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 + ,39 + ,32 + ,16 + ,11 + ,18 + ,11 + ,86 + ,30 + ,35 + ,19 + ,15 + ,11 + ,14 + ,66 + ,31 + ,33 + ,15 + ,6 + ,12 + ,12 + ,67 + ,34 + ,37 + ,14 + ,13 + ,16 + ,21 + ,76 + ,35 + ,29 + ,13 + ,10 + ,18 + ,12 + ,78 + ,39 + ,31 + ,19 + ,12 + ,14 + ,22 + ,53 + ,34 + ,36 + ,15 + ,14 + ,14 + ,11 + ,80 + ,36 + ,35 + ,14 + ,12 + ,15 + ,10 + ,74 + ,37 + ,38 + ,15 + ,6 + ,15 + ,13 + ,76 + ,38 + ,31 + ,16 + ,10 + ,17 + ,10 + ,79 + ,36 + ,34 + ,16 + ,12 + ,19 + ,8 + ,54 + ,38 + ,35 + ,16 + ,12 + ,10 + ,15 + ,67 + ,39 + ,38 + ,16 + ,11 + ,16 + ,14 + ,54 + ,33 + ,37 + ,17 + ,15 + ,18 + ,10 + ,87 + ,32 + ,33 + ,15 + ,12 + ,14 + ,14 + ,58 + ,36 + ,32 + ,15 + ,10 + ,14 + ,14 + ,75 + ,38 + ,38 + ,20 + ,12 + ,17 + ,11 + ,88 + ,39 + ,38 + ,18 + ,11 + ,14 + ,10 + ,64 + ,32 + ,32 + ,16 + ,12 + ,16 + ,13 + ,57 + ,32 + ,33 + ,16 + ,11 + ,18 + ,7 + ,66 + ,31 + ,31 + ,16 + ,12 + ,11 + ,14 + ,68 + ,39 + ,38 + ,19 + ,13 + ,14 + ,12 + ,54 + 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+ ,35 + ,17 + ,15 + ,14 + ,12 + ,86 + ,31 + ,23 + ,16 + ,11 + ,15 + ,12 + ,77 + ,40 + ,31 + ,10 + ,8 + ,16 + ,11 + ,89 + ,32 + ,27 + ,18 + ,13 + ,16 + ,12 + ,76 + ,36 + ,36 + ,13 + ,12 + ,11 + ,13 + ,60 + ,32 + ,31 + ,16 + ,12 + ,12 + ,11 + ,75 + ,35 + ,32 + ,13 + ,9 + ,9 + ,19 + ,73 + ,38 + ,39 + ,10 + ,7 + ,16 + ,12 + ,85 + ,42 + ,37 + ,15 + ,13 + ,13 + ,17 + ,79 + ,34 + ,38 + ,16 + ,9 + ,16 + ,9 + ,71 + ,35 + ,39 + ,16 + ,6 + ,12 + ,12 + ,72 + ,35 + ,34 + ,14 + ,8 + ,9 + ,19 + ,69 + ,33 + ,31 + ,10 + ,8 + ,13 + ,18 + ,78 + ,36 + ,32 + ,17 + ,15 + ,13 + ,15 + ,54 + ,32 + ,37 + ,13 + ,6 + ,14 + ,14 + ,69 + ,33 + ,36 + ,15 + ,9 + ,19 + ,11 + ,81 + ,34 + ,32 + ,16 + ,11 + ,13 + ,9 + ,84 + ,32 + ,35 + ,12 + ,8 + ,12 + ,18 + ,84 + ,34 + ,36 + ,13 + ,8 + ,13 + ,16 + ,69) + ,dim=c(7 + ,162) + ,dimnames=list(c('Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness' + ,'Depression' + ,'Belonging') + ,1:162)) > y <- array(NA,dim=c(7,162),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging'),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 = '4' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '4' > #'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 Software Connected Separate Learning Happiness Depression Belonging 1 12 41 38 13 14 12 53 2 11 39 32 16 18 11 86 3 15 30 35 19 11 14 66 4 6 31 33 15 12 12 67 5 13 34 37 14 16 21 76 6 10 35 29 13 18 12 78 7 12 39 31 19 14 22 53 8 14 34 36 15 14 11 80 9 12 36 35 14 15 10 74 10 6 37 38 15 15 13 76 11 10 38 31 16 17 10 79 12 12 36 34 16 19 8 54 13 12 38 35 16 10 15 67 14 11 39 38 16 16 14 54 15 15 33 37 17 18 10 87 16 12 32 33 15 14 14 58 17 10 36 32 15 14 14 75 18 12 38 38 20 17 11 88 19 11 39 38 18 14 10 64 20 12 32 32 16 16 13 57 21 11 32 33 16 18 7 66 22 12 31 31 16 11 14 68 23 13 39 38 19 14 12 54 24 11 37 39 16 12 14 56 25 9 39 32 17 17 11 86 26 13 41 32 17 9 9 80 27 10 36 35 16 16 11 76 28 14 33 37 15 14 15 69 29 12 33 33 16 15 14 78 30 10 34 33 14 11 13 67 31 12 31 28 15 16 9 80 32 8 27 32 12 13 15 54 33 10 37 31 14 17 10 71 34 12 34 37 16 15 11 84 35 12 34 30 14 14 13 74 36 7 32 33 7 16 8 71 37 6 29 31 10 9 20 63 38 12 36 33 14 15 12 71 39 10 29 31 16 17 10 76 40 10 35 33 16 13 10 69 41 10 37 32 16 15 9 74 42 12 34 33 14 16 14 75 43 15 38 32 20 16 8 54 44 10 35 33 14 12 14 52 45 10 38 28 14 12 11 69 46 12 37 35 11 11 13 68 47 13 38 39 14 15 9 65 48 11 33 34 15 15 11 75 49 11 36 38 16 17 15 74 50 12 38 32 14 13 11 75 51 14 32 38 16 16 10 72 52 10 32 30 14 14 14 67 53 12 32 33 12 11 18 63 54 13 34 38 16 12 14 62 55 5 32 32 9 12 11 63 56 6 37 32 14 15 12 76 57 12 39 34 16 16 13 74 58 12 29 34 16 15 9 67 59 11 37 36 15 12 10 73 60 10 35 34 16 12 15 70 61 7 30 28 12 8 20 53 62 12 38 34 16 13 12 77 63 14 34 35 16 11 12 77 64 11 31 35 14 14 14 52 65 12 34 31 16 15 13 54 66 13 35 37 17 10 11 80 67 14 36 35 18 11 17 66 68 11 30 27 18 12 12 73 69 12 39 40 12 15 13 