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(170588 + ,46 + ,65 + ,26 + ,99 + ,86621 + ,48 + ,54 + ,20 + ,77 + ,113337 + ,37 + ,58 + ,24 + ,90 + ,144530 + ,72 + ,77 + ,25 + ,96 + ,81530 + ,31 + ,41 + ,15 + ,41 + ,35523 + ,17 + ,0 + ,16 + ,64 + ,305115 + ,78 + ,111 + ,20 + ,76 + ,32750 + ,16 + ,1 + ,18 + ,67 + ,115885 + ,37 + ,37 + ,19 + ,72 + ,130539 + ,24 + ,60 + ,20 + ,75 + ,156990 + ,63 + ,64 + ,26 + ,97 + ,128274 + ,74 + ,71 + ,37 + ,139 + ,102350 + ,43 + ,38 + ,23 + ,76 + ,192887 + ,42 + ,76 + ,36 + ,123 + ,129796 + ,55 + ,61 + ,28 + ,106 + ,245478 + ,120 + ,125 + ,35 + ,133 + ,169569 + ,42 + ,85 + ,20 + ,76 + ,185279 + ,100 + ,69 + ,22 + ,83 + ,109598 + ,36 + ,77 + ,19 + ,72 + ,155012 + ,49 + ,100 + ,28 + ,107 + ,154730 + ,46 + ,78 + ,27 + ,99 + ,280379 + ,56 + ,76 + ,25 + ,88 + ,90938 + ,17 + ,40 + ,15 + ,56 + ,101324 + ,31 + ,81 + ,26 + ,104 + ,139502 + ,77 + ,102 + ,27 + ,103 + ,145120 + ,90 + ,70 + ,24 + ,90 + ,161729 + ,80 + ,75 + ,21 + ,78 + ,160905 + ,54 + ,93 + ,27 + ,103 + 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+ ,87 + ,24 + ,92 + ,208823 + ,74 + ,116 + ,26 + ,93 + ,134088 + ,50 + ,54 + ,20 + ,75 + ,245107 + ,86 + ,96 + ,31 + ,118 + ,201409 + ,76 + ,93 + ,21 + ,77 + ,141760 + ,61 + ,49 + ,31 + ,122 + ,170635 + ,59 + ,49 + ,26 + ,99 + ,129100 + ,38 + ,38 + ,19 + ,72 + ,108811 + ,34 + ,64 + ,15 + ,58 + ,113450 + ,85 + ,64 + ,19 + ,73 + ,142286 + ,100 + ,66 + ,28 + ,103 + ,143937 + ,49 + ,98 + ,20 + ,76 + ,89882 + ,35 + ,99 + ,17 + ,65 + ,118807 + ,33 + ,56 + ,25 + ,95 + ,69471 + ,28 + ,22 + ,20 + ,76 + ,126630 + ,44 + ,51 + ,25 + ,95 + ,145908 + ,37 + ,61 + ,24 + ,92 + ,96981 + ,33 + ,94 + ,22 + ,84 + ,189066 + ,44 + ,98 + ,25 + ,95 + ,191467 + ,55 + ,76 + ,20 + ,76 + ,106193 + ,58 + ,57 + ,23 + ,87 + ,89318 + ,36 + ,75 + ,22 + ,84 + ,120362 + ,42 + ,48 + ,25 + ,95 + ,98791 + ,30 + ,48 + ,18 + ,69 + ,274949 + ,66 + ,109 + ,30 + ,115 + ,132798 + ,53 + ,27 + ,22 + ,83 + ,128075 + ,57 + ,83 + ,25 + ,47 + ,80953 + ,25 + ,49 + ,8 + ,28 + ,109237 + ,39 + ,24 + ,21 + ,79 + ,96634 + ,35 + ,46 + ,22 + ,83 + ,226183 + ,114 + ,44 + ,24 + ,92 + ,167226 + ,53 + ,49 + ,30 + ,98 + ,117805 + ,70 + ,108 + ,27 + ,103 + ,121630 + ,48 + ,42 + ,21 + ,77 + ,152193 + ,49 + ,110 + ,25 + ,95 + ,112004 + ,42 + ,28 + ,21 + ,78 + ,169613 + ,51 + ,79 + ,24 + ,92 + ,176577 + ,51 + ,49 + ,20 + ,76 + ,130533 + ,27 + ,64 + ,20 + ,76 + ,142339 + ,29 + ,75 + ,20 + ,67 + ,189764 + ,54 + ,118 + ,24 + ,92 + ,201603 + ,92 + ,95 + ,40 + ,151 + ,243180 + ,72 + ,106 + ,22 + ,83 + ,155931 + ,63 + ,73 + ,31 + ,118 + ,182557 + ,40 + ,108 + ,26 + ,98 + ,106351 + ,108 + ,30 + ,20 + ,76 + ,43287 + ,14 + ,13 + ,19 + ,71 + ,127394 + ,44 + ,69 + ,15 + ,57 + ,127930 + ,91 + ,75 + ,21 + ,79 + ,135306 + ,29 + ,80 + ,22 + ,83 + ,175663 + ,63 + ,106 + ,24 + ,92 + ,74112 + ,32 + ,28 + ,19 + ,75 + ,89059 + ,65 + ,70 + ,20 + ,79 + ,166142 + ,41 + ,51 + ,23 + ,88 + ,141933 + ,55 + ,90 + ,27 + ,99 + ,22938 + ,10 + ,12 + ,1 + ,0 + ,125927 + ,53 + ,87 + ,24 + ,91 + ,61857 + ,25 + ,23 + ,11 + ,32 + ,91185 + ,31 + ,57 + ,27 + ,101 + ,236316 + ,64 + ,85 + ,22 + ,84 + ,21054 + ,16 + ,4 + ,0 + ,0 + ,169093 + ,35 + ,56 + ,17 + ,60 + ,31414 + ,19 + ,18 + ,8 + ,25 + ,183059 + ,74 + ,86 + ,23 + ,86 + ,137544 + ,35 + ,40 + ,31 + ,115 + ,75032 + ,45 + ,16 + ,23 + ,88 + ,71908 + ,28 + ,18 + ,17 + ,59 + ,38214 + ,34 + ,16 + ,8 + ,27 + ,90961 + ,25 + ,42 + ,22 + ,83 + ,193662 + ,48 + ,78 + ,33 + ,126 + ,127185 + ,38 + ,30 + ,33 + ,125 + ,242153 + ,49 + ,104 + ,31 + ,119 + ,201748 + ,65 + ,121 + ,33 + ,127 + ,254599 + ,71 + ,111 + ,35 + ,133 + ,139144 + ,23 + ,57 + ,21 + ,79 + ,76470 + ,29 + ,28 + ,20 + ,76 + ,183260 + ,190 + ,56 + ,24 + ,92 + ,280039 + ,113 + ,82 + ,29 + ,109 + ,50999 + ,15 + ,2 + ,20 + ,76 + ,253056 + ,85 + ,91 + ,27 + ,100 + ,98466 + ,48 + ,41 + ,24 + ,87 + ,168059 + ,33 + ,84 + ,26 + ,97 + ,128768 + ,50 + ,55 + ,26 + ,95 + ,75746 + ,72 + ,3 + ,12 + ,48 + ,244909 + ,79 + ,54 + ,21 + ,80 + ,152366 + ,54 + ,93 + ,24 + ,91 + ,173260 + ,63 + ,41 + ,21 + ,79 + ,197033 + ,68 + ,94 + ,30 + ,114 + ,67507 + ,39 + ,101 + ,32 + ,120 + ,139409 + ,49 + ,70 + ,24 + ,89 + ,185366 + ,67 + ,114 + ,29 + ,111 + ,0 + ,0 + ,0 + ,0 + ,0 + ,14688 + ,10 + ,4 + ,0 + ,0 + ,98 + ,1 + ,0 + ,0 + ,0 + ,455 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,128873 + ,57 + ,42 + ,20 + ,74 + ,185288 + ,71 + ,97 + ,27 + ,107 + ,0 + ,0 + ,0 + ,0 + ,0 + ,203 + ,4 + ,0 + ,0 + ,0 + ,7199 + ,5 + ,7 + ,0 + ,0 + ,46660 + ,20 + ,12 + ,5 + ,15 + ,17547 + ,5 + ,0 + ,1 + ,4 + ,73567 + ,27 + ,37 + ,23 + ,82 + ,969 + ,2 + ,0 + ,0 + ,0 + ,105477 + ,33 + ,39 + ,16 + ,54) + ,dim=c(5 + ,164) + ,dimnames=list(c('Total_Time_spent_in_RFC_in_seconds' + ,'Number_of_Logins' + ,'Total_Number_of_Blogged_Computations' + ,'Total_Number_of_Reviewed_Compendiums' + ,'Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews') + ,1:164)) > y <- array(NA,dim=c(5,164),dimnames=list(c('Total_Time_spent_in_RFC_in_seconds','Number_of_Logins','Total_Number_of_Blogged_Computations','Total_Number_of_Reviewed_Compendiums','Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews'),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 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Total_Time_spent_in_RFC_in_seconds Number_of_Logins 1 170588 46 2 86621 48 3 113337 37 4 144530 72 5 81530 31 6 35523 17 7 305115 78 8 32750 16 9 115885 37 10 130539 24 11 156990 63 12 128274 74 13 102350 43 14 192887 42 15 129796 55 16 245478 120 17 169569 42 18 185279 100 19 109598 36 20 155012 49 21 154730 46 22 280379 56 23 90938 17 24 101324 31 25 139502 77 26 145120 90 27 161729 80 28 160905 54 29 106888 34 30 187289 38 31 181853 53 32 129340 47 33 196862 63 34 62731 25 35 234863 55 36 167255 37 37 264528 83 38 121976 49 39 80964 26 40 209631 107 41 213310 55 42 115911 40 43 131337 46 44 81106 31 45 93125 49 46 305708 95 47 78800 42 48 157566 54 49 213487 68 50 131108 39 51 128734 53 52 24188 24 53 257662 200 54 65029 17 55 98066 58 56 173587 27 57 179571 58 58 197067 113 59 208823 74 60 134088 50 61 245107 86 62 201409 76 63 141760 61 64 170635 59 65 129100 38 66 108811 34 67 113450 85 68 142286 100 69 143937 49 70 89882 35 71 118807 33 72 69471 28 73 126630 44 74 145908 37 75 96981 33 76 189066 44 77 191467 55 78 106193 58 79 89318 36 80 120362 42 81 98791 30 82 274949 66 83 132798 53 84 128075 57 85 80953 25 86 109237 39 87 96634 35 88 226183 114 89 167226 53 90 117805 70 91 121630 48 92 152193 49 93 112004 42 94 169613 51 95 176577 51 96 130533 27 97 142339 29 98 189764 54 99 201603 92 100 243180 72 101 155931 63 102 182557 40 103 106351 108 104 43287 14 105 127394 44 106 127930 91 107 135306 29 108 175663 63 109 74112 32 110 89059 65 111 166142 41 112 141933 55 113 22938 10 114 125927 53 115 61857 25 116 91185 31 117 236316 64 118 21054 16 119 169093 35 120 31414 19 121 183059 74 122 137544 35 123 75032 45 124 71908 28 125 38214 34 126 90961 25 127 193662 48 128 127185 38 129 242153 49 130 201748 65 131 254599 71 132 139144 23 133 76470 29 134 183260 190 135 280039 113 136 50999 15 137 253056 85 138 98466 48 139 168059 33 140 128768 50 141 75746 72 142 244909 79 143 152366 54 144 173260 63 145 197033 68 146 67507 39 147 139409 49 148 185366 67 149 0 0 150 14688 10 151 98 1 152 455 2 153 0 0 154 0 0 155 128873 57 156 185288 71 157 0 0 158 203 4 159 7199 5 160 46660 20 161 17547 5 162 73567 27 163 969 2 164 105477 33 Total_Number_of_Blogged_Computations Total_Number_of_Reviewed_Compendiums 1 65 26 2 54 20 3 58 24 4 77 25 5 41 15 6 0 16 7 111 20 8 1 18 9 37 19 10 60 20 11 64 26 12 71 37 13 38 23 14 76 36 15 61 28 16 125 35 17 85 20 18 69 22 19 77 19 20 100 28 21 78 27 22 76 25 23 40 15 24 81 26 25 102 27 26 70 24 27 75 21 28 93 27 29 42 21 30 95 30 31 87 25 32 44 33 33 87 30 34 28 20 35 87 27 36 71 25 37 70 30 38 50 20 39 30 8 40 87 24 41 78 22 42 48 25 43 52 20 44 31 21 45 30 21 46 70 26 47 20 26 48 84 30 49 81 26 50 79 30 51 72 18 52 8 4 53 67 31 54 21 18 55 30 14 56 70 20 57 87 35 58 87 24 59 116 26 60 54 20 61 96 31 62 93 21 63 49 31 64 49 26 65 38 19 66 64 15 67 64 19 68 66 28 69 98 20 70 99 17 71 56 25 72 22 20 73 51 25 74 61 24 75 94 22 76 98 25 77 76 20 78 57 23 79 75 22 80 48 25 81 48 18 82 109 30 83 27 22 84 83 25 85 49 8 86 24 21 87 46 22 88 44 24 89 49 30 90 108 27 91 42 21 92 110 25 93 28 21 94 79 24 95 49 20 96 64 20 97 75 20 98 118 24 99 95 40 100 106 22 101 73 31 102 108 26 103 30 20 104 13 19 105 69 15 106 75 21 107 80 22 108 106 24 109 28 19 110 70 20 111 51 23 112 90 27 113 12 1 114 87 24 115 23 11 116 57 27 117 85 22 118 4 0 119 56 17 120 18 8 121 86 23 122 40 31 123 16 23 124 18 17 125 16 8 126 42 22 127 78 33 128 30 33 129 104 31 130 121 33 131 111 35 132 57 21 133 28 20 134 56 24 135 82 29 136 2 20 137 91 27 138 41 24 139 84 26 140 55 26 141 3 12 142 54 21 143 93 24 144 41 21 145 94 30 146 101 32 147 70 24 148 114 29 149 0 0 150 4 0 151 0 0 152 0 0 153 0 0 154 0 0 155 42 20 156 97 27 157 0 0 158 0 0 159 7 0 160 12 5 161 0 1 162 37 23 163 0 0 164 39 16 Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews t 1 99 1 2 77 2 3 90 3 4 96 4 5 41 5 6 64 6 7 76 7 8 67 8 9 72 9 10 75 10 11 97 11 12 139 12 13 76 13 14 123 14 15 106 15 16 133 16 17 76 17 18 83 18 19 72 19 20 107 20 21 99 21 22 88 22 23 56 23 24 104 24 25 103 25 26 90 26 27 78 27 28 103 28 29 81 29 30 114 30 31 95 31 32 118 32 33 113 33 34 75 34 35 103 35 36 93 36 37 114 37 38 76 38 39 27 39 40 92 40 41 84 41 42 92 42 43 72 43 44 79 44 45 57 45 46 99 46 47 82 47 48 113 48 49 97 49 50 110 50 51 78 51 52 12 52 53 114 53 54 67 54 55 52 55 56 76 56 57 134 57 58 92 58 59 93 59 60 75 60 61 118 61 62 77 62 63 122 63 64 99 64 65 72 65 66 58 66 67 73 67 68 103 68 69 76 69 70 65 70 71 95 71 72 76 72 73 95 73 74 92 74 75 84 75 76 95 76 77 76 77 78 87 78 79 84 79 80 95 80 81 69 81 82 115 82 83 83 83 84 47 84 85 28 85 86 79 86 87 83 87 88 92 88 89 98 89 90 103 90 91 77 91 92 95 92 93 78 93 94 92 94 95 76 95 96 76 96 97 67 97 98 92 98 99 151 99 100 83 100 101 118 101 102 98 102 103 76 103 104 71 104 105 57 105 106 79 106 107 83 107 108 92 108 109 75 109 110 79 110 111 88 111 112 99 112 113 0 113 114 91 114 115 32 115 116 101 116 117 84 117 118 0 118 119 60 119 120 25 120 121 86 121 122 115 122 123 88 123 124 59 124 125 27 125 126 83 126 127 126 127 128 125 128 129 119 129 130 127 130 131 133 131 132 79 132 133 76 133 134 92 134 135 109 135 136 76 136 137 100 137 138 87 138 139 97 139 140 95 140 141 48 141 142 80 142 143 91 143 144 79 144 145 114 145 146 120 146 147 89 147 148 111 148 149 0 149 150 0 150 151 0 151 152 0 152 153 0 153 154 0 154 155 74 155 156 107 156 157 0 157 158 0 158 159 0 159 160 15 160 161 4 161 162 82 162 163 0 163 164 54 164 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) 6297.40 Number_of_Logins 795.47 Total_Number_of_Blogged_Computations 922.70 Total_Number_of_Reviewed_Compendiums 886.94 Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews 204.46 t -22.65 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -112617 -17895 -3324 14841 119742 Coefficients: Estimate Std. Error (Intercept) 6297.40 10688.93 Number_of_Logins 795.47 113.91 Total_Number_of_Blogged_Computations 922.70 122.88 Total_Number_of_Reviewed_Compendiums 886.94 2033.11 Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews 204.46 535.38 t -22.65 61.94 t value Pr(>|t|) (Intercept) 0.589 0.557 Number_of_Logins 6.984 7.54e-11 Total_Number_of_Blogged_Computations 7.509 4.12e-12 Total_Number_of_Reviewed_Compendiums 0.436 0.663 Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews 0.382 0.703 t -0.366 0.715 (Intercept) Number_of_Logins *** Total_Number_of_Blogged_Computations *** Total_Number_of_Reviewed_Compendiums Total_Number_of_submitted_Feedback_Messages_in_Peer_Reviews t --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 35310 on 158 degrees of freedom Multiple R-squared: 0.7414, Adjusted R-squared: 0.7332 F-statistic: 90.58 on 5 and 158 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,] 0.5438550 9.122899e-01 4.561450e-01 [2,] 0.7110073 5.779854e-01 2.889927e-01 [3,] 0.5804622 8.390757e-01 4.195378e-01 [4,] 0.4759024 9.518047e-01 5.240976e-01 [5,] 0.3783444 7.566887e-01 6.216556e-01 [6,] 0.3887331 7.774661e-01 6.112669e-01 [7,] 0.3559602 7.119204e-01 6.440398e-01 [8,] 0.3040798 6.081595e-01 6.959202e-01 [9,] 0.3613789 7.227578e-01 6.386211e-01 [10,] 0.3150894 6.301787e-01 6.849106e-01 [11,] 0.4578614 9.157228e-01 5.421386e-01 [12,] 0.4369253 8.738506e-01 5.630747e-01 [13,] 0.3622877 7.245755e-01 6.377123e-01 [14,] 0.8969274 2.061452e-01 1.030726e-01 [15,] 0.8627960 2.744079e-01 1.372040e-01 [16,] 0.8643200 2.713601e-01 1.356800e-01 [17,] 0.9262993 1.474014e-01 7.370072e-02 [18,] 0.9110231 1.779538e-01 8.897689e-02 [19,] 0.8830531 2.338939e-01 1.169469e-01 [20,] 0.8513947 2.972106e-01 1.486053e-01 [21,] 0.8329784 3.340432e-01 1.670216e-01 [22,] 0.8101146 3.797708e-01 1.898854e-01 [23,] 0.7743462 4.513075e-01 2.256538e-01 [24,] 0.7398203 5.203594e-01 2.601797e-01 [25,] 0.7075437 5.849126e-01 2.924563e-01 [26,] 0.6644101 6.711799e-01 3.355899e-01 [27,] 0.7613923 4.772153e-01 2.386077e-01 [28,] 0.7270636 5.458728e-01 2.729364e-01 [29,] 0.8970483 2.059034e-01 1.029517e-01 [30,] 0.8726846 2.546309e-01 1.273154e-01 [31,] 0.8488332 3.023336e-01 1.511668e-01 [32,] 0.8164690 3.670619e-01 1.835310e-01 [33,] 0.8333108 3.333784e-01 1.666892e-01 [34,] 0.8035143 3.929715e-01 1.964857e-01 [35,] 0.7688758 4.622484e-01 2.311242e-01 [36,] 0.7335221 5.329558e-01 2.664779e-01 [37,] 0.7370572 5.258856e-01 2.629428e-01 [38,] 0.9580278 8.394435e-02 4.197217e-02 [39,] 