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(11 + ,236496 + ,61 + ,85 + ,34 + ,131 + ,124252 + ,10 + ,130631 + ,58 + ,58 + ,30 + ,117 + ,98956 + ,12 + ,198514 + ,62 + ,62 + ,38 + ,146 + ,98073 + ,11 + ,189326 + ,94 + ,108 + ,34 + ,132 + ,106816 + ,12 + ,137449 + ,43 + ,55 + ,25 + ,80 + ,41449 + ,10 + ,65295 + ,27 + ,8 + ,31 + ,117 + ,76173 + ,10 + ,439387 + ,103 + ,134 + ,29 + ,112 + ,177551 + ,11 + ,33186 + ,19 + ,1 + ,18 + ,67 + ,22807 + ,10 + ,174859 + ,50 + ,63 + ,30 + ,116 + ,126938 + ,10 + ,186657 + ,38 + ,77 + ,29 + ,107 + ,61680 + ,12 + ,261949 + ,95 + ,86 + ,38 + ,140 + ,72117 + ,10 + ,190794 + ,94 + ,93 + ,49 + ,186 + ,79738 + ,11 + ,138866 + ,57 + ,44 + ,33 + ,109 + ,57793 + ,11 + ,296878 + ,65 + ,106 + ,46 + ,159 + ,91677 + ,10 + ,192648 + ,71 + ,63 + ,38 + ,146 + ,64631 + ,12 + ,333348 + ,161 + ,160 + ,52 + ,201 + ,106385 + ,10 + ,242212 + ,57 + ,104 + ,32 + ,124 + ,161961 + ,10 + ,263451 + ,130 + ,86 + ,35 + ,131 + ,112669 + ,12 + ,150733 + ,47 + ,92 + ,25 + ,96 + ,114029 + 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+ ,63995 + ,11 + ,216638 + ,44 + ,100 + ,36 + ,134 + ,84891 + ,10 + ,192853 + ,71 + ,80 + ,36 + ,129 + ,61263 + ,11 + ,173710 + ,103 + ,29 + ,27 + ,107 + ,106221 + ,10 + ,336678 + ,103 + ,95 + ,33 + ,128 + ,113587 + ,10 + ,212961 + ,75 + ,114 + ,35 + ,129 + ,113864 + ,12 + ,173260 + ,63 + ,41 + ,21 + ,79 + ,37238 + ,10 + ,271773 + ,89 + ,128 + ,40 + ,154 + ,119906 + ,12 + ,127096 + ,51 + ,142 + ,47 + ,180 + ,135096 + ,11 + ,203606 + ,73 + ,88 + ,33 + ,122 + ,151611 + ,12 + ,230177 + ,87 + ,132 + ,39 + ,144 + ,144645 + ,10 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,10 + ,14688 + ,10 + ,4 + ,0 + ,0 + ,6023 + ,11 + ,98 + ,1 + ,0 + ,0 + ,0 + ,0 + ,12 + ,455 + ,2 + ,0 + ,0 + ,0 + ,0 + ,12 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,10 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,11 + ,195765 + ,75 + ,56 + ,33 + ,120 + ,77457 + ,10 + ,306514 + ,117 + ,111 + ,42 + ,168 + ,62464 + ,12 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,10 + ,203 + ,4 + ,0 + ,0 + ,0 + ,0 + ,10 + ,7199 + ,5 + ,7 + ,0 + ,0 + ,1644 + ,10 + ,46660 + ,20 + ,12 + ,5 + ,15 + ,6179 + ,11 + ,17547 + ,5 + ,0 + ,1 + ,4 + ,3926 + ,11 + ,105044 + ,37 + ,37 + ,38 + ,133 + ,42087 + ,12 + ,969 + ,2 + ,0 + ,0 + ,0 + ,0 + ,10 + ,165838 + ,56 + ,46 + ,28 + ,101 + ,87656) + ,dim=c(7 + ,164) + ,dimnames=list(c('Maand' + ,'Total_Time_spent_in_RFC' + ,'Number_of_Logins' + ,'Total_Number_of_Blogged_Computations' + ,'Total_Number_of_Reviewed_Compendiums' + ,'Total_Number_of_Feedback_Messages_PeerReviews' + ,'Total_number_of_characters') + ,1:164)) > y <- array(NA,dim=c(7,164),dimnames=list(c('Maand','Total_Time_spent_in_RFC','Number_of_Logins','Total_Number_of_Blogged_Computations','Total_Number_of_Reviewed_Compendiums','Total_Number_of_Feedback_Messages_PeerReviews','Total_number_of_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 = '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 Total_Time_spent_in_RFC Maand Number_of_Logins 1 236496 11 61 2 130631 10 58 3 198514 12 62 4 189326 11 94 5 137449 12 43 6 65295 10 27 7 439387 10 103 8 33186 11 19 9 174859 10 50 10 186657 10 38 11 261949 12 95 12 190794 10 94 13 138866 11 57 14 296878 11 65 15 192648 10 71 16 333348 12 161 17 242212 10 57 18 263451 10 130 19 150733 12 47 20 223226 10 67 21 240028 11 63 22 384138 10 86 23 156540 10 34 24 148421 12 43 25 176502 11 96 26 191441 12 105 27 249735 11 120 28 236812 12 76 29 142329 11 45 30 259667 10 53 31 228871 11 64 32 176054 12 66 33 286683 11 79 34 87485 10 33 35 322865 12 82 36 247013 12 50 37 340093 11 103 38 191653 12 72 39 114673 10 31 40 284210 11 160 41 284195 10 72 42 155363 11 59 43 174198 12 65 44 142986 10 48 45 140319 10 73 46 392666 10 132 47 78800 11 42 48 201970 12 69 49 302674 12 99 50 164733 11 50 51 194221 11 68 52 24188 10 24 53 340411 11 274 54 65029 12 17 55 101097 11 64 56 243889 12 45 57 273003 10 74 58 282220 12 160 59 273495 10 118 60 214872 10 74 61 333165 12 122 62 260981 12 105 63 184474 11 87 64 222366 12 76 65 205675 11 60 66 201345 10 60 67 163043 11 110 68 204250 10 128 69 197760 12 66 70 127260 10 57 71 216092 10 59 72 73566 12 32 73 213198 11 67 74 177949 11 48 75 148698 10 49 76 300103 11 69 77 251437 10 78 78 191971 10 99 79 154651 11 53 80 155473 12 56 81 132672 11 41 82 376465 10 99 83 145869 11 65 84 223666 11 85 85 80953 10 25 86 130789 11 46 87 135042 12 47 88 300074 10 152 89 271757 12 93 90 150949 12 95 91 