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(1772 + ,158258 + ,89 + ,20465 + ,1703 + ,186930 + ,57 + ,33629 + ,192 + ,7215 + ,18 + ,1423 + ,2294 + ,129098 + ,94 + ,25629 + ,3448 + ,230587 + ,134 + ,54002 + ,6813 + ,508313 + ,261 + ,151036 + ,1795 + ,180745 + ,56 + ,33287 + ,1680 + ,185559 + ,58 + ,31172 + ,1896 + ,154581 + ,43 + ,28113 + ,2917 + ,290658 + ,95 + ,57803 + ,1946 + ,121844 + ,75 + ,49830 + ,2148 + ,184039 + ,69 + ,52143 + ,1832 + ,100324 + ,98 + ,21055 + ,3059 + ,209427 + ,114 + ,47007 + ,1469 + ,167592 + ,57 + ,28735 + ,1565 + ,154593 + ,86 + ,59147 + ,1755 + ,142018 + ,56 + ,78950 + ,1234 + ,77855 + ,59 + ,13497 + ,2779 + ,167047 + ,87 + ,46154 + ,726 + ,27997 + ,24 + ,53249 + ,1048 + ,73019 + ,59 + ,10726 + ,2804 + ,241082 + ,99 + ,83700 + ,1760 + ,195820 + ,72 + ,40400 + ,2261 + ,141899 + ,53 + ,33797 + ,1848 + ,145433 + ,86 + ,36205 + ,1647 + ,180241 + ,31 + ,30165 + ,2081 + ,202232 + ,160 + ,58534 + ,1393 + ,190230 + ,91 + ,44663 + ,2741 + ,354924 + ,118 + ,92556 + ,2112 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,41 + ,65897 + ,2227 + ,165577 + ,76 + ,76542 + ,1233 + ,151517 + ,57 + ,37477 + ,1365 + ,133686 + ,58 + ,53216 + ,901 + ,58128 + ,38 + ,40911 + ,2319 + ,245196 + ,117 + ,57021 + ,1857 + ,195576 + ,70 + ,73116 + ,223 + ,19349 + ,12 + ,3895 + ,2390 + ,225371 + ,105 + ,46609 + ,1973 + ,152796 + ,76 + ,29351 + ,699 + ,59117 + ,28 + ,2325 + ,1062 + ,91762 + ,24 + ,31747 + ,1252 + ,127987 + ,52 + ,32665 + ,1154 + ,113552 + ,58 + ,19249 + ,823 + ,85338 + ,40 + ,15292 + ,596 + ,27676 + ,22 + ,5842 + ,1471 + ,147984 + ,47 + ,33994 + ,1130 + ,122417 + ,37 + ,13018 + ,0 + ,0 + ,0 + ,0 + ,1082 + ,91529 + ,32 + ,98177 + ,1134 + ,107205 + ,66 + ,37941 + ,1366 + ,144664 + ,44 + ,31032 + ,1452 + ,136540 + ,62 + ,32683 + ,869 + ,76656 + ,59 + ,34545 + ,78 + ,3616 + ,5 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1127 + ,183065 + ,43 + ,27525 + ,1578 + ,144636 + ,83 + ,66856 + ,2018 + ,156889 + ,97 + ,28549 + ,919 + ,113273 + ,38 + ,38610 + ,778 + ,43410 + ,19 + ,2781 + ,1751 + ,175774 + ,72 + ,41211 + ,956 + ,95401 + ,41 + ,22698 + ,1875 + ,118893 + ,54 + ,41194 + ,731 + ,60493 + ,40 + ,32689 + ,285 + ,19764 + ,12 + ,5752 + ,1833 + ,164062 + ,55 + ,26757 + ,1147 + ,132696 + ,32 + ,22527 + ,1646 + ,155367 + ,54 + ,44810 + ,256 + ,11796 + ,9 + ,0 + ,98 + ,10674 + ,9 + ,0 + ,1403 + ,142261 + ,56 + ,100674 + ,41 + ,6836 + ,3 + ,0 + ,1786 + ,154206 + ,61 + ,57786 + ,42 + ,5118 + ,3 + ,0 + ,528 + ,40248 + ,16 + ,5444 + ,0 + ,0 + ,0 + ,0 + ,1072 + ,122641 + ,46 + ,28470 + ,1305 + ,88837 + ,38 + ,61849 + ,81 + ,7131 + ,4 + ,0 + ,261 + ,9056 + ,14 + ,2179 + ,934 + ,76611 + ,24 + ,8019 + ,1179 + ,132697 + ,50 + ,39644 + ,1147 + ,100681 + ,19 + ,23494) + ,dim=c(4 + ,144) + ,dimnames=list(c('page_views' + ,'Time_spent' + ,'Logins' + ,'Writing') + ,1:144)) > y <- array(NA,dim=c(4,144),dimnames=list(c('page_views','Time_spent','Logins','Writing'),1:144)) > 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 = '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 Time_spent page_views Logins Writing 1 158258 1772 89 20465 2 186930 1703 57 33629 3 7215 192 18 1423 4 129098 2294 94 25629 5 230587 3448 134 54002 6 508313 6813 261 151036 7 180745 1795 56 33287 8 185559 1680 58 31172 9 154581 1896 43 28113 10 290658 2917 95 57803 11 121844 1946 75 49830 12 184039 2148 69 52143 13 100324 1832 98 21055 14 209427 3059 114 47007 15 167592 1469 57 28735 16 154593 1565 86 59147 17 142018 1755 56 78950 18 77855 1234 59 13497 19 167047 2779 87 46154 20 27997 726 24 53249 21 73019 1048 59 10726 22 