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(4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,2 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,1 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,2 + ,0 + ,1 + ,0 + ,1 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,2 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,1 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,4 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,0 + ,4 + ,0 + ,2 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,4 + ,1 + ,2 + ,0 + ,0 + ,0 + ,1 + ,0 + ,4 + ,1 + ,2 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,0 + ,2 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,1 + ,2 + ,0 + ,0 + ,0 + ,1 + ,1 + 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,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,1 + ,1 + ,2 + ,0 + ,0 + ,2 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,0 + ,2 + ,1 + ,1 + ,0 + ,0 + ,2 + ,1 + ,0 + ,2 + ,1 + ,1 + ,1 + ,0 + ,2 + ,1 + ,0 + ,2 + ,1 + ,0 + ,0 + ,0) + ,dim=c(8 + ,154) + ,dimnames=list(c('Weeks' + ,'UseLimit' + ,'T40' + ,'T20' + ,'Used' + ,'CorrectAnalysis' + ,'Useful' + ,'Outcome ') + ,1:154)) > y <- array(NA,dim=c(8,154),dimnames=list(c('Weeks','UseLimit','T40','T20','Used','CorrectAnalysis','Useful','Outcome '),1:154)) > 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 = '5' > 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 Used Weeks UseLimit T40 T20 CorrectAnalysis Useful Outcome\r 1 0 4 1 1 0 0 0 1 2 0 4 0 2 0 0 0 0 3 0 4 0 2 0 0 0 0 4 0 4 0 2 0 0 0 0 5 0 4 0 2 0 0 0 0 6 0 4 1 2 0 0 1 1 7 0 4 0 2 0 0 0 0 8 0 4 0 1 0 0 0 0 9 0 4 0 2 0 0 0 1 10 0 4 1 2 0 0 0 0 11 0 4 1 1 0 0 0 0 12 0 4 0 2 0 0 0 0 13 1 4 0 2 0 0 1 0 14 0 4 1 1 0 0 0 0 15 1 4 0 2 0 0 1 1 16 1 4 0 1 0 0 1 1 17 1 4 1 1 0 1 1 0 18 0 4 1 1 0 0 0 0 19 0 4 0 2 0 0 0 1 20 1 4 0 1 0 1 1 1 21 0 4 1 2 0 0 1 0 22 1 4 1 2 0 0 1 1 23 0 4 0 2 0 0 1 1 24 0 4 1 2 0 0 1 1 25 1 4 0 1 0 0 0 1 26 1 4 0 2 0 0 1 0 27 0 4 1 2 0 0 0 1 28 1 4 0 2 0 0 0 0 29 0 4 0 2 0 0 0 1 30 0 4 0 2 0 0 1 0 31 0 4 0 2 0 0 0 0 32 0 4 1 2 0 0 0 0 33 0 4 1 2 0 0 1 0 34 0 4 0 1 0 0 0 1 35 0 4 0 2 0 0 0 0 36 0 4 0 2 0 0 0 0 37 1 4 1 1 0 0 1 0 38 1 4 0 2 0 0 0 1 39 0 4 0 2 0 0 1 1 40 0 4 0 1 0 0 1 0 41 1 4 0 2 0 1 1 1 42 1 4 0 2 0 0 0 1 43 0 4 1 2 0 0 1 1 44 0 4 1 1 0 0 0 0 45 0 4 0 2 0 0 1 0 46 0 4 0 2 0 0 1 1 47 0 4 0 2 0 0 0 0 48 0 4 0 2 0 0 0 1 49 0 4 0 2 0 0 1 1 50 0 4 0 2 0 0 0 0 51 1 4 0 1 0 0 0 0 52 1 4 1 1 0 1 1 0 53 0 4 0 2 0 0 0 1 54 1 4 0 2 0 1 0 0 55 0 4 0 2 0 0 0 0 56 1 4 0 1 0 0 0 1 57 1 4 0 2 0 0 1 1 58 0 4 0 2 0 0 0 1 59 0 4 0 2 0 0 0 1 60 1 4 1 1 0 1 1 1 61 0 4 1 1 0 0 0 1 62 1 4 0 2 0 0 1 0 63 0 4 0 2 0 0 0 0 64 0 4 1 1 0 0 0 1 65 0 4 0 2 0 0 0 0 66 0 4 0 2 0 0 0 0 67 1 4 0 1 0 1 1 0 68 0 4 1 2 0 0 0 0 69 0 4 0 2 0 0 0 1 70 1 4 0 2 0 0 0 0 71 0 4 0 2 0 0 0 0 72 0 4 0 2 0 0 0 1 73 1 4 0 2 0 0 0 1 74 1 4 1 2 0 0 0 0 75 0 4 0 2 0 0 0 1 76 0 4 0 1 0 0 1 1 77 0 4 0 2 0 0 0 1 78 1 4 0 2 0 0 1 1 79 1 4 0 1 0 1 0 1 80 0 4 0 1 0 0 1 0 81 0 4 0 2 0 0 0 0 82 1 4 1 2 0 0 0 1 83 0 4 0 2 0 0 0 0 84 1 4 0 2 0 1 0 0 85 0 4 0 2 0 0 1 1 86 0 4 1 2 0 0 0 0 87 0 2 1 0 2 0 0 1 88 1 2 1 0 1 0 0 1 89 0 2 0 0 2 0 0 0 90 0 2 0 0 2 0 0 1 91 0 2 0 0 2 0 1 0 92 0 2 1 0 1 0 0 0 93 0 2 1 0 2 0 1 0 94 0 2 0 0 2 0 0 0 95 0 2 0 0 1 0 0 0 96 0 2 0 0 2 0 0 1 97 0 2 1 0 1 0 0 0 98 0 2 0 0 2 0 0 0 99 0 2 1 0 2 0 0 0 100 0 2 0 0 2 0 0 1 101 0 2 1 0 2 0 0 1 102 0 2 0 0 2 0 0 0 103 0 2 0 0 2 0 0 0 104 0 2 0 0 2 0 0 0 105 1 2 0 0 1 0 0 0 106 0 2 0 0 2 0 0 0 107 0 2 0 0 2 0 0 0 108 