R version 2.12.0 (2010-10-15) Copyright (C) 2010 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(11 + ,7 + ,3 + ,2 + ,3 + ,7 + ,6 + ,11 + ,7 + ,5 + ,6 + ,0 + ,7 + ,7 + ,11 + ,6 + ,6 + ,6 + ,0 + ,8 + ,8 + ,11 + ,6 + ,6 + ,6 + ,6 + ,9 + ,8 + ,11 + ,8 + ,7 + ,8 + ,5 + ,5 + ,9 + ,11 + ,8 + ,3 + ,1 + ,0 + ,7 + ,8 + ,11 + ,8 + ,2 + ,9 + ,8 + ,8 + ,8 + ,11 + ,5 + ,4 + ,4 + ,0 + ,7 + ,7 + ,11 + ,4 + ,7 + ,7 + ,0 + ,8 + ,7 + ,11 + ,9 + ,4 + ,4 + ,9 + ,8 + ,4 + ,11 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,11 + ,6 + ,6 + ,5 + ,6 + ,4 + ,7 + ,11 + ,5 + ,7 + ,7 + ,5 + ,8 + ,5 + ,11 + ,6 + ,4 + ,5 + ,4 + ,8 + ,8 + ,11 + ,2 + ,6 + ,6 + ,0 + ,7 + ,5 + ,11 + ,4 + ,5 + ,5 + ,0 + ,9 + ,4 + ,11 + ,2 + ,0 + ,2 + ,2 + ,2 + ,9 + ,11 + ,6 + ,9 + ,9 + ,6 + ,8 + ,8 + ,11 + ,7 + ,4 + ,4 + ,0 + ,8 + ,4 + ,11 + ,8 + ,2 + ,4 + ,4 + ,4 + ,6 + ,11 + ,5 + ,2 + ,5 + ,5 + ,5 + ,6 + ,11 + ,7 + ,7 + ,7 + ,7 + ,7 + ,7 + ,11 + ,5 + ,5 + ,5 + ,5 + ,8 + ,3 + ,11 + ,4 + ,9 + ,9 + ,4 + ,4 + ,4 + ,11 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,11 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,11 + ,7 + ,7 + ,3 + ,0 + 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,9 + ,12 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,12 + ,5 + ,5 + ,5 + ,0 + ,5 + ,5 + ,12 + ,4 + ,4 + ,4 + ,4 + ,9 + ,8 + ,12 + ,6 + ,6 + ,6 + ,6 + ,6 + ,6 + ,12 + ,5 + ,1 + ,1 + ,0 + ,9 + ,6 + ,12 + ,4 + ,4 + ,5 + ,4 + ,3 + ,6 + ,12 + ,7 + ,4 + ,2 + ,7 + ,7 + ,7 + ,12 + ,6 + ,6 + ,6 + ,0 + ,6 + ,7 + ,12 + ,7 + ,5 + ,5 + ,5 + ,5 + ,9 + ,12 + ,6 + ,9 + ,2 + ,6 + ,6 + ,6 + ,12 + ,6 + ,6 + ,6 + ,6 + ,9 + ,6 + ,12 + ,8 + ,8 + ,8 + ,8 + ,8 + ,6 + ,12 + ,7 + ,7 + ,7 + ,2 + ,7 + ,4 + ,12 + ,7 + ,7 + ,7 + ,7 + ,7 + ,7 + ,12 + ,4 + ,0 + ,9 + ,0 + ,4 + ,8 + ,12 + ,6 + ,2 + ,2 + ,0 + ,8 + ,7 + ,12 + ,5 + ,6 + ,6 + ,5 + ,5 + ,9 + ,12 + ,2 + ,5 + ,5 + ,0 + ,9 + ,6) + ,dim=c(7 + ,156) + ,dimnames=list(c('Maand' + ,'Schoolprestaties' + ,'Sport' + ,'GoingOut' + ,'Relation' + ,'Friends' + ,'Job') + ,1:156)) > y <- array(NA,dim=c(7,156),dimnames=list(c('Maand','Schoolprestaties','Sport','GoingOut','Relation','Friends','Job'),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 Schoolprestaties Maand Sport GoingOut Relation Friends Job t 1 7 11 3 2 3 7 6 1 2 7 11 5 6 0 7 7 2 3 6 11 6 6 0 8 8 3 4 6 11 6 6 6 9 8 4 5 8 11 7 8 5 5 9 5 6 8 11 3 1 0 7 8 6 7 8 11 2 9 8 8 8 7 8 5 11 4 4 0 7 7 8 9 4 11 7 7 0 8 7 9 10 9 11 4 4 9 8 4 10 11 6 11 6 6 6 6 6 11 12 6 11 6 5 6 4 7 12 13 5 11 7 7 5 8 5 13 14 6 11 4 5 4 8 8 14 15 2 11 6 6 0 7 5 15 16 4 11 5 5 0 9 4 16 17 2 11 0 2 2 2 9 17 18 6 11 9 9 6 8 8 18 19 7 11 4 4 0 8 4 19 20 8 11 2 4 4 4 6 20 21 5 11 2 5 5 5 6 21 22 7 11 7 7 7 7 7 22 23 5 11 5 5 5 8 3 23 24 4 11 9 9 4 4 4 24 25 6 11 6 6 6 6 6 25 26 6 11 6 6 6 6 6 26 27 7 11 7 3 0 9 7 27 28 7 11 3 3 1 7 5 28 29 8 11 6 5 0 6 8 29 30 4 11 6 5 4 4 6 30 31 4 11 4 4 4 8 4 31 32 7 11 7 7 7 3 9 32 33 7 11 7 6 7 7 7 33 34 4 11 2 7 0 4 4 34 35 7 11 4 4 4 7 6 35 36 5 11 5 5 5 8 8 36 37 6 11 6 6 0 6 6 37 38 5 11 5 5 5 5 5 38 39 6 11 6 0 1 6 6 39 40 7 11 6 6 2 9 6 40 41 6 11 6 5 0 8 4 41 42 9 11 3 3 9 7 7 42 43 7 11 3 3 3 3 9 43 44 4 11 3 3 0 4 8 44 45 6 11 6 7 6 6 6 45 46 5 11 7 7 1 8 6 46 47 5 11 5 1 5 5 5 47 48 4 11 5 5 0 7 7 48 49 7 11 5 5 0 7 5 49 50 6 11 6 6 0 9 8 50 51 6 11 2 2 6 6 6 51 52 7 11 6 6 7 8 8 52 53 5 11 5 5 0 5 5 53 54 4 11 4 2 4 4 4 54 55 5 11 7 7 5 8 5 55 56 5 11 5 5 1 9 6 56 57 4 12 3 3 4 4 4 57 58 9 12 6 6 9 8 6 58 59 8 12 2 2 2 2 9 59 60 8 12 8 8 8 8 7 60 61 3 12 3 5 3 7 3 61 62 6 12 0 2 1 7 6 62 63 6 12 2 6 0 6 6 63 64 6 12 8 2 6 6 6 64 65 5 12 4 1 0 5 5 65 66 5 12 5 5 0 8 5 66 67 6 12 6 6 6 4 5 67 68 7 12 5 2 2 9 9 68 69 6 12 6 6 1 6 8 