241082 2804 99 83700 23 195820 1760 72 40400 24 141899 2261 53 33797 25 145433 1848 86 36205 26 180241 1647 31 30165 27 202232 2081 160 58534 28 190230 1393 91 44663 29 354924 2741 118 92556 30 192399 2112 44 40078 31 182286 1684 44 34711 32 181590 1616 45 31076 33 133801 2227 105 74608 34 233686 3088 123 58092 35 219428 2388 52 42009 36 0 1 1 0 37 223044 2099 63 36022 38 100129 1669 51 23333 39 136733 2094 47 53349 40 249965 2153 64 92596 41 242379 2390 71 49598 42 145794 1701 59 44093 43 96404 983 32 84205 44 195891 2161 78 63369 45 117156 1276 50 60132 46 157787 1189 94 37403 47 81293 744 31 24460 48 224049 2231 100 46456 49 223789 2242 87 66616 50 160344 2638 58 41554 51 48188 658 28 22346 52 152206 1859 68 30874 53 294283 2489 73 68701 54 235223 2025 78 35728 55 195583 1911 59 29010 56 145942 1714 54 23110 57 208834 1851 66 38844 58 93764 980 23 27084 59 151985 1177 66 35139 60 190545 2809 95 57476 61 148922 1688 60 33277 62 132856 2097 80 31141 63 126107 1309 60 61281 64 112718 1243 36 25820 65 160930 1255 34 23284 66 99184 1293 40 35378 67 182022 2259 69 74990 68 138708 2897 65 29653 69 114408 1103 38 64622 70 31970 340 15 4157 71 225558 2791 112 29245 72 137011 1333 71 50008 73 113612 1441 68 52338 74 108641 1622 70 13310 75 162203 2649 66 92901 76 100098 1499 44 10956 77 174768 2302 60 34241 78 158459 2540 97 75043 79 80934 1000 30 21152 80 84971 1234 71 42249 81 80545 927 68 42005 82 287191 2176 64 41152 83 62974 956 27 14399 84 130982 1531 38 28263 85 75555 1013 45 17215 86 162154 1771 54 48140 87 226638 2613 227 62897 88 115019 1203 110 22883 89 105038 1303 60 41622 90 155537 1524 52 40715 91 153133 1829 41 65897 92 165577 2227 76 76542 93 151517 1233 57 37477 94 133686 1365 58 53216 95 58128 901 38 40911 96 245196 2319 117 57021 97 195576 1857 70 73116 98 19349 223 12 3895 99 225371 2390 105 46609 100 152796 1973 76 29351 101 59117 699 28 2325 102 91762 1062 24 31747 103 127987 1252 52 32665 104 113552 1154 58 19249 105 85338 823 40 15292 106 27676 596 22 5842 107 147984 1471 47 33994 108 122417 1130 37 13018 109 0 0 0 0 110 91529 1082 32 98177 111 107205 1134 66 37941 112 144664 1366 44 31032 113 136540 1452 62 32683 114 76656 869 59 34545 115 3616 78 5 0 116 0 0 0 0 117 183065 1127 43 27525 118 144636 1578 83 66856 119 156889 2018 97 28549 120 113273 919 38 38610 121 43410 778 19 2781 122 175774 1751 72 41211 123 95401 956 41 22698 124 118893 1875 54 41194 125 60493 731 40 32689 126 19764 285 12 5752 127 164062 1833 55 26757 128 132696 1147 32 22527 129 155367 1646 54 44810 130 11796 256 9 0 131 10674 98 9 0 132 142261 1403 56 100674 133 6836 41 3 0 134 154206 1786 61 57786 135 5118 42 3 0 136 40248 528 16 5444 137 0 0 0 0 138 122641 1072 46 28470 139 88837 1305 38 61849 140 7131 81 4 0 141 9056 261 14 2179 142 76611 934 24 8019 143 132697 1179 50 39644 144 100681 1147 19 23494 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) page_views Logins Writing 12451.384 63.804 168.300 0.401 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -81416 -19792 -4231 21005 110607 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.245e+04 5.910e+03 2.107 0.0369 * page_views 6.380e+01 6.353e+00 10.044 <2e-16 *** Logins 1.683e+02 1.368e+02 1.231 0.2206 Writing 4.010e-01 1.585e-01 2.530 0.0125 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 34080 on 140 degrees of freedom Multiple R-squared: 0.8051, Adjusted R-squared: 0.8009 F-statistic: 192.7 on 3 and 140 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,] 0.7470010 5.059979e-01 2.529990e-01 [2,] 0.6772405 6.455191e-01 3.227595e-01 [3,] 