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(1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + 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,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,1 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0) + ,dim=c(6 + ,154) + ,dimnames=list(c('UseLim' + ,'T20' + ,'used' + ,'ca' + ,'Useful' + ,'Outcome') + ,1:154)) > y <- array(NA,dim=c(6,154),dimnames=list(c('UseLim','T20','used','ca','Useful','Outcome'),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 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '4' > 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 ca UseLim T20 used Useful Outcome 1 0 1 0 0 0 1 2 0 0 0 0 0 0 3 0 0 0 0 0 0 4 0 0 0 0 0 0 5 0 0 0 0 0 0 6 0 1 0 0 1 1 7 0 0 0 0 0 0 8 0 0 0 0 0 0 9 0 0 0 0 0 1 10 0 1 0 0 0 0 11 0 1 0 0 0 0 12 0 0 0 0 0 0 13 0 0 0 1 1 0 14 0 1 0 0 0 0 15 0 0 0 1 1 1 16 0 0 0 1 1 1 17 1 1 0 1 1 0 18 0 1 0 0 0 0 19 0 0 0 0 0 1 20 1 0 0 1 1 1 21 0 1 0 0 1 0 22 0 1 0 1 1 1 23 0 0 0 0 1 1 24 0 1 0 0 1 1 25 0 0 0 1 0 1 26 0 0 0 1 1 0 27 0 1 0 0 0 1 28 0 0 0 1 0 0 29 0 0 0 0 0 1 30 0 0 0 0 1 0 31 0 0 0 0 0 0 32 0 1 0 0 0 0 33 0 1 0 0 1 0 34 0 0 0 0 0 1 35 0 0 0 0 0 0 36 0 0 0 0 0 0 37 0 1 0 1 1 0 38 0 0 0 1 0 1 39 0 0 0 0 1 1 40 0 0 0 0 1 0 41 1 0 0 1 1 1 42 0 0 0 1 0 1 43 0 1 0 0 1 1 44 0 1 0 0 0 0 45 0 0 0 0 1 0 46 0 0 0 0 1 1 47 0 0 0 0 0 0 48 0 0 0 0 0 1 49 0 0 0 0 1 1 50 0 0 0 0 0 0 51 0 0 0 1 0 0 52 1 1 0 1 1 0 53 0 0 0 0 0 1 54 1 0 0 1 0 0 55 0 0 0 0 0 0 56 0 0 0 1 0 1 57 0 0 0 1 1 1 58 0 0 0 0 0 1 59 0 0 0 0 0 1 60 1 1 0 1 1 1 61 0 1 0 0 0 1 62 0 0 0 1 1 0 63 0 0 0 0 0 0 64 0 1 0 0 0 1 65 0 0 0 0 0 0 66 0 0 0 0 0 0 67 1 0 0 1 1 0 68 0 1 0 0 0 0 69 0 0 0 0 0 1 70 0 0 0 1 0 0 71 0 0 0 0 0 0 72 0 0 0 0 0 1 73 0 0 0 1 0 1 74 0 1 0 1 0 0 75 0 0 0 0 0 1 76 0 0 0 0 1 1 77 0 0 0 0 0 1 78 0 0 0 1 1 1 79 1 0 0 1 0 1 80 0 0 0 0 1 0 81 0 0 0 0 0 0 82 0 1 0 1 0 1 83 0 0 0 0 0 0 84 1 0 0 1 0 0 85 0 0 0 0 1 1 86 0 1 0 0 0 0 87 0 1 0 0 0 1 88 0 1 1 1 0 1 89 0 0 0 0 0 0 90 0 0 0 0 0 1 91 0 0 0 0 1 0 92 0 1 1 0 0 0 93 0 1 0 0 1 0 94 0 0 0 0 0 0 95 0 0 1 0 0 0 96 0 0 0 0 0 1 97 0 1 1 0 0 0 98 0 0 0 0 0 0 99 0 1 0 0 0 0 100 0 0 0 0 0 1 101 0 1 0 0 0 1 102 0 0 0 0 0 0 103 0 0 0 0 0 0 104 0 0 0 0 0 0 105 0 0 1 1 0 0 106 0 0 0 0 0 0 107 0 0 0 0 0 0 108 0 1 1 1 0 0 109 0 0 0 0 0 0 110 0 1 0 0 0 0 111 0 1 1 1 1 0 112 0 0 1 0 0 0 113 0 0 0 1 0 0 114 0 1 1 1 0 0 115 0 1 0 0 0 0 116 0 0 0 0 0 0 117 0 1 0 0 0 1 118 0 1 0 0 0 0 119 0 0 0 0 0 0 120 0 0 0 0 0 1 121 0 1 0 0 0 0 122 0 0 0 0 0 0 123 0 1 1 1 0 0 124 0 0 0 1 1 1 125 0 0 0 0 0 1 126 0 0 1 0 0 0 127 0 0 0 0 1 0 128 0 0 0 0 0 1 129 0 0 0 0 0 0 130 0 0 0 0 0 1 131 0 1 0 0 0 0 132 0 1 0 0 0 1 133 0 1 0 1 0 0 134 0 0 0 0 0 0 135 0 0 0 0 0 0 136 0 0 0 0 0 0 137 0 1 0 1 1 1 138 0 1 1 1 1 1 139 0 0 1 0 0 0 140 0 0 0 0 0 0 141 1 0 0 1 0 1 142 0 0 1 1 0 1 143 0 1 0 0 0 0 144 0 0 0 0 1 1 145 0 0 0 0 1 0 146 0 0 1 0 0 1 147 0 0 1 1 0 0 148 0 0 1 0 0 0 149 0 1 0 0 0 0 150 0 0 0 0 1 1 151 0 0 0 0 0 1 152 1 1 0 1 0 0 153 1 1 0 1 1 0 154 0 1 0 1 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) UseLim T20 used Useful Outcome 0.01295 0.01151 -0.16261 0.27829 0.04480 -0.03548 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.34754 -0.05774 -0.01295 0.02254 0.74425 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.01295 0.03129 0.414 0.6797 UseLim 0.01151 0.04137 0.278 0.7811 T20 -0.16261 0.06361 -2.556 0.0116 * used 0.27829 0.04485 6.204 5.23e-09 *** Useful 0.04480 0.04592 0.976 0.3309 Outcome -0.03548 0.04009 -0.885 0.3775 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.237 on 148 degrees of freedom Multiple R-squared: 0.2488, Adjusted R-squared: 0.2235 F-statistic: 9.806 on 5 and 148 DF, p-value: 4.12e-08 > 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,] 0.000000000 0.000000000 1.0000000000 [2,] 0.000000000 0.000000000 1.0000000000 [3,] 0.000000000 0.000000000 1.0000000000 [4,] 0.000000000 0.000000000 1.0000000000 [5,] 0.000000000 0.000000000 1.0000000000 [6,] 0.000000000 0.000000000 1.0000000000 [7,] 0.000000000 0.000000000 1.0000000000 [8,] 0.000000000 0.000000000 1.0000000000 [9,] 0.354788247 0.709576495 0.6452117526 [10,] 0.306362518 0.612725035 0.6936374824 [11,] 0.275432772 0.550865544 0.7245672278 [12,] 0.851340400 0.297319200 0.1486596002 [13,] 0.803096395 0.393807209 0.1969036045 [14,] 0.870310992 0.259378016 0.1296890078 [15,] 0.831865229 0.336269542 0.1681347711 [16,] 0.783736055 0.432527889 0.2162639445 [17,] 0.772304136 0.455391728 0.2276958640 [18,] 0.800950703 0.398098593 0.1990492967 [19,] 0.751300826 0.497398348 0.2486991739 [20,] 0.738512251 0.522975498 0.2614877490 [21,] 0.687516429 0.624967143 0.3124835714 [22,] 0.631401583 0.737196834 0.3685984170 [23,] 0.571601966 0.856796068 0.4283980339 [24,] 0.510760807 0.978478385 0.4892391926 [25,] 0.458137250 0.916274499 0.5418627505 [26,] 0.400918281 0.801836561 0.5990817193 [27,] 0.344579839 0.689159678 0.6554201610 [28,] 0.291872845 0.583745691 0.7081271546 [29,] 0.318394948 0.636789897 0.6816050516 [30,] 0.295203044 0.590406089 0.7047969556 [31,] 0.248006348 0.496012697 0.7519936515 [32,] 0.206795222 0.413590444 0.7932047778 [33,] 0.662113178 0.675773644 0.3378868222 [34,] 0.648519226 0.702961548 0.3514807741 [35,] 0.601415635 0.797168730 0.3985843651 [36,] 0.549137151 0.901725698 0.4508628492 [37,] 0.499219079 0.998438159 0.5007809206 [38,] 0.448404870 0.896809739 0.5515951303 [39,] 0.397491026 0.794982052 0.6025089739 [40,] 0.348143902 0.696287804 0.6518560979 [41,] 0.302628264 0.605256528 0.6973717361 [42,] 0.259361635 0.518723270 0.7406383650 [43,] 0.257068208 0.514136417 0.7429317916 [44,] 0.598166340 0.803667321 0.4018336605 [45,] 0.551122812 0.897754375 0.4488771875 [46,] 0.871704750 0.256590501 0.1282952503 [47,] 0.844044648 0.311910704 0.1559553521 [48,] 0.844569583 0.310860834 0.1554304170 [49,] 0.857976045 0.284047910 0.1420239550 [50,] 0.830384468 0.339231064 0.1696155320 [51,] 0.799412822 0.401174356 0.2005871780 [52,] 0.952315770 0.095368460 0.0476842302 [53,] 0.939162832 0.121674335 0.0608371677 [54,] 0.950593241 0.098813519 0.0494067594 [55,] 0.937491867 0.125016266 0.0625081328 [56,] 0.921780527 0.156438946 0.0782194731 [57,] 0.903359808 0.193280383 0.0966401916 [58,] 0.881971423 0.236057154 0.1180285772 [59,] 0.977681835 0.044636330 0.0223181649 [60,] 0.970664631 0.058670739 0.0293353693 [61,] 0.962210513 0.075578973 0.0377894867 [62,] 0.967433191 0.065133619 0.0325668093 [63,] 0.958097551 0.083804898 0.0419024491 [64,] 0.946815137 0.106369725 0.0531848627 [65,] 0.951301446 0.097397107 0.0486985537 [66,] 0.959374122 0.081251756 0.0406258780 [67,] 0.948361315 0.103277369 0.0516386846 [68,] 0.934932312 0.130135376 0.0650676881 [69,] 0.919098225 0.161803550 0.0809017750 [70,] 0.930117897 0.139764205 