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(68 + ,13 + ,5 + ,20 + ,0 + ,17 + ,26 + ,7 + ,28 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,114 + ,37 + ,12 + ,40 + ,0 + ,95 + ,47 + ,15 + ,60 + ,0 + ,148 + ,80 + ,16 + ,60 + ,1 + ,56 + ,21 + ,12 + ,44 + ,0 + ,26 + ,36 + ,13 + ,52 + ,0 + ,63 + ,35 + ,15 + ,60 + ,0 + ,96 + ,40 + ,13 + ,52 + ,1 + ,74 + ,35 + ,6 + ,24 + ,1 + ,65 + ,46 + ,16 + ,64 + ,0 + ,40 + ,20 + ,7 + ,26 + ,0 + ,173 + ,24 + ,12 + ,48 + ,4 + ,28 + ,19 + ,9 + ,36 + ,3 + ,55 + ,15 + ,10 + ,40 + ,3 + ,58 + ,48 + ,16 + ,64 + ,0 + ,25 + ,0 + ,5 + ,20 + ,4 + ,103 + ,38 + ,20 + ,79 + ,0 + ,29 + ,12 + ,7 + ,16 + ,0 + ,31 + ,10 + ,13 + ,52 + ,0 + ,43 + ,51 + ,13 + ,52 + ,0 + ,74 + ,4 + ,11 + ,44 + ,0 + ,99 + ,24 + ,9 + ,29 + ,1 + ,25 + ,39 + ,10 + ,40 + ,1 + ,69 + ,19 + ,7 + ,28 + ,0 + ,62 + ,23 + ,13 + ,49 + ,0 + ,25 + ,39 + ,15 + ,60 + ,0 + ,38 + ,37 + ,13 + ,52 + ,0 + ,57 + ,20 + ,7 + ,28 + ,0 + ,52 + ,20 + ,14 + ,56 + ,0 + ,91 + ,41 + ,11 + ,35 + ,2 + ,48 + ,26 + ,3 + ,12 + ,4 + ,52 + ,0 + ,8 + ,32 + ,0 + ,35 + ,31 + ,12 + ,48 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,31 + ,8 + ,12 + ,48 + ,0 + ,107 + ,35 + ,8 + ,31 + ,3 + ,242 + ,3 + ,20 + ,64 + ,4 + ,41 + ,47 + ,18 + ,72 + ,0 + ,57 + ,42 + ,9 + ,36 + ,1 + ,32 + ,11 + ,14 + ,56 + ,0 + ,17 + ,10 + ,7 + ,28 + ,2 + ,36 + ,26 + ,13 + ,52 + ,1 + ,29 + ,27 + ,11 + ,44 + ,1 + ,22 + ,0 + ,11 + ,44 + ,2 + ,21 + ,15 + ,14 + ,55 + ,1 + ,41 + ,32 + ,9 + ,36 + ,0 + ,64 + ,13 + ,12 + ,48 + ,1 + ,71 + ,24 + ,11 + ,44 + ,0 + ,28 + ,10 + ,17 + ,66 + ,0 + ,36 + ,14 + ,10 + ,40 + ,0 + ,45 + ,24 + ,11 + ,44 + ,0 + ,22 + ,29 + ,12 + ,48 + ,0 + ,27 + ,40 + ,17 + ,68 + ,1 + ,38 + ,22 + ,6 + ,24 + ,3 + ,26 + ,27 + ,8 + ,32 + ,0 + ,41 + ,8 + ,12 + ,44 + ,0 + ,21 + ,27 + ,13 + ,52 + ,0 + ,28 + ,0 + ,14 + ,56 + ,4 + ,36 + ,0 + ,17 + ,68 + ,0 + ,58 + ,17 + ,8 + ,32 + ,0 + ,65 + ,7 + ,9 + ,34 + ,4 + ,29 + ,18 + ,9 + ,36 + ,3 + ,21 + ,7 + ,9 + ,34 + ,0 + ,19 + ,24 + ,15 + ,56 + ,0 + ,55 + ,18 + ,16 + ,64 + ,4 + ,119 + ,39 + ,13 + ,52 + ,2 + ,34 + ,17 + ,12 + ,48 + ,0 + ,25 + ,0 + ,10 + ,40 + ,0 + ,113 + ,39 + ,9 + ,36 + ,2 + ,46 + ,20 + ,3 + ,10 + ,0 + ,28 + ,29 + ,12 + ,48 + ,1 + ,63 + ,27 + ,8 + ,25 + ,0 + ,52 + ,23 + ,17 + ,68 + ,0 + ,35 + ,0 + ,9 + ,36 + ,1 + ,32 + ,31 + ,8 + ,32 + ,0 + ,45 + ,19 + ,9 + ,36 + ,0 + ,42 + ,12 + ,12 + ,43 + ,0 + ,28 + ,23 + ,5 + ,17 + ,0 + ,32 + ,33 + ,14 + ,52 + ,0 + ,32 + ,21 + ,14 + ,56 + ,0 + ,27 + ,17 + ,10 + ,40 + ,0 + ,69 + ,27 + ,12 + ,48 + ,0 + ,30 + ,14 + ,10 + ,40 + ,0 + ,48 + ,12 + ,12 + ,48 + ,0 + ,57 + ,21 + ,17 + ,68 + ,0 + ,36 + ,14 + ,11 + ,44 + ,0 + ,20 + ,14 + ,10 + ,40 + ,2 + ,54 + ,22 + ,11 + ,40 + ,0 + ,26 + ,25 + ,7 + ,28 + ,0 + ,58 + ,36 + ,10 + ,40 + ,1 + ,35 + ,10 + ,11 + ,44 + ,0 + ,28 + ,16 + ,5 + ,20 + ,0 + ,8 + ,12 + ,6 + ,22 + ,0 + ,96 + ,20 + ,14 + ,56 + ,0 + ,50 + ,38 + ,13 + ,52 + ,0 + ,15 + ,13 + ,1 + ,2 + ,0 + ,65 + ,12 + ,13 + ,52 + ,0 + ,33 + ,11 + ,9 + ,30 + ,0 + ,7 + ,8 + ,1 + ,3 + ,0 + ,17 + ,22 + ,6 + ,20 + ,0 + ,55 + ,14 + ,12 + ,48 + ,0 + ,32 + ,7 + ,9 + ,32 + ,1 + ,22 + ,14 + ,9 + ,36 + ,0 + ,41 + ,2 + ,12 + ,45 + ,0 + ,50 + ,35 + ,10 + ,40 + ,0 + ,7 + ,5 + ,2 + ,8 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,26 + ,34 + ,8 + ,32 + ,0 + ,22 + ,12 + ,7 + ,28 + ,0 + ,26 + ,34 + ,11 + ,44 + ,0 + ,37 + ,30 + ,14 + ,56 + ,0 + ,29 + ,21 + ,4 + ,13 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,42 + ,28 + ,13 + ,52 + ,0 + ,51 + ,16 + ,17 + ,51 + ,0 + ,77 + ,12 + ,13 + ,52 + ,1 + ,32 + ,14 + ,12 + ,48 + ,0 + ,63 + ,7 + ,1 + ,3 + ,0 + ,50 + ,41 + ,12 + ,48 + ,1 + ,18 + ,21 + ,6 + ,24 + ,0 + ,37 + ,28 + ,11 + ,37 + ,0 + ,23 + ,1 + ,8 + ,32 + ,3 + ,19 + ,10 + ,2 + ,8 + ,1 + ,39 + ,31 + ,12 + ,44 + ,2 + ,38 + ,7 + ,12 + ,48 + ,0 + ,55 + ,26 + ,14 + ,56 + ,0 + ,22 + ,1 + ,2 + ,8 + ,0 + ,7 + ,0 + ,0 + ,0 + ,0 + ,21 + ,12 + ,9 + ,25 + ,0 + ,5 + ,0 + ,1 + ,4 + ,0 + ,21 + ,17 + ,3 + ,12 + ,0 + ,1 + ,5 + ,0 + ,0 + ,0 + ,22 + ,4 + ,2 + ,6 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,31 + ,6 + ,12 + ,48 + ,0 + ,25 + ,0 + ,14 + ,52 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,0 + ,0 + ,0 + ,20 + ,15 + ,4 + ,12 + ,1 + ,29 + ,0 + ,7 + ,28 + ,0 + ,33 + ,12 + ,10 + ,40 + ,0) + ,dim=c(5 + ,144) + ,dimnames=list(c('CompendiumViews' + ,'BloggedComputations' + ,'ReviewedCompendiums' + ,'submittedfeedback' + ,'Sharedcompendiums') + ,1:144)) > y <- array(NA,dim=c(5,144),dimnames=list(c('CompendiumViews','BloggedComputations','ReviewedCompendiums','submittedfeedback','Sharedcompendiums'),1:144)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '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 CompendiumViews BloggedComputations ReviewedCompendiums submittedfeedback 1 68 13 5 20 2 17 26 7 28 3 1 0 0 0 4 114 37 12 40 5 95 47 15 60 6 148 80 16 60 7 56 21 12 44 8 26 36 13 52 9 63 35 15 60 10 96 40 13 52 11 74 35 6 24 12 65 46 16 64 13 40 20 7 26 14 173 24 12 48 15 28 19 9 36 16 55 15 10 40 17 58 48 16 64 18 25 0 5 20 19 103 38 20 79 20 29 12 7 16 21 31 10 13 52 22 43 51 13 52 23 74 4 11 44 24 99 24 9 29 25 25 39 10 40 26 69 19 7 28 27 62 23 13 49 28 25 39 15 60 29 38 37 13 52 30 57 20 7 28 31 52 20 14 56 32 91 41 11 35 33 48 26 3 12 34 52 0 8 32 35 35 31 12 48 36 0 0 0 0 37 31 8 12 48 38 107 35 8 31 39 242 3 20 64 40 41 47 18 72 41 57 42 9 36 42 32 11 14 56 43 17 10 7 28 44 36 26 13 52 45 29 27 11 44 46 22 0 11 44 47 21 15 14 55 48 41 32 9 36 49 64 13 12 48 50 71 24 11 44 51 28 10 17 66 52 36 14 10 40 53 45 24 11 44 54 22 29 12 48 55 27 40 17 68 56 38 22 6 24 57 26 27 8 32 58 41 8 12 44 59 21 27 13 52 60 28 0 14 56 61 36 0 17 68 62 58 17 8 32 63 65 7 9 34 64 29 18 9 36 65 21 7 9 34 66 19 24 15 56 67 55 18 16 64 68 119 39 13 52 69 34 17 12 48 70 25 0 10 40 71 113 39 9 36 72 46 20 3 10 73 28 29 12 48 74 63 27 8 25 75 52 23 17 68 76 35 0 9 36 77 32 31 8 32 78 45 19 9 36 79 42 12 12 43 80 28 23 5 17 81 32 33 14 52 82 32 21 14 56 83 27 17 10 40 84 69 27 12 48 85 30 14 10 40 86 48 12 12 48 87 57 21 17 68 88 36 14 11 44 89 20 14 10 40 90 54 22 11 40 91 26 25 7 28 92 58 36 10 40 93 35 10 11 44 94 28 16 5 20 95 8 12 6 22 96 96 20 14 56 97 50 38 13 52 98 15 13 1 2 99 65 12 13 52 100 33 11 9 30 101 7 8 1 3 102 17 22 6 20 103 55 14 12 48 104 32 7 9 32 105 22 14 9 36 106 41 2 12 45 107 50 35 10 40 108 7 5 2 8 109 0 0 0 0 110 26 34 8 32 111 22 12 7 28 112 26 34 11 44 113 37 30 14 56 114 29 21 4 13 115 0 0 0 0 116 0 0 0 0 117 42 28 13 52 118 51 16 17 51 119 77 12 13 52 120 32 14 12 48 121 63 7 1 3 122 50 41 12 48 123 18 21 6 24 124 37 28 11 37 125 23 1 8 32 126 19 10 2 8 127 39 31 12 44 128 38 7 12 48 129 55 26 14 56 130 22 1 2 8 131 7 0 0 0 132 21 12 9 25 133 5 0 1 4 134 21 17 3 12 135 1 5 0 0 136 22 4 2 6 137 0 0 0 0 138 31 6 12 48 139 25 0 14 52 140 0 0 0 0 141 4 0 0 0 142 20 15 4 12 143 29 0 7 28 144 33 12 10 40 Sharedcompendiums 1 0 2 0 3 0 4 0 5 0 6 1 7 0 8 0 9 0 10 1 11 1 12 0 13 0 14 4 15 3 16 3 17 0 18 4 19 0 20 0 21 0 22 0 23 0 24 1 25 1 26 0 27 0 28 0 29 0 30 0 31 0 32 2 33 4 34 0 35 1 36 0 37 0 38 3 39 4 40 0 41 1 42 0 43 2 44 1 45 1 46 2 47 1 48 0 49 1 50 0 51 0 52 0 53 0 54 0 55 1 56 3 57 0 58 0 59 0 60 4 61 0 62 0 63 4 64 3 65 0 66 0 67 4 68 2 69 0 70 0 71 2 72 0 73 1 74 0 75 0 76 1 77 0 78 0 79 0 80 0 81 0 82 0 83 0 84 0 85 0 86 0 87 0 88 0 89 2 90 0 91 0 92 1 93 0 94 0 95 0 96 0 97 0 98 0 99 0 100 0 101 0 102 0 103 0 104 1 105 0 106 0 107 0 108 0 109 0 110 0 111 0 112 0 113 0 114 0 115 0 116 0 117 0 118 