0.5922460 8.155079e-01 4.077540e-01 [4,] 0.6300715 7.398570e-01 3.699285e-01 [5,] 0.8239123 3.521755e-01 1.760877e-01 [6,] 0.7602065 4.795871e-01 2.397935e-01 [7,] 0.7004888 5.990223e-01 2.995112e-01 [8,] 0.6520962 6.958076e-01 3.479038e-01 [9,] 0.6688889 6.622223e-01 3.311111e-01 [10,] 0.5896467 8.207065e-01 4.103533e-01 [11,] 0.6943234 6.113531e-01 3.056766e-01 [12,] 0.6445481 7.109038e-01 3.554519e-01 [13,] 0.7650239 4.699523e-01 2.349761e-01 [14,] 0.8596466 2.807069e-01 1.403534e-01 [15,] 0.8187048 3.625903e-01 1.812952e-01 [16,] 0.7746472 4.507056e-01 2.253528e-01 [17,] 0.8304130 3.391741e-01 1.695870e-01 [18,] 0.8521322 2.957356e-01 1.478678e-01 [19,] 0.8133696 3.732608e-01 1.866304e-01 [20,] 0.8195633 3.608734e-01 1.804367e-01 [21,] 0.8296630 3.406740e-01 1.703370e-01 [22,] 0.8888952 2.222096e-01 1.111048e-01 [23,] 0.9898397 2.032054e-02 1.016027e-02 [24,] 0.9870414 2.591722e-02 1.295861e-02 [25,] 0.9874928 2.501432e-02 1.250716e-02 [26,] 0.9891402 2.171953e-02 1.085977e-02 [27,] 0.9970151 5.969897e-03 2.984949e-03 [28,] 0.9959655 8.068964e-03 4.034482e-03 [29,] 0.9950133 9.973385e-03 4.986692e-03 [30,] 0.9934572 1.308554e-02 6.542768e-03 [31,] 0.9953704 9.259244e-03 4.629622e-03 [32,] 0.9955840 8.831952e-03 4.415976e-03 [33,] 0.9965331 6.933877e-03 3.466938e-03 [34,] 0.9970697 5.860646e-03 2.930323e-03 [35,] 0.9975736 4.852729e-03 2.426365e-03 [36,] 0.9964485 7.102977e-03 3.551489e-03 [37,] 0.9964586 7.082737e-03 3.541369e-03 [38,] 0.9948600 1.027992e-02 5.139959e-03 [39,] 0.9930564 1.388726e-02 6.943630e-03 [40,] 0.9937053 1.258943e-02 6.294717e-03 [41,] 0.9911441 1.771185e-02 8.855926e-03 [42,] 0.9908808 1.823846e-02 9.119232e-03 [43,] 0.9892284 2.154322e-02 1.077161e-02 [44,] 0.9919874 1.602519e-02 8.012595e-03 [45,] 0.9902377 1.952453e-02 9.762267e-03 [46,] 0.9865164 2.696710e-02 1.348355e-02 [47,] 0.9971833 5.633328e-03 2.816664e-03 [48,] 0.9990318 1.936349e-03 9.681746e-04 [49,] 0.9991589 1.682154e-03 8.410772e-04 [50,] 0.9987421 2.515751e-03 1.257875e-03 [51,] 0.9992719 1.456255e-03 7.281273e-04 [52,] 0.9989207 2.158627e-03 1.079313e-03 [53,] 0.9990400 1.920066e-03 9.600330e-04 [54,] 0.9991575 1.684919e-03 8.424594e-04 [55,] 0.9987467 2.506616e-03 1.253308e-03 [56,] 0.9989200 2.159907e-03 1.079954e-03 [57,] 0.9984675 3.064915e-03 1.532457e-03 [58,] 0.9977657 4.468584e-03 2.234292e-03 [59,] 0.9987903 2.419326e-03 1.209663e-03 [60,] 0.9984020 3.195911e-03 1.597956e-03 [61,] 0.9978809 4.238199e-03 2.119099e-03 [62,] 0.9998226 3.548782e-04 1.774391e-04 [63,] 0.9997488 5.023724e-04 2.511862e-04 [64,] 0.9996164 7.672279e-04 3.836139e-04 [65,] 0.9994562 1.087635e-03 5.438174e-04 [66,] 0.9992231 1.553871e-03 7.769356e-04 [67,] 0.9990343 1.931329e-03 9.656645e-04 [68,] 0.9990185 1.962937e-03 9.814684e-04 [69,] 0.9998272 3.456546e-04 1.728273e-04 [70,] 0.9998197 3.605102e-04 1.802551e-04 [71,] 0.9998056 3.887516e-04 1.943758e-04 [72,] 0.9999828 3.433905e-05 1.716953e-05 [73,] 0.9999727 5.451201e-05 2.725601e-05 [74,] 0.9999750 5.009285e-05 2.504643e-05 [75,] 0.9999612 7.757379e-05 3.878690e-05 [76,] 0.9999998 3.052862e-07 1.526431e-07 [77,] 0.9999998 3.856112e-07 1.928056e-07 [78,] 0.9999996 7.449107e-07 3.724554e-07 [79,] 0.9999994 1.128179e-06 5.640894e-07 [80,] 0.9999989 2.184762e-06 1.092381e-06 [81,] 0.9999990 1.923852e-06 9.619261e-07 [82,] 0.9999986 2.886473e-06 1.443237e-06 [83,] 0.9999980 3.926886e-06 1.963443e-06 [84,] 0.9999971 5.806065e-06 2.903032e-06 [85,] 0.9999946 1.070320e-05 5.351598e-06 [86,] 0.9999970 6.053619e-06 3.026810e-06 [87,] 0.9999977 4.685189e-06 2.342594e-06 [88,] 0.9999955 