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(4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,1 + ,1 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,1 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,1 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,1 + ,1 + ,4 + ,0 + ,0 + ,4 + ,0 + ,1 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,1 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,1 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,1 + ,1 + ,4 + ,1 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,4 + ,0 + ,1 + ,4 + ,0 + ,0 + ,4 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,1 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,1 + ,0 + ,2 + ,1 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,2 + ,0 + ,1 + ,2 + ,0 + ,1 + ,2 + ,0 + ,0) + ,dim=c(3 + ,154) + ,dimnames=list(c('Weeks' + ,'Treatment' + ,'Difference') + ,1:154)) > y <- array(NA,dim=c(3,154),dimnames=list(c('Weeks','Treatment','Difference'),1:154)) > 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 = '3' > par3 <- 'Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '3' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Difference Weeks Treatment t 1 0 4 1 1 2 0 4 0 2 3 0 4 0 3 4 0 4 0 4 5 0 4 0 5 6 0 4 0 6 7 0 4 0 7 8 0 4 1 8 9 0 4 0 9 10 0 4 0 10 11 0 4 1 11 12 0 4 0 12 13 0 4 0 13 14 0 4 1 14 15 0 4 0 15 16 0 4 1 16 17 1 4 1 17 18 0 4 1 18 19 0 4 0 19 20 1 4 1 20 21 0 4 0 21 22 0 4 0 22 23 0 4 0 23 24 0 4 0 24 25 0 4 1 25 26 0 4 0 26 27 0 4 0 27 28 0 4 0 28 29 0 4 0 29 30 0 4 0 30 31 0 4 0 31 32 0 4 0 32 33 0 4 0 33 34 0 4 1 34 35 0 4 0 35 36 0 4 0 36 37 0 4 1 37 38 0 4 0 38 39 0 4 0 39 40 0 4 1 40 41 1 4 0 41 42 0 4 0 42 43 0 4 0 43 44 0 4 1 44 45 0 4 0 45 46 0 4 0 46 47 0 4 0 47 48 0 4 0 48 49 0 4 0 49 50 0 4 0 50 51 0 4 1 51 52 1 4 1 52 53 0 4 0 53 54 1 4 0 54 55 0 4 0 55 56 0 4 1 56 57 0 4 0 57 58 0 4 0 58 59 0 4 0 59 60 1 4 1 60 61 0 4 1 61 62 0 4 0 62 63 0 4 0 63 64 0 4 1 64 65 0 4 0 65 66 0 4 0 66 67 1 4 1 67 68 0 4 0 68 69 0 4 0 69 70 0 4 0 70 71 0 4 0 71 72 0 4 0 72 73 0 4 0 73 74 0 4 0 74 75 0 4 0 75 76 0 4 1 76 77 0 4 0 77 78 0 4 0 78 79 1 4 1 79 80 0 4 1 80 81 0 4 0 81 82 0 4 0 82 83 0 4 0 83 84 1 4 0 84 85 0 4 0 85 86 0 4 0 86 87 0 2 0 87 88 0 2 1 88 89 0 2 0 89 90 0 2 0 90 91 0 2 0 91 92 0 2 1 92 93 0 2 0 93 94 0 2 0 94 95 0 2 1 95 96 0 2 0 96 97 0 2 1 97 98 0 2 0 98 99 0 2 0 99 100 0 2 0 100 101 0 2 0 101 102 0 2 0 102 103 0 2 0 103 104 0 2 0 104 105 0 2 1 105 106 0 2 0 106 107 0 2 0 107 108 0 2 1 108 109 0 2 0 109 110 0 2 0 110 111 0 2 1 111 112 0 2 1 112 113 0 2 0 113 114 0 2 1 114 115 0 2 0 115 116 0 2 0 116 117 0 2 0 117 118 0 2 0 118 119 0 2 0 119 120 0 2 0 120 121 0 2 0 121 122 0 2 0 122 123 0 2 1 123 124 0 2 0 124 125 0 2 0 125 126 0 2 1 126 127 0 2 0 127 128 0 2 0 128 129 0 2 0 129 130 0 2 0 130 131 0 2 0 131 132 0 2 0 132 133 0 2 0 133 134 0 2 0 134 135 0 2 0 135 136 0 2 0 136 137 0 2 0 137 138 0 2 1 138 139 0 2 1 139 140 0 2 0 140 141 1 2 0 141 142 0 2 1 142 143 0 2 0 143 144 0 2 0 144 145 0 2 0 145 146 0 2 1 146 147 0 2 1 147 148 0 2 1 148 149 0 2 0 149 150 0 2 0 150 151 0 2 0 151 152 1 2 0 152 153 1 2 0 153 154 0 2 0 154 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Weeks Treatment t -0.479003 0.114897 0.102581 0.002221 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.26088 -0.11861 -0.05643 -0.00366 0.93599 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.479003 0.197456 -2.426 0.01646 * Weeks 0.114897 0.041637 2.759 0.00651 ** Treatment 0.102581 0.048157 2.130 0.03479 * t 0.002221 0.000931 2.386 0.01828 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2616 on 150 degrees of freedom Multiple R-squared: 0.07246, Adjusted R-squared: 0.05391 F-statistic: 3.906 on 3 and 150 DF, p-value: 