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Type 'q()' to quit R. > x <- array(list(170588 + ,95556 + ,114468 + ,86621 + ,54565 + ,88594 + ,118522 + ,63016 + ,74151 + ,152510 + ,79774 + ,77921 + ,86206 + ,31258 + ,53212 + ,37257 + ,52491 + ,34956 + ,306055 + ,91256 + ,149703 + ,32750 + ,22807 + ,6853 + ,116502 + ,77411 + ,58907 + ,130539 + ,48821 + ,67067 + ,164604 + ,52295 + ,110563 + ,128274 + ,63262 + ,58126 + ,104367 + ,50466 + ,57113 + ,193024 + ,62932 + ,77993 + ,141574 + ,38439 + ,68091 + ,254150 + ,70817 + ,124676 + ,181110 + ,105965 + ,109522 + ,198432 + ,73795 + ,75865 + ,113853 + ,82043 + ,79746 + ,159940 + ,74349 + ,77844 + ,166822 + ,82204 + ,98681 + ,286675 + ,55709 + ,105531 + ,95297 + ,37137 + ,51428 + ,108278 + ,70780 + ,65703 + ,146342 + ,55027 + ,72562 + ,146684 + ,56699 + ,81728 + ,163569 + ,65911 + ,95580 + ,162716 + ,56316 + ,98278 + ,106888 + ,26982 + ,46629 + ,188150 + ,54628 + ,115189 + ,189401 + ,96750 + ,124865 + ,129484 + ,53009 + ,59392 + ,204030 + ,64664 + ,127818 + ,68538 + ,36990 + ,17821 + ,243625 + ,85224 + ,154076 + ,167255 + ,37048 + ,64881 + ,264528 + ,59635 + ,136506 + ,122024 + ,42051 + ,66524 + ,80964 + ,26998 + ,45988 + ,209795 + ,63717 + ,107445 + ,224911 + ,55071 + ,102772 + ,115971 + ,40001 + ,46657 + ,138191 + ,54506 + ,97563 + ,81106 + ,35838 + ,36663 + ,93125 + ,50838 + ,55369 + ,307743 + ,86997 + ,77921 + ,78800 + ,33032 + ,56968 + ,158835 + ,61704 + ,77519 + ,223590 + ,117986 + ,129805 + ,131108 + ,56733 + ,72761 + ,128734 + ,55064 + ,81278 + ,24188 + ,5950 + ,15049 + ,257677 + ,84607 + ,113935 + ,65029 + ,32551 + ,25109 + ,98066 + ,31701 + ,45824 + ,173587 + ,71170 + ,89644 + ,180042 + ,101773 + ,109011 + ,197266 + ,101653 + ,134245 + ,212120 + ,81493 + ,136692 + ,141582 + ,55901 + ,50741 + ,245107 + ,109104 + ,149510 + ,206879 + ,114425 + ,147888 + ,145696 + ,36311 + ,54987 + ,173535 + ,70027 + ,74467 + ,142064 + ,73713 + ,100033 + ,117926 + ,40671 + ,85505 + ,113461 + ,89041 + ,62426 + ,145285 + ,57231 + ,82932 + ,150999 + ,68608 + ,72002 + ,91838 + ,59155 + ,65469 + ,118807 + ,55827 + ,63572 + ,69471 + ,22618 + ,23824 + ,126630 + ,58425 + ,73831 + ,145908 + ,65724 + ,63551 + ,102896 + ,56979 + ,56756 + ,190926 + ,72369 + ,81399 + ,198797 + ,79194 + ,117881 + ,112566 + ,202316 + ,70711 + ,89318 + ,44970 + ,50495 + ,120362 + ,49319 + ,53845 + ,98791 + ,36252 + ,51390 + ,283982 + ,75741 + ,104953 + ,132798 + ,38417 + ,65983 + ,137875 + ,64102 + ,76839 + ,80953 + ,56622 + ,55792 + ,109237 + ,15430 + ,25155 + ,98724 + ,72571 + ,55291 + ,226191 + ,67271 + ,84279 + ,172071 + ,43460 + ,99692 + ,118174 + ,99501 + ,59633 + ,133561 + ,28340 + ,63249 + ,152193 + ,76013 + ,82928 + ,112004 + ,37361 + ,50000 + ,169613 + ,48204 + ,69455 + ,187483 + ,76168 + ,84068 + ,130533 + ,85168 + ,76195 + ,142339 + ,125410 + ,114634 + ,201941 + ,123328 + ,139357 + ,201744 + ,83038 + ,110044 + ,247024 + ,120087 + ,155118 + ,162502 + ,91939 + ,83061 + ,182581 + ,103646 + ,127122 + ,106351 + ,29467 + ,45653 + ,43287 + ,43750 + ,19630 + ,127493 + ,34497 + ,67229 + ,127930 + ,66477 + ,86060 + ,149006 + ,71181 + ,88003 + ,187714 + ,74482 + ,95815 + ,74112 + ,174949 + ,85499 + ,94006 + ,46765 + ,27220 + ,176625 + ,90257 + ,109882 + ,141933 + ,51370 + ,72579 + ,22938 + ,1168 + ,5841 + ,125927 + ,51360 + ,68369 + ,61857 + ,25162 + ,24610 + ,91290 + ,21067 + ,30995 + ,255100 + ,58233 + ,150662 + ,21054 + ,855 + ,6622 + ,174150 + ,85903 + ,93694 + ,31414 + ,14116 + ,13155 + ,189461 + ,57637 + ,111908 + ,137544 + ,94137 + ,57550 + ,77166 + ,62147 + ,16356 + ,74567 + ,62832 + ,40174 + ,38214 + ,8773 + ,13983 + ,90961 + ,63785 + ,52316 + ,194652 + ,65196 + ,99585 + ,135261 + ,73087 + ,86271 + ,248590 + ,72631 + ,131012 + ,201748 + ,86281 + ,130274 + ,256402 + ,162365 + ,159051 + ,139144 + ,56530 + ,76506 + ,76470 + ,35606 + ,49145 + ,193518 + ,70111 + ,66398 + ,280334 + ,92046 + ,127546 + ,50999 + ,63989 + ,6802 + ,254825 + ,104911 + ,99509 + ,103239 + ,43448 + ,43106 + ,168059 + ,60029 + ,108303 + ,136709 + ,38650 + ,64167 + ,78256 + ,47261 + ,8579 + ,249232 + ,73586 + ,97811 + ,152366 + ,83042 + ,84365 + ,173260 + ,37238 + ,10901 + ,197197 + ,63958 + ,91346 + ,68388 + ,78956 + ,33660 + ,139409 + ,99518 + ,93634 + ,185366 + ,111436 + ,109348 + ,0 + ,0 + ,0 + ,14688 + ,6023 + ,7953 + ,98 + ,0 + ,0 + ,455 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,137885 + ,42564 + ,63538 + ,185288 + ,38885 + ,108281 + ,0 + ,0 + ,0 + ,203 + ,0 + ,0 + ,7199 + ,1644 + ,4245 + ,46660 + ,6179 + ,21509 + ,17547 + ,3926 + ,7670 + ,73567 + ,23238 + ,10641 + ,969 + ,0 + ,0 + ,105477 + ,49288 + ,41243) + ,dim=c(3 + ,164) + ,dimnames=list(c('TotalRFC' + ,'TotalCompen' + ,'TotalCharac') + ,1:164)) > y <- array(NA,dim=c(3,164),dimnames=list(c('TotalRFC','TotalCompen','TotalCharac'),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' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x TotalRFC TotalCompen TotalCharac 1 170588 95556 114468 2 86621 54565 88594 3 118522 63016 74151 4 152510 79774 77921 5 86206 31258 53212 6 37257 52491 34956 7 306055 91256 149703 8 32750 22807 6853 9 116502 77411 58907 10 130539 48821 67067 11 164604 52295 110563 12 128274 63262 58126 13 104367 50466 57113 14 193024 62932 77993 15 141574 38439 68091 16 254150 70817 124676 17 181110 105965 109522 18 198432 73795 75865 19 113853 82043 79746 20 159940 74349 77844 21 166822 82204 98681 22 286675 55709 105531 23 95297 37137 51428 24 108278 70780 65703 25 146342 55027 72562 26 146684 56699 81728 27 163569 65911 95580 28 162716 56316 98278 29 106888 26982 46629 30 188150 54628 115189 31 189401 96750 124865 32 129484 53009 59392 33 204030 64664 127818 34 68538 36990 17821 35 243625 85224 154076 36 167255 37048 64881 37 264528 59635 136506 38 122024 42051 66524 39 80964 26998 45988 40 209795 63717 107445 41 224911 55071 102772 42 115971 40001 46657 43 138191 54506 97563 44 81106 35838 36663 45 93125 50838 55369 46 307743 86997 77921 47 78800 33032 56968 48 158835 61704 77519 49 223590 117986 129805 50 131108 56733 72761 51 128734 55064 81278 52 24188 5950 15049 53 257677 84607 113935 54 65029 32551 25109 55 98066 31701 45824 56 173587 71170 89644 57 180042 101773 109011 58 197266 101653 134245 59 212120 81493 136692 60 141582 55901 50741 61 245107 109104 149510 62 206879 114425 147888 63 145696 36311 54987 64 173535 70027 74467 65 142064 73713 100033 66 117926 40671 85505 67 113461 89041 62426 68 145285 57231 82932 69 