9.030277e-06 4.515138e-06 [89,] 0.9999961 7.867000e-06 3.933500e-06 [90,] 0.9999956 8.767816e-06 4.383908e-06 [91,] 0.9999943 1.137240e-05 5.686198e-06 [92,] 0.9999894 2.128742e-05 1.064371e-05 [93,] 0.9999813 3.730563e-05 1.865282e-05 [94,] 0.9999798 4.045295e-05 2.022648e-05 [95,] 0.9999628 7.441683e-05 3.720842e-05 [96,] 0.9999319 1.362860e-04 6.814302e-05 [97,] 0.9998862 2.276710e-04 1.138355e-04 [98,] 0.9997984 4.031321e-04 2.015660e-04 [99,] 0.9996566 6.867657e-04 3.433829e-04 [100,] 0.9996498 7.003041e-04 3.501520e-04 [101,] 0.9994737 1.052582e-03 5.262908e-04 [102,] 0.9993335 1.333004e-03 6.665022e-04 [103,] 0.9988745 2.251074e-03 1.125537e-03 [104,] 0.9984954 3.009267e-03 1.504634e-03 [105,] 0.9974966 5.006796e-03 2.503398e-03 [106,] 0.9969495 6.101051e-03 3.050525e-03 [107,] 0.9950268 9.946308e-03 4.973154e-03 [108,] 0.9928368 1.432638e-02 7.163190e-03 [109,] 0.9889835 2.203297e-02 1.101649e-02 [110,] 0.9830587 3.388257e-02 1.694129e-02 [111,] 0.9997695 4.610258e-04 2.305129e-04 [112,] 0.9995903 8.194085e-04 4.097042e-04 [113,] 0.9998421 3.157803e-04 1.578902e-04 [114,] 0.9998399 3.202463e-04 1.601232e-04 [115,] 0.9997765 4.469174e-04 2.234587e-04 [116,] 0.9995343 9.314003e-04 4.657001e-04 [117,] 0.9990120 1.976026e-03 9.880128e-04 [118,] 0.9999280 1.439992e-04 7.199958e-05 [119,] 0.9999060 1.880226e-04 9.401128e-05 [120,] 0.9997753 4.494360e-04 2.247180e-04 [121,] 0.9995124 9.752191e-04 4.876096e-04 [122,] 0.9998646 2.707212e-04 1.353606e-04 [123,] 0.9996106 7.788883e-04 3.894441e-04 [124,] 0.9991317 1.736657e-03 8.683287e-04 [125,] 0.9976273 4.745490e-03 2.372745e-03 [126,] 0.9970628 5.874479e-03 2.937240e-03 [127,] 0.9924799 1.504012e-02 7.520060e-03 [128,] 0.9857012 2.859754e-02 1.429877e-02 [129,] 0.9666042 6.679157e-02 3.339578e-02 [130,] 0.9251173 1.497653e-01 7.488267e-02 [131,] 0.8640752 2.718496e-01 1.359248e-01 > postscript(file="/var/wessaorg/rcomp/tmp/10gyp1344776219.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/2wizl1344776219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3j3yv1344776219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/472jt1344776219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5ivuw1344776219.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 144 Frequency = 1 1 2 3 4 5 6 9559.2893 42740.2222 -21086.9097 -55819.0696 -46070.8890 -43334.7014 7 8 9 10 11 12 30990.6637 43653.7589 2645.2483 52919.6651 -47376.7720 2011.9398 13 14 15 16 17 18 -53954.2875 -36239.7361 40295.1153 4094.0224 -23496.4491 -28673.5196 19 20 21 22 23 24 -55868.3046 -56170.1442 -20530.6305 -505.1371 42753.4844 -37287.8452 25 26 27 28 29 30 -13922.1828 45389.2452 6601.5473 55672.4283 110607.3112 21714.8434 31 32 33 34 35 36 41062.4915 45994.6441 -68334.5816 -19791.1976 29013.0183 -12683.4883 37 38 39 40 41 42 51618.1842 -36752.6076 -38629.6623 52237.4989 45595.2827 -2801.1602 43 44 45 46 47 48 -17921.6175 7017.8043 -9239.7012 38652.1272 6344.5650 33789.5333 49 50 51 52 53 54 26930.7936 -46849.4301 -19920.5704 -2683.7340 83185.1395 66112.1180 55 56 57 58 59 60 39637.6560 5773.7212 51595.0824 4051.8041 39236.1160 -40171.3150 61 62 63 64 65 66 5325.5523 -39345.8733 -4538.0338 4544.2319 53344.1937 -16686.2486 67 68 69 70 71 72 -16249.7182 -81416.2145 -730.5583 -6366.4908 4450.5795 7504.1845 73 74 75 76 77 78 -23215.2011 -24419.9612 -67630.4328 -19795.1834 -8390.9864 -62475.4292 79 80 81 82 83 84 -8853.4678 -35107.5712 -19342.8473 108626.6495 -20793.0068 3116.2252 85 86 87 88 89 90 -16007.5659 8311.0533 -15962.2310 -1878.9612 -17340.3329 20768.0147 91 92 93 94 95 96 -9343.8183 -32453.4758 