R version 2.11.1 (2010-05-31) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,13 + ,1 + ,19 + ,19 + ,20 + ,20 + ,8 + ,8 + ,7 + ,7 + ,19 + ,25 + ,25 + ,1 + ,20 + ,20 + ,11 + ,11 + ,17 + ,17 + ,8 + ,8 + ,21 + ,23 + ,23 + ,1 + ,17 + ,17 + ,9 + ,9 + ,11 + ,11 + ,5 + ,5 + ,23 + ,26 + ,26 + ,1 + ,21 + ,21 + ,10 + ,10 + ,12 + ,12 + ,8 + ,8 + ,16 + ,23 + ,23 + ,1 + ,26 + ,26 + ,13 + ,13 + ,20 + ,20 + ,10 + ,10 + ,26 + ,22 + ,22 + ,1 + ,17 + ,17 + ,8 + ,8 + ,12 + ,12 + ,9 + ,9 + ,21 + ,24 + ,24 + ,1 + ,21 + ,21 + ,15 + ,15 + ,7 + ,7 + ,5 + ,5 + ,21 + ,24 + ,24 + ,1 + ,28 + ,28 + ,14 + ,14 + ,11 + ,11 + ,8 + ,8 + ,24 + ,24 + ,24) + ,dim=c(12 + ,159) + ,dimnames=list(c('Gender' + ,'CM' + ,'CM_G' + ,'D' + ,'D_G' + ,'PE' + ,'PE_G' + ,'PC' + ,'PC_G' + ,'PS' + ,'O' + ,'O_G') + ,1:159)) > y <- array(NA,dim=c(12,159),dimnames=list(c('Gender','CM','CM_G','D','D_G','PE','PE_G','PC','PC_G','PS','O','O_G'),1:159)) > 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 = '10' > #'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 > 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 PS Gender CM CM_G D D_G PE PE_G PC PC_G O O_G 1 25 0 25 0 11 0 7 0 8 0 23 0 2 30 0 17 0 6 0 17 0 8 0 25 0 3 22 0 18 0 8 0 12 0 9 0 19 0 4 22 0 16 0 10 0 12 0 7 0 29 0 5 25 0 20 0 10 0 11 0 4 0 25 0 6 23 0 16 0 11 0 11 0 11 0 21 0 7 17 0 18 0 16 0 12 0 7 0 22 0 8 21 0 17 0 11 0 13 0 7 0 25 0 9 19 0 30 0 12 0 16 0 10 0 18 0 10 15 0 23 0 8 0 11 0 10 0 22 0 11 16 0 18 0 12 0 10 0 8 0 15 0 12 22 0 21 0 9 0 9 0 9 0 20 0 13 23 0 31 0 14 0 17 0 11 0 20 0 14 23 0 27 0 15 0 11 0 9 0 21 0 15 19 0 21 0 9 0 14 0 13 0 21 0 16 23 0 16 0 8 0 15 0 9 0 24 0 17 25 0 20 0 9 0 15 0 6 0 24 0 18 22 0 17 0 9 0 13 0 6 0 23 0 19 26 0 25 0 16 0 18 0 16 0 24 0 20 29 0 26 0 11 0 18 0 5 0 18 0 21 32 0 25 0 8 0 12 0 7 0 25 0 22 25 0 17 0 9 0 17 0 9 0 21 0 23 28 0 32 0 12 0 18 0 12 0 22 0 24 25 0 22 0 9 0 14 0 9 0 23 0 25 25 0 17 0 9 0 16 0 5 0 23 0 26 18 0 20 0 14 0 14 0 10 0 24 0 27 25 0 29 0 10 0 12 0 8 0 23 0 28 25 0 23 0 14 0 17 0 7 0 21 0 29 20 0 20 0 10 0 12 0 8 0 28 0 30 15 0 11 0 6 0 6 0 4 0 16 0 31 24 0 26 0 13 0 12 0 8 0 29 0 32 26 0 22 0 10 0 12 0 8 0 27 0 33 14 0 14 0 15 0 13 0 8 0 16 0 34 24 0 19 0 12 0 14 0 7 0 28 0 35 25 0 20 0 11 0 11 0 8 0 25 0 36 20 0 28 0 8 0 12 0 7 0 22 0 37 21 0 19 0 9 0 9 0 7 0 23 0 38 27 0 30 0 9 0 15 0 9 0 26 0 39 23 0 29 0 15 0 18 0 11 0 23 0 40 25 0 26 0 9 0 15 0 6 0 25 0 41 20 0 23 0 10 0 12 0 8 0 21 0 42 22 0 21 0 12 0 14 0 9 0 24 0 43 25 0 28 0 11 0 13 0 6 0 22 0 44 25 0 23 0 14 0 13 0 10 0 27 0 45 17 0 18 0 6 0 11 0 8 0 26 0 46 25 0 20 0 8 0 16 0 10 0 24 0 47 26 0 21 0 10 0 11 0 5 0 24 0 48 27 0 28 0 12 0 16 0 14 0 22 0 49 19 0 10 0 5 0 8 0 6 0 24 0 50 22 0 22 0 10 0 15 0 6 0 20 0 51 32 0 31 0 10 0 21 0 12 0 26 0 52 21 0 29 0 13 0 18 0 12 0 21 0 53 18 0 22 0 10 0 13 0 8 0 19 0 54 23 0 23 0 10 0 15 0 10 0 21 0 55 20 0 20 0 9 0 19 0 10 0 16 0 56 21 0 18 0 8 0 15 0 10 0 22 0 57 17 0 25 0 14 0 11 0 5 0 15 0 58 18 0 21 0 8 0 10 0 7 0 17 0 59 19 0 24 0 9 0 13 0 10 0 15 0 60 22 0 25 0 14 0 15 0 11 0 21 0 61 14 0 13 0 8 0 12 0 7 0 19 0 62 18 0 28 0 8 0 16 0 12 0 24 0 63 35 0 25 0 7 0 18 0 11 0 17 0 64 29 0 9 0 6 0 8 0 11 0 23 0 65 21 0 16 0 8 0 13 0 5 0 24 0 66 25 0 19 0 6 0 17 0 8 0 14 0 67 26 0 29 0 11 0 7 0 4 0 22 0 68 17 0 14 0 11 0 12 0 7 0 16 0 69 25 0 22 0 14 0 14 0 11 0 19 0 70 20 0 15 0 8 0 6 0 6 0 25 0 71 22 0 15 0 8 0 10 0 4 0 24 0 72 24 0 20 0 11 0 11 0 8 0 26 0 73 21 0 18 0 10 0 14 0 9 0 26 0 74 26 0 33 0 14 0 11 0 8 0 25 0 75 24 0 22 0 11 0 13 0 11 0 18 0 76 16 0 16 0 9 0 12 0 8 0 21 0 77 18 0 16 0 8 0 9 0 4 0 23 0 78 19 0 18 0 13 0 12 0 6 0 20 0 79 21 0 18 0 12 0 13 0 9 0 13 0 80 22 0 22 0 13 0 12 0 13 0 15 0 81 23 0 30 0 14 0 9 0 9 0 14 0 82 29 0 30 0 12 0 15 0 10 0 22 0 83 21 0 24 0 14 0 24 0 20 0 10 0 84 23 0 21 0 13 0 17 0 11 0 22 0 85 27 0 29 0 