150999 68608 72002 70 91838 59155 65469 71 118807 55827 63572 72 69471 22618 23824 73 126630 58425 73831 74 145908 65724 63551 75 102896 56979 56756 76 190926 72369 81399 77 198797 79194 117881 78 112566 202316 70711 79 89318 44970 50495 80 120362 49319 53845 81 98791 36252 51390 82 283982 75741 104953 83 132798 38417 65983 84 137875 64102 76839 85 80953 56622 55792 86 109237 15430 25155 87 98724 72571 55291 88 226191 67271 84279 89 172071 43460 99692 90 118174 99501 59633 91 133561 28340 63249 92 152193 76013 82928 93 112004 37361 50000 94 169613 48204 69455 95 187483 76168 84068 96 130533 85168 76195 97 142339 125410 114634 98 201941 123328 139357 99 201744 83038 110044 100 247024 120087 155118 101 162502 91939 83061 102 182581 103646 127122 103 106351 29467 45653 104 43287 43750 19630 105 127493 34497 67229 106 127930 66477 86060 107 149006 71181 88003 108 187714 74482 95815 109 74112 174949 85499 110 94006 46765 27220 111 176625 90257 109882 112 141933 51370 72579 113 22938 1168 5841 114 125927 51360 68369 115 61857 25162 24610 116 91290 21067 30995 117 255100 58233 150662 118 21054 855 6622 119 174150 85903 93694 120 31414 14116 13155 121 189461 57637 111908 122 137544 94137 57550 123 77166 62147 16356 124 74567 62832 40174 125 38214 8773 13983 126 90961 63785 52316 127 194652 65196 99585 128 135261 73087 86271 129 248590 72631 131012 130 201748 86281 130274 131 256402 162365 159051 132 139144 56530 76506 133 76470 35606 49145 134 193518 70111 66398 135 280334 92046 127546 136 50999 63989 6802 137 254825 104911 99509 138 103239 43448 43106 139 168059 60029 108303 140 136709 38650 64167 141 78256 47261 8579 142 249232 73586 97811 143 152366 83042 84365 144 173260 37238 10901 145 197197 63958 91346 146 68388 78956 33660 147 139409 99518 93634 148 185366 111436 109348 149 0 0 0 150 14688 6023 7953 151 98 0 0 152 455 0 0 153 0 0 0 154 0 0 0 155 137885 42564 63538 156 185288 38885 108281 157 0 0 0 158 203 0 0 159 7199 1644 4245 160 46660 6179 21509 161 17547 3926 7670 162 73567 23238 10641 163 969 0 0 164 105477 49288 41243 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) TotalCompen TotalCharac 2.745e+04 -3.828e-02 1.561e+00 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -80094 -20605 -7567 13617 161999 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.745e+04 5.653e+03 4.855 2.83e-06 *** TotalCompen -3.828e-02 1.136e-01 -0.337 0.737 TotalCharac 1.561e+00 9.547e-02 16.350 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 33470 on 161 degrees of freedom Multiple R-squared: 0.7726, Adjusted R-squared: 0.7698 F-statistic: 273.5 on 2 and 161 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,] 0.5373269 9.253463e-01 4.626731e-01 [2,] 0.7810087 4.379827e-01 2.189913e-01 [3,] 0.8174105 3.651789e-01 1.825895e-01 [4,] 0.7399447 5.201107e-01 2.600553e-01 [5,] 0.6728808 6.542385e-01 3.271192e-01 [6,] 0.5961834 8.076332e-01 4.038166e-01 [7,] 0.5489926 9.020149e-01 4.510074e-01 [8,] 0.4565524 9.131048e-01 5.434476e-01 [9,] 0.6147336 7.705328e-01 3.852664e-01 [10,] 0.5811587 8.376826e-01 4.188413e-01 [11,] 0.5821948 8.356104e-01 4.178052e-01 [12,] 0.5291352 9.417297e-01 4.708648e-01 [13,] 0.6654188 6.691625e-01 3.345812e-01 [14,] 0.6670022 