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 + ,0 + ,264530 + ,165119 + ,0 + ,0 + ,135248 + ,107269 + ,0 + ,0 + ,207253 + ,93497 + ,0 + ,0 + ,202898 + ,100269 + ,0 + ,0 + ,145249 + ,91627 + ,0 + ,0 + ,65295 + ,47552 + ,0 + ,0 + ,439387 + ,233933 + ,0 + ,0 + ,33186 + ,6853 + ,0 + ,0 + ,183696 + ,104380 + ,0 + ,0 + ,190673 + ,98431 + ,0 + ,0 + ,287239 + ,156949 + ,0 + ,0 + ,205260 + ,81817 + ,0 + ,0 + ,141987 + ,59238 + ,0 + ,0 + ,322679 + ,101138 + ,0 + ,0 + ,199717 + ,107158 + ,0 + ,0 + ,349227 + ,155499 + ,0 + ,0 + ,276709 + ,156274 + ,0 + ,0 + ,273576 + ,121777 + ,0 + ,0 + ,157448 + ,105037 + ,0 + ,0 + ,242782 + ,118661 + ,0 + ,0 + ,256814 + ,131187 + ,0 + ,0 + ,405874 + ,145026 + ,0 + ,0 + ,161189 + ,107016 + ,0 + ,0 + ,156189 + ,87242 + ,0 + ,0 + ,200181 + ,91699 + ,0 + ,0 + ,192645 + ,110087 + ,0 + ,0 + ,249893 + ,145447 + ,0 + ,0 + ,241171 + ,143307 + ,0 + ,0 + ,143182 + ,61678 + ,0 + ,0 + ,285266 + ,210080 + ,0 + ,0 + ,243048 + ,165005 + ,0 + ,0 + ,176062 + ,97806 + ,0 + ,0 + 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+ ,180424 + ,92189 + ,1 + ,1 + ,204450 + ,121158 + ,1 + ,1 + ,197813 + ,96219 + ,1 + ,1 + ,138731 + ,84128 + ,1 + ,1 + ,216153 + ,97960 + ,1 + ,1 + ,73566 + ,23824 + ,1 + ,1 + ,219392 + ,103515 + ,1 + ,1 + ,181728 + ,91313 + ,1 + ,1 + ,150006 + ,85407 + ,1 + ,1 + ,325723 + ,95871 + ,1 + ,1 + ,265348 + ,143846 + ,1 + ,1 + ,202410 + ,155387 + ,1 + ,1 + ,173420 + ,74429 + ,1 + ,1 + ,162366 + ,74004 + ,1 + ,1 + ,136341 + ,71987 + ,1 + ,1 + ,390163 + ,150629 + ,1 + ,1 + ,145905 + ,68580 + ,1 + ,1 + ,238921 + ,119855 + ,1 + ,1 + ,80953 + ,55792 + ,1 + ,1 + ,133301 + ,25157 + ,1 + ,1 + ,138630 + ,90895 + ,1 + ,1 + ,334082 + ,117510 + ,1 + ,1 + ,277542 + ,144774 + ,1 + ,1 + ,170849 + ,77529 + ,1 + ,1 + ,236398 + ,103123 + ,1 + ,1 + ,207178 + ,104669 + ,1 + ,1 + ,157125 + ,82414 + ,1 + ,1 + ,242395 + ,82390 + ,1 + ,1 + ,273632 + ,128446 + ,1 + ,1 + ,178489 + ,111542 + ,1 + ,1 + ,207720 + ,136048 + ,1 + ,1 + ,268066 + ,197257 + ,1 + ,1 + ,349934 + ,162079 + ,1 + ,1 + ,368833 + ,206286 + ,1 + ,1 + ,247804 + ,109858 + ,1 + ,1 + ,265849 + ,182125 + ,1 + ,1 + ,174311 + ,74168 + ,1 + ,1 + ,43287 + ,19630 + ,1 + ,1 + ,176724 + ,88634 + ,1 + ,1 + ,189021 + ,128321 + ,1 + ,1 + ,237531 + ,118936 + ,1 + ,1 + ,279589 + ,127044 + ,1 + ,1 + ,106655 + ,178377 + ,1 + ,1 + ,135798 + ,69581 + ,1 + ,1 + ,290495 + ,168019 + ,1 + ,1 + ,266805 + ,113598 + ,1 + ,1 + ,23623 + ,5841 + ,1 + ,1 + ,174970 + ,93116 + ,1 + ,1 + ,61857 + ,24610 + ,1 + ,1 + ,147760 + ,60611 + ,1 + ,1 + ,358662 + ,226620 + ,1 + ,1 + ,21054 + ,6622 + ,1 + ,1 + ,230091 + ,121996 + ,1 + ,1 + ,31414 + ,13155 + ,1 + ,1 + ,284519 + ,154158 + ,1 + ,1 + ,209481 + ,78489 + ,1 + ,1 + ,161691 + ,22007 + ,1 + ,1 + ,137093 + ,72530 + ,1 + ,1 + ,38214 + ,13983 + ,1 + ,1 + ,166059 + ,73397 + ,1 + ,1 + ,319346 + ,143878 + ,1 + ,1 + ,186273 + ,119956 + ,1 + ,1 + ,374212 + ,181558 + ,1 + ,1 + ,275578 + ,208236 + ,1 + ,1 + ,368863 + ,237085 + ,1 + ,1 + ,179928 + ,110297 + ,1 + ,1 + ,94381 + ,61394 + ,1 + ,1 + ,251253 + ,81420 + ,1 + ,1 + ,382564 + ,191154 + ,1 + ,1 + ,118033 + ,11798 + ,1 + ,1 + ,370878 + ,135724 + ,1 + ,1 + ,147989 + ,68614 + ,1 + ,1 + ,236370 + ,139926 + ,1 + ,1 + ,193220 + ,105203 + ,1 + ,1 + ,189020 + ,80338 + ,1 + ,1 + ,341992 + ,121376 + ,1 + ,1 + ,224936 + ,124922 + ,1 + ,1 + ,173260 + ,10901 + ,1 + ,1 + ,286161 + ,135471 + ,1 + ,1 + ,130908 + ,66395 + ,1 + ,1 + ,209639 + ,134041 + ,1 + ,1 + ,262412 + ,153554 + ,1 + ,1 + ,1 + ,0 + ,1 + ,1 + ,14688 + ,7953 + ,1 + ,1 + ,98 + ,0 + ,1 + ,1 + ,455 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,1 + ,195822 + ,98922 + ,1 + ,1 + ,347930 + ,165395 + ,1 + ,1 + ,0 + ,0 + ,1 + ,1 + ,203 + ,0 + ,1 + ,1 + ,7199 + ,4245 + ,1 + ,1 + ,46660 + ,21509 + ,1 + ,1 + ,17547 + ,7670 + ,1 + ,1 + ,107465 + ,15167 + ,1 + ,1 + ,969 + ,0 + ,1 + ,1 + ,179994 + ,63891) + ,dim=c(4 + ,164) + ,dimnames=list(c('Pop' + ,'Gender' + ,'Time_RFC_sec' + ,'Compendium_writing_time_sec') + ,1:164)) > y <- array(NA,dim=c(4,164),dimnames=list(c('Pop','Gender','Time_RFC_sec','Compendium_writing_time_sec'),1:164)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par20 = '' > par19 = '' > par18 = '' > par17 = '' > par16 = '' > par15 = '' > par14 = '' > par13 = '' > par12 = '' > par11 = '' > par10 = '' > par9 = '' > par8 = '' > par7 = '' > par6 = '' > par5 = '' > par4 = '' > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '3' > 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 Time_RFC_sec Pop Gender Compendium_writing_time_sec 1 264530 0 0 165119 2 135248 0 0 107269 3 207253 0 0 93497 4 202898 0 0 100269 5 145249 0 0 91627 6 65295 0 0 47552 7 439387 0 0 233933 8 33186 0 0 6853 9 183696 0 0 104380 10 190673 0 0 98431 11 287239 0 0 156949 12 205260 0 0 81817 13 141987 0 0 59238 14 322679 0 0 101138 15 199717 0 0 107158 16 349227 0 0 155499 17 276709 0 0 156274 18 273576 0 0 121777 19 157448 0 0 105037 20 242782 0 0 118661 21 256814 0 0 131187 22 405874 0 0 145026 23 161189 0 0 107016 24 156189 0 0 87242 25 200181 0 0 91699 26 192645 0 0 110087 27 249893 0 0 145447 28 241171 0 0 143307 29 143182 0 0 61678 30 285266 0 0 210080 31 243048 0 0 165005 32 176062 0 0 97806 33 305210 0 0 184471 34 87995 0 0 27786 35 343613 0 0 184458 36 264159 0 0 98765 37 394976 0 0 178441 38 192718 0 0 100619 39 114673 0 0 58391 40 310108 0 0 151672 41 292891 0 0 124437 42 157518 0 0 79929 43 180362 0 0 123064 44 146175 0 0 50466 45 140319 0 0 100991 46 405267 0 0 79367 47 78800 0 0 56968 48 201970 0 0 106257 49 305322 0 0 178412 50 164733 0 0 98520 51 199186 0 1 153670 52 24188 0 1 15049 53 346142 0 1 174478 54 65029 0 1 25109 55 101097 0 1 45824 56 255082 0 1 116772 57 287314 0 1 189150 58 308944 1 1 194404 59 280943 1 1 185881 60 225816 1 1 67508 61 348943 1 1 188597 62 283283 1 1 203618 63 199642 1 1 87232 64 232791 1 1 110875 65 212262 1 1 144756 66 201345 1 1 129825 67 180424 1 1 92189 68 204450 1 1 121158 69 197813 1 1 96219 70 138731 1 1 84128 71 216153 1 1 97960 72 73566 1 1 23824 73 219392 1 1 103515 74 181728 1 1 91313 75 150006 1 1 85407 76 325723 1 1 95871 77 265348 1 1 143846 78 202410 1 1 155387 79 173420 1 1 74429 80 162366 1 1 74004 81 136341 1 1 71987 82 390163 1 1 150629 83 145905 1 1 68580 84 238921 1 1 119855 85 80953 1 1 55792 86 133301 1 1 25157 87 138630 1 1 90895 88 334082 1 1 117510 89 277542 1 1 144774 90 170849 1 1 77529 91 236398 1 1 103123 92 207178 1 1 104669 93 157125 1 1 82414 94 242395 1 1 82390 95 273632 1 1 128446 96 178489 1 1 111542 97 207720 1 1 136048 98 268066 1 1 197257 99 349934 1 1 162079 100 368833 1 1 206286 101 247804 1 1 109858 102 265849 1 1 182125 103 174311 1 1 74168 104 43287 1 1 19630 105 176724 1 1 88634 106 189021 1 1 128321 107 237531 1 1 118936 108 279589 1 1 127044 109 106655 1 1 178377 110 135798 1 1 69581 111 290495 1 1 168019 112 266805 1 1 113598 113 23623 1 1 5841 114 174970 1 1 93116 115 61857 1 1 24610 116 147760 1 1 60611 117 358662 1 1 226620 118 21054 1 1 6622 119 230091 1 1 121996 120 31414 1 1 13155 121 284519 1 1 154158 122 209481 1 1 78489 123 161691 1 1 22007 124 137093 1 1 72530 125 38214 1 1 13983 126 166059 1 1 73397 127 319346 1 1 143878 128 186273 1 1 119956 129 374212 1 1 181558 130 275578 1 1 208236 131 368863 1 1 237085 132 179928 1 1 110297 133 94381 1 1 61394 134 251253 1 1 81420 135 382564 1 1 191154 136 118033 1 1 11798 137 370878 1 1 135724 138 147989 1 1 68614 139 236370 1 1 139926 140 193220 1 1 105203 141 189020 1 1 80338 142 341992 1 1 121376 143 224936 1 1 124922 144 173260 1 1 10901 145 286161 1 1 135471 146 130908 1 1 66395 147 209639 1 1 134041 148 262412 1 1 153554 149 1 1 1 0 150 14688 1 1 7953 151 98 1 1 0 152 455 1 1 0 153 0 1 1 0 154 0 1 1 0 155 195822 1 1 98922 156 347930 1 1 165395 157 0 1 1 0 158 203 1 1 0 159 7199 1 1 4245 160 46660 1 1 21509 161 17547 1 1 7670 162 107465 1 1 15167 163 969 1 1 0 164 179994 1 1 63891 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) Pop 48827.344 17275.676 Gender Compendium_writing_time_sec -23157.096 1.525 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -208382 -34430 -8652 23555 235376 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.883e+04 1.111e+04 4.394 2.02e-05 *** Pop 1.728e+04 