16 0 11 0 6 0 24 0 86 25 0 31 0 9 0 17 0 9 0 19 0 87 21 0 20 0 9 0 11 0 7 0 20 0 88 10 0 16 0 9 0 12 0 9 0 13 0 89 20 0 22 0 8 0 14 0 10 0 20 0 90 26 0 20 0 7 0 11 0 9 0 22 0 91 24 0 28 0 16 0 16 0 8 0 24 0 92 29 0 38 0 11 0 21 0 7 0 29 0 93 19 0 22 0 9 0 14 0 6 0 12 0 94 24 0 20 0 11 0 20 0 13 0 20 0 95 19 0 17 0 9 0 13 0 6 0 21 0 96 22 0 22 0 13 0 15 0 10 0 22 0 97 17 0 31 0 16 0 19 0 16 0 20 0 98 24 1 24 24 14 14 11 11 12 12 26 26 99 19 1 18 18 12 12 10 10 8 8 23 23 100 19 1 23 23 13 13 14 14 12 12 24 24 101 23 1 15 15 11 11 11 11 8 8 22 22 102 27 1 12 12 4 4 15 15 4 4 28 28 103 14 1 15 15 8 8 11 11 8 8 12 12 104 22 1 20 20 8 8 17 17 7 7 24 24 105 21 1 34 34 16 16 18 18 11 11 20 20 106 18 1 31 31 14 14 10 10 8 8 23 23 107 20 1 19 19 11 11 11 11 8 8 28 28 108 19 1 21 21 9 9 13 13 9 9 24 24 109 24 1 22 22 9 9 16 16 9 9 23 23 110 25 1 24 24 10 10 9 9 6 6 29 29 111 29 1 32 32 16 16 9 9 6 6 26 26 112 28 1 33 33 11 11 9 9 6 6 22 22 113 17 1 13 13 16 16 12 12 5 5 22 22 114 29 1 25 25 12 12 12 12 7 7 23 23 115 26 1 29 29 14 14 18 18 10 10 30 30 116 14 1 18 18 10 10 15 15 8 8 17 17 117 26 1 20 20 10 10 10 10 8 8 23 23 118 20 1 15 15 12 12 11 11 8 8 25 25 119 32 1 33 33 14 14 9 9 6 6 24 24 120 23 1 26 26 16 16 5 5 4 4 24 24 121 21 1 18 18 9 9 12 12 8 8 24 24 122 30 1 28 28 8 8 24 24 20 20 20 20 123 24 1 17 17 8 8 14 14 6 6 22 22 124 22 1 12 12 7 7 7 7 4 4 28 28 125 24 1 17 17 9 9 12 12 9 9 25 25 126 24 1 21 21 10 10 13 13 6 6 24 24 127 24 1 18 18 13 13 8 8 9 9 24 24 128 19 1 10 10 10 10 11 11 5 5 23 23 129 31 1 29 29 11 11 9 9 5 5 30 30 130 22 1 31 31 8 8 11 11 8 8 24 24 131 27 1 19 19 9 9 13 13 8 8 21 21 132 19 1 9 9 13 13 10 10 6 6 25 25 133 21 1 13 13 14 14 13 13 6 6 25 25 134 23 1 19 19 12 12 10 10 8 8 29 29 135 19 1 21 21 12 12 13 13 8 8 22 22 136 19 1 23 23 14 14 8 8 5 5 27 27 137 20 1 21 21 11 11 16 16 7 7 24 24 138 23 1 15 15 14 14 9 9 8 8 29 29 139 17 1 19 19 10 10 12 12 7 7 21 21 140 17 1 26 26 14 14 14 14 8 8 24 24 141 17 1 16 16 11 11 9 9 5 5 23 23 142 21 1 19 19 9 9 11 11 10 10 27 27 143 21 1 31 31 16 16 14 14 9 9 25 25 144 18 1 19 19 9 9 12 12 7 7 21 21 145 19 1 15 15 7 7 12 12 6 6 21 21 146 20 1 23 23 14 14 11 11 10 10 29 29 147 15 1 17 17 14 14 12 12 6 6 21 21 148 24 1 21 21 8 8 9 9 11 11 20 20 149 20 1 17 17 11 11 9 9 6 6 19 19 150 22 1 25 25 14 14 15 15 9 9 24 24 151 13 1 20 20 11 11 8 8 4 4 13 13 152 19 1 19 19 20 20 8 8 7 7 25 25 153 21 1 20 20 11 11 17 17 8 8 23 23 154 23 1 17 17 9 9 11 11 5 5 26 26 155 16 1 21 21 10 10 12 12 8 8 23 23 156 26 1 26 26 13 13 20 20 10 10 22 22 157 21 1 17 17 8 8 12 12 9 9 24 24 158 21 1 21 21 15 15 7 7 5 5 24 24 159 24 1 28 28 14 14 11 11 8 8 24 24 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Gender CM CM_G D D_G 7.64947 -1.23162 0.29724 0.04480 -0.31399 -0.13065 PE PE_G PC PC_G O O_G 0.28856 -0.28465 -0.03396 0.11031 0.36989 0.16196 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -8.5470 -2.2015 -0.2332 2.2144 11.0089 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.64947 2.77448 2.757 0.006572 ** Gender -1.23162 4.87740 -0.253 0.800995 CM 0.29724 0.07669 3.876 0.000160 *** CM_G 0.04480 0.11390 0.393 0.694667 D -0.31399 0.15102 -2.079 0.039347 * D_G -0.13065 0.22614 -0.578 0.564345 PE 0.28856 0.13292 2.171 0.031543 * PE_G -0.28465 0.21516 -1.323 0.187896 PC -0.03396 0.16002 -0.212 0.832210 PC_G 0.11031 0.27752 0.397 0.691594 O 0.36989 0.09111 4.060 7.95e-05 *** O_G 0.16196 0.15954 1.015 0.311673 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.424 on 147 degrees of freedom Multiple R-squared: 0.3865, Adjusted R-squared: 0.3406 F-statistic: 8.418 on 11 and 147 DF, p-value: 2.193e-11 > 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,] 0.99108993 0.01782014 0.008910072 [2,] 0.97899308 0.04201385 0.021006924 [3,] 0.95823234 0.08353532 0.041767659 [4,] 0.92847997 0.14304005 