0.01011 > 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,] 0.0000000000 0.0000000000 1.0000000000 [2,] 0.0000000000 0.0000000000 1.0000000000 [3,] 0.0000000000 0.0000000000 1.0000000000 [4,] 0.0000000000 0.0000000000 1.0000000000 [5,] 0.0000000000 0.0000000000 1.0000000000 [6,] 0.0000000000 0.0000000000 1.0000000000 [7,] 0.0000000000 0.0000000000 1.0000000000 [8,] 0.0000000000 0.0000000000 1.0000000000 [9,] 0.0000000000 0.0000000000 1.0000000000 [10,] 0.0000000000 0.0000000000 1.0000000000 [11,] 0.3440363214 0.6880726427 0.6559636786 [12,] 0.3321223323 0.6642446647 0.6678776677 [13,] 0.2734414325 0.5468828650 0.7265585675 [14,] 0.7453440909 0.5093118183 0.2546559091 [15,] 0.7123846645 0.5752306710 0.2876153355 [16,] 0.6690703124 0.6618593753 0.3309296876 [17,] 0.6187585437 0.7624829125 0.3812414563 [18,] 0.5637321741 0.8725356517 0.4362678259 [19,] 0.5820866281 0.8358267438 0.4179133719 [20,] 0.5211905186 0.9576189627 0.4788094814 [21,] 0.4596940937 0.9193881875 0.5403059063 [22,] 0.3992731993 0.7985463985 0.6007268007 [23,] 0.3414203864 0.6828407729 0.6585796136 [24,] 0.2873718086 0.5747436172 0.7126281914 [25,] 0.2380564954 0.4761129909 0.7619435046 [26,] 0.1940750667 0.3881501334 0.8059249333 [27,] 0.1557079623 0.3114159246 0.8442920377 [28,] 0.1506213630 0.3012427261 0.8493786370 [29,] 0.1185112020 0.2370224039 0.8814887980 [30,] 0.0918009316 0.1836018633 0.9081990684 [31,] 0.0825401049 0.1650802098 0.9174598951 [32,] 0.0626543531 0.1253087062 0.9373456469 [33,] 0.0468562745 0.0937125489 0.9531437255 [34,] 0.0397276356 0.0794552711 0.9602723644 [35,] 0.5157080718 0.9685838564 0.4842919282 [36,] 0.4667744358 0.9335488716 0.5332255642 [37,] 0.4181712064 0.8363424128 0.5818287936 [38,] 0.3938546479 0.7877092959 0.6061453521 [39,] 0.3468231862 0.6936463723 0.6531768138 [40,] 0.3021432053 0.6042864106 0.6978567947 [41,] 0.2603889730 0.5207779461 0.7396110270 [42,] 0.2219914705 0.4439829411 0.7780085295 [43,] 0.1872318694 0.3744637388 0.8127681306 [44,] 0.1562451792 0.3124903584 0.8437548208 [45,] 0.1395346430 0.2790692861 0.8604653570 [46,] 0.4957145831 0.9914291662 0.5042854169 [47,] 0.4508440055 0.9016880109 0.5491559945 [48,] 0.8774437470 0.2451125059 0.1225562530 [49,] 0.8564384035 0.2871231929 0.1435615965 [50,] 0.8505522649 0.2988954702 0.1494477351 [51,] 0.8256173442 0.3487653116 0.1743826558 [52,] 0.7979973195 0.4040053609 0.2020026805 [53,] 0.7677973472 0.4644053056 0.2322026528 [54,] 0.9492352090 0.1015295819 0.0507647910 [55,] 0.9476336573 0.1047326853 0.0523663427 [56,] 0.9361575124 0.1276849752 0.0638424876 [57,] 0.9227485747 0.1545028507 0.0772514253 [58,] 0.9181086894 0.1637826212 0.0818913106 [59,] 0.9018099349 0.1963801302 0.0981900651 [60,] 0.8833694816 0.2332610367 0.1166305184 [61,] 0.9851068457 0.0297863085 0.0148931543 [62,] 0.9809792201 0.0380415598 0.0190207799 [63,] 0.9759254342 0.0481491316 0.0240745658 [64,] 0.9698275889 0.0603448222 0.0301724111 [65,] 0.9625869005 0.0748261989 0.0374130995 [66,] 0.9541409096 0.0917181808 0.0458590904 [67,] 0.9444867423 0.1110265154 0.0555132577 [68,] 0.9337125522 0.1325748956 0.0662874478 [69,] 0.9220407987 0.1559184026 0.0779592013 [70,] 0.9190589004 0.1618821991 0.0809410996 [71,] 0.9075146834 0.1849706332 0.0924853166 [72,] 0.8969358517 0.2061282966 0.1030641483 [73,] 0.9884742692 0.0230514616 0.0115257308 [74,] 0.9874329173 0.0251341655 0.0125670827 [75,] 0.9847483503 0.0305032993 0.0152516497 [76,] 0.9824871708 0.0350256584 0.0175128292 [77,] 0.9819169822 0.0361660356 0.0180830178 [78,] 0.9996645961 0.0006708077 0.0003354039 [79,] 0.9994980381 0.0010039238 0.0005019619 [80,] 0.9992550888 0.0014898224 0.0007449112 [81,] 0.9988881338 0.0022237324 0.0011118662 [82,] 0.9984995474 0.0030009052 