + ,17 + ,33 + ,37 + ,15 + ,18 + ,10 + ,87 + ,53 + ,15 + ,32 + ,33 + ,12 + ,14 + ,14 + ,58 + ,36 + ,15 + ,36 + ,32 + ,10 + ,14 + ,14 + ,75 + ,45 + ,20 + ,38 + ,38 + ,12 + ,17 + ,11 + ,88 + ,54 + ,18 + ,39 + ,38 + ,11 + ,14 + ,10 + ,64 + ,41 + ,16 + ,32 + ,32 + ,12 + ,16 + ,13 + ,57 + ,36 + ,16 + ,32 + ,33 + ,11 + ,18 + ,7 + ,66 + ,41 + ,16 + ,31 + ,31 + ,12 + ,11 + ,14 + ,68 + ,44 + ,19 + ,39 + ,38 + ,13 + ,14 + ,12 + ,54 + ,33 + ,16 + ,37 + ,39 + ,11 + ,12 + ,14 + ,56 + ,37 + ,17 + ,39 + ,32 + ,9 + ,17 + ,11 + ,86 + ,52 + ,17 + ,41 + ,32 + ,13 + ,9 + ,9 + ,80 + ,47 + ,16 + 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,42 + ,10 + ,28 + ,32 + ,11 + ,9 + ,20 + ,66 + ,40 + ,16 + ,40 + ,37 + ,8 + ,15 + ,10 + ,77 + ,46 + ,12 + ,27 + ,30 + ,11 + ,15 + ,15 + ,65 + ,40 + ,14 + ,37 + ,38 + ,12 + ,15 + ,12 + ,74 + ,46 + ,15 + ,32 + ,29 + ,12 + ,16 + ,14 + ,82 + ,53 + ,13 + ,28 + ,22 + ,9 + ,11 + ,23 + ,54 + ,33 + ,15 + ,34 + ,35 + ,11 + ,14 + ,14 + ,63 + ,42 + ,11 + ,30 + ,35 + ,10 + ,11 + ,16 + ,54 + ,35 + ,12 + ,35 + ,34 + ,8 + ,15 + ,11 + ,64 + ,40 + ,8 + ,31 + ,35 + ,9 + ,13 + ,12 + ,69 + ,41 + ,16 + ,32 + ,34 + ,8 + ,15 + ,10 + ,54 + ,33 + ,15 + ,30 + ,34 + ,9 + ,16 + ,14 + ,84 + ,51 + ,17 + ,30 + ,35 + ,15 + ,14 + ,12 + ,86 + ,53 + ,16 + ,31 + ,23 + ,11 + ,15 + ,12 + ,77 + ,46 + ,10 + ,40 + ,31 + ,8 + ,16 + ,11 + ,89 + ,55 + ,18 + ,32 + ,27 + ,13 + ,16 + ,12 + ,76 + ,47 + ,13 + ,36 + ,36 + ,12 + ,11 + ,13 + ,60 + ,38 + ,16 + ,32 + ,31 + ,12 + ,12 + ,11 + ,75 + ,46 + ,13 + ,35 + ,32 + ,9 + ,9 + ,19 + ,73 + ,46 + ,10 + ,38 + ,39 + ,7 + ,16 + ,12 + ,85 + ,53 + ,15 + ,42 + ,37 + ,13 + ,13 + ,17 + ,79 + ,47 + ,16 + ,34 + ,38 + ,9 + ,16 + ,9 + ,71 + ,41 + ,16 + ,35 + ,39 + ,6 + ,12 + ,12 + ,72 + ,44 + ,14 + ,35 + ,34 + ,8 + ,9 + ,19 + ,69 + ,43 + ,10 + ,33 + ,31 + ,8 + ,13 + ,18 + ,78 + ,51 + ,17 + ,36 + ,32 + ,15 + ,13 + ,15 + ,54 + ,33 + ,13 + ,32 + ,37 + ,6 + ,14 + ,14 + ,69 + ,43 + ,15 + ,33 + ,36 + ,9 + ,19 + ,11 + ,81 + ,53 + ,16 + ,34 + ,32 + ,11 + ,13 + ,9 + ,84 + ,51 + ,12 + ,32 + ,35 + ,8 + ,12 + ,18 + ,84 + ,50 + ,13 + ,34 + ,36 + ,8 + ,13 + ,16 + ,69 + ,46) + ,dim=c(8 + ,156) + ,dimnames=list(c('Learning' + ,'Connected' + ,'Separate' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Belonging' + ,'Belongfinal ') + ,1:156)) > y <- array(NA,dim=c(8,156),dimnames=list(c('Learning','Connected','Separate','Software','Happiness','Depression','Belonging','Belongfinal '),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 = 'Do not include Seasonal Dummies' > par1 = '1' > par3 <- '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 Learning Connected Separate Software Happiness Depression Belonging 1 19 39 31 12 14 22 53 2 15 34 36 14 14 11 80 3 14 36 35 12 15 10 74 4 15 37 38 6 15 13 76 5 16 38 31 10 17 10 79 6 16 36 34 12 19 8 54 7 16 38 35 12 10 15 67 8 16 39 38 11 16 14 54 9 17 33 37 15 18 10 87 10 15 32 33 12 14 14 58 11 15 36 32 10 14 14 75 12 20 38 38 12 17 11 88 13 18 39 38 11 14 10 64 14 16 32 32 12 16 13 57 15 16 32 33 11 18 7 66 16 16 31 31 12 11 14 68 17 19 39 38 13 14 12 54 18 16 37 39 11 12 14 56 19 17 39 32 9 17 11 86 20 17 41 32 13 9 9 80 21 16 36 35 10 16 11 76 22 15 33 37 14 14 15 69 23 16 33 33 12 15 14 78 24 14 34 33 10 11 13 67 25 15 31 28 12 16 9 80 26 12 27 32 8 13 15 54 27 14 37 31 10 17 10 71 28 16 34 37 12 15 11 84 29 14 34 30 12 14 13 74 30 7 32 33 7 16 8 71 31 10 29 31 6 9 20 63 32 14 36 33 12 15 12 71 33 16 29 31 10 17 10 76 34 16 35 33 10 13 10 69 35 16 37 32 10 15 9 74 36 14 34 33 12 16 14 75 37 20 38 32 15 16 8 54 38 14 35 33 10 12 14 52 39 14 38 28 10 12 11 69 40 11 37 35 12 11 13 68 41 14 38 39 13 15 9 65 42 15 33 34 11 15 11 75 43 16 36 38 11 17 15 74 44 14 38 32 12 13 11 75 45 16 32 38 14 16 10 72 46 14 32 30 10 14 14 67 47 12 32 33 12 11 18 63 48 16 34 38 13 12 14 62 49 9 32 32 5 12 11 63 50 14 37 32 6 15 12 76 51 16 39 34 12 16 13 74 52 16 29 34 12 15 9 67 53 15 37 36 11 12 10 73 54 16 35 34 10 12 15 70 55 12 30 28 7 8 20 53 56 16 38 34 12 13 12 77 57 16 34 35 14 11 12 77 58 14 31 35 11 14 14 52 59 16 34 31 12 15 13 54 60 17 35 37 13 10 11 80 61 18 36 35 14 11 17 66 62 18 30 27 11 12 12 73 63 12 39 40 12 15 13 63 64 16 35 37 12 15 14 69 65 10 38 36 8 14 13 67 66 14 31 38 11 16 15 54 67 18 34 39 14 15 