R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-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(0 + ,255202 + ,64 + ,92 + ,34 + ,0 + ,135248 + ,59 + ,58 + ,30 + ,0 + ,207223 + ,64 + ,62 + ,42 + ,1 + ,189326 + ,95 + ,108 + ,34 + ,1 + ,141365 + ,46 + ,55 + ,25 + ,0 + ,65295 + ,27 + ,8 + ,31 + ,0 + ,439387 + ,103 + ,134 + ,29 + ,0 + ,33186 + ,19 + ,1 + ,18 + ,0 + ,183696 + ,51 + ,64 + ,30 + ,0 + ,186657 + ,38 + ,77 + ,29 + ,1 + ,276696 + ,99 + ,86 + ,42 + ,1 + ,194414 + ,98 + ,96 + ,50 + ,0 + ,141409 + ,59 + ,44 + ,33 + ,1 + ,306730 + ,68 + ,108 + ,46 + ,1 + ,192691 + ,74 + ,63 + ,38 + ,1 + ,333497 + ,164 + ,160 + ,52 + ,0 + ,261835 + ,59 + ,109 + ,32 + ,1 + ,263451 + ,130 + ,86 + ,35 + ,1 + ,157448 + ,49 + ,93 + ,25 + ,1 + ,232190 + ,73 + ,126 + ,42 + ,0 + ,245725 + ,64 + ,110 + ,40 + ,0 + ,388603 + ,92 + ,86 + ,35 + ,0 + ,156540 + ,34 + ,50 + ,25 + ,0 + ,156189 + ,47 + ,92 + ,46 + ,0 + ,189726 + ,106 + ,123 + ,39 + ,0 + ,192167 + ,106 + ,81 + ,35 + ,1 + ,249893 + ,122 + ,93 + ,38 + ,1 + ,236812 + ,76 + ,113 + ,35 + ,1 + ,143160 + ,47 + ,52 + 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,95 + ,50 + ,1 + ,164235 + ,99 + ,156 + ,41 + ,1 + ,234092 + ,80 + ,74 + ,37 + ,0 + ,207158 + ,69 + ,137 + ,38 + ,0 + ,156583 + ,57 + ,37 + ,28 + ,0 + ,242395 + ,68 + ,111 + ,36 + ,1 + ,261601 + ,70 + ,58 + ,32 + ,1 + ,178489 + ,35 + ,78 + ,32 + ,0 + ,204221 + ,44 + ,88 + ,33 + ,1 + ,268066 + ,69 + ,152 + ,35 + ,1 + ,327622 + ,133 + ,130 + ,58 + ,1 + ,361799 + ,101 + ,145 + ,27 + ,0 + ,247131 + ,107 + ,108 + ,45 + ,1 + ,265849 + ,58 + ,138 + ,37 + ,0 + ,162336 + ,162 + ,62 + ,32 + ,1 + ,43287 + ,14 + ,13 + ,19 + ,0 + ,172244 + ,68 + ,89 + ,22 + ,0 + ,189021 + ,121 + ,86 + ,35 + ,0 + ,227681 + ,43 + ,116 + ,36 + ,0 + ,269329 + ,81 + ,157 + ,36 + ,0 + ,106503 + ,56 + ,28 + ,23 + ,1 + ,117891 + ,77 + ,83 + ,40 + ,1 + ,287201 + ,59 + ,72 + ,40 + ,0 + ,266805 + ,78 + ,134 + ,42 + ,0 + ,23623 + ,11 + ,12 + ,1 + ,1 + ,174954 + ,69 + ,120 + ,36 + ,0 + ,61857 + ,25 + ,23 + ,11 + ,1 + ,144889 + ,43 + ,83 + ,40 + ,1 + ,347988 + ,103 + ,126 + ,34 + ,0 + ,21054 + ,16 + ,4 + ,0 + ,1 + ,224051 + ,46 + ,71 + ,27 + ,1 + ,31414 + ,19 + ,18 + ,8 + ,1 + ,278660 + ,107 + ,98 + ,35 + ,0 + ,209481 + ,58 + ,68 + ,44 + ,0 + ,156870 + ,75 + ,44 + ,40 + ,1 + ,112933 + ,46 + ,29 + ,28 + ,0 + ,38214 + ,34 + ,16 + ,8 + ,0 + ,166011 + ,35 + ,61 + ,36 + ,1 + ,316044 + ,73 + ,117 + ,47 + ,1 + ,181578 + ,56 + ,46 + ,48 + ,1 + ,358903 + ,72 + ,129 + ,45 + ,1 + ,275578 + ,91 + ,139 + ,48 + ,1 + ,368796 + ,106 + ,136 + ,49 + ,1 + ,172464 + ,31 + ,66 + ,35 + ,1 + ,94381 + ,35 + ,42 + ,32 + ,1 + ,250563 + ,290 + ,75 + ,36 + ,1 + ,382499 + ,154 + ,97 + ,42 + ,1 + ,118010 + ,42 + ,49 + ,35 + ,1 + ,365575 + ,122 + ,127 + ,42 + ,1 + ,147989 + ,72 + ,55 + ,34 + ,1 + ,231681 + ,46 + ,101 + ,41 + ,0 + ,193119 + ,77 + ,80 + ,36 + ,0 + ,189020 + ,108 + ,29 + ,32 + ,0 + ,341958 + ,106 + ,95 + ,33 + ,1 + ,222060 + ,79 + ,120 + ,35 + ,0 + ,173260 + ,63 + ,41 + ,21 + ,0 + ,274787 + ,91 + ,128 + ,42 + ,1 + ,130908 + ,52 + ,142 + ,49 + ,0 + ,204009 + ,75 + ,88 + ,33 + ,0 + ,262412 + ,94 + ,170 + ,39 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,14688 + ,10 + ,4 + ,0 + ,0 + ,98 + ,1 + ,0 + ,0 + ,0 + ,455 + ,2 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,195765 + ,75 + ,56 + ,33 + ,0 + ,334258 + ,129 + ,121 + ,47 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,203 + ,4 + ,0 + ,0 + ,0 + ,7199 + ,5 + ,7 + ,0 + ,1 + ,46660 + ,20 + ,12 + ,5 + ,1 + ,17547 + ,5 + ,0 + ,1 + ,0 + ,107465 + ,38 + ,37 + ,38 + ,1 + ,969 + ,2 + ,0 + ,0 + ,1 + ,179994 + ,58 + ,47 + ,28) + ,dim=c(5 + ,164) + ,dimnames=list(c('Geslacht' + ,'Time_in_RFC' + ,'Logins' + ,'Blogged_computations' + ,'Reviewed_compendiums') + ,1:164)) > y <- array(NA,dim=c(5,164),dimnames=list(c('Geslacht','Time_in_RFC','Logins','Blogged_computations','Reviewed_compendiums'),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 