R version 2.13.0 (2011-04-13) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-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(14 + ,3 + ,2 + ,3 + ,3 + ,7 + ,8 + ,5 + ,6 + ,0 + ,7 + ,2 + ,12 + ,6 + ,6 + ,0 + ,6 + ,3 + ,7 + ,6 + ,6 + ,6 + ,6 + ,8 + ,10 + ,7 + ,8 + ,5 + ,5 + ,7 + ,9 + ,3 + ,1 + ,0 + ,7 + ,7 + ,16 + ,8 + ,9 + ,8 + ,8 + ,9 + ,7 + ,4 + ,4 + ,0 + ,2 + ,2 + ,14 + ,7 + ,7 + ,0 + ,4 + ,4 + ,6 + ,4 + ,4 + ,9 + ,9 + ,4 + ,16 + ,6 + ,6 + ,6 + ,6 + ,6 + ,11 + ,6 + ,5 + ,6 + ,6 + ,4 + ,17 + ,7 + ,7 + ,5 + ,5 + ,9 + ,12 + ,4 + ,5 + ,4 + ,4 + ,8 + ,7 + ,6 + ,6 + ,0 + ,2 + ,7 + ,13 + ,5 + ,5 + ,0 + ,4 + ,4 + ,9 + ,0 + ,2 + ,2 + ,2 + ,2 + ,15 + ,9 + ,9 + ,6 + ,6 + ,8 + ,7 + ,4 + ,4 + ,0 + ,4 + ,4 + ,9 + ,4 + ,4 + ,4 + ,4 + ,4 + ,7 + ,2 + ,5 + ,5 + ,5 + ,2 + ,14 + ,7 + ,7 + ,7 + ,7 + ,9 + ,15 + ,5 + ,5 + ,5 + ,5 + ,3 + ,7 + ,9 + ,9 + ,4 + ,4 + ,4 + ,13 + ,6 + ,6 + ,6 + ,6 + ,6 + ,17 + ,6 + ,6 + ,6 + ,6 + ,6 + ,15 + ,7 + ,3 + ,0 + ,7 + ,7 + ,14 + ,3 + ,3 + ,1 + ,2 + ,2 + ,14 + ,6 + ,5 + ,0 + ,6 + ,6 + ,8 + ,6 + ,5 + ,4 + ,4 + ,4 + ,8 + ,4 + ,4 + ,4 + ,4 + ,2 + ,12 + ,7 + ,7 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,4 + ,2 + ,0 + ,4 + ,4 + ,14 + ,8 + ,8 + ,8 + ,8 + ,8 + ,12 + ,6 + ,6 + ,0 + ,6 + ,9 + ,8 + ,4 + ,4 + ,9 + ,9 + ,2 + ,11 + ,6 + ,6 + ,5 + ,5 + ,5 + ,13 + ,2 + ,5 + ,0 + ,6 + ,6 + ,9 + ,4 + ,4 + ,0 + ,4 + ,4 + ,15 + ,6 + ,2 + ,0 + ,6 + ,6 + ,13 + ,3 + ,3 + ,3 + ,3 + ,3 + ,15 + ,6 + ,6 + ,6 + ,6 + ,6 + ,14 + ,5 + ,5 + ,0 + ,5 + ,5 + ,16 + ,4 + ,4 + ,4 + ,4 + ,8 + ,12 + ,6 + ,6 + ,6 + ,6 + ,6 + ,14 + ,1 + ,1 + ,0 + ,5 + ,5 + ,10 + ,4 + ,5 + ,4 + ,4 + ,3 + ,10 + ,4 + ,2 + ,7 + ,7 + ,2 + ,4 + ,6 + ,6 + ,0 + ,6 + ,6 + ,8 + ,5 + ,5 + ,5 + ,5 + ,5 + ,17 + ,9 + ,2 + ,6 + ,6 + ,6 + ,16 + ,6 + ,6 + ,6 + ,6 + ,6 + ,12 + ,8 + ,8 + ,8 + ,8 + ,9 + ,12 + ,7 + ,7 + ,2 + ,2 + ,4 + ,15 + ,7 + ,7 + ,7 + ,7 + ,7 + ,9 + ,0 + ,9 + ,0 + ,4 + ,4 + ,13 + ,6 + ,2 + ,0 + ,6 + ,7 + ,14 + ,6 + ,6 + ,5 + ,5 + ,5 + ,11 + ,5 + ,5 + ,0 + ,2 + ,2) + ,dim=c(6 + ,156) + ,dimnames=list(c('Schoolprestaties' + ,'Sport' + ,'Goingout' + ,'Relation' + ,'Family' + ,'Coach') + ,1:156)) > y <- array(NA,dim=c(6,156),dimnames=list(c('Schoolprestaties','Sport','Goingout','Relation','Family','Coach'),1:156)) > 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 > 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 Sport Schoolprestaties Goingout Relation Family Coach t 1 3 14 2 3 3 7 1 2 5 8 6 0 7 2 2 3 6 12 6 0 6 3 3 4 6 7 6 6 6 8 4 5 7 10 8 5 5 7 5 6 3 9 1 0 7 7 6 7 8 16 9 8 8 9 7 8 4 7 4 0 2 2 8 9 7 14 7 0 4 4 9 10 4 6 4 9 9 4 10 11 6 16 6 6 6 6 11 12 6 11 5 6 6 4 12 13 7 17 7 5 5 9 13 14 4 12 5 4 4 8 14 15 6 7 6 0 2 7 15 16 5 13 5 0 4 4 16 17 0 9 2 2 2 2 17 18 9 15 9 6 6 8 18 19 4 7 4 0 4 4 19 20 4 9 4 4 4 4 20 21 2 7 5 5 5 2 21 22 7 14 7 7 7 9 22 23 5 15 5 5 5 3 23 24 9 7 9 4 4 4 24 25 6 13 6 6 6 6 25 26 6 17 6 6 6 6 26 27 7 15 3 0 7 7 27 28 3 14 3 1 2 2 28 29 6 14 5 0 6 6 29 30 6 8 5 4 4 4 30 31 4 8 4 4 4 2 31 32 7 12 7 7 7 9 32 33 7 14 6 7 7 7 33 34 7 8 7 0 4 4 34 35 4 11 4 4 4 4 35 36 5 16 5 5 5 7 36 37 6 11 6 0 6 6 37 38 5 8 5 5 5 5 38 39 6 14 0 1 6 6 39 40 6 16 6 2 2 2 40 41 6 14 5 0 6 2 41 42 3 5 3 9 9 7 42 43 3 8 3 3 3 3 43 44 3 10 3 0 4 4 44 45 6 8 7 6 6 6 45 46 7 13 7 1 5 5 46 47 5 15 1 5 5 7 47 48 5 6 5 0 4 4 48 49 5 12 5 0 2 2 49 50 6 14 6 0 6 6 50 51 6 5 2 6 6 9 51 52 6 15 6 7 7 8 52 53 5 11 5 0 5 5 53 54 4 8 2 4 4 4 54 55 7 13 7 5 5 2 55 56 5 14 5 1 5 9 56 57 3 12 3 4 4 4 57 58 6 16 6 9 9 6 58 59 2 10 2 2 2 2 59 60 8 15 8 8 8 8 60 61 3 8 5 3 3 3 61 62 0 16 2 1 6 3 62 63 6 19 6 0 6 7 63 64 8 14 2 6 6 2 64 65 4 7 1 0 5 9 65 66 5 13 5 0 5 5 66 67 6 15 6 6 6 4 67 68 5 7 2 2 2 2 68 69 6 13 6 1 6 6 69 70 2 4 2 5 5 5 70 71 6 14 6 5 5 5 71 72 5 13 5 5 5 9 72 73 5 11 0 5 5 2 73 74 6 14 2 6 6 6 74 75 4 12 4 6 6 6 75 76 6 15 1 0 9 6 76 77 5 14 5 0 5 5 77 78 5 13 