1 2 1 0 1 0 0 0 109 0 2 0 0 2 0 0 0 110 0 2 1 0 2 0 0 0 111 1 2 1 0 1 0 1 0 112 0 2 0 0 1 0 0 0 113 1 2 0 0 2 0 0 0 114 1 2 1 0 1 0 0 0 115 0 2 1 0 2 0 0 0 116 0 2 0 0 2 0 0 0 117 0 2 1 0 2 0 0 1 118 0 2 1 0 2 0 0 0 119 0 2 0 0 2 0 0 0 120 0 2 0 0 2 0 0 1 121 0 2 1 0 2 0 0 0 122 0 2 0 0 2 0 0 0 123 1 2 1 0 1 0 0 0 124 1 2 0 0 2 0 1 1 125 0 2 0 0 2 0 0 1 126 0 2 0 0 1 0 0 0 127 0 2 0 0 2 0 1 0 128 0 2 0 0 2 0 0 1 129 0 2 0 0 2 0 0 0 130 0 2 0 0 2 0 0 1 131 0 2 1 0 2 0 0 0 132 0 2 1 0 2 0 0 1 133 1 2 1 0 2 0 0 0 134 0 2 0 0 2 0 0 0 135 0 2 0 0 2 0 0 0 136 0 2 0 0 2 0 0 0 137 1 2 1 0 2 0 1 1 138 1 2 1 0 1 0 1 1 139 0 2 0 0 1 0 0 0 140 0 2 0 0 2 0 0 0 141 1 2 0 0 2 1 0 1 142 1 2 0 0 1 0 0 1 143 0 2 1 0 2 0 0 0 144 0 2 0 0 2 0 1 1 145 0 2 0 0 2 0 1 0 146 0 2 0 0 1 0 0 1 147 1 2 0 0 1 0 0 0 148 0 2 0 0 1 0 0 0 149 0 2 1 0 2 0 0 0 150 0 2 0 0 2 0 1 1 151 0 2 0 0 2 0 0 1 152 1 2 1 0 2 1 0 0 153 1 2 1 0 2 1 1 0 154 1 2 1 0 2 0 0 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Weeks UseLimit T40 1.61487 -0.36049 0.05226 -0.01806 T20 CorrectAnalysis Useful `Outcome\\r` -0.42636 0.72716 0.16833 0.07430 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.54183 -0.21109 -0.13071 -0.00198 0.95883 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.61487 0.43315 3.728 0.000275 *** Weeks -0.36049 0.13404 -2.690 0.007989 ** UseLimit 0.05226 0.06877 0.760 0.448507 T40 -0.01806 0.09944 -0.182 0.856131 T20 -0.42636 0.10906 -3.909 0.000141 *** CorrectAnalysis 0.72716 0.12353 5.886 2.6e-08 *** Useful 0.16833 0.07456 2.258 0.025459 * `Outcome\\r` 0.07430 0.06554 1.134 0.258845 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3868 on 146 degrees of freedom Multiple R-squared: 0.3143, Adjusted R-squared: 0.2814 F-statistic: 9.559 on 7 and 146 DF, p-value: 9.474e-10 > 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,] 0.000000000 0.00000000 1.00000000 [2,] 0.000000000 0.00000000 1.00000000 [3,] 0.157463047 0.31492609 0.84253695 [4,] 0.084737202 0.16947440 0.91526280 [5,] 0.111679880 0.22335976 0.88832012 [6,] 0.067818874 0.13563775 0.93218113 [7,] 0.036704550 0.07340910 0.96329545 [8,] 0.019437550 0.03887510 0.98056245 [9,] 0.009834794 0.01966959 0.99016521 [10,] 0.005549151 0.01109830 0.99445085 [11,] 0.015682991 0.03136598 0.98431701 [12,] 0.053029417 0.10605883 0.94697058 [13,] 0.163875368 0.32775074 0.83612463 [14,] 0.172315278 0.34463056 0.82768472 [15,] 0.329702667 0.65940533 0.67029733 [16,] 0.349703622 0.69940724 0.65029638 [17,] 0.320015170 0.64003034 0.67998483 [18,] 0.626636731 0.74672654 0.37336327 [19,] 0.570359314 0.85928137 0.42964069 [20,] 0.649212589 0.70157482 0.35078741 [21,] 0.592207482 0.81558504 0.40779252 [22,] 0.546594861 0.90681028 0.45340514 [23,] 0.520409172 0.95918166 0.47959083 [24,] 0.511828299 0.97634340 0.48817170 [25,] 0.455901249 0.91180250 0.54409875 [26,] 0.401129492 0.80225898 0.59887051 [27,] 0.438034526 0.87606905 0.56196547 [28,] 0.658825285 0.68234943 0.34117472 [29,] 0.704461369 0.59107726 0.29553863 [30,] 0.759735607 0.48052879 0.24026439 [31,] 0.714859803 0.57028039 0.28514020 [32,] 0.828631252 0.34273750 0.17136875 [33,] 0.826846686 0.34630663 0.17315331 [34,] 0.795764943 0.40847011 0.20423506 [35,] 0.790613110 0.41877378 0.20938689 [36,] 0.803234792 0.39353042 0.19676521 [37,] 0.769347149 0.46130570 0.23065285 [38,] 0.742094144 0.51581171 0.25790586 [39,] 0.751717376 0.49656525 0.24828262 [40,] 0.714634767 0.57073047 0.28536523 [41,] 0.834384324 0.33123135 0.16561568 [42,] 0.803699426 0.39260115 0.19630057 [43,] 0.779274507 