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Type 'q()' to quit R. > x <- array(list(3 + ,4 + ,3 + ,1 + ,4 + ,4 + ,4 + ,1 + ,4 + ,5 + ,5 + ,3 + ,4 + ,2 + ,3 + ,2 + ,4 + ,5 + ,3 + ,3 + ,3 + ,3 + ,3 + ,2 + ,5 + ,3 + ,5 + ,3 + ,4 + ,5 + ,3 + ,2 + ,3 + ,3 + ,4 + ,2 + ,3 + ,3 + ,4 + ,2 + ,4 + ,4 + ,5 + ,1 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,4 + ,3 + ,4 + ,3 + ,4 + ,3 + ,4 + ,5 + ,4 + ,2 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,5 + ,1 + ,5 + ,4 + ,5 + ,1 + ,4 + ,4 + ,5 + ,2 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,5 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,5 + ,1 + ,4 + ,4 + ,5 + ,2 + ,4 + ,3 + ,4 + ,2 + ,4 + ,5 + ,3 + ,3 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,4 + ,2 + ,3 + ,2 + ,3 + ,1 + ,4 + ,3 + ,3 + ,1 + ,4 + ,4 + ,4 + ,1 + ,3 + ,4 + ,4 + ,2 + ,3 + ,2 + ,2 + ,1 + ,2 + ,4 + ,2 + ,3 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,3 + ,4 + ,1 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,4 + ,3 + ,5 + ,5 + ,5 + ,1 + ,4 + ,3 + ,3 + ,2 + ,4 + ,4 + ,3 + ,2 + ,4 + ,4 + ,2 + ,1 + ,3 + ,4 + ,4 + ,1 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,1 + ,4 + ,5 + ,4 + ,1 + ,3 + ,3 + ,4 + ,3 + ,3 + ,4 + ,3 + ,3 + ,5 + ,4 + ,3 + ,2 + ,2 + ,1 + ,2 + ,3 + ,4 + ,2 + ,3 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,1 + ,3 + ,3 + ,3 + ,1 + ,4 + ,3 + ,4 + ,3 + ,3 + ,2 + ,4 + ,3 + ,4 + ,4 + ,4 + ,2 + ,4 + ,5 + ,4 + ,1 + ,4 + ,4 + ,3 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,5 + ,1 + ,5 + ,5 + ,4 + ,2 + ,5 + ,4 + ,4 + ,1 + ,3 + ,4 + ,3 + ,2 + ,4 + ,4 + ,4 + ,2 + ,3 + ,3 + ,3 + ,2 + ,4 + ,4 + ,4 + ,4 + ,5 + ,5 + ,4 + ,3 + ,5 + ,5 + ,4 + ,1 + ,4 + ,4 + ,4 + ,2 + ,4 + ,2 + ,4 + ,3 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,5 + ,1 + ,5 + ,4 + ,5 + ,1 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,3 + ,3 + ,3 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,3 + ,4 + ,4 + ,4 + ,4 + ,3 + ,3 + ,3 + ,1 + ,4 + ,5 + ,4 + ,3 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,3 + ,2 + ,4 + ,4 + ,2 + ,1 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,4 + ,3 + ,4 + ,3 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,5 + ,4 + ,2 + ,5 + ,4 + ,4 + ,2 + ,5 + ,5 + ,5 + ,2 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,4 + ,2 + ,3 + ,4 + ,3 + ,3 + ,4 + ,4 + ,5 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,3 + ,5 + ,1 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,2 + ,4 + ,5 + ,4 + ,2 + ,4 + ,4 + ,4 + ,1 + ,3 + ,3 + ,3 + ,2 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,4 + ,3 + ,3 + ,5 + ,3 + ,2 + ,4 + ,2 + ,4 + ,4 + ,4 + ,4 + ,3 + ,2 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,4 + ,1 + ,4 + ,4 + ,4 + ,2 + ,4 + ,4 + ,3 + ,2 + ,4 + ,5 + ,4 + ,1 + ,4 + ,4 + ,4 + ,1 + ,4 + ,5 + ,4 + ,1 + ,4 + ,3 + ,4 + ,1 + ,4 + ,4 + ,5 + ,1 + ,4 + ,3 + ,4 + ,2 + ,3 + ,4 + ,3 + ,2 + ,4 + ,4 + ,5 + ,1 + ,3 + ,4 + ,3 + ,3 + ,4 + ,2 + ,4 + ,2 + ,3 + ,4 + ,2 + ,2 + ,3 + ,4 + ,4 + ,3 + ,3 + ,4 + ,4 + ,3 + ,4 + ,3 + ,2 + ,2 + ,4 + ,4 + ,3 + ,2 + ,4 + ,4 + ,2 + ,2 + ,4 + ,3 + ,3 + ,2 + ,3 + ,3 + ,2 + ,4 + ,2 + ,4 + ,1 + ,4 + ,4 + ,4 + ,1 + ,5 + ,5 + ,4 + ,3 + ,4 + ,4 + ,4 + ,2 + ,3 + ,4 + ,2 + ,2 + ,4 + ,4 + ,4 + ,3 + ,3 + ,4 + ,3 + ,3 + ,4 + ,4 + ,4 + ,1 + ,4 + ,3 + ,4 + ,3 + ,3 + ,2 + ,2 + ,2 + ,4 + ,4 + ,3 + ,1 + ,4 + ,2 + ,4 + ,1 + ,4 + ,2 + ,4 + ,2 + ,4 + ,3 + ,3 + ,2 + ,3 + ,3 + ,2 + ,3 + ,4 + ,5 + ,4 + ,3 + ,3 + ,2 + ,4 + ,1 + ,4 + ,3 + ,4 + ,2 + ,4 + ,4 + ,4 + ,2 + ,3 + ,2 + ,3 + ,3 + ,4 + ,3 + ,3 + ,4) + ,dim=c(4 + ,162) + ,dimnames=list(c('Yt' + ,'X1' + ,'X2' + ,'X3') + ,1:162)) > y <- array(NA,dim=c(4,162),dimnames=list(c('Yt','X1','X2','X3'),1:162)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Yt