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > 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 Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > 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 Geslacht Time_in_RFC Logins Blogged_computations Reviewed_compendiums 1 0 255202 64 92 34 2 0 135248 59 58 30 3 0 207223 64 62 42 4 1 189326 95 108 34 5 1 141365 46 55 25 6 0 65295 27 8 31 7 0 439387 103 134 29 8 0 33186 19 1 18 9 0 183696 51 64 30 10 0 186657 38 77 29 11 1 276696 99 86 42 12 1 194414 98 96 50 13 0 141409 59 44 33 14 1 306730 68 108 46 15 1 192691 74 63 38 16 1 333497 164 160 52 17 0 261835 59 109 32 18 1 263451 130 86 35 19 1 157448 49 93 25 20 1 232190 73 126 42 21 0 245725 64 110 40 22 0 388603 92 86 35 23 0 156540 34 50 25 24 0 156189 47 92 46 25 0 189726 106 123 39 26 0 192167 106 81 35 27 1 249893 122 93 38 28 1 236812 76 113 35 29 1 143160 47 52 28 30 0 259667 54 113 37 31 0 243020 68 113 40 32 0 176062 67 44 42 33 0 286683 79 123 44 34 1 87485 33 38 33 35 0 329737 88 111 38 36 1 247082 51 77 37 37 0 378463 108 102 41 38 1 191653 75 74 32 39 0 114673 31 33 17 40 0 301596 167 107 39 41 0 284195 73 108 33 42 1 155568 60 66 35 43 1 177306 67 69 32 44 1 144595 51 62 35 45 0 140319 73 50 45 46 1 405267 135 91 38 47 1 78800 42 20 26 48 1 201970 69 101 45 49 1 302705 101 129 44 50 1 164733 50 93 40 51 1 194221 68 89 33 52 0 24188 24 8 4 53 0 346142 288 80 41 54 0 65029 17 21 18 55 0 101097 64 30 14 56 1 253745 51 86 36 57 0 273513 77 116 49 58 1 282220 160 106 32 59 1 280928 120 132 37 60 1 214872 74 75 32 61 0 342048 127 139 43 62 0 273924 108 121 25 63 1 195726 92 57 42 64 1 231162 80 67 37 65 0 209798 61 45 33 66 1 201345 60 88 28 67 0 180231 118 79 31 68 1 204441 129 75 40 69 0 197813 67 114 32 70 1 136421 60 127 25 71 1 216092 59 86 42 72 1 73566 32 22 23 73 0 213998 70 67 42 74 1 181728 50 77 38 75 0 148758 51 105 34 76 0 308343 71 121 39 77 1 251437 78 88 32 78 0 202388 102 78 37 79 0 173286 56 122 34 80 0 155529 58 66 33 81 0 132672 41 58 25 82 1 390163 102 134 45 83 0 145905 66 30 26 84 0 228012 88 103 40 85 1 80953 25 49 8 86 0 130805 47 26 27 87 1 135163 49 67 32 88 1 333790 168 59 37 89 1 271806 95 95 50 90 1 164235 99 156 41 91 1 234092 80 74 37 92 0 207158 69 137 38 93 0 156583 57 37 28 94 0 242395 68 111 36 95 1 261601 70 58 32 96 1 178489 35 78 32 97 0 204221 44 88 33 98 1 268066 69 152 35 99 1 327622 133 130 58 100 1 361799 101 145 27 101 0 247131 107 108 45 102 1 265849 58 138 37 103 0 162336 162 62 32 104 1 43287 14 13 19 105 0 172244 68 89 22 106 0 189021 121 86 35 107 0 227681 43 116 36 108 0 269329 81 157 36 109 0 106503 56 28 23 110 1 117891 77 83 40 111 1 287201 59 72 40 112 0 266805 78 134 42 113 0 23623 11 12 1 114 1 174954 69 120 36 115 0 61857 25 23 11 116 1 144889 43 83 40 117 1 347988 103 126 34 118 0 21054 16 4 0 119 1 224051 46 71 27 120 1 31414 19 18 8 121 1 278660 107 98 35 122 0 209481 58 68 44 123 0 156870 75 44 40 124 1 112933 46 29 28 125 0 38214 34 16 8 126 0 166011 35 61 36 127 1 316044 73 117 47 128 1 181578 56 46 48 129 1 358903 72 129 45 130 1 275578 91 139 48 131 1 368796 106 136 49 132 1 172464 31 66 35 133 1 94381 35 42 32 134 1 250563 290 75 36 135 1 382499 154 97 42 136 1 118010 42 49 35 137 1 365575 122 127 42 138 1 147989 72 55 34 139 1 231681 46 101 41 140 0 193119 77 80 36 141 0 189020 108 29 32 142 0 341958 106 95 33 143 1 222060 79 120 35 144 0 173260 63 41 21 145 0 274787 91 128 42 146 1 130908 52 142 49 147 0 204009 75 88 33 148 0 262412 94 170 39 149 0 1 0 0 0 150 0 14688 10 4 0 151 0 98 1 0 0 152 0 455 2 0 0 153 1 0 0 0 0 154 0 0 0 0 0 155 1 195765 75 56 33 156 0 334258 129 121 47 157 0 0 0 0 0 158 0 203 4 0 0 159 0 7199 5 7 0 160 1 46660 20 12 5 161 1 17547 5 0 1 162 0 107465 38 37 38 163 1 969 2 0 0 164 1 179994 58 47 28 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Time_in_RFC