35772.1547 3038.8332 -34613.2025 42223.7443 97 98 99 100 101 102 23536.9113 -10912.3976 24063.7648 -10103.0730 -3578.5063 -5220.4713 103 104 105 106 107 108 13801.1324 9989.4061 7510.9666 -28848.2729 20133.4829 26418.8395 109 110 111 112 113 114 -12451.3838 -34716.4692 -3923.9814 25205.7070 7903.0197 -15024.7946 115 116 117 118 119 120 -14653.6325 -12451.3838 80430.6920 -9279.0859 -12093.9473 20306.0900 121 122 123 124 125 126 -22994.2296 22956.4883 5949.6344 -38800.0482 -18440.7634 -15197.9898 127 128 129 130 131 132 14670.1282 32641.2571 10835.0462 -18504.0282 -9544.9220 -9506.2794 133 134 135 136 137 138 -8736.2671 -5640.4616 -10518.0716 -10768.1584 -12451.3838 22632.0633 139 140 141 142 143 144 -38078.0014 -11161.7459 -23278.3978 -2688.8288 20706.6613 2426.3600 > postscript(file="/var/wessaorg/rcomp/tmp/6wdm81344776219.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 = 144 Frequency = 1 lag(myerror, k = 1) myerror 0 9559.2893 NA 1 42740.2222 9559.2893 2 -21086.9097 42740.2222 3 -55819.0696 -21086.9097 4 -46070.8890 -55819.0696 5 -43334.7014 -46070.8890 6 30990.6637 -43334.7014 7 43653.7589 30990.6637 8 2645.2483 43653.7589 9 52919.6651 2645.2483 10 -47376.7720 52919.6651 11 2011.9398 -47376.7720 12 -53954.2875 2011.9398 13 -36239.7361 -53954.2875 14 40295.1153 -36239.7361 15 4094.0224 40295.1153 16 -23496.4491 4094.0224 17 -28673.5196 -23496.4491 18 -55868.3046 -28673.5196 19 -56170.1442 -55868.3046 20 -20530.6305 -56170.1442 21 -505.1371 -20530.6305 22 42753.4844 -505.1371 23 -37287.8452 42753.4844 24 -13922.1828 -37287.8452 25 45389.2452 -13922.1828 26 6601.5473 45389.2452 27 55672.4283 6601.5473 28 110607.3112 55672.4283 29 21714.8434 110607.3112 30 41062.4915 21714.8434 31 45994.6441 41062.4915 32 -68334.5816 45994.6441 33 -19791.1976 -68334.5816 34 29013.0183 -19791.1976 35 -12683.4883 29013.0183 36 51618.1842 -12683.4883 37 -36752.6076 51618.1842 38 -38629.6623 -36752.6076 39 52237.4989 -38629.6623 40 45595.2827 52237.4989 41 -2801.1602 45595.2827 42 -17921.6175 -2801.1602 43 7017.8043 -17921.6175 44 -9239.7012 7017.8043 45 38652.1272 -9239.7012 46 6344.5650 38652.1272 47 33789.5333 6344.5650 48 26930.7936 33789.5333 49 -46849.4301 26930.7936 50 -19920.5704 -46849.4301 51 -2683.7340 -19920.5704 52 83185.1395 -2683.7340 53 66112.1180 83185.1395 54 39637.6560 66112.1180 55 5773.7212 39637.6560 56 51595.0824 5773.7212 57 4051.8041 51595.0824 58 39236.1160 4051.8041 59 -40171.3150 39236.1160 60 5325.5523 -40171.3150 61 -39345.8733 5325.5523 62 -4538.0338 -39345.8733 63 4544.2319 -4538.0338 64 53344.1937 4544.2319 65 -16686.2486 53344.1937 66 -16249.7182 -16686.2486 67 -81416.2145 -16249.7182 68 -730.5583 -81416.2145 69 -6366.4908 -730.5583 70 4450.5795 -6366.4908 71 7504.1845 4450.5795 72 -23215.2011 7504.1845 73 -24419.9612 -23215.2011 74 -67630.4328 -24419.9612 75 -19795.1834 -67630.4328 76 -8390.9864 -19795.1834 77 -62475.4292 -8390.9864 78 -8853.4678 -62475.4292 79 -35107.5712 -8853.4678 80 -19342.8473 -35107.5712 81 108626.6495 -19342.8473 82 -20793.0068 108626.6495 83 3116.2252 -20793.0068 84 -16007.5659 3116.2252 85 8311.0533 -16007.5659 86 -15962.2310 8311.0533 87 -1878.9612 -15962.2310 88 -17340.3329 -1878.9612 89 20768.0147 -17340.3329 90 -9343.8183 20768.0147 91 -32453.4758 -9343.8183 92 35772.1547 -32453.4758 93 3038.8332 35772.1547 94 -34613.2025 3038.8332 95 42223.7443 -34613.2025 96 23536.9113 42223.7443 97 -10912.3976 23536.9113 98 24063.7648 -10912.3976 99 -10103.0730 24063.7648 100 -3578.5063 -10103.0730 101 -5220.4713 -3578.5063 102 13801.1324 -5220.4713 103 9989.4061 13801.1324 