216802 10 77 92 197389 10 67 93 156583 11 56 94 222599 12 65 95 261601 10 70 96 178489 10 35 97 200657 12 43 98 259084 12 67 99 302789 10 129 100 342025 12 98 101 246440 11 104 102 251306 12 56 103 159965 12 156 104 43287 11 14 105 172212 10 67 106 181781 10 117 107 227681 12 43 108 260464 12 80 109 106288 11 54 110 109632 11 76 111 268905 12 58 112 266568 10 77 113 23623 12 11 114 152474 11 65 115 61857 10 25 116 144889 11 43 117 330910 11 95 118 21054 11 16 119 223718 10 44 120 31414 10 19 121 259747 10 103 122 190495 12 55 123 154984 11 73 124 112933 11 45 125 38214 12 34 126 158671 11 33 127 299775 11 68 128 172783 10 54 129 348678 10 69 130 266701 11 89 131 358933 10 99 132 172464 11 31 133 94381 10 35 134 243875 11 274 135 382487 11 153 136 111853 12 39 137 334926 10 117 138 147979 10 72 139 216638 11 44 140 192853 10 71 141 173710 11 103 142 336678 10 103 143 212961 10 75 144 173260 12 63 145 271773 10 89 146 127096 12 51 147 203606 11 73 148 230177 12 87 149 1 10 0 150 14688 10 10 151 98 11 1 152 455 12 2 153 0 12 0 154 0 10 0 155 195765 11 75 156 306514 10 117 157 0 12 0 158 203 10 4 159 7199 10 5 160 46660 10 20 161 17547 11 5 162 105044 11 37 163 969 12 2 164 165838 10 56 Total_Number_of_Blogged_Computations Total_Number_of_Reviewed_Compendiums 1 85 34 2 58 30 3 62 38 4 108 34 5 55 25 6 8 31 7 134 29 8 1 18 9 63 30 10 77 29 11 86 38 12 93 49 13 44 33 14 106 46 15 63 38 16 160 52 17 104 32 18 86 35 19 92 25 20 119 42 21 107 40 22 86 35 23 50 25 24 92 46 25 123 36 26 81 35 27 93 38 28 113 35 29 52 28 30 113 37 31 109 40 32 44 42 33 123 44 34 38 33 35 111 35 36 77 37 37 92 37 38 74 32 39 33 17 40 105 34 41 108 33 42 66 35 43 69 32 44 62 35 45 50 45 46 91 34 47 20 26 48 101 45 49 129 44 50 93 40 51 89 33 52 8 4 53 79 41 54 21 18 55 30 14 56 86 33 57 116 49 58 106 32 59 127 37 60 75 32 61 138 41 62 114 25 63 55 38 64 67 34 65 43 33 66 88 28 67 67 31 68 75 40 69 114 32 70 119 25 71 86 42 72 22 23 73 67 42 74 77 34 75 105 34 76 119 38 77 88 32 78 75 37 79 112 34 80 66 33 81 58 25 82 132 40 83 30 26 84 100 40 85 49 8 86 26 27 87 67 32 88 57 33 89 95 50 90 139 37 91 70 33 92 134 34 93 37 28 94 98 32 95 58 32 96 78 32 97 88 31 98 142 35 99 127 52 100 139 27 101 108 45 102 128 37 103 62 32 104 13 19 105 89 22 106 83 35 107 116 36 108 157 36 109 28 23 110 83 36 111 72 36 112 134 42 113 12 1 114 106 32 115 23 11 116 83 40 117 120 34 118 4 0 119 71 27 120 18 8 121 98 35 122 66 40 123 44 40 124 29 28 125 16 8 126 56 35 127 112 45 128 46 43 129 129 41 130 139 43 131 136 47 132 66 35 133 42 32 134 70 36 135 97 42 136 49 35 137 113 37 138 55 34 139 100 36 140 80 36 141 29 27 142 95 33 143 114 35 144 41 21 145 128 40 146 142 47 147 88 33 148 132 39 149 0 0 150 4 0 151 0 0 152 0 0 153 0 0 154 0 0 155 56 33 156 111 42 157 0 0 158 0 0 159 7 0 160 12 5 161 0 1 162 37 38 163 0 0 164 46 28 Total_Number_of_Feedback_Messages_PeerReviews Total_number_of_characters 1 131 124252 2 117 98956 3 146 98073 4 132 106816 5 80 41449 6 117 76173 7 112 177551 8 67 22807 9 116 126938 10 107 61680 11 140 72117 12 186 79738 13 109 57793 14 159 91677 15 146 64631 16 201 106385 17 124 161961 18 131 112669 19 96 114029 20 163 124550 21 151 105416 22 128 72875 23 89 81964 24 184 104880 25 136 76302 26 134 96740 27 146 93071 28 130 78912 29 105 35224 30 142 90694 31 155 125369 32 154 80849 33 169 104434 34 125 65702 35 135 108179 36 139 63583 37 139 95066 38 124 62486 39 55 31081 40 131 94584 41 125 87408 42 128 68966 43 107 88766 44 130 57139 45 73 90586 46 125 109249 47 82 33032 48 173 96056 49 169 146648 50 145 80613 51 134 87026 52 12 5950 53 151 131106 54 67 32551 55 52 31701 56 121 91072 57 186 159803 58 120 143950 59 135 112368 60 123 82124 61 158 144068 62 90 162627 63 149 55062 64 131 95329 65 125 105612 66 110 62853 67 121 125976 68 151 79146 69 123 108461 70 92 99971 71 162 77826 72 88 22618 73 163 84892 74 120 92059 75 132 77993 76 144 104155 77 124 109840 78 140 238712 79 132 67486 80 122 68007 81 97 48194 82 155 134796 83 99 38692 84 106 93587 85 28 56622 86 101 15986 87 120 113402 88 127 97967 89 178 74844 90 141 136051 91 122 50548 92 127 112215 93 102 59591 94 124 59938 95 124 137639 96 124 143372 97 111 138599 98 129 174110 99 199 135062 100 102 175681 101 174 130307 102 141 139141 103 122 44244 104 71 43750 105 81 48029 106 131 95216 107 139 92288 108 137 94588 109 91 197426 110 142 151244 111 133 139206 112 155 106271 113 0 1168 114 123 71764 115 32 25162 116 149 45635 117 128 101817 118 0 855 