X1 X2 X3 1 3 4 3 1 2 4 4 4 1 3 4 5 5 3 4 4 2 3 2 5 4 5 3 3 6 3 3 3 2 7 5 3 5 3 8 4 5 3 2 9 3 3 4 2 10 3 3 4 2 11 4 4 5 1 12 4 4 4 1 13 4 4 4 3 14 4 3 4 3 15 4 5 4 2 16 4 4 3 3 17 4 4 5 1 18 5 4 5 1 19 4 4 5 2 20 4 4 4 1 21 4 4 4 4 22 4 4 2 5 23 4 4 2 4 24 4 4 2 4 25 5 1 4 4 26 5 2 4 3 27 4 2 4 5 28 3 3 4 4 29 4 3 4 4 30 4 1 4 4 31 4 2 3 2 32 3 1 4 3 33 3 1 4 4 34 4 1 3 4 35 4 2 3 2 36 2 1 2 4 37 2 3 4 4 38 2 4 4 4 39 2 4 3 4 40 1 4 4 4 41 1 4 4 4 42 3 5 5 5 43 1 4 3 3 44 2 4 4 3 45 2 4 4 2 46 1 3 4 4 47 1 4 4 4 48 2 4 4 4 49 3 3 4 4 50 1 4 5 4 51 1 3 3 4 52 3 3 4 3 53 3 5 4 3 54 2 2 1 2 55 3 4 2 3 56 2 4 4 4 57 2 4 4 4 58 1 3 3 3 59 1 4 3 4 60 3 3 2 4 61 3 4 4 4 62 2 4 5 4 63 1 4 4 3 64 2 4 4 4 65 2 4 4 5 66 1 5 5 4 67 2 5 4 4 68 1 3 4 3 69 2 4 4 4 70 2 3 3 3 71 2 4 4 4 72 4 5 5 4 73 3 5 5 4 74 1 4 4 4 75 2 4 2 4 76 3 4 4 4 77 2 4 4 4 78 2 4 4 4 79 2 4 4 4 80 2 4 4 5 81 1 5 4 5 82 1 4 4 4 83 1 4 4 4 84 4 4 4 4 85 2 4 4 4 86 2 4 3 3 87 3 4 4 4 88 2 4 4 4 89 3 4 4 4 90 4 3 3 3 91 1 4 5 4 92 3 4 4 4 93 1 4 4 3 94 2 4 4 2 95 1 4 4 4 96 1 4 4 4 97 3 4 3 4 98 2 4 4 4 99 2 4 4 4 100 2 4 5 4 101 2 5 4 4 102 2 5 5 5 103 2 4 4 4 104 1 4 4 4 105 2 3 4 3 106 3 4 4 5 107 2 4 4 4 108 2 4 3 5 109 1 2 4 4 110 4 4 4 4 111 2 4 5 4 112 2 4 4 4 113 1 3 3 3 114 2 4 4 4 115 2 4 4 4 116 3 3 5 3 117 2 4 2 4 118 4 4 4 3 119 2 4 4 4 120 4 4 4 4 121 1 4 4 4 122 2 4 4 3 123 2 4 5 4 124 1 4 4 4 125 1 4 5 4 126 1 4 3 4 127 1 4 4 5 128 1 4 3 4 129 2 3 4 3 130 2 4 4 5 131 1 3 4 3 132 3 4 2 4 133 2 3 4 2 134 2 3 4 4 135 3 3 4 4 136 3 4 3 2 137 2 4 4 3 138 2 4 4 2 139 2 4 3 3 140 2 3 3 2 141 4 2 4 1 142 4 4 4 1 143 5 5 4 3 144 4 4 4 2 145 3 4 2 2 146 4 4 4 3 147 3 4 3 3 148 4 4 4 1 149 4 3 4 3 150 3 2 2 2 151 4 4 3 1 152 4 2 4 1 153 4 2 4 2 154 4 3 3 2 155 3 3 2 3 156 4 5 4 3 157 3 2 4 1 158 4 3 4 2 159 4 4 4 2 160 3 2 3 3 161 4 3 3 4 162 3 4 3 1 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X1 X2 X3 4.53591 -0.15472 0.03894 -0.44591 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1.8898 -0.7351 -0.2053 0.7108 2.4196 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.53591 0.50595 8.965 8.38e-16 *** X1 -0.15472 0.09558 -1.619 0.108 X2 0.03894 0.10893 0.357 0.721 X3 -0.44591 0.07767 -5.741 4.69e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.042 on 158 degrees of freedom Multiple R-squared: 0.2013, Adjusted R-squared: 0.1862 F-statistic: 13.28 on 3 and 158 DF, p-value: 8.959e-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,] 0.1920188210 3.840376e-01 8.079812e-01 [2,] 0.1344211019 2.688422e-01 8.655789e-01 [3,] 0.1586635614 3.173271e-01 8.413364e-01 [4,] 0.1391514665 2.783029e-01 8.608485e-01 [5,] 0.0832398366 1.664797e-01 9.167602e-01 [6,] 0.0530012648 1.060025e-01 9.469987e-01 [7,] 0.0286720204 5.734404e-02 9.713280e-01 [8,] 0.0147987388 2.959748e-02 9.852013e-01 [9,] 0.0073938755 1.478775e-02 9.926061e-01 [10,] 0.0038919049 7.783810e-03 9.961081e-01 [11,] 0.0017941261 3.588252e-03 9.982059e-01 [12,] 0.0035764257 7.152851e-03 9.964236e-01 [13,] 0.0020159016 4.031803e-03 9.979841e-01 [14,] 0.0010465073 2.093015e-03 9.989535e-01 [15,] 0.0005750506 1.150101e-03 9.994249e-01 [16,] 0.0003910211 7.820421e-04 9.996090e-01 [17,] 0.0002624946 5.249892e-04 9.997375e-01 [18,] 0.0001701604 3.403207e-04 9.998298e-01 [19,] 0.0002925507 5.851013e-04 9.997074e-01 [20,] 0.0004234145 8.468289e-04 9.995766e-01 [21,] 0.0005099395 1.019879e-03 9.994901e-01 [22,] 0.0019144638 3.828928e-03 9.980855e-01 [23,] 0.0013873419 2.774684e-03 9.986127e-01 [24,] 0.0009957739 1.991548e-03 9.990042e-01 [25,] 0.0006262851 1.252570e-03 9.993737e-01 [26,] 0.0010867178 2.173436e-03 9.989133e-01 [27,] 