0.44145099 0.22072549 [44,] 0.751932928 0.49613414 0.24806707 [45,] 0.717310421 0.56537916 0.28268958 [46,] 0.834560788 0.33087842 0.16543921 [47,] 0.863532668 0.27293466 0.13646733 [48,] 0.846131001 0.30773800 0.15386900 [49,] 0.827468779 0.34506244 0.17253122 [50,] 0.797446699 0.40510660 0.20255330 [51,] 0.769179032 0.46164194 0.23082097 [52,] 0.822418688 0.35516262 0.17758131 [53,] 0.794406168 0.41118766 0.20559383 [54,] 0.764851722 0.47029656 0.23514828 [55,] 0.732558415 0.53488317 0.26744159 [56,] 0.698717330 0.60256534 0.30128267 [57,] 0.663579199 0.67284160 0.33642080 [58,] 0.648358337 0.70328333 0.35164166 [59,] 0.624107447 0.75178511 0.37589255 [60,] 0.780137096 0.43972581 0.21986290 [61,] 0.750763155 0.49847369 0.24923685 [62,] 0.730203841 0.53959232 0.26979616 [63,] 0.835301669 0.32939666 0.16469833 [64,] 0.929179512 0.14164098 0.07082049 [65,] 0.917167834 0.16566433 0.08283217 [66,] 0.919175893 0.16164821 0.08082411 [67,] 0.906907389 0.18618522 0.09309261 [68,] 0.932634215 0.13473157 0.06736579 [69,] 0.917043638 0.16591272 0.08295636 [70,] 0.911566553 0.17686689 0.08843345 [71,] 0.892819791 0.21436042 0.10718021 [72,] 0.947957772 0.10408446 0.05204223 [73,] 0.935283036 0.12943393 0.06471696 [74,] 0.927101533 0.14579693 0.07289847 [75,] 0.916944085 0.16611183 0.08305591 [76,] 0.896830089 0.20633982 0.10316991 [77,] 0.880342489 0.23931502 0.11965751 [78,] 0.867538961 0.26492208 0.13246104 [79,] 0.839171157 0.32165769 0.16082884 [80,] 0.807836149 0.38432770 0.19216385 [81,] 0.780252934 0.43949413 0.21974707 [82,] 0.831986384 0.33602723 0.16801362 [83,] 0.829158046 0.34168391 0.17084195 [84,] 0.795510039 0.40897992 0.20448996 [85,] 0.803349333 0.39330133 0.19665067 [86,] 0.766574880 0.46685024 0.23342512 [87,] 0.815340108 0.36931978 0.18465989 [88,] 0.779445685 0.44110863 0.22055432 [89,] 0.752579616 0.49484077 0.24742038 [90,] 0.710286797 0.57942641 0.28971320 [91,] 0.683931611 0.63213678 0.31606839 [92,] 0.636319145 0.72736171 0.36368085 [93,] 0.586245548 0.82750890 0.41375445 [94,] 0.534465846 0.93106831 0.46553415 [95,] 0.604097594 0.79180481 0.39590241 [96,] 0.551946169 0.89610766 0.44805383 [97,] 0.498605620 0.99721124 0.50139438 [98,] 0.506483514 0.98703297 0.49351649 [99,] 0.452318367 0.90463673 0.54768163 [100,] 0.418498892 0.83699778 0.58150111 [101,] 0.379828011 0.75965602 0.62017199 [102,] 0.393451293 0.78690259 0.60654871 [103,] 0.720165640 0.55966872 0.27983436 [104,] 0.716682236 0.56663553 0.28331776 [105,] 0.682536139 0.63492772 0.31746386 [106,] 0.629179385 0.74164123 0.37082062 [107,] 0.619333076 0.76133385 0.38066692 [108,] 0.590089471 0.81982106 0.40991053 [109,] 0.531224735 0.93755053 0.46877526 [110,] 0.471961541 0.94392308 0.52803846 [111,] 0.447677541 0.89535508 0.55232246 [112,] 0.386970167 0.77394033 0.61302983 [113,] 0.369437067 0.73887413 0.63056293 [114,] 0.561684555 0.87663089 0.43831545 [115,] 0.496342170 0.99268434 0.50365783 [116,] 0.494497727 0.98899545 0.50550227 [117,] 0.428765713 0.85753143 0.57123429 [118,] 0.361710746 0.72342149 0.63828925 [119,] 0.297114746 0.59422949 0.70288525 [120,] 0.237903650 0.47580730 0.76209635 [121,] 0.225227834 0.45045567 0.77477217 [122,] 0.284683337 0.56936667 0.71531666 [123,] 0.377631946 0.75526389 0.62236805 [124,] 0.302623265 0.60524653 0.69737674 [125,] 0.234014097 0.46802819 0.76598590 [126,] 0.174288022 0.34857604 0.82571198 [127,] 0.187090306 0.37418061 0.81290969 [128,] 0.137899305 0.27579861 0.86210069 [129,] 0.133433592 0.26686718 0.86656641 [130,] 0.084475110 0.16895022 0.91552489 [131,] 0.052552203 0.10510441 0.94744780 [132,] 0.082252264 0.16450453 0.91774774 [133,] 0.065150199 0.13030040 0.93484980 > postscript(file="/var/fisher/rcomp/tmp/1opu81355428076.