63 70 12 35 37 16 15 14 69 71 8 38 36 10 14 13 67 72 11 31 38 14 16 15 54 73 14 34 39 18 15 13 81 74 14 38 41 18 15 10 69 75 12 34 27 16 13 11 84 76 9 39 30 17 12 19 80 77 13 37 37 16 17 13 70 78 11 34 31 16 13 17 69 79 12 28 31 13 15 13 77 80 12 37 27 16 13 9 54 81 12 33 36 16 15 11 79 82 12 37 38 20 16 10 30 83 12 35 37 16 15 9 71 84 12 37 33 15 16 12 73 85 11 32 34 15 15 12 72 86 10 33 31 16 14 13 77 87 9 38 39 14 15 13 75 88 12 33 34 16 14 12 69 89 12 29 32 16 13 15 54 90 12 33 33 15 7 22 70 91 9 31 36 12 17 13 73 92 15 36 32 17 13 15 54 93 12 35 41 16 15 13 77 94 12 32 28 15 14 15 82 95 12 29 30 13 13 10 80 96 10 39 36 16 16 11 80 97 13 37 35 16 12 16 69 98 9 35 31 16 14 11 78 99 12 37 34 16 17 11 81 100 10 32 36 14 15 10 76 101 14 38 36 16 17 10 76 102 11 37 35 16 12 16 73 103 15 36 37 20 16 12 85 104 11 32 28 15 11 11 66 105 11 33 39 16 15 16 79 106 12 40 32 13 9 19 68 107 12 38 35 17 16 11 76 108 12 41 39 16 15 16 71 109 11 36 35 16 10 15 54 110 7 43 42 12 10 24 46 111 12 30 34 16 15 14 82 112 14 31 33 16 11 15 74 113 11 32 41 17 13 11 88 114 11 32 33 13 14 15 38 115 10 37 34 12 18 12 76 116 13 37 32 18 16 10 86 117 13 33 40 14 14 14 54 118 8 34 40 14 14 13 70 119 11 33 35 13 14 9 69 120 12 38 36 16 14 15 90 121 11 33 37 13 12 15 54 122 13 31 27 16 14 14 76 123 12 38 39 13 15 11 89 124 14 37 38 16 15 8 76 125 13 33 31 15 15 11 73 126 15 31 33 16 13 11 79 127 10 39 32 15 17 8 90 128 11 44 39 17 17 10 74 129 9 33 36 15 19 11 81 130 11 35 33 12 15 13 72 131 10 32 33 16 13 11 71 132 11 28 32 10 9 20 66 133 8 40 37 16 15 10 77 134 11 27 30 12 15 15 65 135 12 37 38 14 15 12 74 136 12 32 29 15 16 14 82 137 9 28 22 13 11 23 54 138 11 34 35 15 14 14 63 139 10 30 35 11 11 16 54 140 8 35 34 12 15 11 64 141 9 31 35 8 13 12 69 142 8 32 34 16 15 10 54 143 9 30 34 15 16 14 84 144 15 30 35 17 14 12 86 145 11 31 23 16 15 12 77 146 8 40 31 10 16 11 89 147 13 32 27 18 16 12 76 148 12 36 36 13 11 13 60 149 12 32 31 16 12 11 75 150 9 35 32 13 9 19 73 151 7 38 39 10 16 12 85 152 13 42 37 15 13 17 79 153 9 34 38 16 16 9 71 154 6 35 39 16 12 12 72 155 8 35 34 14 9 19 69 156 8 33 31 10 13 18 78 157 15 36 32 17 13 15 54 158 6 32 37 13 14 14 69 159 9 33 36 15 19 11 81 160 11 34 32 16 13 9 84 161 8 32 35 12 12 18 84 162 8 34 36 13 13 16 69 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Connected Separate Learning Happiness Depression 4.536742 -0.047566 0.033265 0.529087 -0.040261 -0.023069 Belonging -0.000217 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -5.8591 -0.9791 0.2434 1.3513 3.1690 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.536742 2.552828 1.777 0.0775 . Connected -0.047566 0.046846 -1.015 0.3115 Separate 0.033265 0.043892 0.758 0.4497 Learning 0.529087 0.066724 7.929 4.06e-13 *** Happiness -0.040261 0.075143 -0.536 0.5929 Depression -0.023068 0.055311 -0.417 0.6772 Belonging -0.000217 0.014179 -0.015 0.9878 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.82 on 155 degrees of freedom Multiple R-squared: 0.3045, Adjusted R-squared: 0.2776 F-statistic: 11.31 on 6 and 155 DF, p-value: 1.844e-10 > 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,] 0.99986270 0.0002745959 0.0001372979 [2,] 0.99964384 0.0007123106 0.0003561553 [3,] 0.99952185 0.0009562934 0.0004781467 [4,] 0.99909014 0.0018197167 0.0009098583 [5,] 0.99833092 0.0033381545 0.0016690772 [6,] 0.99799609 0.0040078259 0.0020039129 [7,] 0.99661985 0.0067603079 0.0033801540 [8,] 0.99393542 0.0121291681 0.0060645840 [9,] 0.99257305 0.0148538927 0.0074269463 [10,] 0.98888885 0.0222222921 0.0111111460 [11,] 0.98197735 0.0360452988 0.0180226494 [12,] 0.97350450 0.0529909989 0.0264954994 [13,] 0.96240498 0.0751900336 0.0375950168 [14,] 0.94674351 0.1065129806 0.0532564903 [15,] 0.92822882 0.1435423556 0.0717711778 [16,] 0.92556537 0.1488692510 0.0744346255 [17,] 0.94367604 0.1126479285 0.0563239643 [18,] 0.93319641 0.1336071779 0.0668035889 [19,] 0.93964148 