0 119 1 120 0 121 0 122 1 123 0 124 0 125 3 126 1 127 2 128 0 129 0 130 0 131 0 132 0 133 0 134 0 135 0 136 1 137 0 138 0 139 0 140 1 141 0 142 1 143 0 144 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) BloggedComputations ReviewedCompendiums 1.1404 0.6575 13.7596 submittedfeedback Sharedcompendiums -2.9244 9.7061 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -45.428 -15.928 -1.508 7.235 112.033 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.1404 4.9526 0.230 0.818218 BloggedComputations 0.6575 0.1680 3.914 0.000142 *** ReviewedCompendiums 13.7596 2.9358 4.687 6.54e-06 *** submittedfeedback -2.9244 0.7450 -3.925 0.000136 *** Sharedcompendiums 9.7061 1.9425 4.997 1.72e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 25.21 on 139 degrees of freedom Multiple R-squared: 0.4429, Adjusted R-squared: 0.4268 F-statistic: 27.62 on 4 and 139 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,] 0.9106352 1.787296e-01 8.936479e-02 [2,] 0.8409102 3.181795e-01 1.590898e-01 [3,] 0.7650387 4.699225e-01 2.349613e-01 [4,] 0.6691248 6.617504e-01 3.308752e-01 [5,] 0.5606150 8.787699e-01 4.393850e-01 [6,] 0.4600757 9.201514e-01 5.399243e-01 [7,] 0.4563327 9.126655e-01 5.436673e-01 [8,] 0.9716409 5.671814e-02 2.835907e-02 [9,] 0.9729443 5.411144e-02 2.705572e-02 [10,] 0.9618801 7.623988e-02 3.811994e-02 [11,] 0.9764849 4.703014e-02 2.351507e-02 [12,] 0.9765459 4.690816e-02 2.345408e-02 [13,] 0.9827950 3.441003e-02 1.720502e-02 [14,] 0.9736937 5.261255e-02 2.630627e-02 [15,] 0.9785363 4.292740e-02 2.146370e-02 [16,] 0.9906605 1.867902e-02 9.339508e-03 [17,] 0.9908358 1.832843e-02 9.164215e-03 [18,] 0.9940181 1.196382e-02 5.981910e-03 [19,] 0.9963016 7.396713e-03 3.698356e-03 [20,] 0.9943988 1.120245e-02 5.601225e-03 [21,] 0.9966316 6.736823e-03 3.368412e-03 [22,] 0.9957535 8.492913e-03 4.246456e-03 [23,] 0.9956648 8.670468e-03 4.335234e-03 [24,] 0.9935601 1.287981e-02 6.439906e-03 [25,] 0.9915823 1.683542e-02 8.417709e-03 [26,] 0.9895435 2.091291e-02 1.045645e-02 [27,] 0.9899057 2.018851e-02 1.009426e-02 [28,] 0.9896669 2.066616e-02 1.033308e-02 [29,] 0.9852262 2.954763e-02 1.477382e-02 [30,] 0.9800923 3.981548e-02 1.990774e-02 [31,] 0.9840033 3.199340e-02 1.599670e-02 [32,] 0.9999954 9.271641e-06 4.635820e-06 [33,] 0.9999973 5.343558e-06 2.671779e-06 [34,] 0.9999952 9.668664e-06 4.834332e-06 [35,] 0.9999930 1.398752e-05 6.993762e-06 [36,] 0.9999940 1.207771e-05 6.038856e-06 [37,] 0.9999933 1.335409e-05 6.677045e-06 [38,] 0.9999931 1.387126e-05 6.935629e-06 [39,] 0.9999937 1.264776e-05 6.323879e-06 [40,] 0.9999966 6.772305e-06 3.386153e-06 [41,] 0.9999940 1.208712e-05 6.043559e-06 [42,] 0.9999931 1.385853e-05 6.929263e-06 [43,] 0.9999955 9.009419e-06 4.504709e-06 [44,] 0.9999958 8.315497e-06 4.157749e-06 [45,] 0.9999927 1.459112e-05 7.295558e-06 [46,] 0.9999875 2.500769e-05 1.250385e-05 [47,] 0.9999881 2.384938e-05 1.192469e-05 [48,] 0.9999975 5.019577e-06 2.509789e-06 [49,] 0.9999964 7.153136e-06 3.576568e-06 [50,] 0.9999945 1.099279e-05 5.496397e-06 [51,] 0.9999915 1.698163e-05 8.490814e-06 [52,] 0.9999934 1.329354e-05 6.646771e-06 [53,] 0.9999965 6.915366e-06 3.457683e-06 [54,] 0.9999940 1.209575e-05 6.047873e-06 [55,] 0.9999954 9.196001e-06 4.598000e-06 [56,] 0.9999940 1.193260e-05 5.966302e-06 [57,] 0.9999945 1.107867e-05 5.539337e-06 [58,] 0.9999913 1.749351e-05 8.746754e-06 [59,] 0.9999973 5.383564e-06 2.691782e-06 [60,] 0.9999971 5.726324e-06 2.863162e-06 [61,] 0.9999998 3.408313e-07 1.704157e-07 [62,] 0.9999997 6.157161e-07 3.078581e-07 [63,] 0.9999994 1.136436e-06 5.682181e-07 [64,] 1.0000000 3.621266e-09 1.810633e-09 [65,] 1.0000000 2.503432e-09 1.251716e-09 [66,] 1.0000000 2.544876e-09 1.272438e-09 [67,] 