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/2pqje1355428076.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3oom91355428076.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/46yaq1355428076.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5wo2z1355428076.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 154 Frequency = 1 1 2 3 4 5 6 -0.28141376 -0.13679278 -0.13679278 -0.13679278 -0.13679278 -0.43168088 7 8 9 10 11 12 -0.13679278 -0.15485263 -0.21108946 -0.18905724 -0.20711708 -0.13679278 13 14 15 16 17 18 0.69488025 -0.20711708 0.62058357 0.60252373 -0.10260196 -0.20711708 19 20 21 22 23 24 -0.21108946 -0.12463419 -0.35738420 0.56831912 -0.37941643 -0.43168088 25 26 27 28 29 30 0.77085070 0.69488025 -0.26335391 0.86320722 -0.21108946 -0.30511975 31 32 33 34 35 36 -0.13679278 -0.18905724 -0.35738420 -0.22914930 -0.13679278 -0.13679278 37 38 39 40 41 42 0.62455595 0.78891054 -0.37941643 -0.32317959 -0.10657434 0.78891054 43 44 45 46 47 48 -0.43168088 -0.20711708 -0.30511975 -0.37941643 -0.13679278 -0.21108946 49 50 51 52 53 54 -0.37941643 -0.13679278 0.84514737 -0.10260196 -0.21108946 0.13604930 55 56 57 58 59 60 -0.13679278 0.77085070 0.62058357 -0.21108946 -0.21108946 -0.17689864 61 62 63 64 65 66 -0.28141376 0.69488025 -0.13679278 -0.28141376 -0.13679278 -0.13679278 67 68 69 70 71 72 -0.05033751 -0.18905724 -0.21108946 0.86320722 -0.13679278 -0.21108946 73 74 75 76 77 78 0.78891054 0.81094276 -0.21108946 -0.39747627 -0.21108946 0.62058357 79 80 81 82 83 84 0.04369278 -0.32317959 -0.13679278 0.73664609 -0.13679278 0.13604930 85 86 87 88 89 90 -0.37941643 -0.18905724 -0.16773328 0.40590689 -0.04117215 -0.11546883 91 92 93 94 95 96 -0.20949912 -0.51979644 -0.26176357 -0.04117215 -0.46753198 -0.11546883 97 98 99 100 101 102 -0.51979644 -0.04117215 -0.09343661 -0.11546883 -0.16773328 -0.04117215 103 104 105 106 107 108 -0.04117215 -0.04117215 0.53246802 -0.04117215 -0.04117215 0.48020356 109 110 111 112 113 114 -0.04117215 -0.09343661 0.31187660 -0.46753198 0.95882785 0.48020356 115 116 117 118 119 120 -0.09343661 -0.04117215 -0.16773328 -0.09343661 -0.04117215 -0.11546883 121 122 123 124 125 126 -0.09343661 -0.04117215 0.48020356 0.71620420 -0.11546883 -0.46753198 127 128 129 130 131 132 -0.20949912 -0.11546883 -0.04117215 -0.11546883 -0.09343661 -0.16773328 133 134 135 136 137 138 0.90656339 -0.04117215 -0.04117215 -0.04117215 0.66393975 0.23757992 139 140 141 142 143 144 -0.46753198 -0.04117215 0.15737325 0.45817134 -0.09343661 -0.28379580 145 146 147 148 149 150 -0.20949912 -0.54182866 0.53246802 -0.46753198 -0.09343661 -0.28379580 151 152 153 154 -0.11546883 0.17940547 0.01107851 0.90656339 > postscript(file="/var/fisher/rcomp/tmp/6l1ci1355428076.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 = 154 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.28141376 NA 1 -0.13679278 -0.28141376 2 -0.13679278 -0.13679278 3 -0.13679278 -0.13679278 4 -0.13679278 -0.13679278 5 -0.43168088 -0.13679278 6 -0.13679278 -0.43168088 7 -0.15485263 -0.13679278 8 -0.21108946 -0.15485263 9 -0.18905724 -0.21108946 10 -0.20711708 -0.18905724 11 -0.13679278 -0.20711708 12 0.69488025 -0.13679278 13 -0.20711708 0.69488025 14 0.62058357 -0.20711708 15 0.60252373 0.62058357 16 -0.10260196 0.60252373 17 -0.20711708 -0.10260196 18 -0.21108946 -0.20711708 19 -0.12463419 -0.21108946 