0.1207170309 0.0603585155 [20,] 0.91886097 0.1622780587 0.0811390293 [21,] 0.89839910 0.2032018036 0.1016009018 [22,] 0.87963674 0.2407265139 0.1203632569 [23,] 0.89698233 0.2060353322 0.1030176661 [24,] 0.86784535 0.2643092910 0.1321546455 [25,] 0.83353679 0.3329264243 0.1664632122 [26,] 0.82250186 0.3549962782 0.1774981391 [27,] 0.78564799 0.4287040292 0.2143520146 [28,] 0.82570143 0.3485971333 0.1742985666 [29,] 0.81699378 0.3660124336 0.1830062168 [30,] 0.80653398 0.3869320419 0.1934660210 [31,] 0.78780291 0.4243941715 0.2121970858 [32,] 0.76310074 0.4737985193 0.2368992596 [33,] 0.74724666 0.5055066797 0.2527533399 [34,] 0.74819473 0.5036105439 0.2518052719 [35,] 0.70562059 0.5887588245 0.2943794122 [36,] 0.66139306 0.6772138723 0.3386069362 [37,] 0.72869343 0.5426131389 0.2713065695 [38,] 0.73483145 0.5303370917 0.2651685459 [39,] 0.69084112 0.6183177510 0.3091588755 [40,] 0.65302594 0.6939481152 0.3469740576 [41,] 0.63903319 0.7219336237 0.3609668118 [42,] 0.64450924 0.7109815279 0.3554907639 [43,] 0.59790162 0.8041967537 0.4020983768 [44,] 0.62591875 0.7481625010 0.3740812505 [45,] 0.59094335 0.8181132951 0.4090566475 [46,] 0.68528918 0.6294216447 0.3147108224 [47,] 0.84430680 0.3113863990 0.1556931995 [48,] 0.81714871 0.3657025893 0.1828512947 [49,] 0.78322245 0.4335551060 0.2167775530 [50,] 0.74682278 0.5063544459 0.2531772229 [51,] 0.73536072 0.5292785678 0.2646392839 [52,] 0.75321041 0.4935791812 0.2467895906 [53,] 0.71706726 0.5658654722 0.2829327361 [54,] 0.73169654 0.5366069231 0.2683034616 [55,] 0.69144000 0.6171200023 0.3085600011 [56,] 0.65689978 0.6862004491 0.3431002246 [57,] 0.61526775 0.7694645100 0.3847322550 [58,] 0.59480577 0.8103884598 0.4051942299 [59,] 0.57748973 0.8450205390 0.4225102695 [60,] 0.59610780 0.8077844097 0.4038922049 [61,] 0.55232250 0.8953550047 0.4476775023 [62,] 0.51217300 0.9756540082 0.4878270041 [63,] 0.46906605 0.9381320935 0.5309339532 [64,] 0.44068879 0.8813775864 0.5593112068 [65,] 0.41666626 0.8333325122 0.5833337439 [66,] 0.39064291 0.7812858108 0.6093570946 [67,] 0.44057883 0.8811576502 0.5594211749 [68,] 0.42678357 0.8535671424 0.5732164288 [69,] 0.38603383 0.7720676615 0.6139661692 [70,] 0.39089122 0.7817824445 0.6091087777 [71,] 0.36831449 0.7366289859 0.6316855070 [72,] 0.32762555 0.6552511046 0.6723744477 [73,] 0.32424182 0.6484836322 0.6757581839 [74,] 0.28590204 0.5718040847 0.7140979577 [75,] 0.26083756 0.5216751117 0.7391624442 [76,] 0.22495044 0.4499008816 0.7750495592 [77,] 0.21708678 0.4341735509 0.7829132245 [78,] 0.21679389 0.4335877864 0.7832061068 [79,] 0.18479645 0.3695928915 0.8152035542 [80,] 0.15667696 0.3133539128 0.8433230436 [81,] 0.13571116 0.2714223270 0.8642888365 [82,] 0.11594805 0.2318960969 0.8840519515 [83,] 0.16207714 0.3241542706 0.8379228647 [84,] 0.14130885 0.2826177082 0.8586911459 [85,] 0.12599725 0.2519944951 0.8740027525 [86,] 0.12165917 0.2433183414 0.8783408293 [87,] 0.11321085 0.2264216932 0.8867891534 [88,] 0.10449403 0.2089880637 0.8955059682 [89,] 0.12893683 0.2578736550 0.8710631725 [90,] 0.10760992 0.2152198313 0.8923900844 [91,] 0.09085602 0.1817120393 0.9091439803 [92,] 0.11157534 0.2231506833 0.8884246584 [93,] 0.09231257 0.1846251420 0.9076874290 [94,] 0.08806149 0.1761229860 0.9119385070 [95,] 0.07421863 0.1484372669 0.9257813665 [96,] 0.06253472 0.1250694435 0.9374652783 [97,] 0.06364992 0.1272998445 0.9363500777 [98,] 0.05012751 0.1002550117 0.9498724942 [99,] 0.04432144 0.0886428811 0.9556785594 [100,] 0.03533076 0.0706615261 0.9646692369 [101,] 0.03665618 0.0733123695 0.9633438153 [102,] 0.02880205 0.0576040951 0.9711979525 [103,] 0.03221319 0.0644263756 0.9677868122 [104,] 0.02881520 0.0576303970 0.9711848015 [105,] 0.02313138 0.0462627558 0.9768686221 [106,] 0.01822136 0.0364427130 0.9817786435 [107,] 0.01376606 0.0275321243 0.9862339379 [108,] 0.02205201 0.0441040299 0.9779479850 [109,] 0.02522014 0.0504402761 0.9747798619 [110,] 0.01937500 0.0387499975 0.9806250012 [111,] 0.01502528 0.0300505631 0.9849747185 [112,] 0.01254553 0.0250910685 0.9874544657 [113,] 0.01047970 0.0209594008 0.9895202996 [114,] 0.01235574 0.0247114814 0.9876442593 [115,] 0.01945483 0.0389096533 0.9805451734 [116,] 0.02053953 0.0410790637 0.9794604681 [117,] 0.04897786 0.0979557227 0.9510221386 [118,] 0.03743636 0.0748727243 0.9625636379 [119,] 0.02873530 0.0574705975 0.9712647013 [120,] 0.02403466 0.0480693135 0.9759653433 [121,] 0.02257273 0.0451454607 0.9774272697 [122,] 0.01831186 0.0366237257 0.9816881371 [123,] 0.02083405 0.0416680930 0.9791659535 [124,] 0.03294417 0.0658883421 0.9670558289 [125,] 0.03574487 0.0714897347 0.9642551326 [126,] 0.03969924 0.0793984784 0.9603007608 [127,] 0.03388490 0.0677698040 0.9661150980 [128,] 0.02918492 0.0583698333 0.9708150834 [129,] 0.02109372 0.0421874350 0.9789062825 [130,] 0.02106918 0.0421383546 0.9789308227 [131,] 0.01497114 0.0299422743 0.9850288629 [132,] 0.04530952 0.0906190482 0.9546904759 [133,] 0.05053176 0.1010635277 0.9494682362 [134,] 0.03721931 0.0744386167 0.9627806916 [135,] 0.36436167 0.7287233368 0.6356383316 [136,] 0.37203608 0.7440721559 0.6279639221 [137,] 0.63569541 0.7286091823 0.3643045911 [138,] 0.61905646 0.7618870890 0.3809435445 [139,] 0.82839364 0.3432127142 0.1716063571 [140,] 0.81617407 0.3676518538 0.1838259269 [141,] 0.72591887 0.5481622601 0.2740811300 [142,] 0.61978583 0.7604283371 0.3802141685 [143,] 0.60086371 0.7982725887 0.3991362944 > postscript(file="/var/fisher/rcomp/tmp/1ixjd1352140365.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2ob4e1352140365.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/37nl21352140365.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4snpf1352140365.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5uf311352140365.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 2.12326799 -0.21439813 1.45348666 -5.32172902 2.58761229 0.30372382 7 8 9 10 11 12 -0.68284659 2.78145070 1.45482560 -5.05684974 -1.29354850 0.54048473 13 14 15 16 17 18 0.40430169 -0.43224837 2.78194174 0.85054377 -0.92223700 -1.61772765 19 20 21 22 23 24 -1.66104842 0.41195884 -0.67724135 0.22180655 -0.14616831 -0.72125690 25 26 27 28 29 30 -2.78374619 0.94185705 -1.53958415 2.79050649 0.41362481 -0.66713610 31 32 33 34 35 36 0.93925576 -1.78482344 -0.28467734 0.26022844 1.55496125 0.02817069 37 38 39 40 41 42 -2.64200194 1.56684119 -1.72229629 -1.66599229 -1.47905575 1.55897493 43 44 45 46 47 48 1.46501596 -0.55949517 -0.31598856 2.99651057 2.39187738 -0.16040977 49 50 51 52 53 54 -0.50727726 1.59251558 2.14641966 -0.51862193 2.41037935 1.17061105 55 56 57 58 59 60 -3.09031378 -4.35124264 0.68208257 0.07236511 -0.18096055 -1.62395882 61 62 63 64 65 66 -2.59524619 0.49131494 2.18726234 0.26423256 0.49944393 0.57653349 67 68 69 70 71 72 1.33717680 -1.75566478 2.55619246 0.37374510 -0.33953430 0.26846325 73 74 75 76 77 78 1.18101125 1.23293720 0.51235392 -2.73527701 1.52654879 -0.48554942 79 80 81 82 83 84 1.80629771 0.60240546 0.24484188 -1.74121043 0.25883658 1.12601603 85 86 87 88 89 90 -0.18555861 -1.58339239 -1.51367846 0.29200860 0.19396205 0.80343329 91 92 93 94 95 96 -0.60858608 2.99783941 0.21935347 1.04514458 1.73805176 -1.42928198 97 98 99 100 101 102 1.46076048 -2.53417978 0.58259331 -0.76827040 2.53947644 -0.53837147 103 104 105 106 107 108 1.30256073 -0.17138550 -0.73960998 2.23871882 0.02646168 0.63918437 109 110 111 112 113 114 -0.69365208 -2.27131395 0.23852914 2.17964752 -1.57670469 0.92744560 115 116 117 118 119 120 0.76118517 0.52870462 2.19347518 -2.77855484 0.77679861 0.53707329 121 122 123 124 125 126 0.76490231 1.47738572 1.97230946 2.29872093 1.93895062 3.16898111 127 128 129 130 131 132 -0.79391011 -0.80444094 -2.06459217 1.60073397 -1.78518868 2.27781798 133 134 135 136 137 138 -3.47896131 1.36461587 1.44873451 1.06933384 -0.82967037 -0.11976817 139 140 141 142 143 144 0.72971379 -1.48040395 1.35604415 -3.76468862 -2.19168877 2.59064729 145 146 147 148 149 150 -0.39521379 -0.03891786 0.50116401 1.85576980 0.24194770 -0.99802765 151 152 153 154 155 156 -1.37796844 2.22664910 -2.78173325 -5.85905435 -2.59451209 -0.33357342 157 158 159 160 161 162 2.99783941 -4.22195476 -2.06459217 -0.70010712 -1.61133172 -2.08768161 > postscript(file="/var/fisher/rcomp/tmp/6ot7r1352140365.