6.659955e-01 3.329978e-01 [15,] 0.6125380 7.749241e-01 3.874620e-01 [16,] 0.5521145 8.957710e-01 4.478855e-01 [17,] 0.8705577 2.588846e-01 1.294423e-01 [18,] 0.8337661 3.324678e-01 1.662339e-01 [19,] 0.7960910 4.078179e-01 2.039090e-01 [20,] 0.7500664 4.998672e-01 2.499336e-01 [21,] 0.7001444 5.997112e-01 2.998556e-01 [22,] 0.6551593 6.896813e-01 3.448407e-01 [23,] 0.6220776 7.558448e-01 3.779224e-01 [24,] 0.5709490 8.581019e-01 4.290510e-01 [25,] 0.5485120 9.029760e-01 4.514880e-01 [26,] 0.5445329 9.109342e-01 4.554671e-01 [27,] 0.4989108 9.978215e-01 5.010892e-01 [28,] 0.4755004 9.510007e-01 5.244996e-01 [29,] 0.4413570 8.827139e-01 5.586430e-01 [30,] 0.4104867 8.209734e-01 5.895133e-01 [31,] 0.4315824 8.631648e-01 5.684176e-01 [32,] 0.4008129 8.016258e-01 5.991871e-01 [33,] 0.3521252 7.042504e-01 6.478748e-01 [34,] 0.3173951 6.347903e-01 6.826049e-01 [35,] 0.2807186 5.614371e-01 7.192814e-01 [36,] 0.2895359 5.790718e-01 7.104641e-01 [37,] 0.2578051 5.156103e-01 7.421949e-01 [38,] 0.2852430 5.704859e-01 7.147570e-01 [39,] 0.2423198 4.846396e-01 7.576802e-01 [40,] 0.2132777 4.265555e-01 7.867223e-01 [41,] 0.9701476 5.970483e-02 2.985241e-02 [42,] 0.9695715 6.085705e-02 3.042852e-02 [43,] 0.9612362 7.752753e-02 3.876377e-02 [44,] 0.9518268 9.634647e-02 4.817324e-02 [45,] 0.9392583 1.214834e-01 6.074172e-02 [46,] 0.9309043 1.381915e-01 6.909574e-02 [47,] 0.9204265 1.591469e-01 7.957347e-02 [48,] 0.9405462 1.189076e-01 5.945378e-02 [49,] 0.9252829 1.494341e-01 7.471706e-02 [50,] 0.9074022 1.851957e-01 9.259785e-02 [51,] 0.8872337 2.255326e-01 1.127663e-01 [52,] 0.8733364 2.533271e-01 1.266636e-01 [53,] 0.8831595 2.336809e-01 1.168405e-01 [54,] 0.8735769 2.528462e-01 1.264231e-01 [55,] 0.8760192 2.479617e-01 1.239808e-01 [56,] 0.8556007 2.887986e-01 1.443993e-01 [57,] 0.8819416 2.361168e-01 1.180584e-01 [58,] 0.8816145 2.367710e-01 1.183855e-01 [59,] 0.8775897 2.448206e-01 1.224103e-01 [60,] 0.8847012 2.305976e-01 1.152988e-01 [61,] 0.8930014 2.139971e-01 1.069986e-01 [62,] 0.8737312 2.525376e-01 1.262688e-01 [63,] 0.8505555 2.988890e-01 1.494445e-01 [64,] 0.8263851 3.472299e-01 1.736149e-01 [65,] 0.8307419 3.385162e-01 1.692581e-01 [66,] 0.8015694 3.968612e-01 1.984306e-01 [67,] 0.7693430 4.613139e-01 2.306570e-01 [68,] 0.7399422 5.201155e-01 2.600578e-01 [69,] 0.7160277 5.679445e-01 2.839723e-01 [70,] 0.6815230 6.369541e-01 3.184770e-01 [71,] 0.6921431 6.157138e-01 3.078569e-01 [72,] 0.6548983 6.902033e-01 3.451017e-01 [73,] 0.6339898 7.320205e-01 3.660102e-01 [74,] 0.6000104 7.999791e-01 3.999896e-01 [75,] 0.5599506 8.800989e-01 4.400494e-01 [76,] 0.5175210 9.649579e-01 4.824790e-01 [77,] 0.7965520 4.068960e-01 2.034480e-01 [78,] 0.7634999 4.730002e-01 2.365001e-01 [79,] 0.7288552 5.422897e-01 2.711448e-01 [80,] 0.7237278 5.525444e-01 2.762722e-01 [81,] 0.7465170 5.069659e-01 2.534830e-01 [82,] 0.7140491 5.719017e-01 2.859509e-01 [83,] 0.8267617 3.464767e-01 1.732383e-01 [84,] 0.7982313 4.035375e-01 2.017687e-01 [85,] 0.7648112 4.703777e-01 2.351888e-01 [86,] 0.7309312 5.381376e-01 2.690688e-01 [87,] 0.6920678 6.158644e-01 3.079322e-01 [88,] 0.6533261 6.933477e-01 3.466739e-01 [89,] 0.6596941 6.806118e-01 3.403059e-01 [90,] 0.6570877 6.858245e-01 