104 7510.9666 9989.4061 105 -28848.2729 7510.9666 106 20133.4829 -28848.2729 107 26418.8395 20133.4829 108 -12451.3838 26418.8395 109 -34716.4692 -12451.3838 110 -3923.9814 -34716.4692 111 25205.7070 -3923.9814 112 7903.0197 25205.7070 113 -15024.7946 7903.0197 114 -14653.6325 -15024.7946 115 -12451.3838 -14653.6325 116 80430.6920 -12451.3838 117 -9279.0859 80430.6920 118 -12093.9473 -9279.0859 119 20306.0900 -12093.9473 120 -22994.2296 20306.0900 121 22956.4883 -22994.2296 122 5949.6344 22956.4883 123 -38800.0482 5949.6344 124 -18440.7634 -38800.0482 125 -15197.9898 -18440.7634 126 14670.1282 -15197.9898 127 32641.2571 14670.1282 128 10835.0462 32641.2571 129 -18504.0282 10835.0462 130 -9544.9220 -18504.0282 131 -9506.2794 -9544.9220 132 -8736.2671 -9506.2794 133 -5640.4616 -8736.2671 134 -10518.0716 -5640.4616 135 -10768.1584 -10518.0716 136 -12451.3838 -10768.1584 137 22632.0633 -12451.3838 138 -38078.0014 22632.0633 139 -11161.7459 -38078.0014 140 -23278.3978 -11161.7459 141 -2688.8288 -23278.3978 142 20706.6613 -2688.8288 143 2426.3600 20706.6613 144 NA 2426.3600 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 42740.2222 9559.2893 [2,] -21086.9097 42740.2222 [3,] -55819.0696 -21086.9097 [4,] -46070.8890 -55819.0696 [5,] -43334.7014 -46070.8890 [6,] 30990.6637 -43334.7014 [7,] 43653.7589 30990.6637 [8,] 2645.2483 43653.7589 [9,] 52919.6651 2645.2483 [10,] -47376.7720 52919.6651 [11,] 2011.9398 -47376.7720 [12,] -53954.2875 2011.9398 [13,] -36239.7361 -53954.2875 [14,] 40295.1153 -36239.7361 [15,] 4094.0224 40295.1153 [16,] -23496.4491 4094.0224 [17,] -28673.5196 -23496.4491 [18,] -55868.3046 -28673.5196 [19,] -56170.1442 -55868.3046 [20,] -20530.6305 -56170.1442 [21,] -505.1371 -20530.6305 [22,] 42753.4844 -505.1371 [23,] -37287.8452 42753.4844 [24,] -13922.1828 -37287.8452 [25,] 45389.2452 -13922.1828 [26,] 6601.5473 45389.2452 [27,] 55672.4283 6601.5473 [28,] 110607.3112 55672.4283 [29,] 21714.8434 110607.3112 [30,] 41062.4915 21714.8434 [31,] 45994.6441 41062.4915 [32,] -68334.5816 45994.6441 [33,] -19791.1976 -68334.5816 [34,] 29013.0183 -19791.1976 [35,] -12683.4883 29013.0183 [36,] 51618.1842 -12683.4883 [37,] -36752.6076 51618.1842 [38,] -38629.6623 -36752.6076 [39,] 52237.4989 -38629.6623 [40,] 45595.2827 52237.4989 [41,] -2801.1602 45595.2827 [42,] -17921.6175 -2801.1602 [43,] 7017.8043 -17921.6175 [44,] -9239.7012 7017.8043 [45,] 38652.1272 -9239.7012 [46,] 6344.5650 38652.1272 [47,] 33789.5333 6344.5650 [48,] 26930.7936 33789.5333 [49,] -46849.4301 26930.7936 [50,] -19920.5704 -46849.4301 [51,] -2683.7340 -19920.5704 [52,] 83185.1395 -2683.7340 [53,] 66112.1180 83185.1395 [54,] 39637.6560 66112.1180 [55,] 5773.7212 39637.6560 [56,] 51595.0824 5773.7212 [57,] 4051.8041 51595.0824 [58,] 39236.1160 4051.8041 [59,] -40171.3150 39236.1160 [60,] 5325.5523 -40171.3150 [61,] -39345.8733 5325.5523 [62,] -4538.0338 -39345.8733 [63,] 4544.2319 -4538.0338 [64,] 53344.1937 4544.2319 [65,] -16686.2486 53344.1937 [66,] -16249.7182 -16686.2486 [67,] -81416.2145 -16249.7182 [68,] -730.5583 -81416.2145 [69,] -6366.4908 -730.5583 [70,] 4450.5795 -6366.4908 [71,] 7504.1845 4450.5795 [72,] -23215.2011 7504.1845 [73,] -24419.9612 -23215.2011 [74,] -67630.4328 -24419.9612 [75,] -19795.1834 -67630.4328 [76,] -8390.9864 -19795.1834 [77,] -62475.4292 -8390.9864 [78,] -8853.4678 -62475.4292 [79,] -35107.5712 -8853.4678 [80,] -19342.8473 -35107.5712 [81,] 108626.6495 -19342.8473 [82,] -20793.0068 108626.6495 [83,] 3116.2252 -20793.0068 [84,] -16007.5659 3116.2252 [85,] 8311.0533 -16007.5659 [86,] -15962.2310 