119 99 100174 120 25 14116 121 132 85008 122 151 124254 123 151 105793 124 103 117129 125 27 8773 126 131 94747 127 170 107549 128 165 97392 129 159 126893 130 167 118850 131 178 234853 132 135 74783 133 118 66089 134 140 95684 135 158 139537 136 132 144253 137 136 153824 138 123 63995 139 134 84891 140 129 61263 141 107 106221 142 128 113587 143 129 113864 144 79 37238 145 154 119906 146 180 135096 147 122 151611 148 144 144645 149 0 0 150 0 6023 151 0 0 152 0 0 153 0 0 154 0 0 155 120 77457 156 168 62464 157 0 0 158 0 0 159 0 1644 160 15 6179 161 4 3926 162 133 42087 163 0 0 164 101 87656 t 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 51 51 52 52 53 53 54 54 55 55 56 56 57 57 58 58 59 59 60 60 61 61 62 62 63 63 64 64 65 65 66 66 67 67 68 68 69 69 70 70 71 71 72 72 73 73 74 74 75 75 76 76 77 77 78 78 79 79 80 80 81 81 82 82 83 83 84 84 85 85 86 86 87 87 88 88 89 89 90 90 91 91 92 92 93 93 94 94 95 95 96 96 97 97 98 98 99 99 100 100 101 101 102 102 103 103 104 104 105 105 106 106 107 107 108 108 109 109 110 110 111 111 112 112 113 113 114 114 115 115 116 116 117 117 118 118 119 119 120 120 121 121 122 122 123 123 124 124 125 125 126 126 127 127 128 128 129 129 130 130 131 131 132 132 133 133 134 134 135 135 136 136 137 137 138 138 139 139 140 140 141 141 142 142 143 143 144 144 145 145 146 146 147 147 148 148 149 149 150 150 151 151 152 152 153 153 154 154 155 155 156 156 157 157 158 158 159 159 160 160 161 161 162 162 163 163 164 164 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) 1.098e+05 Maand -8.640e+03 Number_of_Logins 7.302e+02 Total_Number_of_Blogged_Computations 1.033e+03 Total_Number_of_Reviewed_Compendiums 3.791e+02 Total_Number_of_Feedback_Messages_PeerReviews 1.689e+02 Total_number_of_characters 2.543e-01 t -9.239e+01 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -132230 -25148 -2183 21576 157722 Coefficients: Estimate Std. Error t value (Intercept) 1.098e+05 5.076e+04 2.164 Maand -8.640e+03 4.460e+03 -1.937 Number_of_Logins 7.302e+02 1.090e+02 6.701 Total_Number_of_Blogged_Computations 1.033e+03 1.490e+02 6.933 Total_Number_of_Reviewed_Compendiums 3.791e+02 1.499e+03 0.253 Total_Number_of_Feedback_Messages_PeerReviews 1.689e+02 3.985e+02 0.424 Total_number_of_characters 2.543e-01 1.141e-01 2.228 t -9.239e+01 8.125e+01 -1.137 Pr(>|t|) (Intercept) 0.0320 * Maand 0.0545 . Number_of_Logins 3.56e-10 *** Total_Number_of_Blogged_Computations 1.03e-10 *** Total_Number_of_Reviewed_Compendiums 0.8007 Total_Number_of_Feedback_Messages_PeerReviews 0.6722 Total_number_of_characters 0.0273 * t 0.2572 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 46220 on 156 degrees of freedom Multiple R-squared: 0.7784, Adjusted R-squared: 0.7685 F-statistic: 78.29 on 7 and 156 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,] 0.7831098 4.337805e-01 2.168902e-01 [2,] 0.6586632 6.826736e-01 3.413368e-01 [3,] 0.5739875 8.520250e-01 4.260125e-01 [4,] 0.4507410 9.014819e-01 5.492590e-01 [5,] 0.3684177 7.368353e-01 6.315823e-01 [6,] 0.4002980 8.005959e-01 5.997020e-01 [7,] 0.6391237 7.217526e-01 3.608763e-01 [8,] 0.5463135 9.073730e-01 4.536865e-01 [9,] 0.5690245 8.619509e-01 4.309755e-01 [10,] 0.4988622 9.977244e-01 5.011378e-01 [11,] 0.4367840 8.735679e-01 5.632160e-01 [12,] 0.9379754 1.240492e-01 6.202458e-02 [13,] 0.9144616 1.710769e-01 8.553843e-02 [14,] 0.8976924 2.046151e-01 1.023076e-01 [15,] 0.9497246 1.005508e-01 5.027541e-02 [16,] 0.9331552 1.336895e-01 6.684476e-02 [17,] 0.9094068 1.811863e-01 9.059315e-02 [18,] 0.8833995 2.332010e-01 1.166005e-01 [19,] 0.8661019 2.677963e-01 1.338981e-01 [20,] 0.8410964 3.178073e-01 1.589036e-01 [21,] 0.8014197 3.971607e-01 1.985803e-01 [22,] 0.7655360 4.689280e-01 2.344640e-01 [23,] 0.7302402 5.395197e-01 2.697598e-01 [24,] 0.7146495 5.707010e-01 2.853505e-01 [25,] 0.7983147 4.033706e-01 2.016853e-01 [26,] 0.8538462 2.923077e-01 1.461538e-01 [27,] 0.8946184 2.107631e-01 1.053816e-01 [28,] 0.8671799 2.656402e-01 1.328201e-01 [29,] 0.8524926 2.950147e-01 1.475074e-01 [30,] 0.8383756 3.232488e-01 1.616244e-01 [31,] 0.8181149 3.637702e-01 1.818851e-01 [32,] 0.8107410 3.785180e-01 1.892590e-01 [33,] 0.8295490 3.409021e-01 1.704510e-01 [34,] 0.8066141 3.867717e-01 1.933859e-01 [35,] 0.8793121 2.413758e-01 1.206879e-01 [36,] 0.9529029 9.419414e-02 4.709707e-02 [37,] 0.9417685 1.164631e-01 5.823154e-02 [38,] 0.9301469 1.397062e-01 6.985311e-02 [39,] 0.9132191 1.735618e-01 8.678091e-02 [40,] 0.9060313 1.879374e-01 9.396869e-02 [41,] 0.8899277 2.201446e-01 1.100723e-01 [42,] 0.8866292 2.267417e-01 1.133708e-01 [43,] 0.8814053 2.371895e-01 1.185947e-01 [44,] 0.8549592 2.900816e-01 1.450408e-01 [45,] 0.8283003 3.433994e-01 1.716997e-01 [46,] 0.8459088 3.081824e-01 1.540912e-01 [47,] 0.8172795 3.654410e-01 1.827205e-01 [48,] 0.7996175 4.007650e-01 2.003825e-01 [49,] 0.7875273 4.249455e-01 2.124727e-01 [50,] 0.7539788 4.920424e-01 2.460212e-01 [51,] 0.7241739 5.516522e-01 2.758261e-01 [52,] 0.6999319 6.001362e-01 3.000681e-01 [53,] 0.6602410 6.795180e-01 3.397590e-01 [54,] 0.6510997 6.978006e-01 3.489003e-01 [55,] 0.6596521 6.806957e-01 3.403479e-01 [56,] 0.6187665 7.624670e-01 3.812335e-01 [57,] 0.6422510 7.154980e-01 3.577490e-01 [58,] 0.6252298 7.495404e-01 3.747702e-01 [59,] 0.6057690 7.884621e-01 3.942310e-01 [60,] 0.7686111 4.627778e-01 2.313889e-01 [61,] 0.7348873 5.302254e-01 2.651127e-01 [62,] 0.6952779 6.094442e-01 3.047221e-01 [63,] 0.6679988 6.640025e-01 3.320012e-01 [64,] 0.6243741 7.512518e-01 3.756259e-01 [65,] 0.6554489 6.891022e-01 3.445511e-01 [66,] 0.6760760 6.478481e-01 3.239240e-01 [67,] 0.6511923 6.976153e-01 3.488077e-01 [68,] 0.6974616 6.050768e-01 3.025384e-01 [69,] 0.7135707 5.728586e-01 2.864293e-01 [70,] 0.6725843 6.548313e-01 3.274157e-01 [71,] 0.6297403 7.405194e-01 3.702597e-01 [72,] 0.7111103 5.777794e-01 2.888897e-01 [73,] 0.6860042 6.279915e-01 3.139958e-01 [74,] 0.6497527 7.004946e-01 3.502473e-01 [75,] 0.6157934 7.684132e-01 3.842066e-01 [76,] 0.5989693 8.020614e-01 4.010307e-01 [77,] 0.5642726 8.714549e-01 4.357274e-01 [78,] 0.5990895 8.018210e-01 4.009105e-01 [79,] 0.5876821 8.246358e-01 4.123179e-01 [80,] 0.8310715 3.378570e-01 1.689285e-01 [81,] 0.8160750 3.678501e-01 1.839250e-01 [82,] 0.8463829 3.072342e-01 1.536171e-01 [83,] 0.8340623 3.318753e-01 1.659377e-01 [84,] 0.8242780 3.514439e-01 1.757220e-01 [85,] 0.8714359 2.571283e-01 1.285641e-01 [86,] 0.8448759 3.102482e-01 1.551241e-01 [87,] 0.8190451 3.619099e-01 1.809549e-01 [88,] 0.7920008 4.159984e-01 2.079992e-01 [89,] 0.7605704 4.788591e-01 2.394296e-01 [90,] 0.7777801 4.444399e-01 2.222199e-01 [91,] 0.7454144 5.091712e-01 2.545856e-01 [92,] 0.7080286 5.839428e-01 2.919714e-01 [93,] 0.7074874 5.850252e-01 2.925126e-01 [94,] 0.6653083 6.693833e-01 3.346917e-01 [95,] 0.6225727 7.548546e-01 3.774273e-01 [96,] 0.6494571 7.010858e-01 3.505429e-01 [97,] 0.6170910 7.658179e-01 3.829090e-01 [98,] 0.5729254 8.541491e-01 4.270746e-01 [99,] 0.5568453 8.863094e-01 4.431547e-01 [100,] 0.7997472 4.005055e-01 2.002528e-01 [101,] 0.8712647 2.574706e-01 1.287353e-01 [102,] 0.8457979 3.084042e-01 1.542021e-01 [103,] 0.8161643 3.676714e-01 1.838357e-01 [104,] 0.8485061 3.029879e-01 1.514939e-01 [105,] 0.8187888 3.624224e-01 1.812112e-01 [106,] 0.7996194 4.007612e-01 2.003806e-01 [107,] 0.8410894 3.178212e-01 1.589106e-01 [108,] 0.8062386 3.875228e-01 1.937614e-01 [109,] 0.8073981 3.852038e-01 1.926019e-01 [110,] 0.7849794 4.300413e-01 2.150206e-01 [111,] 0.7442891 5.114218e-01 2.557109e-01 [112,] 0.7042632 5.914736e-01 2.957368e-01 [113,] 0.6613141 6.773719e-01 3.386859e-01 [114,] 0.6160008 7.679984e-01 3.839992e-01 [115,] 0.5607166 8.785667e-01 4.392834e-01 [116,] 0.5053887 9.892226e-01 4.946113e-01 [117,] 0.5336167 9.327667e-01 4.663833e-01 [118,] 0.4788084 9.576168e-01 5.211916e-01 [119,] 0.5637109 8.725782e-01 4.362891e-01 [120,] 0.5056447 9.887105e-01 4.943553e-01 [121,] 0.4539752 9.079504e-01 5.460248e-01 [122,] 0.4182338 8.364676e-01 5.817662e-01 [123,] 0.3943596 7.887191e-01 6.056404e-01 [124,] 0.9944899 1.102018e-02 5.510091e-03 [125,] 0.9925518 1.489648e-02 7.448241e-03 [126,] 0.9912675 1.746500e-02 8.732498e-03 [127,] 0.9868676 2.626476e-02 1.313238e-02 [128,] 0.9891065 2.178700e-02 1.089350e-02 [129,] 0.9987700 2.459956e-03 1.229978e-03 [130,] 0.9979680 4.063905e-03 2.031952e-03 [131,] 0.9999962 7.598594e-06 3.799297e-06 [132,] 0.9999999 2.736840e-07 1.368420e-07 [133,] 0.9999995 1.083853e-06 5.419264e-07 [134,] 0.9999980 3.940607e-06 1.970303e-06 [135,] 1.0000000 6.207831e-09 3.103916e-09 [136,] 1.0000000 5.047765e-09 2.523882e-09 [137,] 1.0000000 3.772954e-08 1.886477e-08 [138,] 0.9999999 2.020254e-07 1.010127e-07 [139,] 0.9999993 1.425704e-06 7.128520e-07 [140,] 0.9999942 1.152924e-05 5.764618e-06 [141,] 0.9999437 1.126828e-04 5.634141e-05 [142,] 0.9994828 1.034473e-03 5.172367e-04 [143,] 0.9960042 7.991586e-03 3.995793e-03 > postscript(file="/var/wessaorg/rcomp/tmp/154kq1323344450.