3.429123e-01 [91,] 0.6194627 7.610745e-01 3.805373e-01 [92,] 0.7042761 5.914479e-01 2.957239e-01 [93,] 0.7153483 5.693034e-01 2.846517e-01 [94,] 0.6760669 6.478662e-01 3.239331e-01 [95,] 0.6463216 7.073568e-01 3.536784e-01 [96,] 0.6048284 7.903433e-01 3.951716e-01 [97,] 0.6251412 7.497176e-01 3.748588e-01 [98,] 0.5835021 8.329957e-01 4.164979e-01 [99,] 0.5451913 9.096173e-01 4.548087e-01 [100,] 0.4989507 9.979014e-01 5.010493e-01 [101,] 0.4950901 9.901802e-01 5.049099e-01 [102,] 0.4566696 9.133392e-01 5.433304e-01 [103,] 0.4157425 8.314849e-01 5.842575e-01 [104,] 0.7762085 4.475829e-01 2.237915e-01 [105,] 0.7543802 4.912395e-01 2.456198e-01 [106,] 0.7417707 5.164587e-01 2.582293e-01 [107,] 0.7006660 5.986680e-01 2.993340e-01 [108,] 0.6637674 6.724653e-01 3.362326e-01 [109,] 0.6198295 7.603410e-01 3.801705e-01 [110,] 0.5724461 8.551078e-01 4.275539e-01 [111,] 0.5420426 9.159148e-01 4.579574e-01 [112,] 0.4940289 9.880578e-01 5.059711e-01 [113,] 0.4530218 9.060436e-01 5.469782e-01 [114,] 0.4066489 8.132977e-01 5.933511e-01 [115,] 0.3662502 7.325004e-01 6.337498e-01 [116,] 0.3215649 6.431298e-01 6.784351e-01 [117,] 0.2865549 5.731098e-01 7.134451e-01 [118,] 0.2544650 5.089301e-01 7.455350e-01 [119,] 0.2316817 4.633635e-01 7.683183e-01 [120,] 0.1960560 3.921121e-01 8.039440e-01 [121,] 0.1788314 3.576629e-01 8.211686e-01 [122,] 0.1504370 3.008740e-01 8.495630e-01 [123,] 0.1451576 2.903152e-01 8.548424e-01 [124,] 0.1264264 2.528528e-01 8.735736e-01 [125,] 0.1207064 2.414128e-01 8.792936e-01 [126,] 0.1931908 3.863816e-01 8.068092e-01 [127,] 0.1620016 3.240033e-01 8.379984e-01 [128,] 0.1500907 3.001814e-01 8.499093e-01 [129,] 0.1929805 3.859610e-01 8.070195e-01 [130,] 0.2179020 4.358039e-01 7.820980e-01 [131,] 0.1839983 3.679966e-01 8.160017e-01 [132,] 0.2491240 4.982479e-01 7.508760e-01 [133,] 0.2043361 4.086722e-01 7.956639e-01 [134,] 0.1815614 3.631228e-01 8.184386e-01 [135,] 0.1477892 2.955785e-01 8.522108e-01 [136,] 0.1380124 2.760247e-01 8.619876e-01 [137,] 0.2708299 5.416598e-01 7.291701e-01 [138,] 0.2210945 4.421891e-01 7.789055e-01 [139,] 0.9967536 6.492725e-03 3.246363e-03 [140,] 0.9982744 3.451224e-03 1.725612e-03 [141,] 0.9965129 6.974209e-03 3.487105e-03 [142,] 0.9984880 3.024032e-03 1.512016e-03 [143,] 0.9999970 5.991881e-06 2.995940e-06 [144,] 0.9999894 2.127166e-05 1.063583e-05 [145,] 0.9999676 6.473868e-05 3.236934e-05 [146,] 0.9998914 2.171777e-04 1.085889e-04 [147,] 0.9996461 7.078889e-04 3.539444e-04 [148,] 0.9989125 2.174950e-03 1.087475e-03 [149,] 0.9968314 6.337253e-03 3.168626e-03 [150,] 0.9904739 1.905222e-02 9.526112e-03 [151,] 0.9756468 4.870648e-02 2.435324e-02 [152,] 0.9414184 1.171632e-01 5.858161e-02 [153,] 0.8711509 2.576982e-01 1.288491e-01 > postscript(file="/var/wessaorg/rcomp/tmp/1lpbj1321907957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2h34o1321907957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3bbqk1321907957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4xrpe1321907957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/51jwu1321907957.