8311.0533 [87,] -1878.9612 -15962.2310 [88,] -17340.3329 -1878.9612 [89,] 20768.0147 -17340.3329 [90,] -9343.8183 20768.0147 [91,] -32453.4758 -9343.8183 [92,] 35772.1547 -32453.4758 [93,] 3038.8332 35772.1547 [94,] -34613.2025 3038.8332 [95,] 42223.7443 -34613.2025 [96,] 23536.9113 42223.7443 [97,] -10912.3976 23536.9113 [98,] 24063.7648 -10912.3976 [99,] -10103.0730 24063.7648 [100,] -3578.5063 -10103.0730 [101,] -5220.4713 -3578.5063 [102,] 13801.1324 -5220.4713 [103,] 9989.4061 13801.1324 [104,] 7510.9666 9989.4061 [105,] -28848.2729 7510.9666 [106,] 20133.4829 -28848.2729 [107,] 26418.8395 20133.4829 [108,] -12451.3838 26418.8395 [109,] -34716.4692 -12451.3838 [110,] -3923.9814 -34716.4692 [111,] 25205.7070 -3923.9814 [112,] 7903.0197 25205.7070 [113,] -15024.7946 7903.0197 [114,] -14653.6325 -15024.7946 [115,] -12451.3838 -14653.6325 [116,] 80430.6920 -12451.3838 [117,] -9279.0859 80430.6920 [118,] -12093.9473 -9279.0859 [119,] 20306.0900 -12093.9473 [120,] -22994.2296 20306.0900 [121,] 22956.4883 -22994.2296 [122,] 5949.6344 22956.4883 [123,] -38800.0482 5949.6344 [124,] -18440.7634 -38800.0482 [125,] -15197.9898 -18440.7634 [126,] 14670.1282 -15197.9898 [127,] 32641.2571 14670.1282 [128,] 10835.0462 32641.2571 [129,] -18504.0282 10835.0462 [130,] -9544.9220 -18504.0282 [131,] -9506.2794 -9544.9220 [132,] -8736.2671 -9506.2794 [133,] -5640.4616 -8736.2671 [134,] -10518.0716 -5640.4616 [135,] -10768.1584 -10518.0716 [136,] -12451.3838 -10768.1584 [137,] 22632.0633 -12451.3838 [138,] -38078.0014 22632.0633 [139,] -11161.7459 -38078.0014 [140,] -23278.3978 -11161.7459 [141,] -2688.8288 -23278.3978 [142,] 20706.6613 -2688.8288 [143,] 2426.3600 20706.6613 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 42740.2222 9559.2893 2 -21086.9097 42740.2222 3 -55819.0696 -21086.9097 4 -46070.8890 -55819.0696 5 -43334.7014 -46070.8890 6 30990.6637 -43334.7014 7 43653.7589 30990.6637 8 2645.2483 43653.7589 9 52919.6651 2645.2483 10 -47376.7720 52919.6651 11 2011.9398 -47376.7720 12 -53954.2875 2011.9398 13 -36239.7361 -53954.2875 14 40295.1153 -36239.7361 15 4094.0224 40295.1153 16 -23496.4491 4094.0224 17 -28673.5196 -23496.4491 18 -55868.3046 -28673.5196 19 -56170.1442 -55868.3046 20 -20530.6305 -56170.1442 21 -505.1371 -20530.6305 22 42753.4844 -505.1371 23 -37287.8452 42753.4844 24 -13922.1828 -37287.8452 25 45389.2452 -13922.1828 26 6601.5473 45389.2452 27 55672.4283 6601.5473 28 110607.3112 55672.4283 29 21714.8434 110607.3112 30 41062.4915 21714.8434 31 45994.6441 41062.4915 32 -68334.5816 45994.6441 33 -19791.1976 -68334.5816 34 29013.0183 -19791.1976 35 -12683.4883 29013.0183 36 51618.1842 -12683.4883 37 -36752.6076 51618.1842 38 -38629.6623 -36752.6076 39 52237.4989 -38629.6623 40 45595.2827 52237.4989 41 -2801.1602 45595.2827 42 -17921.6175 -2801.1602 43 7017.8043 -17921.6175 44 -9239.7012 7017.8043 45 38652.1272 -9239.7012 46 6344.5650 38652.1272 47 33789.5333 6344.5650 48 26930.7936 33789.5333 49 -46849.4301 26930.7936 50 -19920.5704 -46849.4301 51 -2683.7340 -19920.5704 52 83185.1395 -2683.7340 53 66112.1180 83185.1395 54 39637.6560 66112.1180 55 5773.7212 39637.6560 56 51595.0824 5773.7212 57 4051.8041 51595.0824 58 39236.1160 4051.8041 59 -40171.3150 39236.1160 60 5325.5523 -40171.3150 61 -39345.8733 5325.5523 62 -4538.0338 -39345.8733 63 4544.2319 -4538.0338 64 53344.1937 4544.2319 65 -16686.2486 53344.1937 66 -16249.7182 -16686.2486 67 -81416.2145 -16249.7182 68 -730.5583 -81416.2145 69 -6366.4908 -730.5583 70 4450.5795 -6366.4908 71 7504.1845 4450.5795 72 -23215.2011 7504.1845 73 -24419.9612 -23215.2011 