1.0000000 9.044829e-10 4.522414e-10 [68,] 1.0000000 1.855946e-09 9.279729e-10 [69,] 1.0000000 3.909139e-09 1.954569e-09 [70,] 1.0000000 8.222106e-09 4.111053e-09 [71,] 1.0000000 1.425234e-08 7.126170e-09 [72,] 1.0000000 2.703713e-08 1.351857e-08 [73,] 1.0000000 5.046432e-08 2.523216e-08 [74,] 1.0000000 3.622697e-08 1.811348e-08 [75,] 1.0000000 4.092322e-08 2.046161e-08 [76,] 1.0000000 6.749504e-08 3.374752e-08 [77,] 1.0000000 4.759758e-08 2.379879e-08 [78,] 1.0000000 9.067553e-08 4.533776e-08 [79,] 0.9999999 1.675896e-07 8.379479e-08 [80,] 0.9999998 3.334603e-07 1.667302e-07 [81,] 0.9999997 6.518631e-07 3.259315e-07 [82,] 0.9999997 5.798399e-07 2.899200e-07 [83,] 0.9999996 8.263401e-07 4.131700e-07 [84,] 0.9999992 1.582188e-06 7.910940e-07 [85,] 0.9999989 2.182216e-06 1.091108e-06 [86,] 0.9999980 4.083548e-06 2.041774e-06 [87,] 0.9999962 7.682807e-06 3.841404e-06 [88,] 0.9999957 8.627954e-06 4.313977e-06 [89,] 0.9999999 1.326390e-07 6.631951e-08 [90,] 0.9999999 2.920002e-07 1.460001e-07 [91,] 0.9999997 6.099683e-07 3.049841e-07 [92,] 0.9999998 3.462657e-07 1.731329e-07 [93,] 0.9999997 6.655881e-07 3.327940e-07 [94,] 0.9999993 1.416028e-06 7.080138e-07 [95,] 0.9999989 2.244929e-06 1.122465e-06 [96,] 0.9999988 2.456329e-06 1.228164e-06 [97,] 0.9999975 4.935682e-06 2.467841e-06 [98,] 0.9999956 8.729842e-06 4.364921e-06 [99,] 0.9999916 1.679952e-05 8.399758e-06 [100,] 0.9999868 2.630861e-05 1.315430e-05 [101,] 0.9999748 5.038966e-05 2.519483e-05 [102,] 0.9999548 9.034942e-05 4.517471e-05 [103,] 0.9999208 1.583179e-04 7.915893e-05 [104,] 0.9998499 3.001152e-04 1.500576e-04 [105,] 0.9998271 3.457131e-04 1.728565e-04 [106,] 0.9997550 4.899694e-04 2.449847e-04 [107,] 0.9995521 8.957018e-04 4.478509e-04 [108,] 0.9992428 1.514456e-03 7.572278e-04 [109,] 0.9987638 2.472416e-03 1.236208e-03 [110,] 0.9978605 4.279089e-03 2.139544e-03 [111,] 0.9979262 4.147524e-03 2.073762e-03 [112,] 0.9997170 5.660818e-04 2.830409e-04 [113,] 0.9994416 1.116824e-03 5.584118e-04 [114,] 1.0000000 1.722364e-08 8.611820e-09 [115,] 1.0000000 7.139544e-08 3.569772e-08 [116,] 1.0000000 6.535110e-08 3.267555e-08 [117,] 0.9999999 2.643546e-07 1.321773e-07 [118,] 0.9999995 1.066473e-06 5.332363e-07 [119,] 0.9999980 3.911128e-06 1.955564e-06 [120,] 0.9999962 7.564138e-06 3.782069e-06 [121,] 0.9999854 2.919801e-05 1.459900e-05 [122,] 0.9999509 9.819867e-05 4.909933e-05 [123,] 0.9999644 7.115451e-05 3.557725e-05 [124,] 0.9998707 2.586575e-04 1.293287e-04 [125,] 0.9995253 9.494642e-04 4.747321e-04 [126,] 0.9980860 3.827950e-03 1.913975e-03 [127,] 0.9929560 1.408792e-02 7.043958e-03 [128,] 0.9786775 4.264500e-02 2.132250e-02 [129,] 0.9721315 5.573705e-02 2.786853e-02 > postscript(file="/var/wessaorg/rcomp/tmp/1mnvb1322146673.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2crhy1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3gxyz1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4xq841322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5jda31322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 144 Frequency = 1 1 2 3 4 5 6 48.0026941 -15.6683131 -0.1404419 40.3944757 32.0292105 39.8669929 7 8 9 10 11 12 4.6116297 -25.6147211 7.9188438 32.0492702 27.7702833 0.6247277 13 14 15 16 17 18 5.4276820 92.5123408 -33.3083243 -5.7403988 -7.6902112 -25.2747268 19 20 21 22 23 24 32.7122634 -29.5566700 -3.5205157 -18.4767627 47.5482055 33.3457151 25 26 27 28 29 30 -32.1074035 40.9339729 10.1591493 -32.7110340 -14.2721906 28.2765035 31 32 33 34 35 36 8.8428376 -5.5101223 -15.2450277 34.3639400 -20.9715526 -1.1404419 37 38 39 40 41 42 -0.1436246 34.3097062 112.0330089 -28.1566463 -0.0178596 -5.2399374 43 44 45 46 47 48 -24.5610639 -18.7461576 -22.2797225 -21.2341786 -31.5003568 0.2629657 49 50 51 52 53 54 19.8628973 31.3988167 -20.6171463 5.0354633 5.3988167 -22.9504828 55 56 57 58 59 60 -45.1985388 -19.0948758 -9.3877349 -1.8412676 -24.6974962 -40.8322972 61 62 63 64 65 66 -0.1936304 29.1869595 -3.9736434 -31.6508549 -9.1491198 -40.5466354 67 68 69 70 71 72 -29.7906516 46.0006088 -3.0608495 3.2400355 48.2484178 19.6754910 73 74 75 76 77 78 -26.6566137 7.1413899 0.6845725 5.5958568 -6.0176126 12.8100684 79 80 81 82 83 84 -6.3955561 -7.3452326 -31.4019081 -11.8146318 -5.9369450 25.3644561 85 86 87 88 89 90 -0.9645367 14.2264977 6.9995114 2.9735111 -30.3767985 4.0161125 91 92 93 94 95 96 -6.0108437 2.8650048 4.6033888 6.0302858 -19.2506102 52.8428376 97 98 99 100 101 102 -2.9296600 -2.5983184 29.1645454 -11.4766406 -4.3865604 -22.6741261 103 104 105 106 107 108 19.9115588 -13.7040722 -6.9025844 5.0279598 5.2286051 -1.5516936 109 110 111 112 113 114 -1.1404419 -13.9900209 -1.4637410 -20.1758777 -12.7318568 -2.9683414 115 116 117 118 119 120 -1.1404419 -1.1404419 -4.3549656 -45.4281242 31.4584145 -3.0884412 121 122 123 124 125 126 52.2709090 -12.5462470 -9.3190137 -25.7019363 -24.4119221 -2.5451717 127 128 129 130 131 132 -38.3753265 7.5138449 7.8980210 16.0781842 5.8595581 -38.7561638 133 134 135 136 137 138 1.7976059 2.4967209 -3.4277891 -1.4491766 -1.1404419 1.1713143 139 140 141 142 143 144 -16.7054166 -10.8465728 2.8595581 -20.6540664 13.4258923 3.3504022 > postscript(file="/var/wessaorg/rcomp/tmp/6wxwe1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 144 Frequency = 1 lag(myerror, k = 1) myerror 0 48.0026941 NA 1 -15.6683131 48.0026941 2 -0.1404419 -15.6683131 3 40.3944757 -0.1404419 4 32.0292105 40.3944757 5 39.8669929 32.0292105 6 4.6116297 39.8669929 7 -25.6147211 4.6116297 8 7.9188438 -25.6147211 9 32.0492702 7.9188438 10 27.7702833 32.0492702 11 0.6247277 27.7702833 12 5.4276820 0.6247277 13 92.5123408 5.4276820 14 -33.3083243 92.5123408 15 -5.7403988 -33.3083243 16 -7.6902112 -5.7403988 17 -25.2747268 -7.6902112 18 32.7122634 -25.2747268 19 -29.5566700 32.7122634 20 -3.5205157 -29.5566700 21 -18.4767627 -3.5205157 22 47.5482055 -18.4767627 23 33.3457151 47.5482055 24 -32.1074035 33.3457151 25 40.9339729 -32.1074035 26 10.1591493 40.9339729 27 -32.7110340 10.1591493 28 -14.2721906 -32.7110340 29 28.2765035 -14.2721906 30 8.8428376 28.2765035 31 -5.5101223 8.8428376 32 -15.2450277 -5.5101223 33 34.3639400 -15.2450277 34 -20.9715526 34.3639400 35 -1.1404419 -20.9715526 36 -0.1436246 -1.1404419 37 34.3097062 -0.1436246 38 112.0330089 34.3097062 39 -28.1566463 112.0330089 40 -0.0178596 -28.1566463 41 -5.2399374 -0.0178596 42 -24.5610639 -5.2399374 43 -18.7461576 -24.5610639 44 -22.2797225 -18.7461576 45 -21.2341786 -22.2797225 46 -31.5003568 -21.2341786 47 0.2629657 -31.5003568 48 19.8628973 0.2629657 49 31.3988167 19.8628973 50 -20.6171463 31.3988167 51 5.0354633 -20.6171463 52 5.3988167 5.0354633 53 -22.9504828 5.3988167 54 -45.1985388 -22.9504828 55 -19.0948758 -45.1985388 56 -9.3877349 -19.0948758 57 -1.8412676 -9.3877349 58 -24.6974962 -1.8412676 59 -40.8322972 -24.6974962 60 -0.1936304 -40.8322972 61 29.1869595 -0.1936304 62 -3.9736434 29.1869595 63 -31.6508549 -3.9736434 64 -9.1491198 -31.6508549 65 -40.5466354 -9.1491198 66 -29.7906516 -40.5466354 67 46.0006088 -29.7906516 68 -3.0608495 46.0006088 69 3.2400355 -3.0608495 70 48.2484178 3.2400355 71 19.6754910 48.2484178 72 -26.6566137 19.6754910 73 7.1413899 -26.6566137 74 0.6845725 7.1413899 75 5.5958568 0.6845725 76 -6.0176126 5.5958568 77 12.8100684 -6.0176126 78 -6.3955561 12.8100684 79 -7.3452326 -6.3955561 80 -31.4019081 -7.3452326 81 -11.8146318 -31.4019081 82 -5.9369450 -11.8146318 83 25.3644561 -5.9369450 84 -0.9645367 25.3644561 85 14.2264977 -0.9645367 86 6.9995114 14.2264977 87 2.9735111 6.9995114 88 -30.3767985 2.9735111 89 4.0161125 -30.3767985 90 -6.0108437 4.0161125 91 2.8650048 -6.0108437 92 4.6033888 2.8650048 93 6.0302858 4.6033888 94 -19.2506102 6.0302858 95 52.8428376 -19.2506102 96 -2.9296600 52.8428376 97 -2.5983184 -2.9296600 98 29.1645454 -2.5983184 99 -11.4766406 29.1645454 100 -4.3865604 -11.4766406 101 -22.6741261 -4.3865604 102 19.9115588 -22.6741261 103 -13.7040722 19.9115588 104 -6.9025844 -13.7040722 105 5.0279598 -6.9025844 106 5.2286051 5.0279598 107 -1.5516936 5.2286051 108 -1.1404419 -1.5516936 109 -13.9900209 -1.1404419 110 -1.4637410 -13.9900209 111 -20.1758777 -1.4637410 112 -12.7318568 -20.1758777 113 -2.9683414 -12.7318568 114 -1.1404419 -2.9683414 115 -1.1404419 -1.1404419 116 -4.3549656 -1.1404419 117 -45.4281242 -4.3549656 118 31.4584145 -45.4281242 119 -3.0884412 31.4584145 120 52.2709090 -3.0884412 121 -12.5462470 52.2709090 122 -9.3190137 -12.5462470 123 -25.7019363 -9.3190137 124 -24.4119221 -25.7019363 125 -2.5451717 -24.4119221 126 -38.3753265 -2.5451717 127 7.5138449 -38.3753265 128 7.8980210 7.5138449 129 16.0781842 7.8980210 130 5.8595581 16.0781842 131 -38.7561638 5.8595581 132 1.7976059 -38.7561638 133 2.4967209 1.7976059 134 -3.4277891 2.4967209 135 -1.4491766 -3.4277891 136 -1.1404419 -1.4491766 137 1.1713143 -1.1404419 138 -16.7054166 1.1713143 139 -10.8465728 -16.7054166 140 2.8595581 -10.8465728 141 -20.6540664 2.8595581 142 13.4258923 -20.6540664 143 3.3504022 13.4258923 144 NA 3.3504022 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -15.6683131 48.0026941 [2,] -0.1404419 -15.6683131 [3,] 40.3944757 -0.1404419 [4,] 32.0292105 40.3944757 [5,] 39.8669929 32.0292105 [6,] 4.6116297 39.8669929 [7,] -25.6147211 4.6116297 [8,] 7.9188438 -25.6147211 [9,] 32.0492702 7.9188438 [10,] 27.7702833 32.0492702 [11,] 0.6247277 27.7702833 [12,] 5.4276820 0.6247277 [13,] 92.5123408 5.4276820 [14,] -33.3083243 92.5123408 [15,] -5.7403988 -33.3083243 [16,] -7.6902112 -5.7403988 [17,] -25.2747268 -7.6902112 [18,] 32.7122634 -25.2747268 [19,] -29.5566700 32.7122634 [20,] -3.5205157 -29.5566700 [21,] -18.4767627 -3.5205157 [22,] 47.5482055 -18.4767627 [23,] 33.3457151 47.5482055 [24,] -32.1074035 33.3457151 [25,] 40.9339729 -32.1074035 [26,] 10.1591493 40.9339729 [27,] -32.7110340 10.1591493 [28,] -14.2721906 -32.7110340 [29,] 28.2765035 -14.2721906 [30,] 8.8428376 28.2765035 [31,] -5.5101223 8.8428376 [32,] -15.2450277 -5.5101223 [33,] 34.3639400 -15.2450277 [34,] -20.9715526 34.3639400 [35,] -1.1404419 -20.9715526 [36,] -0.1436246 -1.1404419 [37,] 34.3097062 -0.1436246 [38,] 112.0330089 34.3097062 [39,] -28.1566463 112.0330089 [40,] -0.0178596 -28.1566463 [41,] -5.2399374 -0.0178596 [42,] -24.5610639 -5.2399374 [43,] -18.7461576 -24.5610639 [44,] -22.2797225 -18.7461576 [45,] -21.2341786 -22.2797225 [46,] -31.5003568 -21.2341786 [47,] 0.2629657 -31.5003568 [48,] 19.8628973 0.2629657 [49,] 31.3988167 19.8628973 [50,] -20.6171463 31.3988167 [51,] 5.0354633 -20.6171463 [52,] 5.3988167 5.0354633 [53,] -22.9504828 5.3988167 [54,] -45.1985388 -22.9504828 [55,] -19.0948758 -45.1985388 [56,] -9.3877349 -19.0948758 [57,] -1.8412676 -9.3877349 [58,] -24.6974962 -1.8412676 [59,] -40.8322972 -24.6974962 [60,] -0.1936304 -40.8322972 [61,] 29.1869595 -0.1936304 [62,] -3.9736434 29.1869595 [63,] -31.6508549 -3.9736434 [64,] -9.1491198 -31.6508549 [65,] -40.5466354 -9.1491198 [66,] -29.7906516 -40.5466354 [67,] 46.0006088 -29.7906516 [68,] -3.0608495 46.0006088 [69,] 3.2400355 -3.0608495 [70,] 48.2484178 3.2400355 [71,] 19.6754910 48.2484178 [72,] -26.6566137 19.6754910 [73,] 7.1413899 -26.6566137 [74,] 