0.071520026 [5,] 0.92191489 0.15617021 0.078085105 [6,] 0.94087275 0.11825451 0.059127254 [7,] 0.96765162 0.06469677 0.032348383 [8,] 0.95410712 0.09178575 0.045892877 [9,] 0.93153299 0.13693403 0.068467015 [10,] 0.90082869 0.19834262 0.099171311 [11,] 0.86662761 0.26674477 0.133372387 [12,] 0.86450669 0.27098663 0.135493313 [13,] 0.82166555 0.35666890 0.178334452 [14,] 0.78178417 0.43643167 0.218215833 [15,] 0.80695676 0.38608647 0.193043236 [16,] 0.76459403 0.47081193 0.235405966 [17,] 0.71500370 0.56999261 0.284996304 [18,] 0.66522336 0.66955328 0.334776641 [19,] 0.61429273 0.77141455 0.385707273 [20,] 0.55060939 0.89878123 0.449390613 [21,] 0.52831697 0.94336606 0.471683031 [22,] 0.62544708 0.74910584 0.374552922 [23,] 0.56439064 0.87121871 0.435609356 [24,] 0.50883956 0.98232089 0.491160443 [25,] 0.46694829 0.93389659 0.533051706 [26,] 0.42742388 0.85484776 0.572576120 [27,] 0.39087524 0.78175047 0.609124765 [28,] 0.33695800 0.67391601 0.663041997 [29,] 0.28650779 0.57301557 0.713492214 [30,] 0.25195171 0.50390343 0.748048286 [31,] 0.39047418 0.78094837 0.609525816 [32,] 0.33860307 0.67720614 0.661396932 [33,] 0.33040181 0.66080361 0.669598194 [34,] 0.32028074 0.64056148 0.679719261 [35,] 0.27429162 0.54858325 0.725708376 [36,] 0.23692998 0.47385996 0.763070022 [37,] 0.21782868 0.43565735 0.782171323 [38,] 0.22858005 0.45716010 0.771419952 [39,] 0.22576129 0.45152257 0.774238714 [40,] 0.18733045 0.37466089 0.812669555 [41,] 0.16189069 0.32378138 0.838109310 [42,] 0.13581845 0.27163690 0.864181551 [43,] 0.11568956 0.23137913 0.884310437 [44,] 0.09954430 0.19908859 0.900455705 [45,] 0.08452768 0.16905536 0.915472321 [46,] 0.06613969 0.13227937 0.933860314 [47,] 0.08447735 0.16895470 0.915522650 [48,] 0.25378019 0.50756037 0.746219813 [49,] 0.70212923 0.59574155 0.297870774 [50,] 0.92393450 0.15213101 0.076065503 [51,] 0.90820296 0.18359408 0.091797042 [52,] 0.91921244 0.16157512 0.080787560 [53,] 0.91842337 0.16315325 0.081576625 [54,] 0.89828556 0.20342889 0.101714443 [55,] 0.91000670 0.17998660 0.089993299 [56,] 0.88909901 0.22180198 0.110900991 [57,] 0.86865680 0.26268640 0.131343202 [58,] 0.84584342 0.30831317 0.154156584 [59,] 0.82426934 0.35146133 0.175730663 [60,] 0.79675760 0.40648480 0.203242402 [61,] 0.78983341 0.42033318 0.210166589 [62,] 0.80241005 0.39517991 0.197589953 [63,] 0.78838462 0.42323076 0.211615379 [64,] 0.75338393 0.49323214 0.246616068 [65,] 0.75390148 0.49219704 0.246098518 [66,] 0.73806175 0.52387651 0.261938253 [67,] 0.72442218 0.55115564 0.275577820 [68,] 0.74486934 0.51026132 0.255130661 [69,] 0.77248842 0.45502316 0.227511579 [70,] 0.73885238 0.52229525 0.261147625 [71,] 0.74770661 0.50458679 0.252293394 [72,] 0.72367134 0.55265733 0.276328663 [73,] 0.68172991 0.63654018 0.318270090 [74,] 0.78495550 0.43008900 0.215044502 [75,] 0.76943384 0.46113232 0.230566161 [76,] 0.79904655 0.40190691 0.200953454 [77,] 0.77723359 0.44553283 0.222766414 [78,] 0.75319644 0.49360713 0.246803564 [79,] 0.71232586 0.57534829 0.287674143 [80,] 0.66877667 0.66244666 0.331223331 [81,] 0.62779186 0.74441628 0.372208140 [82,] 0.57943612 0.84112776 0.420563878 [83,] 0.61080319 0.77839362 0.389196808 [84,] 0.56305988 0.87388023 0.436940117 [85,] 0.51541973 0.96916055 0.484580274 [86,] 0.49330808 0.98661615 0.506691924 [87,] 0.48315620 0.96631241 0.516843795 [88,] 0.46795335 0.93590670 0.532046652 [89,] 0.42090422 0.84180845 0.579095775 [90,] 0.38947912 0.77895824 0.610520881 [91,] 0.36252535 0.72505070 0.637474648 [92,] 0.43332425 0.86664850 0.566675750 [93,] 0.42889567 0.85779135 0.571104326 [94,] 0.43141143 0.86282286 0.568588571 [95,] 0.39037007 0.78074014 0.609629929 [96,] 0.35274073 0.70548146 0.647259268 [97,] 0.36819093 0.73638185 0.631809073 [98,] 0.36632494 0.73264988 0.633675062 [99,] 0.32570388 0.65140776 0.674296121 [100,] 0.44854404 0.89708808 0.551455962 [101,] 0.39479667 0.78959334 0.605203329 [102,] 0.40619515 0.81239031 0.593804847 [103,] 0.42856377 0.85712754 0.571436229 [104,] 0.37211672 0.74423344 0.627883280 [105,] 0.62121122 0.75757757 0.378788783 [106,] 0.64055886 0.71888227 0.359441137 [107,] 0.58710406 0.82579187 0.412895937 [108,] 0.62510511 0.74978979 0.374894894 [109,] 0.61744445 0.76511110 0.382555550 [110,] 0.55548693 0.88902615 0.444513073 [111,] 0.50427777 0.99144446 0.495722231 [112,] 0.47054992 0.94109984 0.529450082 [113,] 0.48003251 0.96006503 0.519967487 [114,] 0.41266492 0.82532984 0.587335078 [115,] 0.63514124 0.72971753 0.364858765 [116,] 0.59430100 0.81139799 0.405698997 [117,] 0.79940146 0.40119707 0.200598535 [118,] 0.73820363 0.52359275 0.261796373 [119,] 0.68865210 0.62269580 0.311347899 [120,] 0.61932478 0.76135044 0.380675218 [121,] 0.54299860 0.91400280 0.457001402 [122,] 0.46803679 0.93607359 0.531963206 [123,] 0.38223554 0.76447108 0.617764460 [124,] 0.32610718 0.65221437 0.673892816 [125,] 0.27225777 0.54451553 0.727742234 [126,] 0.30833798 0.61667597 0.691662016 [127,] 0.22511896 0.45023793 0.774881037 [128,] 0.15891998 0.31783996 0.841080018 [129,] 0.11619229 0.23238457 0.883807714 [130,] 0.06795274 0.13590549 0.932047257 > postscript(file="/var/www/rcomp/tmp/1eamp1290474916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/26j391290474916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/36j391290474916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/46j391290474916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/56j391290474916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 159 Frequency = 1 1 2 3 4 5 6 3.11771681 5.30030204 1.32716052 -1.21724770 2.26003903 3.48030491 7 8 9 10 11 12 -2.33853505 -1.00948171 -4.73411693 -7.94618710 -1.39415618 1.24521807 13 14 15 16 17 18 -1.39775779 1.39871375 -3.43161123 0.20649365 1.22963728 0.06836235 19 20 21 22 23 24 2.41533880 5.39390698 6.95920466 2.75580755 1.68264466 1.39550632 25 26 27 28 29 30 2.16872190 -3.77600062 0.17198180 2.47439606 -4.00234231 -1.54894073 31 32 33 34 35 36 -1.21369678 1.77307489 -2.49880573 0.31179238 2.70988575 -4.82282944 37 38 39 40 41 42 -0.33791471 -0.38063736 -1.88753130 -0.92368472 -2.30480281 -0.73518145 43 44 45 46 47 48 0.79661501 1.51116358 -6.63547733 0.76294695 3.36665872 2.51664321 49 50 51 52 53 54 -1.03402761 -0.57127615 3.00665474 -3.74175847 -3.55633660 -0.10255010 55 56 57 58 59 60 -1.82959274 -1.61423136 -2.23729637 -2.31557837 -1.91729960 -0.40710521 61 62 63 64 65 66 -5.25457775 -8.54702907 11.00886809 10.11691499 -1.35224651 3.77465324 67 68 69 70 71 72 3.16280027 -0.50016813 4.51295457 -0.37102661 0.77670327 1.33999235 73 74 75 76 77 78 -2.21123230 0.78775799 3.22943913 -4.53812529 -2.86208274 -0.57468016 79 80 81 82 83 84 3.51391893 3.32358515 3.35938145 4.07486815 0.66761156 0.52084723 85 86 87 88 89 90 3.90665337 -0.66573903 -0.10258998 -7.54501370 -2.77483827 3.59757364 91 92 93 94 95 96 -0.17097321 -3.03952490 -0.63755943 1.13214648 -2.19185085 -0.23323788 97 98 99 100 101 102 -7.17707488 0.81084742 -1.12137196 -3.23975459 3.98804710 3.00030153 103 104 105 106 107 108 -1.02727404 -1.06683477 -1.48007167 -5.67854154 -3.57123689 -4.10129373 109 110 111 112 113 114 1.07681299 -1.09740289 4.42970806 2.99193536 1.12040525 6.55292184 115 116 117 118 119 120 -0.90141206 -3.83902111 4.30529191 -0.16289312 7.26212203 1.71393366 121 122 123 124 125 126 -0.99494503 5.30454560 3.11104214 -0.63454807 1.73888886 1.57236552 127 128 129 130 131 132 3.72287440 0.95074804 3.18154633 -4.88211174 6.25468990 1.49053270 133 134 135 136 137 138 2.55531444 -0.65455481 -1.62733267 -3.83286785 -2.07105952 1.60675465 139 140 141 142 143 144 -3.22042819 -5.51585484 -2.64900806 -3.08133067 -2.94495454 -2.66506254 145 146 