5 1 5 3 78 79 4 7 2 7 7 2 79 80 2 5 2 2 2 2 80 81 7 7 7 4 4 4 81 82 5 13 5 0 6 8 82 83 6 13 2 5 5 5 83 84 5 11 5 5 5 9 84 85 3 6 3 3 3 2 85 86 6 12 6 0 6 6 86 87 4 8 1 4 4 4 87 88 5 11 5 9 9 5 88 89 7 12 7 0 8 8 89 90 4 9 2 4 4 3 90 91 6 12 6 2 2 2 91 92 8 13 8 7 7 7 92 93 7 16 7 7 7 7 93 94 6 16 6 6 6 9 94 95 7 11 7 0 5 5 95 96 4 8 4 5 5 5 96 97 0 4 5 6 6 2 97 98 3 7 2 0 3 3 98 99 5 14 5 5 5 5 99 100 6 11 2 9 9 2 100 101 5 17 5 0 7 7 101 102 7 15 7 7 7 7 102 103 6 14 5 1 6 6 103 104 8 5 8 3 3 3 104 105 7 4 2 7 7 3 105 106 8 19 8 8 8 2 106 107 3 11 3 0 3 3 107 108 8 15 2 5 5 5 108 109 3 10 3 3 3 3 109 110 4 9 5 0 4 4 110 111 2 12 2 5 5 5 111 112 7 15 2 7 7 7 112 113 6 7 6 0 6 6 113 114 2 13 2 0 7 7 114 115 7 14 7 0 9 2 115 116 6 14 6 6 6 6 116 117 6 14 2 0 6 9 117 118 6 8 2 6 6 4 118 119 6 15 5 6 6 6 119 120 6 15 6 2 2 2 120 121 4 9 4 5 5 2 121 122 5 16 5 0 5 5 122 123 7 9 7 4 4 4 123 124 6 15 6 0 7 7 124 125 6 15 6 6 6 6 125 126 5 6 5 5 5 7 126 127 8 8 2 8 8 8 127 128 6 15 6 6 6 6 128 129 0 10 3 5 5 3 129 130 4 9 2 0 4 4 130 131 8 14 8 8 8 8 131 132 6 12 6 0 6 9 132 133 4 8 4 9 9 2 133 134 6 11 6 5 5 5 134 135 2 13 5 0 6 6 135 136 4 9 4 0 4 4 136 137 6 15 2 0 6 6 137 138 3 13 3 3 3 3 138 139 6 15 6 6 6 6 139 140 5 14 5 0 5 5 140 141 4 16 4 4 4 8 141 142 6 12 6 6 6 6 142 143 1 14 1 0 5 5 143 144 4 10 5 4 4 3 144 145 4 10 2 7 7 2 145 146 6 4 6 0 6 6 146 147 5 8 5 5 5 5 147 148 9 17 2 6 6 6 148 149 6 16 6 6 6 6 149 150 8 12 8 8 8 9 150 151 7 12 7 2 2 4 151 152 7 15 7 7 7 7 152 153 0 9 9 0 4 4 153 154 6 13 2 0 6 7 154 155 6 14 6 5 5 5 155 156 5 11 5 0 2 2 156 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Schoolprestaties Goingout Relation 0.8608072 0.0940010 0.3511947 0.0700473 Family Coach t 0.1550685 0.1203996 -0.0007138 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -5.8602 -0.6727 -0.0275 0.8040 3.8713 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.8608072 0.5568904 1.546 0.1243 Schoolprestaties 0.0940010 0.0374443 2.510 0.0131 * Goingout 0.3511947 0.0601972 5.834 3.25e-08 *** Relation 0.0700473 0.0465097 1.506 0.1342 Family 0.1550685 0.0860917 1.801 0.0737 . Coach 0.1203996 0.0641044 1.878 0.0623 . t -0.0007138 0.0026876 -0.266 0.7909 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.497 on 149 degrees of freedom Multiple R-squared: 0.3826, Adjusted R-squared: 0.3577 F-statistic: 15.39 on 6 and 149 DF, p-value: 1.101e-13 > 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,] 2.138175e-02 0.0427634901 0.9786183 [2,] 4.541182e-03 0.0090823640 0.9954588 [3,] 6.592911e-03 0.0131858213 0.9934071 [4,] 2.806287e-03 0.0056125733 0.9971937 [5,] 1.482900e-02 0.0296580097 0.9851710 [6,] 7.325859e-03 0.0146517176 0.9926741 [7,] 3.160718e-03 0.0063214365 0.9968393 [8,] 3.086381e-02 0.0617276171 0.9691362 [9,] 2.126112e-02 0.0425222313 0.9787389 [10,] 1.109192e-02 0.0221838398 0.9889081 [11,] 6.145269e-03 0.0122905390 0.9938547 [12,] 1.678617e-02 0.0335723395 0.9832138 [13,] 9.349298e-03 0.0186985955 0.9906507 [14,] 6.629670e-03 0.0132593409 0.9933703 [15,] 1.324367e-02 0.0264873487 0.9867563 [16,] 7.789474e-03 0.0155789474 0.9922105 [17,] 4.430940e-03 0.0088618807 0.9955691 [18,] 8.247462e-03 0.0164949236 0.9917525 [19,] 4.936431e-03 0.0098728615 0.9950636 [20,] 3.506627e-03 0.0070132536 0.9964934 [21,] 3.788835e-03 0.0075776706 0.9962112 [22,] 2.255177e-03 0.0045103536 0.9977448 [23,] 1.622125e-03 0.0032442491 0.9983779 [24,] 9.528814e-04 0.0019057627 0.9990471 [25,] 6.179724e-04 0.0012359448 0.9993820 [26,] 3.533707e-04 0.0007067414 0.9996466 [27,] 2.707132e-04 0.0005414264 0.9997293 [28,] 2.982537e-04 0.0005965074 0.9997017 [29,] 1.608785e-04 0.0003217570 0.9998391 [30,] 2.091865e-03 0.0041837309 0.9979081 [31,] 1.372487e-03 0.0027449737 0.9986275 [32,] 8.835603e-04 0.0017671205 0.9991164 [33,] 1.129340e-03 0.0022586807 0.9988707 [34,] 