0.0015004526 [83,] 0.9978359989 0.0043280022 0.0021640011 [84,] 0.9968937671 0.0062124657 0.0031062329 [85,] 0.9955822147 0.0088355706 0.0044177853 [86,] 0.9942620614 0.0114758772 0.0057379386 [87,] 0.9920272907 0.0159454186 0.0079727093 [88,] 0.9890339466 0.0219321067 0.0109660534 [89,] 0.9861315802 0.0277368395 0.0138684198 [90,] 0.9813370488 0.0373259024 0.0186629512 [91,] 0.9768640439 0.0462719121 0.0231359561 [92,] 0.9695170430 0.0609659140 0.0304829570 [93,] 0.9602594965 0.0794810070 0.0397405035 [94,] 0.9487507396 0.1024985207 0.0512492604 [95,] 0.9346373028 0.1307253943 0.0653626972 [96,] 0.9175676111 0.1648647778 0.0824323889 [97,] 0.8972104205 0.2055791590 0.1027895795 [98,] 0.8732764384 0.2534471232 0.1267235616 [99,] 0.8533746359 0.2932507282 0.1466253641 [100,] 0.8228619369 0.3542761261 0.1771380631 [101,] 0.7883818968 0.4232362064 0.2116181032 [102,] 0.7612908525 0.4774182950 0.2387091475 [103,] 0.7203591824 0.5592816351 0.2796408176 [104,] 0.6759538264 0.6480923471 0.3240461736 [105,] 0.6434562600 0.7130874801 0.3565437400 [106,] 0.6130009992 0.7739980016 0.3869990008 [107,] 0.5630098727 0.8739802546 0.4369901273 [108,] 0.5360919446 0.9278161108 0.4639080554 [109,] 0.4846607511 0.9693215022 0.5153392489 [110,] 0.4329319252 0.8658638505 0.5670680748 [111,] 0.3817648142 0.7635296284 0.6182351858 [112,] 0.3320194019 0.6640388039 0.6679805981 [113,] 0.2845136676 0.5690273352 0.7154863324 [114,] 0.2399818659 0.4799637317 0.7600181341 [115,] 0.1990371883 0.3980743766 0.8009628117 [116,] 0.1621420810 0.3242841619 0.8378579190 [117,] 0.1442135725 0.2884271450 0.8557864275 [118,] 0.1141965630 0.2283931260 0.8858034370 [119,] 0.0885333571 0.1770667143 0.9114666429 [120,] 0.0791198834 0.1582397668 0.9208801166 [121,] 0.0595247122 0.1190494244 0.9404752878 [122,] 0.0436685399 0.0873370798 0.9563314601 [123,] 0.0311846074 0.0623692148 0.9688153926 [124,] 0.0216404561 0.0432809123 0.9783595439 [125,] 0.0145704193 0.0291408385 0.9854295807 [126,] 0.0095072661 0.0190145322 0.9904927339 [127,] 0.0060100680 0.0120201359 0.9939899320 [128,] 0.0036858676 0.0073717353 0.9963141324 [129,] 0.0022036733 0.0044073466 0.9977963267 [130,] 0.0013006361 0.0026012722 0.9986993639 [131,] 0.0007823025 0.0015646050 0.9992176975 [132,] 0.0004362406 0.0008724812 0.9995637594 [133,] 0.0002341005 0.0004682011 0.9997658995 [134,] 0.0001427624 0.0002855248 0.9998572376 [135,] 0.0229572638 0.0459145276 0.9770427362 [136,] 0.0169774062 0.0339548123 0.9830225938 [137,] 0.0099224482 0.0198448965 0.9900775518 [138,] 0.0054392170 0.0108784340 0.9945607830 [139,] 0.0030673422 0.0061346844 0.9969326578 [140,] 0.0014371087 0.0028742174 0.9985628913 [141,] 0.0005365976 0.0010731952 0.9994634024 > postscript(file="/var/fisher/rcomp/tmp/1q4xw1356097571.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2gg7p1356097571.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/34mdf1356097571.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4cqg81356097571.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5qff51356097571.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 = 154 Frequency = 1 1 2 3 4 5 -0.0853874026 0.0149724718 0.0127510476 0.0105296235 0.0083081994 6 7 8 9 10 0.0060867753 0.0038653511 -0.1009373715 -0.0005774971 -0.0027989212 11 12 13 14 15 -0.1076016439 -0.0072417695 -0.0094631936 -0.1142659162 -0.0139060419 16 17 18 19 20 -0.1187087645 0.8790698114 -0.1231516127 -0.0227917384 0.8724055390 21 22 23 24 25 -0.0272345866 -0.0294560108 -0.0316774349 -0.0338988590 -0.1387015816 26 27 28 29 30 -0.0383417073 -0.0405631314 -0.0427845555 -0.0450059796 -0.0472274038 31 32 33 34 35 -0.0494488279 -0.0516702520 -0.0538916761 -0.1586943988 -0.0583345244 36 37 38 39 40 -0.0605559485 -0.1653586711 -0.0649987968 -0.0672202209 -0.1720229435 41 42 43 44 45 0.9283369308 -0.0738844933 -0.0761059174 -0.1809086400 -0.0805487657 46 47 48 49 50 -0.0827701898 -0.0849916139 -0.0872130380 -0.0894344622 -0.0916558863 51 52 53 54 55 -0.1964586089 0.8013199670 -0.0983201587 0.8994584172 -0.1027630069 56 57 58 59 60 -0.2075657295 -0.1072058552 -0.1094272793 -0.1116487034 0.7835485740 61 62 63 64 65 -0.2186728502 -0.1183129758 -0.1205343999 -0.2253371226 -0.1249772482 66 67 68 69 70 -0.1271986723 0.7679986051 -0.1316415206 -0.1338629447 -0.1360843688 71 72 73 74 75 -0.1383057929 -0.1405272171 -0.1427486412 -0.1449700653 -0.1471914895 76 77 78 79 80 -0.2519942121 -0.1516343377 -0.1538557618 0.7413415156 -0.2608799086 81 82 83 84 85 -0.1605200342 -0.1627414583 -0.1649628825 0.8328156934 -0.1694057307 86 87 88 89 90 -0.1716271548 0.0559453858 -0.0488573368 0.0515025375 0.0492811134 91 92 93 94 95 0.0470596893 -0.0577430333 0.0426168410 0.0403954169 -0.0644073057 96 97 98 99 100 0.0359525687 -0.0688501540 0.0315097204 0.0292882963 0.0270668722 101 102 103 104 105 0.0248454480 0.0226240239 0.0204025998 0.0181811756 -0.0866215470 106 107 108 109 110 0.0137383274 0.0115169033 -0.0932858193 0.0070740550 0.0048526309 111 112 113 114 115 -0.0999500917 -0.1021715159 -0.0018116415 -0.1066143641 -0.0062544897 116 117 118 119 120 -0.0084759139 -0.0106973380 -0.0129187621 -0.0151401862 -0.0173616104 121 122 123 124 125 -0.0195830345 -0.0218044586 -0.1266071812 -0.0262473069 -0.0284687310 126 127 128 129 130 -0.1332714536 -0.0329115793 -0.0351330034 -0.0373544275 -0.0395758516 131 132 133 134 135 -0.0417972758 -0.0440186999 -0.0462401240 -0.0484615481 -0.0506829723 136 137 138 139 140 -0.0529043964 -0.0551258205 -0.1599285431 -0.1621499673 -0.0617900929 141 142 143 144 145 0.9359884830 -0.1688142396 -0.0684543653 -0.0706757894 -0.0728972135 146 147 148 149 150 -0.1776999361 -0.1799213603 -0.1821427844 -0.0817829100 -0.0840043342 151 152 153 154 -0.0862257583 0.9115528176 0.9093313935 -0.0928900307 > postscript(file="/var/fisher/rcomp/tmp/6w7781356097571.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 = 154 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.0853874026 NA 1 0.0149724718 -0.0853874026 2 0.0127510476 0.0149724718 3 0.0105296235 0.0127510476 4 0.0083081994 0.0105296235 5 0.0060867753 0.0083081994 6 0.0038653511 0.0060867753 7 -0.1009373715 0.0038653511 8 -0.0005774971 -0.1009373715 9 -0.0027989212 -0.0005774971 10 -0.1076016439 -0.0027989212 11 -0.0072417695 -0.1076016439 12 -0.0094631936 -0.0072417695 13 -0.1142659162 -0.0094631936 14 -0.0139060419 -0.1142659162 15 -0.1187087645 -0.0139060419 16 0.8790698114 -0.1187087645 17 -0.1231516127 0.8790698114 18 -0.0227917384 -0.1231516127 19 0.8724055390 -0.0227917384 20 -0.0272345866 0.8724055390 21 -0.0294560108 -0.0272345866 22 -0.0316774349 -0.0294560108 23 -0.0338988590 -0.0316774349 24 -0.1387015816 -0.0338988590 25 -0.0383417073 -0.1387015816 26 -0.0405631314 -0.0383417073 27 -0.0427845555 -0.0405631314 28 -0.0450059796 -0.0427845555 29 -0.0472274038 -0.0450059796 30 -0.0494488279 -0.0472274038 31 -0.0516702520 -0.0494488279 32 -0.0538916761 -0.0516702520 33 -0.1586943988 -0.0538916761 34 -0.0583345244 -0.1586943988 35 -0.0605559485 -0.0583345244 36 -0.1653586711 -0.0605559485 37 -0.0649987968 -0.1653586711 38 -0.0672202209 -0.0649987968 39 -0.1720229435 -0.0672202209 40 0.9283369308 -0.1720229435 41 -0.0738844933 0.9283369308 42 -0.0761059174 -0.0738844933 43 -0.1809086400 -0.0761059174 44 -0.0805487657 -0.1809086400 45 -0.0827701898 -0.0805487657 46 -0.0849916139 -0.0827701898 47 -0.0872130380 -0.0849916139 48 -0.0894344622 -0.0872130380 49 -0.0916558863 -0.0894344622 50 -0.1964586089 -0.0916558863 51 