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 2.12326799 NA 1 -0.21439813 2.12326799 2 1.45348666 -0.21439813 3 -5.32172902 1.45348666 4 2.58761229 -5.32172902 5 0.30372382 2.58761229 6 -0.68284659 0.30372382 7 2.78145070 -0.68284659 8 1.45482560 2.78145070 9 -5.05684974 1.45482560 10 -1.29354850 -5.05684974 11 0.54048473 -1.29354850 12 0.40430169 0.54048473 13 -0.43224837 0.40430169 14 2.78194174 -0.43224837 15 0.85054377 2.78194174 16 -0.92223700 0.85054377 17 -1.61772765 -0.92223700 18 -1.66104842 -1.61772765 19 0.41195884 -1.66104842 20 -0.67724135 0.41195884 21 0.22180655 -0.67724135 22 -0.14616831 0.22180655 23 -0.72125690 -0.14616831 24 -2.78374619 -0.72125690 25 0.94185705 -2.78374619 26 -1.53958415 0.94185705 27 2.79050649 -1.53958415 28 0.41362481 2.79050649 29 -0.66713610 0.41362481 30 0.93925576 -0.66713610 31 -1.78482344 0.93925576 32 -0.28467734 -1.78482344 33 0.26022844 -0.28467734 34 1.55496125 0.26022844 35 0.02817069 1.55496125 36 -2.64200194 0.02817069 37 1.56684119 -2.64200194 38 -1.72229629 1.56684119 39 -1.66599229 -1.72229629 40 -1.47905575 -1.66599229 41 1.55897493 -1.47905575 42 1.46501596 1.55897493 43 -0.55949517 1.46501596 44 -0.31598856 -0.55949517 45 2.99651057 -0.31598856 46 2.39187738 2.99651057 47 -0.16040977 2.39187738 48 -0.50727726 -0.16040977 49 1.59251558 -0.50727726 50 2.14641966 1.59251558 51 -0.51862193 2.14641966 52 2.41037935 -0.51862193 53 1.17061105 2.41037935 54 -3.09031378 1.17061105 55 -4.35124264 -3.09031378 56 0.68208257 -4.35124264 57 0.07236511 0.68208257 58 -0.18096055 0.07236511 59 -1.62395882 -0.18096055 60 -2.59524619 -1.62395882 61 0.49131494 -2.59524619 62 2.18726234 0.49131494 63 0.26423256 2.18726234 64 0.49944393 0.26423256 65 0.57653349 0.49944393 66 1.33717680 0.57653349 67 -1.75566478 1.33717680 68 2.55619246 -1.75566478 69 0.37374510 2.55619246 70 -0.33953430 0.37374510 71 0.26846325 -0.33953430 72 1.18101125 0.26846325 73 1.23293720 1.18101125 74 0.51235392 1.23293720 75 -2.73527701 0.51235392 76 1.52654879 -2.73527701 77 -0.48554942 1.52654879 78 1.80629771 -0.48554942 79 0.60240546 1.80629771 80 0.24484188 0.60240546 81 -1.74121043 0.24484188 82 0.25883658 -1.74121043 83 1.12601603 0.25883658 84 -0.18555861 1.12601603 85 -1.58339239 -0.18555861 86 -1.51367846 -1.58339239 87 0.29200860 -1.51367846 88 0.19396205 0.29200860 89 0.80343329 0.19396205 90 -0.60858608 0.80343329 91 2.99783941 -0.60858608 92 0.21935347 2.99783941 93 1.04514458 0.21935347 94 1.73805176 1.04514458 95 -1.42928198 1.73805176 96 1.46076048 -1.42928198 97 -2.53417978 1.46076048 98 0.58259331 -2.53417978 99 -0.76827040 0.58259331 100 2.53947644 -0.76827040 101 -0.53837147 2.53947644 102 1.30256073 -0.53837147 103 -0.17138550 1.30256073 104 -0.73960998 -0.17138550 105 2.23871882 -0.73960998 106 0.02646168 2.23871882 107 0.63918437 0.02646168 108 -0.69365208 0.63918437 109 -2.27131395 -0.69365208 110 0.23852914 -2.27131395 111 2.17964752 0.23852914 112 -1.57670469 2.17964752 113 0.92744560 -1.57670469 114 0.76118517 0.92744560 115 0.52870462 0.76118517 116 2.19347518 0.52870462 117 -2.77855484 2.19347518 118 0.77679861 -2.77855484 119 0.53707329 0.77679861 120 0.76490231 0.53707329 121 1.47738572 0.76490231 122 1.97230946 1.47738572 123 2.29872093 1.97230946 124 1.93895062 2.29872093 125 3.16898111 1.93895062 126 -0.79391011 3.16898111 127 -0.80444094 -0.79391011 128 -2.06459217 -0.80444094 129 1.60073397 -2.06459217 130 -1.78518868 1.60073397 131 2.27781798 -1.78518868 132 -3.47896131 2.27781798 133 1.36461587 -3.47896131 134 1.44873451 1.36461587 135 1.06933384 1.44873451 136 -0.82967037 1.06933384 137 -0.11976817 -0.82967037 138 0.72971379 -0.11976817 139 -1.48040395 0.72971379 140 1.35604415 -1.48040395 141 -3.76468862 1.35604415 142 -2.19168877 -3.76468862 143 2.59064729 -2.19168877 144 -0.39521379 2.59064729 145 -0.03891786 -0.39521379 146 0.50116401 -0.03891786 147 1.85576980 0.50116401 148 0.24194770 1.85576980 149 -0.99802765 0.24194770 150 -1.37796844 -0.99802765 151 2.22664910 -1.37796844 152 -2.78173325 2.22664910 153 -5.85905435 -2.78173325 154 -2.59451209 -5.85905435 155 -0.33357342 -2.59451209 156 2.99783941 -0.33357342 157 -4.22195476 2.99783941 158 -2.06459217 -4.22195476 159 -0.70010712 -2.06459217 160 -1.61133172 -0.70010712 161 -2.08768161 -1.61133172 162 NA -2.08768161 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.21439813 2.12326799 [2,] 1.45348666 -0.21439813 [3,] -5.32172902 1.45348666 [4,] 2.58761229 -5.32172902 [5,] 0.30372382 2.58761229 [6,] -0.68284659 0.30372382 [7,] 2.78145070 -0.68284659 [8,] 1.45482560 2.78145070 [9,] -5.05684974 1.45482560 [10,] -1.29354850 -5.05684974 [11,] 0.54048473 -1.29354850 [12,] 0.40430169 0.54048473 [13,] -0.43224837 0.40430169 [14,] 2.78194174 -0.43224837 [15,] 0.85054377 2.78194174 [16,] -0.92223700 0.85054377 [17,] -1.61772765 -0.92223700 [18,] -1.66104842 -1.61772765 [19,] 0.41195884 -1.66104842 [20,] -0.67724135 0.41195884 [21,] 0.22180655 -0.67724135 [22,] -0.14616831 0.22180655 [23,] -0.72125690 -0.14616831 [24,] -2.78374619 -0.72125690 [25,] 0.94185705 -2.78374619 [26,] -1.53958415 0.94185705 [27,] 2.79050649 -1.53958415 [28,] 0.41362481 2.79050649 [29,] -0.66713610 0.41362481 [30,] 0.93925576 -0.66713610 [31,] -1.78482344 0.93925576 [32,] -0.28467734 -1.78482344 [33,] 0.26022844 -0.28467734 [34,] 1.55496125 0.26022844 [35,] 0.02817069 1.55496125 [36,] -2.64200194 0.02817069 [37,] 1.56684119 -2.64200194 [38,] -1.72229629 1.56684119 [39,] -1.66599229 -1.72229629 [40,] -1.47905575 -1.66599229 [41,] 1.55897493 -1.47905575 [42,] 1.46501596 1.55897493 [43,] -0.55949517 1.46501596 [44,] -0.31598856 -0.55949517 [45,] 2.99651057 -0.31598856 [46,] 2.39187738 2.99651057 [47,] -0.16040977 2.39187738 [48,] -0.50727726 -0.16040977 [49,] 1.59251558 -0.50727726 [50,] 2.14641966 1.59251558 [51,] -0.51862193 2.14641966 [52,] 2.41037935 -0.51862193 [53,] 1.17061105 2.41037935 [54,] -3.09031378 1.17061105 [55,] -4.35124264 -3.09031378 [56,] 0.68208257 -4.35124264 [57,] 0.07236511 0.68208257 [58,] -0.18096055 0.07236511 [59,] -1.62395882 -0.18096055 [60,] -2.59524619 -1.62395882 [61,] 0.49131494 -2.59524619 [62,] 2.18726234 0.49131494 [63,] 0.26423256 2.18726234 [64,] 0.49944393 0.26423256 [65,] 0.57653349 0.49944393 [66,] 1.33717680 0.57653349 [67,] -1.75566478 1.33717680 [68,] 2.55619246 -1.75566478 [69,] 0.37374510 2.55619246 [70,] -0.33953430 0.37374510 [71,] 0.26846325 -0.33953430 [72,] 1.18101125 0.26846325 [73,] 1.23293720 1.18101125 [74,] 0.51235392 1.23293720 [75,] -2.73527701 0.51235392 [76,] 1.52654879 -2.73527701 [77,] -0.48554942 1.52654879 [78,] 1.80629771 -0.48554942 [79,] 0.60240546 1.80629771 [80,] 0.24484188 0.60240546 [81,] -1.74121043 0.24484188 [82,] 0.25883658 -1.74121043 [83,] 1.12601603 0.25883658 [84,] -0.18555861 1.12601603 [85,] -1.58339239 -0.18555861 [86,] -1.51367846 -1.58339239 [87,] 0.29200860 -1.51367846 [88,] 0.19396205 0.29200860 [89,] 0.80343329 0.19396205 [90,] -0.60858608 0.80343329 [91,] 2.99783941 -0.60858608 [92,] 0.21935347 2.99783941 [93,] 1.04514458 0.21935347 [94,] 1.73805176 1.04514458 [95,] -1.42928198 1.73805176 [96,] 1.46076048 -1.42928198 [97,] -2.53417978 1.46076048 [98,] 0.58259331 -2.53417978 [99,] -0.76827040 0.58259331 [100,] 2.53947644 -0.76827040 [101,] -0.53837147 2.53947644 [102,] 1.30256073 -0.53837147 [103,] -0.17138550 1.30256073 [104,] -0.73960998 -0.17138550 [105,] 2.23871882 -0.73960998 [106,] 0.02646168 2.23871882 [107,] 0.63918437 0.02646168 [108,] -0.69365208 0.63918437 [109,] -2.27131395 -0.69365208 [110,] 0.23852914 -2.27131395 [111,] 2.17964752 0.23852914 [112,] -1.57670469 2.17964752 [113,] 0.92744560 -1.57670469 [114,] 0.76118517 0.92744560 [115,] 0.52870462 0.76118517 [116,] 2.19347518 0.52870462 [117,] -2.77855484 2.19347518 [118,] 0.77679861 -2.77855484 [119,] 0.53707329 0.77679861 [120,] 0.76490231 0.53707329 [121,] 1.47738572 0.76490231 [122,] 1.97230946 1.47738572 [123,] 2.29872093 1.97230946 [124,] 1.93895062 2.29872093 [125,] 3.16898111 1.93895062 [126,] -0.79391011 3.16898111 [127,] -0.80444094 -0.79391011 [128,] -2.06459217 -0.80444094 [129,] 1.60073397 -2.06459217 [130,] -1.78518868 1.60073397 [131,] 2.27781798 -1.78518868 [132,] -3.47896131 2.27781798 [133,] 1.36461587 -3.47896131 [134,] 1.44873451 1.36461587 [135,] 1.06933384 1.44873451 [136,] -0.82967037 1.06933384 [137,] -0.11976817 -0.82967037 [138,] 0.72971379 -0.11976817 [139,] -1.48040395 0.72971379 [140,] 1.35604415 -1.48040395 [141,] -3.76468862 1.35604415 [142,] -2.19168877 -3.76468862 [143,] 2.59064729 -2.19168877 [144,] -0.39521379 2.59064729 [145,] -0.03891786 -0.39521379 [146,] 0.50116401 -0.03891786 [147,] 1.85576980 0.50116401 [148,] 0.24194770 1.85576980 [149,] -0.99802765 0.24194770 [150,] -1.37796844 -0.99802765 [151,] 2.22664910 -1.37796844 [152,] -2.78173325 2.22664910 [153,] -5.85905435 -2.78173325 [154,] -2.59451209 -5.85905435 [155,] -0.33357342 -2.59451209 [156,] 2.99783941 -0.33357342 [157,] -4.22195476 2.99783941 [158,] -2.06459217 -4.22195476 [159,] -0.70010712 -2.06459217 [160,] -1.61133172 -0.70010712 [161,] -2.08768161 -1.61133172 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.21439813 2.12326799 2 1.45348666 -0.21439813 3 -5.32172902 1.45348666 4 2.58761229 -5.32172902 5 0.30372382 2.58761229 6 -0.68284659 0.30372382 7 2.78145070 -0.68284659 8 1.45482560 2.78145070 9 -5.05684974 1.45482560 10 -1.29354850 -5.05684974 11 0.54048473 -1.29354850 12 0.40430169 0.54048473 13 -0.43224837 0.40430169 14 2.78194174 -0.43224837 15 0.85054377 2.78194174 16 -0.92223700 0.85054377 17 -1.61772765 -0.92223700 18 -1.66104842 -1.61772765 19 0.41195884 -1.66104842 20 -0.67724135 0.41195884 21 0.22180655 -0.67724135 22 -0.14616831 0.22180655 23 -0.72125690 -0.14616831 24 -2.78374619 -0.72125690 25 0.94185705 -2.78374619 26 -1.53958415 0.94185705 27 2.79050649 -1.53958415 28 0.41362481 2.79050649 29 -0.66713610 0.41362481 30 0.93925576 -0.66713610 31 -1.78482344 0.93925576 32 -0.28467734 -1.78482344 33 0.26022844 -0.28467734 34 1.55496125 0.26022844 35 0.02817069 1.55496125 36 -2.64200194 0.02817069 37 1.56684119 -2.64200194 38 -1.72229629 1.56684119 39 -1.66599229 -1.72229629 40 -1.47905575 -1.66599229 41 1.55897493 -1.47905575 42 1.46501596 1.55897493 43 -0.55949517 1.46501596 44 -0.31598856 -0.55949517 45 2.99651057 -0.31598856 46 2.39187738 2.99651057 47 -0.16040977 2.39187738 48 -0.50727726 -0.16040977 49 1.59251558 -0.50727726 50 2.14641966 1.59251558 51 -0.51862193 2.14641966 52 2.41037935 -0.51862193 53 1.17061105 2.41037935 54 -3.09031378 1.17061105 55 -4.35124264 -3.09031378 56 0.68208257 -4.35124264 57 0.07236511 0.68208257 58 -0.18096055 0.07236511 59 -1.62395882 -0.18096055 60 -2.59524619 -1.62395882 61 0.49131494 -2.59524619 62 2.18726234 0.49131494 63 0.26423256 2.18726234 64 0.49944393 0.26423256 65 0.57653349 0.49944393 66 1.33717680 0.57653349 67 -1.75566478 1.33717680 68 2.55619246 -1.75566478 69 0.37374510 2.55619246 70 -0.33953430 0.37374510 71 0.26846325 -0.33953430 72 1.18101125 0.26846325 73 1.23293720 1.18101125 74 0.51235392 1.23293720 75 -2.73527701 0.51235392 76 1.52654879 -2.73527701 77 -0.48554942 1.52654879 78 1.80629771 -0.48554942 79 0.60240546 1.80629771 80 0.24484188 0.60240546 81 -1.74121043 0.24484188 82 0.25883658 -1.74121043 83 1.12601603 0.25883658 84 -0.18555861 1.12601603 85 -1.58339239 -0.18555861 86 -1.51367846 -1.58339239 87 0.29200860 -1.51367846 88 0.19396205 0.29200860 89 0.80343329 0.19396205 90 -0.60858608 0.80343329 91 2.99783941 -0.60858608 92 0.21935347 2.99783941 93 1.04514458 0.21935347 94 1.73805176 1.04514458 95 -1.42928198 1.73805176 96 1.46076048 -1.42928198 97 -2.53417978 1.46076048 98 0.58259331 -2.53417978 99 -0.76827040 0.58259331 100 2.53947644 -0.76827040 101 -0.53837147 2.53947644 102 1.30256073 -0.53837147 103 -0.17138550 1.30256073 104 -0.73960998 -0.17138550 105 2.23871882 -0.73960998 106 0.02646168 2.23871882 107 0.63918437 0.02646168 108 -0.69365208 0.63918437 109 -2.27131395 -0.69365208 110 0.23852914 -2.27131395 111 2.17964752 0.23852914 112 -1.57670469 2.17964752 113 0.92744560 -1.57670469 114 0.76118517 0.92744560 115 0.52870462 0.76118517 116 2.19347518 0.52870462 117 -2.77855484 2.19347518 118 0.77679861 -2.77855484 119 0.53707329 0.77679861 120 0.76490231 0.53707329 121 1.47738572 0.76490231 122 1.97230946 1.47738572 123 2.29872093 1.97230946 124 1.93895062 2.29872093 125 3.16898111 1.93895062 126 -0.79391011 3.16898111 127 -0.80444094 -0.79391011 128 -2.06459217 -0.80444094 129 1.60073397 -2.06459217 130 -1.78518868 1.60073397 131 2.27781798 -1.78518868 132 -3.47896131 2.27781798 133 1.36461587 -3.47896131 134 1.44873451 1.36461587 135 1.06933384 1.44873451 136 -0.82967037 1.06933384 137 -0.11976817 -0.82967037 138 0.72971379 -0.11976817 139 -1.48040395 0.72971379 140 1.35604415 -1.48040395 141 -3.76468862 1.35604415 142 -2.19168877 -3.76468862 143 2.59064729 -2.19168877 144 -0.39521379 2.59064729 145 -0.03891786 -0.39521379 146 0.50116401 -0.03891786 147 1.85576980 0.50116401 148 0.24194770 1.85576980 149 -0.99802765 0.24194770 150 -1.37796844 -0.99802765 151 2.22664910 -1.37796844 152 -2.78173325 2.22664910 153 -5.85905435 -2.78173325 154 -2.59451209 -5.85905435 155 -0.33357342 -2.59451209 156 2.99783941 -0.33357342 157 -4.22195476 2.99783941 158 -2.06459217 -4.22195476 159 -0.70010712 -2.06459217 160 -1.61133172 -0.70010712 161 -2.08768161 -1.61133172 > 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/fisher/rcomp/tmp/7ayyt1352140365.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8x9591352140365.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/95p381352140365.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10dik91352140365.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/fisher/rcomp/tmp/11ex121352140365.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/fisher/rcomp/tmp/12otuw1352140365.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/fisher/rcomp/tmp/131bvd1352140365.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/fisher/rcomp/tmp/14s6jc1352140365.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/fisher/rcomp/tmp/15ces91352140365.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/fisher/rcomp/tmp/16za591352140365.tab") + } > > try(system("convert tmp/1ixjd1352140365.ps tmp/1ixjd1352140365.png",intern=TRUE)) character(0) > try(system("convert tmp/2ob4e1352140365.ps tmp/2ob4e1352140365.png",intern=TRUE)) character(0) > try(system("convert tmp/37nl21352140365.ps tmp/37nl21352140365.png",intern=TRUE)) character(0) > try(system("convert tmp/4snpf1352140365.ps tmp/4snpf1352140365.png",intern=TRUE)) character(0) > try(system("convert tmp/5uf311352140365.ps tmp/5uf311352140365.png",intern=TRUE)) character(0) > try(system("convert tmp/6ot7r1352140365.ps tmp/6ot7r1352140365.png",intern=TRUE)) character(0) > try(system("convert tmp/7ayyt1352140365.ps tmp/7ayyt1352140365.png",intern=TRUE)) character(0) > try(system("convert tmp/8x9591352140365.ps tmp/8x9591352140365.png",intern=TRUE)) character(0) > try(system("convert tmp/95p381352140365.ps tmp/95p381352140365.png",intern=TRUE)) character(0) > try(system("convert tmp/10dik91352140365.ps tmp/10dik91352140365.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.207 1.113 9.319