Logins 2.515e-01 -1.760e-07 -1.899e-04 Blogged_computations Reviewed_compendiums 2.700e-04 8.641e-03 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.64349 -0.51140 0.02463 0.46718 0.74903 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.515e-01 1.040e-01 2.419 0.0167 * Time_in_RFC -1.760e-07 8.058e-07 -0.218 0.8274 Logins -1.899e-04 1.267e-03 -0.150 0.8810 Blogged_computations 2.700e-04 1.631e-03 0.166 0.8688 Reviewed_compendiums 8.641e-03 4.890e-03 1.767 0.0791 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.497 on 159 degrees of freedom Multiple R-squared: 0.0421, Adjusted R-squared: 0.018 F-statistic: 1.747 on 4 and 159 DF, p-value: 0.1423 > 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,] 0.5464957 0.9070087 0.4535043 [2,] 0.4168327 0.8336653 0.5831673 [3,] 0.3062774 0.6125548 0.6937226 [4,] 0.3767013 0.7534026 0.6232987 [5,] 0.2680332 0.5360663 0.7319668 [6,] 0.2411647 0.4823293 0.7588353 [7,] 0.3819712 0.7639423 0.6180288 [8,] 0.3968483 0.7936967 0.6031517 [9,] 0.3801995 0.7603990 0.6198005 [10,] 0.3330565 0.6661130 0.6669435 [11,] 0.2780567 0.5561134 0.7219433 [12,] 0.3340444 0.6680888 0.6659556 [13,] 0.2773613 0.5547226 0.7226387 [14,] 0.2971584 0.5943168 0.7028416 [15,] 0.2418876 0.4837751 0.7581124 [16,] 0.1932613 0.3865226 0.8067387 [17,] 0.2111471 0.4222941 0.7888529 [18,] 0.3831194 0.7662389 0.6168806 [19,] 0.4323001 0.8646001 0.5676999 [20,] 0.3984158 0.7968316 0.6015842 [21,] 0.4078618 0.8157236 0.5921382 [22,] 0.4771746 0.9543491 0.5228254 [23,] 0.4485756 0.8971512 0.5514244 [24,] 0.4379691 0.8759383 0.5620309 [25,] 0.4196648 0.8393296 0.5803352 [26,] 0.4097691 0.8195383 0.5902309 [27,] 0.4669991 0.9339982 0.5330009 [28,] 0.4433740 0.8867480 0.5566260 [29,] 0.5257825 0.9484350 0.4742175 [30,] 0.5094211 0.9811577 0.4905789 [31,] 0.5144784 0.9710433 0.4855216 [32,] 0.4799782 0.9599563 0.5200218 [33,] 0.5425815 0.9148369 0.4574185 [34,] 0.5223086 0.9553828 0.4776914 [35,] 0.5292575 0.9414850 0.4707425 [36,] 0.5406891 0.9186218 0.4593109 [37,] 0.5432662 0.9134677 0.4567338 [38,] 0.5637363 0.8725273 0.4362637 [39,] 0.5965463 0.8069074 0.4034537 [40,] 0.6053809 0.7892382 0.3946191 [41,] 0.5962949 0.8074102 0.4037051 [42,] 0.5857653 0.8284693 0.4142347 [43,] 0.5766236 0.8467527 0.4233764 [44,] 0.5727157 0.8545687 0.4272843 [45,] 0.5421110 0.9157779 0.4578890 [46,] 0.5656465 0.8687070 0.4343535 [47,] 0.5403947 0.9192106 0.4596053 [48,] 0.5081295 0.9837411 0.4918705 [49,] 0.5144811 0.9710378 0.4855189 [50,] 0.5491685 0.9016629 0.4508315 [51,] 0.5524585 0.8950830 0.4475415 [52,] 0.5369866 0.9260268 0.4630134 [53,] 0.5439556 0.9120888 0.4560444 [54,] 0.5640735 0.8718530 0.4359265 [55,] 0.5564865 0.8870269 0.4435135 [56,] 0.5473385 0.9053229 0.4526615 [57,] 0.5480104 0.9039792 0.4519896 [58,] 0.5410506 0.9178988 0.4589494 [59,] 0.5479645 0.9040710 0.4520355 [60,] 0.5486354 0.9027292 0.4513646 [61,] 0.5371640 0.9256721 0.4628360 [62,] 0.5425938 0.9148124 0.4574062 [63,] 0.5435583 0.9128834 0.4564417 [64,] 0.5288421 0.9423158 0.4711579 [65,] 0.5450624 0.9098752 0.4549376 [66,] 0.5591483 0.8817035 0.4408517 [67,] 0.5487441 0.9025118 0.4512559 [68,] 0.5623658 0.8752683 0.4376342 [69,] 0.5743646 0.8512708 0.4256354 [70,] 0.5777066 0.8445868 0.4222934 [71,] 0.5852986 0.8294028 0.4147014 [72,] 0.5942359 0.8115282 0.4057641 [73,] 0.5967970 0.8064060 0.4032030 [74,] 0.5880184 0.8239632 0.4119816 [75,] 0.5741915 0.8516171 0.4258085 [76,] 0.5644900 0.8710200 0.4355100 [77,] 0.5804548 0.8390903 0.4195452 [78,] 0.6216458 0.7567085 0.3783542 [79,] 0.6179497 0.7641005 0.3820503 [80,] 0.6145065 0.7709870 0.3854935 [81,] 0.6119987 0.7760026 0.3880013 [82,] 0.5879121 0.8241757 0.4120879 [83,] 0.5813757 0.8372486 0.4186243 [84,] 0.5717428 0.8565144 0.4282572 [85,] 0.5805096 0.8389808 0.4194904 [86,] 0.5795952 0.8408096 0.4204048 [87,] 0.5901826 0.8196348 0.4098174 [88,] 0.5857542 0.8284915 