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 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,1 + ,4 + ,1 + ,1 + ,0 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,1 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,4 + ,1 + ,1 + ,1 + ,4 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,1 + ,4 + ,1 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,1 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,2 + ,0 + ,1 + ,1 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,2 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,2 + ,0 + ,0 + ,1 + ,2 + ,0 + ,0 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0) + ,dim=c(4 + ,154) + ,dimnames=list(c('Weeks*t' + ,'CorrectAnalysis' + ,'Useful' + ,'Outcome') + ,1:154)) > y <- array(NA,dim=c(4,154),dimnames=list(c('Weeks*t','CorrectAnalysis','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 = 'Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '2' > 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 CorrectAnalysis Weeks*t Useful Outcome t 1 0 4 0 1 1 2 0 0 0 0 2 3 0 0 0 0 3 4 0 0 0 0 4 5 0 0 0 0 5 6 0 0 1 1 6 7 0 0 0 0 7 8 0 4 0 0 8 9 0 0 0 1 9 10 0 0 0 0 10 11 0 4 0 0 11 12 0 0 0 0 12 13 0 0 1 0 13 14 0 4 0 0 14 15 0 0 1 1 15 16 0 4 1 1 16 17 1 4 1 0 17 18 0 4 0 0 18 19 0 0 0 1 19 20 1 4 1 1 20 21 0 0 1 0 21 22 0 0 1 1 22 23 0 0 1 1 23 24 0 0 1 1 24 25 0 4 0 1 25 26 0 0 1 0 26 27 0 0 0 1 27 28 0 0 0 0 28 29 0 0 0 1 29 30 0 0 1 0 30 31 0 0 0 0 31 32 0 0 0 0 32 33 0 0 1 0 33 34 0 4 0 1 34 35 0 0 0 0 35 36 0 0 0 0 36 37 0 4 1 0 37 38 0 0 0 1 38 39 0 0 1 1 39 40 0 4 1 0 40 41 1 0 1 1 41 42 0 0 0 1 42 43 0 0 1 1 43 44 0 4 0 0 44 45 0 0 1 0 45 46 0 0 1 1 46 47 0 0 0 0 47 48 0 0 0 1 48 49 0 0 1 1 49 50 0 0 0 0 50 51 0 4 0 0 51 52 1 4 1 0 52 53 0 0 0 1 53 54 1 0 0 0 54 55 0 0 0 0 55 56 0 4 0 1 56 57 0 0 1 1 57 58 0 0 0 1 58 59 0 0 0 1 59 60 1 4 1 1 60 61 0 4 0 1 61 62 0 0 1 0 62 63 0 0 0 0 63 64 0 4 0 1 64 65 0 0 0 0 65 66 0 0 0 0 66 67 1 4 1 0 67 68 0 0 0 0 68 69 0 0 0 1 69 70 0 0 0 0 70 71 0 0 0 0 71 72 0 0 0 1 72 73 0 0 0 1 73 74 0 0 0 0 74 75 0 0 0 1 75 76 0 4 1 1 76 77 0 0 0 1 77 78 0 0 1 1 78 79 1 4 0 1 79 80 0 4 1 0 80 81 0 0 0 0 81 82 0 0 0 1 82 83 0 0 0 0 83 84 1 0 0 0 84 85 0 0 1 1 85 86 0 0 0 0 86 87 0 0 0 1 87 88 0 2 0 1 88 89 0 0 0 0 89 90 0 0 0 1 90 91 0 0 1 0 91 92 0 2 0 0 92 93 0 0 1 0 93 94 0 0 0 0 94 95 0 2 0 0 95 96 0 0 0 1 96 97 0 2 0 0 97 98 0 0 0 0 98 99 0 0 0 0 99 100 0 0 0 1 100 101 0 0 0 1 101 102 0 0 0 0 102 103 0 0 0 0 103 104 0 0 0 0 104 105 0 2 0 0 105 106 0 0 0 0 106 107 0 0 0 0 107 108 0 2 0 0 108 109 0 0 0 0 109 110 0 0 0 0 110 111 0 2 1 0 111 112 0 2 0 0 112 113 0 0 0 0 113 114 0 2 0 0 114 115 0 0 0 0 115 116 0 0 0 0 116 117 0 0 0 1 117 118 0 0 0 0 118 119 0 0 0 0 119 120 0 0 0 1 120 121 0 0 0 0 121 122 0 0 0 0 122 123 0 2 0 0 123 124 0 0 1 1 124 125 0 0 0 1 125 126 0 2 0 0 126 127 0 0 1 0 127 128 0 0 0 1 128 129 0 0 0 0 129 130 0 0 0 1 130 131 0 0 0 0 131 132 0 0 0 1 132 133 0 0 0 0 133 134 0 0 0 0 134 135 0 0 0 0 135 136 0 0 0 0 136 137 0 0 1 1 137 138 0 2 1 1 138 139 0 2 0 0 139 140 0 0 0 0 140 141 1 0 0 1 141 142 0 2 0 1 142 143 0 0 0 0 143 144 0 0 1 1 144 145 0 0 1 0 145 146 0 2 0 1 146 147 0 2 0 0 147 148 0 2 0 0 148 149 0 0 0 0 149 150 0 0 1 1 150 151 0 0 0 1 151 152 1 0 0 0 152 153 1 0 1 0 153 154 0 0 0 0 154 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `Weeks*t` Useful Outcome t -0.0184517 0.0414689 0.1222127 -0.0112666 0.0004435 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.30512 -0.11760 -0.03277 -0.00359 0.99450 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0184517 0.0525576 -0.351 0.72603 `Weeks*t` 0.0414689 0.0145332 2.853 0.00494 ** Useful 0.1222127 0.0488134 2.504 0.01337 * Outcome -0.0112666 0.0434525 -0.259 0.79577 