0.0016105972 3.221194e-03 9.983894e-01 [28,] 0.0014651090 2.930218e-03 9.985349e-01 [29,] 0.0010967500 2.193500e-03 9.989033e-01 [30,] 0.0037875106 7.575021e-03 9.962125e-01 [31,] 0.0242510714 4.850214e-02 9.757489e-01 [32,] 0.0715593117 1.431186e-01 9.284407e-01 [33,] 0.1129629590 2.259259e-01 8.870370e-01 [34,] 0.3494169557 6.988339e-01 6.505830e-01 [35,] 0.5701559825 8.596880e-01 4.298440e-01 [36,] 0.5409270319 9.181459e-01 4.590730e-01 [37,] 0.7420659408 5.158681e-01 2.579341e-01 [38,] 0.7667386847 4.665226e-01 2.332613e-01 [39,] 0.8133269464 3.733461e-01 1.866731e-01 [40,] 0.8902555515 2.194889e-01 1.097444e-01 [41,] 0.9283316851 1.433366e-01 7.166831e-02 [42,] 0.9193896038 1.612208e-01 8.061040e-02 [43,] 0.9076361410 1.847277e-01 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2.634209e-01 [110,] 0.6955760323 6.088479e-01 3.044240e-01 [111,] 0.6506542087 6.986916e-01 3.493458e-01 [112,] 0.6724737701 6.550525e-01 3.275262e-01 [113,] 0.6254976383 7.490047e-01 3.745024e-01 [114,] 0.7267080058 5.465840e-01 2.732920e-01 [115,] 0.7390638124 5.218724e-01 2.609362e-01 [116,] 0.7163769157 5.672462e-01 2.836231e-01 [117,] 0.6700464419 6.599071e-01 3.299536e-01 [118,] 0.6933624100 6.132752e-01 3.066376e-01 [119,] 0.7357955463 5.284089e-01 2.642045e-01 [120,] 0.7706891617 4.586217e-01 2.293108e-01 [121,] 0.7826091507 4.347817e-01 2.173908e-01 [122,] 0.8435176126 3.129648e-01 1.564824e-01 [123,] 0.8438819171 3.122362e-01 1.561181e-01 [124,] 0.8223657675 3.552685e-01 1.776342e-01 [125,] 0.9399068200 1.201864e-01 6.009318e-02 [126,] 0.9222159216 1.555682e-01 7.778408e-02 [127,] 0.9530042180 9.399156e-02 4.699578e-02 [128,] 0.9660455104 6.790898e-02 3.395449e-02 [129,] 0.9567810407 8.643792e-02 4.321896e-02 [130,] 0.9398430775 1.203138e-01 6.015692e-02 [131,] 0.9790654989 4.186900e-02 2.093450e-02 [132,] 0.9976289414 4.742117e-03 2.371059e-03 [133,] 0.9997026040 5.947920e-04 2.973960e-04 [134,] 0.9999861142 2.777151e-05 1.388575e-05 [135,] 0.9999701460 5.970800e-05 2.985400e-05 [136,] 0.9999223847 1.552306e-04 7.761528e-05 [137,] 0.9999675708 6.485838e-05 3.242919e-05 [138,] 0.9999109289 1.781422e-04 8.907111e-05 [139,] 0.9997590353 4.819295e-04 2.409647e-04 [140,] 0.9993944006 1.211199e-03 6.055994e-04 [141,] 0.9994369349 1.126130e-03 5.630651e-04 [142,] 0.9984992944 3.001411e-03 1.500706e-03 [143,] 0.9961967101 7.606580e-03 3.803290e-03 [144,] 0.9905697940 1.886041e-02 9.430206e-03 [145,] 0.9895820630 2.083587e-02 1.041794e-02 [146,] 0.9835254749 3.294905e-02 1.647453e-02 [147,] 0.9682523593 6.349528e-02 3.174764e-02 [148,] 0.9843294592 3.134108e-02 1.567054e-02 [149,] 0.9455683102 1.088634e-01 5.443169e-02 > postscript(file="/var/wessaorg/rcomp/tmp/1yji41324664466.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2mlux1324664466.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3eedu1324664466.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4nrse1324664466.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/52iuh1324664466.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 162 Frequency = 1 1 2 3 4 5 6 -0.587965036 0.373093801 1.380680566 0.548510291 1.458562892 -0.296774254 7 8 9 10 11 12 2.071249656 1.012656655 -0.335715417 -0.335715417 0.334152639 0.373093801 13 14 15 16 17 18 1.264906274 1.110190819 0.973715493 1.303847437 0.334152639 1.334152639 19 20 21 22 23 24 0.780058875 0.373093801 1.710812510 2.234601072 1.788694836 1.788694836 25 26 27 28 29 30 2.246666145 1.955475364 1.847287837 0.556097055 1.556097055 1.246666145 31 32 33 34 35 36 0.548510291 -0.199240091 0.246666145 1.285607308 0.548510291 -0.675451529 37 38 39 40 41 42 -0.443902945 -0.289187490 -0.250246327 -1.289187490 -1.289187490 1.272493038 43 44 45 46 47 48 -1.696152563 -0.735093726 -1.180999962 -1.443902945 -1.289187490 -0.289187490 49 50 51 52 53 54 0.556097055 -1.328128653 -1.404961782 0.110190819 0.419621729 -1.373607384 55 56 57 58 59 60 0.342788600 -0.289187490 -0.289187490 -1.850868018 -1.250246327 0.633979381 61 62 63 64 65 66 0.710812510 -0.328128653 -1.735093726 -0.289187490 0.156718746 -1.173413198 67 68 69 70 71 72 -0.134472035 -1.889809181 -0.289187490 -0.850868018 -0.289187490 1.826586802 73 74 75 76 77 78 0.826586802 -1.289187490 -0.211305164 0.710812510 -0.289187490 -0.289187490 79 80 81 82 83 84 -0.289187490 0.156718746 -0.688565799 -1.289187490 -1.289187490 1.710812510 85 86 87 88 89 90 -0.289187490 -0.696152563 0.710812510 -0.289187490 0.710812510 1.149131982 91 92 93 94 95 96 -1.328128653 0.710812510 -1.735093726 -1.180999962 -1.289187490 -1.289187490 97 98 99 100 101 102 0.749753673 -0.289187490 -0.289187490 -0.328128653 -0.134472035 0.272493038 103 104 105 106 107 108 -0.289187490 -1.289187490 -0.889809181 1.156718746 -0.289187490 0.195659909 109 110 111 112 113 114 -1.598618400 1.710812510 -0.328128653 -0.289187490 -1.850868018 -0.289187490 115 116 117 118 119 120 -0.289187490 0.071249656 -0.211305164 1.264906274 -0.289187490 1.710812510 121 122 123 124 125 126 -1.289187490 -0.735093726 -0.328128653 -1.289187490 -1.328128653 -1.250246327 127 128 129 130 131 132 -0.843281254 -1.250246327 -0.889809181 0.156718746 -1.889809181 0.788694836 133 134 135 136 137 138 -1.335715417 -0.443902945 0.556097055 -0.142058800 -0.735093726 -1.180999962 139 140 141 142 143 144 -0.696152563 -1.296774254 0.063662892 0.373093801 2.419621729 0.819000038 145 146 147 148 149 150 -0.103117637 1.264906274 0.303847437 0.373093801 1.110190819 -0.412548546 151 152 153 154 155 156 0.412034964 0.063662892 0.509569128 0.703225746 0.188073145 1.419621729 157 158 159 160 161 162 -0.936337108 0.664284583 0.819000038 -0.005583473 1.595038218 -0.587965036 > postscript(file="/var/wessaorg/rcomp/tmp/6fgcc1324664466.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 162 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.587965036 NA 1 0.373093801 -0.587965036 2 1.380680566 0.373093801 3 0.548510291 1.380680566 4 1.458562892 0.548510291 5 -0.296774254 1.458562892 6 2.071249656 -0.296774254 7 1.012656655 2.071249656 8 -0.335715417 1.012656655 9 -0.335715417 -0.335715417 10 0.334152639 -0.335715417 11 0.373093801 0.334152639 12 1.264906274 0.373093801 13 1.110190819 1.264906274 14 0.973715493 1.110190819 15 1.303847437 0.973715493 16 0.334152639 1.303847437 17 1.334152639 0.334152639 18 0.780058875 1.334152639 19 0.373093801 0.780058875 20 1.710812510 0.373093801 21 2.234601072 1.710812510 22 1.788694836 2.234601072 23 1.788694836 1.788694836 24 2.246666145 1.788694836 25 1.955475364 2.246666145 26 1.847287837 1.955475364 27 0.556097055 1.847287837 28 1.556097055 0.556097055 29 1.246666145 1.556097055 30 0.548510291 1.246666145 31 -0.199240091 0.548510291 32 0.246666145 -0.199240091 33 1.285607308 0.246666145 34 0.548510291 1.285607308 35 -0.675451529 0.548510291 36 -0.443902945 -0.675451529 37 -0.289187490 -0.443902945 38 -0.250246327 -0.289187490 39 -1.289187490 -0.250246327 40 -1.289187490 -1.289187490 41 1.272493038 -1.289187490 42 -1.696152563 1.272493038 43 -0.735093726 -1.696152563 44 -1.180999962 -0.735093726 45 -1.443902945 -1.180999962 46 -1.289187490 -1.443902945 47 -0.289187490 -1.289187490 48 0.556097055 -0.289187490 49 -1.328128653 0.556097055 50 -1.404961782 -1.328128653 51 0.110190819 -1.404961782 52 0.419621729 0.110190819 53 -1.373607384 0.419621729 54 0.342788600 -1.373607384 55 -0.289187490 0.342788600 56 -0.289187490 -0.289187490 57 -1.850868018 -0.289187490 58 -1.250246327 -1.850868018 59 0.633979381 -1.250246327 60 0.710812510 0.633979381 61 -0.328128653 0.710812510 62 -1.735093726 -0.328128653 63 -0.289187490 -1.735093726 64 0.156718746 -0.289187490 65 -1.173413198 0.156718746 66 -0.134472035 -1.173413198 67 -1.889809181 -0.134472035 68 -0.289187490 -1.889809181 69 -0.850868018 -0.289187490 70 -0.289187490 -0.850868018 71 1.826586802 -0.289187490 72 0.826586802 1.826586802 73 -1.289187490 0.826586802 74 -0.211305164 -1.289187490 75 0.710812510 -0.211305164 76 -0.289187490 0.710812510 77 -0.289187490 -0.289187490 78 -0.289187490 -0.289187490 79 0.156718746 -0.289187490 80 -0.688565799 0.156718746 81 -1.289187490 -0.688565799 82 -1.289187490 -1.289187490 83 1.710812510 -1.289187490 84 -0.289187490 1.710812510 85 -0.696152563 -0.289187490 86 0.710812510 -0.696152563 87 -0.289187490 0.710812510 88 0.710812510 -0.289187490 89 1.149131982 0.710812510 90 -1.328128653 1.149131982 91 0.710812510 -1.328128653 92 -1.735093726 0.710812510 93 -1.180999962 -1.735093726 94 -1.289187490 -1.180999962 95 -1.289187490 -1.289187490 96 0.749753673 -1.289187490 97 -0.289187490 0.749753673 98 -0.289187490 -0.289187490 99 -0.328128653 -0.289187490 100 -0.134472035 -0.328128653 101 0.272493038 -0.134472035 102 -0.289187490 0.272493038 103 -1.289187490 -0.289187490 104 -0.889809181 -1.289187490 105 1.156718746 -0.889809181 106 -0.289187490 1.156718746 107 0.195659909 -0.289187490 108 -1.598618400 0.195659909 109 1.710812510 -1.598618400 110 -0.328128653 1.710812510 111 -0.289187490 -0.328128653 112 -1.850868018 -0.289187490 113 -0.289187490 -1.850868018 114 -0.289187490 -0.289187490 115 0.071249656 -0.289187490 116 -0.211305164 0.071249656 117 1.264906274 -0.211305164 118 -0.289187490 1.264906274 119 1.710812510 -0.289187490 120 -1.289187490 1.710812510 121 -0.735093726 -1.289187490 122 -0.328128653 -0.735093726 123 -1.289187490 -0.328128653 124 -1.328128653 -1.289187490 125 -1.250246327 -1.328128653 126 -0.843281254 -1.250246327 127 -1.250246327 -0.843281254 128 -0.889809181 -1.250246327 129 0.156718746 -0.889809181 130 -1.889809181 0.156718746 131 0.788694836 -1.889809181 132 -1.335715417 0.788694836 133 -0.443902945 -1.335715417 134 0.556097055 -0.443902945 135 -0.142058800 0.556097055 136 -0.735093726 -0.142058800 137 -1.180999962 -0.735093726 138 -0.696152563 -1.180999962 139 -1.296774254 -0.696152563 140 0.063662892 -1.296774254 141 0.373093801 0.063662892 142 2.419621729 0.373093801 143 0.819000038 2.419621729 144 -0.103117637 0.819000038 145 1.264906274 -0.103117637 146 0.303847437 1.264906274 147 0.373093801 0.303847437 148 1.110190819 0.373093801 149 -0.412548546 1.110190819 150 0.412034964 -0.412548546 151 0.063662892 0.412034964 152 0.509569128 0.063662892 153 0.703225746 0.509569128 154 0.188073145 0.703225746 155 1.419621729 0.188073145 156 -0.936337108 1.419621729 157 0.664284583 -0.936337108 158 0.819000038 0.664284583 159 -0.005583473 0.819000038 160 1.595038218 -0.005583473 161 -0.587965036 1.595038218 162 NA -0.587965036 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.373093801 -0.587965036 [2,] 1.380680566 0.373093801 [3,] 0.548510291 1.380680566 [4,] 1.458562892 0.548510291 [5,] -0.296774254 1.458562892 [6,] 2.071249656 -0.296774254 [7,] 1.012656655 2.071249656 [8,] -0.335715417 1.012656655 [9,] -0.335715417 -0.335715417 [10,] 0.334152639 -0.335715417 [11,] 0.373093801 0.334152639 [12,] 1.264906274 0.373093801 [13,] 1.110190819 1.264906274 [14,] 0.973715493 1.110190819 [15,] 1.303847437 0.973715493 [16,] 0.334152639 1.303847437 [17,] 1.334152639 0.334152639 [18,] 0.780058875 1.334152639 [19,] 