20 -0.35738420 -0.12463419 21 0.56831912 -0.35738420 22 -0.37941643 0.56831912 23 -0.43168088 -0.37941643 24 0.77085070 -0.43168088 25 0.69488025 0.77085070 26 -0.26335391 0.69488025 27 0.86320722 -0.26335391 28 -0.21108946 0.86320722 29 -0.30511975 -0.21108946 30 -0.13679278 -0.30511975 31 -0.18905724 -0.13679278 32 -0.35738420 -0.18905724 33 -0.22914930 -0.35738420 34 -0.13679278 -0.22914930 35 -0.13679278 -0.13679278 36 0.62455595 -0.13679278 37 0.78891054 0.62455595 38 -0.37941643 0.78891054 39 -0.32317959 -0.37941643 40 -0.10657434 -0.32317959 41 0.78891054 -0.10657434 42 -0.43168088 0.78891054 43 -0.20711708 -0.43168088 44 -0.30511975 -0.20711708 45 -0.37941643 -0.30511975 46 -0.13679278 -0.37941643 47 -0.21108946 -0.13679278 48 -0.37941643 -0.21108946 49 -0.13679278 -0.37941643 50 0.84514737 -0.13679278 51 -0.10260196 0.84514737 52 -0.21108946 -0.10260196 53 0.13604930 -0.21108946 54 -0.13679278 0.13604930 55 0.77085070 -0.13679278 56 0.62058357 0.77085070 57 -0.21108946 0.62058357 58 -0.21108946 -0.21108946 59 -0.17689864 -0.21108946 60 -0.28141376 -0.17689864 61 0.69488025 -0.28141376 62 -0.13679278 0.69488025 63 -0.28141376 -0.13679278 64 -0.13679278 -0.28141376 65 -0.13679278 -0.13679278 66 -0.05033751 -0.13679278 67 -0.18905724 -0.05033751 68 -0.21108946 -0.18905724 69 0.86320722 -0.21108946 70 -0.13679278 0.86320722 71 -0.21108946 -0.13679278 72 0.78891054 -0.21108946 73 0.81094276 0.78891054 74 -0.21108946 0.81094276 75 -0.39747627 -0.21108946 76 -0.21108946 -0.39747627 77 0.62058357 -0.21108946 78 0.04369278 0.62058357 79 -0.32317959 0.04369278 80 -0.13679278 -0.32317959 81 0.73664609 -0.13679278 82 -0.13679278 0.73664609 83 0.13604930 -0.13679278 84 -0.37941643 0.13604930 85 -0.18905724 -0.37941643 86 -0.16773328 -0.18905724 87 0.40590689 -0.16773328 88 -0.04117215 0.40590689 89 -0.11546883 -0.04117215 90 -0.20949912 -0.11546883 91 -0.51979644 -0.20949912 92 -0.26176357 -0.51979644 93 -0.04117215 -0.26176357 94 -0.46753198 -0.04117215 95 -0.11546883 -0.46753198 96 -0.51979644 -0.11546883 97 -0.04117215 -0.51979644 98 -0.09343661 -0.04117215 99 -0.11546883 -0.09343661 100 -0.16773328 -0.11546883 101 -0.04117215 -0.16773328 102 -0.04117215 -0.04117215 103 -0.04117215 -0.04117215 104 0.53246802 -0.04117215 105 -0.04117215 0.53246802 106 -0.04117215 -0.04117215 107 0.48020356 -0.04117215 108 -0.04117215 0.48020356 109 -0.09343661 -0.04117215 110 0.31187660 -0.09343661 111 -0.46753198 0.31187660 112 0.95882785 -0.46753198 113 0.48020356 0.95882785 114 -0.09343661 0.48020356 115 -0.04117215 -0.09343661 116 -0.16773328 -0.04117215 117 -0.09343661 -0.16773328 118 -0.04117215 -0.09343661 119 -0.11546883 -0.04117215 120 -0.09343661 -0.11546883 121 -0.04117215 -0.09343661 122 0.48020356 -0.04117215 123 0.71620420 0.48020356 124 -0.11546883 0.71620420 125 -0.46753198 -0.11546883 126 -0.20949912 -0.46753198 127 -0.11546883 -0.20949912 128 -0.04117215 -0.11546883 129 -0.11546883 -0.04117215 130 -0.09343661 -0.11546883 131 -0.16773328 -0.09343661 132 0.90656339 -0.16773328 133 -0.04117215 0.90656339 134 -0.04117215 -0.04117215 135 -0.04117215 -0.04117215 136 0.66393975 -0.04117215 137 0.23757992 0.66393975 138 -0.46753198 0.23757992 139 -0.04117215 -0.46753198 140 0.15737325 -0.04117215 141 0.45817134 0.15737325 142 -0.09343661 0.45817134 143 -0.28379580 -0.09343661 144 -0.20949912 -0.28379580 145 -0.54182866 -0.20949912 146 0.53246802 -0.54182866 147 -0.46753198 0.53246802 148 -0.09343661 -0.46753198 149 -0.28379580 -0.09343661 150 -0.11546883 -0.28379580 