13 81 68 18 38 41 14 15 10 69 69 16 34 27 12 13 11 84 70 17 39 30 9 12 19 80 71 16 37 37 13 17 13 70 72 16 34 31 11 13 17 69 73 13 28 31 12 15 13 77 74 16 37 27 12 13 9 54 75 16 33 36 12 15 11 79 76 20 37 38 12 16 10 30 77 16 35 37 12 15 9 71 78 15 37 33 12 16 12 73 79 15 32 34 11 15 12 72 80 16 33 31 10 14 13 77 81 14 38 39 9 15 13 75 82 16 33 34 12 14 12 69 83 16 29 32 12 13 15 54 84 15 33 33 12 7 22 70 85 12 31 36 9 17 13 73 86 17 36 32 15 13 15 54 87 16 35 41 12 15 13 77 88 15 32 28 12 14 15 82 89 13 29 30 12 13 10 80 90 16 39 36 10 16 11 80 91 16 37 35 13 12 16 69 92 16 35 31 9 14 11 78 93 16 37 34 12 17 11 81 94 14 32 36 10 15 10 76 95 16 38 36 14 17 10 76 96 16 37 35 11 12 16 73 97 20 36 37 15 16 12 85 98 15 32 28 11 11 11 66 99 16 33 39 11 15 16 79 100 13 40 32 12 9 19 68 101 17 38 35 12 16 11 76 102 16 41 39 12 15 16 71 103 16 36 35 11 10 15 54 104 12 43 42 7 10 24 46 105 16 30 34 12 15 14 82 106 16 31 33 14 11 15 74 107 17 32 41 11 13 11 88 108 13 32 33 11 14 15 38 109 12 37 34 10 18 12 76 110 18 37 32 13 16 10 86 111 14 33 40 13 14 14 54 112 14 34 40 8 14 13 70 113 13 33 35 11 14 9 69 114 16 38 36 12 14 15 90 115 13 33 37 11 12 15 54 116 16 31 27 13 14 14 76 117 13 38 39 12 15 11 89 118 16 37 38 14 15 8 76 119 15 33 31 13 15 11 73 120 16 31 33 15 13 11 79 121 15 39 32 10 17 8 90 122 17 44 39 11 17 10 74 123 15 33 36 9 19 11 81 124 12 35 33 11 15 13 72 125 16 32 33 10 13 11 71 126 10 28 32 11 9 20 66 127 16 40 37 8 15 10 77 128 12 27 30 11 15 15 65 129 14 37 38 12 15 12 74 130 15 32 29 12 16 14 82 131 13 28 22 9 11 23 54 132 15 34 35 11 14 14 63 133 11 30 35 10 11 16 54 134 12 35 34 8 15 11 64 135 8 31 35 9 13 12 69 136 16 32 34 8 15 10 54 137 15 30 34 9 16 14 84 138 17 30 35 15 14 12 86 139 16 31 23 11 15 12 77 140 10 40 31 8 16 11 89 141 18 32 27 13 16 12 76 142 13 36 36 12 11 13 60 143 16 32 31 12 12 11 75 144 13 35 32 9 9 19 73 145 10 38 39 7 16 12 85 146 15 42 37 13 13 17 79 147 16 34 38 9 16 9 71 148 16 35 39 6 12 12 72 149 14 35 34 8 9 19 69 150 10 33 31 8 13 18 78 151 17 36 32 15 13 15 54 152 13 32 37 6 14 14 69 153 15 33 36 9 19 11 81 154 16 34 32 11 13 9 84 155 12 32 35 8 12 18 84 156 13 34 36 8 13 16 69 Belongfinal\r t 1 37 1 2 49 2 3 45 3 4 47 4 5 49 5 6 33 6 7 42 7 8 33 8 9 53 9 10 36 10 11 45 11 12 54 12 13 41 13 14 36 14 15 41 15 16 44 16 17 33 17 18 37 18 19 52 19 20 47 20 21 43 21 22 44 22 23 45 23 24 44 24 25 49 25 26 33 26 27 43 27 28 54 28 29 42 29 30 44 30 31 37 31 32 43 32 33 46 33 34 42 34 35 45 35 36 44 36 37 33 37 38 31 38 39 42 39 40 40 40 41 43 41 42 46 42 43 42 43 44 45 44 45 44 45 46 40 46 47 37 47 48 46 48 49 36 49 50 47 50 51 45 51 52 42 52 53 43 53 54 43 54 55 32 55 56 45 56 57 45 57 58 31 58 59 33 59 60 49 60 61 42 61 62 41 62 63 38 63 64 42 64 65 44 65 66 33 66 67 48 67 68 40 68 69 50 69 70 49 70 71 43 71 72 44 72 73 47 73 74 33 74 75 46 75 76 0 76 77 45 77 78 43 78 79 44 79 80 47 80 81 45 81 82 42 82 83 33 83 84 43 84 85 46 85 86 33 86 87 46 87 88 48 88 89 47 89 90 47 90 91 43 91 92 46 92 93 48 93 94 46 94 95 45 95 96 45 96 97 52 97 98 42 98 99 47 99 100 41 100 101 47 101 102 43 102 103 33 103 104 30 104 105 49 105 106 44 106 107 55 107 108 11 108 109 47 109 110 53 110 111 33 111 112 44 112 113 42 113 114 55 114 115 33 115 116 46 116 117 54 117 118 47 118 119 45 119 120 47 120 121 55 121 122 44 122 123 53 123 124 44 124 125 42 125 126 40 126 127 46 127 128 40 128 129 46 129 130 53 130 131 33 131 132 42 132 133 35 133 134 40 134 135 41 135 136 33 136 137 51 137 138 53 138 139 46 139 140 55 140 141 47 141 142 38 142 143 46 143 144 46 144 145 53 145 146 47 146 147 41 147 148 44 148 149 43 149 150 51 150 151 33 151 152 43 152 153 53 153 154 51 154 155 50 155 156 46 156 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Connected Separate Software 3.88264 0.13322 -0.02400 0.54336 Happiness Depression Belonging `Belongfinal\\r` 0.10811 -0.02974 0.04661 -0.06506 t -0.00453 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -5.9600 -1.0391 0.2202 1.1992 4.3670 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.882640 2.683792 1.447 0.15011 Connected 0.133224 0.047820 2.786 0.00604 ** Separate -0.024001 0.045101 -0.532 0.59543 Software 0.543359 0.070891 7.665 2.24e-12 *** Happiness 0.108110 0.078736 