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(1173 + ,70 + ,95556 + ,669 + ,44 + ,54565 + ,1154 + ,39 + ,63016 + ,1948 + ,119 + ,79774 + ,705 + ,31 + ,31258 + ,332 + ,23 + ,52491 + ,2726 + ,46 + ,91256 + ,345 + ,39 + ,22807 + ,1385 + ,58 + ,77411 + ,1161 + ,51 + ,48821 + ,1431 + ,65 + ,52295 + ,1228 + ,42 + ,63262 + ,1205 + ,45 + ,50466 + ,1732 + ,76 + ,62932 + ,1214 + ,33 + ,38439 + ,3221 + ,90 + ,70817 + ,1385 + ,36 + ,105965 + ,1953 + ,68 + ,73795 + ,883 + ,28 + ,82043 + ,1631 + ,38 + ,74349 + ,1459 + ,75 + ,82204 + ,1929 + ,76 + ,55709 + ,860 + ,34 + ,37137 + ,1165 + ,44 + ,70780 + ,2115 + ,126 + ,55027 + ,1939 + ,59 + ,56699 + ,1844 + ,64 + ,65911 + ,1346 + ,46 + ,56316 + ,1093 + ,36 + ,26982 + ,1625 + ,108 + ,54628 + ,1551 + ,34 + ,96750 + ,1267 + ,54 + ,53009 + ,1478 + ,40 + ,64664 + ,670 + ,29 + ,36990 + ,2040 + ,46 + ,85224 + ,1561 + ,49 + ,37048 + ,2078 + ,56 + ,59635 + ,1113 + ,38 + ,42051 + ,686 + ,19 + ,26998 + ,2065 + ,29 + ,63717 + ,2251 + ,26 + ,55071 + ,1106 + ,56 + ,40001 + ,1244 + ,60 + ,54506 + ,1021 + ,45 + ,35838 + ,1735 + ,56 + ,50838 + ,3681 + ,596 + ,86997 + ,918 + ,57 + ,33032 + ,1582 + ,55 + ,61704 + ,2900 + ,99 + ,117986 + ,1496 + ,51 + ,56733 + ,1116 + ,21 + ,55064 + ,496 + ,20 + ,5950 + ,1777 + ,58 + ,84607 + ,744 + ,21 + ,32551 + ,1101 + ,66 + ,31701 + ,1612 + ,47 + ,71170 + ,1849 + ,58 + ,101773 + ,2460 + ,158 + ,101653 + ,1701 + ,49 + ,81493 + ,1334 + ,53 + ,55901 + ,2549 + ,46 + ,109104 + ,2218 + ,117 + ,114425 + ,1633 + ,56 + ,36311 + ,1724 + ,30 + ,70027 + ,973 + ,45 + ,73713 + ,1171 + ,42 + ,40671 + ,1282 + ,36 + ,89041 + ,1977 + ,61 + ,57231 + ,1521 + ,63 + ,68608 + ,1071 + ,46 + ,59155 + ,1425 + ,39 + ,55827 + ,852 + ,36 + ,22618 + ,1363 + ,40 + ,58425 + ,1150 + ,73 + ,65724 + ,1100 + ,49 + ,56979 + ,1393 + ,58 + ,72369 + ,1521 + ,29 + ,79194 + ,1015 + ,27 + ,202316 + ,993 + ,41 + ,44970 + ,1189 + ,52 + ,49319 + ,1244 + ,31 + ,36252 + ,2622 + ,89 + ,75741 + ,1177 + ,36 + ,38417 + ,1333 + ,39 + ,64102 + ,870 + ,31 + ,56622 + ,1473 + ,142 + ,15430 + ,881 + ,52 + ,72571 + ,2489 + ,223 + ,67271 + ,1429 + ,52 + ,43460 + ,1995 + ,51 + ,99501 + ,1247 + ,45 + ,28340 + ,1357 + ,51 + ,76013 + ,1316 + ,67 + ,37361 + ,1980 + ,66 + ,48204 + ,1454 + ,81 + ,76168 + ,1030 + ,43 + ,85168 + ,1154 + ,45 + ,125410 + ,1521 + ,35 + ,123328 + ,2294 + ,97 + ,83038 + ,2274 + ,41 + ,120087 + ,1371 + ,44 + ,91939 + ,1624 + ,61 + ,103646 + ,999 + ,35 + ,29467 + ,602 + ,43 + ,43750 + ,1380 + ,57 + ,34497 + ,1207 + ,34 + ,66477 + ,1405 + ,69 + ,71181 + ,1800 + ,39 + ,74482 + ,682 + ,25 + ,174949 + ,1151 + ,56 + ,46765 + ,1270 + ,42 + ,90257 + ,1381 + ,48 + ,51370 + ,391 + ,9 + ,1168 + ,1264 + ,36 + ,51360 + ,530 + ,25 + ,25162 + ,1123 + ,92 + ,21067 + ,1980 + ,42 + ,58233 + ,387 + ,2 + ,855 + ,1485 + ,46 + ,85903 + ,449 + ,22 + ,14116 + ,2209 + ,137 + ,57637 + ,1135 + ,51 + ,94137 + ,813 + ,67 + ,62147 + ,1015 + ,38 + ,62832 + ,568 + ,52 + ,8773 + ,936 + ,64 + ,63785 + ,1585 + ,75 + ,65196 + ,871 + ,37 + ,73087 + ,2275 + ,107 + ,72631 + ,1637 + ,84 + ,86281 + ,2238 + ,68 + ,162365 + ,829 + ,30 + ,56530 + ,809 + ,31 + ,35606 + ,1904 + ,117 + ,70111 + ,3053 + ,120 + ,92046 + ,655 + ,36 + ,63989 + ,2617 + ,106 + ,104911 + ,1311 + ,50 + ,43448 + ,1154 + ,54 + ,60029 + ,1496 + ,134 + ,38650 + ,742 + ,48 + ,47261 + ,2831 + ,81 + ,73586 + ,1281 + ,40 + ,83042 + ,2035 + ,37 + ,37238 + ,1894 + ,41 + ,63958 + ,1268 + ,100 + ,78956 + ,1713 + ,37 + ,99518 + ,1568 + ,38 + ,111436 + ,0 + ,0 + ,0 + ,207 + ,0 + ,6023 + ,5 + ,0 + ,0 + ,8 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1301 + ,36 + ,42564 + ,1761 + ,68 + ,38885 + ,0 + ,0 + ,0 + ,4 + ,0 + ,0 + ,151 + ,0 + ,1644 + ,474 + ,7 + ,6179 + ,141 + ,3 + ,3926 + ,705 + ,53 + ,23238 + ,29 + ,0 + ,0 + ,1021 + ,25 + ,49288) + ,dim=c(3 + ,164) + ,dimnames=list(c('Pageviews' + ,'Compendiumviews' + ,'CW:characters') + ,1:164)) > y <- array(NA,dim=c(3,164),dimnames=list(c('Pageviews','Compendiumviews','CW:characters'),1:164)) > 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 