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/2au1u1323344450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3vley1323344450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4tm6h1323344450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5ps2r1323344450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 22875.2183 -51149.2657 19346.7595 -67601.8293 10044.9826 -36429.4585 7 8 9 10 11 12 127962.0911 -19698.5774 -12542.8866 12144.0270 42252.8332 -66467.5649 13 14 15 16 17 18 -7388.9607 58853.3963 -1791.3120 -34826.3041 -2919.2472 -6087.8135 19 20 21 22 23 24 -37667.3926 -45287.9328 3210.5463 157721.7572 13432.3509 -57097.9973 25 26 27 28 29 30 -89112.1675 -33117.3150 -8947.9340 5774.8551 6362.5402 22547.6598 31 32 33 34 35 36 -15567.3810 16746.2792 18460.0638 -46462.8932 81872.6094 74497.1905 37 38 39 40 41 42 96834.0825 11064.8511 14505.4214 -11205.9708 44606.1756 -19202.0784 43 44 45 46 47 48 536.2925 -25201.8971 -36304.9636 121352.4305 -15064.2820 -25129.7625 49 50 51 52 53 54 13035.4736 -38134.2233 -14684.5478 -25266.0475 -25511.8003 3362.3179 55 56 57 58 59 60 -8463.4879 65144.8732 -9608.5955 -13861.6253 -27193.3134 11719.1454 61 62 63 64 65 66 22198.1309 139.5256 1614.7827 38196.6868 48204.7348 4156.0515 67 68 69 70 71 72 -59280.9724 -44599.1008 -28413.6231 -104643.6056 4261.2625 -1340.5234 73 74 75 76 77 78 22006.9228 -1129.0320 -67024.9293 53854.5874 26292.2061 -72356.4846 79 80 81 82 83 84 -59540.7083 -2735.0919 -2574.8817 76392.5654 23898.4063 -5555.8748 85 86 87 88 89 90 -25628.3545 32156.3647 -27806.1510 56060.3709 39770.7816 -132229.6707 91 92 93 94 95 96 27305.1049 -67712.8346 28299.6826 28158.2030 67874.2910 -11701.6317 97 98 99 100 101 102 15458.6219 -12900.7017 -24504.5053 57882.5319 -26066.1969 8287.3055 103 104 105 106 107 108 -58583.3724 -15850.0060 -16574.5760 -62599.0637 19639.7541 -17090.5532 109 110 111 112 113 114 -41055.6453 -112285.0509 84804.7966 -10239.4657 6826.4386 -59858.8351 115 116 117 118 119 120 -8915.1873 -28223.6029 73246.2149 1149.2382 53412.8589 -24222.9057 121 122 123 124 125 126 13910.8885 15042.6518 -14744.7032 -10991.5293 -7547.6213 14116.6052 127 128 129 130 131 132 58288.7974 5317.9528 78906.1154 -19334.0933 27275.8871 23997.4847 133 134 135 136 137 138 -34548.1933 -92508.7752 90196.9451 -33046.3622 45922.7268 -22000.8014 139 140 141 142 143 144 21432.5025 -3107.4422 11482.9131 90047.4888 -33751.8179 61309.3919 145 146 147 148 149 150 -7091.0313 -132015.9595 -13443.0448 -38022.6514 -9648.3483 -7833.1412 151 152 153 154 155 156 -1456.8496 6902.2576 8000.0014 -9187.3907 30235.4040 37260.4353 157 158 159 160 161 162 8369.5675 -11535.5289 -12824.4947 5029.8452 11942.2417 -7568.3338 163 164 8432.5645 19212.7666 > postscript(file="/var/wessaorg/rcomp/tmp/6lyqd1323344450.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 22875.2183 NA 1 -51149.2657 22875.2183 2 19346.7595 -51149.2657 3 -67601.8293 19346.7595 4 10044.9826 -67601.8293 5 -36429.4585 10044.9826 6 127962.0911 -36429.4585 7 -19698.5774 127962.0911 8 -12542.8866 -19698.5774 9 12144.0270 -12542.8866 10 42252.8332 12144.0270 11 -66467.5649 42252.8332 12 -7388.9607 -66467.5649 13 58853.3963 -7388.9607 14 -1791.3120 58853.3963 15 -34826.3041 -1791.3120 16 -2919.2472 -34826.3041 17 -6087.8135 -2919.2472 18 -37667.3926 -6087.8135 19 -45287.9328 -37667.3926 20 3210.5463 -45287.9328 21 157721.7572 3210.5463 22 13432.3509 157721.7572 23 -57097.9973 13432.3509 24 -89112.1675 -57097.9973 25 -33117.3150 -89112.1675 26 -8947.9340 -33117.3150 27 5774.8551 -8947.9340 28 6362.5402 5774.8551 29 22547.6598 6362.5402 30 -15567.3810 22547.6598 31 16746.2792 -15567.3810 32 18460.0638 16746.2792 33 -46462.8932 18460.0638 34 81872.6094 -46462.8932 35 74497.1905 81872.6094 36 96834.0825 74497.1905 37 11064.8511 96834.0825 38 14505.4214 11064.8511 39 -11205.9708 14505.4214 40 44606.1756 -11205.9708 41 -19202.0784 44606.1756 42 536.2925 -19202.0784 43 -25201.8971 536.2925 44 -36304.9636 -25201.8971 45 121352.4305 -36304.9636 46 -15064.2820 121352.4305 47 -25129.7625 -15064.2820 48 13035.4736 -25129.7625 49 -38134.2233 13035.4736 50 -14684.5478 -38134.2233 51 -25266.0475 -14684.5478 52 -25511.8003 -25266.0475 53 3362.3179 -25511.8003 54 -8463.4879 3362.3179 55 65144.8732 -8463.4879 56 -9608.5955 65144.8732 57 -13861.6253 -9608.5955 58 -27193.3134 -13861.6253 59 11719.1454 -27193.3134 60 22198.1309 11719.1454 61 139.5256 22198.1309 62 1614.7827 139.5256 63 38196.6868 1614.7827 64 48204.7348 38196.6868 65 4156.0515 