0.9510646 9.787079e-02 4.893539e-02 [40,] 0.9448766 1.102469e-01 5.512345e-02 [41,] 0.9391869 1.216262e-01 6.081310e-02 [42,] 0.9367596 1.264809e-01 6.324043e-02 [43,] 0.9302587 1.394826e-01 6.974131e-02 [44,] 0.9218386 1.563228e-01 7.816142e-02 [45,] 0.9110301 1.779398e-01 8.896990e-02 [46,] 0.8898128 2.203743e-01 1.101872e-01 [47,] 0.8664445 2.671110e-01 1.335555e-01 [48,] 0.8747186 2.505628e-01 1.252814e-01 [49,] 0.8510384 2.979232e-01 1.489616e-01 [50,] 0.8384875 3.230250e-01 1.615125e-01 [51,] 0.8267772 3.464457e-01 1.732228e-01 [52,] 0.7956167 4.087665e-01 2.043833e-01 [53,] 0.7849772 4.300457e-01 2.150228e-01 [54,] 0.7596652 4.806697e-01 2.403348e-01 [55,] 0.7229693 5.540614e-01 2.770307e-01 [56,] 0.7145317 5.709366e-01 2.854683e-01 [57,] 0.6962061 6.075877e-01 3.037939e-01 [58,] 0.6697807 6.604386e-01 3.302193e-01 [59,] 0.7162805 5.674390e-01 2.837195e-01 [60,] 0.7457135 5.085730e-01 2.542865e-01 [61,] 0.7392608 5.214785e-01 2.607392e-01 [62,] 0.8211326 3.577349e-01 1.788674e-01 [63,] 0.7895288 4.209423e-01 2.104712e-01 [64,] 0.7570129 4.859743e-01 2.429871e-01 [65,] 0.7196504 5.606991e-01 2.803496e-01 [66,] 0.6883029 6.233942e-01 3.116971e-01 [67,] 0.7478345 5.043310e-01 2.521655e-01 [68,] 0.7194377 5.611246e-01 2.805623e-01 [69,] 0.7305642 5.388716e-01 2.694358e-01 [70,] 0.7255186 5.489629e-01 2.744814e-01 [71,] 0.7566812 4.866375e-01 2.433188e-01 [72,] 0.7208754 5.582492e-01 2.791246e-01 [73,] 0.6820793 6.358414e-01 3.179207e-01 [74,] 0.7786679 4.426643e-01 2.213321e-01 [75,] 0.7632329 4.735341e-01 2.367671e-01 [76,] 0.7670946 4.658108e-01 2.329054e-01 [77,] 0.7303151 5.393698e-01 2.696849e-01 [78,] 0.7023867 5.952265e-01 2.976133e-01 [79,] 0.6662433 6.675135e-01 3.337567e-01 [80,] 0.7176671 5.646658e-01 2.823329e-01 [81,] 0.6939713 6.120574e-01 3.060287e-01 [82,] 0.8514558 2.970883e-01 1.485442e-01 [83,] 0.8239320 3.521361e-01 1.760680e-01 [84,] 0.8204524 3.590952e-01 1.795476e-01 [85,] 0.7944942 4.110116e-01 2.055058e-01 [86,] 0.7648197 4.703607e-01 2.351803e-01 [87,] 0.8163107 3.673785e-01 1.836893e-01 [88,] 0.7900193 4.199614e-01 2.099807e-01 [89,] 0.7588123 4.823754e-01 2.411877e-01 [90,] 0.7210295 5.579411e-01 2.789705e-01 [91,] 0.7029926 5.940148e-01 2.970074e-01 [92,] 0.7430067 5.139866e-01 2.569933e-01 [93,] 0.7087178 5.825645e-01 2.912822e-01 [94,] 0.6680090 6.639820e-01 3.319910e-01 [95,] 0.6793049 6.413902e-01 3.206951e-01 [96,] 0.6410772 7.178456e-01 3.589228e-01 [97,] 0.5960350 8.079300e-01 4.039650e-01 [98,] 0.6399722 7.200556e-01 3.600278e-01 [99,] 0.5932523 8.134954e-01 4.067477e-01 [100,] 0.5530583 8.938834e-01 4.469417e-01 [101,] 0.5088712 9.822575e-01 4.911288e-01 [102,] 0.6317372 7.365256e-01 3.682628e-01 [103,] 0.6483871 7.032259e-01 3.516129e-01 [104,] 0.6529979 6.940041e-01 3.470021e-01 [105,] 0.6072554 7.854891e-01 3.927446e-01 [106,] 0.6509455 6.981089e-01 3.490545e-01 [107,] 0.6077758 7.844483e-01 3.922242e-01 [108,] 0.6304216 7.391568e-01 3.695784e-01 [109,] 0.7038912 5.922175e-01 2.961088e-01 [110,] 0.6600847 6.798307e-01 3.399153e-01 [111,] 0.7127774 5.744451e-01 2.872226e-01 [112,] 0.6839299 6.321402e-01 3.160701e-01 [113,] 0.6370896 7.258209e-01 3.629104e-01 [114,] 0.5940446 8.119108e-01 4.059554e-01 [115,] 0.5622386 8.755228e-01 4.377614e-01 [116,] 0.5106947 9.786106e-01 4.893053e-01 [117,] 0.5201577 9.596847e-01 4.798423e-01 [118,] 0.4907119 9.814238e-01 5.092881e-01 [119,] 0.4480981 8.961962e-01 5.519019e-01 [120,] 0.3952210 7.904420e-01 6.047790e-01 [121,] 0.4296002 8.592004e-01 5.703998e-01 [122,] 0.3984996 7.969992e-01 6.015004e-01 [123,] 0.3797541 7.595082e-01 6.202459e-01 [124,] 0.3479330 6.958659e-01 6.520670e-01 [125,] 0.2980232 5.960464e-01 7.019768e-01 [126,] 0.9386941 1.226118e-01 6.130588e-02 [127,] 0.9268738 1.462525e-01 7.312624e-02 [128,] 0.9496898 1.006205e-01 5.031023e-02 [129,] 0.9341005 1.317990e-01 6.589949e-02 [130,] 0.9210626 1.578747e-01 7.893736e-02 [131,] 0.9740835 5.183307e-02 2.591653e-02 [132,] 0.9614481 7.710378e-02 3.855189e-02 [133,] 0.9999871 2.580013e-05 1.290006e-05 [134,] 0.9999899 2.028277e-05 1.014138e-05 [135,] 0.9999738 5.247002e-05 2.623501e-05 [136,] 0.9999352 1.296354e-04 6.481769e-05 [137,] 0.9999519 9.610221e-05 4.805110e-05 [138,] 1.0000000 3.067975e-08 1.533987e-08 [139,] 0.9999999 1.023768e-07 5.118838e-08 [140,] 0.9999998 3.546880e-07 1.773440e-07 [141,] 0.9999990 1.997149e-06 9.985747e-07 [142,] 0.9999944 1.129063e-05 5.645317e-06 [143,] 0.9999651 6.975403e-05 3.487702e-05 [144,] 0.9997936 4.127817e-04 2.063909e-04 [145,] 0.9989824 2.035227e-03 1.017613e-03 [146,] 0.9962456 7.508715e-03 3.754358e-03 [147,] 0.9809053 3.818939e-02 1.909469e-02 > postscript(file="/var/wessaorg/rcomp/tmp/1hb1z1321982611.