1.373 0.17182 Depression -0.029736 0.059321 -0.501 0.61693 Belonging 0.046608 0.044483 1.048 0.29646 `Belongfinal\\r` -0.065057 0.063180 -1.030 0.30484 t -0.004530 0.003414 -1.327 0.18666 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.815 on 147 degrees of freedom Multiple R-squared: 0.3895, Adjusted R-squared: 0.3562 F-statistic: 11.72 on 8 and 147 DF, p-value: 8.021e-13 > 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,] 0.069570859 0.139141718 0.9304291 [2,] 0.064225909 0.128451817 0.9357741 [3,] 0.027940821 0.055881642 0.9720592 [4,] 0.010119047 0.020238093 0.9898810 [5,] 0.003576985 0.007153971 0.9964230 [6,] 0.002247084 0.004494168 0.9977529 [7,] 0.029668453 0.059336906 0.9703315 [8,] 0.030700691 0.061401381 0.9692993 [9,] 0.017232467 0.034464933 0.9827675 [10,] 0.009147692 0.018295384 0.9908523 [11,] 0.056860156 0.113720313 0.9431398 [12,] 0.036738597 0.073477193 0.9632614 [13,] 0.042741849 0.085483699 0.9572582 [14,] 0.027835204 0.055670408 0.9721648 [15,] 0.016850252 0.033700505 0.9831497 [16,] 0.045794601 0.091589203 0.9542054 [17,] 0.032593948 0.065187895 0.9674061 [18,] 0.026893154 0.053786307 0.9731068 [19,] 0.412319687 0.824639374 0.5876803 [20,] 0.351601041 0.703202083 0.6483990 [21,] 0.354085700 0.708171399 0.6459143 [22,] 0.494849119 0.989698239 0.5051509 [23,] 0.508155019 0.983689962 0.4918450 [24,] 0.460876418 0.921752836 0.5391236 [25,] 0.456392667 0.912785334 0.5436073 [26,] 0.473815199 0.947630398 0.5261848 [27,] 0.415863422 0.831726844 0.5841366 [28,] 0.377928853 0.755857705 0.6220711 [29,] 0.638049652 0.723900696 0.3619503 [30,] 0.678477209 0.643045582 0.3215228 [31,] 0.642849491 0.714301018 0.3571505 [32,] 0.602075028 0.795849945 0.3979250 [33,] 0.590084118 0.819831764 0.4099159 [34,] 0.548633198 0.902733605 0.4513668 [35,] 0.502502827 0.994994345 0.4974972 [36,] 0.518602895 0.962794209 0.4813971 [37,] 0.474660026 0.949320051 0.5253400 [38,] 0.482899936 0.965799872 0.5171001 [39,] 0.448387791 0.896775581 0.5516122 [40,] 0.399243753 0.798487506 0.6007562 [41,] 0.434570207 0.869140414 0.5654298 [42,] 0.405954883 0.811909766 0.5940451 [43,] 0.424826148 0.849652296 0.5751739 [44,] 0.399487449 0.798974899 0.6005126 [45,] 0.357784265 0.715568530 0.6422157 [46,] 0.333157098 0.666314195 0.6668429 [47,] 0.291565595 0.583131190 0.7084344 [48,] 0.255260450 0.510520901 0.7447395 [49,] 0.256303581 0.512607163 0.7436964 [50,] 0.247204362 0.494408724 0.7527956 [51,] 0.437931446 0.875862892 0.5620686 [52,] 0.609362276 0.781275448 0.3906377 [53,] 0.566258444 0.867483112 0.4337416 [54,] 0.704820806 0.590358388 0.2951792 [55,] 0.664660816 0.670678368 0.3353392 [56,] 0.654438334 0.691123331 0.3455617 [57,] 0.632099527 0.735800947 0.3679005 [58,] 0.586375458 0.827249084 0.4136245 [59,] 0.618066761 0.763866478 0.3819332 [60,] 0.576235078 0.847529845 0.4237649 [61,] 0.551248896 0.897502208 0.4487511 [62,] 0.555722310 0.888555380 0.4442777 [63,] 0.508838715 0.982322570 0.4911613 [64,] 0.470430393 0.940860786 0.5295696 [65,] 0.592216251 0.815567499 0.4077837 [66,] 0.553213227 0.893573547 0.4467868 [67,] 0.526213909 0.947572182 0.4737861 [68,] 0.481839335 0.963678670 0.5181607 [69,] 0.473211776 0.946423552 0.5267882 [70,] 0.426856866 0.853713731 0.5731431 [71,] 0.386849990 0.773699981 0.6131500 [72,] 0.370062208 0.740124417 0.6299378 [73,] 0.333434476 0.666868952 0.6665655 [74,] 0.320944179 0.641888358 0.6790558 [75,] 0.286406268 0.572812536 0.7135937 [76,] 0.249850845 0.499701691 0.7501492 [77,] 0.215711741 0.431423482 0.7842883 [78,] 0.229400584 0.458801168 0.7705994 [79,] 0.198465332 0.396930664 0.8015347 [80,] 0.167901353 0.335802705 0.8320986 [81,] 0.166779868 0.333559736 0.8332201 [82,] 0.138707632 0.277415265 0.8612924 [83,] 0.116974438 0.233948876 0.8830256 [84,] 0.106003387 0.212006774 0.8939966 [85,] 0.093892349 0.187784699 0.9061077 [86,] 0.119716160 0.239432319 0.8802838 [87,] 0.099602804 0.199205609 0.9003972 [88,] 0.094810031 0.189620061 0.9051900 [89,] 0.110088713 0.220177425 0.8899113 [90,] 0.096069457 0.192138915 0.9039305 [91,] 0.083924286 0.167848573 0.9160757 [92,] 0.083085615 0.166171230 0.9169144 [93,] 0.082503789 0.165007578 0.9174962 [94,] 0.073922294 0.147844587 0.9260777 [95,] 0.061355217 0.122710434 0.9386448 [96,] 0.094897342 0.189794684 0.9051027 [97,] 0.088934120 0.177868240 0.9110659 [98,] 0.107380542 0.214761084 0.8926195 [99,] 0.113314956 0.226629912 0.8866850 [100,] 0.094283065 0.188566130 0.9057169 [101,] 0.097231114 0.194462227 0.9027689 [102,] 0.086218952 0.172437905 0.9137810 [103,] 0.098085074 0.196170148 0.9019149 [104,] 0.077839118 0.155678235 0.9221609 [105,] 0.067556814 0.135113628 0.9324432 [106,] 0.066554627 0.133109254 0.9334454 [107,] 0.050509952 0.101019904 0.9494900 [108,] 0.037830375 0.075660751 0.9621696 [109,] 0.027665080 0.055330160 0.9723349 [110,] 0.019631357 0.039262715 0.9803686 [111,] 0.018414953 0.036829907 0.9815850 [112,] 0.020584229 0.041168458 0.9794158 [113,] 0.020988277 0.041976553 0.9790117 [114,] 0.024291874 0.048583747 0.9757081 [115,] 0.025714326 0.051428651 0.9742857 [116,] 0.056961083 0.113922166 0.9430389 [117,] 0.059641247 0.119282494 0.9403588 [118,] 0.045298552 0.090597104 0.9547014 [119,] 0.036536617 0.073073233 0.9634634 [120,] 0.025865150 0.051730300 0.9741349 [121,] 0.031730047 0.063460094 0.9682700 [122,] 0.026087730 0.052175460 0.9739123 [123,] 0.016939916 0.033879831 0.9830601 [124,] 0.478863203 0.957726407 0.5211368 [125,] 0.428493245 0.856986491 0.5715068 [126,] 0.359670544 0.719341088 0.6403295 [127,] 0.276475881 0.552951762 0.7235241 [128,] 0.203333755 0.406667509 0.7966662 [129,] 0.251312021 0.502624043 0.7486880 [130,] 0.283396714 0.566793429 0.7166033 [131,] 0.573714358 0.852571285 0.4262856 [132,] 0.585905621 0.828188757 0.4140944 [133,] 0.588018003 0.823963994 0.4119820 > postscript(file="/var/wessaorg/rcomp/tmp/1isgt1352156658.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2i4kp1352156658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3a6mh1352156658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4bs7v1352156658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/570d81352156658.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 = 156 Frequency = 1 1 2 3 4 5 3.2273654694 -1.8735239681 -2.1911503419 2.1384162991 0.3531443497 6 7 8 9 10 -0.5419952395 0.3808343832 0.2094961522 -0.7561536623 -0.2872759082 11 12 13 14 15 0.0402496508 3.4016865651 2.3837990760 0.5074954535 0.5905637462 16 17 18 19 20 1.2038325893 2.3202958724 1.1446969846 1.7493477865 0.0737653185 21 22 23 24 25 0.6754007108 -1.3193551967 0.1836290477 -0.0080096409 -0.7506406031 26 27 28 29 30 -0.2701326505 -1.4314514031 0.3857146531 -1.9247818273 -5.9599649593 31 32 33 34 35 -1.0293409101 -2.0386035642 1.6236538320 1.3753072983 0.8055626401 36 37 38 39 40 -1.9240497255 1.9782340024 -0.3028106433 -0.9838734727 -4.6807587878 41 42 43 44 45 -2.4731972845 -0.0472686062 0.3426916799 -2.1445280255 -0.5626654667 46 47 48 49 50 -0.2687283207 -2.8443814476 0.8754129609 -2.4371240524 1.1730521148 51 52 53 54 55 -0.4162933783 1.0407343219 -0.2896968154 1.7651422326 0.5796970867 56 57 58 59 60 -0.1056491962 -0.4147192290 -0.3908937652 0.4736538906 1.2557848135 61 62 63 64 65 1.8031474905 3.3969952277 -4.0525317894 0.4232093413 -3.5207927225 66 67 68 69 70 -0.4322399364 1.3326154478 0.8018828872 0.2874263021 2.7952831429 71 72 73 74 75 -0.5823963351 1.4275700053 -1.8247690589 0.1432048235 0.4204266681 76 77 78 79 80 3.0933860168 0.4353751106 -1.1647799501 0.2930071136 1.7356464779 81 82 83 84 85 -0.3355904943 0.7478334921 1.5481849494 0.8054680315 -1.5148484125 86 87 88 89 90 -0.0008738667 0.4810263663 -0.3621225123 -1.9223267710 0.6860855480 91 92 93 94 95 0.1365656310 1.8957790016 -0.2582565106 -0.1634739400 -1.4130046417 96 97 98 99 100 1.1896150160 2.5466530059 0.7873113407 1.3582393581 -2.4209501698 101 102 103 104 105 0.9448523861 -0.1246888017 1.6459027047 -0.4953842650 1.0525488314 106 107 108 109 110 0.3228910665 2.7442299786 -1.9645180315 -3.0094510254 1.3980106676 111 112 113 114 115 -1.3470768088 1.1811869010 -1.6335871883 0.2308466236 -1.0682846977 116 117 118 119 120 0.4503779648 -2.9290634678 -0.8407285582 -0.8290285959 -0.5300816171 121 122 123 124 125 -0.4124315481 0.6401938508 1.1976814818 -2.8970865310 2.1237188379 126 127 128 129 130 -3.1032263746 1.9843274510 -1.7596735479 -1.5570801664 -0.0685372267 131 132 133 134 135 0.7430205646 0.7475827523 -1.8237555503 -1.1445440615 -5.0485030121 136 137 138 139 140 3.2451350661 1.7563703220 0.7183927653 1.3310921106 -4.1529455067 141 142 143 144 145 2.2197663207 -1.8187351618 1.2524432959 0.1668090060 -3.0424300555 146 147 148 149 150 -1.5166396589 2.1714305230 4.3670243756 1.7720798506 -2.3901354037 151 152 153 154 155 0.2935683065 1.6548342970 1.3335778694 1.3414138557 -0.3748554371 156 0.6585386991 > postscript(file="/var/wessaorg/rcomp/tmp/6n5qt1352156658.