147 148 149 150 -1.10985475 -4.29001016 -2.68148174 2.44444509 2.06004935 -0.25406868 151 152 153 154 155 156 -2.61831346 1.11410620 -0.27741517 0.51630312 -6.04455364 3.92710841 157 158 159 -1.17388730 0.89531441 0.81179549 > postscript(file="/var/www/rcomp/tmp/6hbku1290474916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 159 Frequency = 1 lag(myerror, k = 1) myerror 0 3.11771681 NA 1 5.30030204 3.11771681 2 1.32716052 5.30030204 3 -1.21724770 1.32716052 4 2.26003903 -1.21724770 5 3.48030491 2.26003903 6 -2.33853505 3.48030491 7 -1.00948171 -2.33853505 8 -4.73411693 -1.00948171 9 -7.94618710 -4.73411693 10 -1.39415618 -7.94618710 11 1.24521807 -1.39415618 12 -1.39775779 1.24521807 13 1.39871375 -1.39775779 14 -3.43161123 1.39871375 15 0.20649365 -3.43161123 16 1.22963728 0.20649365 17 0.06836235 1.22963728 18 2.41533880 0.06836235 19 5.39390698 2.41533880 20 6.95920466 5.39390698 21 2.75580755 6.95920466 22 1.68264466 2.75580755 23 1.39550632 1.68264466 24 2.16872190 1.39550632 25 -3.77600062 2.16872190 26 0.17198180 -3.77600062 27 2.47439606 0.17198180 28 -4.00234231 2.47439606 29 -1.54894073 -4.00234231 30 -1.21369678 -1.54894073 31 1.77307489 -1.21369678 32 -2.49880573 1.77307489 33 0.31179238 -2.49880573 34 2.70988575 0.31179238 35 -4.82282944 2.70988575 36 -0.33791471 -4.82282944 37 -0.38063736 -0.33791471 38 -1.88753130 -0.38063736 39 -0.92368472 -1.88753130 40 -2.30480281 -0.92368472 41 -0.73518145 -2.30480281 42 0.79661501 -0.73518145 43 1.51116358 0.79661501 44 -6.63547733 1.51116358 45 0.76294695 -6.63547733 46 3.36665872 0.76294695 47 2.51664321 3.36665872 48 -1.03402761 2.51664321 49 -0.57127615 -1.03402761 50 3.00665474 -0.57127615 51 -3.74175847 3.00665474 52 -3.55633660 -3.74175847 53 -0.10255010 -3.55633660 54 -1.82959274 -0.10255010 55 -1.61423136 -1.82959274 56 -2.23729637 -1.61423136 57 -2.31557837 -2.23729637 58 -1.91729960 -2.31557837 59 -0.40710521 -1.91729960 60 -5.25457775 -0.40710521 61 -8.54702907 -5.25457775 62 11.00886809 -8.54702907 63 10.11691499 11.00886809 64 -1.35224651 10.11691499 65 3.77465324 -1.35224651 66 3.16280027 3.77465324 67 -0.50016813 3.16280027 68 4.51295457 -0.50016813 69 -0.37102661 4.51295457 70 0.77670327 -0.37102661 71 1.33999235 0.77670327 72 -2.21123230 1.33999235 73 0.78775799 -2.21123230 74 3.22943913 0.78775799 75 -4.53812529 3.22943913 76 -2.86208274 -4.53812529 77 -0.57468016 -2.86208274 78 3.51391893 -0.57468016 79 3.32358515 3.51391893 80 3.35938145 3.32358515 81 4.07486815 3.35938145 82 0.66761156 4.07486815 83 0.52084723 0.66761156 84 3.90665337 0.52084723 85 -0.66573903 3.90665337 86 -0.10258998 -0.66573903 87 -7.54501370 -0.10258998 88 -2.77483827 -7.54501370 89 3.59757364 -2.77483827 90 -0.17097321 3.59757364 91 -3.03952490 -0.17097321 92 -0.63755943 -3.03952490 93 1.13214648 -0.63755943 94 -2.19185085 1.13214648 95 -0.23323788 -2.19185085 96 -7.17707488 -0.23323788 97 0.81084742 -7.17707488 98 -1.12137196 0.81084742 99 -3.23975459 -1.12137196 100 3.98804710 -3.23975459 101 3.00030153 3.98804710 102 -1.02727404 3.00030153 103 -1.06683477 -1.02727404 104 -1.48007167 -1.06683477 105 -5.67854154 -1.48007167 106 -3.57123689 -5.67854154 107 -4.10129373 -3.57123689 108 1.07681299 -4.10129373 109 -1.09740289 1.07681299 110 4.42970806 -1.09740289 111 2.99193536 4.42970806 112 1.12040525 2.99193536 113 6.55292184 1.12040525 114 -0.90141206 6.55292184 115 -3.83902111 -0.90141206 116 4.30529191 -3.83902111 117 -0.16289312 4.30529191 118 7.26212203 -0.16289312 119 1.71393366 7.26212203 120 -0.99494503 1.71393366 121 5.30454560 -0.99494503 122 3.11104214 5.30454560 123 -0.63454807 3.11104214 124 1.73888886 -0.63454807 125 1.57236552 1.73888886 126 3.72287440 1.57236552 127 0.95074804 3.72287440 128 3.18154633 0.95074804 129 -4.88211174 3.18154633 130 6.25468990 -4.88211174 131 