69 70 5 12 2 2 5 5 5 70 71 5 12 6 6 5 5 6 71 72 7 12 2 5 5 7 7 72 73 5 12 5 0 5 8 5 73 74 6 12 6 2 6 9 6 74 75 6 12 4 4 6 6 6 75 76 9 12 6 1 0 6 6 76 77 8 12 5 5 0 5 6 77 78 5 12 5 5 1 3 9 78 79 7 12 4 2 7 7 7 79 80 7 12 2 2 2 9 9 80 81 4 12 7 7 4 7 4 81 82 6 12 5 5 0 8 8 82 83 5 12 6 2 5 5 5 83 84 5 12 5 5 5 5 8 84 85 3 12 3 3 3 8 9 85 86 6 12 6 6 0 6 6 86 87 4 12 4 1 4 9 4 87 88 9 12 5 5 9 5 7 88 89 8 12 7 7 0 8 8 89 90 4 12 4 2 4 8 9 90 91 2 12 6 6 2 7 9 91 92 7 12 8 8 7 7 7 92 93 7 12 7 7 7 8 8 93 94 6 12 6 6 6 4 4 94 95 5 12 7 7 0 5 6 95 96 8 12 4 4 5 9 7 96 97 6 12 0 5 6 6 6 97 98 3 12 3 2 0 7 7 98 99 5 12 5 5 5 5 5 99 100 9 12 6 2 9 2 9 100 101 7 12 5 5 0 7 7 101 102 7 12 7 7 7 7 7 102 103 6 12 6 5 1 6 6 103 104 3 12 8 8 3 8 6 104 105 7 12 7 2 7 9 9 105 106 8 12 8 8 8 8 9 106 107 3 12 3 3 0 3 8 107 108 5 12 8 2 5 5 8 108 109 8 12 3 3 3 7 3 109 110 7 12 4 5 0 8 6 110 111 5 12 2 2 5 5 5 111 112 7 12 7 2 7 9 7 112 113 6 12 6 6 0 6 6 113 114 7 12 2 2 0 7 7 114 115 9 12 7 7 0 7 7 115 116 6 12 6 6 6 6 6 116 117 6 12 6 2 0 3 8 117 118 6 12 6 2 6 9 9 118 119 6 12 6 5 6 6 6 119 120 2 12 6 6 2 2 9 120 121 5 12 4 4 5 5 5 121 122 5 12 2 5 0 5 6 122 123 4 12 7 7 4 9 4 123 124 7 12 6 6 0 7 7 124 125 6 12 6 6 6 6 6 125 126 5 12 5 5 5 8 8 126 127 8 12 8 2 8 8 8 127 128 7 12 6 6 6 6 9 128 129 5 12 0 3 5 3 8 129 130 4 12 4 2 0 7 4 130 131 8 12 8 8 8 9 6 131 132 6 12 6 6 0 7 6 132 133 9 12 4 4 9 4 7 133 134 5 12 6 6 5 5 9 134 135 6 12 2 5 0 6 8 135 136 4 12 4 4 0 4 4 136 137 6 12 2 2 0 6 6 137 138 3 12 3 3 3 7 9 138 139 6 12 6 6 6 6 6 139 140 5 12 5 5 0 5 5 140 141 4 12 4 4 4 9 8 141 142 6 12 6 6 6 6 6 142 143 5 12 1 1 0 9 6 143 144 4 12 4 5 4 3 6 144 145 7 12 4 2 7 7 7 145 146 6 12 6 6 0 6 7 146 147 7 12 5 5 5 5 9 147 148 6 12 9 2 6 6 6 148 149 6 12 6 6 6 9 6 149 150 8 12 8 8 8 8 6 150 151 7 12 7 7 2 7 4 151 152 7 12 7 7 7 7 7 152 153 4 12 0 9 0 4 8 153 154 6 12 2 2 0 8 7 154 155 5 12 6 6 5 5 9 155 156 2 12 5 5 0 9 6 156 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Maand Sport GoingOut Relation Friends -0.635810 0.384537 0.051560 -0.023449 0.167531 0.117834 Job t 0.154860 -0.006159 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -4.1405 -0.9250 0.0328 0.9134 3.6240 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.635810 5.097830 -0.125 0.90091 Maand 0.384537 0.458197 0.839 0.40269 Sport 0.051560 0.071786 0.718 0.47374 GoingOut -0.023449 0.065672 -0.357 0.72155 Relation 0.167531 0.043424 3.858 0.00017 *** Friends 0.117834 0.068863 1.711 0.08915 . Job 0.154860 0.077927 1.987 0.04874 * t -0.006159 0.004840 -1.272 0.20520 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.506 on 148 degrees of freedom Multiple R-squared: 0.1627, Adjusted R-squared: 0.1231 F-statistic: 4.108 on 7 and 148 DF, p-value: 0.0003757 > 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,] 0.65214761 0.69570478 0.34785239 [2,] 0.58004075 0.83991850 0.41995925 [3,] 0.43671467 0.87342933 0.56328533 [4,] 0.31097658 0.62195317 0.68902342 [5,] 0.31995820 0.63991640 0.68004180 [6,] 0.27164899 0.54329798 0.72835101 [7,] 0.48225547 0.96451094 0.51774453 [8,] 0.44767535 0.89535070 0.55232465 [9,] 0.78592548 0.42814903 0.21407452 [10,] 0.91474405 0.17051191 0.08525595 [11,] 0.88280694 0.23438612 0.11719306 [12,] 0.86322733 0.27354534 0.13677267 [13,] 0.82768003 0.34463993 0.17231997 [14,] 0.78734765 0.42530469 0.21265235 [15,] 0.73963928 0.52072144 0.26036072 [16,] 0.68693785 0.62612429 0.31306215 [17,] 0.70452711 0.59094579 0.29547289 [18,] 0.72670079 0.54659842 0.27329921 [19,] 0.83008767 0.33982466 0.16991233 [20,] 0.81968747 0.36062506 0.18031253 [21,] 0.84153057 0.31693885 0.15846943 [22,] 0.81241958 0.37516085 