7.169963e-04 0.0014339926 0.9992830 [35,] 8.072668e-04 0.0016145336 0.9991927 [36,] 5.364492e-04 0.0010728985 0.9994636 [37,] 3.691951e-04 0.0007383902 0.9996308 [38,] 4.393497e-04 0.0008786993 0.9995607 [39,] 2.765873e-04 0.0005531747 0.9997234 [40,] 1.700682e-04 0.0003401365 0.9998299 [41,] 1.904182e-04 0.0003808363 0.9998096 [42,] 3.692753e-04 0.0007385506 0.9996307 [43,] 3.271607e-04 0.0006543215 0.9996728 [44,] 2.701057e-04 0.0005402113 0.9997299 [45,] 1.898193e-04 0.0003796385 0.9998102 [46,] 1.523683e-04 0.0003047365 0.9998476 [47,] 2.376244e-04 0.0004752488 0.9997624 [48,] 2.178515e-04 0.0004357029 0.9997821 [49,] 1.681601e-04 0.0003363203 0.9998318 [50,] 1.283960e-04 0.0002567921 0.9998716 [51,] 7.657657e-05 0.0001531531 0.9999234 [52,] 9.164928e-05 0.0001832986 0.9999084 [53,] 3.395163e-03 0.0067903270 0.9966048 [54,] 2.734650e-03 0.0054693000 0.9972653 [55,] 5.055111e-02 0.1011022289 0.9494489 [56,] 3.932975e-02 0.0786594966 0.9606703 [57,] 3.104034e-02 0.0620806889 0.9689597 [58,] 2.353623e-02 0.0470724678 0.9764638 [59,] 3.311036e-02 0.0662207238 0.9668896 [60,] 2.560813e-02 0.0512162529 0.9743919 [61,] 2.729021e-02 0.0545804258 0.9727098 [62,] 2.050713e-02 0.0410142547 0.9794929 [63,] 1.843144e-02 0.0368628772 0.9815686 [64,] 2.689069e-02 0.0537813717 0.9731093 [65,] 2.528443e-02 0.0505688661 0.9747156 [66,] 2.596146e-02 0.0519229111 0.9740385 [67,] 2.381134e-02 0.0476226898 0.9761887 [68,] 1.882225e-02 0.0376444988 0.9811778 [69,] 1.420035e-02 0.0284006910 0.9857997 [70,] 1.053627e-02 0.0210725416 0.9894637 [71,] 8.472429e-03 0.0169448583 0.9915276 [72,] 8.173188e-03 0.0163463769 0.9918268 [73,] 6.858685e-03 0.0137173698 0.9931413 [74,] 7.037514e-03 0.0140750284 0.9929625 [75,] 6.060362e-03 0.0121207242 0.9939396 [76,] 4.495813e-03 0.0089916253 0.9955042 [77,] 3.208956e-03 0.0064179129 0.9967910 [78,] 2.401203e-03 0.0048024063 0.9975988 [79,] 2.281015e-03 0.0045620295 0.9977190 [80,] 1.599015e-03 0.0031980295 0.9984010 [81,] 1.098532e-03 0.0021970646 0.9989015 [82,] 9.214432e-04 0.0018428864 0.9990786 [83,] 6.406084e-04 0.0012812167 0.9993594 [84,] 4.301127e-04 0.0008602253 0.9995699 [85,] 3.638634e-04 0.0007277267 0.9996361 [86,] 3.196723e-04 0.0006393446 0.9996803 [87,] 2.441062e-04 0.0004882125 0.9997559 [88,] 8.420789e-03 0.0168415782 0.9915792 [89,] 6.082992e-03 0.0121659849 0.9939170 [90,] 4.886558e-03 0.0097731153 0.9951134 [91,] 4.458161e-03 0.0089163223 0.9955418 [92,] 3.923186e-03 0.0078463719 0.9960768 [93,] 2.911944e-03 0.0058238872 0.9970881 [94,] 1.994818e-03 0.0039896363 0.9980052 [95,] 4.316648e-03 0.0086332962 0.9956834 [96,] 1.068764e-02 0.0213752866 0.9893124 [97,] 8.062011e-03 0.0161240219 0.9919380 [98,] 6.208943e-03 0.0124178861 0.9937911 [99,] 1.775993e-02 0.0355198579 0.9822401 [100,] 1.420557e-02 0.0284111441 0.9857944 [101,] 1.079154e-02 0.0215830883 0.9892085 [102,] 2.019786e-02 0.0403957296 0.9798021 [103,] 1.812464e-02 0.0362492802 0.9818754 [104,] 1.485214e-02 0.0297042844 0.9851479 [105,] 3.230728e-02 0.0646145675 0.9676927 [106,] 4.978369e-02 0.0995673734 0.9502163 [107,] 3.808426e-02 0.0761685156 0.9619157 [108,] 3.000117e-02 0.0600023414 0.9699988 [109,] 2.931166e-02 0.0586233236 0.9706883 [110,] 2.138821e-02 0.0427764106 0.9786118 [111,] 2.405190e-02 0.0481038016 0.9759481 [112,] 1.804773e-02 0.0360954632 0.9819523 [113,] 1.435941e-02 0.0287188221 0.9856406 [114,] 2.685817e-02 0.0537163491 0.9731418 [115,] 2.621112e-02 0.0524222489 0.9737889 [116,] 2.030797e-02 0.0406159432 0.9796920 [117,] 1.491109e-02 0.0298221844 0.9850889 [118,] 1.594764e-02 0.0318952705 0.9840524 [119,] 1.196089e-02 0.0239217783 0.9880391 [120,] 7.083095e-02 0.1416618978 0.9291691 [121,] 5.361728e-02 0.1072345594 0.9463827 [122,] 4.570740e-02 0.0914148053 0.9542926 [123,] 3.664031e-02 0.0732806111 0.9633597 [124,] 2.611010e-02 