0.0698821026 [71,] 0.993369829 0.013260342 0.0066301711 [72,] 0.990842170 0.018315660 0.0091578298 [73,] 0.987556511 0.024886979 0.0124434894 [74,] 0.989010413 0.021979175 0.0109895874 [75,] 0.985145791 0.029708417 0.0148542085 [76,] 0.999278580 0.001442840 0.0007214202 [77,] 0.998904378 0.002191245 0.0010956223 [78,] 0.998367714 0.003264572 0.0016322860 [79,] 0.997588157 0.004823686 0.0024118428 [80,] 0.996680881 0.006638238 0.0033191190 [81,] 0.995223528 0.009552944 0.0047764721 [82,] 0.993231104 0.013537792 0.0067688962 [83,] 0.990536310 0.018927379 0.0094636895 [84,] 0.988373200 0.023253601 0.0116268005 [85,] 0.984201982 0.031596036 0.0157980182 [86,] 0.978571053 0.042857894 0.0214289470 [87,] 0.973779481 0.052441038 0.0262205192 [88,] 0.965217262 0.069565476 0.0347827379 [89,] 0.958266970 0.083466059 0.0417330296 [90,] 0.945719829 0.108560341 0.0542801706 [91,] 0.930218817 0.139562367 0.0697811833 [92,] 0.911644490 0.176711019 0.0883555096 [93,] 0.889198415 0.221603171 0.1108015853 [94,] 0.863078917 0.273842165 0.1369210827 [95,] 0.832943188 0.334113623 0.1670568116 [96,] 0.798729742 0.402540515 0.2012702576 [97,] 0.774242314 0.451515372 0.2257576860 [98,] 0.733492538 0.533014924 0.2665074619 [99,] 0.689207083 0.621585834 0.3107929172 [100,] 0.656056238 0.687887523 0.3439437615 [101,] 0.606630867 0.786738265 0.3933691325 [102,] 0.554364023 0.891271954 0.4456359771 [103,] 0.518384616 0.963230768 0.4816153840 [104,] 0.482885186 0.965770371 0.5171148144 [105,] 0.537422977 0.925154046 0.4625770230 [106,] 0.501663853 0.996672294 0.4983361469 [107,] 0.445940703 0.891881406 0.5540592970 [108,] 0.393351516 0.786703033 0.6066484836 [109,] 0.340169744 0.680339487 0.6598302565 [110,] 0.289338953 0.578677905 0.7106610473 [111,] 0.244510523 0.489021046 0.7554894768 [112,] 0.201193047 0.402386094 0.7988069528 [113,] 0.162537939 0.325075878 0.8374620612 [114,] 0.130807314 0.261614627 0.8691926864 [115,] 0.113786107 0.227572214 0.8862138930 [116,] 0.143105433 0.286210866 0.8568945672 [117,] 0.111222884 0.222445767 0.8887771164 [118,] 0.093618512 0.187237024 0.9063814879 [119,] 0.071062557 0.142125114 0.9289374430 [120,] 0.051875384 0.103750768 0.9481246159 [121,] 0.037854564 0.075709128 0.9621454360 [122,] 0.026330013 0.052660025 0.9736699874 [123,] 0.017570110 0.035140220 0.9824298900 [124,] 0.011574935 0.023149871 0.9884250647 [125,] 0.021972178 0.043944357 0.9780278216 [126,] 0.015200173 0.030400346 0.9847998271 [127,] 0.010521405 0.021042811 0.9894785945 [128,] 0.007469504 0.014939008 0.9925304961 [129,] 0.013505224 0.027010448 0.9864947758 [130,] 0.011755740 0.023511481 0.9882442595 [131,] 0.010055937 0.020111875 0.9899440625 [132,] 0.005439065 0.010878130 0.9945609348 [133,] 0.052500846 0.105001691 0.9474991544 [134,] 0.045111357 0.090222714 0.9548886431 [135,] 0.027504673 0.055009347 0.9724953266 [136,] 0.015119306 0.030238612 0.9848806938 [137,] 0.006243364 0.012486729 0.9937566355 > postscript(file="/var/fisher/rcomp/tmp/1xchv1356107400.