0.8013199670 -0.1964586089 52 -0.0983201587 0.8013199670 53 0.8994584172 -0.0983201587 54 -0.1027630069 0.8994584172 55 -0.2075657295 -0.1027630069 56 -0.1072058552 -0.2075657295 57 -0.1094272793 -0.1072058552 58 -0.1116487034 -0.1094272793 59 0.7835485740 -0.1116487034 60 -0.2186728502 0.7835485740 61 -0.1183129758 -0.2186728502 62 -0.1205343999 -0.1183129758 63 -0.2253371226 -0.1205343999 64 -0.1249772482 -0.2253371226 65 -0.1271986723 -0.1249772482 66 0.7679986051 -0.1271986723 67 -0.1316415206 0.7679986051 68 -0.1338629447 -0.1316415206 69 -0.1360843688 -0.1338629447 70 -0.1383057929 -0.1360843688 71 -0.1405272171 -0.1383057929 72 -0.1427486412 -0.1405272171 73 -0.1449700653 -0.1427486412 74 -0.1471914895 -0.1449700653 75 -0.2519942121 -0.1471914895 76 -0.1516343377 -0.2519942121 77 -0.1538557618 -0.1516343377 78 0.7413415156 -0.1538557618 79 -0.2608799086 0.7413415156 80 -0.1605200342 -0.2608799086 81 -0.1627414583 -0.1605200342 82 -0.1649628825 -0.1627414583 83 0.8328156934 -0.1649628825 84 -0.1694057307 0.8328156934 85 -0.1716271548 -0.1694057307 86 0.0559453858 -0.1716271548 87 -0.0488573368 0.0559453858 88 0.0515025375 -0.0488573368 89 0.0492811134 0.0515025375 90 0.0470596893 0.0492811134 91 -0.0577430333 0.0470596893 92 0.0426168410 -0.0577430333 93 0.0403954169 0.0426168410 94 -0.0644073057 0.0403954169 95 0.0359525687 -0.0644073057 96 -0.0688501540 0.0359525687 97 0.0315097204 -0.0688501540 98 0.0292882963 0.0315097204 99 0.0270668722 0.0292882963 100 0.0248454480 0.0270668722 101 0.0226240239 0.0248454480 102 0.0204025998 0.0226240239 103 0.0181811756 0.0204025998 104 -0.0866215470 0.0181811756 105 0.0137383274 -0.0866215470 106 0.0115169033 0.0137383274 107 -0.0932858193 0.0115169033 108 0.0070740550 -0.0932858193 109 0.0048526309 0.0070740550 110 -0.0999500917 0.0048526309 111 -0.1021715159 -0.0999500917 112 -0.0018116415 -0.1021715159 113 -0.1066143641 -0.0018116415 114 -0.0062544897 -0.1066143641 115 -0.0084759139 -0.0062544897 116 -0.0106973380 -0.0084759139 117 -0.0129187621 -0.0106973380 118 -0.0151401862 -0.0129187621 119 -0.0173616104 -0.0151401862 120 -0.0195830345 -0.0173616104 121 -0.0218044586 -0.0195830345 122 -0.1266071812 -0.0218044586 123 -0.0262473069 -0.1266071812 124 -0.0284687310 -0.0262473069 125 -0.1332714536 -0.0284687310 126 -0.0329115793 -0.1332714536 127 -0.0351330034 -0.0329115793 128 -0.0373544275 -0.0351330034 129 -0.0395758516 -0.0373544275 130 -0.0417972758 -0.0395758516 131 -0.0440186999 -0.0417972758 132 -0.0462401240 -0.0440186999 133 -0.0484615481 -0.0462401240 134 -0.0506829723 -0.0484615481 135 -0.0529043964 -0.0506829723 136 -0.0551258205 -0.0529043964 137 -0.1599285431 -0.0551258205 138 -0.1621499673 -0.1599285431 139 -0.0617900929 -0.1621499673 140 0.9359884830 -0.0617900929 141 -0.1688142396 0.9359884830 142 -0.0684543653 -0.1688142396 143 -0.0706757894 -0.0684543653 144 -0.0728972135 -0.0706757894 145 -0.1776999361 -0.0728972135 146 -0.1799213603 -0.1776999361 147 -0.1821427844 -0.1799213603 148 -0.0817829100 -0.1821427844 149 -0.0840043342 -0.0817829100 150 -0.0862257583 -0.0840043342 151 0.9115528176 -0.0862257583 152 0.9093313935 0.9115528176 153 -0.0928900307 0.9093313935 154 NA -0.0928900307 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.0149724718 -0.0853874026 [2,] 0.0127510476 0.0149724718 [3,] 0.0105296235 0.0127510476 [4,] 0.0083081994 0.0105296235 [5,] 0.0060867753 0.0083081994 [6,] 0.0038653511 0.0060867753 [7,] -0.1009373715 0.0038653511 [8,] -0.0005774971 -0.1009373715 [9,] -0.0027989212 -0.0005774971 [10,] -0.1076016439 -0.0027989212 [11,] -0.0072417695 -0.1076016439 [12,] -0.0094631936 -0.0072417695 [13,] -0.1142659162 -0.0094631936 [14,] -0.0139060419 -0.1142659162 [15,] -0.1187087645 -0.0139060419 [16,] 0.8790698114 -0.1187087645 [17,] -0.1231516127 0.8790698114 [18,] -0.0227917384 -0.1231516127 [19,] 0.8724055390 -0.0227917384 [20,] -0.0272345866 0.8724055390 [21,] -0.0294560108 -0.0272345866 [22,] -0.0316774349 -0.0294560108 [23,] -0.0338988590 -0.0316774349 [24,] -0.1387015816 -0.0338988590 [25,] -0.0383417073 -0.1387015816 [26,] -0.0405631314 -0.0383417073 [27,] -0.0427845555 -0.0405631314 [28,] -0.0450059796 -0.0427845555 [29,] -0.0472274038 -0.0450059796 [30,] -0.0494488279 -0.0472274038 [31,] -0.0516702520 -0.0494488279 [32,] -0.0538916761 -0.0516702520 [33,] -0.1586943988 -0.0538916761 [34,] -0.0583345244 -0.1586943988 [35,] -0.0605559485 -0.0583345244 [36,] -0.1653586711 -0.0605559485 [37,] -0.0649987968 -0.1653586711 [38,] -0.0672202209 -0.0649987968 [39,] -0.1720229435 -0.0672202209 [40,] 0.9283369308 -0.1720229435 [41,] -0.0738844933 0.9283369308 [42,] -0.0761059174 -0.0738844933 [43,] -0.1809086400 -0.0761059174 [44,] -0.0805487657 -0.1809086400 [45,] -0.0827701898 -0.0805487657 [46,] -0.0849916139 -0.0827701898 [47,] -0.0872130380 -0.0849916139 [48,] -0.0894344622 -0.0872130380 [49,] -0.0916558863 -0.0894344622 [50,] -0.1964586089 -0.0916558863 [51,] 0.8013199670 -0.1964586089 [52,] -0.0983201587 0.8013199670 [53,] 0.8994584172 -0.0983201587 [54,] -0.1027630069 0.8994584172 [55,] -0.2075657295 -0.1027630069 [56,] -0.1072058552 -0.2075657295 [57,] -0.1094272793 -0.1072058552 [58,] -0.1116487034 -0.1094272793 [59,] 0.7835485740 -0.1116487034 [60,] -0.2186728502 0.7835485740 [61,] -0.1183129758 -0.2186728502 [62,] -0.1205343999 -0.1183129758 [63,] -0.2253371226 -0.1205343999 [64,] -0.1249772482 -0.2253371226 [65,] -0.1271986723 -0.1249772482 [66,] 0.7679986051 -0.1271986723 [67,] -0.1316415206 0.7679986051 [68,] -0.1338629447 -0.1316415206 [69,] -0.1360843688 -0.1338629447 [70,] -0.1383057929 -0.1360843688 [71,] -0.1405272171 -0.1383057929 [72,] -0.1427486412 -0.1405272171 [73,] -0.1449700653 -0.1427486412 [74,] -0.1471914895 -0.1449700653 [75,] -0.2519942121 -0.1471914895 [76,] -0.1516343377 -0.2519942121 [77,] -0.1538557618 -0.1516343377 [78,] 0.7413415156 -0.1538557618 [79,] -0.2608799086 0.7413415156 [80,] -0.1605200342 -0.2608799086 [81,] -0.1627414583 -0.1605200342 [82,] -0.1649628825 -0.1627414583 [83,] 0.8328156934 -0.1649628825 [84,] -0.1694057307 0.8328156934 [85,] -0.1716271548 -0.1694057307 [86,] 0.0559453858 -0.1716271548 [87,] -0.0488573368 0.0559453858 [88,] 0.0515025375 -0.0488573368 [89,] 0.0492811134 0.0515025375 [90,] 0.0470596893 0.0492811134 [91,] -0.0577430333 0.0470596893 [92,] 0.0426168410 -0.0577430333 [93,] 0.0403954169 0.0426168410 [94,] -0.0644073057 0.0403954169 [95,] 0.0359525687 -0.0644073057 [96,] -0.0688501540 0.0359525687 [97,] 0.0315097204 -0.0688501540 [98,] 0.0292882963 0.0315097204 [99,] 0.0270668722 0.0292882963 [100,] 0.0248454480 0.0270668722 [101,] 0.0226240239 0.0248454480 [102,] 0.0204025998 0.0226240239 [103,] 0.0181811756 0.0204025998 [104,] -0.0866215470 0.0181811756 [105,] 0.0137383274 -0.0866215470 [106,] 0.0115169033 0.0137383274 [107,] -0.0932858193 0.0115169033 [108,] 0.0070740550 -0.0932858193 [109,] 0.0048526309 0.0070740550 [110,] -0.0999500917 0.0048526309 [111,] -0.1021715159 -0.0999500917 [112,] -0.0018116415 -0.1021715159 [113,] -0.1066143641 -0.0018116415 [114,] -0.0062544897 -0.1066143641 [115,] -0.0084759139 -0.0062544897 [116,] -0.0106973380 -0.0084759139 [117,] -0.0129187621 -0.0106973380 [118,] -0.0151401862 -0.0129187621 [119,] -0.0173616104 -0.0151401862 [120,] -0.0195830345 -0.0173616104 [121,] -0.0218044586 -0.0195830345 [122,] -0.1266071812 -0.0218044586 [123,] -0.0262473069 -0.1266071812 [124,] -0.0284687310 -0.0262473069 [125,] -0.1332714536 -0.0284687310 [126,] -0.0329115793 -0.1332714536 [127,] -0.0351330034 -0.0329115793 [128,] -0.0373544275 -0.0351330034 [129,] -0.0395758516 -0.0373544275 [130,] -0.0417972758 -0.0395758516 [131,] -0.0440186999 -0.0417972758 [132,] -0.0462401240 -0.0440186999 [133,] -0.0484615481 -0.0462401240 [134,] -0.0506829723 -0.0484615481 [135,] -0.0529043964 -0.0506829723 [136,] -0.0551258205 -0.0529043964 [137,] -0.1599285431 -0.0551258205 [138,] -0.1621499673 -0.1599285431 [139,] -0.0617900929 -0.1621499673 [140,] 0.9359884830 -0.0617900929 [141,] -0.1688142396 0.9359884830 [142,] -0.0684543653 -0.1688142396 [143,] -0.0706757894 -0.0684543653 [144,] -0.0728972135 -0.0706757894 [145,] -0.1776999361 -0.0728972135 [146,] -0.1799213603 -0.1776999361 [147,] -0.1821427844 -0.1799213603 [148,] -0.0817829100 -0.1821427844 [149,] -0.0840043342 -0.0817829100 [150,] -0.0862257583 -0.0840043342 [151,] 0.9115528176 -0.0862257583 [152,] 0.9093313935 0.9115528176 [153,] -0.0928900307 0.9093313935 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.0149724718 -0.0853874026 2 0.0127510476 0.0149724718 3 0.0105296235 0.0127510476 4 0.0083081994 0.0105296235 5 0.0060867753 0.0083081994 6 0.0038653511 0.0060867753 7 -0.1009373715 0.0038653511 8 -0.0005774971 -0.1009373715 9 -0.0027989212 -0.0005774971 10 -0.1076016439 -0.0027989212 11 -0.0072417695 -0.1076016439 12 -0.0094631936 -0.0072417695 13 -0.1142659162 -0.0094631936 14 -0.0139060419 -0.1142659162 15 -0.1187087645 -0.0139060419 16 0.8790698114 -0.1187087645 17 -0.1231516127 0.8790698114 18 -0.0227917384 -0.1231516127 19 0.8724055390 -0.0227917384 20 -0.0272345866 0.8724055390 21 -0.0294560108 -0.0272345866 22 -0.0316774349 -0.0294560108 23 -0.0338988590 -0.0316774349 24 -0.1387015816 -0.0338988590 25 -0.0383417073 -0.1387015816 26 -0.0405631314 -0.0383417073 27 -0.0427845555 -0.0405631314 28 -0.0450059796 -0.0427845555 29 -0.0472274038 -0.0450059796 30 -0.0494488279 -0.0472274038 31 -0.0516702520 -0.0494488279 32 -0.0538916761 -0.0516702520 33 -0.1586943988 -0.0538916761 34 -0.0583345244 -0.1586943988 35 -0.0605559485 -0.0583345244 36 -0.1653586711 -0.0605559485 37 -0.0649987968 -0.1653586711 38 -0.0672202209 -0.0649987968 39 -0.1720229435 -0.0672202209 40 0.9283369308 -0.1720229435 41 -0.0738844933 0.9283369308 42 -0.0761059174 -0.0738844933 43 -0.1809086400 -0.0761059174 44 -0.0805487657 -0.1809086400 45 -0.0827701898 -0.0805487657 46 -0.0849916139 -0.0827701898 47 -0.0872130380 -0.0849916139 48 -0.0894344622 -0.0872130380 49 -0.0916558863 -0.0894344622 50 -0.1964586089 -0.0916558863 51 0.8013199670 -0.1964586089 52 -0.0983201587 0.8013199670 53 0.8994584172 -0.0983201587 54 -0.1027630069 0.8994584172 55 -0.2075657295 -0.1027630069 56 -0.1072058552 -0.2075657295 57 -0.1094272793 -0.1072058552 58 -0.1116487034 -0.1094272793 59 0.7835485740 -0.1116487034 60 -0.2186728502 0.7835485740 61 -0.1183129758 -0.2186728502 62 -0.1205343999 -0.1183129758 63 -0.2253371226 -0.1205343999 64 -0.1249772482 -0.2253371226 65 -0.1271986723 -0.1249772482 66 0.7679986051 -0.1271986723 67 -0.1316415206 0.7679986051 68 -0.1338629447 -0.1316415206 69 -0.1360843688 -0.1338629447 70 -0.1383057929 -0.1360843688 71 -0.1405272171 -0.1383057929 72 -0.1427486412 -0.1405272171 73 -0.1449700653 -0.1427486412 74 -0.1471914895 -0.1449700653 75 -0.2519942121 -0.1471914895 76 -0.1516343377 -0.2519942121 77 -0.1538557618 -0.1516343377 78 0.7413415156 -0.1538557618 79 -0.2608799086 0.7413415156 80 -0.1605200342 -0.2608799086 81 -0.1627414583 -0.1605200342 82 -0.1649628825 -0.1627414583 83 0.8328156934 -0.1649628825 84 -0.1694057307 0.8328156934 85 -0.1716271548 -0.1694057307 86 0.0559453858 -0.1716271548 87 -0.0488573368 0.0559453858 88 0.0515025375 -0.0488573368 89 0.0492811134 0.0515025375 