48204.7348 66 -59280.9724 4156.0515 67 -44599.1008 -59280.9724 68 -28413.6231 -44599.1008 69 -104643.6056 -28413.6231 70 4261.2625 -104643.6056 71 -1340.5234 4261.2625 72 22006.9228 -1340.5234 73 -1129.0320 22006.9228 74 -67024.9293 -1129.0320 75 53854.5874 -67024.9293 76 26292.2061 53854.5874 77 -72356.4846 26292.2061 78 -59540.7083 -72356.4846 79 -2735.0919 -59540.7083 80 -2574.8817 -2735.0919 81 76392.5654 -2574.8817 82 23898.4063 76392.5654 83 -5555.8748 23898.4063 84 -25628.3545 -5555.8748 85 32156.3647 -25628.3545 86 -27806.1510 32156.3647 87 56060.3709 -27806.1510 88 39770.7816 56060.3709 89 -132229.6707 39770.7816 90 27305.1049 -132229.6707 91 -67712.8346 27305.1049 92 28299.6826 -67712.8346 93 28158.2030 28299.6826 94 67874.2910 28158.2030 95 -11701.6317 67874.2910 96 15458.6219 -11701.6317 97 -12900.7017 15458.6219 98 -24504.5053 -12900.7017 99 57882.5319 -24504.5053 100 -26066.1969 57882.5319 101 8287.3055 -26066.1969 102 -58583.3724 8287.3055 103 -15850.0060 -58583.3724 104 -16574.5760 -15850.0060 105 -62599.0637 -16574.5760 106 19639.7541 -62599.0637 107 -17090.5532 19639.7541 108 -41055.6453 -17090.5532 109 -112285.0509 -41055.6453 110 84804.7966 -112285.0509 111 -10239.4657 84804.7966 112 6826.4386 -10239.4657 113 -59858.8351 6826.4386 114 -8915.1873 -59858.8351 115 -28223.6029 -8915.1873 116 73246.2149 -28223.6029 117 1149.2382 73246.2149 118 53412.8589 1149.2382 119 -24222.9057 53412.8589 120 13910.8885 -24222.9057 121 15042.6518 13910.8885 122 -14744.7032 15042.6518 123 -10991.5293 -14744.7032 124 -7547.6213 -10991.5293 125 14116.6052 -7547.6213 126 58288.7974 14116.6052 127 5317.9528 58288.7974 128 78906.1154 5317.9528 129 -19334.0933 78906.1154 130 27275.8871 -19334.0933 131 23997.4847 27275.8871 132 -34548.1933 23997.4847 133 -92508.7752 -34548.1933 134 90196.9451 -92508.7752 135 -33046.3622 90196.9451 136 45922.7268 -33046.3622 137 -22000.8014 45922.7268 138 21432.5025 -22000.8014 139 -3107.4422 21432.5025 140 11482.9131 -3107.4422 141 90047.4888 11482.9131 142 -33751.8179 90047.4888 143 61309.3919 -33751.8179 144 -7091.0313 61309.3919 145 -132015.9595 -7091.0313 146 -13443.0448 -132015.9595 147 -38022.6514 -13443.0448 148 -9648.3483 -38022.6514 149 -7833.1412 -9648.3483 150 -1456.8496 -7833.1412 151 6902.2576 -1456.8496 152 8000.0014 6902.2576 153 -9187.3907 8000.0014 154 30235.4040 -9187.3907 155 37260.4353 30235.4040 156 8369.5675 37260.4353 157 -11535.5289 8369.5675 158 -12824.4947 -11535.5289 159 5029.8452 -12824.4947 160 11942.2417 5029.8452 161 -7568.3338 11942.2417 162 8432.5645 -7568.3338 163 19212.7666 8432.5645 164 NA 19212.7666 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -51149.2657 22875.2183 [2,] 19346.7595 -51149.2657 [3,] -67601.8293 19346.7595 [4,] 10044.9826 -67601.8293 [5,] -36429.4585 10044.9826 [6,] 127962.0911 -36429.4585 [7,] -19698.5774 127962.0911 [8,] -12542.8866 -19698.5774 [9,] 12144.0270 -12542.8866 [10,] 42252.8332 12144.0270 [11,] -66467.5649 42252.8332 [12,] -7388.9607 -66467.5649 [13,] 58853.3963 -7388.9607 [14,] -1791.3120 58853.3963 [15,] -34826.3041 -1791.3120 [16,] -2919.2472 -34826.3041 [17,] -6087.8135 -2919.2472 [18,] -37667.3926 -6087.8135 [19,] -45287.9328 -37667.3926 [20,] 3210.5463 -45287.9328 [21,] 157721.7572 3210.5463 [22,] 13432.3509 157721.7572 [23,] -57097.9973 13432.3509 [24,] -89112.1675 -57097.9973 [25,] -33117.3150 -89112.1675 [26,] -8947.9340 -33117.3150 [27,] 5774.8551 -8947.9340 [28,] 6362.5402 5774.8551 [29,] 22547.6598 6362.5402 [30,] -15567.3810 22547.6598 [31,] 16746.2792 -15567.3810 [32,] 18460.0638 16746.2792 [33,] -46462.8932 18460.0638 [34,] 81872.6094 -46462.8932 [35,] 74497.1905 81872.6094 [36,] 96834.0825 74497.1905 [37,] 11064.8511 96834.0825 [38,] 14505.4214 11064.8511 [39,] -11205.9708 14505.4214 [40,] 44606.1756 -11205.9708 [41,] -19202.0784 44606.1756 [42,] 536.2925 -19202.0784 [43,] -25201.8971 536.2925 [44,] -36304.9636 -25201.8971 [45,] 121352.4305 -36304.9636 [46,] -15064.2820 121352.4305 [47,] -25129.7625 -15064.2820 [48,] 13035.4736 -25129.7625 [49,] -38134.2233 13035.4736 [50,] -14684.5478 -38134.2233 [51,] -25266.0475 -14684.5478 [52,] -25511.8003 -25266.0475 [53,] 3362.3179 -25511.8003 [54,] -8463.4879 3362.3179 [55,] 65144.8732 -8463.4879 [56,] -9608.5955 65144.8732 [57,] -13861.6253 -9608.5955 [58,] -27193.3134 -13861.6253 [59,] 11719.1454 -27193.3134 [60,] 22198.1309 11719.1454 [61,] 139.5256 22198.1309 [62,] 1614.7827 139.5256 [63,] 38196.6868 1614.7827 [64,] 48204.7348 