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2ecvd1321982611.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3xfzw1321982611.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4u2bi1321982611.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5kbcc1321982611.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 24444.1363 -41121.7586 -15529.3898 -31800.3870 -8831.4854 -11437.7870 7 8 9 10 11 12 101231.6115 -16679.9640 14646.1781 16941.5074 -1118.8269 -63364.8050 13 14 15 16 17 18 -8859.3551 26293.3949 -22704.3101 -29487.4058 18539.4654 -307.2166 19 20 21 22 23 24 -27527.0001 -28792.1119 -3842.9755 119742.2315 9976.6889 -48152.3677 25 26 27 28 29 30 -66602.7960 -36458.0579 -11370.8926 -18531.6080 261.0721 13870.0390 31 32 33 34 35 36 12225.5595 -7613.6968 11210.2085 -21591.8911 60325.6782 25640.5606 37 38 39 40 41 42 78538.8170 -1851.6149 14570.6303 -1247.9143 55532.2610 -6527.3355 43 44 45 46 47 48 8981.4694 -12236.1257 -9092.2740 116991.5582 -18122.7198 -17818.6832 49 50 51 52 53 54 36575.4874 -27072.3838 -16915.5938 -13405.9167 -19154.1391 -2608.6740 55 56 57 58 59 60 -3853.2101 49213.4269 -10288.1271 -18177.0868 -4111.6690 6476.8214 61 62 63 64 65 66 31579.9060 15879.4986 -9285.6997 30340.0832 27411.3193 -7253.2836 67 68 69 70 71 72 -49775.4267 -48810.4332 -23478.2755 -62386.8550 -5401.2032 -11045.9049 73 74 75 76 77 78 -1669.6073 15472.6346 -57289.5065 17467.2761 39759.5425 -35256.7002 79 80 81 82 83 84 -49716.9699 -3420.0082 -3898.2361 67312.1759 24824.7815 -30029.2477 85 86 87 88 89 90 -1338.8837 16941.2425 -14461.4506 50499.1439 28926.8476 -86795.5398 91 92 93 94 95 96 6088.4661 -34093.1605 13994.0017 11885.3019 53372.1073 12601.5898 97 98 99 100 101 102 14529.6584 -6244.9618 -29643.0929 47584.2205 -17172.0611 4001.6946 103 104 105 106 107 108 -44483.3796 -15154.9588 -150.7950 -52335.3315 -1935.5505 -16206.3044 109 110 111 112 113 114 -13193.6323 -64932.9475 44294.5314 -32810.6681 -714.2370 -40115.8471 115 116 117 118 119 120 756.2233 -34336.3869 66641.3573 1010.7250 58632.4542 -16095.1619 121 122 123 124 125 126 3301.6533 18252.4248 -17430.9627 2396.2054 -19677.5409 -7605.5991 127 128 129 130 131 132 25056.8735 11051.4882 52012.4768 -20192.9505 34134.2721 30168.4525 133 134 135 136 137 138 -8997.0382 -62910.5187 63241.9215 726.8389 53887.1538 -19793.7257 139 140 141 142 143 144 18259.3511 -7364.7524 -7857.3814 94177.0255 -19351.7342 47500.5143 145 146 147 148 149 150 3277.0941 -112617.0378 -6609.6759 -24480.2478 -2922.7655 142.3252 151 152 153 154 155 156 -3574.9428 -3990.7687 -2832.1713 -2809.5228 9122.0110 -9281.2969 157 158 159 160 161 162 -2741.5771 -5697.8262 -5933.5762 9502.9040 9213.8818 -21844.1644 163 164 -3227.6346 15426.2227 > postscript(file="/var/wessaorg/rcomp/tmp/6q4zt1321982611.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 24444.1363 NA 1 -41121.7586 24444.1363 2 -15529.3898 -41121.7586 3 -31800.3870 -15529.3898 4 -8831.4854 -31800.3870 5 -11437.7870 -8831.4854 6 101231.6115 -11437.7870 7 -16679.9640 101231.6115 8 14646.1781 -16679.9640 9 16941.5074 14646.1781 10 -1118.8269 16941.5074 11 -63364.8050 -1118.8269 12 -8859.3551 -63364.8050 13 26293.3949 -8859.3551 14 -22704.3101 26293.3949 15 -29487.4058 -22704.3101 16 18539.4654 -29487.4058 17 -307.2166 18539.4654 18 -27527.0001 -307.2166 19 -28792.1119 -27527.0001 20 -3842.9755 -28792.1119 21 119742.2315 -3842.9755 22 9976.6889 119742.2315 23 -48152.3677 9976.6889 24 -66602.7960 -48152.3677 25 -36458.0579 -66602.7960 26 -11370.8926 -36458.0579 27 -18531.6080 -11370.8926 28 261.0721 -18531.6080 29 13870.0390 261.0721 30 12225.5595 13870.0390 31 -7613.6968 12225.5595 32 11210.2085 -7613.6968 33 -21591.8911 11210.2085 34 60325.6782 -21591.8911 35 25640.5606 60325.6782 36 78538.8170 25640.5606 37 -1851.6149 78538.8170 38 14570.6303 -1851.6149 39 -1247.9143 14570.6303 40 55532.2610 -1247.9143 41 -6527.3355 55532.2610 42 8981.4694 -6527.3355 43 -12236.1257 8981.4694 44 -9092.2740 -12236.1257 45 116991.5582 -9092.2740 46 -18122.7198 116991.5582 47 -17818.6832 -18122.7198 48 36575.4874 -17818.6832 49 -27072.3838 36575.4874 50 -16915.5938 -27072.3838 51 -13405.9167 -16915.5938 52 -19154.1391 -13405.9167 53 -2608.6740 -19154.1391 54 -3853.2101 -2608.6740 55 49213.4269 -3853.2101 56 -10288.1271 49213.4269 57 -18177.0868 -10288.1271 58 -4111.6690 -18177.0868 59 6476.8214 -4111.6690 60 31579.9060 6476.8214 61 15879.4986 31579.9060 62 -9285.6997 15879.4986 63 30340.0832 -9285.6997 64 27411.3193 30340.0832 65 -7253.2836 27411.3193 66 -49775.4267 -7253.2836 67 -48810.4332 -49775.4267 68 -23478.2755 -48810.4332 69 -62386.8550 -23478.2755 70 -5401.2032 -62386.8550 71 -11045.9049 -5401.2032 72 -1669.6073 -11045.9049 73 15472.6346 -1669.6073 74 -57289.5065 15472.6346 75 17467.2761 -57289.5065 76 39759.5425 17467.2761 77 -35256.7002 39759.5425 78 -49716.9699 -35256.7002 79 -3420.0082 -49716.9699 80 -3898.2361 -3420.0082 81 67312.1759 -3898.2361 82 24824.7815 67312.1759 83 -30029.2477 24824.7815 84 -1338.8837 -30029.2477 85 16941.2425 -1338.8837 86 -14461.4506 16941.2425 87 50499.1439 -14461.4506 88 28926.8476 50499.1439 89 -86795.5398 28926.8476 90 6088.4661 -86795.5398 91 -34093.1605 6088.4661 92 13994.0017 -34093.1605 93 11885.3019 13994.0017 94 53372.1073 11885.3019 95 12601.5898 53372.1073 96 14529.6584 12601.5898 97 -6244.9618 14529.6584 98 -29643.0929 -6244.9618 99 47584.2205 -29643.0929 100 -17172.0611 47584.2205 101 4001.6946 -17172.0611 102 -44483.3796 4001.6946 103 -15154.9588 -44483.3796 104 -150.7950 -15154.9588 105 -52335.3315 -150.7950 106 -1935.5505 -52335.3315 107 -16206.3044 -1935.5505 108 -13193.6323 -16206.3044 109 -64932.9475 -13193.6323 110 44294.5314 -64932.9475 111 -32810.6681 44294.5314 112 -714.2370 -32810.6681 113 -40115.8471 -714.2370 114 756.2233 -40115.8471 115 -34336.3869 756.2233 116 66641.3573 -34336.3869 117 1010.7250 66641.3573 118 58632.4542 1010.7250 119 -16095.1619 58632.4542 120 3301.6533 -16095.1619 121 18252.4248 3301.6533 122 -17430.9627 18252.4248 123 2396.2054 -17430.9627 124 -19677.5409 2396.2054 125 -7605.5991 -19677.5409 126 25056.8735 -7605.5991 127 11051.4882 25056.8735 128 52012.4768 11051.4882 129 -20192.9505 52012.4768 130 34134.2721 -20192.9505 131 30168.4525 34134.2721 132 -8997.0382 30168.4525 133 -62910.5187 -8997.0382 134 63241.9215 -62910.5187 135 726.8389 63241.9215 136 53887.1538 726.8389 137 -19793.7257 53887.1538 138 18259.3511 -19793.7257 139 -7364.7524 18259.3511 140 -7857.3814 -7364.7524 141 94177.0255 -7857.3814 142 -19351.7342 94177.0255 143 47500.5143 -19351.7342 144 3277.0941 47500.5143 145 -112617.0378 3277.0941 146 -6609.6759 -112617.0378 147 -24480.2478 -6609.6759 148 -2922.7655 -24480.2478 149 142.3252 -2922.7655 150 -3574.9428 142.3252 151 -3990.7687 -3574.9428 152 -2832.1713 -3990.7687 153 -2809.5228 -2832.1713 154 9122.0110 -2809.5228 155 -9281.2969 9122.0110 156 -2741.5771 -9281.2969 157 -5697.8262 -2741.5771 158 -5933.5762 -5697.8262 159 9502.9040 -5933.5762 160 9213.8818 9502.9040 161 -21844.1644 9213.8818 162 -3227.6346 -21844.1644 163 15426.2227 -3227.6346 164 NA 15426.2227 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -41121.7586 24444.1363 [2,] -15529.3898 -41121.7586 [3,] -31800.3870 -15529.3898 [4,] -8831.4854 -31800.3870 [5,] -11437.7870 -8831.4854 [6,] 101231.6115 -11437.7870 [7,] -16679.9640 101231.6115 [8,] 14646.1781 -16679.9640 [9,] 16941.5074 14646.1781 [10,] -1118.8269 16941.5074 [11,] -63364.8050 -1118.8269 [12,] -8859.3551 -63364.8050 [13,] 26293.3949 -8859.3551 [14,] -22704.3101 26293.3949 [15,] -29487.4058 -22704.3101 [16,] 18539.4654 -29487.4058 [17,] -307.2166 18539.4654 [18,] -27527.0001 -307.2166 [19,] -28792.1119 -27527.0001 [20,] -3842.9755 -28792.1119 [21,] 119742.2315 -3842.9755 [22,] 9976.6889 119742.2315 [23,] -48152.3677 9976.6889 [24,] -66602.7960 -48152.3677 [25,] -36458.0579 -66602.7960 [26,] -11370.8926 -36458.0579 [27,] -18531.6080 -11370.8926 [28,] 261.0721 -18531.6080 [29,] 13870.0390 261.0721 [30,] 12225.5595 13870.0390 [31,] -7613.6968 12225.5595 [32,] 11210.2085 -7613.6968 [33,] -21591.8911 11210.2085 [34,] 60325.6782 -21591.8911 [35,] 25640.5606 60325.6782 [36,] 78538.8170 25640.5606 [37,] -1851.6149 78538.8170 [38,] 14570.6303 -1851.6149 [39,] -1247.9143 14570.6303 [40,] 55532.2610 -1247.9143 [41,] -6527.3355 55532.2610 [42,] 8981.4694 -6527.3355 [43,] -12236.1257 8981.4694 [44,] -9092.2740 -12236.1257 [45,] 116991.5582 -9092.2740 [46,] -18122.7198 116991.5582 [47,] -17818.6832 -18122.7198 [48,] 36575.4874 -17818.6832 [49,] -27072.3838 36575.4874 [50,] -16915.5938 -27072.3838 [51,] -13405.9167 -16915.5938 [52,] -19154.1391 -13405.9167 [53,] -2608.6740 -19154.1391 [54,] -3853.2101 -2608.6740 [55,] 49213.4269 -3853.2101 [56,] -10288.1271 49213.4269 [57,] -18177.0868 -10288.1271 [58,] -4111.6690 -18177.0868 [59,] 6476.8214 -4111.6690 [60,] 