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/2x8vr1356107400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/306xx1356107400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4ve3z1356107400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/58c4j1356107400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 6 0.01102373 -0.01294597 -0.01294597 -0.01294597 -0.01294597 -0.03377194 7 8 9 10 11 12 -0.01294597 -0.01294597 0.02253840 -0.02446064 -0.02446064 -0.01294597 13 14 15 16 17 18 -0.33602952 -0.02446064 -0.30054515 -0.30054515 0.65245581 -0.02446064 19 20 21 22 23 24 0.02253840 0.69945485 -0.06925631 -0.31205982 -0.02225727 -0.03377194 25 26 27 28 29 30 -0.25574948 -0.33602952 0.01102373 -0.29123385 0.02253840 -0.05774164 31 32 33 34 35 36 -0.01294597 -0.02446064 -0.06925631 0.02253840 -0.01294597 -0.01294597 37 38 39 40 41 42 -0.34754419 -0.25574948 -0.02225727 -0.05774164 0.69945485 -0.25574948 43 44 45 46 47 48 -0.03377194 -0.02446064 -0.05774164 -0.02225727 -0.01294597 0.02253840 49 50 51 52 53 54 -0.02225727 -0.01294597 -0.29123385 0.65245581 0.02253840 0.70876615 55 56 57 58 59 60 -0.01294597 -0.25574948 -0.30054515 0.02253840 0.02253840 0.68794018 61 62 63 64 65 66 0.01102373 -0.33602952 -0.01294597 0.01102373 -0.01294597 -0.01294597 67 68 69 70 71 72 0.66397048 -0.02446064 0.02253840 -0.29123385 -0.01294597 0.02253840 73 74 75 76 77 78 -0.25574948 -0.30274852 0.02253840 -0.02225727 0.02253840 -0.30054515 79 80 81 82 83 84 0.74425052 -0.05774164 -0.01294597 -0.26726415 -0.01294597 0.70876615 85 86 87 88 89 90 -0.02225727 -0.02446064 0.01102373 -0.10464982 -0.01294597 0.02253840 91 92 93 94 95 96 -0.05774164 0.13815369 -0.06925631 -0.01294597 0.14966836 0.02253840 97 98 99 100 101 102 0.13815369 -0.01294597 -0.02446064 0.02253840 0.01102373 -0.01294597 103 104 105 106 107 108 -0.01294597 -0.01294597 -0.12861952 -0.01294597 -0.01294597 -0.14013419 109 110 111 112 113 114 -0.01294597 -0.02446064 -0.18492985 0.14966836 -0.29123385 -0.14013419 115 116 117 118 119 120 -0.02446064 -0.01294597 0.01102373 -0.02446064 -0.01294597 0.02253840 121 122 123 124 125 126 -0.02446064 -0.01294597 -0.14013419 -0.30054515 0.02253840 0.14966836 127 128 129 130 131 132 -0.05774164 0.02253840 -0.01294597 0.02253840 -0.02446064 0.01102373 133 134 135 136 137 138 -0.30274852 -0.01294597 -0.01294597 -0.01294597 -0.31205982 -0.14944548 139 140 141 142 143 144 0.14966836 -0.01294597 0.74425052 -0.09313515 -0.02446064 -0.02225727 145 146 147 148 149 150 -0.05774164 0.18515273 -0.12861952 0.14966836 -0.02446064 -0.02225727 151 152 153 154 0.02253840 0.69725148 0.65245581 -0.30274852 > postscript(file="/var/fisher/rcomp/tmp/65lqf1356107400.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 0.01102373 NA 1 -0.01294597 0.01102373 2 -0.01294597 -0.01294597 3 -0.01294597 -0.01294597 4 -0.01294597 -0.01294597 5 -0.03377194 -0.01294597 6 -0.01294597 -0.03377194 7 -0.01294597 -0.01294597 8 0.02253840 -0.01294597 9 -0.02446064 0.02253840 10 -0.02446064 -0.02446064 11 -0.01294597 -0.02446064 12 -0.33602952 -0.01294597 13 -0.02446064 -0.33602952 14 -0.30054515 -0.02446064 15 -0.30054515 -0.30054515 16 0.65245581 -0.30054515 17 -0.02446064 0.65245581 18 0.02253840 -0.02446064 19 0.69945485 0.02253840 20 -0.06925631 0.69945485 21 -0.31205982 -0.06925631 22 -0.02225727 -0.31205982 23 -0.03377194 -0.02225727 24 -0.25574948 -0.03377194 25 -0.33602952 -0.25574948 26 0.01102373 -0.33602952 27 -0.29123385 0.01102373 28 0.02253840 -0.29123385 29 -0.05774164 0.02253840 30 -0.01294597 -0.05774164 31 -0.02446064 -0.01294597 32 -0.06925631 -0.02446064 33 0.02253840 -0.06925631 34 -0.01294597 0.02253840 35 -0.01294597 -0.01294597 36 -0.34754419 -0.01294597 37 -0.25574948 -0.34754419 38 -0.02225727 -0.25574948 39 -0.05774164 -0.02225727 40 0.69945485 -0.05774164 41 -0.25574948 0.69945485 42 -0.03377194 -0.25574948 43 -0.02446064 -0.03377194 44 -0.05774164 -0.02446064 45 -0.02225727 -0.05774164 46 -0.01294597 -0.02225727 47 0.02253840 -0.01294597 48 -0.02225727 0.02253840 49 -0.01294597 -0.02225727 50 -0.29123385 -0.01294597 51 0.65245581 -0.29123385 52 0.02253840 0.65245581 53 0.70876615 0.02253840 54 -0.01294597 0.70876615 55 -0.25574948 -0.01294597 56 -0.30054515 -0.25574948 57 0.02253840 -0.30054515 58 0.02253840 0.02253840 59 0.68794018 0.02253840 60 0.01102373 0.68794018 61 -0.33602952 0.01102373 62 -0.01294597 -0.33602952 63 0.01102373 -0.01294597 64 -0.01294597 0.01102373 65 -0.01294597 -0.01294597 66 0.66397048 -0.01294597 67 -0.02446064 0.66397048 68 0.02253840 -0.02446064 69 -0.29123385 0.02253840 70 -0.01294597 -0.29123385 71 0.02253840 -0.01294597 72 -0.25574948 0.02253840 73 -0.30274852 -0.25574948 74 0.02253840 -0.30274852 75 -0.02225727 0.02253840 76 0.02253840 -0.02225727 77 -0.30054515 0.02253840 78 0.74425052 -0.30054515 79 -0.05774164 0.74425052 80 -0.01294597 -0.05774164 81 -0.26726415 -0.01294597 82 -0.01294597 -0.26726415 83 0.70876615 -0.01294597 84 -0.02225727 0.70876615 85 -0.02446064 -0.02225727 86 0.01102373 -0.02446064 87 -0.10464982 0.01102373 88 -0.01294597 -0.10464982 89 0.02253840 -0.01294597 90 -0.05774164 0.02253840 91 0.13815369 -0.05774164 92 -0.06925631 0.13815369 93 -0.01294597 -0.06925631 94 0.14966836 -0.01294597 95 0.02253840 0.14966836 96 0.13815369 0.02253840 97 -0.01294597 0.13815369 98 -0.02446064 -0.01294597 99 0.02253840 -0.02446064 100 0.01102373 0.02253840 101 -0.01294597 0.01102373 102 -0.01294597 -0.01294597 103 -0.01294597 -0.01294597 104 -0.12861952 -0.01294597 105 -0.01294597 -0.12861952 106 -0.01294597 -0.01294597 107 -0.14013419 -0.01294597 108 -0.01294597 -0.14013419 109 -0.02446064 -0.01294597 110 -0.18492985 -0.02446064 111 0.14966836 -0.18492985 112 -0.29123385 0.14966836 113 -0.14013419 -0.29123385 114 -0.02446064 -0.14013419 115 -0.01294597 -0.02446064 116 0.01102373 -0.01294597 117 -0.02446064 0.01102373 118 -0.01294597 -0.02446064 119 0.02253840 -0.01294597 120 -0.02446064 0.02253840 121 -0.01294597 -0.02446064 122 -0.14013419 -0.01294597 123 -0.30054515 -0.14013419 124 0.02253840 -0.30054515 125 0.14966836 0.02253840 126 -0.05774164 0.14966836 127 0.02253840 -0.05774164 128 -0.01294597 0.02253840 129 0.02253840 -0.01294597 130 -0.02446064 0.02253840 131 0.01102373 -0.02446064 132 -0.30274852 0.01102373 133 -0.01294597 -0.30274852 134 -0.01294597 -0.01294597 135 -0.01294597 -0.01294597 136 -0.31205982 -0.01294597 137 -0.14944548 -0.31205982 138 0.14966836 -0.14944548 139 -0.01294597 0.14966836 140 0.74425052 -0.01294597 141 -0.09313515 0.74425052 142 -0.02446064 -0.09313515 143 -0.02225727 -0.02446064 144 -0.05774164 -0.02225727 145 0.18515273 -0.05774164 146 -0.12861952 0.18515273 147 0.14966836 -0.12861952 148 -0.02446064 0.14966836 149 -0.02225727 -0.02446064 150 0.02253840 -0.02225727 151 0.69725148 0.02253840 152 0.65245581 0.69725148 153 -0.30274852 0.65245581 154 NA -0.30274852 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.01294597 0.01102373 [2,] -0.01294597 -0.01294597 [3,] -0.01294597 -0.01294597 [4,] -0.01294597 -0.01294597 [5,] -0.03377194 -0.01294597 [6,] -0.01294597 -0.03377194 [7,] -0.01294597 -0.01294597 [8,] 0.02253840 -0.01294597 [9,] -0.02446064 0.02253840 [10,] -0.02446064 -0.02446064 [11,] -0.01294597 -0.02446064 [12,] -0.33602952 -0.01294597 [13,] -0.02446064 -0.33602952 [14,] -0.30054515 -0.02446064 [15,] -0.30054515 -0.30054515 [16,] 0.65245581 -0.30054515 [17,] -0.02446064 0.65245581 [18,] 0.02253840 -0.02446064 [19,] 0.69945485 0.02253840 [20,] -0.06925631 0.69945485 [21,] -0.31205982 -0.06925631 [22,] -0.02225727 -0.31205982 [23,] -0.03377194 -0.02225727 [24,] -0.25574948 -0.03377194 [25,] -0.33602952 -0.25574948 [26,] 0.01102373 -0.33602952 [27,] -0.29123385 0.01102373 [28,] 0.02253840 -0.29123385 [29,] -0.05774164 0.02253840 [30,] -0.01294597 -0.05774164 [31,] -0.02446064 -0.01294597 [32,] -0.06925631 -0.02446064 [33,] 0.02253840 -0.06925631 [34,] -0.01294597 0.02253840 [35,] -0.01294597 -0.01294597 [36,] -0.34754419 -0.01294597 [37,] -0.25574948 -0.34754419 [38,] -0.02225727 -0.25574948 [39,] -0.05774164 -0.02225727 [40,] 0.69945485 -0.05774164 [41,] -0.25574948 0.69945485 [42,] -0.03377194 -0.25574948 [43,] -0.02446064 -0.03377194 [44,] -0.05774164 -0.02446064 [45,] -0.02225727 -0.05774164 [46,] -0.01294597 -0.02225727 [47,] 0.02253840 -0.01294597 [48,] -0.02225727 0.02253840 [49,] -0.01294597 -0.02225727 [50,] -0.29123385 -0.01294597 [51,] 0.65245581 -0.29123385 [52,] 0.02253840 0.65245581 [53,] 0.70876615 0.02253840 [54,] -0.01294597 0.70876615 [55,] -0.25574948 -0.01294597 [56,] -0.30054515 -0.25574948 [57,] 0.02253840 -0.30054515 [58,] 0.02253840 0.02253840 [59,] 0.68794018 0.02253840 [60,] 0.01102373 0.68794018 [61,] -0.33602952 0.01102373 [62,] -0.01294597 -0.33602952 [63,] 0.01102373 -0.01294597 [64,] -0.01294597 0.01102373 [65,] -0.01294597 -0.01294597 [66,] 0.66397048 -0.01294597 [67,] -0.02446064 0.66397048 [68,] 0.02253840 -0.02446064 [69,] -0.29123385 0.02253840 [70,] -0.01294597 -0.29123385 [71,] 0.02253840 -0.01294597 [72,] -0.25574948 0.02253840 [73,] -0.30274852 -0.25574948 [74,] 0.02253840 -0.30274852 [75,] -0.02225727 0.02253840 [76,] 0.02253840 -0.02225727 [77,] -0.30054515 0.02253840 [78,] 0.74425052 -0.30054515 [79,] -0.05774164 0.74425052 [80,] -0.01294597 -0.05774164 [81,] -0.26726415 -0.01294597 [82,] -0.01294597 -0.26726415 [83,] 0.70876615 -0.01294597 [84,] -0.02225727 0.70876615 [85,] -0.02446064 -0.02225727 [86,] 0.01102373 -0.02446064 [87,] -0.10464982 0.01102373 [88,] -0.01294597 -0.10464982 [89,] 0.02253840 -0.01294597 [90,] -0.05774164 0.02253840 [91,] 0.13815369 -0.05774164 [92,] -0.06925631 0.13815369 [93,] -0.01294597 -0.06925631 [94,] 0.14966836 -0.01294597 [95,] 0.02253840 0.14966836 [96,] 0.13815369 0.02253840 [97,] -0.01294597 0.13815369 [98,] -0.02446064 -0.01294597 [99,] 0.02253840 -0.02446064 [100,] 0.01102373 0.02253840 [101,] -0.01294597 0.01102373 [102,] -0.01294597 -0.01294597 [103,] -0.01294597 -0.01294597 [104,] -0.12861952 -0.01294597 [105,] -0.01294597 -0.12861952 [106,] -0.01294597 -0.01294597 [107,] -0.14013419 -0.01294597 [108,] -0.01294597 -0.14013419 [109,] -0.02446064 -0.01294597 [110,] -0.18492985 -0.02446064 [111,] 0.14966836 -0.18492985 [112,] -0.29123385 0.14966836 [113,] -0.14013419 -0.29123385 [114,] -0.02446064 -0.14013419 [115,] -0.01294597 -0.02446064 [116,] 0.01102373 -0.01294597 [117,] -0.02446064 0.01102373 [118,] -0.01294597 -0.02446064 [119,] 0.02253840 -0.01294597 [120,] -0.02446064 0.02253840 [121,] -0.01294597 -0.02446064 [122,] -0.14013419 -0.01294597 [123,] -0.30054515 -0.14013419 [124,] 0.02253840 -0.30054515 [125,] 0.14966836 0.02253840 [126,] -0.05774164 0.14966836 [127,] 0.02253840 -0.05774164 [128,] -0.01294597 0.02253840 [129,] 0.02253840 -0.01294597 [130,] -0.02446064 0.02253840 [131,] 0.01102373 -0.02446064 [132,] -0.30274852 0.01102373 [133,] -0.01294597 -0.30274852 [134,] -0.01294597 -0.01294597 [135,] -0.01294597 -0.01294597 [136,] -0.31205982 -0.01294597 [137,] -0.14944548 -0.31205982 [138,] 0.14966836 -0.14944548 [139,] -0.01294597 0.14966836 [140,] 0.74425052 -0.01294597 [141,] -0.09313515 0.74425052 [142,] -0.02446064 -0.09313515 [143,] -0.02225727 -0.02446064 [144,] -0.05774164 -0.02225727 [145,] 0.18515273 -0.05774164 [146,] -0.12861952 0.18515273 [147,] 0.14966836 -0.12861952 [148,] -0.02446064 0.14966836 [149,] -0.02225727 -0.02446064 [150,] 0.02253840 -0.02225727 [151,] 0.69725148 0.02253840 [152,] 0.65245581 0.69725148 [153,] -0.30274852 0.65245581 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.01294597 0.01102373 2 -0.01294597 -0.01294597 3 -0.01294597 -0.01294597 4 -0.01294597 -0.01294597 5 -0.03377194 -0.01294597 6 -0.01294597 -0.03377194 7 -0.01294597 -0.01294597 8 0.02253840 -0.01294597 9 -0.02446064 0.02253840 10 -0.02446064 -0.02446064 11 -0.01294597 -0.02446064 12 -0.33602952 -0.01294597 13 -0.02446064 -0.33602952 14 -0.30054515 -0.02446064 15 -0.30054515 -0.30054515 16 0.65245581 -0.30054515 17 -0.02446064 0.65245581 18 0.02253840 -0.02446064 19 0.69945485 0.02253840 20 -0.06925631 0.69945485 21 -0.31205982 -0.06925631 22 -0.02225727 -0.31205982 23 -0.03377194 -0.02225727 24 -0.25574948 -0.03377194 25 -0.33602952 -0.25574948 26 0.01102373 -0.33602952 27 -0.29123385 0.01102373 28 0.02253840 -0.29123385 29 -0.05774164 0.02253840 30 -0.01294597 -0.05774164 31 -0.02446064 -0.01294597 32 -0.06925631 -0.02446064 33 0.02253840 -0.06925631 34 -0.01294597 0.02253840 35 -0.01294597 -0.01294597 36 -0.34754419 -0.01294597 37 -0.25574948 -0.34754419 38 -0.02225727 -0.25574948 39 -0.05774164 -0.02225727 40 0.69945485 -0.05774164 41 -0.25574948 0.69945485 42 -0.03377194 -0.25574948 43 -0.02446064 -0.03377194 44 -0.05774164 -0.02446064 45 -0.02225727 -0.05774164 46 -0.01294597 -0.02225727 47 0.02253840 -0.01294597 48 -0.02225727 0.02253840 49 -0.01294597 -0.02225727 50 -0.29123385 -0.01294597 51 0.65245581 -0.29123385 52 0.02253840 0.65245581 53 0.70876615 0.02253840 54 -0.01294597 0.70876615 55 -0.25574948 -0.01294597 56 -0.30054515 -0.25574948 57 0.02253840 -0.30054515 58 0.02253840 0.02253840 59 0.68794018 0.02253840 60 0.01102373 0.68794018 61 -0.33602952 0.01102373 62 -0.01294597 -0.33602952 63 0.01102373 -0.01294597 64 -0.01294597 0.01102373 65 -0.01294597 -0.01294597 66 0.66397048 -0.01294597 67 -0.02446064 0.66397048 68 0.02253840 -0.02446064 69 -0.29123385 0.02253840 70 -0.01294597 -0.29123385 71 0.02253840 -0.01294597 72 -0.25574948 0.02253840 73 -0.30274852 -0.25574948 74 0.02253840 -0.30274852 75 -0.02225727 0.02253840 76 0.02253840 -0.02225727 77 -0.30054515 0.02253840 78 0.74425052 -0.30054515 79 -0.05774164 0.74425052 80 -0.01294597 -0.05774164 81 -0.26726415 -0.01294597 82 -0.01294597 -0.26726415 83 0.70876615 -0.01294597 84 -0.02225727 0.70876615 85 -0.02446064 -0.02225727 86 0.01102373 -0.02446064 87 -0.10464982 0.01102373 88 -0.01294597 -0.10464982 89 0.02253840 -0.01294597 90 -0.05774164 0.02253840 91 0.13815369 -0.05774164 92 -0.06925631 0.13815369 93 -0.01294597 -0.06925631 94 0.14966836 -0.01294597 95 0.02253840 0.14966836 96 0.13815369 0.02253840 97 -0.01294597 0.13815369 98 -0.02446064 -0.01294597 99 0.02253840 -0.02446064 100 0.01102373 0.02253840 101 -0.01294597 0.01102373 102 -0.01294597 -0.01294597 103 -0.01294597 -0.01294597 104 -0.12861952 -0.01294597 105 -0.01294597 -0.12861952 106 -0.01294597 -0.01294597 107 -0.14013419 -0.01294597 108 -0.01294597 -0.14013419 109 -0.02446064 -0.01294597 110 -0.18492985 -0.02446064 111 0.14966836 -0.18492985 112 -0.29123385 0.14966836 113 -0.14013419 -0.29123385 114 -0.02446064 -0.14013419 115 -0.01294597 -0.02446064 116 0.01102373 -0.01294597 117 -0.02446064 0.01102373 118 -0.01294597 -0.02446064 119 0.02253840 -0.01294597 120 -0.02446064 0.02253840 121 -0.01294597 -0.02446064 122 -0.14013419 -0.01294597 123 -0.30054515 -0.14013419 124 0.02253840 -0.30054515 125 0.14966836 0.02253840 126 -0.05774164 0.14966836 127 0.02253840 -0.05774164 128 -0.01294597 0.02253840 129 0.02253840 -0.01294597 130 -0.02446064 0.02253840 131 0.01102373 -0.02446064 132 -0.30274852 0.01102373 133 -0.01294597 -0.30274852 134 -0.01294597 -0.01294597 135 -0.01294597 -0.01294597 136 -0.31205982 -0.01294597 137 -0.14944548 -0.31205982 138 0.14966836 -0.14944548 139 -0.01294597 0.14966836 140 0.74425052 -0.01294597 141 -0.09313515 0.74425052 142 -0.02446064 -0.09313515 143 -0.02225727 -0.02446064 144 -0.05774164 -0.02225727 145 0.18515273 -0.05774164 146 -0.12861952 0.18515273 147 0.14966836 -0.12861952 148 -0.02446064 0.14966836 149 -0.02225727 -0.02446064 150 0.02253840 -0.02225727 151 0.69725148 0.02253840 152 0.65245581 0.69725148 153 -0.30274852 0.65245581 > 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/7mskn1356107400.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/8z43j1356107400.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/9blv01356107400.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/10cuz21356107400.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/115qfv1356107400.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/1226gn1356107400.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/132tyx1356107400.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14eihz1356107400.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/15a6781356107400.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/161l8w1356107400.tab") + } > > try(system("convert tmp/1xchv1356107400.ps tmp/1xchv1356107400.png",intern=TRUE)) character(0) > try(system("convert tmp/2x8vr1356107400.ps tmp/2x8vr1356107400.png",intern=TRUE)) character(0) > try(system("convert tmp/306xx1356107400.ps tmp/306xx1356107400.png",intern=TRUE)) character(0) > try(system("convert tmp/4ve3z1356107400.ps tmp/4ve3z1356107400.png",intern=TRUE)) character(0) > try(system("convert tmp/58c4j1356107400.ps tmp/58c4j1356107400.png",intern=TRUE)) character(0) > try(system("convert tmp/65lqf1356107400.ps tmp/65lqf1356107400.png",intern=TRUE)) character(0) > try(system("convert tmp/7mskn1356107400.ps tmp/7mskn1356107400.png",intern=TRUE)) character(0) > try(system("convert tmp/8z43j1356107400.ps tmp/8z43j1356107400.png",intern=TRUE)) character(0) > try(system("convert tmp/9blv01356107400.ps tmp/9blv01356107400.png",intern=TRUE)) character(0) > try(system("convert tmp/10cuz21356107400.ps tmp/10cuz21356107400.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.497 2.323 13.839