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 = 156 Frequency = 1 lag(myerror, k = 1) myerror 0 3.2273654694 NA 1 -1.8735239681 3.2273654694 2 -2.1911503419 -1.8735239681 3 2.1384162991 -2.1911503419 4 0.3531443497 2.1384162991 5 -0.5419952395 0.3531443497 6 0.3808343832 -0.5419952395 7 0.2094961522 0.3808343832 8 -0.7561536623 0.2094961522 9 -0.2872759082 -0.7561536623 10 0.0402496508 -0.2872759082 11 3.4016865651 0.0402496508 12 2.3837990760 3.4016865651 13 0.5074954535 2.3837990760 14 0.5905637462 0.5074954535 15 1.2038325893 0.5905637462 16 2.3202958724 1.2038325893 17 1.1446969846 2.3202958724 18 1.7493477865 1.1446969846 19 0.0737653185 1.7493477865 20 0.6754007108 0.0737653185 21 -1.3193551967 0.6754007108 22 0.1836290477 -1.3193551967 23 -0.0080096409 0.1836290477 24 -0.7506406031 -0.0080096409 25 -0.2701326505 -0.7506406031 26 -1.4314514031 -0.2701326505 27 0.3857146531 -1.4314514031 28 -1.9247818273 0.3857146531 29 -5.9599649593 -1.9247818273 30 -1.0293409101 -5.9599649593 31 -2.0386035642 -1.0293409101 32 1.6236538320 -2.0386035642 33 1.3753072983 1.6236538320 34 0.8055626401 1.3753072983 35 -1.9240497255 0.8055626401 36 1.9782340024 -1.9240497255 37 -0.3028106433 1.9782340024 38 -0.9838734727 -0.3028106433 39 -4.6807587878 -0.9838734727 40 -2.4731972845 -4.6807587878 41 -0.0472686062 -2.4731972845 42 0.3426916799 -0.0472686062 43 -2.1445280255 0.3426916799 44 -0.5626654667 -2.1445280255 45 -0.2687283207 -0.5626654667 46 -2.8443814476 -0.2687283207 47 0.8754129609 -2.8443814476 48 -2.4371240524 0.8754129609 49 1.1730521148 -2.4371240524 50 -0.4162933783 1.1730521148 51 1.0407343219 -0.4162933783 52 -0.2896968154 1.0407343219 53 1.7651422326 -0.2896968154 54 0.5796970867 1.7651422326 55 -0.1056491962 0.5796970867 56 -0.4147192290 -0.1056491962 57 -0.3908937652 -0.4147192290 58 0.4736538906 -0.3908937652 59 1.2557848135 0.4736538906 60 1.8031474905 1.2557848135 61 3.3969952277 1.8031474905 62 -4.0525317894 3.3969952277 63 0.4232093413 -4.0525317894 64 -3.5207927225 0.4232093413 65 -0.4322399364 -3.5207927225 66 1.3326154478 -0.4322399364 67 0.8018828872 1.3326154478 68 0.2874263021 0.8018828872 69 2.7952831429 0.2874263021 70 -0.5823963351 2.7952831429 71 1.4275700053 -0.5823963351 72 -1.8247690589 1.4275700053 73 0.1432048235 -1.8247690589 74 0.4204266681 0.1432048235 75 3.0933860168 0.4204266681 76 0.4353751106 3.0933860168 77 -1.1647799501 0.4353751106 78 0.2930071136 -1.1647799501 79 1.7356464779 0.2930071136 80 -0.3355904943 1.7356464779 81 0.7478334921 -0.3355904943 82 1.5481849494 0.7478334921 83 0.8054680315 1.5481849494 84 -1.5148484125 0.8054680315 85 -0.0008738667 -1.5148484125 86 0.4810263663 -0.0008738667 87 -0.3621225123 0.4810263663 88 -1.9223267710 -0.3621225123 89 0.6860855480 -1.9223267710 90 0.1365656310 0.6860855480 91 1.8957790016 0.1365656310 92 -0.2582565106 1.8957790016 93 -0.1634739400 -0.2582565106 94 -1.4130046417 -0.1634739400 95 1.1896150160 -1.4130046417 96 2.5466530059 1.1896150160 97 0.7873113407 2.5466530059 98 1.3582393581 0.7873113407 99 -2.4209501698 1.3582393581 100 0.9448523861 -2.4209501698 101 -0.1246888017 0.9448523861 102 1.6459027047 -0.1246888017 103 -0.4953842650 1.6459027047 104 1.0525488314 -0.4953842650 105 0.3228910665 1.0525488314 106 2.7442299786 0.3228910665 107 -1.9645180315 2.7442299786 108 -3.0094510254 -1.9645180315 109 1.3980106676 -3.0094510254 110 -1.3470768088 1.3980106676 111 1.1811869010 -1.3470768088 112 -1.6335871883 1.1811869010 113 0.2308466236 -1.6335871883 114 -1.0682846977 0.2308466236 115 0.4503779648 -1.0682846977 116 -2.9290634678 0.4503779648 117 -0.8407285582 -2.9290634678 118 -0.8290285959 -0.8407285582 119 -0.5300816171 -0.8290285959 120 -0.4124315481 -0.5300816171 121 0.6401938508 -0.4124315481 122 1.1976814818 0.6401938508 123 -2.8970865310 