0.4142458 [89,] 0.5805996 0.8388007 0.4194004 [90,] 0.5893981 0.8212039 0.4106019 [91,] 0.5827283 0.8345434 0.4172717 [92,] 0.5492618 0.9014764 0.4507382 [93,] 0.5574375 0.8851250 0.4425625 [94,] 0.5815580 0.8368840 0.4184420 [95,] 0.5730627 0.8538746 0.4269373 [96,] 0.5703924 0.8592152 0.4296076 [97,] 0.5822872 0.8354256 0.4177128 [98,] 0.5665601 0.8668798 0.4334399 [99,] 0.5753873 0.8492253 0.4246127 [100,] 0.5866693 0.8266613 0.4133307 [101,] 0.6012239 0.7975521 0.3987761 [102,] 0.5951765 0.8096471 0.4048235 [103,] 0.5747804 0.8504392 0.4252196 [104,] 0.5542924 0.8914151 0.4457076 [105,] 0.5910987 0.8178025 0.4089013 [106,] 0.5571878 0.8856243 0.4428122 [107,] 0.5371157 0.9257686 0.4628843 [108,] 0.5139055 0.9721890 0.4860945 [109,] 0.4921576 0.9843152 0.5078424 [110,] 0.4760364 0.9520728 0.5239636 [111,] 0.4411594 0.8823188 0.5588406 [112,] 0.4392057 0.8784114 0.5607943 [113,] 0.4754895 0.9509790 0.5245105 [114,] 0.4627747 0.9255495 0.5372253 [115,] 0.5012310 0.9975380 0.4987690 [116,] 0.5502923 0.8994153 0.4497077 [117,] 0.5356781 0.9286437 0.4643219 [118,] 0.5064667 0.9870665 0.4935333 [119,] 0.5466422 0.9067156 0.4533578 [120,] 0.5106891 0.9786217 0.4893109 [121,] 0.4627661 0.9255321 0.5372339 [122,] 0.4405040 0.8810080 0.5594960 [123,] 0.4093377 0.8186754 0.5906623 [124,] 0.3912535 0.7825069 0.6087465 [125,] 0.3755350 0.7510699 0.6244650 [126,] 0.3538371 0.7076742 0.6461629 [127,] 0.3697899 0.7395798 0.6302101 [128,] 0.4309036 0.8618072 0.5690964 [129,] 0.3979989 0.7959979 0.6020011 [130,] 0.4544220 0.9088440 0.5455780 [131,] 0.4951715 0.9903430 0.5048285 [132,] 0.4441137 0.8882275 0.5558863 [133,] 0.4127185 0.8254369 0.5872815 [134,] 0.3554752 0.7109504 0.6445248 [135,] 0.3050648 0.6101295 0.6949352 [136,] 0.3524754 0.7049508 0.6475246 [137,] 0.3060441 0.6120882 0.6939559 [138,] 0.2869137 0.5738274 0.7130863 [139,] 0.5847735 0.8304529 0.4152265 [140,] 0.5374685 0.9250629 0.4625315 [141,] 0.7140733 0.5718534 0.2859267 [142,] 0.6583108 0.6833784 0.3416892 [143,] 0.6479510 0.7040979 0.3520490 [144,] 0.6024191 0.7951618 0.3975809 [145,] 0.5791090 0.8417821 0.4208910 [146,] 0.6909761 0.6180478 0.3090239 [147,] 0.6295064 0.7409872 0.3704936 [148,] 0.5148602 0.9702796 0.4851398 [149,] 0.3694248 0.7388497 0.6305752 > postscript(file="/var/fisher/rcomp/tmp/1bhgg1355065323.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2f3wo1355065323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3tdsy1355065323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/43alr1355065323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5rrgk1355065323.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 7 -0.5130816 -0.4914017 -0.5825564 0.4768902 0.5512218 -0.5049340 -0.4413876 8 9 10 11 12 13 14 -0.3978805 -0.4860130 -0.4828296 0.4298400 0.3433372 -0.5124607 0.3887346 15 16 17 18 19 20 21 0.4510794 0.3457921 -0.5001713 0.4938841 0.5443627 0.4062679 -0.5714566 22 23 24 25 26 27 28 -0.4913031 -0.4470361 -0.6374328 -0.5682059 -0.5218718 0.4621648 0.4716493 29 30 31 32 33 34 35 0.5266142 -0.5457883 -0.5719831 -0.5826118 -0.5994727 0.4747293 -0.5350980 36 37 38 39 40 41 42 0.4611466 -0.5462160 0.4999635 -0.3812564 -0.5326088 -0.5019477 0.4669994 43 44 45 46 47 48 49 0.4972687 0.4644385 -0.6153073 0.4925235 0.5402578 0.3810153 0.4059059 50 51 52 53 54 55 56 0.4162178 0.4863950 -0.2794287 -0.5117793 -0.3980551 -0.3506453 0.4685306 57 58 59 60 61 62 63 -0.6434865 0.5234091 0.4653591 0.5035907 -0.5762896 -0.4314891 0.4220877 64 65 66 67 68 69 70 0.4665520 -0.5003127 0.5296053 -0.4865892 0.4430712 -0.5112714 0.5335707 71 72 73 74 75 76 77 0.4115752 0.5628207 -0.5815743 0.4408116 -0.5377974 -0.5534337 0.5072768 78 79 80 81 82 83 84 -0.5373046 -0.5371202 -0.5161051 -0.4520680 0.4114995 -0.4460718 -0.5681264 85 86 87 88 89 90 91 0.6851188 -0.4598995 0.4869719 0.5034902 0.3566604 0.3997854 0.4651777 92 93 94 95 96 97 98 -0.5673034 -0.4650738 -0.5369885 0.5156465 0.4889694 -0.5161329 0.4652914 99 100 101 102 103 104 105 0.2951234 0.5588874 -0.6057082 0.4493097 -0.4854338 0.5910668 -0.4224209 106 107 108 109 110 111 112 -0.5209267 -0.5456766 -0.5421983 -0.4284434 0.4158003 0.4451544 -0.5888493 113 114 115 116 117 118 119 -0.2571537 0.4489001 -0.3371461 0.4140952 0.5014781 -0.2458552 0.5441743 120 121 122 123 124 125 126 0.6836291 0.4889531 -0.6022007 -0.5671884 0.5273135 -0.3117851 -0.5432016 127 128 129 130 131 132 133 0.3802526 0.3638831 0.4016492 0.3619671 0.3733935 0.4644657 0.4838842 134 135 136 137 138 139 140 0.5163320 0.4559397 0.4615596 0.4387832 0.4795555 0.4164415 -0.5355831 141 142 143 144 145 146 147 -0.4820826 -0.4820025 0.4677323 -0.4015907 -0.5833553 0.3196434 -0.5102826 148 149 150 151 152 153 154 -0.5703803 -0.2515198 -0.2481153 -0.2513128 -0.2510601 0.7484800 -0.2515200 155 156 157 158 159 160 161 0.4969061 -0.6069856 -0.2515200 -0.2507246 -0.2511932 0.7140461 0.7438772 162 163 164 -0.5637398 0.7490304 0.5365371 > postscript(file="/var/fisher/rcomp/tmp/6qo1y1355065323.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 -0.5130816 NA 1 -0.4914017 -0.5130816 2 -0.5825564 -0.4914017 3 0.4768902 -0.5825564 4 0.5512218 0.4768902 5 -0.5049340 0.5512218 6 -0.4413876 -0.5049340 7 -0.3978805 -0.4413876 8 -0.4860130 -0.3978805 9 -0.4828296 -0.4860130 10 0.4298400 -0.4828296 11 0.3433372 0.4298400 12 -0.5124607 0.3433372 13 0.3887346 -0.5124607 14 0.4510794 0.3887346 15 0.3457921 0.4510794 16 -0.5001713 0.3457921 17 0.4938841 -0.5001713 18 0.5443627 0.4938841 19 0.4062679 0.5443627 20 -0.5714566 0.4062679 21 -0.4913031 -0.5714566 22 -0.4470361 -0.4913031 23 -0.6374328 -0.4470361 24 -0.5682059 -0.6374328 25 -0.5218718 -0.5682059 26 0.4621648 -0.5218718 27 0.4716493 0.4621648 28 0.5266142 0.4716493 29 -0.5457883 0.5266142 30 -0.5719831 -0.5457883 31 -0.5826118 -0.5719831 32 -0.5994727 -0.5826118 33 0.4747293 -0.5994727 34 -0.5350980 0.4747293 35 0.4611466 -0.5350980 36 -0.5462160 0.4611466 37 0.4999635 -0.5462160 38 -0.3812564 0.4999635 39 -0.5326088 -0.3812564 40 -0.5019477 -0.5326088 41 0.4669994 -0.5019477 42 0.4972687 0.4669994 43 0.4644385 0.4972687 44 -0.6153073 0.4644385 45 0.4925235 -0.6153073 46 0.5402578 0.4925235 47 0.3810153 0.5402578 48 0.4059059 0.3810153 49 0.4162178 0.4059059 50 0.4863950 0.4162178 51 -0.2794287 0.4863950 52 -0.5117793 -0.2794287 53 -0.3980551 -0.5117793 54 -0.3506453 -0.3980551 55 0.4685306 -0.3506453 56 -0.6434865 0.4685306 57 0.5234091 -0.6434865 58 0.4653591 0.5234091 59 0.5035907 0.4653591 60 -0.5762896 0.5035907 61 -0.4314891 -0.5762896 62 0.4220877 -0.4314891 63 0.4665520 0.4220877 64 -0.5003127 0.4665520 65 0.5296053 -0.5003127 66 -0.4865892 0.5296053 67 0.4430712 -0.4865892 68 -0.5112714 0.4430712 69 0.5335707 -0.5112714 70 0.4115752 0.5335707 71 0.5628207 0.4115752 72 -0.5815743 0.5628207 73 0.4408116 -0.5815743 74 -0.5377974 0.4408116 75 -0.5534337 -0.5377974 76 0.5072768 -0.5534337 77 -0.5373046 0.5072768 78 -0.5371202 -0.5373046 79 -0.5161051 -0.5371202 80 -0.4520680 -0.5161051 81 0.4114995 -0.4520680 82 -0.4460718 0.4114995 83 -0.5681264 -0.4460718 84 0.6851188 -0.5681264 85 -0.4598995 0.6851188 86 0.4869719 -0.4598995 87 0.5034902 0.4869719 88 0.3566604 0.5034902 89 0.3997854 0.3566604 90 0.4651777 0.3997854 91 -0.5673034 0.4651777 92 -0.4650738 -0.5673034 93 -0.5369885 -0.4650738 94 0.5156465 -0.5369885 95 0.4889694 0.5156465 96 -0.5161329 0.4889694 97 0.4652914 -0.5161329 98 0.2951234 0.4652914 99 0.5588874 0.2951234 100 -0.6057082 0.5588874 101 0.4493097 -0.6057082 102 -0.4854338 0.4493097 103 0.5910668 -0.4854338 104 -0.4224209 0.5910668 105 -0.5209267 -0.4224209 106 -0.5456766 -0.5209267 107 -0.5421983 -0.5456766 108 -0.4284434 -0.5421983 109 0.4158003 -0.4284434 110 0.4451544 0.4158003 111 -0.5888493 0.4451544 112 -0.2571537 -0.5888493 113 0.4489001 -0.2571537 