31579.9060 6476.8214 [61,] 15879.4986 31579.9060 [62,] -9285.6997 15879.4986 [63,] 30340.0832 -9285.6997 [64,] 27411.3193 30340.0832 [65,] -7253.2836 27411.3193 [66,] -49775.4267 -7253.2836 [67,] -48810.4332 -49775.4267 [68,] -23478.2755 -48810.4332 [69,] -62386.8550 -23478.2755 [70,] -5401.2032 -62386.8550 [71,] -11045.9049 -5401.2032 [72,] -1669.6073 -11045.9049 [73,] 15472.6346 -1669.6073 [74,] -57289.5065 15472.6346 [75,] 17467.2761 -57289.5065 [76,] 39759.5425 17467.2761 [77,] -35256.7002 39759.5425 [78,] -49716.9699 -35256.7002 [79,] -3420.0082 -49716.9699 [80,] -3898.2361 -3420.0082 [81,] 67312.1759 -3898.2361 [82,] 24824.7815 67312.1759 [83,] -30029.2477 24824.7815 [84,] -1338.8837 -30029.2477 [85,] 16941.2425 -1338.8837 [86,] -14461.4506 16941.2425 [87,] 50499.1439 -14461.4506 [88,] 28926.8476 50499.1439 [89,] -86795.5398 28926.8476 [90,] 6088.4661 -86795.5398 [91,] -34093.1605 6088.4661 [92,] 13994.0017 -34093.1605 [93,] 11885.3019 13994.0017 [94,] 53372.1073 11885.3019 [95,] 12601.5898 53372.1073 [96,] 14529.6584 12601.5898 [97,] -6244.9618 14529.6584 [98,] -29643.0929 -6244.9618 [99,] 47584.2205 -29643.0929 [100,] -17172.0611 47584.2205 [101,] 4001.6946 -17172.0611 [102,] -44483.3796 4001.6946 [103,] -15154.9588 -44483.3796 [104,] -150.7950 -15154.9588 [105,] -52335.3315 -150.7950 [106,] -1935.5505 -52335.3315 [107,] -16206.3044 -1935.5505 [108,] -13193.6323 -16206.3044 [109,] -64932.9475 -13193.6323 [110,] 44294.5314 -64932.9475 [111,] -32810.6681 44294.5314 [112,] -714.2370 -32810.6681 [113,] -40115.8471 -714.2370 [114,] 756.2233 -40115.8471 [115,] -34336.3869 756.2233 [116,] 66641.3573 -34336.3869 [117,] 1010.7250 66641.3573 [118,] 58632.4542 1010.7250 [119,] -16095.1619 58632.4542 [120,] 3301.6533 -16095.1619 [121,] 18252.4248 3301.6533 [122,] -17430.9627 18252.4248 [123,] 2396.2054 -17430.9627 [124,] -19677.5409 2396.2054 [125,] -7605.5991 -19677.5409 [126,] 25056.8735 -7605.5991 [127,] 11051.4882 25056.8735 [128,] 52012.4768 11051.4882 [129,] -20192.9505 52012.4768 [130,] 34134.2721 -20192.9505 [131,] 30168.4525 34134.2721 [132,] -8997.0382 30168.4525 [133,] -62910.5187 -8997.0382 [134,] 63241.9215 -62910.5187 [135,] 726.8389 63241.9215 [136,] 53887.1538 726.8389 [137,] -19793.7257 53887.1538 [138,] 18259.3511 -19793.7257 [139,] -7364.7524 18259.3511 [140,] -7857.3814 -7364.7524 [141,] 94177.0255 -7857.3814 [142,] -19351.7342 94177.0255 [143,] 47500.5143 -19351.7342 [144,] 3277.0941 47500.5143 [145,] -112617.0378 3277.0941 [146,] -6609.6759 -112617.0378 [147,] -24480.2478 -6609.6759 [148,] -2922.7655 -24480.2478 [149,] 142.3252 -2922.7655 [150,] -3574.9428 142.3252 [151,] -3990.7687 -3574.9428 [152,] -2832.1713 -3990.7687 [153,] -2809.5228 -2832.1713 [154,] 9122.0110 -2809.5228 [155,] -9281.2969 9122.0110 [156,] -2741.5771 -9281.2969 [157,] -5697.8262 -2741.5771 [158,] -5933.5762 -5697.8262 [159,] 9502.9040 -5933.5762 [160,] 9213.8818 9502.9040 [161,] -21844.1644 9213.8818 [162,] -3227.6346 -21844.1644 [163,] 15426.2227 -3227.6346 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -41121.7586 24444.1363 2 -15529.3898 -41121.7586 3 -31800.3870 -15529.3898 4 -8831.4854 -31800.3870 5 -11437.7870 -8831.4854 6 101231.6115 -11437.7870 7 -16679.9640 101231.6115 8 14646.1781 -16679.9640 9 16941.5074 14646.1781 10 -1118.8269 16941.5074 11 -63364.8050 -1118.8269 12 -8859.3551 -63364.8050 13 26293.3949 -8859.3551 14 -22704.3101 26293.3949 15 -29487.4058 -22704.3101 16 18539.4654 -29487.4058 17 -307.2166 18539.4654 18 -27527.0001 -307.2166 19 -28792.1119 -27527.0001 20 -3842.9755 -28792.1119 21 119742.2315 -3842.9755 22 9976.6889 119742.2315 23 -48152.3677 9976.6889 24 -66602.7960 -48152.3677 25 -36458.0579 -66602.7960 26 -11370.8926 -36458.0579 27 -18531.6080 -11370.8926 28 261.0721 -18531.6080 29 13870.0390 261.0721 30 12225.5595 13870.0390 31 -7613.6968 12225.5595 32 11210.2085 -7613.6968 33 -21591.8911 11210.2085 34 60325.6782 -21591.8911 35 25640.5606 60325.6782 36 78538.8170 25640.5606 37 -1851.6149 78538.8170 38 14570.6303 -1851.6149 39 -1247.9143 14570.6303 40 55532.2610 -1247.9143 41 -6527.3355 55532.2610 42 8981.4694 -6527.3355 43 -12236.1257 8981.4694 44 -9092.2740 -12236.1257 45 116991.5582 -9092.2740 46 -18122.7198 116991.5582 47 -17818.6832 -18122.7198 48 36575.4874 -17818.6832 49 -27072.3838 36575.4874 50 -16915.5938 -27072.3838 51 -13405.9167 -16915.5938 52 -19154.1391 -13405.9167 53 -2608.6740 -19154.1391 54 -3853.2101 -2608.6740 55 49213.4269 -3853.2101 56 -10288.1271 49213.4269 57 -18177.0868 -10288.1271 58 -4111.6690 -18177.0868 59 6476.8214 -4111.6690 60 31579.9060 6476.8214 61 15879.4986 31579.9060 62 -9285.6997 15879.4986 63 30340.0832 -9285.6997 64 27411.3193 30340.0832 65 -7253.2836 27411.3193 66 -49775.4267 -7253.2836 67 -48810.4332 -49775.4267 68 -23478.2755 -48810.4332 69 -62386.8550 -23478.2755 70 -5401.2032 -62386.8550 71 -11045.9049 -5401.2032 72 -1669.6073 -11045.9049 73 15472.6346 -1669.6073 74 -57289.5065 15472.6346 75 17467.2761 -57289.5065 76 39759.5425 17467.2761 77 -35256.7002 39759.5425 78 -49716.9699 -35256.7002 79 -3420.0082 -49716.9699 80 -3898.2361 -3420.0082 81 67312.1759 -3898.2361 82 24824.7815 67312.1759 83 -30029.2477 24824.7815 84 -1338.8837 -30029.2477 85 16941.2425 -1338.8837 86 -14461.4506 16941.2425 87 50499.1439 -14461.4506 88 28926.8476 50499.1439 89 -86795.5398 28926.8476 90 6088.4661 -86795.5398 91 -34093.1605 6088.4661 92 13994.0017 -34093.1605 93 11885.3019 13994.0017 94 53372.1073 11885.3019 95 12601.5898 53372.1073 96 14529.6584 12601.5898 97 -6244.9618 14529.6584 98 -29643.0929 -6244.9618 99 47584.2205 -29643.0929 100 -17172.0611 47584.2205 101 4001.6946 -17172.0611 102 -44483.3796 4001.6946 103 -15154.9588 -44483.3796 104 -150.7950 -15154.9588 105 -52335.3315 -150.7950 106 -1935.5505 -52335.3315 107 -16206.3044 -1935.5505 108 -13193.6323 -16206.3044 109 -64932.9475 -13193.6323 110 44294.5314 -64932.9475 111 -32810.6681 44294.5314 112 -714.2370 -32810.6681 113 -40115.8471 -714.2370 114 756.2233 -40115.8471 115 -34336.3869 756.2233 116 66641.3573 -34336.3869 117 1010.7250 66641.3573 118 58632.4542 1010.7250 119 -16095.1619 58632.4542 120 3301.6533 -16095.1619 121 18252.4248 3301.6533 122 -17430.9627 18252.4248 123 2396.2054 -17430.9627 124 -19677.5409 2396.2054 125 -7605.5991 -19677.5409 126 25056.8735 -7605.5991 127 11051.4882 25056.8735 128 52012.4768 11051.4882 129 -20192.9505 52012.4768 130 34134.2721 -20192.9505 131 30168.4525 34134.2721 132 -8997.0382 30168.4525 133 -62910.5187 -8997.0382 134 63241.9215 -62910.5187 135 726.8389 63241.9215 136 53887.1538 726.8389 137 -19793.7257 53887.1538 138 18259.3511 -19793.7257 139 -7364.7524 18259.3511 140 -7857.3814 -7364.7524 141 94177.0255 -7857.3814 142 -19351.7342 94177.0255 143 47500.5143 -19351.7342 144 3277.0941 47500.5143 145 -112617.0378 3277.0941 146 -6609.6759 -112617.0378 147 -24480.2478 -6609.6759 148 -2922.7655 -24480.2478 149 142.3252 -2922.7655 150 -3574.9428 142.3252 151 -3990.7687 -3574.9428 152 -2832.1713 -3990.7687 153 -2809.5228 -2832.1713 154 9122.0110 -2809.5228 155 -9281.2969 9122.0110 156 -2741.5771 -9281.2969 157 -5697.8262 -2741.5771 158 -5933.5762 -5697.8262 159 9502.9040 -5933.5762 160 9213.8818 9502.9040 161 -21844.1644 9213.8818 162 -3227.6346 -21844.1644 163 15426.2227 -3227.6346 > 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/7mfa01321982611.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8rgi81321982611.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9rk851321982611.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10w4ju1321982611.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11kire1321982611.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/12l2x51321982611.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/13e9i51321982611.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/14dhy01321982611.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/15x80o1321982611.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/16piwg1321982611.tab") + } > > try(system("convert tmp/1hb1z1321982611.ps tmp/1hb1z1321982611.png",intern=TRUE)) character(0) > try(system("convert tmp/2ecvd1321982611.ps tmp/2ecvd1321982611.png",intern=TRUE)) character(0) > try(system("convert tmp/3xfzw1321982611.ps tmp/3xfzw1321982611.png",intern=TRUE)) character(0) > try(system("convert tmp/4u2bi1321982611.ps tmp/4u2bi1321982611.png",intern=TRUE)) character(0) > try(system("convert tmp/5kbcc1321982611.ps tmp/5kbcc1321982611.png",intern=TRUE)) character(0) > try(system("convert tmp/6q4zt1321982611.ps tmp/6q4zt1321982611.png",intern=TRUE)) character(0) > try(system("convert tmp/7mfa01321982611.ps tmp/7mfa01321982611.png",intern=TRUE)) character(0) > try(system("convert tmp/8rgi81321982611.ps tmp/8rgi81321982611.png",intern=TRUE)) character(0) > try(system("convert tmp/9rk851321982611.ps tmp/9rk851321982611.png",intern=TRUE)) character(0) > try(system("convert tmp/10w4ju1321982611.ps tmp/10w4ju1321982611.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.934 0.545 5.666