1.1976814818 124 2.1237188379 -2.8970865310 125 -3.1032263746 2.1237188379 126 1.9843274510 -3.1032263746 127 -1.7596735479 1.9843274510 128 -1.5570801664 -1.7596735479 129 -0.0685372267 -1.5570801664 130 0.7430205646 -0.0685372267 131 0.7475827523 0.7430205646 132 -1.8237555503 0.7475827523 133 -1.1445440615 -1.8237555503 134 -5.0485030121 -1.1445440615 135 3.2451350661 -5.0485030121 136 1.7563703220 3.2451350661 137 0.7183927653 1.7563703220 138 1.3310921106 0.7183927653 139 -4.1529455067 1.3310921106 140 2.2197663207 -4.1529455067 141 -1.8187351618 2.2197663207 142 1.2524432959 -1.8187351618 143 0.1668090060 1.2524432959 144 -3.0424300555 0.1668090060 145 -1.5166396589 -3.0424300555 146 2.1714305230 -1.5166396589 147 4.3670243756 2.1714305230 148 1.7720798506 4.3670243756 149 -2.3901354037 1.7720798506 150 0.2935683065 -2.3901354037 151 1.6548342970 0.2935683065 152 1.3335778694 1.6548342970 153 1.3414138557 1.3335778694 154 -0.3748554371 1.3414138557 155 0.6585386991 -0.3748554371 156 NA 0.6585386991 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -1.8735239681 3.2273654694 [2,] -2.1911503419 -1.8735239681 [3,] 2.1384162991 -2.1911503419 [4,] 0.3531443497 2.1384162991 [5,] -0.5419952395 0.3531443497 [6,] 0.3808343832 -0.5419952395 [7,] 0.2094961522 0.3808343832 [8,] -0.7561536623 0.2094961522 [9,] -0.2872759082 -0.7561536623 [10,] 0.0402496508 -0.2872759082 [11,] 3.4016865651 0.0402496508 [12,] 2.3837990760 3.4016865651 [13,] 0.5074954535 2.3837990760 [14,] 0.5905637462 0.5074954535 [15,] 1.2038325893 0.5905637462 [16,] 2.3202958724 1.2038325893 [17,] 1.1446969846 2.3202958724 [18,] 1.7493477865 1.1446969846 [19,] 0.0737653185 1.7493477865 [20,] 0.6754007108 0.0737653185 [21,] -1.3193551967 0.6754007108 [22,] 0.1836290477 -1.3193551967 [23,] -0.0080096409 0.1836290477 [24,] -0.7506406031 -0.0080096409 [25,] -0.2701326505 -0.7506406031 [26,] -1.4314514031 -0.2701326505 [27,] 0.3857146531 -1.4314514031 [28,] -1.9247818273 0.3857146531 [29,] -5.9599649593 -1.9247818273 [30,] -1.0293409101 -5.9599649593 [31,] -2.0386035642 -1.0293409101 [32,] 1.6236538320 -2.0386035642 [33,] 1.3753072983 1.6236538320 [34,] 0.8055626401 1.3753072983 [35,] -1.9240497255 0.8055626401 [36,] 1.9782340024 -1.9240497255 [37,] -0.3028106433 1.9782340024 [38,] -0.9838734727 -0.3028106433 [39,] -4.6807587878 -0.9838734727 [40,] -2.4731972845 -4.6807587878 [41,] -0.0472686062 -2.4731972845 [42,] 0.3426916799 -0.0472686062 [43,] -2.1445280255 0.3426916799 [44,] -0.5626654667 -2.1445280255 [45,] -0.2687283207 -0.5626654667 [46,] -2.8443814476 -0.2687283207 [47,] 0.8754129609 -2.8443814476 [48,] -2.4371240524 0.8754129609 [49,] 1.1730521148 -2.4371240524 [50,] -0.4162933783 1.1730521148 [51,] 1.0407343219 -0.4162933783 [52,] -0.2896968154 1.0407343219 [53,] 1.7651422326 -0.2896968154 [54,] 0.5796970867 1.7651422326 [55,] -0.1056491962 0.5796970867 [56,] -0.4147192290 -0.1056491962 [57,] -0.3908937652 -0.4147192290 [58,] 0.4736538906 -0.3908937652 [59,] 1.2557848135 0.4736538906 [60,] 1.8031474905 1.2557848135 [61,] 3.3969952277 1.8031474905 [62,] -4.0525317894 3.3969952277 [63,] 0.4232093413 -4.0525317894 [64,] -3.5207927225 0.4232093413 [65,] -0.4322399364 -3.5207927225 [66,] 1.3326154478 -0.4322399364 [67,] 0.8018828872 1.3326154478 [68,] 0.2874263021 0.8018828872 [69,] 2.7952831429 0.2874263021 [70,] -0.5823963351 2.7952831429 [71,] 1.4275700053 -0.5823963351 [72,] -1.8247690589 1.4275700053 [73,] 0.1432048235 -1.8247690589 [74,] 0.4204266681 0.1432048235 [75,] 3.0933860168 0.4204266681 [76,] 0.4353751106 3.0933860168 [77,] -1.1647799501 0.4353751106 [78,] 0.2930071136 -1.1647799501 [79,] 1.7356464779 0.2930071136 [80,] -0.3355904943 1.7356464779 [81,] 0.7478334921 -0.3355904943 [82,] 1.5481849494 0.7478334921 [83,] 0.8054680315 1.5481849494 [84,] -1.5148484125 0.8054680315 [85,] -0.0008738667 -1.5148484125 [86,] 0.4810263663 -0.0008738667 [87,] -0.3621225123 0.4810263663 [88,] -1.9223267710 -0.3621225123 [89,] 0.6860855480 -1.9223267710 [90,] 0.1365656310 0.6860855480 [91,] 1.8957790016 0.1365656310 [92,] -0.2582565106 1.8957790016 [93,] -0.1634739400 -0.2582565106 [94,] -1.4130046417 -0.1634739400 [95,] 1.1896150160 -1.4130046417 [96,] 2.5466530059 1.1896150160 [97,] 