0.18758042 [23,] 0.76793805 0.46412390 0.23206195 [24,] 0.73660154 0.52679692 0.26339846 [25,] 0.69734629 0.60530742 0.30265371 [26,] 0.72499305 0.55001390 0.27500695 [27,] 0.70673271 0.58653458 0.29326729 [28,] 0.66205326 0.67589347 0.33794674 [29,] 0.60935649 0.78128702 0.39064351 [30,] 0.58222205 0.83555589 0.41777795 [31,] 0.54639673 0.90720653 0.45360327 [32,] 0.54749244 0.90501511 0.45250756 [33,] 0.51740358 0.96519285 0.48259642 [34,] 0.50929253 0.98141494 0.49070747 [35,] 0.45455856 0.90911712 0.54544144 [36,] 0.40564049 0.81128097 0.59435951 [37,] 0.38067178 0.76134357 0.61932822 [38,] 0.37307860 0.74615720 0.62692140 [39,] 0.41682888 0.83365776 0.58317112 [40,] 0.36875991 0.73751982 0.63124009 [41,] 0.33170321 0.66340642 0.66829679 [42,] 0.29118601 0.58237201 0.70881399 [43,] 0.25585863 0.51171726 0.74414137 [44,] 0.23242438 0.46484875 0.76757562 [45,] 0.20446552 0.40893105 0.79553448 [46,] 0.17712637 0.35425273 0.82287363 [47,] 0.15643810 0.31287620 0.84356190 [48,] 0.17839841 0.35679682 0.82160159 [49,] 0.20259971 0.40519941 0.79740029 [50,] 0.17238594 0.34477187 0.82761406 [51,] 0.24076052 0.48152104 0.75923948 [52,] 0.20733529 0.41467058 0.79266471 [53,] 0.18343474 0.36686948 0.81656526 [54,] 0.16359083 0.32718165 0.83640917 [55,] 0.13538181 0.27076363 0.86461819 [56,] 0.11272951 0.22545901 0.88727049 [57,] 0.09278050 0.18556100 0.90721950 [58,] 0.08024252 0.16048503 0.91975748 [59,] 0.06370041 0.12740083 0.93629959 [60,] 0.05489406 0.10978812 0.94510594 [61,] 0.04784915 0.09569831 0.95215085 [62,] 0.03876766 0.07753531 0.96123234 [63,] 0.03947105 0.07894209 0.96052895 [64,] 0.03346983 0.06693967 0.96653017 [65,] 0.02566326 0.05132651 0.97433674 [66,] 0.08455994 0.16911989 0.91544006 [67,] 0.14564909 0.29129818 0.85435091 [68,] 0.12745571 0.25491142 0.87254429 [69,] 0.10604193 0.21208386 0.89395807 [70,] 0.09878782 0.19757563 0.90121218 [71,] 0.10768215 0.21536429 0.89231785 [72,] 0.09077200 0.18154401 0.90922800 [73,] 0.07924063 0.15848126 0.92075937 [74,] 0.07381010 0.14762020 0.92618990 [75,] 0.16434732 0.32869465 0.83565268 [76,] 0.14538916 0.29077833 0.85461084 [77,] 0.16907997 0.33815994 0.83092003 [78,] 0.20872684 0.41745369 0.79127316 [79,] 0.26513026 0.53026052 0.73486974 [80,] 0.33026153 0.66052307 0.66973847 [81,] 0.59171014 0.81657973 0.40828986 [82,] 0.54903927 0.90192146 0.45096073 [83,] 0.50177420 0.99645161 0.49822580 [84,] 0.46177100 0.92354200 0.53822900 [85,] 0.41565364 0.83130729 0.58434636 [86,] 0.41508186 0.83016372 0.58491814 [87,] 0.36809043 0.73618085 0.63190957 [88,] 0.43317163 0.86634327 0.56682837 [89,] 0.41197590 0.82395180 0.58802410 [90,] 0.47593341 0.95186683 0.52406659 [91,] 0.47772237 0.95544473 0.52227763 [92,] 0.42952152 0.85904304 0.57047848 [93,] 0.38717723 0.77435445 0.61282277 [94,] 0.58441867 0.83116266 0.41558133 [95,] 0.53394001 0.93211998 0.46605999 [96,] 0.49041779 0.98083558 0.50958221 [97,] 0.51861252 0.96277497 0.48138748 [98,] 0.51054303 0.97891395 0.48945697 [99,] 0.58575409 0.82849182 0.41424591 [100,] 0.58064141 0.83871719 0.41935859 [101,] 0.53730578 0.92538845 0.46269422 [102,] 0.48334762 0.96669523 0.51665238 [103,] 0.43756634 0.87513269 0.56243366 [104,] 0.46156812 0.92313625 0.53843188 [105,] 0.72544624 0.54910753 0.27455376 [106,] 0.67560660 0.64878680 0.32439340 [107,] 0.67292350 0.65415300 0.32707650 [108,] 0.62499076 0.75001848 0.37500924 [109,] 0.56783739 0.86432522 0.43216261 [110,] 0.75763749 0.48472502 0.24236251 [111,] 0.72801829 0.54396342 0.27198171 [112,] 0.67505591 0.64988818 0.32494409 [113,] 0.75328981 0.49342038 0.24671019 [114,] 0.77537050 0.44925899 0.22462950 [115,] 0.73693300 0.52613400 0.26306700 [116,] 0.72844325 0.54311350 0.27155675 [117,] 0.68835400 0.62329201 0.31164600 [118,] 0.63526371 0.72947257 0.36473629 [119,] 0.58925649 0.82148703 0.41074351 [120,] 0.56874268 0.86251463 0.43125732 [121,] 0.50613377 0.98773245 0.49386623 [122,] 0.46959148 0.93918296 0.53040852 [123,] 0.51737651 0.96524699 0.48262349 [124,] 0.45124603 0.90249206 0.54875397 [125,] 0.47934527 0.95869054 0.52065473 [126,] 0.43832693 0.87665387 0.56167307 [127,] 0.43448812 0.86897624 0.56551188 [128,] 0.45102889 0.90205779 0.54897111 [129,] 0.36138117 0.72276235 0.63861883 [130,] 0.27235649 0.54471299 0.72764351 [131,] 0.34331364 0.68662728 0.65668636 [132,] 0.27633609 0.55267218 0.72366391 [133,] 0.19224564 0.38449127 0.80775436 [134,] 0.27968254 0.55936509 0.72031746 [135,] 0.20344610 0.40689221 0.79655390 > postscript(file="/var/www/rcomp/tmp/170am1324494755.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/www/rcomp/tmp/2pi4m1324494755.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/3u52c1324494755.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4h37w1324494755.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5fwm61324494755.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 1.04768603 1.39225380 0.07415896 -1.04269925 1.44280495 2.24790415 7 8 9 10 11 12 1.03513891 -0.56613006 -1.76213736 2.28515885 -0.33636379 -0.27284584 13 14 15 16 17 18 -1.26543394 -0.44854230 -3.26951819 -1.31605639 -3.40696736 -0.92297026 19 20 21 22 23 24 1.84836570 2.44913891 -0.80661729 0.26305107 -0.83790157 -1.46017903 25 26 27 28 29 30 -0.25013640 -0.24397730 1.13709542 1.72735195 2.44651428 -1.67206106 31 32 33 34 35 36 -1.74784731 0.48625827 0.30735191 -0.41444396 1.08490330 -1.53213273 37 38 39 40 41 42 0.82895610 -0.70173287 0.53304823 1.15887031 0.90419503 2.16361515 43 44 45 46 47 48 1.33657376 -1.11764959 -0.10350516 -0.54692135 -0.74009799 -1.34787689 49 50 51 52 53 54 1.96800198 0.24580262 0.02244338 0.20324096 0.22830637 -1.18175055 55 56 57 58 59 60 -1.00675177 -0.54694276 -1.47280140 1.83031677 2.36405724 0.79908403 61 62 63 64 65 66 -2.43237805 0.52859488 0.81079543 -0.59138610 -0.12455885 -0.42966478 67 68 69 70 71 72 0.01453619 0.43997100 0.16425949 -0.80484675 -1.06599074 0.73243143 73 74 75 76 77 78 -1.34145011 -0.78017699 -0.27049729 3.56737725 2.83672741 -0.55355572 79 80 81 82 83 84 0.26701616 0.66856035 -1.79092821 0.20430114 -0.93101867 -1.26753143 85 86 87 88 89 90 -3.37845151 0.74621450 -1.97565697 2.24184265 2.19119323 -2.59019587 91 92 93 94 95 96 -4.14046477 0.28153975 0.04311577 0.33569176 -0.10863041 1.51801246 97 98 99 100 101 102 0.09469235 -2.39168704 -0.71056528 2.23762624 1.59401791 0.37124155 103 104 105 106 107 108 0.65993939 -2.93740296 -0.27291519 0.77268282 -1.99632980 -1.34474097 109 110 111 112 113 114 2.81636021 1.73803575 -0.55232368 0.07991828 0.91251018 1.75841860 115 116 117 118 119 120 3.62402369 -0.07419583 0.88713178 -0.97375630 -0.07916778 -3.37268091 121 122 123 124 125 126 -0.54695429 0.26856703 -1.76791404 1.70756639 -0.01876393 -1.36235125 127 128 129 130 131 132 0.91618827 0.53513369 -0.54380222 -0.78157625 1.27340597 0.91169907 133 134 135 136 137 138 2.66494691 -1.14254716 0.92108155 -0.34422116 1.17277176 -2.93418525 139 140 141 142 143 144 0.06746345 0.37961055 -2.19215741 0.08594075 -0.11566483 -1.13350711 145 146 147 148 149 150 0.67351671 0.96090057 0.96563193 -0.12558182 -0.22444754 1.50826285 151 152 153 154 155 156 1.97526983 0.67919651 -0.53546956 0.88694857 -1.01320608 -3.14803974 > postscript(file="/var/www/rcomp/tmp/6nvdb1324494755.