Pageviews Compendiumviews CW:characters 1 1173 70 95556 2 669 44 54565 3 1154 39 63016 4 1948 119 79774 5 705 31 31258 6 332 23 52491 7 2726 46 91256 8 345 39 22807 9 1385 58 77411 10 1161 51 48821 11 1431 65 52295 12 1228 42 63262 13 1205 45 50466 14 1732 76 62932 15 1214 33 38439 16 3221 90 70817 17 1385 36 105965 18 1953 68 73795 19 883 28 82043 20 1631 38 74349 21 1459 75 82204 22 1929 76 55709 23 860 34 37137 24 1165 44 70780 25 2115 126 55027 26 1939 59 56699 27 1844 64 65911 28 1346 46 56316 29 1093 36 26982 30 1625 108 54628 31 1551 34 96750 32 1267 54 53009 33 1478 40 64664 34 670 29 36990 35 2040 46 85224 36 1561 49 37048 37 2078 56 59635 38 1113 38 42051 39 686 19 26998 40 2065 29 63717 41 2251 26 55071 42 1106 56 40001 43 1244 60 54506 44 1021 45 35838 45 1735 56 50838 46 3681 596 86997 47 918 57 33032 48 1582 55 61704 49 2900 99 117986 50 1496 51 56733 51 1116 21 55064 52 496 20 5950 53 1777 58 84607 54 744 21 32551 55 1101 66 31701 56 1612 47 71170 57 1849 58 101773 58 2460 158 101653 59 1701 49 81493 60 1334 53 55901 61 2549 46 109104 62 2218 117 114425 63 1633 56 36311 64 1724 30 70027 65 973 45 73713 66 1171 42 40671 67 1282 36 89041 68 1977 61 57231 69 1521 63 68608 70 1071 46 59155 71 1425 39 55827 72 852 36 22618 73 1363 40 58425 74 1150 73 65724 75 1100 49 56979 76 1393 58 72369 77 1521 29 79194 78 1015 27 202316 79 993 41 44970 80 1189 52 49319 81 1244 31 36252 82 2622 89 75741 83 1177 36 38417 84 1333 39 64102 85 870 31 56622 86 1473 142 15430 87 881 52 72571 88 2489 223 67271 89 1429 52 43460 90 1995 51 99501 91 1247 45 28340 92 1357 51 76013 93 1316 67 37361 94 1980 66 48204 95 1454 81 76168 96 1030 43 85168 97 1154 45 125410 98 1521 35 123328 99 2294 97 83038 100 2274 41 120087 101 1371 44 91939 102 1624 61 103646 103 999 35 29467 104 602 43 43750 105 1380 57 34497 106 1207 34 66477 107 1405 69 71181 108 1800 39 74482 109 682 25 174949 110 1151 56 46765 111 1270 42 90257 112 1381 48 51370 113 391 9 1168 114 1264 36 51360 115 530 25 25162 116 1123 92 21067 117 1980 42 58233 118 387 2 855 119 1485 46 85903 120 449 22 14116 121 2209 137 57637 122 1135 51 94137 123 813 67 62147 124 1015 38 62832 125 568 52 8773 126 936 64 63785 127 1585 75 65196 128 871 37 73087 129 2275 107 72631 130 1637 84 86281 131 2238 68 162365 132 829 30 56530 133 809 31 35606 134 1904 117 70111 135 3053 120 92046 136 655 36 63989 137 2617 106 104911 138 1311 50 43448 139 1154 54 60029 140 1496 134 38650 141 742 48 47261 142 2831 81 73586 143 1281 40 83042 144 2035 37 37238 145 1894 41 63958 146 1268 100 78956 147 1713 37 99518 148 1568 38 111436 149 0 0 0 150 207 0 6023 151 5 0 0 152 8 0 0 153 0 0 0 154 0 0 0 155 1301 36 42564 156 1761 68 38885 157 0 0 0 158 4 0 0 159 151 0 1644 160 474 7 6179 161 141 3 3926 162 705 53 23238 163 29 0 0 164 1021 25 49288 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Compendiumviews `CW:characters` 4.601e+02 6.260e+00 9.059e-03 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1519.4 -262.9 -44.4 216.0 1556.0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.601e+02 7.570e+01 6.078 8.51e-09 *** Compendiumviews 6.260e+00 6.901e-01 9.071 3.94e-16 *** `CW:characters` 9.059e-03 1.103e-03 8.213 6.64e-14 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 456.9 on 161 degrees of freedom Multiple R-squared: 0.5531, Adjusted R-squared: 0.5475 F-statistic: 99.62 on 2 and 161 DF, p-value: < 2.2e-16 > 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,] 0.2175337 4.350675e-01 7.824663e-01 [2,] 0.9517515 9.649706e-02 4.824853e-02 [3,] 0.9161990 1.676019e-01 8.380095e-02 [4,] 0.8640486 2.719028e-01 1.359514e-01 [5,] 0.8166137 3.667726e-01 1.833863e-01 [6,] 0.7805077 4.389846e-01 2.194923e-01 [7,] 0.7016752 5.966497e-01 2.983248e-01 [8,] 0.6360889 7.278222e-01 3.639111e-01 [9,] 0.5894534 8.210931e-01 4.105466e-01 [10,] 0.5869953 8.260093e-01 4.130047e-01 [11,] 0.9531690 9.366202e-02 4.683101e-02 [12,] 0.9394993 1.210014e-01 6.050069e-02 [13,] 0.9218570 1.562859e-01 7.814297e-02 [14,] 