0.6845725 7.1413899 [75,] 5.5958568 0.6845725 [76,] -6.0176126 5.5958568 [77,] 12.8100684 -6.0176126 [78,] -6.3955561 12.8100684 [79,] -7.3452326 -6.3955561 [80,] -31.4019081 -7.3452326 [81,] -11.8146318 -31.4019081 [82,] -5.9369450 -11.8146318 [83,] 25.3644561 -5.9369450 [84,] -0.9645367 25.3644561 [85,] 14.2264977 -0.9645367 [86,] 6.9995114 14.2264977 [87,] 2.9735111 6.9995114 [88,] -30.3767985 2.9735111 [89,] 4.0161125 -30.3767985 [90,] -6.0108437 4.0161125 [91,] 2.8650048 -6.0108437 [92,] 4.6033888 2.8650048 [93,] 6.0302858 4.6033888 [94,] -19.2506102 6.0302858 [95,] 52.8428376 -19.2506102 [96,] -2.9296600 52.8428376 [97,] -2.5983184 -2.9296600 [98,] 29.1645454 -2.5983184 [99,] -11.4766406 29.1645454 [100,] -4.3865604 -11.4766406 [101,] -22.6741261 -4.3865604 [102,] 19.9115588 -22.6741261 [103,] -13.7040722 19.9115588 [104,] -6.9025844 -13.7040722 [105,] 5.0279598 -6.9025844 [106,] 5.2286051 5.0279598 [107,] -1.5516936 5.2286051 [108,] -1.1404419 -1.5516936 [109,] -13.9900209 -1.1404419 [110,] -1.4637410 -13.9900209 [111,] -20.1758777 -1.4637410 [112,] -12.7318568 -20.1758777 [113,] -2.9683414 -12.7318568 [114,] -1.1404419 -2.9683414 [115,] -1.1404419 -1.1404419 [116,] -4.3549656 -1.1404419 [117,] -45.4281242 -4.3549656 [118,] 31.4584145 -45.4281242 [119,] -3.0884412 31.4584145 [120,] 52.2709090 -3.0884412 [121,] -12.5462470 52.2709090 [122,] -9.3190137 -12.5462470 [123,] -25.7019363 -9.3190137 [124,] -24.4119221 -25.7019363 [125,] -2.5451717 -24.4119221 [126,] -38.3753265 -2.5451717 [127,] 7.5138449 -38.3753265 [128,] 7.8980210 7.5138449 [129,] 16.0781842 7.8980210 [130,] 5.8595581 16.0781842 [131,] -38.7561638 5.8595581 [132,] 1.7976059 -38.7561638 [133,] 2.4967209 1.7976059 [134,] -3.4277891 2.4967209 [135,] -1.4491766 -3.4277891 [136,] -1.1404419 -1.4491766 [137,] 1.1713143 -1.1404419 [138,] -16.7054166 1.1713143 [139,] -10.8465728 -16.7054166 [140,] 2.8595581 -10.8465728 [141,] -20.6540664 2.8595581 [142,] 13.4258923 -20.6540664 [143,] 3.3504022 13.4258923 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -15.6683131 48.0026941 2 -0.1404419 -15.6683131 3 40.3944757 -0.1404419 4 32.0292105 40.3944757 5 39.8669929 32.0292105 6 4.6116297 39.8669929 7 -25.6147211 4.6116297 8 7.9188438 -25.6147211 9 32.0492702 7.9188438 10 27.7702833 32.0492702 11 0.6247277 27.7702833 12 5.4276820 0.6247277 13 92.5123408 5.4276820 14 -33.3083243 92.5123408 15 -5.7403988 -33.3083243 16 -7.6902112 -5.7403988 17 -25.2747268 -7.6902112 18 32.7122634 -25.2747268 19 -29.5566700 32.7122634 20 -3.5205157 -29.5566700 21 -18.4767627 -3.5205157 22 47.5482055 -18.4767627 23 33.3457151 47.5482055 24 -32.1074035 33.3457151 25 40.9339729 -32.1074035 26 10.1591493 40.9339729 27 -32.7110340 10.1591493 28 -14.2721906 -32.7110340 29 28.2765035 -14.2721906 30 8.8428376 28.2765035 31 -5.5101223 8.8428376 32 -15.2450277 -5.5101223 33 34.3639400 -15.2450277 34 -20.9715526 34.3639400 35 -1.1404419 -20.9715526 36 -0.1436246 -1.1404419 37 34.3097062 -0.1436246 38 112.0330089 34.3097062 39 -28.1566463 112.0330089 40 -0.0178596 -28.1566463 41 -5.2399374 -0.0178596 42 -24.5610639 -5.2399374 43 -18.7461576 -24.5610639 44 -22.2797225 -18.7461576 45 -21.2341786 -22.2797225 46 -31.5003568 -21.2341786 47 0.2629657 -31.5003568 48 19.8628973 0.2629657 49 31.3988167 19.8628973 50 -20.6171463 31.3988167 51 5.0354633 -20.6171463 52 5.3988167 5.0354633 53 -22.9504828 5.3988167 54 -45.1985388 -22.9504828 55 -19.0948758 -45.1985388 56 -9.3877349 -19.0948758 57 -1.8412676 -9.3877349 58 -24.6974962 -1.8412676 59 -40.8322972 -24.6974962 60 -0.1936304 -40.8322972 61 29.1869595 -0.1936304 62 -3.9736434 29.1869595 63 -31.6508549 -3.9736434 64 -9.1491198 -31.6508549 65 -40.5466354 -9.1491198 66 -29.7906516 -40.5466354 67 46.0006088 -29.7906516 68 -3.0608495 46.0006088 69 3.2400355 -3.0608495 70 48.2484178 3.2400355 71 19.6754910 48.2484178 72 -26.6566137 19.6754910 73 7.1413899 -26.6566137 74 0.6845725 7.1413899 75 5.5958568 0.6845725 76 -6.0176126 5.5958568 77 12.8100684 -6.0176126 78 -6.3955561 12.8100684 79 -7.3452326 -6.3955561 80 -31.4019081 -7.3452326 81 -11.8146318 -31.4019081 82 -5.9369450 -11.8146318 83 25.3644561 -5.9369450 84 -0.9645367 25.3644561 85 14.2264977 -0.9645367 86 6.9995114 14.2264977 87 2.9735111 6.9995114 88 -30.3767985 2.9735111 89 4.0161125 -30.3767985 90 -6.0108437 4.0161125 91 2.8650048 -6.0108437 92 4.6033888 2.8650048 93 6.0302858 4.6033888 94 -19.2506102 6.0302858 95 52.8428376 -19.2506102 96 -2.9296600 52.8428376 97 -2.5983184 -2.9296600 98 29.1645454 -2.5983184 99 -11.4766406 29.1645454 100 -4.3865604 -11.4766406 101 -22.6741261 -4.3865604 102 19.9115588 -22.6741261 103 -13.7040722 19.9115588 104 -6.9025844 -13.7040722 105 5.0279598 -6.9025844 106 5.2286051 5.0279598 107 -1.5516936 5.2286051 108 -1.1404419 -1.5516936 109 -13.9900209 -1.1404419 110 -1.4637410 -13.9900209 111 -20.1758777 -1.4637410 112 -12.7318568 -20.1758777 113 -2.9683414 -12.7318568 114 -1.1404419 -2.9683414 115 -1.1404419 -1.1404419 116 -4.3549656 -1.1404419 117 -45.4281242 -4.3549656 118 31.4584145 -45.4281242 119 -3.0884412 31.4584145 120 52.2709090 -3.0884412 121 -12.5462470 52.2709090 122 -9.3190137 -12.5462470 123 -25.7019363 -9.3190137 124 -24.4119221 -25.7019363 125 -2.5451717 -24.4119221 126 -38.3753265 -2.5451717 127 7.5138449 -38.3753265 128 7.8980210 7.5138449 129 16.0781842 7.8980210 130 5.8595581 16.0781842 131 -38.7561638 5.8595581 132 1.7976059 -38.7561638 133 2.4967209 1.7976059 134 -3.4277891 2.4967209 135 -1.4491766 -3.4277891 136 -1.1404419 -1.4491766 137 1.1713143 -1.1404419 138 -16.7054166 1.1713143 139 -10.8465728 -16.7054166 140 2.8595581 -10.8465728 141 -20.6540664 2.8595581 142 13.4258923 -20.6540664 143 3.3504022 13.4258923 > 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/76bur1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8we9j1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/925ve1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10wdcd1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11abd71322146673.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/121lcl1322146673.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/13ymov1322146673.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/14jkaq1322146673.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/15w5it1322146673.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/16krdm1322146673.tab") + } > > try(system("convert tmp/1mnvb1322146673.ps tmp/1mnvb1322146673.png",intern=TRUE)) character(0) > try(system("convert tmp/2crhy1322146673.ps tmp/2crhy1322146673.png",intern=TRUE)) character(0) > try(system("convert tmp/3gxyz1322146673.ps tmp/3gxyz1322146673.png",intern=TRUE)) character(0) > try(system("convert tmp/4xq841322146673.ps tmp/4xq841322146673.png",intern=TRUE)) character(0) > try(system("convert tmp/5jda31322146673.ps tmp/5jda31322146673.png",intern=TRUE)) character(0) > try(system("convert tmp/6wxwe1322146673.ps tmp/6wxwe1322146673.png",intern=TRUE)) character(0) > try(system("convert tmp/76bur1322146673.ps tmp/76bur1322146673.png",intern=TRUE)) character(0) > try(system("convert tmp/8we9j1322146673.ps tmp/8we9j1322146673.png",intern=TRUE)) character(0) > try(system("convert tmp/925ve1322146673.ps tmp/925ve1322146673.png",intern=TRUE)) character(0) > try(system("convert tmp/10wdcd1322146673.ps tmp/10wdcd1322146673.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.710 0.511 5.367