90 0.0470596893 0.0492811134 91 -0.0577430333 0.0470596893 92 0.0426168410 -0.0577430333 93 0.0403954169 0.0426168410 94 -0.0644073057 0.0403954169 95 0.0359525687 -0.0644073057 96 -0.0688501540 0.0359525687 97 0.0315097204 -0.0688501540 98 0.0292882963 0.0315097204 99 0.0270668722 0.0292882963 100 0.0248454480 0.0270668722 101 0.0226240239 0.0248454480 102 0.0204025998 0.0226240239 103 0.0181811756 0.0204025998 104 -0.0866215470 0.0181811756 105 0.0137383274 -0.0866215470 106 0.0115169033 0.0137383274 107 -0.0932858193 0.0115169033 108 0.0070740550 -0.0932858193 109 0.0048526309 0.0070740550 110 -0.0999500917 0.0048526309 111 -0.1021715159 -0.0999500917 112 -0.0018116415 -0.1021715159 113 -0.1066143641 -0.0018116415 114 -0.0062544897 -0.1066143641 115 -0.0084759139 -0.0062544897 116 -0.0106973380 -0.0084759139 117 -0.0129187621 -0.0106973380 118 -0.0151401862 -0.0129187621 119 -0.0173616104 -0.0151401862 120 -0.0195830345 -0.0173616104 121 -0.0218044586 -0.0195830345 122 -0.1266071812 -0.0218044586 123 -0.0262473069 -0.1266071812 124 -0.0284687310 -0.0262473069 125 -0.1332714536 -0.0284687310 126 -0.0329115793 -0.1332714536 127 -0.0351330034 -0.0329115793 128 -0.0373544275 -0.0351330034 129 -0.0395758516 -0.0373544275 130 -0.0417972758 -0.0395758516 131 -0.0440186999 -0.0417972758 132 -0.0462401240 -0.0440186999 133 -0.0484615481 -0.0462401240 134 -0.0506829723 -0.0484615481 135 -0.0529043964 -0.0506829723 136 -0.0551258205 -0.0529043964 137 -0.1599285431 -0.0551258205 138 -0.1621499673 -0.1599285431 139 -0.0617900929 -0.1621499673 140 0.9359884830 -0.0617900929 141 -0.1688142396 0.9359884830 142 -0.0684543653 -0.1688142396 143 -0.0706757894 -0.0684543653 144 -0.0728972135 -0.0706757894 145 -0.1776999361 -0.0728972135 146 -0.1799213603 -0.1776999361 147 -0.1821427844 -0.1799213603 148 -0.0817829100 -0.1821427844 149 -0.0840043342 -0.0817829100 150 -0.0862257583 -0.0840043342 151 0.9115528176 -0.0862257583 152 0.9093313935 0.9115528176 153 -0.0928900307 0.9093313935 > 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/726a01356097571.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8ruz41356097571.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9fyaj1356097571.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10k6co1356097571.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11ygo71356097571.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/12y5di1356097571.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/136sda1356097571.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/14lw5f1356097571.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/15kh821356097571.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/16svmq1356097571.tab") + } > > try(system("convert tmp/1q4xw1356097571.ps tmp/1q4xw1356097571.png",intern=TRUE)) character(0) > try(system("convert tmp/2gg7p1356097571.ps tmp/2gg7p1356097571.png",intern=TRUE)) character(0) > try(system("convert tmp/34mdf1356097571.ps tmp/34mdf1356097571.png",intern=TRUE)) character(0) > try(system("convert tmp/4cqg81356097571.ps tmp/4cqg81356097571.png",intern=TRUE)) character(0) > try(system("convert tmp/5qff51356097571.ps tmp/5qff51356097571.png",intern=TRUE)) character(0) > try(system("convert tmp/6w7781356097571.ps tmp/6w7781356097571.png",intern=TRUE)) character(0) > try(system("convert tmp/726a01356097571.ps tmp/726a01356097571.png",intern=TRUE)) character(0) > try(system("convert tmp/8ruz41356097571.ps tmp/8ruz41356097571.png",intern=TRUE)) character(0) > try(system("convert tmp/9fyaj1356097571.ps tmp/9fyaj1356097571.png",intern=TRUE)) character(0) > try(system("convert tmp/10k6co1356097571.ps tmp/10k6co1356097571.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.557 2.018 10.594