0.373093801 0.780058875 [20,] 1.710812510 0.373093801 [21,] 2.234601072 1.710812510 [22,] 1.788694836 2.234601072 [23,] 1.788694836 1.788694836 [24,] 2.246666145 1.788694836 [25,] 1.955475364 2.246666145 [26,] 1.847287837 1.955475364 [27,] 0.556097055 1.847287837 [28,] 1.556097055 0.556097055 [29,] 1.246666145 1.556097055 [30,] 0.548510291 1.246666145 [31,] -0.199240091 0.548510291 [32,] 0.246666145 -0.199240091 [33,] 1.285607308 0.246666145 [34,] 0.548510291 1.285607308 [35,] -0.675451529 0.548510291 [36,] -0.443902945 -0.675451529 [37,] -0.289187490 -0.443902945 [38,] -0.250246327 -0.289187490 [39,] -1.289187490 -0.250246327 [40,] -1.289187490 -1.289187490 [41,] 1.272493038 -1.289187490 [42,] -1.696152563 1.272493038 [43,] -0.735093726 -1.696152563 [44,] -1.180999962 -0.735093726 [45,] -1.443902945 -1.180999962 [46,] -1.289187490 -1.443902945 [47,] -0.289187490 -1.289187490 [48,] 0.556097055 -0.289187490 [49,] -1.328128653 0.556097055 [50,] -1.404961782 -1.328128653 [51,] 0.110190819 -1.404961782 [52,] 0.419621729 0.110190819 [53,] -1.373607384 0.419621729 [54,] 0.342788600 -1.373607384 [55,] -0.289187490 0.342788600 [56,] -0.289187490 -0.289187490 [57,] -1.850868018 -0.289187490 [58,] -1.250246327 -1.850868018 [59,] 0.633979381 -1.250246327 [60,] 0.710812510 0.633979381 [61,] -0.328128653 0.710812510 [62,] -1.735093726 -0.328128653 [63,] -0.289187490 -1.735093726 [64,] 0.156718746 -0.289187490 [65,] -1.173413198 0.156718746 [66,] -0.134472035 -1.173413198 [67,] -1.889809181 -0.134472035 [68,] -0.289187490 -1.889809181 [69,] -0.850868018 -0.289187490 [70,] -0.289187490 -0.850868018 [71,] 1.826586802 -0.289187490 [72,] 0.826586802 1.826586802 [73,] -1.289187490 0.826586802 [74,] -0.211305164 -1.289187490 [75,] 0.710812510 -0.211305164 [76,] -0.289187490 0.710812510 [77,] -0.289187490 -0.289187490 [78,] -0.289187490 -0.289187490 [79,] 0.156718746 -0.289187490 [80,] -0.688565799 0.156718746 [81,] -1.289187490 -0.688565799 [82,] -1.289187490 -1.289187490 [83,] 1.710812510 -1.289187490 [84,] -0.289187490 1.710812510 [85,] -0.696152563 -0.289187490 [86,] 0.710812510 -0.696152563 [87,] -0.289187490 0.710812510 [88,] 0.710812510 -0.289187490 [89,] 1.149131982 0.710812510 [90,] -1.328128653 1.149131982 [91,] 0.710812510 -1.328128653 [92,] -1.735093726 0.710812510 [93,] -1.180999962 -1.735093726 [94,] -1.289187490 -1.180999962 [95,] -1.289187490 -1.289187490 [96,] 0.749753673 -1.289187490 [97,] -0.289187490 0.749753673 [98,] -0.289187490 -0.289187490 [99,] -0.328128653 -0.289187490 [100,] -0.134472035 -0.328128653 [101,] 0.272493038 -0.134472035 [102,] -0.289187490 0.272493038 [103,] -1.289187490 -0.289187490 [104,] -0.889809181 -1.289187490 [105,] 1.156718746 -0.889809181 [106,] -0.289187490 1.156718746 [107,] 0.195659909 -0.289187490 [108,] -1.598618400 0.195659909 [109,] 1.710812510 -1.598618400 [110,] -0.328128653 1.710812510 [111,] -0.289187490 -0.328128653 [112,] -1.850868018 -0.289187490 [113,] -0.289187490 -1.850868018 [114,] -0.289187490 -0.289187490 [115,] 0.071249656 -0.289187490 [116,] -0.211305164 0.071249656 [117,] 1.264906274 -0.211305164 [118,] -0.289187490 1.264906274 [119,] 1.710812510 -0.289187490 [120,] -1.289187490 1.710812510 [121,] -0.735093726 -1.289187490 [122,] -0.328128653 -0.735093726 [123,] -1.289187490 -0.328128653 [124,] -1.328128653 -1.289187490 [125,] -1.250246327 -1.328128653 [126,] -0.843281254 -1.250246327 [127,] -1.250246327 -0.843281254 [128,] -0.889809181 -1.250246327 [129,] 0.156718746 -0.889809181 [130,] -1.889809181 0.156718746 [131,] 0.788694836 -1.889809181 [132,] -1.335715417 0.788694836 [133,] -0.443902945 -1.335715417 [134,] 0.556097055 -0.443902945 [135,] -0.142058800 0.556097055 [136,] -0.735093726 -0.142058800 [137,] -1.180999962 -0.735093726 [138,] -0.696152563 -1.180999962 [139,] -1.296774254 -0.696152563 [140,] 0.063662892 -1.296774254 [141,] 0.373093801 0.063662892 [142,] 2.419621729 0.373093801 [143,] 0.819000038 2.419621729 [144,] -0.103117637 0.819000038 [145,] 1.264906274 -0.103117637 [146,] 0.303847437 1.264906274 [147,] 0.373093801 0.303847437 [148,] 1.110190819 0.373093801 [149,] -0.412548546 1.110190819 [150,] 0.412034964 -0.412548546 [151,] 0.063662892 0.412034964 [152,] 0.509569128 0.063662892 [153,] 0.703225746 0.509569128 [154,] 0.188073145 0.703225746 [155,] 1.419621729 0.188073145 [156,] -0.936337108 1.419621729 [157,] 0.664284583 -0.936337108 [158,] 0.819000038 0.664284583 [159,] -0.005583473 0.819000038 [160,] 1.595038218 -0.005583473 [161,] -0.587965036 1.595038218 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.373093801 -0.587965036 2 1.380680566 0.373093801 3 0.548510291 1.380680566 4 1.458562892 0.548510291 5 -0.296774254 1.458562892 6 2.071249656 -0.296774254 7 1.012656655 2.071249656 8 -0.335715417 1.012656655 9 -0.335715417 -0.335715417 10 0.334152639 -0.335715417 11 0.373093801 0.334152639 12 1.264906274 0.373093801 13 1.110190819 1.264906274 14 0.973715493 1.110190819 15 1.303847437 0.973715493 16 0.334152639 1.303847437 17 1.334152639 0.334152639 18 0.780058875 1.334152639 19 0.373093801 0.780058875 20 1.710812510 0.373093801 21 2.234601072 1.710812510 22 1.788694836 2.234601072 23 1.788694836 1.788694836 24 2.246666145 1.788694836 25 1.955475364 2.246666145 26 1.847287837 1.955475364 27 0.556097055 1.847287837 28 1.556097055 0.556097055 29 1.246666145 1.556097055 30 0.548510291 1.246666145 31 -0.199240091 0.548510291 32 0.246666145 -0.199240091 33 1.285607308 0.246666145 34 0.548510291 1.285607308 35 -0.675451529 0.548510291 36 -0.443902945 -0.675451529 37 -0.289187490 -0.443902945 38 -0.250246327 -0.289187490 39 -1.289187490 -0.250246327 40 -1.289187490 -1.289187490 41 1.272493038 -1.289187490 42 -1.696152563 1.272493038 43 -0.735093726 -1.696152563 44 -1.180999962 -0.735093726 45 -1.443902945 -1.180999962 46 -1.289187490 -1.443902945 47 -0.289187490 -1.289187490 48 0.556097055 -0.289187490 49 -1.328128653 0.556097055 50 -1.404961782 -1.328128653 51 0.110190819 -1.404961782 52 0.419621729 0.110190819 53 -1.373607384 0.419621729 54 0.342788600 -1.373607384 55 -0.289187490 0.342788600 56 -0.289187490 -0.289187490 57 -1.850868018 -0.289187490 58 -1.250246327 -1.850868018 59 0.633979381 -1.250246327 60 0.710812510 0.633979381 61 -0.328128653 0.710812510 62 -1.735093726 -0.328128653 63 -0.289187490 -1.735093726 64 0.156718746 -0.289187490 65 -1.173413198 0.156718746 66 -0.134472035 -1.173413198 67 -1.889809181 -0.134472035 68 -0.289187490 -1.889809181 69 -0.850868018 -0.289187490 70 -0.289187490 -0.850868018 71 1.826586802 -0.289187490 72 0.826586802 1.826586802 73 -1.289187490 0.826586802 74 -0.211305164 -1.289187490 75 0.710812510 -0.211305164 76 -0.289187490 0.710812510 77 -0.289187490 -0.289187490 78 -0.289187490 -0.289187490 79 0.156718746 -0.289187490 80 -0.688565799 0.156718746 81 -1.289187490 -0.688565799 82 -1.289187490 -1.289187490 83 1.710812510 -1.289187490 84 -0.289187490 1.710812510 85 -0.696152563 -0.289187490 86 0.710812510 -0.696152563 87 -0.289187490 0.710812510 88 0.710812510 -0.289187490 89 1.149131982 0.710812510 90 -1.328128653 1.149131982 91 0.710812510 -1.328128653 92 -1.735093726 0.710812510 93 -1.180999962 -1.735093726 94 -1.289187490 -1.180999962 95 -1.289187490 -1.289187490 96 0.749753673 -1.289187490 97 -0.289187490 0.749753673 98 -0.289187490 -0.289187490 99 -0.328128653 -0.289187490 100 -0.134472035 -0.328128653 101 0.272493038 -0.134472035 102 -0.289187490 0.272493038 103 -1.289187490 -0.289187490 104 -0.889809181 -1.289187490 105 1.156718746 -0.889809181 106 -0.289187490 1.156718746 107 0.195659909 -0.289187490 108 -1.598618400 0.195659909 109 1.710812510 -1.598618400 110 -0.328128653 1.710812510 111 -0.289187490 -0.328128653 112 -1.850868018 -0.289187490 113 -0.289187490 -1.850868018 114 -0.289187490 -0.289187490 115 0.071249656 -0.289187490 116 -0.211305164 0.071249656 117 1.264906274 -0.211305164 118 -0.289187490 1.264906274 119 1.710812510 -0.289187490 120 -1.289187490 1.710812510 121 -0.735093726 -1.289187490 122 -0.328128653 -0.735093726 123 -1.289187490 -0.328128653 124 -1.328128653 -1.289187490 125 -1.250246327 -1.328128653 126 -0.843281254 -1.250246327 127 -1.250246327 -0.843281254 128 -0.889809181 -1.250246327 129 0.156718746 -0.889809181 130 -1.889809181 0.156718746 131 0.788694836 -1.889809181 132 -1.335715417 0.788694836 133 -0.443902945 -1.335715417 134 0.556097055 -0.443902945 135 -0.142058800 0.556097055 136 -0.735093726 -0.142058800 137 -1.180999962 -0.735093726 138 -0.696152563 -1.180999962 139 -1.296774254 -0.696152563 140 0.063662892 -1.296774254 141 0.373093801 0.063662892 142 2.419621729 0.373093801 143 0.819000038 2.419621729 144 -0.103117637 0.819000038 145 1.264906274 -0.103117637 146 0.303847437 1.264906274 147 0.373093801 0.303847437 148 1.110190819 0.373093801 149 -0.412548546 1.110190819 150 0.412034964 -0.412548546 151 0.063662892 0.412034964 152 0.509569128 0.063662892 153 0.703225746 0.509569128 154 0.188073145 0.703225746 155 1.419621729 0.188073145 156 -0.936337108 1.419621729 157 0.664284583 -0.936337108 158 0.819000038 0.664284583 159 -0.005583473 0.819000038 160 1.595038218 -0.005583473 161 -0.587965036 1.595038218 > 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/wessaorg/rcomp/tmp/7u0rl1324664466.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/80bvy1324664466.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9ue2h1324664466.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/100xee1324664466.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/wessaorg/rcomp/tmp/11cs8z1324664466.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/wessaorg/rcomp/tmp/12j1em1324664466.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/wessaorg/rcomp/tmp/13j83z1324664466.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/wessaorg/rcomp/tmp/14pgbj1324664466.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/wessaorg/rcomp/tmp/15g24h1324664466.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/wessaorg/rcomp/tmp/16e0f81324664466.tab") + } > > try(system("convert tmp/1yji41324664466.ps tmp/1yji41324664466.png",intern=TRUE)) character(0) > try(system("convert tmp/2mlux1324664466.ps tmp/2mlux1324664466.png",intern=TRUE)) character(0) > try(system("convert tmp/3eedu1324664466.ps tmp/3eedu1324664466.png",intern=TRUE)) character(0) > try(system("convert tmp/4nrse1324664466.ps tmp/4nrse1324664466.png",intern=TRUE)) character(0) > try(system("convert tmp/52iuh1324664466.ps tmp/52iuh1324664466.png",intern=TRUE)) character(0) > try(system("convert tmp/6fgcc1324664466.ps tmp/6fgcc1324664466.png",intern=TRUE)) character(0) > try(system("convert tmp/7u0rl1324664466.ps tmp/7u0rl1324664466.png",intern=TRUE)) character(0) > try(system("convert tmp/80bvy1324664466.ps tmp/80bvy1324664466.png",intern=TRUE)) character(0) > try(system("convert tmp/9ue2h1324664466.ps tmp/9ue2h1324664466.png",intern=TRUE)) character(0) > try(system("convert tmp/100xee1324664466.ps tmp/100xee1324664466.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.743 0.777 5.550