114 -0.3371461 0.4489001 115 0.4140952 -0.3371461 116 0.5014781 0.4140952 117 -0.2458552 0.5014781 118 0.5441743 -0.2458552 119 0.6836291 0.5441743 120 0.4889531 0.6836291 121 -0.6022007 0.4889531 122 -0.5671884 -0.6022007 123 0.5273135 -0.5671884 124 -0.3117851 0.5273135 125 -0.5432016 -0.3117851 126 0.3802526 -0.5432016 127 0.3638831 0.3802526 128 0.4016492 0.3638831 129 0.3619671 0.4016492 130 0.3733935 0.3619671 131 0.4644657 0.3733935 132 0.4838842 0.4644657 133 0.5163320 0.4838842 134 0.4559397 0.5163320 135 0.4615596 0.4559397 136 0.4387832 0.4615596 137 0.4795555 0.4387832 138 0.4164415 0.4795555 139 -0.5355831 0.4164415 140 -0.4820826 -0.5355831 141 -0.4820025 -0.4820826 142 0.4677323 -0.4820025 143 -0.4015907 0.4677323 144 -0.5833553 -0.4015907 145 0.3196434 -0.5833553 146 -0.5102826 0.3196434 147 -0.5703803 -0.5102826 148 -0.2515198 -0.5703803 149 -0.2481153 -0.2515198 150 -0.2513128 -0.2481153 151 -0.2510601 -0.2513128 152 0.7484800 -0.2510601 153 -0.2515200 0.7484800 154 0.4969061 -0.2515200 155 -0.6069856 0.4969061 156 -0.2515200 -0.6069856 157 -0.2507246 -0.2515200 158 -0.2511932 -0.2507246 159 0.7140461 -0.2511932 160 0.7438772 0.7140461 161 -0.5637398 0.7438772 162 0.7490304 -0.5637398 163 0.5365371 0.7490304 164 NA 0.5365371 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.4914017 -0.5130816 [2,] -0.5825564 -0.4914017 [3,] 0.4768902 -0.5825564 [4,] 0.5512218 0.4768902 [5,] -0.5049340 0.5512218 [6,] -0.4413876 -0.5049340 [7,] -0.3978805 -0.4413876 [8,] -0.4860130 -0.3978805 [9,] -0.4828296 -0.4860130 [10,] 0.4298400 -0.4828296 [11,] 0.3433372 0.4298400 [12,] -0.5124607 0.3433372 [13,] 0.3887346 -0.5124607 [14,] 0.4510794 0.3887346 [15,] 0.3457921 0.4510794 [16,] -0.5001713 0.3457921 [17,] 0.4938841 -0.5001713 [18,] 0.5443627 0.4938841 [19,] 0.4062679 0.5443627 [20,] -0.5714566 0.4062679 [21,] -0.4913031 -0.5714566 [22,] -0.4470361 -0.4913031 [23,] -0.6374328 -0.4470361 [24,] -0.5682059 -0.6374328 [25,] -0.5218718 -0.5682059 [26,] 0.4621648 -0.5218718 [27,] 0.4716493 0.4621648 [28,] 0.5266142 0.4716493 [29,] -0.5457883 0.5266142 [30,] -0.5719831 -0.5457883 [31,] -0.5826118 -0.5719831 [32,] -0.5994727 -0.5826118 [33,] 0.4747293 -0.5994727 [34,] -0.5350980 0.4747293 [35,] 0.4611466 -0.5350980 [36,] -0.5462160 0.4611466 [37,] 0.4999635 -0.5462160 [38,] -0.3812564 0.4999635 [39,] -0.5326088 -0.3812564 [40,] -0.5019477 -0.5326088 [41,] 0.4669994 -0.5019477 [42,] 0.4972687 0.4669994 [43,] 0.4644385 0.4972687 [44,] -0.6153073 0.4644385 [45,] 0.4925235 -0.6153073 [46,] 0.5402578 0.4925235 [47,] 0.3810153 0.5402578 [48,] 0.4059059 0.3810153 [49,] 0.4162178 0.4059059 [50,] 0.4863950 0.4162178 [51,] -0.2794287 0.4863950 [52,] -0.5117793 -0.2794287 [53,] -0.3980551 -0.5117793 [54,] -0.3506453 -0.3980551 [55,] 0.4685306 -0.3506453 [56,] -0.6434865 0.4685306 [57,] 0.5234091 -0.6434865 [58,] 0.4653591 0.5234091 [59,] 0.5035907 0.4653591 [60,] -0.5762896 0.5035907 [61,] -0.4314891 -0.5762896 [62,] 0.4220877 -0.4314891 [63,] 0.4665520 0.4220877 [64,] -0.5003127 0.4665520 [65,] 0.5296053 -0.5003127 [66,] -0.4865892 0.5296053 [67,] 0.4430712 -0.4865892 [68,] -0.5112714 0.4430712 [69,] 0.5335707 -0.5112714 [70,] 0.4115752 0.5335707 [71,] 0.5628207 0.4115752 [72,] -0.5815743 0.5628207 [73,] 0.4408116 -0.5815743 [74,] -0.5377974 0.4408116 [75,] -0.5534337 -0.5377974 [76,] 0.5072768 -0.5534337 [77,] -0.5373046 0.5072768 [78,] -0.5371202 -0.5373046 [79,] -0.5161051 -0.5371202 [80,] -0.4520680 -0.5161051 [81,] 0.4114995 -0.4520680 [82,] -0.4460718 0.4114995 [83,] -0.5681264 -0.4460718 [84,] 0.6851188 -0.5681264 [85,] -0.4598995 0.6851188 [86,] 0.4869719 -0.4598995 [87,] 0.5034902 0.4869719 [88,] 0.3566604 0.5034902 [89,] 0.3997854 0.3566604 [90,] 0.4651777 0.3997854 [91,] -0.5673034 0.4651777 [92,] -0.4650738 -0.5673034 [93,] -0.5369885 -0.4650738 [94,] 0.5156465 -0.5369885 [95,] 0.4889694 0.5156465 [96,] -0.5161329 0.4889694 [97,] 0.4652914 -0.5161329 [98,] 0.2951234 0.4652914 [99,] 0.5588874 0.2951234 [100,] -0.6057082 0.5588874 [101,] 0.4493097 -0.6057082 [102,] -0.4854338 0.4493097 [103,] 0.5910668 -0.4854338 [104,] -0.4224209 0.5910668 [105,] -0.5209267 -0.4224209 [106,] -0.5456766 -0.5209267 [107,] -0.5421983 -0.5456766 [108,] -0.4284434 -0.5421983 [109,] 0.4158003 -0.4284434 [110,] 0.4451544 0.4158003 [111,] -0.5888493 0.4451544 [112,] -0.2571537 -0.5888493 [113,] 0.4489001 -0.2571537 [114,] -0.3371461 0.4489001 [115,] 0.4140952 -0.3371461 [116,] 0.5014781 0.4140952 [117,] -0.2458552 0.5014781 [118,] 0.5441743 -0.2458552 [119,] 0.6836291 0.5441743 [120,] 0.4889531 0.6836291 [121,] -0.6022007 0.4889531 [122,] -0.5671884 -0.6022007 [123,] 0.5273135 -0.5671884 [124,] -0.3117851 0.5273135 [125,] -0.5432016 -0.3117851 [126,] 0.3802526 -0.5432016 [127,] 0.3638831 0.3802526 [128,] 0.4016492 0.3638831 [129,] 0.3619671 0.4016492 [130,] 0.3733935 0.3619671 [131,] 0.4644657 0.3733935 [132,] 0.4838842 0.4644657 [133,] 0.5163320 0.4838842 [134,] 0.4559397 0.5163320 [135,] 0.4615596 0.4559397 [136,] 0.4387832 0.4615596 [137,] 0.4795555 0.4387832 [138,] 0.4164415 0.4795555 [139,] -0.5355831 0.4164415 [140,] -0.4820826 -0.5355831 [141,] -0.4820025 -0.4820826 [142,] 0.4677323 -0.4820025 [143,] -0.4015907 0.4677323 [144,] -0.5833553 -0.4015907 [145,] 0.3196434 -0.5833553 [146,] -0.5102826 0.3196434 [147,] -0.5703803 -0.5102826 [148,] -0.2515198 -0.5703803 [149,] -0.2481153 -0.2515198 [150,] -0.2513128 -0.2481153 [151,] -0.2510601 -0.2513128 [152,] 0.7484800 -0.2510601 [153,] -0.2515200 0.7484800 [154,] 0.4969061 -0.2515200 [155,] -0.6069856 0.4969061 [156,] -0.2515200 -0.6069856 [157,] -0.2507246 -0.2515200 [158,] -0.2511932 -0.2507246 [159,] 0.7140461 -0.2511932 [160,] 0.7438772 0.7140461 [161,] -0.5637398 0.7438772 [162,] 0.7490304 -0.5637398 [163,] 0.5365371 0.7490304 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.4914017 -0.5130816 2 -0.5825564 -0.4914017 3 0.4768902 -0.5825564 4 0.5512218 0.4768902 5 -0.5049340 0.5512218 6 -0.4413876 -0.5049340 7 -0.3978805 -0.4413876 8 -0.4860130 -0.3978805 9 -0.4828296 -0.4860130 10 0.4298400 -0.4828296 11 0.3433372 0.4298400 12 -0.5124607 0.3433372 13 0.3887346 -0.5124607 14 0.4510794 0.3887346 15 0.3457921 0.4510794 16 -0.5001713 0.3457921 17 0.4938841 -0.5001713 18 0.5443627 0.4938841 19 0.4062679 0.5443627 20 -0.5714566 0.4062679 21 -0.4913031 -0.5714566 22 -0.4470361 -0.4913031 23 -0.6374328 -0.4470361 24 -0.5682059 -0.6374328 25 -0.5218718 -0.5682059 26 0.4621648 -0.5218718 27 0.4716493 0.4621648 28 0.5266142 0.4716493 29 -0.5457883 0.5266142 30 -0.5719831 -0.5457883 31 -0.5826118 -0.5719831 32 -0.5994727 -0.5826118 33 0.4747293 -0.5994727 34 -0.5350980 0.4747293 35 0.4611466 -0.5350980 36 -0.5462160 0.4611466 37 0.4999635 -0.5462160 38 -0.3812564 0.4999635 39 -0.5326088 -0.3812564 40 -0.5019477 -0.5326088 41 0.4669994 -0.5019477 42 0.4972687 0.4669994 43 0.4644385 0.4972687 44 -0.6153073 0.4644385 45 0.4925235 -0.6153073 46 0.5402578 0.4925235 47 0.3810153 0.5402578 48 0.4059059 0.3810153 49 0.4162178 0.4059059 50 0.4863950 0.4162178 51 -0.2794287 0.4863950 52 -0.5117793 -0.2794287 53 -0.3980551 -0.5117793 54 -0.3506453 -0.3980551 55 0.4685306 -0.3506453 56 -0.6434865 0.4685306 57 0.5234091 -0.6434865 58 0.4653591 0.5234091 59 0.5035907 0.4653591 60 -0.5762896 0.5035907 61 -0.4314891 -0.5762896 62 0.4220877 -0.4314891 63 0.4665520 0.4220877 64 -0.5003127 0.4665520 65 0.5296053 -0.5003127 66 -0.4865892 0.5296053 67 0.4430712 -0.4865892 68 -0.5112714 0.4430712 69 0.5335707 -0.5112714 70 0.4115752 0.5335707 71 0.5628207 0.4115752 72 -0.5815743 0.5628207 73 0.4408116 -0.5815743 74 -0.5377974 0.4408116 75 -0.5534337 -0.5377974 76 0.5072768 -0.5534337 77 -0.5373046 0.5072768 78 -0.5371202 -0.5373046 79 -0.5161051 -0.5371202 80 -0.4520680 -0.5161051 81 0.4114995 -0.4520680 