0.0522201930 0.9738899 [125,] 2.258034e-02 0.0451606854 0.9774197 [126,] 2.816226e-02 0.0563245250 0.9718377 [127,] 2.052660e-02 0.0410531929 0.9794734 [128,] 2.772477e-02 0.0554495417 0.9722752 [129,] 1.845154e-02 0.0369030795 0.9815485 [130,] 1.254352e-02 0.0250870325 0.9874565 [131,] 6.997880e-02 0.1399576053 0.9300212 [132,] 1.522278e-01 0.3044555927 0.8477722 [133,] 1.104333e-01 0.2208666963 0.8895667 [134,] 2.754846e-01 0.5509691017 0.7245154 [135,] 2.219048e-01 0.4438095242 0.7780952 [136,] 1.401095e-01 0.2802190403 0.8598905 [137,] 8.056128e-01 0.3887744461 0.1943872 > postscript(file="/var/wessaorg/rcomp/tmp/1kz9q1321989367.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/23nzi1321989367.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3ag9r1321989367.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4rqdo1321989367.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5402a1321989367.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 156 Frequency = 1 1 2 3 4 5 6 -1.396641562 -0.044834608 0.614543951 0.062981067 0.424817707 -0.982005095 7 8 9 10 11 12 -0.405102073 0.531180719 1.269367053 -1.330094812 -0.537232595 0.525480162 13 14 15 16 17 18 -0.117083896 -1.598460281 1.231789499 0.070753750 -3.088102682 1.267381313 19 20 21 22 23 24 -0.011904196 -0.479381584 -2.626177054 -0.278888605 -0.497157280 2.955501876 25 26 27 28 29 30 -0.245237044 -0.620527353 1.766588148 -0.831403938 0.435095321 1.270562221 31 32 33 34 35 36 -0.136730096 -0.083749071 0.320956583 1.851216904 -0.656677357 -1.063477974 37 38 39 40 41 42 0.371613655 -0.069243137 2.128159119 0.865527618 0.925258762 -2.223257895 43 44 45 46 47 48 -0.672254232 -0.924868764 -0.112151675 1.044261473 0.443153152 0.751600868 49 50 51 52 53 54 0.739244700 0.098889366 1.568908616 -0.879882782 0.009696461 0.341276375 55 56 57 58 59 60 1.131694945 -0.821811045 -1.383781147 -1.179033583 -1.152126177 0.198322040 61 62 63 64 65 66 -1.361796152 -4.384617251 -0.482236560 3.574975502 0.317445947 -0.169026812 67 68 69 70 71 72 -0.162462336 2.136300621 0.136404363 -1.616814921 0.039109825 -0.996579131 73 74 75 76 77 78 1.790907483 1.100514571 -1.413159077 1.314214006 -0.255176572 0.010290134 79 80 81 82 83 84 0.018573109 -0.667132343 1.698575082 -0.673874108 1.546454703 -0.800012096 85 86 87 88 89 90 -0.333875074 0.312586413 0.716024855 -1.216021672 0.412596780 0.393369986 91 92 93 94 95 96 1.277932952 0.754679447 -0.175415133 -0.839190133 1.337284553 -0.676650896 97 98 99 100 101 102 -4.515044727 0.022339605 -0.589710447 1.207326303 -1.070985775 -0.074990364 103 104 105 106 107 108 0.417865578 2.897314020 3.198734057 0.577548156 -0.698435414 3.376296435 109 110 111 112 113 114 -0.813148730 -0.486149647 -2.339559270 1.688120712 0.801862749 -2.632118817 115 116 117 118 119 120 0.810481440 -0.274286766 1.190290678 1.936724898 -0.014951815 1.016628671 121 122 123 124 125 126 -0.391609322 -0.411059830 1.540550572 -0.217762199 -0.361864026 -0.059230284 127 128 129 130 131 132 3.011318710 -0.359722775 -4.249105228 0.581709505 0.342999338 -0.015779892 133 134 135 136 137 138 -1.189506316 0.366079149 -3.395246115 -0.116397421 1.471763499 -1.074453011 139 140 141 142 143 144 -0.351871520 -0.210210292 -1.532623316 -0.067727224 -2.803290184 -0.715672646 145 146 147 148 149 150 -0.216322404 1.107419559 0.008555664 3.871329062 -0.438735030 0.424163017 151 152 153 154 155 156 1.728764048 -0.039302840 -5.860237234 1.551499678 0.099064865 0.909617016 > postscript(file="/var/wessaorg/rcomp/tmp/6f0mt1321989367.