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 1.04768603 NA 1 1.39225380 1.04768603 2 0.07415896 1.39225380 3 -1.04269925 0.07415896 4 1.44280495 -1.04269925 5 2.24790415 1.44280495 6 1.03513891 2.24790415 7 -0.56613006 1.03513891 8 -1.76213736 -0.56613006 9 2.28515885 -1.76213736 10 -0.33636379 2.28515885 11 -0.27284584 -0.33636379 12 -1.26543394 -0.27284584 13 -0.44854230 -1.26543394 14 -3.26951819 -0.44854230 15 -1.31605639 -3.26951819 16 -3.40696736 -1.31605639 17 -0.92297026 -3.40696736 18 1.84836570 -0.92297026 19 2.44913891 1.84836570 20 -0.80661729 2.44913891 21 0.26305107 -0.80661729 22 -0.83790157 0.26305107 23 -1.46017903 -0.83790157 24 -0.25013640 -1.46017903 25 -0.24397730 -0.25013640 26 1.13709542 -0.24397730 27 1.72735195 1.13709542 28 2.44651428 1.72735195 29 -1.67206106 2.44651428 30 -1.74784731 -1.67206106 31 0.48625827 -1.74784731 32 0.30735191 0.48625827 33 -0.41444396 0.30735191 34 1.08490330 -0.41444396 35 -1.53213273 1.08490330 36 0.82895610 -1.53213273 37 -0.70173287 0.82895610 38 0.53304823 -0.70173287 39 1.15887031 0.53304823 40 0.90419503 1.15887031 41 2.16361515 0.90419503 42 1.33657376 2.16361515 43 -1.11764959 1.33657376 44 -0.10350516 -1.11764959 45 -0.54692135 -0.10350516 46 -0.74009799 -0.54692135 47 -1.34787689 -0.74009799 48 1.96800198 -1.34787689 49 0.24580262 1.96800198 50 0.02244338 0.24580262 51 0.20324096 0.02244338 52 0.22830637 0.20324096 53 -1.18175055 0.22830637 54 -1.00675177 -1.18175055 55 -0.54694276 -1.00675177 56 -1.47280140 -0.54694276 57 1.83031677 -1.47280140 58 2.36405724 1.83031677 59 0.79908403 2.36405724 60 -2.43237805 0.79908403 61 0.52859488 -2.43237805 62 0.81079543 0.52859488 63 -0.59138610 0.81079543 64 -0.12455885 -0.59138610 65 -0.42966478 -0.12455885 66 0.01453619 -0.42966478 67 0.43997100 0.01453619 68 0.16425949 0.43997100 69 -0.80484675 0.16425949 70 -1.06599074 -0.80484675 71 0.73243143 -1.06599074 72 -1.34145011 0.73243143 73 -0.78017699 -1.34145011 74 -0.27049729 -0.78017699 75 3.56737725 -0.27049729 76 2.83672741 3.56737725 77 -0.55355572 2.83672741 78 0.26701616 -0.55355572 79 0.66856035 0.26701616 80 -1.79092821 0.66856035 81 0.20430114 -1.79092821 82 -0.93101867 0.20430114 83 -1.26753143 -0.93101867 84 -3.37845151 -1.26753143 85 0.74621450 -3.37845151 86 -1.97565697 0.74621450 87 2.24184265 -1.97565697 88 2.19119323 2.24184265 89 -2.59019587 2.19119323 90 -4.14046477 -2.59019587 91 0.28153975 -4.14046477 92 0.04311577 0.28153975 93 0.33569176 0.04311577 94 -0.10863041 0.33569176 95 1.51801246 -0.10863041 96 0.09469235 1.51801246 97 -2.39168704 0.09469235 98 -0.71056528 -2.39168704 99 2.23762624 -0.71056528 100 1.59401791 2.23762624 101 0.37124155 1.59401791 102 0.65993939 0.37124155 103 -2.93740296 0.65993939 104 -0.27291519 -2.93740296 105 0.77268282 -0.27291519 106 -1.99632980 0.77268282 107 -1.34474097 -1.99632980 108 2.81636021 -1.34474097 109 1.73803575 2.81636021 110 -0.55232368 1.73803575 111 0.07991828 -0.55232368 112 0.91251018 0.07991828 113 1.75841860 0.91251018 114 3.62402369 1.75841860 115 -0.07419583 3.62402369 116 0.88713178 -0.07419583 117 -0.97375630 0.88713178 118 -0.07916778 -0.97375630 119 -3.37268091 -0.07916778 120 -0.54695429 -3.37268091 121 0.26856703 -0.54695429 122 -1.76791404 0.26856703 123 1.70756639 -1.76791404 124 -0.01876393 1.70756639 125 -1.36235125 -0.01876393 126 0.91618827 -1.36235125 127 0.53513369 0.91618827 128 -0.54380222 0.53513369 129 -0.78157625 -0.54380222 130 1.27340597 -0.78157625 131 0.91169907 1.27340597 132 2.66494691 0.91169907 133 -1.14254716 2.66494691 134 0.92108155 -1.14254716 135 -0.34422116 0.92108155 136 1.17277176 -0.34422116 137 -2.93418525 1.17277176 138 0.06746345 -2.93418525 139 0.37961055 0.06746345 140 -2.19215741 0.37961055 141 0.08594075 -2.19215741 142 -0.11566483 0.08594075 143 -1.13350711 -0.11566483 144 0.67351671 -1.13350711 145 0.96090057 0.67351671 146 0.96563193 0.96090057 147 -0.12558182 0.96563193 148 -0.22444754 -0.12558182 149 1.50826285 -0.22444754 150 1.97526983 1.50826285 151 0.67919651 1.97526983 152 -0.53546956 0.67919651 153 0.88694857 -0.53546956 154 -1.01320608 0.88694857 155 -3.14803974 -1.01320608 156 NA -3.14803974 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.39225380 1.04768603 [2,] 0.07415896 1.39225380 [3,] -1.04269925 0.07415896 [4,] 1.44280495 -1.04269925 [5,] 2.24790415 1.44280495 [6,] 1.03513891 2.24790415 [7,] -0.56613006 1.03513891 [8,] -1.76213736 -0.56613006 [9,] 2.28515885 -1.76213736 [10,] -0.33636379 2.28515885 [11,] -0.27284584 -0.33636379 [12,] -1.26543394 -0.27284584 [13,] -0.44854230 -1.26543394 [14,] -3.26951819 -0.44854230 [15,] -1.31605639 -3.26951819 [16,] -3.40696736 -1.31605639 [17,] -0.92297026 -3.40696736 [18,] 1.84836570 -0.92297026 [19,] 2.44913891 1.84836570 [20,] -0.80661729 2.44913891 [21,] 0.26305107 -0.80661729 [22,] -0.83790157 0.26305107 [23,] -1.46017903 -0.83790157 [24,] -0.25013640 -1.46017903 [25,] -0.24397730 -0.25013640 [26,] 1.13709542 -0.24397730 [27,] 1.72735195 1.13709542 [28,] 2.44651428 1.72735195 [29,] -1.67206106 2.44651428 [30,] -1.74784731 -1.67206106 [31,] 0.48625827 -1.74784731 [32,] 0.30735191 0.48625827 [33,] -0.41444396 0.30735191 [34,] 1.08490330 -0.41444396 [35,] -1.53213273 1.08490330 [36,] 0.82895610 -1.53213273 [37,] -0.70173287 0.82895610 [38,] 0.53304823 -0.70173287 [39,] 1.15887031 0.53304823 [40,] 0.90419503 1.15887031 [41,] 2.16361515 0.90419503 [42,] 1.33657376 2.16361515 [43,] -1.11764959 1.33657376 [44,] -0.10350516 -1.11764959 [45,] -0.54692135 -0.10350516 [46,] -0.74009799 -0.54692135 [47,] -1.34787689 -0.74009799 [48,] 1.96800198 -1.34787689 [49,] 0.24580262 1.96800198 [50,] 0.02244338 0.24580262 [51,] 0.20324096 0.02244338 [52,] 0.22830637 0.20324096 [53,] -1.18175055 0.22830637 [54,] -1.00675177 -1.18175055 [55,] -0.54694276 -1.00675177 [56,] -1.47280140 -0.54694276 [57,] 1.83031677 -1.47280140 [58,] 2.36405724 1.83031677 [59,] 0.79908403 2.36405724 [60,] -2.43237805 0.79908403 [61,] 0.52859488 -2.43237805 [62,] 0.81079543 0.52859488 [63,] -0.59138610 0.81079543 [64,] -0.12455885 -0.59138610 [65,] -0.42966478 -0.12455885 [66,] 0.01453619 -0.42966478 [67,] 0.43997100 0.01453619 [68,] 0.16425949 0.43997100 [69,] -0.80484675 0.16425949 [70,] -1.06599074 -0.80484675 [71,] 0.73243143 -1.06599074 [72,] -1.34145011 0.73243143 [73,] -0.78017699 -1.34145011 [74,] -0.27049729 -0.78017699 [75,] 3.56737725 -0.27049729 [76,] 2.83672741 3.56737725 [77,] -0.55355572 2.83672741 [78,] 0.26701616 -0.55355572 [79,] 0.66856035 0.26701616 [80,] -1.79092821 0.66856035 [81,] 0.20430114 -1.79092821 [82,] -0.93101867 0.20430114 [83,] -1.26753143 -0.93101867 [84,] -3.37845151 -1.26753143 [85,] 0.74621450 -3.37845151 [86,] -1.97565697 0.74621450 [87,] 2.24184265 -1.97565697 [88,] 2.19119323 2.24184265 [89,] -2.59019587 2.19119323 [90,] -4.14046477 -2.59019587 [91,] 0.28153975 -4.14046477 [92,] 0.04311577 0.28153975 [93,] 0.33569176 0.04311577 [94,] -0.10863041 0.33569176 [95,] 1.51801246 -0.10863041 [96,] 0.09469235 1.51801246 [97,] -2.39168704 0.09469235 [98,] -0.71056528 -2.39168704 [99,] 2.23762624 -0.71056528 [100,] 1.59401791 2.23762624 [101,] 0.37124155 1.59401791 [102,] 0.65993939 0.37124155 [103,] -2.93740296 0.65993939 [104,] -0.27291519 -2.93740296 [105,] 0.77268282 -0.27291519 [106,] -1.99632980 0.77268282 [107,] -1.34474097 -1.99632980 [108,] 2.81636021 -1.34474097 [109,] 1.73803575 2.81636021 [110,] -0.55232368 1.73803575 [111,] 0.07991828 -0.55232368 [112,] 0.91251018 0.07991828 [113,] 1.75841860 0.91251018 [114,] 3.62402369 1.75841860 [115,] -0.07419583 3.62402369 [116,] 0.88713178 -0.07419583 [117,] -0.97375630 0.88713178 [118,] -0.07916778 -0.97375630 [119,] -3.37268091 -0.07916778 [120,] -0.54695429 -3.37268091 [121,] 0.26856703 -0.54695429 [122,] -1.76791404 0.26856703 [123,] 1.70756639 -1.76791404 [124,] -0.01876393 1.70756639 [125,] -1.36235125 -0.01876393 [126,] 0.91618827 -1.36235125 [127,] 0.53513369 0.91618827 [128,] -0.54380222 0.53513369 [129,] -0.78157625 -0.54380222 [130,] 1.27340597 -0.78157625 [131,] 0.91169907 1.27340597 [132,] 2.66494691 0.91169907 [133,] -1.14254716 2.66494691 [134,] 0.92108155 -1.14254716 [135,] -0.34422116 0.92108155 [136,] 1.17277176 -0.34422116 [137,] -2.93418525 1.17277176 [138,] 0.06746345 -2.93418525 [139,] 0.37961055 0.06746345 [140,] -2.19215741 0.37961055 [141,] 0.08594075 -2.19215741 [142,] -0.11566483 0.08594075 [143,] -1.13350711 -0.11566483 [144,] 0.67351671 -1.13350711 [145,] 0.96090057 0.67351671 [146,] 0.96563193 0.96090057 [147,] -0.12558182 0.96563193 [148,] -0.22444754 -0.12558182 [149,] 1.50826285 -0.22444754 [150,] 1.97526983 1.50826285 [151,] 0.67919651 1.97526983 [152,] -0.53546956 0.67919651 [153,] 0.88694857 -0.53546956 [154,] -1.01320608 0.88694857 [155,] -3.14803974 -1.01320608 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.39225380 1.04768603 2 0.07415896 1.39225380 3 -1.04269925 0.07415896 4 1.44280495 -1.04269925 5 2.24790415 1.44280495 6 1.03513891 2.24790415 7 -0.56613006 1.03513891 8 -1.76213736 -0.56613006 9 2.28515885 -1.76213736 10 -0.33636379 2.28515885 11 -0.27284584 -0.33636379 12 -1.26543394 -0.27284584 13 -0.44854230 -1.26543394 14 -3.26951819 -0.44854230 15 -1.31605639 -3.26951819 16 -3.40696736 -1.31605639 17 -0.92297026 -3.40696736 18 1.84836570 -0.92297026 19 2.44913891 1.84836570 20 -0.80661729 2.44913891 21 0.26305107 -0.80661729 22 -0.83790157 0.26305107 23 -1.46017903 -0.83790157 24 -0.25013640 -1.46017903 25 -0.24397730 -0.25013640 26 1.13709542 -0.24397730 27 1.72735195 1.13709542 28 2.44651428 1.72735195 29 -1.67206106 2.44651428 30 -1.74784731 -1.67206106 31 0.48625827 -1.74784731 32 0.30735191 0.48625827 33 -0.41444396 0.30735191 34 1.08490330 -0.41444396 35 -1.53213273 1.08490330 36 0.82895610 -1.53213273 37 -0.70173287 0.82895610 38 0.53304823 -0.70173287 39 1.15887031 0.53304823 40 0.90419503 1.15887031 41 2.16361515 0.90419503 42 1.33657376 2.16361515 43 -1.11764959 1.33657376 44 -0.10350516 -1.11764959 45 -0.54692135 -0.10350516 46 -0.74009799 -0.54692135 47 -1.34787689 -0.74009799 48 1.96800198 -1.34787689 49 0.24580262 1.96800198 50 0.02244338 0.24580262 51 0.20324096 0.02244338 52 0.22830637 0.20324096 53 -1.18175055 0.22830637 54 -1.00675177 -1.18175055 55 -0.54694276 -1.00675177 56 -1.47280140 -0.54694276 57 1.83031677 -1.47280140 58 2.36405724 1.83031677 59 0.79908403 2.36405724 60 -2.43237805 0.79908403 61 0.52859488 -2.43237805 62 0.81079543 0.52859488 63 -0.59138610 0.81079543 64 -0.12455885 -0.59138610 65 -0.42966478 -0.12455885 66 0.01453619 -0.42966478 67 0.43997100 0.01453619 68 0.16425949 0.43997100 69 -0.80484675 0.16425949 70 -1.06599074 -0.80484675 71 0.73243143 -1.06599074 72 -1.34145011 0.73243143 73 -0.78017699 -1.34145011 74 -0.27049729 -0.78017699 75 3.56737725 -0.27049729 76 2.83672741 3.56737725 77 -0.55355572 2.83672741 78 0.26701616 -0.55355572 79 0.66856035 0.26701616 80 -1.79092821 0.66856035 81 0.20430114 -1.79092821 82 -0.93101867 0.20430114 83 -1.26753143 -0.93101867 84 -3.37845151 -1.26753143 85 0.74621450 -3.37845151 86 -1.97565697 0.74621450 87 2.24184265 -1.97565697 88 2.19119323 2.24184265 89 -2.59019587 2.19119323 90 -4.14046477 -2.59019587 91 0.28153975 -4.14046477 92 0.04311577 0.28153975 93 0.33569176 0.04311577 94 -0.10863041 0.33569176 95 1.51801246 -0.10863041 96 0.09469235 1.51801246 97 -2.39168704 0.09469235 98 -0.71056528 -2.39168704 99 2.23762624 -0.71056528 100 1.59401791 2.23762624 101 0.37124155 1.59401791 102 0.65993939 0.37124155 103 -2.93740296 0.65993939 104 -0.27291519 -2.93740296 105 0.77268282 -0.27291519 106 -1.99632980 0.77268282 107 -1.34474097 -1.99632980 108 2.81636021 -1.34474097 109 1.73803575 2.81636021 110 -0.55232368 1.73803575 111 0.07991828 -0.55232368 112 0.91251018 0.07991828 113 1.75841860 0.91251018 114 3.62402369 1.75841860 115 -0.07419583 3.62402369 116 0.88713178 -0.07419583 117 -0.97375630 0.88713178 118 -0.07916778 -0.97375630 119 -3.37268091 -0.07916778 120 -0.54695429 -3.37268091 121 0.26856703 -0.54695429 122 -1.76791404 0.26856703 123 1.70756639 -1.76791404 124 -0.01876393 1.70756639 125 -1.36235125 -0.01876393 126 0.91618827 -1.36235125 127 0.53513369 0.91618827 128 -0.54380222 0.53513369 129 -0.78157625 -0.54380222 130 1.27340597 -0.78157625 131 0.91169907 1.27340597 132 2.66494691 0.91169907 133 -1.14254716 2.66494691 134 0.92108155 -1.14254716 135 -0.34422116 0.92108155 136 1.17277176 -0.34422116 137 -2.93418525 1.17277176 138 0.06746345 -2.93418525 139 0.37961055 0.06746345 140 -2.19215741 0.37961055 141 0.08594075 -2.19215741 142 -0.11566483 0.08594075 143 -1.13350711 -0.11566483 144 0.67351671 -1.13350711 145 0.96090057 0.67351671 146 0.96563193 0.96090057 147 -0.12558182 0.96563193 148 -0.22444754 -0.12558182 149 1.50826285 -0.22444754 150 1.97526983 1.50826285 151 0.67919651 1.97526983 152 -0.53546956 0.67919651 153 0.88694857 -0.53546956 154 -1.01320608 0.88694857 155 -3.14803974 -1.01320608 > 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/www/rcomp/tmp/71jag1324494755.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/www/rcomp/tmp/8urf11324494755.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/www/rcomp/tmp/9rpoh1324494755.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/www/rcomp/tmp/108s321324494755.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/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/11y6ff1324494755.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/12ambp1324494755.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/13ixv61324494755.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/14t9hu1324494755.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/www/rcomp/tmp/15ssbm1324494755.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/www/rcomp/tmp/16jqnb1324494755.tab") + } > > try(system("convert tmp/170am1324494755.ps tmp/170am1324494755.png",intern=TRUE)) character(0) > try(system("convert tmp/2pi4m1324494755.ps tmp/2pi4m1324494755.png",intern=TRUE)) character(0) > try(system("convert tmp/3u52c1324494755.ps tmp/3u52c1324494755.png",intern=TRUE)) character(0) > try(system("convert tmp/4h37w1324494755.ps tmp/4h37w1324494755.png",intern=TRUE)) character(0) > try(system("convert tmp/5fwm61324494755.ps tmp/5fwm61324494755.png",intern=TRUE)) character(0) > try(system("convert tmp/6nvdb1324494755.ps tmp/6nvdb1324494755.png",intern=TRUE)) character(0) > try(system("convert tmp/71jag1324494755.ps tmp/71jag1324494755.png",intern=TRUE)) character(0) > try(system("convert tmp/8urf11324494755.ps tmp/8urf11324494755.png",intern=TRUE)) character(0) > try(system("convert tmp/9rpoh1324494755.ps tmp/9rpoh1324494755.png",intern=TRUE)) character(0) > try(system("convert tmp/108s321324494755.ps tmp/108s321324494755.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.440 0.360 5.793