0.7873113407 2.5466530059 [98,] 1.3582393581 0.7873113407 [99,] -2.4209501698 1.3582393581 [100,] 0.9448523861 -2.4209501698 [101,] -0.1246888017 0.9448523861 [102,] 1.6459027047 -0.1246888017 [103,] -0.4953842650 1.6459027047 [104,] 1.0525488314 -0.4953842650 [105,] 0.3228910665 1.0525488314 [106,] 2.7442299786 0.3228910665 [107,] -1.9645180315 2.7442299786 [108,] -3.0094510254 -1.9645180315 [109,] 1.3980106676 -3.0094510254 [110,] -1.3470768088 1.3980106676 [111,] 1.1811869010 -1.3470768088 [112,] -1.6335871883 1.1811869010 [113,] 0.2308466236 -1.6335871883 [114,] -1.0682846977 0.2308466236 [115,] 0.4503779648 -1.0682846977 [116,] -2.9290634678 0.4503779648 [117,] -0.8407285582 -2.9290634678 [118,] -0.8290285959 -0.8407285582 [119,] -0.5300816171 -0.8290285959 [120,] -0.4124315481 -0.5300816171 [121,] 0.6401938508 -0.4124315481 [122,] 1.1976814818 0.6401938508 [123,] -2.8970865310 1.1976814818 [124,] 2.1237188379 -2.8970865310 [125,] -3.1032263746 2.1237188379 [126,] 1.9843274510 -3.1032263746 [127,] -1.7596735479 1.9843274510 [128,] -1.5570801664 -1.7596735479 [129,] -0.0685372267 -1.5570801664 [130,] 0.7430205646 -0.0685372267 [131,] 0.7475827523 0.7430205646 [132,] -1.8237555503 0.7475827523 [133,] -1.1445440615 -1.8237555503 [134,] -5.0485030121 -1.1445440615 [135,] 3.2451350661 -5.0485030121 [136,] 1.7563703220 3.2451350661 [137,] 0.7183927653 1.7563703220 [138,] 1.3310921106 0.7183927653 [139,] -4.1529455067 1.3310921106 [140,] 2.2197663207 -4.1529455067 [141,] -1.8187351618 2.2197663207 [142,] 1.2524432959 -1.8187351618 [143,] 0.1668090060 1.2524432959 [144,] -3.0424300555 0.1668090060 [145,] -1.5166396589 -3.0424300555 [146,] 2.1714305230 -1.5166396589 [147,] 4.3670243756 2.1714305230 [148,] 1.7720798506 4.3670243756 [149,] -2.3901354037 1.7720798506 [150,] 0.2935683065 -2.3901354037 [151,] 1.6548342970 0.2935683065 [152,] 1.3335778694 1.6548342970 [153,] 1.3414138557 1.3335778694 [154,] -0.3748554371 1.3414138557 [155,] 0.6585386991 -0.3748554371 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -1.8735239681 3.2273654694 2 -2.1911503419 -1.8735239681 3 2.1384162991 -2.1911503419 4 0.3531443497 2.1384162991 5 -0.5419952395 0.3531443497 6 0.3808343832 -0.5419952395 7 0.2094961522 0.3808343832 8 -0.7561536623 0.2094961522 9 -0.2872759082 -0.7561536623 10 0.0402496508 -0.2872759082 11 3.4016865651 0.0402496508 12 2.3837990760 3.4016865651 13 0.5074954535 2.3837990760 14 0.5905637462 0.5074954535 15 1.2038325893 0.5905637462 16 2.3202958724 1.2038325893 17 1.1446969846 2.3202958724 18 1.7493477865 1.1446969846 19 0.0737653185 1.7493477865 20 0.6754007108 0.0737653185 21 -1.3193551967 0.6754007108 22 0.1836290477 -1.3193551967 23 -0.0080096409 0.1836290477 24 -0.7506406031 -0.0080096409 25 -0.2701326505 -0.7506406031 26 -1.4314514031 -0.2701326505 27 0.3857146531 -1.4314514031 28 -1.9247818273 0.3857146531 29 -5.9599649593 -1.9247818273 30 -1.0293409101 -5.9599649593 31 -2.0386035642 -1.0293409101 32 1.6236538320 -2.0386035642 33 1.3753072983 1.6236538320 34 0.8055626401 1.3753072983 35 -1.9240497255 0.8055626401 36 1.9782340024 -1.9240497255 37 -0.3028106433 1.9782340024 38 -0.9838734727 -0.3028106433 39 -4.6807587878 -0.9838734727 40 -2.4731972845 -4.6807587878 41 -0.0472686062 -2.4731972845 42 0.3426916799 -0.0472686062 43 -2.1445280255 0.3426916799 44 -0.5626654667 -2.1445280255 45 -0.2687283207 -0.5626654667 46 -2.8443814476 -0.2687283207 47 0.8754129609 -2.8443814476 48 -2.4371240524 0.8754129609 49 1.1730521148 -2.4371240524 50 -0.4162933783 1.1730521148 51 1.0407343219 -0.4162933783 52 -0.2896968154 1.0407343219 53 1.7651422326 -0.2896968154 54 0.5796970867 1.7651422326 55 -0.1056491962 0.5796970867 56 -0.4147192290 -0.1056491962 57 -0.3908937652 -0.4147192290 58 0.4736538906 -0.3908937652 59 1.2557848135 0.4736538906 60 1.8031474905 1.2557848135 61 3.3969952277 1.8031474905 62 -4.0525317894 3.3969952277 63 0.4232093413 -4.0525317894 64 -3.5207927225 0.4232093413 65 -0.4322399364 -3.5207927225 66 1.3326154478 -0.4322399364 67 0.8018828872 1.3326154478 68 0.2874263021 0.8018828872 69 2.7952831429 0.2874263021 70 -0.5823963351 2.7952831429 71 1.4275700053 -0.5823963351 72 -1.8247690589 1.4275700053 73 0.1432048235 -1.8247690589 74 