0.9066507 1.866987e-01 9.334934e-02 [15,] 0.8923413 2.153173e-01 1.076587e-01 [16,] 0.8836380 2.327240e-01 1.163620e-01 [17,] 0.8656431 2.687137e-01 1.343569e-01 [18,] 0.8273486 3.453027e-01 1.726514e-01 [19,] 0.7870252 4.259495e-01 2.129748e-01 [20,] 0.7537508 4.924984e-01 2.462492e-01 [21,] 0.7746939 4.506122e-01 2.253061e-01 [22,] 0.7459621 5.080758e-01 2.540379e-01 [23,] 0.6979018 6.041963e-01 3.020982e-01 [24,] 0.6628661 6.742677e-01 3.371339e-01 [25,] 0.6476054 7.047891e-01 3.523946e-01 [26,] 0.5918822 8.162356e-01 4.081178e-01 [27,] 0.5341595 9.316811e-01 4.658405e-01 [28,] 0.4901717 9.803434e-01 5.098283e-01 [29,] 0.4425314 8.850628e-01 5.574686e-01 [30,] 0.4529692 9.059385e-01 5.470308e-01 [31,] 0.4598218 9.196436e-01 5.401782e-01 [32,] 0.5245002 9.509995e-01 4.754998e-01 [33,] 0.4711615 9.423231e-01 5.288385e-01 [34,] 0.4185835 8.371671e-01 5.814165e-01 [35,] 0.5689361 8.621278e-01 4.310639e-01 [36,] 0.8062335 3.875330e-01 1.937665e-01 [37,] 0.7722686 4.554628e-01 2.277314e-01 [38,] 0.7384781 5.230437e-01 2.615219e-01 [39,] 0.6960428 6.079144e-01 3.039572e-01 [40,] 0.6852528 6.294945e-01 3.147472e-01 [41,] 0.9469171 1.061658e-01 5.308290e-02 [42,] 0.9360276 1.279448e-01 6.397242e-02 [43,] 0.9217014 1.565971e-01 7.829856e-02 [44,] 0.9325627 1.348747e-01 6.743734e-02 [45,] 0.9177674 1.644652e-01 8.223260e-02 [46,] 0.9008588 1.982825e-01 9.914124e-02 [47,] 0.8786656 2.426688e-01 1.213344e-01 [48,] 0.8561850 2.876299e-01 1.438150e-01 [49,] 0.8309853 3.380295e-01 1.690147e-01 [50,] 0.8003686 3.992628e-01 1.996314e-01 [51,] 0.7720285 4.559430e-01 2.279715e-01 [52,] 0.7435976 5.128048e-01 2.564024e-01 [53,] 0.7103937 5.792126e-01 2.896063e-01 [54,] 0.6766790 6.466420e-01 3.233210e-01 [55,] 0.6342697 7.314605e-01 3.657303e-01 [56,] 0.7090082 5.819836e-01 2.909918e-01 [57,] 0.6803281 6.393438e-01 3.196719e-01 [58,] 0.6950420 6.099161e-01 3.049580e-01 [59,] 0.6990885 6.018229e-01 3.009115e-01 [60,] 0.7217565 5.564869e-01 2.782435e-01 [61,] 0.6840966 6.318067e-01 3.159034e-01 [62,] 0.6747578 6.504845e-01 3.252422e-01 [63,] 0.7102152 5.795695e-01 2.897848e-01 [64,] 0.6714766 6.570468e-01 3.285234e-01 [65,] 0.6459246 7.081508e-01 3.540754e-01 [66,] 0.6166632 7.666735e-01 3.833368e-01 [67,] 0.5727297 8.545406e-01 4.272703e-01 [68,] 0.5351911 9.296178e-01 4.648089e-01 [69,] 0.5354055 9.291890e-01 4.645945e-01 [70,] 0.5025532 9.948936e-01 4.974468e-01 [71,] 0.4644638 9.289277e-01 5.355362e-01 [72,] 0.4387783 8.775565e-01 5.612217e-01 [73,] 0.8405224 3.189551e-01 1.594776e-01 [74,] 0.8163893 3.672214e-01 1.836107e-01 [75,] 0.7863739 4.272523e-01 2.136261e-01 [76,] 0.7687384 4.625232e-01 2.312616e-01 [77,] 0.8594385 2.811229e-01 1.405615e-01 [78,] 0.8386153 3.227695e-01 1.613847e-01 [79,] 0.8128864 3.742272e-01 1.871136e-01 [80,] 0.7960922 4.078157e-01 2.039078e-01 [81,] 0.7796066 4.407867e-01 2.203934e-01 [82,] 0.7993177 4.013646e-01 2.006823e-01 [83,] 0.8434431 3.131139e-01 1.565569e-01 [84,] 0.8232849 3.534303e-01 1.767151e-01 [85,] 0.8190044 3.619913e-01 1.809956e-01 [86,] 0.7978742 4.042517e-01 2.021258e-01 [87,] 0.7666550 4.666900e-01 2.333450e-01 [88,] 0.7302066 5.395868e-01 2.697934e-01 [89,] 0.7691385 4.617230e-01 2.308615e-01 [90,] 0.7477263 5.045473e-01 2.522737e-01 [91,] 0.7441877 5.116245e-01 2.558123e-01 [92,] 0.7769306 4.461388e-01 2.230694e-01 [93,] 0.7449398 5.101205e-01 2.550602e-01 [94,] 0.7368106 5.263789e-01 2.631894e-01 [95,] 0.7821162 4.357676e-01 2.178838e-01 [96,] 0.7498140 5.003721e-01 2.501860e-01 [97,] 0.7129781 5.740438e-01 2.870219e-01 [98,] 0.6775056 6.449887e-01 3.224944e-01 [99,] 0.6942695 6.114611e-01 3.057305e-01 [100,] 0.6623091 6.753819e-01 3.376909e-01 [101,] 0.6226541 7.546918e-01 3.773459e-01 [102,] 0.5822801 8.354398e-01 4.177199e-01 [103,] 0.6109181 7.781639e-01 3.890819e-01 [104,] 0.8849815 2.300371e-01 1.150185e-01 [105,] 0.8611189 2.777621e-01 1.388811e-01 [106,] 0.8404090 3.191821e-01 1.595910e-01 [107,] 0.8153281 3.693437e-01 1.846719e-01 [108,] 0.7943879 4.112243e-01 2.056121e-01 [109,] 0.7662296 