2.054e+04 0.841 0.402 Gender -2.316e+04 2.125e+04 -1.090 0.278 Compendium_writing_time_sec 1.525e+00 7.249e-02 21.043 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 52630 on 160 degrees of freedom Multiple R-squared: 0.7408, Adjusted R-squared: 0.736 F-statistic: 152.4 on 3 and 160 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,] 0.5245297 0.950940629 0.475470315 [2,] 0.4061374 0.812274871 0.593862565 [3,] 0.2668683 0.533736517 0.733131741 [4,] 0.1748654 0.349730781 0.825134610 [5,] 0.1041179 0.208235779 0.895882110 [6,] 0.1389171 0.277834161 0.861082919 [7,] 0.1018670 0.203733913 0.898133043 [8,] 0.5705283 0.858943360 0.429471680 [9,] 0.4803202 0.960640465 0.519679768 [10,] 0.4914322 0.982864371 0.508567814 [11,] 0.4177232 0.835446307 0.582276847 [12,] 0.3775597 0.755119361 0.622440319 [13,] 0.3714623 0.742924611 0.628537695 [14,] 0.3035967 0.607193306 0.696403347 [15,] 0.2399123 0.479824592 0.760087704 [16,] 0.5730181 0.853963847 0.426981923 [17,] 0.5693222 0.861355663 0.430677831 [18,] 0.5101862 0.979627564 0.489813782 [19,] 0.4483580 0.896715992 0.551642004 [20,] 0.3974810 0.794962047 0.602518977 [21,] 0.3570596 0.714119156 0.642940422 [22,] 0.3233149 0.646629841 0.676685079 [23,] 0.2720398 0.544079642 0.727960179 [24,] 0.4185221 0.837044283 0.581477858 [25,] 0.4315139 0.863027776 0.568486112 [26,] 0.3803223 0.760644612 0.619677694 [27,] 0.3373028 0.674605692 0.662697154 [28,] 0.2863150 0.572630016 0.713684992 [29,] 0.2423758 0.484751600 0.757624200 [30,] 0.2753071 0.550614207 0.724692897 [31,] 0.3201637 0.640327396 0.679836302 [32,] 0.2726710 0.545342041 0.727328980 [33,] 0.2341690 0.468338011 0.765830995 [34,] 0.2046619 0.409323752 0.795338124 [35,] 0.2078775 0.415754920 0.792122540 [36,] 0.1723458 0.344691569 0.827654216 [37,] 0.1804445 0.360888993 0.819555504 [38,] 0.1552654 0.310530888 0.844734556 [39,] 0.1730973 0.346194580 0.826902710 [40,] 0.9261856 0.147628849 0.073814425 [41,] 0.9230414 0.153917236 0.076958618 [42,] 0.9043043 0.191391483 0.095695742 [43,] 0.8840177 0.231964555 0.115982278 [44,] 0.8650237 0.269952680 0.134976340 [45,] 0.8515267 0.296946635 0.148473318 [46,] 0.8325415 0.334917064 0.167458532 [47,] 0.8467220 0.306556092 0.153278046 [48,] 0.8181186 0.363762832 0.181881416 [49,] 0.7862422 0.427515533 0.213757766 [50,] 0.7827518 0.434496471 0.217248236 [51,] 0.7551694 0.489661121 0.244830560 [52,] 0.7208317 0.558336569 0.279168285 [53,] 0.6922017 0.615596638 0.307798319 [54,] 0.7632906 0.473418855 0.236709428 [55,] 0.7291298 0.541740406 0.270870203 [56,] 0.7530563 0.493887416 0.246943708 [57,] 0.7234387 0.553122581 0.276561290 [58,] 0.6890137 0.621972670 0.310986335 [59,] 0.6838681 0.632263830 0.316131915 [60,] 0.6612388 0.677522384 0.338761192 [61,] 0.6181509 0.763698100 0.381849050 [62,] 0.5795465 0.840907021 0.420453510 [63,] 0.5366049 0.926790105 0.463395053 [64,] 0.5042021 0.991595777 0.495797889 [65,] 0.4710698 0.942139663 0.528930169 [66,] 0.4258212 0.851642470 0.574178765 [67,] 0.3888382 0.777676330 0.611161835 [68,] 0.3458455 0.691691062 0.654154469 [69,] 0.3109353 0.621870644 0.689064678 [70,] 0.5596182 0.880763573 0.440381786 [71,] 0.5145172 0.970965598 0.485482799 [72,] 0.5630099 0.873980202 0.436990101 [73,] 0.5221342 0.955731697 0.477865849 [74,] 0.4772747 0.954549368 0.522725316 [75,] 0.4367465 0.873493084 0.563253458 [76,] 0.6101120 0.779775999 0.389887999 [77,] 0.5661688 0.867662342 0.433831171 [78,] 0.5233942 0.953211549 0.476605775 [79,] 0.5156154 0.968769131 0.484384566 [80,] 0.5107776 0.978444746 0.489222373 [81,] 0.4961032 0.992206333 0.503896833 [82,] 0.6443400 0.711319910 0.355659955 [83,] 0.6035208 0.792958372 0.396479186 [84,] 0.5601934 0.879613263 0.439806631 [85,] 0.5344121 0.931175772 0.465587886 [86,] 0.4891419 0.978283857 0.510858071 [87,] 0.4465598 0.893119563 0.553440219 [88,] 0.4854667 0.970933366 0.514533317 [89,] 0.4585209 0.917041806 0.541479097 [90,] 0.4335251 0.867050110 0.566474945 [91,] 0.4180179 0.836035725 0.581982138 [92,] 0.4647343 0.929468583 0.535265708 [93,] 0.4755341 0.951068277 0.524465861 [94,] 0.4314090 0.862818089 