t 0.0004435 0.0004867 0.911 0.36364 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2593 on 149 degrees of freedom Multiple R-squared: 0.09453, Adjusted R-squared: 0.07022 F-statistic: 3.889 on 4 and 149 DF, p-value: 0.004927 > 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,] 0.000000e+00 0.0000000000 1.0000000000 [2,] 0.000000e+00 0.0000000000 1.0000000000 [3,] 0.000000e+00 0.0000000000 1.0000000000 [4,] 0.000000e+00 0.0000000000 1.0000000000 [5,] 0.000000e+00 0.0000000000 1.0000000000 [6,] 0.000000e+00 0.0000000000 1.0000000000 [7,] 0.000000e+00 0.0000000000 1.0000000000 [8,] 0.000000e+00 0.0000000000 1.0000000000 [9,] 0.000000e+00 0.0000000000 1.0000000000 [10,] 2.294448e-01 0.4588896333 0.7705551833 [11,] 1.818746e-01 0.3637492885 0.8181253557 [12,] 1.587934e-01 0.3175868300 0.8412065850 [13,] 5.786855e-01 0.8426290157 0.4213145079 [14,] 5.897706e-01 0.8204587086 0.4102293543 [15,] 5.457763e-01 0.9084474436 0.4542237218 [16,] 4.908836e-01 0.9817671641 0.5091164179 [17,] 4.314686e-01 0.8629371372 0.5685314314 [18,] 3.716081e-01 0.7432162567 0.6283918717 [19,] 3.277409e-01 0.6554817245 0.6722591377 [20,] 2.914972e-01 0.5829943355 0.7085028323 [21,] 2.405275e-01 0.4810549480 0.7594725260 [22,] 1.990446e-01 0.3980892397 0.8009553801 [23,] 1.694961e-01 0.3389921087 0.8305039457 [24,] 1.334509e-01 0.2669018591 0.8665490705 [25,] 1.028478e-01 0.2056955435 0.8971522282 [26,] 8.413743e-02 0.1682748631 0.9158625685 [27,] 6.879671e-02 0.1375934236 0.9312032882 [28,] 5.162318e-02 0.1032463578 0.9483768211 [29,] 3.793304e-02 0.0758660724 0.9620669638 [30,] 4.618108e-02 0.0923621656 0.9538189172 [31,] 3.468080e-02 0.0693616033 0.9653191984 [32,] 2.577622e-02 0.0515524478 0.9742237761 [33,] 2.659797e-02 0.0531959391 0.9734020305 [34,] 3.834185e-01 0.7668370307 0.6165814847 [35,] 3.320251e-01 0.6640502069 0.6679748966 [36,] 2.994190e-01 0.5988379171 0.7005810414 [37,] 2.666317e-01 0.5332633753 0.7333683123 [38,] 2.323754e-01 0.4647508192 0.7676245904 [39,] 2.029080e-01 0.4058160800 0.7970919600 [40,] 1.695463e-01 0.3390925284 0.8304537358 [41,] 1.383608e-01 0.2767215904 0.8616392048 [42,] 1.174841e-01 0.2349681160 0.8825159420 [43,] 9.498759e-02 0.1899751834 0.9050124083 [44,] 8.109603e-02 0.1621920548 0.9189039726 [45,] 2.948156e-01 0.5896311798 0.7051844101 [46,] 2.528520e-01 0.5057039726 0.7471480137 [47,] 8.135086e-01 0.3729828072 0.1864914036 [48,] 7.811336e-01 0.4377327476 0.2188663738 [49,] 7.643651e-01 0.4712698546 0.2356349273 [50,] 7.392685e-01 0.5214629435 0.2607314717 [51,] 6.983350e-01 0.6033300277 0.3016650138 [52,] 6.548420e-01 0.6903159298 0.3451579649 [53,] 8.618211e-01 0.2763578719 0.1381789359 [54,] 8.496831e-01 0.3006337168 0.1503168584 [55,] 8.358398e-01 0.3283203201 0.1641601600 [56,] 8.054024e-01 0.3891951622 0.1945975811 [57,] 7.888885e-01 0.4222230788 0.2111115394 [58,] 7.534971e-01 0.4930058477 0.2465029239 [59,] 7.151341e-01 0.5697318843 0.2848659421 [60,] 9.091773e-01 0.1816454763 0.0908227381 [61,] 8.890958e-01 0.2218083754 0.1109041877 [62,] 8.651417e-01 0.2697165623 0.1348582811 [63,] 8.388088e-01 0.3223823575 0.1611911788 [64,] 8.092396e-01 0.3815208444 0.1907604222 [65,] 7.755826e-01 0.4488347404 0.2244173702 [66,] 7.388499e-01 0.5223002335 0.2611501167 [67,] 7.003656e-01 0.5992687964 0.2996343982 [68,] 6.584209e-01 0.6831582494 0.3415791247 [69,] 6.739467e-01 0.6521065126 0.3260532563 [70,] 6.306489e-01 0.7387021596 0.3693510798 [71,] 5.990342e-01 0.8019315530 0.4009657765 [72,] 9.495161e-01 0.1009677842 0.0504838921 [73,] 9.540481e-01 0.0919037782 0.0459518891 [74,] 9.416624e-01 0.1166752903 0.0583376452 [75,] 9.266013e-01 0.1467973282 0.0733986641 [76,] 9.088953e-01 0.1822093588 0.0911046794 [77,] 9.991397e-01 0.0017206873 0.0008603436 [78,] 9.988303e-01 0.0023393527 0.0011696764 [79,] 9.983181e-01 0.0033638803 0.0016819402 [80,] 9.975912e-01 0.0048176639 0.0024088320 [81,] 9.969091e-01 0.0061818685 0.0030909342 [82,] 9.956671e-01 0.0086658962 0.0043329481 [83,] 9.940144e-01 0.0119712283 0.0059856142 [84,] 9.922937e-01 0.0154126244 0.0077063122 [85,] 9.903104e-01 0.0193792001 0.0096896000 [86,] 9.874448e-01 0.0251104725 0.0125552362 [87,] 9.830393e-01 0.0339213130 0.0169606565 [88,] 9.789570e-01 0.0420860377 0.0210430189 [89,] 9.724281e-01 0.0551437723 0.0275718862 [90,] 9.664267e-01 0.0671466013 0.0335733006 [91,] 9.563384e-01 0.0873231941 0.0436615971 [92,] 9.438587e-01 0.1122825860 0.0561412930 [93,] 9.295533e-01 0.1408933215 0.0704466608 [94,] 9.129191e-01 0.1741618020 0.0870809010 [95,] 8.917562e-01 0.2164875763 0.1082437882 [96,] 8.669564e-01 0.2660872292 0.1330436146 [97,] 8.383077e-01 0.3233846682 0.1616923341 [98,] 8.142255e-01 0.3715489479 0.1857744739 [99,] 7.786138e-01 0.4427723980 0.2213861990 [100,] 7.391066e-01 0.5217868803 0.2608934402 [101,] 7.074942e-01 0.5850115859 0.2925057930 [102,] 6.619098e-01 0.6761804712 0.3380902356 [103,] 6.134442e-01 0.7731115076 0.3865557538 [104,] 5.843777e-01 0.8312445945 0.4156222973 [105,] 5.498363e-01 0.9003274918 0.4501637459 [106,] 4.975355e-01 0.9950710136 0.5024644932 [107,] 4.672744e-01 0.9345488528 0.5327255736 [108,] 4.152487e-01 0.8304973682 0.5847513159 [109,] 3.641944e-01 0.7283888679 0.6358055661 [110,] 3.162742e-01 0.6325484457 0.6837257772 [111,] 2.694754e-01 0.5389508500 0.7305245750 [112,] 2.259700e-01 0.4519400858 0.7740299571 [113,] 1.872371e-01 0.3744741108 0.8127629446 [114,] 1.516442e-01 0.3032883663 0.8483558168 [115,] 1.204995e-01 0.2409989919 0.8795005041 [116,] 1.043594e-01 0.2087187783 0.8956406108 [117,] 8.183890e-02 0.1636777981 0.9181611010 [118,] 6.189502e-02 0.1237900327 0.9381049837 [119,] 5.395674e-02 0.1079134786 0.9460432607 [120,] 4.036676e-02 0.0807335225 0.9596332387 [121,] 2.866763e-02 0.0573352654 0.9713323673 [122,] 1.987955e-02 0.0397591046 0.9801204477 [123,] 1.331818e-02 0.0266363617 0.9866818192 [124,] 8.667477e-03 0.0173349541 0.9913325229 [125,] 5.449939e-03 0.0108998787 0.9945500606 [126,] 3.314779e-03 0.0066295571 0.9966852215 [127,] 1.951928e-03 0.0039038555 0.9980480722 [128,] 1.120056e-03 0.0022401125 0.9988799438 [129,] 6.407532e-04 0.0012815063 0.9993592468 [130,] 3.705062e-04 0.0007410124 0.9996294938 [131,] 2.069430e-04 0.0004138860 0.9997930570 [132,] 1.033850e-04 0.0002067701 0.9998966150 [133,] 6.497748e-05 0.0001299550 0.9999350225 [134,] 2.532494e-02 0.0506498832 0.9746750584 [135,] 2.622321e-02 0.0524464251 0.9737767875 [136,] 1.703412e-02 0.0340682477 0.9829658762 [137,] 1.067354e-02 0.0213470701 0.9893264650 [138,] 5.327487e-03 0.0106549748 0.9946725126 [139,] 6.229888e-03 0.0124597751 0.9937701125 > postscript(file="/var/fisher/rcomp/tmp/1wxmb1356170620.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/20ejq1356170620.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3lybj1356170620.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4343j1356170620.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5vnze1356170620.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 -0.1366006449 0.0175647052 0.0171212177 0.0166777302 0.0162342427 6 7 8 9 10 -0.0951553662 0.0153472676 -0.1509716434 0.0257268783 0.0140168050 11 12 13 14 15 -0.1523021060 0.0131298299 -0.1095263647 -0.1536325686 -0.0991467540 16 17 18 19 20 -0.2654656650 0.7228242617 -0.1554065187 0.0212920030 0.7327603849 21 22 23 24 25 -0.1130742650 -0.1022511667 -0.1026946542 -0.1031381418 -0.1472443456 26 27 28 29 30 -0.1152917026 0.0177441028 0.0060340295 0.0168571277 -0.1170656527 31 32 33 34 35 0.0047035669 0.0042600793 -0.1183961153 -0.1512357334 0.0029296168 36 37 38 39 40 0.0024861292 -0.2860454889 0.0128657400 -0.1097904547 -0.2873759515 41 42 43 44 45 0.8893225702 0.0110917898 -0.1115644048 -0.1669371945 -0.1237179657 46 47 48 49 50 -0.1128948674 -0.0023922336 0.0084308647 -0.1142253300 -0.0037226962 51 52 53 54 55 -0.1700416072 0.7073021981 0.0062134270 0.9945033537 -0.0059401338 56 57 58 59 60 -0.1609924590 -0.1177732303 0.0039959894 0.0035525018 0.7150208837 61 62 63 64 65 -0.1632098967 -0.1312572537 -0.0094880341 -0.1645403593 -0.0103750091 66 67 68 69 70 -0.0108184967 0.7006498852 -0.0117054717 -0.0008823735 -0.0125924468 71 72 73 74 75 -0.0130359343 -0.0022128360 -0.0026563236 -0.0143663969 -0.0035432986 76 77 78 79 80 -0.2920749168 -0.0044302737 -0.1270864684 0.8288073278 -0.3051154527 81 82 83 84 85 -0.0174708096 -0.0066477113 -0.0183577847 0.9811987278 -0.1301908811 86 87 88 89 90 -0.0196882473 -0.0088651490 -0.0922463483 -0.0210187098 -0.0101956116 91 92 93 94 95 -0.1441183921 -0.1052868842 -0.1450053671 -0.0232361475 -0.1066173468 96 97 98 99 100 -0.0128565368 -0.1075043218 -0.0250100976 -0.0254535851 -0.0146304869 101 102 103 104 105 -0.0150739744 -0.0267840477 -0.0272275353 -0.0276710228 -0.1110522221 106 107 108 109 110 -0.0285579978 -0.0290014854 -0.1123826846 -0.0298884604 -0.0303319480 111 112 113 114 115 -0.2359258544 -0.1141566348 -0.0316624106 -0.1150436098 -0.0325493856 116 117 118 119 120 -0.0329928731 -0.0221697749 -0.0338798482 -0.0343233357 -0.0235002375 121 122 123 124 125 -0.0352103108 -0.0356537983 -0.1190349976 -0.1474868947 -0.0257176751 126 127 128 129 130 -0.1203654602 -0.1600839431 -0.0270481377 -0.0387582110 -0.0279351128 131 132 133 134 135 -0.0396451861 -0.0288220878 -0.0405321612 -0.0409756487 -0.0414191362 136 137 138 139 140 -0.0418626237 -0.1532522326 -0.2366334319 -0.1261307981 -0.0436365739 141 142 143 144 145 0.9671865244 -0.1161946749 -0.0449670364 -0.1563566453 -0.1680667187 146 147 148 149 150 -0.1179686250 -0.1296786983 -0.1301221858 -0.0476279616 -0.1590175705 151 152 153 154 -0.0372483509 0.9510415758 0.8283853811 -0.0498453993 > postscript(file="/var/fisher/rcomp/tmp/6qd501356170620.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 -0.1366006449 NA 1 0.0175647052 -0.1366006449 2 0.0171212177 0.0175647052 3 0.0166777302 0.0171212177 4 0.0162342427 0.0166777302 5 -0.0951553662 0.0162342427 6 0.0153472676 -0.0951553662 7 -0.1509716434 0.0153472676 8 0.0257268783 -0.1509716434 9 0.0140168050 0.0257268783 10 -0.1523021060 0.0140168050 11 0.0131298299 -0.1523021060 12 -0.1095263647 0.0131298299 13 -0.1536325686 -0.1095263647 14 -0.0991467540 -0.1536325686 15 -0.2654656650 -0.0991467540 16 0.7228242617 -0.2654656650 17 -0.1554065187 0.7228242617 18 0.0212920030 -0.1554065187 19 0.7327603849 0.0212920030 20 -0.1130742650 0.7327603849 21 -0.1022511667 -0.1130742650 22 -0.1026946542 -0.1022511667 23 -0.1031381418 -0.1026946542 24 -0.1472443456 -0.1031381418 25 -0.1152917026 -0.1472443456 26 0.0177441028 -0.1152917026 27 0.0060340295 0.0177441028 28 0.0168571277 0.0060340295 29 -0.1170656527 0.0168571277 30 0.0047035669 -0.1170656527 31 0.0042600793 0.0047035669 32 -0.1183961153 0.0042600793 33 -0.1512357334 -0.1183961153 34 0.0029296168 -0.1512357334 35 0.0024861292 0.0029296168 36 -0.2860454889 0.0024861292 37 0.0128657400 -0.2860454889 38 -0.1097904547 0.0128657400 39 -0.2873759515 -0.1097904547 40 0.8893225702 -0.2873759515 41 0.0110917898 0.8893225702 42 -0.1115644048 0.0110917898 43 -0.1669371945 -0.1115644048 44 -0.1237179657 -0.1669371945 45 -0.1128948674 -0.1237179657 46 -0.0023922336 -0.1128948674 47 0.0084308647 -0.0023922336 48 -0.1142253300 0.0084308647 49 -0.0037226962 -0.1142253300 50 -0.1700416072 -0.0037226962 51 0.7073021981 -0.1700416072 52 0.0062134270 0.7073021981 53 0.9945033537 0.0062134270 54 -0.0059401338 0.9945033537 55 -0.1609924590 -0.0059401338 56 -0.1177732303 -0.1609924590 57 0.0039959894 -0.1177732303 58 0.0035525018 0.0039959894 59 0.7150208837 0.0035525018 60 -0.1632098967 0.7150208837 61 -0.1312572537 -0.1632098967 62 -0.0094880341 -0.1312572537 63 -0.1645403593 -0.0094880341 64 -0.0103750091 -0.1645403593 65 -0.0108184967 -0.0103750091 66 0.7006498852 -0.0108184967 67 -0.0117054717 0.7006498852 68 -0.0008823735 -0.0117054717 69 -0.0125924468 -0.0008823735 70 -0.0130359343 -0.0125924468 71 -0.0022128360 -0.0130359343 72 -0.0026563236 -0.0022128360 73 -0.0143663969 -0.0026563236 74 -0.0035432986 -0.0143663969 75 -0.2920749168 -0.0035432986 76 -0.0044302737 -0.2920749168 77 -0.1270864684 -0.0044302737 78 0.8288073278 -0.1270864684 79 -0.3051154527 0.8288073278 80 -0.0174708096 -0.3051154527 81 -0.0066477113 -0.0174708096 82 -0.0183577847 -0.0066477113 83 0.9811987278 -0.0183577847 84 -0.1301908811 0.9811987278 85 -0.0196882473 -0.1301908811 86 -0.0088651490 -0.0196882473 87 -0.0922463483 -0.0088651490 88 -0.0210187098 -0.0922463483 89 -0.0101956116 -0.0210187098 90 -0.1441183921 -0.0101956116 91 -0.1052868842 -0.1441183921 92 -0.1450053671 -0.1052868842 93 -0.0232361475 -0.1450053671 94 -0.1066173468 -0.0232361475 95 -0.0128565368 -0.1066173468 96 -0.1075043218 -0.0128565368 97 -0.0250100976 -0.1075043218 98 -0.0254535851 -0.0250100976 99 -0.0146304869 -0.0254535851 100 -0.0150739744 -0.0146304869 101 -0.0267840477 -0.0150739744 102 -0.0272275353 -0.0267840477 103 -0.0276710228 -0.0272275353 104 -0.1110522221 -0.0276710228 105 -0.0285579978 -0.1110522221 106 -0.0290014854 -0.0285579978 107 -0.1123826846 -0.0290014854 108 -0.0298884604 -0.1123826846 109 -0.0303319480 -0.0298884604 110 -0.2359258544 -0.0303319480 111 -0.1141566348 -0.2359258544 112 -0.0316624106 -0.1141566348 113 -0.1150436098 -0.0316624106 114 -0.0325493856 -0.1150436098 115 -0.0329928731 -0.0325493856 116 -0.0221697749 -0.0329928731 117 -0.0338798482 -0.0221697749 118 -0.0343233357 -0.0338798482 119 -0.0235002375 -0.0343233357 120 -0.0352103108 -0.0235002375 121 -0.0356537983 -0.0352103108 122 -0.1190349976 -0.0356537983 123 -0.1474868947 -0.1190349976 124 -0.0257176751 -0.1474868947 125 -0.1203654602 -0.0257176751 126 -0.1600839431 -0.1203654602 127 -0.0270481377 -0.1600839431 128 -0.0387582110 -0.0270481377 129 -0.0279351128 -0.0387582110 130 -0.0396451861 -0.0279351128 131 -0.0288220878 -0.0396451861 132 -0.0405321612 -0.0288220878 133 -0.0409756487 -0.0405321612 134 -0.0414191362 -0.0409756487 135 -0.0418626237 -0.0414191362 136 -0.1532522326 -0.0418626237 137 -0.2366334319 -0.1532522326 138 -0.1261307981 -0.2366334319 139 -0.0436365739 -0.1261307981 140 0.9671865244 -0.0436365739 141 -0.1161946749 0.9671865244 142 -0.0449670364 -0.1161946749 143 -0.1563566453 -0.0449670364 144 -0.1680667187 -0.1563566453 145 -0.1179686250 -0.1680667187 146 -0.1296786983 -0.1179686250 147 -0.1301221858 -0.1296786983 148 -0.0476279616 -0.1301221858 149 -0.1590175705 -0.0476279616 150 -0.0372483509 -0.1590175705 151 0.9510415758 -0.0372483509 152 0.8283853811 0.9510415758 153 -0.0498453993 0.8283853811 154 NA -0.0498453993 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.0175647052 -0.1366006449 [2,] 0.0171212177 0.0175647052 [3,] 0.0166777302 0.0171212177 [4,] 0.0162342427 0.0166777302 [5,] -0.0951553662 0.0162342427 [6,] 0.0153472676 -0.0951553662 [7,] -0.1509716434 0.0153472676 [8,] 0.0257268783 -0.1509716434 [9,] 0.0140168050 0.0257268783 [10,] -0.1523021060 0.0140168050 [11,] 0.0131298299 -0.1523021060 [12,] -0.1095263647 0.0131298299 [13,] -0.1536325686 -0.1095263647 [14,] -0.0991467540 -0.1536325686 [15,] -0.2654656650 -0.0991467540 [16,] 0.7228242617 -0.2654656650 [17,] -0.1554065187 0.7228242617 [18,] 0.0212920030 -0.1554065187 [19,] 0.7327603849 0.0212920030 [20,] -0.1130742650 0.7327603849 [21,] -0.1022511667 -0.1130742650 [22,] -0.1026946542 -0.1022511667 [23,] -0.1031381418 -0.1026946542 [24,] -0.1472443456 -0.1031381418 [25,] -0.1152917026 -0.1472443456 [26,] 0.0177441028 -0.1152917026 [27,] 0.0060340295 0.0177441028 [28,] 0.0168571277 0.0060340295 [29,] -0.1170656527 0.0168571277 [30,] 0.0047035669 -0.1170656527 [31,] 0.0042600793 0.0047035669 [32,] -0.1183961153 0.0042600793 [33,] -0.1512357334 -0.1183961153 [34,] 0.0029296168 -0.1512357334 [35,] 0.0024861292 0.0029296168 [36,] -0.2860454889 0.0024861292 [37,] 0.0128657400 -0.2860454889 [38,] -0.1097904547 0.0128657400 [39,] -0.2873759515 -0.1097904547 [40,] 0.8893225702 -0.2873759515 [41,] 0.0110917898 0.8893225702 [42,] -0.1115644048 0.0110917898 [43,] -0.1669371945 -0.1115644048 [44,] -0.1237179657 -0.1669371945 [45,] -0.1128948674 -0.1237179657 [46,] -0.0023922336 -0.1128948674 [47,] 0.0084308647 -0.0023922336 [48,] -0.1142253300 0.0084308647 [49,] -0.0037226962 -0.1142253300 [50,] -0.1700416072 -0.0037226962 [51,] 0.7073021981 -0.1700416072 [52,] 0.0062134270 0.7073021981 [53,] 0.9945033537 0.0062134270 [54,] -0.0059401338 0.9945033537 [55,] -0.1609924590 -0.0059401338 [56,] -0.1177732303 -0.1609924590 [57,] 0.0039959894 -0.1177732303 [58,] 0.0035525018 0.0039959894 [59,] 0.7150208837 0.0035525018 [60,] -0.1632098967 0.7150208837 [61,] -0.1312572537 -0.1632098967 [62,] -0.0094880341 -0.1312572537 [63,] -0.1645403593 -0.0094880341 [64,] -0.0103750091 -0.1645403593 [65,] -0.0108184967 -0.0103750091 [66,] 0.7006498852 -0.0108184967 [67,] -0.0117054717 0.7006498852 [68,] -0.0008823735 -0.0117054717 [69,] -0.0125924468 -0.0008823735 [70,] -0.0130359343 -0.0125924468 [71,] -0.0022128360 -0.0130359343 [72,] -0.0026563236 -0.0022128360 [73,] -0.0143663969 -0.0026563236 [74,] -0.0035432986 -0.0143663969 [75,] -0.2920749168 -0.0035432986 [76,] -0.0044302737 -0.2920749168 [77,] -0.1270864684 -0.0044302737 [78,] 0.8288073278 -0.1270864684 [79,] -0.3051154527 0.8288073278 [80,] -0.0174708096 -0.3051154527 [81,] -0.0066477113 -0.0174708096 [82,] -0.0183577847 -0.0066477113 [83,] 0.9811987278 -0.0183577847 [84,] -0.1301908811 0.9811987278 [85,] -0.0196882473 -0.1301908811 [86,] -0.0088651490 -0.0196882473 [87,] -0.0922463483 -0.0088651490 [88,] -0.0210187098 -0.0922463483 [89,] -0.0101956116 -0.0210187098 [90,] -0.1441183921 -0.0101956116 [91,] -0.1052868842 -0.1441183921 [92,] -0.1450053671 -0.1052868842 [93,] -0.0232361475 -0.1450053671 [94,] -0.1066173468 -0.0232361475 [95,] -0.0128565368 -0.1066173468 [96,] -0.1075043218 -0.0128565368 [97,] -0.0250100976 -0.1075043218 [98,] -0.0254535851 -0.0250100976 [99,] -0.0146304869 -0.0254535851 [100,] -0.0150739744 -0.0146304869 [101,] -0.0267840477 -0.0150739744 [102,] -0.0272275353 -0.0267840477 [103,] -0.0276710228 -0.0272275353 [104,] -0.1110522221 -0.0276710228 [105,] -0.0285579978 -0.1110522221 [106,] -0.0290014854 -0.0285579978 [107,] -0.1123826846 -0.0290014854 [108,] -0.0298884604 -0.1123826846 [109,] -0.0303319480 -0.0298884604 [110,] -0.2359258544 -0.0303319480 [111,] -0.1141566348 -0.2359258544 [112,] -0.0316624106 -0.1141566348 [113,] -0.1150436098 -0.0316624106 [114,] -0.0325493856 -0.1150436098 [115,] -0.0329928731 -0.0325493856 [116,] -0.0221697749 -0.0329928731 [117,] -0.0338798482 -0.0221697749 [118,] -0.0343233357 -0.0338798482 [119,] -0.0235002375 -0.0343233357 [120,] -0.0352103108 -0.0235002375 [121,] -0.0356537983 -0.0352103108 [122,] -0.1190349976 -0.0356537983 [123,] -0.1474868947 -0.1190349976 [124,] -0.0257176751 -0.1474868947 [125,] -0.1203654602 -0.0257176751 [126,] -0.1600839431 -0.1203654602 [127,] -0.0270481377 -0.1600839431 [128,] -0.0387582110 -0.0270481377 [129,] -0.0279351128 -0.0387582110 [130,] -0.0396451861 -0.0279351128 [131,] -0.0288220878 -0.0396451861 [132,] -0.0405321612 -0.0288220878 [133,] -0.0409756487 -0.0405321612 [134,] -0.0414191362 -0.0409756487 [135,] -0.0418626237 -0.0414191362 [136,] -0.1532522326 -0.0418626237 [137,] -0.2366334319 -0.1532522326 [138,] -0.1261307981 -0.2366334319 [139,] -0.0436365739 -0.1261307981 [140,] 0.9671865244 -0.0436365739 [141,] -0.1161946749 0.9671865244 [142,] -0.0449670364 -0.1161946749 [143,] -0.1563566453 -0.0449670364 [144,] -0.1680667187 -0.1563566453 [145,] -0.1179686250 -0.1680667187 [146,] -0.1296786983 -0.1179686250 [147,] -0.1301221858 -0.1296786983 [148,] -0.0476279616 -0.1301221858 [149,] -0.1590175705 -0.0476279616 [150,] -0.0372483509 -0.1590175705 [151,] 0.9510415758 -0.0372483509 [152,] 0.8283853811 0.9510415758 [153,] -0.0498453993 0.8283853811 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.0175647052 -0.1366006449 2 0.0171212177 0.0175647052 3 0.0166777302 0.0171212177 4 0.0162342427 0.0166777302 5 -0.0951553662 0.0162342427 6 0.0153472676 -0.0951553662 7 -0.1509716434 0.0153472676 8 0.0257268783 -0.1509716434 9 0.0140168050 0.0257268783 10 -0.1523021060 0.0140168050 11 0.0131298299 -0.1523021060 12 -0.1095263647 0.0131298299 13 -0.1536325686 -0.1095263647 14 -0.0991467540 -0.1536325686 15 -0.2654656650 -0.0991467540 16 0.7228242617 -0.2654656650 17 -0.1554065187 0.7228242617 18 0.0212920030 -0.1554065187 19 0.7327603849 0.0212920030 20 -0.1130742650 0.7327603849 21 -0.1022511667 -0.1130742650 22 -0.1026946542 -0.1022511667 23 -0.1031381418 -0.1026946542 24 -0.1472443456 -0.1031381418 25 -0.1152917026 -0.1472443456 26 0.0177441028 -0.1152917026 27 0.0060340295 0.0177441028 28 0.0168571277 0.0060340295 29 -0.1170656527 0.0168571277 30 0.0047035669 -0.1170656527 31 0.0042600793 0.0047035669 32 -0.1183961153 0.0042600793 33 -0.1512357334 -0.1183961153 34 0.0029296168 -0.1512357334 35 0.0024861292 0.0029296168 36 -0.2860454889 0.0024861292 37 0.0128657400 -0.2860454889 38 -0.1097904547 0.0128657400 39 -0.2873759515 -0.1097904547 40 0.8893225702 -0.2873759515 41 0.0110917898 0.8893225702 42 -0.1115644048 0.0110917898 43 -0.1669371945 -0.1115644048 44 -0.1237179657 -0.1669371945 45 -0.1128948674 -0.1237179657 46 -0.0023922336 -0.1128948674 47 0.0084308647 -0.0023922336 48 -0.1142253300 0.0084308647 49 -0.0037226962 -0.1142253300 50 -0.1700416072 -0.0037226962 51 0.7073021981 -0.1700416072 52 0.0062134270 0.7073021981 53 0.9945033537 0.0062134270 54 -0.0059401338 0.9945033537 55 -0.1609924590 -0.0059401338 56 -0.1177732303 -0.1609924590 57 0.0039959894 -0.1177732303 58 0.0035525018 0.0039959894 59 0.7150208837 0.0035525018 60 -0.1632098967 0.7150208837 61 -0.1312572537 -0.1632098967 62 -0.0094880341 -0.1312572537 63 -0.1645403593 -0.0094880341 64 -0.0103750091 -0.1645403593 65 -0.0108184967 -0.0103750091 66 0.7006498852 -0.0108184967 67 -0.0117054717 0.7006498852 68 -0.0008823735 -0.0117054717 69 -0.0125924468 -0.0008823735 70 -0.0130359343 -0.0125924468 71 -0.0022128360 -0.0130359343 72 -0.0026563236 -0.0022128360 73 -0.0143663969 -0.0026563236 74 -0.0035432986 -0.0143663969 75 -0.2920749168 -0.0035432986 76 -0.0044302737 -0.2920749168 77 -0.1270864684 -0.0044302737 78 0.8288073278 -0.1270864684 79 -0.3051154527 0.8288073278 80 -0.0174708096 -0.3051154527 81 -0.0066477113 -0.0174708096 82 -0.0183577847 -0.0066477113 83 0.9811987278 -0.0183577847 84 -0.1301908811 0.9811987278 85 -0.0196882473 -0.1301908811 86 -0.0088651490 -0.0196882473 87 -0.0922463483 -0.0088651490 88 -0.0210187098 -0.0922463483 89 -0.0101956116 -0.0210187098 90 -0.1441183921 -0.0101956116 91 -0.1052868842 -0.1441183921 92 -0.1450053671 -0.1052868842 93 -0.0232361475 -0.1450053671 94 -0.1066173468 -0.0232361475 95 -0.0128565368 -0.1066173468 96 -0.1075043218 -0.0128565368 97 -0.0250100976 -0.1075043218 98 -0.0254535851 -0.0250100976 99 -0.0146304869 -0.0254535851 100 -0.0150739744 -0.0146304869 101 -0.0267840477 -0.0150739744 102 -0.0272275353 -0.0267840477 103 -0.0276710228 -0.0272275353 104 -0.1110522221 -0.0276710228 105 -0.0285579978 -0.1110522221 106 -0.0290014854 -0.0285579978 107 -0.1123826846 -0.0290014854 108 -0.0298884604 -0.1123826846 109 -0.0303319480 -0.0298884604 110 -0.2359258544 -0.0303319480 111 -0.1141566348 -0.2359258544 112 -0.0316624106 -0.1141566348 113 -0.1150436098 -0.0316624106 114 -0.0325493856 -0.1150436098 115 -0.0329928731 -0.0325493856 116 -0.0221697749 -0.0329928731 117 -0.0338798482 -0.0221697749 118 -0.0343233357 -0.0338798482 119 -0.0235002375 -0.0343233357 120 -0.0352103108 -0.0235002375 121 -0.0356537983 -0.0352103108 122 -0.1190349976 -0.0356537983 123 -0.1474868947 -0.1190349976 124 -0.0257176751 -0.1474868947 125 -0.1203654602 -0.0257176751 126 -0.1600839431 -0.1203654602 127 -0.0270481377 -0.1600839431 128 -0.0387582110 -0.0270481377 129 -0.0279351128 -0.0387582110 130 -0.0396451861 -0.0279351128 131 -0.0288220878 -0.0396451861 132 -0.0405321612 -0.0288220878 133 -0.0409756487 -0.0405321612 134 -0.0414191362 -0.0409756487 135 -0.0418626237 -0.0414191362 136 -0.1532522326 -0.0418626237 137 -0.2366334319 -0.1532522326 138 -0.1261307981 -0.2366334319 139 -0.0436365739 -0.1261307981 140 0.9671865244 -0.0436365739 141 -0.1161946749 0.9671865244 142 -0.0449670364 -0.1161946749 143 -0.1563566453 -0.0449670364 144 -0.1680667187 -0.1563566453 145 -0.1179686250 -0.1680667187 146 -0.1296786983 -0.1179686250 147 -0.1301221858 -0.1296786983 148 -0.0476279616 -0.1301221858 149 -0.1590175705 -0.0476279616 150 -0.0372483509 -0.1590175705 151 0.9510415758 -0.0372483509 152 0.8283853811 0.9510415758 153 -0.0498453993 0.8283853811 > 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/7bihz1356170620.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/8lh591356170620.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/9jszb1356170620.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/105z7p1356170620.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/11q8pg1356170620.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/123un61356170620.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/13mkat1356170620.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14wfrr1356170620.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/15zf691356170620.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/161bae1356170620.tab") + } > > try(system("convert tmp/1wxmb1356170620.ps tmp/1wxmb1356170620.png",intern=TRUE)) character(0) > try(system("convert tmp/20ejq1356170620.ps tmp/20ejq1356170620.png",intern=TRUE)) character(0) > try(system("convert tmp/3lybj1356170620.ps tmp/3lybj1356170620.png",intern=TRUE)) character(0) > try(system("convert tmp/4343j1356170620.ps tmp/4343j1356170620.png",intern=TRUE)) character(0) > try(system("convert tmp/5vnze1356170620.ps tmp/5vnze1356170620.png",intern=TRUE)) character(0) > try(system("convert tmp/6qd501356170620.ps tmp/6qd501356170620.png",intern=TRUE)) character(0) > try(system("convert tmp/7bihz1356170620.ps tmp/7bihz1356170620.png",intern=TRUE)) character(0) > try(system("convert tmp/8lh591356170620.ps tmp/8lh591356170620.png",intern=TRUE)) character(0) > try(system("convert tmp/9jszb1356170620.ps tmp/9jszb1356170620.png",intern=TRUE)) character(0) > try(system("convert tmp/105z7p1356170620.ps tmp/105z7p1356170620.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.818 1.810 9.634