82 -0.4460718 0.4114995 83 -0.5681264 -0.4460718 84 0.6851188 -0.5681264 85 -0.4598995 0.6851188 86 0.4869719 -0.4598995 87 0.5034902 0.4869719 88 0.3566604 0.5034902 89 0.3997854 0.3566604 90 0.4651777 0.3997854 91 -0.5673034 0.4651777 92 -0.4650738 -0.5673034 93 -0.5369885 -0.4650738 94 0.5156465 -0.5369885 95 0.4889694 0.5156465 96 -0.5161329 0.4889694 97 0.4652914 -0.5161329 98 0.2951234 0.4652914 99 0.5588874 0.2951234 100 -0.6057082 0.5588874 101 0.4493097 -0.6057082 102 -0.4854338 0.4493097 103 0.5910668 -0.4854338 104 -0.4224209 0.5910668 105 -0.5209267 -0.4224209 106 -0.5456766 -0.5209267 107 -0.5421983 -0.5456766 108 -0.4284434 -0.5421983 109 0.4158003 -0.4284434 110 0.4451544 0.4158003 111 -0.5888493 0.4451544 112 -0.2571537 -0.5888493 113 0.4489001 -0.2571537 114 -0.3371461 0.4489001 115 0.4140952 -0.3371461 116 0.5014781 0.4140952 117 -0.2458552 0.5014781 118 0.5441743 -0.2458552 119 0.6836291 0.5441743 120 0.4889531 0.6836291 121 -0.6022007 0.4889531 122 -0.5671884 -0.6022007 123 0.5273135 -0.5671884 124 -0.3117851 0.5273135 125 -0.5432016 -0.3117851 126 0.3802526 -0.5432016 127 0.3638831 0.3802526 128 0.4016492 0.3638831 129 0.3619671 0.4016492 130 0.3733935 0.3619671 131 0.4644657 0.3733935 132 0.4838842 0.4644657 133 0.5163320 0.4838842 134 0.4559397 0.5163320 135 0.4615596 0.4559397 136 0.4387832 0.4615596 137 0.4795555 0.4387832 138 0.4164415 0.4795555 139 -0.5355831 0.4164415 140 -0.4820826 -0.5355831 141 -0.4820025 -0.4820826 142 0.4677323 -0.4820025 143 -0.4015907 0.4677323 144 -0.5833553 -0.4015907 145 0.3196434 -0.5833553 146 -0.5102826 0.3196434 147 -0.5703803 -0.5102826 148 -0.2515198 -0.5703803 149 -0.2481153 -0.2515198 150 -0.2513128 -0.2481153 151 -0.2510601 -0.2513128 152 0.7484800 -0.2510601 153 -0.2515200 0.7484800 154 0.4969061 -0.2515200 155 -0.6069856 0.4969061 156 -0.2515200 -0.6069856 157 -0.2507246 -0.2515200 158 -0.2511932 -0.2507246 159 0.7140461 -0.2511932 160 0.7438772 0.7140461 161 -0.5637398 0.7438772 162 0.7490304 -0.5637398 163 0.5365371 0.7490304 > 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/fisher/rcomp/tmp/7vtyy1355065323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8pggk1355065323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9lk3g1355065323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10az2o1355065323.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/fisher/rcomp/tmp/11r6ld1355065323.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/fisher/rcomp/tmp/12a87g1355065323.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/fisher/rcomp/tmp/13zrf31355065323.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/fisher/rcomp/tmp/14cyjc1355065323.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/fisher/rcomp/tmp/152hrc1355065323.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/fisher/rcomp/tmp/16b0jp1355065323.tab") + } > > try(system("convert tmp/1bhgg1355065323.ps tmp/1bhgg1355065323.png",intern=TRUE)) character(0) > try(system("convert tmp/2f3wo1355065323.ps tmp/2f3wo1355065323.png",intern=TRUE)) character(0) > try(system("convert tmp/3tdsy1355065323.ps tmp/3tdsy1355065323.png",intern=TRUE)) character(0) > try(system("convert tmp/43alr1355065323.ps tmp/43alr1355065323.png",intern=TRUE)) character(0) > try(system("convert tmp/5rrgk1355065323.ps tmp/5rrgk1355065323.png",intern=TRUE)) character(0) > try(system("convert tmp/6qo1y1355065323.ps tmp/6qo1y1355065323.png",intern=TRUE)) character(0) > try(system("convert tmp/7vtyy1355065323.ps tmp/7vtyy1355065323.png",intern=TRUE)) character(0) > try(system("convert tmp/8pggk1355065323.ps tmp/8pggk1355065323.png",intern=TRUE)) character(0) > try(system("convert tmp/9lk3g1355065323.ps tmp/9lk3g1355065323.png",intern=TRUE)) character(0) > try(system("convert tmp/10az2o1355065323.ps tmp/10az2o1355065323.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.225 1.638 9.863