4.675408e-01 2.337704e-01 [110,] 0.7428909 5.142183e-01 2.571091e-01 [111,] 0.7119549 5.760902e-01 2.880451e-01 [112,] 0.8101397 3.797206e-01 1.898603e-01 [113,] 0.7910622 4.178756e-01 2.089378e-01 [114,] 0.7521570 4.956861e-01 2.478430e-01 [115,] 0.7220257 5.559487e-01 2.779743e-01 [116,] 0.6850205 6.299589e-01 3.149795e-01 [117,] 0.6922713 6.154574e-01 3.077287e-01 [118,] 0.7554383 4.891233e-01 2.445617e-01 [119,] 0.7202511 5.594977e-01 2.797489e-01 [120,] 0.6940749 6.118503e-01 3.059251e-01 [121,] 0.7236486 5.527028e-01 2.763514e-01 [122,] 0.6761154 6.477692e-01 3.238846e-01 [123,] 0.6768913 6.462174e-01 3.231087e-01 [124,] 0.6476036 7.047928e-01 3.523964e-01 [125,] 0.6205573 7.588854e-01 3.794427e-01 [126,] 0.6205035 7.589929e-01 3.794965e-01 [127,] 0.5921320 8.157359e-01 4.078680e-01 [128,] 0.5382034 9.235933e-01 4.617966e-01 [129,] 0.5025717 9.948567e-01 4.974283e-01 [130,] 0.5868248 8.263503e-01 4.131752e-01 [131,] 0.6519854 6.960293e-01 3.480146e-01 [132,] 0.6143414 7.713171e-01 3.856586e-01 [133,] 0.5592863 8.814273e-01 4.407137e-01 [134,] 0.5142178 9.715644e-01 4.857822e-01 [135,] 0.5247968 9.504064e-01 4.752032e-01 [136,] 0.5537216 8.925568e-01 4.462784e-01 [137,] 0.7854650 4.290701e-01 2.145350e-01 [138,] 0.7484194 5.031612e-01 2.515806e-01 [139,] 0.9735444 5.291114e-02 2.645557e-02 [140,] 0.9928223 1.435533e-02 7.177663e-03 [141,] 0.9999467 1.065861e-04 5.329303e-05 [142,] 0.9998720 2.560296e-04 1.280148e-04 [143,] 0.9999517 9.652582e-05 4.826291e-05 [144,] 0.9998662 2.676485e-04 1.338243e-04 [145,] 0.9996426 7.147132e-04 3.573566e-04 [146,] 0.9990404 1.919273e-03 9.596367e-04 [147,] 0.9974981 5.003831e-03 2.501916e-03 [148,] 0.9937779 1.244427e-02 6.222134e-03 [149,] 0.9852067 2.958654e-02 1.479327e-02 [150,] 0.9697843 6.043139e-02 3.021570e-02 [151,] 0.9954922 9.015513e-03 4.507756e-03 [152,] 0.9863469 2.730613e-02 1.365307e-02 [153,] 0.9623249 7.535016e-02 3.767508e-02 > postscript(file="/var/wessaorg/rcomp/tmp/1vltr1321974143.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2esq11321974143.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/39dgj1321974143.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/49d5v1321974143.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/531ad1321974143.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 = 164 Frequency = 1 1 2 3 4 5 6 -590.886372 -560.812855 -121.070107 20.359640 -232.307572 -747.574406 7 8 9 10 11 12 1151.295409 -565.828993 -139.401637 -60.596609 90.299672 -68.077220 13 14 15 16 17 18 6.059183 226.087185 199.122815 1556.025557 -260.353376 398.759031 19 20 21 22 23 24 -495.574784 259.527247 -215.232450 488.518148 -149.342320 -211.699613 25 26 27 28 29 30 367.718289 595.962521 386.216023 87.806253 163.129624 -5.995251 31 32 33 34 35 36 1.641625 -11.313102 181.741604 -306.712903 519.937464 458.570537 37 38 39 40 41 42 727.144862 34.105016 -137.602833 846.175325 1129.275485 -66.996697 43 44 45 46 47 48 -85.431316 -45.430210 463.834212 -1297.880387 -198.126202 218.661978 49 50 51 52 53 54 751.399897 202.730987 25.636685 -143.194954 187.411986 -142.424888 55 56 57 58 59 60 -59.405097 212.988822 103.910408 90.041672 195.956786 35.748707 61 62 63 64 65 66 812.615799 -11.014126 493.429891 441.755394 -436.528327 79.567785 67 68 69 70 71 72 -210.044002 616.624179 45.044277 -212.911386 215.052859 -38.338226 73 74 75 76 77 78 123.258809 -362.426021 -182.978336 -85.727690 161.973884 -1446.831790 79 80 81 82 83 84 -131.115992 -43.367397 261.453299 918.680095 143.543433 48.092149 85 86 87 88 89 90 -297.072342 -15.737444 -562.000200 23.626508 249.707504 314.308670 91 92 93 94 95 96 248.491894 -110.920675 98.063125 670.099240 -203.111506 -470.776576 97 98 99 100 101 102 -723.835743 -275.379959 474.502327 469.421928 -197.372565 -156.835204 103 104 105 106 107 108 52.878322 -523.583498 250.602809 -68.124466 -131.821095 421.062883 109 110 111 112 113 114 -1519.403336 -83.269718 -270.616723 155.091448 -136.021133 113.296729 115 116 117 118 119 120 -314.528414 -103.823424 729.478963 -93.368857 -41.213391 -276.687382 121 122 123 124 125 126 369.219956 -497.100484 -629.465718 -252.143749 -297.073502 -502.525189 127 128 129 130 131 132 64.837855 -482.781121 487.180600 -130.500774 -118.569688 -330.979384 133 134 135 136 137 138 -167.694783 76.412936 1007.931762 -610.105545 543.025445 144.335324 139 140 141 142 143 144 -187.905149 -153.003906 -446.686369 1197.278047 -181.739114 1005.964078 145 146 147 148 149 150 597.877485 -533.298749 119.788481 -139.432618 -460.104556 -307.665083 151 152 153 154 155 156 -455.104556 -452.104556 -460.104556 -460.104556 229.977020 522.998114 157 158 159 160 161 162 -460.104556 -456.104556 -323.997053 -85.895144 -373.447671 -297.367100 163 164 -431.104556 -42.078517 > postscript(file="/var/wessaorg/rcomp/tmp/6vuhq1321974143.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 = 164 Frequency = 1 lag(myerror, k = 1) myerror 0 -590.886372 NA 1 -560.812855 -590.886372 2 -121.070107 -560.812855 3 20.359640 -121.070107 4 -232.307572 20.359640 5 -747.574406 -232.307572 6 1151.295409 -747.574406 7 -565.828993 1151.295409 8 -139.401637 -565.828993 9 -60.596609 -139.401637 10 90.299672 -60.596609 11 -68.077220 90.299672 12 6.059183 -68.077220 13 226.087185 6.059183 14 199.122815 226.087185 15 1556.025557 199.122815 16 -260.353376 1556.025557 17 398.759031 -260.353376 18 -495.574784 398.759031 19 259.527247 -495.574784 20 -215.232450 259.527247 21 488.518148 -215.232450 22 -149.342320 488.518148 23 -211.699613 -149.342320 24 367.718289 -211.699613 25 595.962521 367.718289 26 386.216023 595.962521 27 87.806253 386.216023 28 163.129624 87.806253 29 -5.995251 163.129624 30 1.641625 -5.995251 31 -11.313102 1.641625 32 181.741604 -11.313102 33 -306.712903 181.741604 34 519.937464 -306.712903 35 458.570537 519.937464 36 727.144862 458.570537 37 34.105016 727.144862 38 -137.602833 34.105016 39 846.175325 -137.602833 40 1129.275485 846.175325 41 -66.996697 1129.275485 42 -85.431316 -66.996697 43 -45.430210 -85.431316 44 463.834212 -45.430210 45 -1297.880387 463.834212 46 -198.126202 -1297.880387 47 218.661978 -198.126202 48 751.399897 218.661978 49 202.730987 751.399897 50 25.636685 202.730987 51 -143.194954 25.636685 52 187.411986 -143.194954 53 -142.424888 187.411986 54 -59.405097 -142.424888 55 212.988822 -59.405097 56 103.910408 212.988822 57 90.041672 103.910408 58 195.956786 90.041672 59 35.748707 195.956786 60 812.615799 35.748707 61 -11.014126 812.615799 62 493.429891 -11.014126 63 441.755394 493.429891 64 -436.528327 441.755394 65 79.567785 -436.528327 66 -210.044002 79.567785 67 616.624179 -210.044002 68 45.044277 616.624179 69 -212.911386 45.044277 70 215.052859 -212.911386 71 -38.338226 215.052859 72 123.258809 -38.338226 73 -362.426021 123.258809 74 -182.978336 -362.426021 75 -85.727690 -182.978336 76 161.973884 -85.727690 77 -1446.831790 161.973884 78 -131.115992 -1446.831790 79 -43.367397 -131.115992 80 261.453299 -43.367397 81 918.680095 261.453299 82 143.543433 918.680095 83 48.092149 143.543433 84 -297.072342 48.092149 85 -15.737444 -297.072342 86 -562.000200 -15.737444 87 23.626508 -562.000200 88 249.707504 23.626508 89 314.308670 249.707504 90 248.491894 314.308670 91 -110.920675 248.491894 92 98.063125 -110.920675 93 670.099240 98.063125 94 -203.111506 670.099240 95 -470.776576 -203.111506 96 -723.835743 -470.776576 97 -275.379959 -723.835743 98 474.502327 -275.379959 99 469.421928 474.502327 100 -197.372565 469.421928 101 -156.835204 -197.372565 102 52.878322 -156.835204 103 -523.583498 52.878322 104 250.602809 -523.583498 105 -68.124466 250.602809 106 -131.821095 -68.124466 107 421.062883 -131.821095 108 -1519.403336 421.062883 109 -83.269718 -1519.403336 110 -270.616723 -83.269718 111 155.091448 -270.616723 112 -136.021133 155.091448 113 113.296729 -136.021133 114 -314.528414 113.296729 115 -103.823424 -314.528414 116 729.478963 -103.823424 117 -93.368857 729.478963 118 -41.213391 -93.368857 119 -276.687382 -41.213391 120 369.219956 -276.687382 121 -497.100484 369.219956 122 -629.465718 -497.100484 123 -252.143749 -629.465718 124 -297.073502 -252.143749 125 -502.525189 -297.073502 126 64.837855 -502.525189 127 -482.781121 64.837855 128 487.180600 -482.781121 129 -130.500774 487.180600 130 -118.569688 -130.500774 131 -330.979384 -118.569688 132 -167.694783 -330.979384 133 76.412936 -167.694783 134 1007.931762 76.412936 135 -610.105545 1007.931762 136 543.025445 -610.105545 137 144.335324 543.025445 138 -187.905149 144.335324 139 -153.003906 -187.905149 140 -446.686369 -153.003906 141 1197.278047 -446.686369 142 -181.739114 1197.278047 143 1005.964078 -181.739114 144 597.877485 1005.964078 145 -533.298749 597.877485 146 119.788481 -533.298749 147 -139.432618 119.788481 148 -460.104556 -139.432618 149 -307.665083 -460.104556 150 -455.104556 -307.665083 151 -452.104556 -455.104556 152 -460.104556 -452.104556 153 -460.104556 -460.104556 154 229.977020 -460.104556 155 522.998114 229.977020 156 -460.104556 522.998114 157 -456.104556 -460.104556 158 -323.997053 -456.104556 159 -85.895144 -323.997053 160 -373.447671 -85.895144 161 -297.367100 -373.447671 162 -431.104556 -297.367100 163 -42.078517 -431.104556 164 NA -42.078517 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -560.812855 -590.886372 [2,] -121.070107 -560.812855 [3,] 20.359640 -121.070107 [4,] -232.307572 20.359640 [5,] -747.574406 -232.307572 [6,] 1151.295409 -747.574406 [7,] -565.828993 1151.295409 [8,] -139.401637 -565.828993 [9,] -60.596609 -139.401637 [10,] 90.299672 -60.596609 [11,] -68.077220 90.299672 [12,] 6.059183 -68.077220 [13,] 226.087185 6.059183 [14,] 199.122815 226.087185 [15,] 1556.025557 199.122815 [16,] -260.353376 1556.025557 [17,] 398.759031 -260.353376 [18,] -495.574784 398.759031 [19,] 259.527247 -495.574784 [20,] -215.232450 259.527247 [21,] 488.518148 -215.232450 [22,] -149.342320 488.518148 [23,] -211.699613 -149.342320 [24,] 367.718289 -211.699613 [25,] 595.962521 367.718289 [26,] 386.216023 595.962521 [27,] 87.806253 386.216023 [28,] 163.129624 87.806253 [29,] -5.995251 163.129624 [30,] 1.641625 -5.995251 [31,] -11.313102 1.641625 [32,] 181.741604 -11.313102 [33,] -306.712903 181.741604 [34,] 519.937464 -306.712903 [35,] 458.570537 519.937464 [36,] 727.144862 458.570537 [37,] 34.105016 727.144862 [38,] -137.602833 34.105016 [39,] 846.175325 -137.602833 [40,] 1129.275485 846.175325 [41,] -66.996697 1129.275485 [42,] -85.431316 -66.996697 [43,] -45.430210 -85.431316 [44,] 463.834212 -45.430210 [45,] -1297.880387 463.834212 [46,] -198.126202 -1297.880387 [47,] 218.661978 -198.126202 [48,] 751.399897 218.661978 [49,] 202.730987 751.399897 [50,] 25.636685 202.730987 [51,] -143.194954 25.636685 [52,] 187.411986 -143.194954 [53,] -142.424888 187.411986 [54,] -59.405097 -142.424888 [55,] 212.988822 -59.405097 [56,] 103.910408 212.988822 [57,] 90.041672 103.910408 [58,] 195.956786 90.041672 [59,] 35.748707 195.956786 [60,] 812.615799 35.748707 [61,] -11.014126 812.615799 [62,] 493.429891 -11.014126 [63,] 441.755394 493.429891 [64,] -436.528327 441.755394 [65,] 79.567785 -436.528327 [66,] -210.044002 79.567785 [67,] 616.624179 -210.044002 [68,] 45.044277 616.624179 [69,] -212.911386 45.044277 [70,] 215.052859 -212.911386 [71,] -38.338226 215.052859 [72,] 123.258809 -38.338226 [73,] -362.426021 123.258809 [74,] -182.978336 -362.426021 [75,] -85.727690 -182.978336 [76,] 161.973884 -85.727690 [77,] -1446.831790 161.973884 [78,] -131.115992 -1446.831790 [79,] -43.367397 -131.115992 [80,] 261.453299 -43.367397 [81,] 918.680095 261.453299 [82,] 143.543433 918.680095 [83,] 48.092149 143.543433 [84,] -297.072342 48.092149 [85,] -15.737444 -297.072342 [86,] -562.000200 -15.737444 [87,] 23.626508 -562.000200 [88,] 249.707504 23.626508 [89,] 314.308670 249.707504 [90,] 248.491894 314.308670 [91,] -110.920675 248.491894 [92,] 98.063125 -110.920675 [93,] 670.099240 98.063125 [94,] -203.111506 670.099240 [95,] -470.776576 -203.111506 [96,] -723.835743 -470.776576 [97,] -275.379959 -723.835743 [98,] 474.502327 -275.379959 [99,] 469.421928 474.502327 [100,] -197.372565 469.421928 [101,] -156.835204 -197.372565 [102,] 52.878322 -156.835204 [103,] -523.583498 52.878322 [104,] 250.602809 -523.583498 [105,] -68.124466 250.602809 [106,] -131.821095 -68.124466 [107,] 421.062883 -131.821095 [108,] -1519.403336 421.062883 [109,] -83.269718 -1519.403336 [110,] -270.616723 -83.269718 [111,] 155.091448 -270.616723 [112,] -136.021133 155.091448 [113,] 113.296729 -136.021133 [114,] -314.528414 113.296729 [115,] -103.823424 -314.528414 [116,] 729.478963 -103.823424 [117,] -93.368857 729.478963 [118,] -41.213391 -93.368857 [119,] -276.687382 -41.213391 [120,] 369.219956 -276.687382 [121,] -497.100484 369.219956 [122,] -629.465718 -497.100484 [123,] -252.143749 -629.465718 [124,] -297.073502 -252.143749 [125,] -502.525189 -297.073502 [126,] 64.837855 -502.525189 [127,] -482.781121 64.837855 [128,] 487.180600 -482.781121 [129,] -130.500774 487.180600 [130,] -118.569688 -130.500774 [131,] -330.979384 -118.569688 [132,] -167.694783 -330.979384 [133,] 76.412936 -167.694783 [134,] 1007.931762 76.412936 [135,] -610.105545 1007.931762 [136,] 543.025445 -610.105545 [137,] 144.335324 543.025445 [138,] -187.905149 144.335324 [139,] -153.003906 -187.905149 [140,] -446.686369 -153.003906 [141,] 1197.278047 -446.686369 [142,] -181.739114 1197.278047 [143,] 1005.964078 -181.739114 [144,] 597.877485 1005.964078 [145,] -533.298749 597.877485 [146,] 119.788481 -533.298749 [147,] -139.432618 119.788481 [148,] -460.104556 -139.432618 [149,] -307.665083 -460.104556 [150,] -455.104556 -307.665083 [151,] -452.104556 -455.104556 [152,] -460.104556 -452.104556 [153,] -460.104556 -460.104556 [154,] 229.977020 -460.104556 [155,] 522.998114 229.977020 [156,] -460.104556 522.998114 [157,] -456.104556 -460.104556 [158,] -323.997053 -456.104556 [159,] -85.895144 -323.997053 [160,] -373.447671 -85.895144 [161,] -297.367100 -373.447671 [162,] -431.104556 -297.367100 [163,] -42.078517 -431.104556 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -560.812855 -590.886372 2 -121.070107 -560.812855 3 20.359640 -121.070107 4 -232.307572 20.359640 5 -747.574406 -232.307572 6 1151.295409 -747.574406 7 -565.828993 1151.295409 8 -139.401637 -565.828993 9 -60.596609 -139.401637 10 90.299672 -60.596609 11 -68.077220 90.299672 12 6.059183 -68.077220 13 226.087185 6.059183 14 199.122815 226.087185 15 1556.025557 199.122815 16 -260.353376 1556.025557 17 398.759031 -260.353376 18 -495.574784 398.759031 19 259.527247 -495.574784 20 -215.232450 259.527247 21 488.518148 -215.232450 22 -149.342320 488.518148 23 -211.699613 -149.342320 24 367.718289 -211.699613 25 595.962521 367.718289 26 386.216023 595.962521 27 87.806253 386.216023 28 163.129624 87.806253 29 -5.995251 163.129624 30 1.641625 -5.995251 31 -11.313102 1.641625 32 181.741604 -11.313102 33 -306.712903 181.741604 34 519.937464 -306.712903 35 458.570537 519.937464 36 727.144862 458.570537 37 34.105016 727.144862 38 -137.602833 34.105016 39 846.175325 -137.602833 40 1129.275485 846.175325 41 -66.996697 1129.275485 42 -85.431316 -66.996697 43 -45.430210 -85.431316 44 463.834212 -45.430210 45 -1297.880387 463.834212 46 -198.126202 -1297.880387 47 218.661978 -198.126202 48 751.399897 218.661978 49 202.730987 751.399897 50 25.636685 202.730987 51 -143.194954 25.636685 52 187.411986 -143.194954 53 -142.424888 187.411986 54 -59.405097 -142.424888 55 212.988822 -59.405097 56 103.910408 212.988822 57 90.041672 103.910408 58 195.956786 90.041672 59 35.748707 195.956786 60 812.615799 35.748707 61 -11.014126 812.615799 62 493.429891 -11.014126 63 441.755394 493.429891 64 -436.528327 441.755394 65 79.567785 -436.528327 66 -210.044002 79.567785 67 616.624179 -210.044002 68 45.044277 616.624179 69 -212.911386 45.044277 70 215.052859 -212.911386 71 -38.338226 215.052859 72 123.258809 -38.338226 73 -362.426021 123.258809 74 -182.978336 -362.426021 75 -85.727690 -182.978336 76 161.973884 -85.727690 77 -1446.831790 161.973884 78 -131.115992 -1446.831790 79 -43.367397 -131.115992 80 261.453299 -43.367397 81 918.680095 261.453299 82 143.543433 918.680095 83 48.092149 143.543433 84 -297.072342 48.092149 85 -15.737444 -297.072342 86 -562.000200 -15.737444 87 23.626508 -562.000200 88 249.707504 23.626508 89 314.308670 249.707504 90 248.491894 314.308670 91 -110.920675 248.491894 92 98.063125 -110.920675 93 670.099240 98.063125 94 -203.111506 670.099240 95 -470.776576 -203.111506 96 -723.835743 -470.776576 97 -275.379959 -723.835743 98 474.502327 -275.379959 99 469.421928 474.502327 100 -197.372565 469.421928 101 -156.835204 -197.372565 102 52.878322 -156.835204 103 -523.583498 52.878322 104 250.602809 -523.583498 105 -68.124466 250.602809 106 -131.821095 -68.124466 107 421.062883 -131.821095 108 -1519.403336 421.062883 109 -83.269718 -1519.403336 110 -270.616723 -83.269718 111 155.091448 -270.616723 112 -136.021133 155.091448 113 113.296729 -136.021133 114 -314.528414 113.296729 115 -103.823424 -314.528414 116 729.478963 -103.823424 117 -93.368857 729.478963 118 -41.213391 -93.368857 119 -276.687382 -41.213391 120 369.219956 -276.687382 121 -497.100484 369.219956 122 -629.465718 -497.100484 123 -252.143749 -629.465718 124 -297.073502 -252.143749 125 -502.525189 -297.073502 126 64.837855 -502.525189 127 -482.781121 64.837855 128 487.180600 -482.781121 129 -130.500774 487.180600 130 -118.569688 -130.500774 131 -330.979384 -118.569688 132 -167.694783 -330.979384 133 76.412936 -167.694783 134 1007.931762 76.412936 135 -610.105545 1007.931762 136 543.025445 -610.105545 137 144.335324 543.025445 138 -187.905149 144.335324 139 -153.003906 -187.905149 140 -446.686369 -153.003906 141 1197.278047 -446.686369 142 -181.739114 1197.278047 143 1005.964078 -181.739114 144 597.877485 1005.964078 145 -533.298749 597.877485 146 119.788481 -533.298749 147 -139.432618 119.788481 148 -460.104556 -139.432618 149 -307.665083 -460.104556 150 -455.104556 -307.665083 151 -452.104556 -455.104556 152 -460.104556 -452.104556 153 -460.104556 -460.104556 154 229.977020 -460.104556 155 522.998114 229.977020 156 -460.104556 522.998114 157 -456.104556 -460.104556 158 -323.997053 -456.104556 159 -85.895144 -323.997053 160 -373.447671 -85.895144 161 -297.367100 -373.447671 162 -431.104556 -297.367100 163 -42.078517 -431.104556 > 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/7d0n51321974143.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/806zv1321974143.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9rtn61321974143.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10ld8u1321974143.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11rpe01321974143.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/122kw51321974143.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/13l6jh1321974143.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/14108e1321974143.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/151vj11321974143.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/162jxu1321974143.tab") + } > > try(system("convert tmp/1vltr1321974143.ps tmp/1vltr1321974143.png",intern=TRUE)) character(0) > try(system("convert tmp/2esq11321974143.ps tmp/2esq11321974143.png",intern=TRUE)) character(0) > try(system("convert tmp/39dgj1321974143.ps tmp/39dgj1321974143.png",intern=TRUE)) character(0) > try(system("convert tmp/49d5v1321974143.ps tmp/49d5v1321974143.png",intern=TRUE)) character(0) > try(system("convert tmp/531ad1321974143.ps tmp/531ad1321974143.png",intern=TRUE)) character(0) > try(system("convert tmp/6vuhq1321974143.ps tmp/6vuhq1321974143.png",intern=TRUE)) character(0) > try(system("convert tmp/7d0n51321974143.ps tmp/7d0n51321974143.png",intern=TRUE)) character(0) > try(system("convert tmp/806zv1321974143.ps tmp/806zv1321974143.png",intern=TRUE)) character(0) > try(system("convert tmp/9rtn61321974143.ps tmp/9rtn61321974143.png",intern=TRUE)) character(0) > try(system("convert tmp/10ld8u1321974143.ps tmp/10ld8u1321974143.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.528 0.481 5.059