74 -67630.4328 -24419.9612 75 -19795.1834 -67630.4328 76 -8390.9864 -19795.1834 77 -62475.4292 -8390.9864 78 -8853.4678 -62475.4292 79 -35107.5712 -8853.4678 80 -19342.8473 -35107.5712 81 108626.6495 -19342.8473 82 -20793.0068 108626.6495 83 3116.2252 -20793.0068 84 -16007.5659 3116.2252 85 8311.0533 -16007.5659 86 -15962.2310 8311.0533 87 -1878.9612 -15962.2310 88 -17340.3329 -1878.9612 89 20768.0147 -17340.3329 90 -9343.8183 20768.0147 91 -32453.4758 -9343.8183 92 35772.1547 -32453.4758 93 3038.8332 35772.1547 94 -34613.2025 3038.8332 95 42223.7443 -34613.2025 96 23536.9113 42223.7443 97 -10912.3976 23536.9113 98 24063.7648 -10912.3976 99 -10103.0730 24063.7648 100 -3578.5063 -10103.0730 101 -5220.4713 -3578.5063 102 13801.1324 -5220.4713 103 9989.4061 13801.1324 104 7510.9666 9989.4061 105 -28848.2729 7510.9666 106 20133.4829 -28848.2729 107 26418.8395 20133.4829 108 -12451.3838 26418.8395 109 -34716.4692 -12451.3838 110 -3923.9814 -34716.4692 111 25205.7070 -3923.9814 112 7903.0197 25205.7070 113 -15024.7946 7903.0197 114 -14653.6325 -15024.7946 115 -12451.3838 -14653.6325 116 80430.6920 -12451.3838 117 -9279.0859 80430.6920 118 -12093.9473 -9279.0859 119 20306.0900 -12093.9473 120 -22994.2296 20306.0900 121 22956.4883 -22994.2296 122 5949.6344 22956.4883 123 -38800.0482 5949.6344 124 -18440.7634 -38800.0482 125 -15197.9898 -18440.7634 126 14670.1282 -15197.9898 127 32641.2571 14670.1282 128 10835.0462 32641.2571 129 -18504.0282 10835.0462 130 -9544.9220 -18504.0282 131 -9506.2794 -9544.9220 132 -8736.2671 -9506.2794 133 -5640.4616 -8736.2671 134 -10518.0716 -5640.4616 135 -10768.1584 -10518.0716 136 -12451.3838 -10768.1584 137 22632.0633 -12451.3838 138 -38078.0014 22632.0633 139 -11161.7459 -38078.0014 140 -23278.3978 -11161.7459 141 -2688.8288 -23278.3978 142 20706.6613 -2688.8288 143 2426.3600 20706.6613 > 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/7mmje1344776219.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/87h481344776219.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/9x3xt1344776219.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/10lwe41344776219.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11zd071344776219.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12fj0y1344776219.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13evub1344776219.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/149hkw1344776219.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/1534ab1344776219.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/16lfjj1344776219.tab") + } > > try(system("convert tmp/10gyp1344776219.ps tmp/10gyp1344776219.png",intern=TRUE)) character(0) > try(system("convert tmp/2wizl1344776219.ps tmp/2wizl1344776219.png",intern=TRUE)) character(0) > try(system("convert tmp/3j3yv1344776219.ps tmp/3j3yv1344776219.png",intern=TRUE)) character(0) > try(system("convert tmp/472jt1344776219.ps tmp/472jt1344776219.png",intern=TRUE)) character(0) > try(system("convert tmp/5ivuw1344776219.ps tmp/5ivuw1344776219.png",intern=TRUE)) character(0) > try(system("convert tmp/6wdm81344776219.ps tmp/6wdm81344776219.png",intern=TRUE)) character(0) > try(system("convert tmp/7mmje1344776219.ps tmp/7mmje1344776219.png",intern=TRUE)) character(0) > try(system("convert tmp/87h481344776219.ps tmp/87h481344776219.png",intern=TRUE)) character(0) > try(system("convert tmp/9x3xt1344776219.ps tmp/9x3xt1344776219.png",intern=TRUE)) character(0) > try(system("convert tmp/10lwe41344776219.ps tmp/10lwe41344776219.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.232 0.789 7.043