1.49053270 6.25468990 132 2.55531444 1.49053270 133 -0.65455481 2.55531444 134 -1.62733267 -0.65455481 135 -3.83286785 -1.62733267 136 -2.07105952 -3.83286785 137 1.60675465 -2.07105952 138 -3.22042819 1.60675465 139 -5.51585484 -3.22042819 140 -2.64900806 -5.51585484 141 -3.08133067 -2.64900806 142 -2.94495454 -3.08133067 143 -2.66506254 -2.94495454 144 -1.10985475 -2.66506254 145 -4.29001016 -1.10985475 146 -2.68148174 -4.29001016 147 2.44444509 -2.68148174 148 2.06004935 2.44444509 149 -0.25406868 2.06004935 150 -2.61831346 -0.25406868 151 1.11410620 -2.61831346 152 -0.27741517 1.11410620 153 0.51630312 -0.27741517 154 -6.04455364 0.51630312 155 3.92710841 -6.04455364 156 -1.17388730 3.92710841 157 0.89531441 -1.17388730 158 0.81179549 0.89531441 159 NA 0.81179549 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 5.30030204 3.11771681 [2,] 1.32716052 5.30030204 [3,] -1.21724770 1.32716052 [4,] 2.26003903 -1.21724770 [5,] 3.48030491 2.26003903 [6,] -2.33853505 3.48030491 [7,] -1.00948171 -2.33853505 [8,] -4.73411693 -1.00948171 [9,] -7.94618710 -4.73411693 [10,] -1.39415618 -7.94618710 [11,] 1.24521807 -1.39415618 [12,] -1.39775779 1.24521807 [13,] 1.39871375 -1.39775779 [14,] -3.43161123 1.39871375 [15,] 0.20649365 -3.43161123 [16,] 1.22963728 0.20649365 [17,] 0.06836235 1.22963728 [18,] 2.41533880 0.06836235 [19,] 5.39390698 2.41533880 [20,] 6.95920466 5.39390698 [21,] 2.75580755 6.95920466 [22,] 1.68264466 2.75580755 [23,] 1.39550632 1.68264466 [24,] 2.16872190 1.39550632 [25,] -3.77600062 2.16872190 [26,] 0.17198180 -3.77600062 [27,] 2.47439606 0.17198180 [28,] -4.00234231 2.47439606 [29,] -1.54894073 -4.00234231 [30,] -1.21369678 -1.54894073 [31,] 1.77307489 -1.21369678 [32,] -2.49880573 1.77307489 [33,] 0.31179238 -2.49880573 [34,] 2.70988575 0.31179238 [35,] -4.82282944 2.70988575 [36,] -0.33791471 -4.82282944 [37,] -0.38063736 -0.33791471 [38,] -1.88753130 -0.38063736 [39,] -0.92368472 -1.88753130 [40,] -2.30480281 -0.92368472 [41,] -0.73518145 -2.30480281 [42,] 0.79661501 -0.73518145 [43,] 1.51116358 0.79661501 [44,] -6.63547733 1.51116358 [45,] 0.76294695 -6.63547733 [46,] 3.36665872 0.76294695 [47,] 2.51664321 3.36665872 [48,] -1.03402761 2.51664321 [49,] -0.57127615 -1.03402761 [50,] 3.00665474 -0.57127615 [51,] -3.74175847 3.00665474 [52,] -3.55633660 -3.74175847 [53,] -0.10255010 -3.55633660 [54,] -1.82959274 -0.10255010 [55,] -1.61423136 -1.82959274 [56,] -2.23729637 -1.61423136 [57,] -2.31557837 -2.23729637 [58,] -1.91729960 -2.31557837 [59,] -0.40710521 -1.91729960 [60,] -5.25457775 -0.40710521 [61,] -8.54702907 -5.25457775 [62,] 11.00886809 -8.54702907 [63,] 10.11691499 11.00886809 [64,] -1.35224651 10.11691499 [65,] 3.77465324 -1.35224651 [66,] 3.16280027 3.77465324 [67,] -0.50016813 3.16280027 [68,] 4.51295457 -0.50016813 [69,] -0.37102661 4.51295457 [70,] 0.77670327 -0.37102661 [71,] 1.33999235 0.77670327 [72,] -2.21123230 1.33999235 [73,] 0.78775799 -2.21123230 [74,] 3.22943913 0.78775799 [75,] -4.53812529 3.22943913 [76,] -2.86208274 -4.53812529 [77,] -0.57468016 -2.86208274 [78,] 3.51391893 -0.57468016 [79,] 3.32358515 3.51391893 [80,] 3.35938145 3.32358515 [81,] 4.07486815 3.35938145 [82,] 0.66761156 4.07486815 [83,] 0.52084723 0.66761156 [84,] 3.90665337 0.52084723 [85,] -0.66573903 3.90665337 [86,] -0.10258998 -0.66573903 [87,] -7.54501370 -0.10258998 [88,] -2.77483827 -7.54501370 [89,] 3.59757364 -2.77483827 [90,] -0.17097321 3.59757364 [91,] -3.03952490 -0.17097321 [92,] -0.63755943 -3.03952490 [93,] 1.13214648 -0.63755943 [94,] -2.19185085 1.13214648 [95,] -0.23323788 -2.19185085 [96,] -7.17707488 -0.23323788 [97,] 0.81084742 -7.17707488 [98,] -1.12137196 0.81084742 [99,] -3.23975459 -1.12137196 [100,] 3.98804710 -3.23975459 [101,] 3.00030153 3.98804710 [102,] -1.02727404 3.00030153 [103,] -1.06683477 -1.02727404 [104,] -1.48007167 -1.06683477 [105,] -5.67854154 -1.48007167 [106,] -3.57123689 -5.67854154 [107,] -4.10129373 -3.57123689 [108,] 1.07681299 -4.10129373 [109,] -1.09740289 1.07681299 [110,] 4.42970806 -1.09740289 [111,] 2.99193536 4.42970806 [112,] 1.12040525 2.99193536 [113,] 6.55292184 1.12040525 [114,] -0.90141206 6.55292184 [115,] -3.83902111 -0.90141206 [116,] 4.30529191 -3.83902111 [117,] -0.16289312 4.30529191 [118,] 7.26212203 -0.16289312 [119,] 1.71393366 7.26212203 [120,] -0.99494503 1.71393366 [121,] 5.30454560 -0.99494503 [122,] 3.11104214 5.30454560 [123,] -0.63454807 3.11104214 [124,] 1.73888886 -0.63454807 [125,] 1.57236552 1.73888886 [126,] 3.72287440 1.57236552 [127,] 0.95074804 3.72287440 [128,] 3.18154633 0.95074804 [129,] -4.88211174 3.18154633 [130,] 6.25468990 -4.88211174 [131,] 1.49053270 6.25468990 [132,] 2.55531444 1.49053270 [133,] -0.65455481 2.55531444 [134,] -1.62733267 -0.65455481 [135,] -3.83286785 -1.62733267 [136,] -2.07105952 -3.83286785 [137,] 1.60675465 -2.07105952 [138,] -3.22042819 1.60675465 [139,] -5.51585484 -3.22042819 [140,] -2.64900806 -5.51585484 [141,] -3.08133067 -2.64900806 [142,] -2.94495454 -3.08133067 [143,] -2.66506254 -2.94495454 [144,] -1.10985475 -2.66506254 [145,] -4.29001016 -1.10985475 [146,] -2.68148174 -4.29001016 [147,] 2.44444509 -2.68148174 [148,] 2.06004935 2.44444509 [149,] -0.25406868 2.06004935 [150,] -2.61831346 -0.25406868 [151,] 1.11410620 -2.61831346 [152,] -0.27741517 1.11410620 [153,] 0.51630312 -0.27741517 [154,] -6.04455364 0.51630312 [155,] 3.92710841 -6.04455364 [156,] -1.17388730 3.92710841 [157,] 0.89531441 -1.17388730 [158,] 0.81179549 0.89531441 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 5.30030204 3.11771681 2 1.32716052 5.30030204 3 -1.21724770 1.32716052 4 2.26003903 -1.21724770 5 3.48030491 2.26003903 6 -2.33853505 3.48030491 7 -1.00948171 -2.33853505 8 -4.73411693 -1.00948171 9 -7.94618710 -4.73411693 10 -1.39415618 -7.94618710 11 1.24521807 -1.39415618 12 -1.39775779 1.24521807 13 1.39871375 -1.39775779 14 -3.43161123 1.39871375 15 0.20649365 -3.43161123 16 1.22963728 0.20649365 17 0.06836235 1.22963728 18 2.41533880 0.06836235 19 5.39390698 2.41533880 20 6.95920466 5.39390698 21 2.75580755 6.95920466 22 1.68264466 2.75580755 23 1.39550632 1.68264466 24 2.16872190 1.39550632 25 -3.77600062 2.16872190 26 0.17198180 -3.77600062 27 2.47439606 0.17198180 28 -4.00234231 2.47439606 29 -1.54894073 -4.00234231 30 -1.21369678 -1.54894073 31 1.77307489 -1.21369678 32 -2.49880573 1.77307489 33 0.31179238 -2.49880573 34 2.70988575 0.31179238 35 -4.82282944 2.70988575 36 -0.33791471 -4.82282944 37 -0.38063736 -0.33791471 38 -1.88753130 -0.38063736 39 -0.92368472 -1.88753130 40 -2.30480281 -0.92368472 41 -0.73518145 -2.30480281 42 0.79661501 -0.73518145 43 1.51116358 0.79661501 44 -6.63547733 1.51116358 45 0.76294695 -6.63547733 46 3.36665872 0.76294695 47 2.51664321 3.36665872 48 -1.03402761 2.51664321 49 -0.57127615 -1.03402761 50 3.00665474 -0.57127615 51 -3.74175847 3.00665474 52 -3.55633660 -3.74175847 53 -0.10255010 -3.55633660 54 -1.82959274 -0.10255010 55 -1.61423136 -1.82959274 56 -2.23729637 -1.61423136 57 -2.31557837 -2.23729637 58 -1.91729960 -2.31557837 59 -0.40710521 -1.91729960 60 -5.25457775 -0.40710521 61 -8.54702907 -5.25457775 62 11.00886809 -8.54702907 63 10.11691499 11.00886809 64 -1.35224651 10.11691499 65 3.77465324 -1.35224651 66 3.16280027 3.77465324 67 -0.50016813 3.16280027 68 4.51295457 -0.50016813 69 -0.37102661 4.51295457 70 0.77670327 -0.37102661 71 1.33999235 0.77670327 72 -2.21123230 1.33999235 73 0.78775799 -2.21123230 74 3.22943913 0.78775799 75 -4.53812529 3.22943913 76 -2.86208274 -4.53812529 77 -0.57468016 -2.86208274 78 3.51391893 -0.57468016 79 3.32358515 3.51391893 80 3.35938145 3.32358515 81 4.07486815 