38196.6868 [65,] 4156.0515 48204.7348 [66,] -59280.9724 4156.0515 [67,] -44599.1008 -59280.9724 [68,] -28413.6231 -44599.1008 [69,] -104643.6056 -28413.6231 [70,] 4261.2625 -104643.6056 [71,] -1340.5234 4261.2625 [72,] 22006.9228 -1340.5234 [73,] -1129.0320 22006.9228 [74,] -67024.9293 -1129.0320 [75,] 53854.5874 -67024.9293 [76,] 26292.2061 53854.5874 [77,] -72356.4846 26292.2061 [78,] -59540.7083 -72356.4846 [79,] -2735.0919 -59540.7083 [80,] -2574.8817 -2735.0919 [81,] 76392.5654 -2574.8817 [82,] 23898.4063 76392.5654 [83,] -5555.8748 23898.4063 [84,] -25628.3545 -5555.8748 [85,] 32156.3647 -25628.3545 [86,] -27806.1510 32156.3647 [87,] 56060.3709 -27806.1510 [88,] 39770.7816 56060.3709 [89,] -132229.6707 39770.7816 [90,] 27305.1049 -132229.6707 [91,] -67712.8346 27305.1049 [92,] 28299.6826 -67712.8346 [93,] 28158.2030 28299.6826 [94,] 67874.2910 28158.2030 [95,] -11701.6317 67874.2910 [96,] 15458.6219 -11701.6317 [97,] -12900.7017 15458.6219 [98,] -24504.5053 -12900.7017 [99,] 57882.5319 -24504.5053 [100,] -26066.1969 57882.5319 [101,] 8287.3055 -26066.1969 [102,] -58583.3724 8287.3055 [103,] -15850.0060 -58583.3724 [104,] -16574.5760 -15850.0060 [105,] -62599.0637 -16574.5760 [106,] 19639.7541 -62599.0637 [107,] -17090.5532 19639.7541 [108,] -41055.6453 -17090.5532 [109,] -112285.0509 -41055.6453 [110,] 84804.7966 -112285.0509 [111,] -10239.4657 84804.7966 [112,] 6826.4386 -10239.4657 [113,] -59858.8351 6826.4386 [114,] -8915.1873 -59858.8351 [115,] -28223.6029 -8915.1873 [116,] 73246.2149 -28223.6029 [117,] 1149.2382 73246.2149 [118,] 53412.8589 1149.2382 [119,] -24222.9057 53412.8589 [120,] 13910.8885 -24222.9057 [121,] 15042.6518 13910.8885 [122,] -14744.7032 15042.6518 [123,] -10991.5293 -14744.7032 [124,] -7547.6213 -10991.5293 [125,] 14116.6052 -7547.6213 [126,] 58288.7974 14116.6052 [127,] 5317.9528 58288.7974 [128,] 78906.1154 5317.9528 [129,] -19334.0933 78906.1154 [130,] 27275.8871 -19334.0933 [131,] 23997.4847 27275.8871 [132,] -34548.1933 23997.4847 [133,] -92508.7752 -34548.1933 [134,] 90196.9451 -92508.7752 [135,] -33046.3622 90196.9451 [136,] 45922.7268 -33046.3622 [137,] -22000.8014 45922.7268 [138,] 21432.5025 -22000.8014 [139,] -3107.4422 21432.5025 [140,] 11482.9131 -3107.4422 [141,] 90047.4888 11482.9131 [142,] -33751.8179 90047.4888 [143,] 61309.3919 -33751.8179 [144,] -7091.0313 61309.3919 [145,] -132015.9595 -7091.0313 [146,] -13443.0448 -132015.9595 [147,] -38022.6514 -13443.0448 [148,] -9648.3483 -38022.6514 [149,] -7833.1412 -9648.3483 [150,] -1456.8496 -7833.1412 [151,] 6902.2576 -1456.8496 [152,] 8000.0014 6902.2576 [153,] -9187.3907 8000.0014 [154,] 30235.4040 -9187.3907 [155,] 37260.4353 30235.4040 [156,] 8369.5675 37260.4353 [157,] -11535.5289 8369.5675 [158,] -12824.4947 -11535.5289 [159,] 5029.8452 -12824.4947 [160,] 11942.2417 5029.8452 [161,] -7568.3338 11942.2417 [162,] 8432.5645 -7568.3338 [163,] 19212.7666 8432.5645 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -51149.2657 22875.2183 2 19346.7595 -51149.2657 3 -67601.8293 19346.7595 4 10044.9826 -67601.8293 5 -36429.4585 10044.9826 6 127962.0911 -36429.4585 7 -19698.5774 127962.0911 8 -12542.8866 -19698.5774 9 12144.0270 -12542.8866 10 42252.8332 12144.0270 11 -66467.5649 42252.8332 12 -7388.9607 -66467.5649 13 58853.3963 -7388.9607 14 -1791.3120 58853.3963 15 -34826.3041 -1791.3120 16 -2919.2472 -34826.3041 17 -6087.8135 -2919.2472 18 -37667.3926 -6087.8135 19 -45287.9328 -37667.3926 20 3210.5463 -45287.9328 21 157721.7572 3210.5463 22 13432.3509 157721.7572 23 -57097.9973 13432.3509 24 -89112.1675 -57097.9973 25 -33117.3150 -89112.1675 26 -8947.9340 -33117.3150 27 5774.8551 -8947.9340 28 6362.5402 5774.8551 29 22547.6598 6362.5402 30 -15567.3810 22547.6598 31 16746.2792 -15567.3810 32 18460.0638 16746.2792 33 -46462.8932 18460.0638 34 81872.6094 -46462.8932 35 74497.1905 81872.6094 36 96834.0825 74497.1905 37 11064.8511 96834.0825 38 14505.4214 11064.8511 39 -11205.9708 14505.4214 40 44606.1756 -11205.9708 41 -19202.0784 44606.1756 42 536.2925 -19202.0784 43 -25201.8971 536.2925 44 -36304.9636 -25201.8971 45 121352.4305 -36304.9636 46 -15064.2820 121352.4305 47 -25129.7625 -15064.2820 48 13035.4736 -25129.7625 49 -38134.2233 13035.4736 50 -14684.5478 -38134.2233 51 -25266.0475 -14684.5478 52 -25511.8003 -25266.0475 53 3362.3179 -25511.8003 54 -8463.4879 3362.3179 55 65144.8732 -8463.4879 56 -9608.5955 65144.8732 57 -13861.6253 -9608.5955 58 -27193.3134 -13861.6253 59 11719.1454 -27193.3134 60 22198.1309 11719.1454 61 139.5256 22198.1309 62 1614.7827 139.5256 63 38196.6868 1614.7827 64 48204.7348 38196.6868 65 4156.0515 48204.7348 66 -59280.9724 4156.0515 67 -44599.1008 -59280.9724 68 -28413.6231 -44599.1008 69 -104643.6056 -28413.6231 70 4261.2625 -104643.6056 71 -1340.5234 4261.2625 72 22006.9228 -1340.5234 73 -1129.0320 22006.9228 74 -67024.9293 -1129.0320 75 53854.5874 -67024.9293 76 26292.2061 53854.5874 77 -72356.4846 26292.2061 78 -59540.7083 -72356.4846 79 -2735.0919 -59540.7083 80 -2574.8817 -2735.0919 81 76392.5654 -2574.8817 82 23898.4063 76392.5654 83 -5555.8748 23898.4063 84 -25628.3545 -5555.8748 85 32156.3647 -25628.3545 86 -27806.1510 32156.3647 87 56060.3709 -27806.1510 88 39770.7816 56060.3709 89 -132229.6707 39770.7816 90 27305.1049 -132229.6707 91 -67712.8346 27305.1049 92 28299.6826 -67712.8346 93 28158.2030 28299.6826 94 67874.2910 28158.2030 95 -11701.6317 67874.2910 96 15458.6219 -11701.6317 97 -12900.7017 15458.6219 98 -24504.5053 -12900.7017 99 57882.5319 -24504.5053 100 -26066.1969 57882.5319 101 8287.3055 -26066.1969 102 -58583.3724 8287.3055 103 -15850.0060 -58583.3724 104 -16574.5760 -15850.0060 105 -62599.0637 -16574.5760 106 19639.7541 -62599.0637 107 -17090.5532 19639.7541 108 -41055.6453 -17090.5532 109 -112285.0509 -41055.6453 110 84804.7966 -112285.0509 111 -10239.4657 84804.7966 112 6826.4386 -10239.4657 113 -59858.8351 6826.4386 114 -8915.1873 -59858.8351 115 -28223.6029 -8915.1873 116 73246.2149 -28223.6029 117 1149.2382 73246.2149 118 53412.8589 1149.2382 119 -24222.9057 53412.8589 120 13910.8885 -24222.9057 121 15042.6518 13910.8885 122 -14744.7032 15042.6518 123 -10991.5293 -14744.7032 124 -7547.6213 -10991.5293 125 14116.6052 -7547.6213 126 58288.7974 14116.6052 127 5317.9528 58288.7974 128 78906.1154 5317.9528 129 -19334.0933 78906.1154 130 27275.8871 -19334.0933 131 23997.4847 27275.8871 132 -34548.1933 23997.4847 133 -92508.7752 -34548.1933 134 90196.9451 -92508.7752 135 -33046.3622 90196.9451 136 45922.7268 -33046.3622 137 -22000.8014 45922.7268 138 21432.5025 -22000.8014 139 -3107.4422 21432.5025 140 11482.9131 -3107.4422 141 90047.4888 11482.9131 142 -33751.8179 90047.4888 143 61309.3919 -33751.8179 144 -7091.0313 61309.3919 145 -132015.9595 -7091.0313 146 -13443.0448 -132015.9595 147 -38022.6514 -13443.0448 148 -9648.3483 -38022.6514 149 -7833.1412 -9648.3483 150 -1456.8496 -7833.1412 151 6902.2576 -1456.8496 152 8000.0014 6902.2576 153 -9187.3907 8000.0014 154 30235.4040 -9187.3907 155 37260.4353 30235.4040 156 8369.5675 37260.4353 157 -11535.5289 8369.5675 158 -12824.4947 -11535.5289 159 5029.8452 -12824.4947 160 11942.2417 5029.8452 161 -7568.3338 11942.2417 162 8432.5645 -7568.3338 163 19212.7666 8432.5645 > 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/7sn7p1323344450.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/819u71323344450.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/9eexo1323344450.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/109zj41323344450.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11ky3e1323344450.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12kav91323344450.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/132x0n1323344450.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14wr0o1323344450.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/15e8dg1323344450.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/16i8nw1323344451.tab") + } > > try(system("convert tmp/154kq1323344450.ps tmp/154kq1323344450.png",intern=TRUE)) character(0) > try(system("convert tmp/2au1u1323344450.ps tmp/2au1u1323344450.png",intern=TRUE)) character(0) > try(system("convert tmp/3vley1323344450.ps tmp/3vley1323344450.png",intern=TRUE)) character(0) > try(system("convert tmp/4tm6h1323344450.ps tmp/4tm6h1323344450.png",intern=TRUE)) character(0) > try(system("convert tmp/5ps2r1323344450.ps tmp/5ps2r1323344450.png",intern=TRUE)) character(0) > try(system("convert tmp/6lyqd1323344450.ps tmp/6lyqd1323344450.png",intern=TRUE)) character(0) > try(system("convert tmp/7sn7p1323344450.ps tmp/7sn7p1323344450.png",intern=TRUE)) character(0) > try(system("convert tmp/819u71323344450.ps tmp/819u71323344450.png",intern=TRUE)) character(0) > try(system("convert tmp/9eexo1323344450.ps tmp/9eexo1323344450.png",intern=TRUE)) character(0) > try(system("convert tmp/109zj41323344450.ps tmp/109zj41323344450.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.082 0.507 5.600