0.568590956 [95,] 0.4075266 0.815053229 0.592473385 [96,] 0.4105424 0.821084802 0.589457599 [97,] 0.3706905 0.741381037 0.629309481 [98,] 0.3421112 0.684222395 0.657888802 [99,] 0.3000609 0.600121871 0.699939064 [100,] 0.2949270 0.589854044 0.705072978 [101,] 0.2572758 0.514551568 0.742724216 [102,] 0.2427681 0.485536281 0.757231860 [103,] 0.8722584 0.255483268 0.127741634 [104,] 0.8468296 0.306340712 0.153170356 [105,] 0.8198303 0.360339425 0.180169712 [106,] 0.8138647 0.372270565 0.186135282 [107,] 0.7880850 0.423830086 0.211915043 [108,] 0.7519022 0.496195564 0.248097782 [109,] 0.7150020 0.569996069 0.284998034 [110,] 0.6729016 0.654196715 0.327098358 [111,] 0.6625513 0.674897352 0.337448676 [112,] 0.6288067 0.742386572 0.371193286 [113,] 0.5797538 0.840492421 0.420246210 [114,] 0.5432841 0.913431792 0.456715896 [115,] 0.4925285 0.985056901 0.507471550 [116,] 0.4768837 0.953767489 0.523116256 [117,] 0.5776514 0.844697274 0.422348637 [118,] 0.5295146 0.940970845 0.470485422 [119,] 0.4839893 0.967978558 0.516010721 [120,] 0.4318279 0.863655713 0.568172143 [121,] 0.4247622 0.849524347 0.575237827 [122,] 0.4080752 0.816150349 0.591924826 [123,] 0.3900434 0.780086856 0.609956572 [124,] 0.5552159 0.889568190 0.444784095 [125,] 0.6318862 0.736227550 0.368113775 [126,] 0.6243026 0.751394790 0.375697395 [127,] 0.6116941 0.776611748 0.388305874 [128,] 0.6785713 0.642857326 0.321428663 [129,] 0.6309145 0.738170967 0.369085483 [130,] 0.6889729 0.622054230 0.311027115 [131,] 0.8401481 0.319703886 0.159851943 [132,] 0.7956068 0.408786440 0.204393220 [133,] 0.7785288 0.442942300 0.221471150 [134,] 0.7349528 0.530094458 0.265047229 [135,] 0.6822804 0.635439183 0.317719592 [136,] 0.8563654 0.287269264 0.143634632 [137,] 0.8153686 0.369262867 0.184631434 [138,] 0.9966490 0.006702089 0.003351044 [139,] 0.9951446 0.009710750 0.004855375 [140,] 0.9907756 0.018448748 0.009224374 [141,] 0.9944333 0.011133346 0.005566673 [142,] 0.9977865 0.004426986 0.002213493 [143,] 0.9953123 0.009375456 0.004687728 [144,] 0.9905306 0.018938878 0.009469439 [145,] 0.9811783 0.037643473 0.018821736 [146,] 0.9638874 0.072225226 0.036112613 [147,] 0.9339240 0.132151947 0.066075973 [148,] 0.8847762 0.230447548 0.115223774 [149,] 0.8258637 0.348272652 0.174136326 [150,] 0.8291104 0.341779270 0.170889635 [151,] 0.6889763 0.622047317 0.311023659 > postscript(file="/var/fisher/rcomp/tmp/12sjj1354889340.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/263k41354889340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3nqb81354889340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4xlce1354889340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/55fpf1354889340.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 -36165.0380 -77204.3396 15808.0665 1123.2557 -43343.4902 -56066.7740 7 8 9 10 11 12 33725.0985 -26094.7095 -24349.5435 -8298.1129 -993.7589 31631.3969 13 14 15 16 17 18 2799.7433 119578.7085 -12566.0235 63206.0287 -10494.1337 38993.5814 19 20 21 22 23 24 -51599.7121 12952.6367 7877.8426 135828.2366 -50877.4208 -25714.7394 25 26 27 28 29 30 11478.6831 -24105.8344 -20794.9445 -26252.6510 272.8387 -84011.2328 31 32 33 34 35 36 -57473.1458 -21955.7561 -25004.0130 -3216.2956 13418.8168 64678.4133 37 38 39 40 41 42 73959.9727 -9590.6241 -23222.2676 29924.6226 54251.0952 -13230.7031 43 44 45 46 47 48 -56183.5708 20368.2956 -62557.0620 235375.5553 -56924.6651 -8938.6644 49 50 51 52 53 54 -15649.7916 -34373.8708 -60887.9723 -24437.5523 54328.1129 1058.2179 55 56 57 58 59 60 5528.1626 51291.1580 -26880.1268 -30540.1013 -45540.3663 79895.3466 61 62 63 64 65 66 18316.7267 -70255.8674 23634.9337 20719.5929 -51490.4938 -39632.1830 67 68 69 70 71 72 -3144.3291 -23306.7946 8097.4267 -32541.3154 23781.7562 -5720.3571 73 74 75 76 77 78 18547.3217 -504.1043 -23217.2646 136538.2558 2983.5936 -77558.7104 79 80 81 82 83 84 16942.2556 6536.5382 -16411.7890 117452.0037 -1650.8508 13151.7635 85 86 87 88 89 90 -47096.4096 51981.3236 -42964.4993 111889.7579 13762.0495 9642.6062 91 92 93 94 95 96 36151.2670 4573.0438 -11532.8299 73773.7790 34758.3033 -34599.8294 97 98 99 100 101 102 -42749.5652 -75769.9840 59757.5085 11224.4434 37283.8949 -54905.0737 103 104 105 106 107 108 18231.3773 -29601.9522 -1421.6361 -49662.0254 13163.5792 42853.8731 109 110 111 112 113 114 -208381.9841 -13284.7470 -8742.1938 50580.0083 -28232.6148 -10012.3478 115 116 117 118 119 120 -18628.2986 12359.8287 -29963.4457 -31992.9293 1055.9447 -31598.1765 121 122 123 124 125 126 6424.9703 46810.2503 85176.2415 -16488.0653 -26061.1835 11155.4382 127 128 129 130 131 132 56932.7817 -39650.2990 54322.8115 -85005.0296 -35725.4507 -31261.7428 133 134 135 136 137 138 -42213.5366 84111.3886 48037.3537 57090.7516 120902.6549 381.2865 139 140 141 142 143 144 -20014.9530 -10199.5042 23528.8398 113902.6745 -8562.2902 113686.0092 145 146 147 148 149 150 36571.5737 -13314.9157 -37769.1461 -14760.7058 -42944.9242 -40389.1978 151 152 153 154 155 156 -42847.9242 -42490.9242 -42945.9242 -42945.9242 1983.3496 52695.3791 157 158 159 160 161 162 -42945.9242 -42742.9242 -42222.1230 -29095.1238 -37098.5179 41383.7775 163 164 -41976.9242 39590.6126 > postscript(file="/var/fisher/rcomp/tmp/61nmw1354889340.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 -36165.0380 NA 1 -77204.3396 -36165.0380 2 15808.0665 -77204.3396 3 1123.2557 15808.0665 4 -43343.4902 1123.2557 5 -56066.7740 -43343.4902 6 33725.0985 -56066.7740 7 -26094.7095 33725.0985 8 -24349.5435 -26094.7095 9 -8298.1129 -24349.5435 10 -993.7589 -8298.1129 11 31631.3969 -993.7589 12 2799.7433 31631.3969 13 119578.7085 2799.7433 14 -12566.0235 119578.7085 15 63206.0287 -12566.0235 16 -10494.1337 63206.0287 17 38993.5814 -10494.1337 18 -51599.7121 38993.5814 19 12952.6367 -51599.7121 20 7877.8426 12952.6367 21 135828.2366 7877.8426 22 -50877.4208 135828.2366 23 -25714.7394 -50877.4208 24 11478.6831 -25714.7394 25 -24105.8344 11478.6831 26 -20794.9445 -24105.8344 27 -26252.6510 -20794.9445 28 272.8387 -26252.6510 29 -84011.2328 272.8387 30 -57473.1458 -84011.2328 31 -21955.7561 -57473.1458 32 -25004.0130 -21955.7561 33 -3216.2956 -25004.0130 34 13418.8168 -3216.2956 35 64678.4133 13418.8168 36 73959.9727 64678.4133 37 -9590.6241 73959.9727 38 -23222.2676 -9590.6241 39 29924.6226 -23222.2676 40 54251.0952 29924.6226 41 -13230.7031 54251.0952 42 -56183.5708 -13230.7031 43 20368.2956 -56183.5708 44 -62557.0620 20368.2956 45 235375.5553 -62557.0620 46 -56924.6651 235375.5553 47 -8938.6644 -56924.6651 48 -15649.7916 -8938.6644 49 -34373.8708 -15649.7916 50 -60887.9723 -34373.8708 51 -24437.5523 -60887.9723 52 54328.1129 -24437.5523 53 1058.2179 54328.1129 54 5528.1626 1058.2179 55 51291.1580 5528.1626 56 -26880.1268 51291.1580 57 -30540.1013 -26880.1268 58 -45540.3663 -30540.1013 59 79895.3466 -45540.3663 60 18316.7267 79895.3466 61 -70255.8674 18316.7267 62 23634.9337 -70255.8674 63 20719.5929 23634.9337 64 -51490.4938 20719.5929 65 -39632.1830 -51490.4938 66 -3144.3291 -39632.1830 67 -23306.7946 -3144.3291 68 8097.4267 -23306.7946 69 -32541.3154 8097.4267 70 23781.7562 -32541.3154 71 -5720.3571 23781.7562 72 18547.3217 -5720.3571 73 -504.1043 18547.3217 74 -23217.2646 -504.1043 75 136538.2558 -23217.2646 76 2983.5936 136538.2558 77 -77558.7104 2983.5936 78 16942.2556 -77558.7104 79 6536.5382 16942.2556 80 -16411.7890 6536.5382 81 117452.0037 -16411.7890 82 -1650.8508 117452.0037 83 13151.7635 -1650.8508 84 -47096.4096 13151.7635 85 51981.3236 -47096.4096 86 -42964.4993 51981.3236 87 111889.7579 -42964.4993 88 13762.0495 111889.7579 89 9642.6062 13762.0495 90 36151.2670 9642.6062 91 4573.0438 36151.2670 92 -11532.8299 4573.0438 93 73773.7790 -11532.8299 94 34758.3033 73773.7790 95 -34599.8294 34758.3033 96 -42749.5652 -34599.8294 97 -75769.9840 -42749.5652 98 59757.5085 -75769.9840 99 11224.4434 59757.5085 100 37283.8949 11224.4434 101 -54905.0737 37283.8949 102 18231.3773 -54905.0737 103 -29601.9522 18231.3773 104 -1421.6361 -29601.9522 105 -49662.0254 -1421.6361 106 13163.5792 -49662.0254 107 42853.8731 13163.5792 108 -208381.9841 42853.8731 109 -13284.7470 -208381.9841 110 -8742.1938 -13284.7470 111 50580.0083 -8742.1938 112 -28232.6148 50580.0083 113 -10012.3478 -28232.6148 114 -18628.2986 -10012.3478 115 12359.8287 -18628.2986 116 -29963.4457 12359.8287 117 -31992.9293 -29963.4457 118 1055.9447 -31992.9293 119 -31598.1765 1055.9447 120 6424.9703 -31598.1765 121 46810.2503 6424.9703 122 85176.2415 46810.2503 123 -16488.0653 85176.2415 124 -26061.1835 -16488.0653 125 11155.4382 -26061.1835 126 56932.7817 11155.4382 127 -39650.2990 56932.7817 128 54322.8115 -39650.2990 129 -85005.0296 54322.8115 130 -35725.4507 -85005.0296 131 -31261.7428 -35725.4507 132 -42213.5366 -31261.7428 133 84111.3886 -42213.5366 134 48037.3537 84111.3886 135 57090.7516 48037.3537 136 120902.6549 57090.7516 137 381.2865 120902.6549 138 -20014.9530 381.2865 139 -10199.5042 -20014.9530 140 23528.8398 -10199.5042 141 113902.6745 23528.8398 142 -8562.2902 113902.6745 143 113686.0092 -8562.2902 144 36571.5737 113686.0092 145 -13314.9157 36571.5737 146 -37769.1461 -13314.9157 147 -14760.7058 -37769.1461 148 -42944.9242 -14760.7058 149 -40389.1978 -42944.9242 150 -42847.9242 -40389.1978 151 -42490.9242 -42847.9242 152 -42945.9242 -42490.9242 153 -42945.9242 -42945.9242 154 1983.3496 -42945.9242 155 52695.3791 1983.3496 156 -42945.9242 52695.3791 157 -42742.9242 -42945.9242 158 -42222.1230 -42742.9242 159 -29095.1238 -42222.1230 160 -37098.5179 -29095.1238 161 41383.7775 -37098.5179 162 -41976.9242 41383.7775 163 39590.6126 -41976.9242 164 NA 39590.6126 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -77204.3396 -36165.0380 [2,] 15808.0665 -77204.3396 [3,] 1123.2557 15808.0665 [4,] -43343.4902 1123.2557 [5,] -56066.7740 -43343.4902 [6,] 33725.0985 -56066.7740 [7,] -26094.7095 33725.0985 [8,] -24349.5435 -26094.7095 [9,] -8298.1129 -24349.5435 [10,] -993.7589 -8298.1129 [11,] 31631.3969 -993.7589 [12,] 2799.7433 31631.3969 [13,] 119578.7085 2799.7433 [14,] -12566.0235 119578.7085 [15,] 63206.0287 -12566.0235 [16,] -10494.1337 63206.0287 [17,] 38993.5814 -10494.1337 [18,] -51599.7121 38993.5814 [19,] 12952.6367 -51599.7121 [20,] 7877.8426 12952.6367 [21,] 135828.2366 7877.8426 [22,] -50877.4208 135828.2366 [23,] -25714.7394 -50877.4208 [24,] 11478.6831 -25714.7394 [25,] -24105.8344 11478.6831 [26,] -20794.9445 -24105.8344 [27,] -26252.6510 -20794.9445 [28,] 272.8387 -26252.6510 [29,] -84011.2328 272.8387 [30,] -57473.1458 -84011.2328 [31,] -21955.7561 -57473.1458 [32,] -25004.0130 -21955.7561 [33,] -3216.2956 -25004.0130 [34,] 13418.8168 -3216.2956 [35,] 64678.4133 13418.8168 [36,] 73959.9727 64678.4133 [37,] -9590.6241 73959.9727 [38,] -23222.2676 -9590.6241 [39,] 29924.6226 -23222.2676 [40,] 54251.0952 29924.6226 [41,] -13230.7031 54251.0952 [42,] -56183.5708 -13230.7031 [43,] 20368.2956 -56183.5708 [44,] -62557.0620 20368.2956 [45,] 235375.5553 -62557.0620 [46,] -56924.6651 235375.5553 [47,] -8938.6644 -56924.6651 [48,] -15649.7916 -8938.6644 [49,] -34373.8708 -15649.7916 [50,] -60887.9723 -34373.8708 [51,] -24437.5523 -60887.9723 [52,] 54328.1129 -24437.5523 [53,] 1058.2179 54328.1129 [54,] 5528.1626 1058.2179 [55,] 51291.1580 5528.1626 [56,] -26880.1268 51291.1580 [57,] -30540.1013 -26880.1268 [58,] -45540.3663 -30540.1013 [59,] 79895.3466 -45540.3663 [60,] 18316.7267 79895.3466 [61,] -70255.8674 18316.7267 [62,] 23634.9337 -70255.8674 [63,] 20719.5929 23634.9337 [64,] -51490.4938 20719.5929 [65,] -39632.1830 -51490.4938 [66,] -3144.3291 -39632.1830 [67,] -23306.7946 -3144.3291 [68,] 8097.4267 -23306.7946 [69,] -32541.3154 8097.4267 [70,] 23781.7562 -32541.3154 [71,] -5720.3571 23781.7562 [72,] 18547.3217 -5720.3571 [73,] -504.1043 18547.3217 [74,] -23217.2646 -504.1043 [75,] 136538.2558 -23217.2646 [76,] 2983.5936 136538.2558 [77,] -77558.7104 2983.5936 [78,] 16942.2556 -77558.7104 [79,] 6536.5382 16942.2556 [80,] -16411.7890 6536.5382 [81,] 117452.0037 -16411.7890 [82,] -1650.8508 117452.0037 [83,] 13151.7635 -1650.8508 [84,] -47096.4096 13151.7635 [85,] 51981.3236 -47096.4096 [86,] -42964.4993 51981.3236 [87,] 111889.7579 -42964.4993 [88,] 13762.0495 111889.7579 [89,] 9642.6062 13762.0495 [90,] 36151.2670 9642.6062 [91,] 4573.0438 36151.2670 [92,] -11532.8299 4573.0438 [93,] 73773.7790 -11532.8299 [94,] 34758.3033 73773.7790 [95,] -34599.8294 34758.3033 [96,] -42749.5652 -34599.8294 [97,] -75769.9840 -42749.5652 [98,] 59757.5085 -75769.9840 [99,] 11224.4434 59757.5085 [100,] 37283.8949 11224.4434 [101,] -54905.0737 37283.8949 [102,] 18231.3773 -54905.0737 [103,] -29601.9522 18231.3773 [104,] -1421.6361 -29601.9522 [105,] -49662.0254 -1421.6361 [106,] 13163.5792 -49662.0254 [107,] 42853.8731 13163.5792 [108,] -208381.9841 42853.8731 [109,] -13284.7470 -208381.9841 [110,] -8742.1938 -13284.7470 [111,] 50580.0083 -8742.1938 [112,] -28232.6148 50580.0083 [113,] -10012.3478 -28232.6148 [114,] -18628.2986 -10012.3478 [115,] 12359.8287 -18628.2986 [116,] -29963.4457 12359.8287 [117,] -31992.9293 -29963.4457 [118,] 1055.9447 -31992.9293 [119,] -31598.1765 1055.9447 [120,] 6424.9703 -31598.1765 [121,] 46810.2503 6424.9703 [122,] 85176.2415 46810.2503 [123,] -16488.0653 85176.2415 [124,] -26061.1835 -16488.0653 [125,] 11155.4382 -26061.1835 [126,] 56932.7817 11155.4382 [127,] -39650.2990 56932.7817 [128,] 54322.8115 -39650.2990 [129,] -85005.0296 54322.8115 [130,] -35725.4507 -85005.0296 [131,] -31261.7428 -35725.4507 [132,] -42213.5366 -31261.7428 [133,] 84111.3886 -42213.5366 [134,] 48037.3537 84111.3886 [135,] 57090.7516 48037.3537 [136,] 120902.6549 57090.7516 [137,] 381.2865 120902.6549 [138,] -20014.9530 381.2865 [139,] -10199.5042 -20014.9530 [140,] 23528.8398 -10199.5042 [141,] 113902.6745 23528.8398 [142,] -8562.2902 113902.6745 [143,] 113686.0092 -8562.2902 [144,] 36571.5737 113686.0092 [145,] -13314.9157 36571.5737 [146,] -37769.1461 -13314.9157 [147,] -14760.7058 -37769.1461 [148,] -42944.9242 -14760.7058 [149,] -40389.1978 -42944.9242 [150,] -42847.9242 -40389.1978 [151,] -42490.9242 -42847.9242 [152,] -42945.9242 -42490.9242 [153,] -42945.9242 -42945.9242 [154,] 1983.3496 -42945.9242 [155,] 52695.3791 1983.3496 [156,] -42945.9242 52695.3791 [157,] -42742.9242 -42945.9242 [158,] -42222.1230 -42742.9242 [159,] -29095.1238 -42222.1230 [160,] -37098.5179 -29095.1238 [161,] 41383.7775 -37098.5179 [162,] -41976.9242 41383.7775 [163,] 39590.6126 -41976.9242 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -77204.3396 -36165.0380 2 15808.0665 -77204.3396 3 1123.2557 15808.0665 4 -43343.4902 1123.2557 5 -56066.7740 -43343.4902 6 33725.0985 -56066.7740 7 -26094.7095 33725.0985 8 -24349.5435 -26094.7095 9 -8298.1129 -24349.5435 10 -993.7589 -8298.1129 11 31631.3969 -993.7589 12 2799.7433 31631.3969 13 119578.7085 2799.7433 14 -12566.0235 119578.7085 15 63206.0287 -12566.0235 16 -10494.1337 63206.0287 17 38993.5814 -10494.1337 18 -51599.7121 38993.5814 19 12952.6367 -51599.7121 20 7877.8426 12952.6367 21 135828.2366 7877.8426 22 -50877.4208 135828.2366 23 -25714.7394 -50877.4208 24 11478.6831 -25714.7394 25 -24105.8344 11478.6831 26 -20794.9445 -24105.8344 27 -26252.6510 -20794.9445 28 272.8387 -26252.6510 29 -84011.2328 272.8387 30 -57473.1458 -84011.2328 31 -21955.7561 -57473.1458 32 -25004.0130 -21955.7561 33 -3216.2956 -25004.0130 34 13418.8168 -3216.2956 35 64678.4133 13418.8168 36 73959.9727 64678.4133 37 -9590.6241 73959.9727 38 -23222.2676 -9590.6241 39 29924.6226 -23222.2676 40 54251.0952 29924.6226 41 -13230.7031 54251.0952 42 -56183.5708 -13230.7031 43 20368.2956 -56183.5708 44 -62557.0620 20368.2956 45 235375.5553 -62557.0620 46 -56924.6651 235375.5553 47 -8938.6644 -56924.6651 48 -15649.7916 -8938.6644 49 -34373.8708 -15649.7916 50 -60887.9723 -34373.8708 51 -24437.5523 -60887.9723 52 54328.1129 -24437.5523 53 1058.2179 54328.1129 54 5528.1626 1058.2179 55 51291.1580 5528.1626 56 -26880.1268 51291.1580 57 -30540.1013 -26880.1268 58 -45540.3663 -30540.1013 59 79895.3466 -45540.3663 60 18316.7267 79895.3466 61 -70255.8674 18316.7267 62 23634.9337 -70255.8674 63 20719.5929 23634.9337 64 -51490.4938 20719.5929 65 -39632.1830 -51490.4938 66 -3144.3291 -39632.1830 67 -23306.7946 -3144.3291 68 8097.4267 -23306.7946 69 -32541.3154 8097.4267 70 23781.7562 -32541.3154 71 -5720.3571 23781.7562 72 18547.3217 -5720.3571 73 -504.1043 18547.3217 74 -23217.2646 -504.1043 75 136538.2558 -23217.2646 76 2983.5936 136538.2558 77 -77558.7104 2983.5936 78 16942.2556 -77558.7104 79 6536.5382 16942.2556 80 -16411.7890 6536.5382 81 117452.0037 -16411.7890 82 -1650.8508 117452.0037 83 13151.7635 -1650.8508 84 -47096.4096 13151.7635 85 51981.3236 -47096.4096 86 -42964.4993 51981.3236 87 111889.7579 -42964.4993 88 13762.0495 111889.7579 89 9642.6062 13762.0495 90 36151.2670 9642.6062 91 4573.0438 36151.2670 92 -11532.8299 4573.0438 93 73773.7790 -11532.8299 94 34758.3033 73773.7790 95 -34599.8294 34758.3033 96 -42749.5652 -34599.8294 97 -75769.9840 -42749.5652 98 59757.5085 -75769.9840 99 11224.4434 59757.5085 100 37283.8949 11224.4434 101 -54905.0737 37283.8949 102 18231.3773 -54905.0737 103 -29601.9522 18231.3773 104 -1421.6361 -29601.9522 105 -49662.0254 -1421.6361 106 13163.5792 -49662.0254 107 42853.8731 13163.5792 108 -208381.9841 42853.8731 109 -13284.7470 -208381.9841 110 -8742.1938 -13284.7470 111 50580.0083 -8742.1938 112 -28232.6148 50580.0083 113 -10012.3478 -28232.6148 114 -18628.2986 -10012.3478 115 12359.8287 -18628.2986 116 -29963.4457 12359.8287 117 -31992.9293 -29963.4457 118 1055.9447 -31992.9293 119 -31598.1765 1055.9447 120 6424.9703 -31598.1765 121 46810.2503 6424.9703 122 85176.2415 46810.2503 123 -16488.0653 85176.2415 124 -26061.1835 -16488.0653 125 11155.4382 -26061.1835 126 56932.7817 11155.4382 127 -39650.2990 56932.7817 128 54322.8115 -39650.2990 129 -85005.0296 54322.8115 130 -35725.4507 -85005.0296 131 -31261.7428 -35725.4507 132 -42213.5366 -31261.7428 133 84111.3886 -42213.5366 134 48037.3537 84111.3886 135 57090.7516 48037.3537 136 120902.6549 57090.7516 137 381.2865 120902.6549 138 -20014.9530 381.2865 139 -10199.5042 -20014.9530 140 23528.8398 -10199.5042 141 113902.6745 23528.8398 142 -8562.2902 113902.6745 143 113686.0092 -8562.2902 144 36571.5737 113686.0092 145 -13314.9157 36571.5737 146 -37769.1461 -13314.9157 147 -14760.7058 -37769.1461 148 -42944.9242 -14760.7058 149 -40389.1978 -42944.9242 150 -42847.9242 -40389.1978 151 -42490.9242 -42847.9242 152 -42945.9242 -42490.9242 153 -42945.9242 -42945.9242 154 1983.3496 -42945.9242 155 52695.3791 1983.3496 156 -42945.9242 52695.3791 157 -42742.9242 -42945.9242 158 -42222.1230 -42742.9242 159 -29095.1238 -42222.1230 160 -37098.5179 -29095.1238 161 41383.7775 -37098.5179 162 -41976.9242 41383.7775 163 39590.6126 -41976.9242 > 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/70ivr1354889340.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/8vb4v1354889340.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/9eng41354889340.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/10yknv1354889340.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/11mbp51354889340.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/12pqbo1354889340.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/13vjs71354889340.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14gx6i1354889340.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/15yanc1354889340.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/16vttn1354889340.tab") + } > > try(system("convert tmp/12sjj1354889340.ps tmp/12sjj1354889340.png",intern=TRUE)) character(0) > try(system("convert tmp/263k41354889340.ps tmp/263k41354889340.png",intern=TRUE)) character(0) > try(system("convert tmp/3nqb81354889340.ps tmp/3nqb81354889340.png",intern=TRUE)) character(0) > try(system("convert tmp/4xlce1354889340.ps tmp/4xlce1354889340.png",intern=TRUE)) character(0) > try(system("convert tmp/55fpf1354889340.ps tmp/55fpf1354889340.png",intern=TRUE)) character(0) > try(system("convert tmp/61nmw1354889340.ps tmp/61nmw1354889340.png",intern=TRUE)) character(0) > try(system("convert tmp/70ivr1354889340.ps tmp/70ivr1354889340.png",intern=TRUE)) character(0) > try(system("convert tmp/8vb4v1354889340.ps tmp/8vb4v1354889340.png",intern=TRUE)) character(0) > try(system("convert tmp/9eng41354889340.ps tmp/9eng41354889340.png",intern=TRUE)) character(0) > try(system("convert tmp/10yknv1354889340.ps tmp/10yknv1354889340.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.296 1.559 9.854