151 0.17940547 -0.11546883 152 0.01107851 0.17940547 153 0.90656339 0.01107851 154 NA 0.90656339 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.13679278 -0.28141376 [2,] -0.13679278 -0.13679278 [3,] -0.13679278 -0.13679278 [4,] -0.13679278 -0.13679278 [5,] -0.43168088 -0.13679278 [6,] -0.13679278 -0.43168088 [7,] -0.15485263 -0.13679278 [8,] -0.21108946 -0.15485263 [9,] -0.18905724 -0.21108946 [10,] -0.20711708 -0.18905724 [11,] -0.13679278 -0.20711708 [12,] 0.69488025 -0.13679278 [13,] -0.20711708 0.69488025 [14,] 0.62058357 -0.20711708 [15,] 0.60252373 0.62058357 [16,] -0.10260196 0.60252373 [17,] -0.20711708 -0.10260196 [18,] -0.21108946 -0.20711708 [19,] -0.12463419 -0.21108946 [20,] -0.35738420 -0.12463419 [21,] 0.56831912 -0.35738420 [22,] -0.37941643 0.56831912 [23,] -0.43168088 -0.37941643 [24,] 0.77085070 -0.43168088 [25,] 0.69488025 0.77085070 [26,] -0.26335391 0.69488025 [27,] 0.86320722 -0.26335391 [28,] -0.21108946 0.86320722 [29,] -0.30511975 -0.21108946 [30,] -0.13679278 -0.30511975 [31,] -0.18905724 -0.13679278 [32,] -0.35738420 -0.18905724 [33,] -0.22914930 -0.35738420 [34,] -0.13679278 -0.22914930 [35,] -0.13679278 -0.13679278 [36,] 0.62455595 -0.13679278 [37,] 0.78891054 0.62455595 [38,] -0.37941643 0.78891054 [39,] -0.32317959 -0.37941643 [40,] -0.10657434 -0.32317959 [41,] 0.78891054 -0.10657434 [42,] -0.43168088 0.78891054 [43,] -0.20711708 -0.43168088 [44,] -0.30511975 -0.20711708 [45,] -0.37941643 -0.30511975 [46,] -0.13679278 -0.37941643 [47,] -0.21108946 -0.13679278 [48,] -0.37941643 -0.21108946 [49,] -0.13679278 -0.37941643 [50,] 0.84514737 -0.13679278 [51,] -0.10260196 0.84514737 [52,] -0.21108946 -0.10260196 [53,] 0.13604930 -0.21108946 [54,] -0.13679278 0.13604930 [55,] 0.77085070 -0.13679278 [56,] 0.62058357 0.77085070 [57,] -0.21108946 0.62058357 [58,] -0.21108946 -0.21108946 [59,] -0.17689864 -0.21108946 [60,] -0.28141376 -0.17689864 [61,] 0.69488025 -0.28141376 [62,] -0.13679278 0.69488025 [63,] -0.28141376 -0.13679278 [64,] -0.13679278 -0.28141376 [65,] -0.13679278 -0.13679278 [66,] -0.05033751 -0.13679278 [67,] -0.18905724 -0.05033751 [68,] -0.21108946 -0.18905724 [69,] 0.86320722 -0.21108946 [70,] -0.13679278 0.86320722 [71,] -0.21108946 -0.13679278 [72,] 0.78891054 -0.21108946 [73,] 0.81094276 0.78891054 [74,] -0.21108946 0.81094276 [75,] -0.39747627 -0.21108946 [76,] -0.21108946 -0.39747627 [77,] 0.62058357 -0.21108946 [78,] 0.04369278 0.62058357 [79,] -0.32317959 0.04369278 [80,] -0.13679278 -0.32317959 [81,] 0.73664609 -0.13679278 [82,] -0.13679278 0.73664609 [83,] 0.13604930 -0.13679278 [84,] -0.37941643 0.13604930 [85,] -0.18905724 -0.37941643 [86,] -0.16773328 -0.18905724 [87,] 0.40590689 -0.16773328 [88,] -0.04117215 0.40590689 [89,] -0.11546883 -0.04117215 [90,] -0.20949912 -0.11546883 [91,] -0.51979644 -0.20949912 [92,] -0.26176357 -0.51979644 [93,] -0.04117215 -0.26176357 [94,] -0.46753198 -0.04117215 [95,] -0.11546883 -0.46753198 [96,] -0.51979644 -0.11546883 [97,] -0.04117215 -0.51979644 [98,] -0.09343661 -0.04117215 [99,] -0.11546883 -0.09343661 [100,] -0.16773328 -0.11546883 [101,] -0.04117215 -0.16773328 [102,] -0.04117215 -0.04117215 [103,] -0.04117215 -0.04117215 [104,] 0.53246802 -0.04117215 [105,] -0.04117215 0.53246802 [106,] -0.04117215 -0.04117215 [107,] 0.48020356 -0.04117215 [108,] -0.04117215 0.48020356 [109,] -0.09343661 -0.04117215 [110,] 0.31187660 -0.09343661 [111,] -0.46753198 0.31187660 [112,] 0.95882785 -0.46753198 [113,] 0.48020356 0.95882785 [114,] -0.09343661 0.48020356 [115,] -0.04117215 -0.09343661 [116,] -0.16773328 -0.04117215 [117,] -0.09343661 -0.16773328 [118,] -0.04117215 -0.09343661 [119,] -0.11546883 -0.04117215 [120,] -0.09343661 -0.11546883 [121,] -0.04117215 -0.09343661 [122,] 0.48020356 -0.04117215 [123,] 0.71620420 0.48020356 [124,] -0.11546883 0.71620420 [125,] -0.46753198 -0.11546883 [126,] -0.20949912 -0.46753198 [127,] -0.11546883 -0.20949912 [128,] -0.04117215 -0.11546883 [129,] -0.11546883 -0.04117215 [130,] -0.09343661 -0.11546883 [131,] -0.16773328 -0.09343661 [132,] 0.90656339 -0.16773328 [133,] -0.04117215 0.90656339 [134,] -0.04117215 -0.04117215 [135,] -0.04117215 -0.04117215 [136,] 0.66393975 -0.04117215 [137,] 0.23757992 0.66393975 [138,] -0.46753198 0.23757992 [139,] -0.04117215 -0.46753198 [140,] 0.15737325 -0.04117215 [141,] 0.45817134 0.15737325 [142,] -0.09343661 0.45817134 [143,] -0.28379580 -0.09343661 [144,] -0.20949912 -0.28379580 [145,] -0.54182866 -0.20949912 [146,] 0.53246802 -0.54182866 [147,] -0.46753198 0.53246802 [148,] -0.09343661 -0.46753198 [149,] -0.28379580 -0.09343661 [150,] -0.11546883 -0.28379580 [151,] 0.17940547 -0.11546883 [152,] 0.01107851 0.17940547 [153,] 0.90656339 0.01107851 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.13679278 -0.28141376 2 -0.13679278 -0.13679278 3 -0.13679278 -0.13679278 4 -0.13679278 -0.13679278 5 -0.43168088 -0.13679278 6 -0.13679278 -0.43168088 7 -0.15485263 -0.13679278 8 -0.21108946 -0.15485263 9 -0.18905724 -0.21108946 10 -0.20711708 -0.18905724 11 -0.13679278 -0.20711708 12 0.69488025 -0.13679278 13 -0.20711708 0.69488025 14 0.62058357 -0.20711708 15 0.60252373 0.62058357 16 -0.10260196 0.60252373 17 -0.20711708 -0.10260196 18 -0.21108946 -0.20711708 19 -0.12463419 -0.21108946 20 -0.35738420 -0.12463419 21 0.56831912 -0.35738420 22 -0.37941643 0.56831912 23 -0.43168088 -0.37941643 24 0.77085070 -0.43168088 25 0.69488025 0.77085070 26 -0.26335391 0.69488025 27 0.86320722 -0.26335391 28 -0.21108946 0.86320722 29 -0.30511975 -0.21108946 30 -0.13679278 -0.30511975 31 -0.18905724 -0.13679278 32 -0.35738420 -0.18905724 33 -0.22914930 -0.35738420 34 -0.13679278 -0.22914930 35 -0.13679278 -0.13679278 36 0.62455595 -0.13679278 37 0.78891054 0.62455595 38 -0.37941643 0.78891054 39 -0.32317959 -0.37941643 40 -0.10657434 -0.32317959 41 0.78891054 -0.10657434 42 -0.43168088 0.78891054 43 -0.20711708 -0.43168088 44 -0.30511975 -0.20711708 45 -0.37941643 -0.30511975 46 -0.13679278 -0.37941643 47 -0.21108946 -0.13679278 48 -0.37941643 -0.21108946 49 -0.13679278 -0.37941643 50 0.84514737 -0.13679278 51 -0.10260196 0.84514737 52 -0.21108946 -0.10260196 53 0.13604930 -0.21108946 54 -0.13679278 0.13604930 55 0.77085070 -0.13679278 56 0.62058357 0.77085070 57 -0.21108946 0.62058357 58 -0.21108946 -0.21108946 59 -0.17689864 -0.21108946 60 -0.28141376 -0.17689864 61 0.69488025 -0.28141376 62 -0.13679278 0.69488025 63 -0.28141376 -0.13679278 64 -0.13679278 -0.28141376 65 -0.13679278 -0.13679278 66 -0.05033751 -0.13679278 67 -0.18905724 -0.05033751 68 -0.21108946 -0.18905724 69 0.86320722 -0.21108946 70 -0.13679278 0.86320722 71 -0.21108946 -0.13679278 72 0.78891054 -0.21108946 73 0.81094276 0.78891054 74 -0.21108946 0.81094276 75 -0.39747627 -0.21108946 76 -0.21108946 -0.39747627 77 0.62058357 -0.21108946 78 0.04369278 0.62058357 79 -0.32317959 0.04369278 80 -0.13679278 -0.32317959 81 0.73664609 -0.13679278 82 -0.13679278 0.73664609 83 0.13604930 -0.13679278 84 -0.37941643 0.13604930 85 -0.18905724 -0.37941643 86 -0.16773328 -0.18905724 87 0.40590689 -0.16773328 88 -0.04117215 0.40590689 89 -0.11546883 -0.04117215 90 -0.20949912 -0.11546883 91 -0.51979644 -0.20949912 92 -0.26176357 -0.51979644 93 -0.04117215 -0.26176357 94 -0.46753198 -0.04117215 95 -0.11546883 -0.46753198 96 -0.51979644 -0.11546883 97 -0.04117215 -0.51979644 98 -0.09343661 -0.04117215 99 -0.11546883 -0.09343661 100 -0.16773328 -0.11546883 101 -0.04117215 -0.16773328 102 -0.04117215 -0.04117215 103 -0.04117215 -0.04117215 104 0.53246802 -0.04117215 105 -0.04117215 0.53246802 106 -0.04117215 -0.04117215 107 0.48020356 -0.04117215 108 -0.04117215 0.48020356 109 -0.09343661 -0.04117215 110 0.31187660 -0.09343661 111 -0.46753198 0.31187660 112 0.95882785 -0.46753198 113 0.48020356 0.95882785 114 -0.09343661 0.48020356 115 -0.04117215 -0.09343661 116 -0.16773328 -0.04117215 117 -0.09343661 -0.16773328 118 -0.04117215 -0.09343661 119 -0.11546883 -0.04117215 120 -0.09343661 -0.11546883 121 -0.04117215 -0.09343661 122 0.48020356 -0.04117215 123 0.71620420 0.48020356 124 -0.11546883 0.71620420 125 -0.46753198 -0.11546883 126 -0.20949912 -0.46753198 127 -0.11546883 -0.20949912 128 -0.04117215 -0.11546883 129 -0.11546883 -0.04117215 130 -0.09343661 -0.11546883 131 -0.16773328 -0.09343661 132 0.90656339 -0.16773328 133 -0.04117215 0.90656339 134 -0.04117215 -0.04117215 135 -0.04117215 -0.04117215 136 0.66393975 -0.04117215 137 0.23757992 0.66393975 138 -0.46753198 0.23757992 139 -0.04117215 -0.46753198 140 0.15737325 -0.04117215 141 0.45817134 0.15737325 142 -0.09343661 0.45817134 143 -0.28379580 -0.09343661 144 -0.20949912 -0.28379580 145 -0.54182866 -0.20949912 146 0.53246802 -0.54182866 147 -0.46753198 0.53246802 148 -0.09343661 -0.46753198 149 -0.28379580 -0.09343661 150 -0.11546883 -0.28379580 151 0.17940547 -0.11546883 152 0.01107851 0.17940547 153 0.90656339 0.01107851 > 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/7uq551355428076.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/8o1cj1355428076.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/9dir11355428076.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/10ilr51355428076.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/11tysd1355428076.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/12n2ic1355428076.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/13r71s1355428076.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14mhnk1355428076.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/15jh5u1355428076.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/16ioe61355428076.tab") + } > > try(system("convert tmp/1opu81355428076.ps tmp/1opu81355428076.png",intern=TRUE)) character(0) > try(system("convert tmp/2pqje1355428076.ps tmp/2pqje1355428076.png",intern=TRUE)) character(0) > try(system("convert tmp/3oom91355428076.ps tmp/3oom91355428076.png",intern=TRUE)) character(0) > try(system("convert tmp/46yaq1355428076.ps tmp/46yaq1355428076.png",intern=TRUE)) character(0) > try(system("convert tmp/5wo2z1355428076.ps tmp/5wo2z1355428076.png",intern=TRUE)) character(0) > try(system("convert tmp/6l1ci1355428076.ps tmp/6l1ci1355428076.png",intern=TRUE)) character(0) > try(system("convert tmp/7uq551355428076.ps tmp/7uq551355428076.png",intern=TRUE)) character(0) > try(system("convert tmp/8o1cj1355428076.ps tmp/8o1cj1355428076.png",intern=TRUE)) character(0) > try(system("convert tmp/9dir11355428076.ps tmp/9dir11355428076.png",intern=TRUE)) character(0) > try(system("convert tmp/10ilr51355428076.ps tmp/10ilr51355428076.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.546 1.744 10.342