3.35938145 82 0.66761156 4.07486815 83 0.52084723 0.66761156 84 3.90665337 0.52084723 85 -0.66573903 3.90665337 86 -0.10258998 -0.66573903 87 -7.54501370 -0.10258998 88 -2.77483827 -7.54501370 89 3.59757364 -2.77483827 90 -0.17097321 3.59757364 91 -3.03952490 -0.17097321 92 -0.63755943 -3.03952490 93 1.13214648 -0.63755943 94 -2.19185085 1.13214648 95 -0.23323788 -2.19185085 96 -7.17707488 -0.23323788 97 0.81084742 -7.17707488 98 -1.12137196 0.81084742 99 -3.23975459 -1.12137196 100 3.98804710 -3.23975459 101 3.00030153 3.98804710 102 -1.02727404 3.00030153 103 -1.06683477 -1.02727404 104 -1.48007167 -1.06683477 105 -5.67854154 -1.48007167 106 -3.57123689 -5.67854154 107 -4.10129373 -3.57123689 108 1.07681299 -4.10129373 109 -1.09740289 1.07681299 110 4.42970806 -1.09740289 111 2.99193536 4.42970806 112 1.12040525 2.99193536 113 6.55292184 1.12040525 114 -0.90141206 6.55292184 115 -3.83902111 -0.90141206 116 4.30529191 -3.83902111 117 -0.16289312 4.30529191 118 7.26212203 -0.16289312 119 1.71393366 7.26212203 120 -0.99494503 1.71393366 121 5.30454560 -0.99494503 122 3.11104214 5.30454560 123 -0.63454807 3.11104214 124 1.73888886 -0.63454807 125 1.57236552 1.73888886 126 3.72287440 1.57236552 127 0.95074804 3.72287440 128 3.18154633 0.95074804 129 -4.88211174 3.18154633 130 6.25468990 -4.88211174 131 1.49053270 6.25468990 132 2.55531444 1.49053270 133 -0.65455481 2.55531444 134 -1.62733267 -0.65455481 135 -3.83286785 -1.62733267 136 -2.07105952 -3.83286785 137 1.60675465 -2.07105952 138 -3.22042819 1.60675465 139 -5.51585484 -3.22042819 140 -2.64900806 -5.51585484 141 -3.08133067 -2.64900806 142 -2.94495454 -3.08133067 143 -2.66506254 -2.94495454 144 -1.10985475 -2.66506254 145 -4.29001016 -1.10985475 146 -2.68148174 -4.29001016 147 2.44444509 -2.68148174 148 2.06004935 2.44444509 149 -0.25406868 2.06004935 150 -2.61831346 -0.25406868 151 1.11410620 -2.61831346 152 -0.27741517 1.11410620 153 0.51630312 -0.27741517 154 -6.04455364 0.51630312 155 3.92710841 -6.04455364 156 -1.17388730 3.92710841 157 0.89531441 -1.17388730 158 0.81179549 0.89531441 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/7s2jf1290474916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/8s2jf1290474916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/92tj01290474916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/rcomp/tmp/102tj01290474916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/11och61290474916.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/129vyu1290474916.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/13gdv61290474916.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/141etc1290474916.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/155fs01290474916.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/16qx8n1290474916.tab") + } > > try(system("convert tmp/1eamp1290474916.ps tmp/1eamp1290474916.png",intern=TRUE)) character(0) > try(system("convert tmp/26j391290474916.ps tmp/26j391290474916.png",intern=TRUE)) character(0) > try(system("convert tmp/36j391290474916.ps tmp/36j391290474916.png",intern=TRUE)) character(0) > try(system("convert tmp/46j391290474916.ps tmp/46j391290474916.png",intern=TRUE)) character(0) > try(system("convert tmp/56j391290474916.ps tmp/56j391290474916.png",intern=TRUE)) character(0) > try(system("convert tmp/6hbku1290474916.ps tmp/6hbku1290474916.png",intern=TRUE)) character(0) > try(system("convert tmp/7s2jf1290474916.ps tmp/7s2jf1290474916.png",intern=TRUE)) character(0) > try(system("convert tmp/8s2jf1290474916.ps tmp/8s2jf1290474916.png",intern=TRUE)) character(0) > try(system("convert tmp/92tj01290474916.ps tmp/92tj01290474916.png",intern=TRUE)) character(0) > try(system("convert tmp/102tj01290474916.ps tmp/102tj01290474916.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.300 2.080 8.475