0.4204266681 0.1432048235 75 3.0933860168 0.4204266681 76 0.4353751106 3.0933860168 77 -1.1647799501 0.4353751106 78 0.2930071136 -1.1647799501 79 1.7356464779 0.2930071136 80 -0.3355904943 1.7356464779 81 0.7478334921 -0.3355904943 82 1.5481849494 0.7478334921 83 0.8054680315 1.5481849494 84 -1.5148484125 0.8054680315 85 -0.0008738667 -1.5148484125 86 0.4810263663 -0.0008738667 87 -0.3621225123 0.4810263663 88 -1.9223267710 -0.3621225123 89 0.6860855480 -1.9223267710 90 0.1365656310 0.6860855480 91 1.8957790016 0.1365656310 92 -0.2582565106 1.8957790016 93 -0.1634739400 -0.2582565106 94 -1.4130046417 -0.1634739400 95 1.1896150160 -1.4130046417 96 2.5466530059 1.1896150160 97 0.7873113407 2.5466530059 98 1.3582393581 0.7873113407 99 -2.4209501698 1.3582393581 100 0.9448523861 -2.4209501698 101 -0.1246888017 0.9448523861 102 1.6459027047 -0.1246888017 103 -0.4953842650 1.6459027047 104 1.0525488314 -0.4953842650 105 0.3228910665 1.0525488314 106 2.7442299786 0.3228910665 107 -1.9645180315 2.7442299786 108 -3.0094510254 -1.9645180315 109 1.3980106676 -3.0094510254 110 -1.3470768088 1.3980106676 111 1.1811869010 -1.3470768088 112 -1.6335871883 1.1811869010 113 0.2308466236 -1.6335871883 114 -1.0682846977 0.2308466236 115 0.4503779648 -1.0682846977 116 -2.9290634678 0.4503779648 117 -0.8407285582 -2.9290634678 118 -0.8290285959 -0.8407285582 119 -0.5300816171 -0.8290285959 120 -0.4124315481 -0.5300816171 121 0.6401938508 -0.4124315481 122 1.1976814818 0.6401938508 123 -2.8970865310 1.1976814818 124 2.1237188379 -2.8970865310 125 -3.1032263746 2.1237188379 126 1.9843274510 -3.1032263746 127 -1.7596735479 1.9843274510 128 -1.5570801664 -1.7596735479 129 -0.0685372267 -1.5570801664 130 0.7430205646 -0.0685372267 131 0.7475827523 0.7430205646 132 -1.8237555503 0.7475827523 133 -1.1445440615 -1.8237555503 134 -5.0485030121 -1.1445440615 135 3.2451350661 -5.0485030121 136 1.7563703220 3.2451350661 137 0.7183927653 1.7563703220 138 1.3310921106 0.7183927653 139 -4.1529455067 1.3310921106 140 2.2197663207 -4.1529455067 141 -1.8187351618 2.2197663207 142 1.2524432959 -1.8187351618 143 0.1668090060 1.2524432959 144 -3.0424300555 0.1668090060 145 -1.5166396589 -3.0424300555 146 2.1714305230 -1.5166396589 147 4.3670243756 2.1714305230 148 1.7720798506 4.3670243756 149 -2.3901354037 1.7720798506 150 0.2935683065 -2.3901354037 151 1.6548342970 0.2935683065 152 1.3335778694 1.6548342970 153 1.3414138557 1.3335778694 154 -0.3748554371 1.3414138557 155 0.6585386991 -0.3748554371 > 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/74mzk1352156658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8kq7g1352156658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9b1hy1352156658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10rtni1352156658.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11w82a1352156658.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/12gwa91352156658.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/136hw21352156658.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/14tuz91352156658.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/15e0gj1352156658.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/16a3e01352156658.tab") + } > > try(system("convert tmp/1isgt1352156658.ps tmp/1isgt1352156658.png",intern=TRUE)) character(0) > try(system("convert tmp/2i4kp1352156658.ps tmp/2i4kp1352156658.png",intern=TRUE)) character(0) > try(system("convert tmp/3a6mh1352156658.ps tmp/3a6mh1352156658.png",intern=TRUE)) character(0) > try(system("convert tmp/4bs7v1352156658.ps tmp/4bs7v1352156658.png",intern=TRUE)) character(0) > try(system("convert tmp/570d81352156658.ps tmp/570d81352156658.png",intern=TRUE)) character(0) > try(system("convert tmp/6n5qt1352156658.ps tmp/6n5qt1352156658.png",intern=TRUE)) character(0) > try(system("convert tmp/74mzk1352156658.ps tmp/74mzk1352156658.png",intern=TRUE)) character(0) > try(system("convert tmp/8kq7g1352156658.ps tmp/8kq7g1352156658.png",intern=TRUE)) character(0) > try(system("convert tmp/9b1hy1352156658.ps tmp/9b1hy1352156658.png",intern=TRUE)) character(0) > try(system("convert tmp/10rtni1352156658.ps tmp/10rtni1352156658.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.720 0.891 8.609