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 = 156 Frequency = 1 lag(myerror, k = 1) myerror 0 -1.396641562 NA 1 -0.044834608 -1.396641562 2 0.614543951 -0.044834608 3 0.062981067 0.614543951 4 0.424817707 0.062981067 5 -0.982005095 0.424817707 6 -0.405102073 -0.982005095 7 0.531180719 -0.405102073 8 1.269367053 0.531180719 9 -1.330094812 1.269367053 10 -0.537232595 -1.330094812 11 0.525480162 -0.537232595 12 -0.117083896 0.525480162 13 -1.598460281 -0.117083896 14 1.231789499 -1.598460281 15 0.070753750 1.231789499 16 -3.088102682 0.070753750 17 1.267381313 -3.088102682 18 -0.011904196 1.267381313 19 -0.479381584 -0.011904196 20 -2.626177054 -0.479381584 21 -0.278888605 -2.626177054 22 -0.497157280 -0.278888605 23 2.955501876 -0.497157280 24 -0.245237044 2.955501876 25 -0.620527353 -0.245237044 26 1.766588148 -0.620527353 27 -0.831403938 1.766588148 28 0.435095321 -0.831403938 29 1.270562221 0.435095321 30 -0.136730096 1.270562221 31 -0.083749071 -0.136730096 32 0.320956583 -0.083749071 33 1.851216904 0.320956583 34 -0.656677357 1.851216904 35 -1.063477974 -0.656677357 36 0.371613655 -1.063477974 37 -0.069243137 0.371613655 38 2.128159119 -0.069243137 39 0.865527618 2.128159119 40 0.925258762 0.865527618 41 -2.223257895 0.925258762 42 -0.672254232 -2.223257895 43 -0.924868764 -0.672254232 44 -0.112151675 -0.924868764 45 1.044261473 -0.112151675 46 0.443153152 1.044261473 47 0.751600868 0.443153152 48 0.739244700 0.751600868 49 0.098889366 0.739244700 50 1.568908616 0.098889366 51 -0.879882782 1.568908616 52 0.009696461 -0.879882782 53 0.341276375 0.009696461 54 1.131694945 0.341276375 55 -0.821811045 1.131694945 56 -1.383781147 -0.821811045 57 -1.179033583 -1.383781147 58 -1.152126177 -1.179033583 59 0.198322040 -1.152126177 60 -1.361796152 0.198322040 61 -4.384617251 -1.361796152 62 -0.482236560 -4.384617251 63 3.574975502 -0.482236560 64 0.317445947 3.574975502 65 -0.169026812 0.317445947 66 -0.162462336 -0.169026812 67 2.136300621 -0.162462336 68 0.136404363 2.136300621 69 -1.616814921 0.136404363 70 0.039109825 -1.616814921 71 -0.996579131 0.039109825 72 1.790907483 -0.996579131 73 1.100514571 1.790907483 74 -1.413159077 1.100514571 75 1.314214006 -1.413159077 76 -0.255176572 1.314214006 77 0.010290134 -0.255176572 78 0.018573109 0.010290134 79 -0.667132343 0.018573109 80 1.698575082 -0.667132343 81 -0.673874108 1.698575082 82 1.546454703 -0.673874108 83 -0.800012096 1.546454703 84 -0.333875074 -0.800012096 85 0.312586413 -0.333875074 86 0.716024855 0.312586413 87 -1.216021672 0.716024855 88 0.412596780 -1.216021672 89 0.393369986 0.412596780 90 1.277932952 0.393369986 91 0.754679447 1.277932952 92 -0.175415133 0.754679447 93 -0.839190133 -0.175415133 94 1.337284553 -0.839190133 95 -0.676650896 1.337284553 96 -4.515044727 -0.676650896 97 0.022339605 -4.515044727 98 -0.589710447 0.022339605 99 1.207326303 -0.589710447 100 -1.070985775 1.207326303 101 -0.074990364 -1.070985775 102 0.417865578 -0.074990364 103 2.897314020 0.417865578 104 3.198734057 2.897314020 105 0.577548156 3.198734057 106 -0.698435414 0.577548156 107 3.376296435 -0.698435414 108 -0.813148730 3.376296435 109 -0.486149647 -0.813148730 110 -2.339559270 -0.486149647 111 1.688120712 -2.339559270 112 0.801862749 1.688120712 113 -2.632118817 0.801862749 114 0.810481440 -2.632118817 115 -0.274286766 0.810481440 116 1.190290678 -0.274286766 117 1.936724898 1.190290678 118 -0.014951815 1.936724898 119 1.016628671 -0.014951815 120 -0.391609322 1.016628671 121 -0.411059830 -0.391609322 122 1.540550572 -0.411059830 123 -0.217762199 1.540550572 124 -0.361864026 -0.217762199 125 -0.059230284 -0.361864026 126 3.011318710 -0.059230284 127 -0.359722775 3.011318710 128 -4.249105228 -0.359722775 129 0.581709505 -4.249105228 130 0.342999338 0.581709505 131 -0.015779892 0.342999338 132 -1.189506316 -0.015779892 133 0.366079149 -1.189506316 134 -3.395246115 0.366079149 135 -0.116397421 -3.395246115 136 1.471763499 -0.116397421 137 -1.074453011 1.471763499 138 -0.351871520 -1.074453011 139 -0.210210292 -0.351871520 140 -1.532623316 -0.210210292 141 -0.067727224 -1.532623316 142 -2.803290184 -0.067727224 143 -0.715672646 -2.803290184 144 -0.216322404 -0.715672646 145 1.107419559 -0.216322404 146 0.008555664 1.107419559 147 3.871329062 0.008555664 148 -0.438735030 3.871329062 149 0.424163017 -0.438735030 150 1.728764048 0.424163017 151 -0.039302840 1.728764048 152 -5.860237234 -0.039302840 153 1.551499678 -5.860237234 154 0.099064865 1.551499678 155 0.909617016 0.099064865 156 NA 0.909617016 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.044834608 -1.396641562 [2,] 0.614543951 -0.044834608 [3,] 0.062981067 0.614543951 [4,] 0.424817707 0.062981067 [5,] -0.982005095 0.424817707 [6,] -0.405102073 -0.982005095 [7,] 0.531180719 -0.405102073 [8,] 1.269367053 0.531180719 [9,] -1.330094812 1.269367053 [10,] -0.537232595 -1.330094812 [11,] 0.525480162 -0.537232595 [12,] -0.117083896 0.525480162 [13,] -1.598460281 -0.117083896 [14,] 1.231789499 -1.598460281 [15,] 0.070753750 1.231789499 [16,] -3.088102682 0.070753750 [17,] 1.267381313 -3.088102682 [18,] -0.011904196 1.267381313 [19,] -0.479381584 -0.011904196 [20,] -2.626177054 -0.479381584 [21,] -0.278888605 -2.626177054 [22,] -0.497157280 -0.278888605 [23,] 2.955501876 -0.497157280 [24,] -0.245237044 2.955501876 [25,] -0.620527353 -0.245237044 [26,] 1.766588148 -0.620527353 [27,] -0.831403938 1.766588148 [28,] 0.435095321 -0.831403938 [29,] 1.270562221 0.435095321 [30,] -0.136730096 1.270562221 [31,] -0.083749071 -0.136730096 [32,] 0.320956583 -0.083749071 [33,] 1.851216904 0.320956583 [34,] -0.656677357 1.851216904 [35,] -1.063477974 -0.656677357 [36,] 0.371613655 -1.063477974 [37,] -0.069243137 0.371613655 [38,] 2.128159119 -0.069243137 [39,] 0.865527618 2.128159119 [40,] 0.925258762 0.865527618 [41,] -2.223257895 0.925258762 [42,] -0.672254232 -2.223257895 [43,] -0.924868764 -0.672254232 [44,] -0.112151675 -0.924868764 [45,] 1.044261473 -0.112151675 [46,] 0.443153152 1.044261473 [47,] 0.751600868 0.443153152 [48,] 0.739244700 0.751600868 [49,] 0.098889366 0.739244700 [50,] 1.568908616 0.098889366 [51,] -0.879882782 1.568908616 [52,] 0.009696461 -0.879882782 [53,] 0.341276375 0.009696461 [54,] 1.131694945 0.341276375 [55,] -0.821811045 1.131694945 [56,] -1.383781147 -0.821811045 [57,] -1.179033583 -1.383781147 [58,] -1.152126177 -1.179033583 [59,] 0.198322040 -1.152126177 [60,] -1.361796152 0.198322040 [61,] -4.384617251 -1.361796152 [62,] -0.482236560 -4.384617251 [63,] 3.574975502 -0.482236560 [64,] 0.317445947 3.574975502 [65,] -0.169026812 0.317445947 [66,] -0.162462336 -0.169026812 [67,] 2.136300621 -0.162462336 [68,] 0.136404363 2.136300621 [69,] -1.616814921 0.136404363 [70,] 0.039109825 -1.616814921 [71,] -0.996579131 0.039109825 [72,] 1.790907483 -0.996579131 [73,] 1.100514571 1.790907483 [74,] -1.413159077 1.100514571 [75,] 1.314214006 -1.413159077 [76,] -0.255176572 1.314214006 [77,] 0.010290134 -0.255176572 [78,] 0.018573109 0.010290134 [79,] -0.667132343 0.018573109 [80,] 1.698575082 -0.667132343 [81,] -0.673874108 1.698575082 [82,] 1.546454703 -0.673874108 [83,] -0.800012096 1.546454703 [84,] -0.333875074 -0.800012096 [85,] 0.312586413 -0.333875074 [86,] 0.716024855 0.312586413 [87,] -1.216021672 0.716024855 [88,] 0.412596780 -1.216021672 [89,] 0.393369986 0.412596780 [90,] 1.277932952 0.393369986 [91,] 0.754679447 1.277932952 [92,] -0.175415133 0.754679447 [93,] -0.839190133 -0.175415133 [94,] 1.337284553 -0.839190133 [95,] -0.676650896 1.337284553 [96,] -4.515044727 -0.676650896 [97,] 0.022339605 -4.515044727 [98,] -0.589710447 0.022339605 [99,] 1.207326303 -0.589710447 [100,] -1.070985775 1.207326303 [101,] -0.074990364 -1.070985775 [102,] 0.417865578 -0.074990364 [103,] 2.897314020 0.417865578 [104,] 3.198734057 2.897314020 [105,] 0.577548156 3.198734057 [106,] -0.698435414 0.577548156 [107,] 3.376296435 -0.698435414 [108,] -0.813148730 3.376296435 [109,] -0.486149647 -0.813148730 [110,] -2.339559270 -0.486149647 [111,] 1.688120712 -2.339559270 [112,] 0.801862749 1.688120712 [113,] -2.632118817 0.801862749 [114,] 0.810481440 -2.632118817 [115,] -0.274286766 0.810481440 [116,] 1.190290678 -0.274286766 [117,] 1.936724898 1.190290678 [118,] -0.014951815 1.936724898 [119,] 1.016628671 -0.014951815 [120,] -0.391609322 1.016628671 [121,] -0.411059830 -0.391609322 [122,] 1.540550572 -0.411059830 [123,] -0.217762199 1.540550572 [124,] -0.361864026 -0.217762199 [125,] -0.059230284 -0.361864026 [126,] 3.011318710 -0.059230284 [127,] -0.359722775 3.011318710 [128,] -4.249105228 -0.359722775 [129,] 0.581709505 -4.249105228 [130,] 0.342999338 0.581709505 [131,] -0.015779892 0.342999338 [132,] -1.189506316 -0.015779892 [133,] 0.366079149 -1.189506316 [134,] -3.395246115 0.366079149 [135,] -0.116397421 -3.395246115 [136,] 1.471763499 -0.116397421 [137,] -1.074453011 1.471763499 [138,] -0.351871520 -1.074453011 [139,] -0.210210292 -0.351871520 [140,] -1.532623316 -0.210210292 [141,] -0.067727224 -1.532623316 [142,] -2.803290184 -0.067727224 [143,] -0.715672646 -2.803290184 [144,] -0.216322404 -0.715672646 [145,] 1.107419559 -0.216322404 [146,] 0.008555664 1.107419559 [147,] 3.871329062 0.008555664 [148,] -0.438735030 3.871329062 [149,] 0.424163017 -0.438735030 [150,] 1.728764048 0.424163017 [151,] -0.039302840 1.728764048 [152,] -5.860237234 -0.039302840 [153,] 1.551499678 -5.860237234 [154,] 0.099064865 1.551499678 [155,] 0.909617016 0.099064865 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.044834608 -1.396641562 2 0.614543951 -0.044834608 3 0.062981067 0.614543951 4 0.424817707 0.062981067 5 -0.982005095 0.424817707 6 -0.405102073 -0.982005095 7 0.531180719 -0.405102073 8 1.269367053 0.531180719 9 -1.330094812 1.269367053 10 -0.537232595 -1.330094812 11 0.525480162 -0.537232595 12 -0.117083896 0.525480162 13 -1.598460281 -0.117083896 14 1.231789499 -1.598460281 15 0.070753750 1.231789499 16 -3.088102682 0.070753750 17 1.267381313 -3.088102682 18 -0.011904196 1.267381313 19 -0.479381584 -0.011904196 20 -2.626177054 -0.479381584 21 -0.278888605 -2.626177054 22 -0.497157280 -0.278888605 23 2.955501876 -0.497157280 24 -0.245237044 2.955501876 25 -0.620527353 -0.245237044 26 1.766588148 -0.620527353 27 -0.831403938 1.766588148 28 0.435095321 -0.831403938 29 1.270562221 0.435095321 30 -0.136730096 1.270562221 31 -0.083749071 -0.136730096 32 0.320956583 -0.083749071 33 1.851216904 0.320956583 34 -0.656677357 1.851216904 35 -1.063477974 -0.656677357 36 0.371613655 -1.063477974 37 -0.069243137 0.371613655 38 2.128159119 -0.069243137 39 0.865527618 2.128159119 40 0.925258762 0.865527618 41 -2.223257895 0.925258762 42 -0.672254232 -2.223257895 43 -0.924868764 -0.672254232 44 -0.112151675 -0.924868764 45 1.044261473 -0.112151675 46 0.443153152 1.044261473 47 0.751600868 0.443153152 48 0.739244700 0.751600868 49 0.098889366 0.739244700 50 1.568908616 0.098889366 51 -0.879882782 1.568908616 52 0.009696461 -0.879882782 53 0.341276375 0.009696461 54 1.131694945 0.341276375 55 -0.821811045 1.131694945 56 -1.383781147 -0.821811045 57 -1.179033583 -1.383781147 58 -1.152126177 -1.179033583 59 0.198322040 -1.152126177 60 -1.361796152 0.198322040 61 -4.384617251 -1.361796152 62 -0.482236560 -4.384617251 63 3.574975502 -0.482236560 64 0.317445947 3.574975502 65 -0.169026812 0.317445947 66 -0.162462336 -0.169026812 67 2.136300621 -0.162462336 68 0.136404363 2.136300621 69 -1.616814921 0.136404363 70 0.039109825 -1.616814921 71 -0.996579131 0.039109825 72 1.790907483 -0.996579131 73 1.100514571 1.790907483 74 -1.413159077 1.100514571 75 1.314214006 -1.413159077 76 -0.255176572 1.314214006 77 0.010290134 -0.255176572 78 0.018573109 0.010290134 79 -0.667132343 0.018573109 80 1.698575082 -0.667132343 81 -0.673874108 1.698575082 82 1.546454703 -0.673874108 83 -0.800012096 1.546454703 84 -0.333875074 -0.800012096 85 0.312586413 -0.333875074 86 0.716024855 0.312586413 87 -1.216021672 0.716024855 88 0.412596780 -1.216021672 89 0.393369986 0.412596780 90 1.277932952 0.393369986 91 0.754679447 1.277932952 92 -0.175415133 0.754679447 93 -0.839190133 -0.175415133 94 1.337284553 -0.839190133 95 -0.676650896 1.337284553 96 -4.515044727 -0.676650896 97 0.022339605 -4.515044727 98 -0.589710447 0.022339605 99 1.207326303 -0.589710447 100 -1.070985775 1.207326303 101 -0.074990364 -1.070985775 102 0.417865578 -0.074990364 103 2.897314020 0.417865578 104 3.198734057 2.897314020 105 0.577548156 3.198734057 106 -0.698435414 0.577548156 107 3.376296435 -0.698435414 108 -0.813148730 3.376296435 109 -0.486149647 -0.813148730 110 -2.339559270 -0.486149647 111 1.688120712 -2.339559270 112 0.801862749 1.688120712 113 -2.632118817 0.801862749 114 0.810481440 -2.632118817 115 -0.274286766 0.810481440 116 1.190290678 -0.274286766 117 1.936724898 1.190290678 118 -0.014951815 1.936724898 119 1.016628671 -0.014951815 120 -0.391609322 1.016628671 121 -0.411059830 -0.391609322 122 1.540550572 -0.411059830 123 -0.217762199 1.540550572 124 -0.361864026 -0.217762199 125 -0.059230284 -0.361864026 126 3.011318710 -0.059230284 127 -0.359722775 3.011318710 128 -4.249105228 -0.359722775 129 0.581709505 -4.249105228 130 0.342999338 0.581709505 131 -0.015779892 0.342999338 132 -1.189506316 -0.015779892 133 0.366079149 -1.189506316 134 -3.395246115 0.366079149 135 -0.116397421 -3.395246115 136 1.471763499 -0.116397421 137 -1.074453011 1.471763499 138 -0.351871520 -1.074453011 139 -0.210210292 -0.351871520 140 -1.532623316 -0.210210292 141 -0.067727224 -1.532623316 142 -2.803290184 -0.067727224 143 -0.715672646 -2.803290184 144 -0.216322404 -0.715672646 145 1.107419559 -0.216322404 146 0.008555664 1.107419559 147 3.871329062 0.008555664 148 -0.438735030 3.871329062 149 0.424163017 -0.438735030 150 1.728764048 0.424163017 151 -0.039302840 1.728764048 152 -5.860237234 -0.039302840 153 1.551499678 -5.860237234 154 0.099064865 1.551499678 155 0.909617016 0.099064865 > 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/7vsgf1321989367.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/8enxz1321989367.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/9n7pr1321989367.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/10up951321989367.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/116iwx1321989367.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/129ame1321989367.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13apop1321989367.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14pajl1321989367.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/15300i1321989367.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/16nyhb1321989367.tab") + } > > try(system("convert tmp/1kz9q1321989367.ps tmp/1kz9q1321989367.png",intern=TRUE)) character(0) > try(system("convert tmp/23nzi1321989367.ps tmp/23nzi1321989367.png",intern=TRUE)) character(0) > try(system("convert tmp/3ag9r1321989367.ps tmp/3ag9r1321989367.png",intern=TRUE)) character(0) > try(system("convert tmp/4rqdo1321989367.ps tmp/4rqdo1321989367.png",intern=TRUE)) character(0) > try(system("convert tmp/5402a1321989367.ps tmp/5402a1321989367.png",intern=TRUE)) character(0) > try(system("convert tmp/6f0mt1321989367.ps tmp/6f0mt1321989367.png",intern=TRUE)) character(0) > try(system("convert tmp/7vsgf1321989367.ps tmp/7vsgf1321989367.png",intern=TRUE)) character(0) > try(system("convert tmp/8enxz1321989367.ps tmp/8enxz1321989367.png",intern=TRUE)) character(0) > try(system("convert tmp/9n7pr1321989367.ps tmp/9n7pr1321989367.png",intern=TRUE)) character(0) > try(system("convert tmp/10up951321989367.ps tmp/10up951321989367.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.867 0.501 5.560