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 -31875.49563 -77024.54714 -22255.76693 6489.08346 -23103.48897 -42743.65079 7 8 9 10 11 12 48428.10654 -4520.59453 69.84104 275.36212 -33420.15561 12519.29194 13 14 15 16 17 18 -10296.33245 46245.98713 9314.56428 34805.69020 -13234.76071 55391.44633 19 20 21 22 23 24 -34929.72688 13831.60521 -11510.46468 96636.04979 -11002.77124 -19015.96265 25 26 27 28 29 30 7738.70659 -6162.62433 -10546.76617 -15978.40724 7690.32093 -17005.63530 31 32 33 34 35 36 -29245.60911 11360.69109 -20454.23703 14690.22825 -21058.67359 39952.84987 37 38 39 40 41 42 26290.03500 -7651.21685 -17232.52091 17075.00664 39154.18809 17227.98173 43 44 45 46 47 48 -39456.64062 -2196.60612 -18801.85883 161998.57938 -36304.37223 12749.85247 49 50 51 52 53 54 -1954.61022 -7740.60968 -23472.80085 -26521.12794 55626.34444 -364.63538 55 56 57 58 59 60 305.49943 8938.13168 -13665.60327 -35834.29311 -25571.57228 37072.86104 61 62 63 64 65 66 -11535.41696 -47027.92744 33809.31769 32532.36491 -38703.85487 -41429.74605 67 68 69 70 71 72 -8018.81870 -9420.59950 13789.69796 -35535.71301 -5733.05533 5702.90420 73 74 75 76 77 78 -13824.01770 21779.58031 -10960.76909 39192.76275 -9620.23026 -17509.82982 79 80 81 82 83 84 -15225.59208 10755.82644 -7483.33425 95612.08143 3828.12820 -7056.93095 85 86 87 88 89 90 -31412.71422 43116.17194 -12249.16988 69767.18023 -9322.66596 1454.22131 91 92 93 94 95 96 8472.91941 -1787.38223 7941.78823 35598.28150 31729.10954 -12587.29756 97 98 99 100 101 102 -59240.79833 -38308.96649 5706.79843 -17951.59253 8923.66238 -39324.61294 103 104 105 106 107 108 8771.89832 -13125.69231 -3571.82759 -31304.20133 -13080.98786 13559.51637 109 110 111 112 113 114 -80094.22838 25861.38671 -18882.99075 3163.18136 -13581.28923 -6271.75478 115 116 117 118 119 120 -3040.58983 16269.21990 -5287.92776 -16696.34445 3743.40938 -16026.16351 121 122 123 124 125 126 -10458.09316 23870.27101 26567.97497 -13182.64573 -10723.12971 -15704.77908 127 128 129 130 131 132 14257.40432 -24049.52380 19425.18080 -25742.34387 -13094.26730 -5558.00231 133 134 135 136 137 138 -26324.81184 65113.59306 57322.51248 15384.45607 76069.32111 10170.73718 139 140 141 142 143 144 -26141.43369 10582.66670 39227.36579 71927.63382 -3588.34560 130223.24447 145 146 147 148 149 150 29615.38172 -8576.62834 -30382.75457 -8497.73224 -27446.70328 -24942.08991 151 152 153 154 155 156 -27348.70328 -26991.70328 -27446.70328 -27446.70328 12890.30905 -9687.48439 157 158 159 160 161 162 -27446.70328 -27243.70328 -26810.84972 -14123.86433 -21721.62434 30400.16303 163 164 -26477.70328 15540.27418 > postscript(file="/var/wessaorg/rcomp/tmp/6s3zh1321907957.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 -31875.49563 NA 1 -77024.54714 -31875.49563 2 -22255.76693 -77024.54714 3 6489.08346 -22255.76693 4 -23103.48897 6489.08346 5 -42743.65079 -23103.48897 6 48428.10654 -42743.65079 7 -4520.59453 48428.10654 8 69.84104 -4520.59453 9 275.36212 69.84104 10 -33420.15561 275.36212 11 12519.29194 -33420.15561 12 -10296.33245 12519.29194 13 46245.98713 -10296.33245 14 9314.56428 46245.98713 15 34805.69020 9314.56428 16 -13234.76071 34805.69020 17 55391.44633 -13234.76071 18 -34929.72688 55391.44633 19 13831.60521 -34929.72688 20 -11510.46468 13831.60521 21 96636.04979 -11510.46468 22 -11002.77124 96636.04979 23 -19015.96265 -11002.77124 24 7738.70659 -19015.96265 25 -6162.62433 7738.70659 26 -10546.76617 -6162.62433 27 -15978.40724 -10546.76617 28 7690.32093 -15978.40724 29 -17005.63530 7690.32093 30 -29245.60911 -17005.63530 31 11360.69109 -29245.60911 32 -20454.23703 11360.69109 33 14690.22825 -20454.23703 34 -21058.67359 14690.22825 35 39952.84987 -21058.67359 36 26290.03500 39952.84987 37 -7651.21685 26290.03500 38 -17232.52091 -7651.21685 39 17075.00664 -17232.52091 40 39154.18809 17075.00664 41 17227.98173 39154.18809 42 -39456.64062 17227.98173 43 -2196.60612 -39456.64062 44 -18801.85883 -2196.60612 45 161998.57938 -18801.85883 46 -36304.37223 161998.57938 47 12749.85247 -36304.37223 48 -1954.61022 12749.85247 49 -7740.60968 -1954.61022 50 -23472.80085 -7740.60968 51 -26521.12794 -23472.80085 52 55626.34444 -26521.12794 53 -364.63538 55626.34444 54 305.49943 -364.63538 55 8938.13168 305.49943 56 -13665.60327 8938.13168 57 -35834.29311 -13665.60327 58 -25571.57228 -35834.29311 59 37072.86104 -25571.57228 60 -11535.41696 37072.86104 61 -47027.92744 -11535.41696 62 33809.31769 -47027.92744 63 32532.36491 33809.31769 64 -38703.85487 32532.36491 65 -41429.74605 -38703.85487 66 -8018.81870 -41429.74605 67 -9420.59950 -8018.81870 68 13789.69796 -9420.59950 69 -35535.71301 13789.69796 70 -5733.05533 -35535.71301 71 5702.90420 -5733.05533 72 -13824.01770 5702.90420 73 21779.58031 -13824.01770 74 -10960.76909 21779.58031 75 39192.76275 -10960.76909 76 -9620.23026 39192.76275 77 -17509.82982 -9620.23026 78 -15225.59208 -17509.82982 79 10755.82644 -15225.59208 80 -7483.33425 10755.82644 81 95612.08143 -7483.33425 82 3828.12820 95612.08143 83 -7056.93095 3828.12820 84 -31412.71422 -7056.93095 85 43116.17194 -31412.71422 86 -12249.16988 43116.17194 87 69767.18023 -12249.16988 88 -9322.66596 69767.18023 89 1454.22131 -9322.66596 90 8472.91941 1454.22131 91 -1787.38223 8472.91941 92 7941.78823 -1787.38223 93 35598.28150 7941.78823 94 31729.10954 35598.28150 95 -12587.29756 31729.10954 96 -59240.79833 -12587.29756 97 -38308.96649 -59240.79833 98 5706.79843 -38308.96649 99 -17951.59253 5706.79843 100 8923.66238 -17951.59253 101 -39324.61294 8923.66238 102 8771.89832 -39324.61294 103 -13125.69231 8771.89832 104 -3571.82759 -13125.69231 105 -31304.20133 -3571.82759 106 -13080.98786 -31304.20133 107 13559.51637 -13080.98786 108 -80094.22838 13559.51637 109 25861.38671 -80094.22838 110 -18882.99075 25861.38671 111 3163.18136 -18882.99075 112 -13581.28923 3163.18136 113 -6271.75478 -13581.28923 114 -3040.58983 -6271.75478 115 16269.21990 -3040.58983 116 -5287.92776 16269.21990 117 -16696.34445 -5287.92776 118 3743.40938 -16696.34445 119 -16026.16351 3743.40938 120 -10458.09316 -16026.16351 121 23870.27101 -10458.09316 122 26567.97497 23870.27101 123 -13182.64573 26567.97497 124 -10723.12971 -13182.64573 125 -15704.77908 -10723.12971 126 14257.40432 -15704.77908 127 -24049.52380 14257.40432 128 19425.18080 -24049.52380 129 -25742.34387 19425.18080 130 -13094.26730 -25742.34387 131 -5558.00231 -13094.26730 132 -26324.81184 -5558.00231 133 65113.59306 -26324.81184 134 57322.51248 65113.59306 135 15384.45607 57322.51248 136 76069.32111 15384.45607 137 10170.73718 76069.32111 138 -26141.43369 10170.73718 139 10582.66670 -26141.43369 140 39227.36579 10582.66670 141 71927.63382 39227.36579 142 -3588.34560 71927.63382 143 130223.24447 -3588.34560 144 29615.38172 130223.24447 145 -8576.62834 29615.38172 146 -30382.75457 -8576.62834 147 -8497.73224 -30382.75457 148 -27446.70328 -8497.73224 149 -24942.08991 -27446.70328 150 -27348.70328 -24942.08991 151 -26991.70328 -27348.70328 152 -27446.70328 -26991.70328 153 -27446.70328 -27446.70328 154 12890.30905 -27446.70328 155 -9687.48439 12890.30905 156 -27446.70328 -9687.48439 157 -27243.70328 -27446.70328 158 -26810.84972 -27243.70328 159 -14123.86433 -26810.84972 160 -21721.62434 -14123.86433 161 30400.16303 -21721.62434 162 -26477.70328 30400.16303 163 15540.27418 -26477.70328 164 NA 15540.27418 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -77024.54714 -31875.49563 [2,] -22255.76693 -77024.54714 [3,] 6489.08346 -22255.76693 [4,] -23103.48897 6489.08346 [5,] -42743.65079 -23103.48897 [6,] 48428.10654 -42743.65079 [7,] -4520.59453 48428.10654 [8,] 69.84104 -4520.59453 [9,] 275.36212 69.84104 [10,] -33420.15561 275.36212 [11,] 12519.29194 -33420.15561 [12,] -10296.33245 12519.29194 [13,] 46245.98713 -10296.33245 [14,] 9314.56428 46245.98713 [15,] 34805.69020 9314.56428 [16,] -13234.76071 34805.69020 [17,] 55391.44633 -13234.76071 [18,] -34929.72688 55391.44633 [19,] 13831.60521 -34929.72688 [20,] -11510.46468 13831.60521 [21,] 96636.04979 -11510.46468 [22,] -11002.77124 96636.04979 [23,] -19015.96265 -11002.77124 [24,] 7738.70659 -19015.96265 [25,] -6162.62433 7738.70659 [26,] -10546.76617 -6162.62433 [27,] -15978.40724 -10546.76617 [28,] 7690.32093 -15978.40724 [29,] -17005.63530 7690.32093 [30,] -29245.60911 -17005.63530 [31,] 11360.69109 -29245.60911 [32,] -20454.23703 11360.69109 [33,] 14690.22825 -20454.23703 [34,] -21058.67359 14690.22825 [35,] 39952.84987 -21058.67359 [36,] 26290.03500 39952.84987 [37,] -7651.21685 26290.03500 [38,] -17232.52091 -7651.21685 [39,] 17075.00664 -17232.52091 [40,] 39154.18809 17075.00664 [41,] 17227.98173 39154.18809 [42,] -39456.64062 17227.98173 [43,] -2196.60612 -39456.64062 [44,] -18801.85883 -2196.60612 [45,] 161998.57938 -18801.85883 [46,] -36304.37223 161998.57938 [47,] 12749.85247 -36304.37223 [48,] -1954.61022 12749.85247 [49,] -7740.60968 -1954.61022 [50,] -23472.80085 -7740.60968 [51,] -26521.12794 -23472.80085 [52,] 55626.34444 -26521.12794 [53,] -364.63538 55626.34444 [54,] 305.49943 -364.63538 [55,] 8938.13168 305.49943 [56,] -13665.60327 8938.13168 [57,] -35834.29311 -13665.60327 [58,] -25571.57228 -35834.29311 [59,] 37072.86104 -25571.57228 [60,] -11535.41696 37072.86104 [61,] -47027.92744 -11535.41696 [62,] 33809.31769 -47027.92744 [63,] 32532.36491 33809.31769 [64,] -38703.85487 32532.36491 [65,] -41429.74605 -38703.85487 [66,] -8018.81870 -41429.74605 [67,] -9420.59950 -8018.81870 [68,] 13789.69796 -9420.59950 [69,] -35535.71301 13789.69796 [70,] -5733.05533 -35535.71301 [71,] 5702.90420 -5733.05533 [72,] -13824.01770 5702.90420 [73,] 21779.58031 -13824.01770 [74,] -10960.76909 21779.58031 [75,] 39192.76275 -10960.76909 [76,] -9620.23026 39192.76275 [77,] -17509.82982 -9620.23026 [78,] -15225.59208 -17509.82982 [79,] 10755.82644 -15225.59208 [80,] -7483.33425 10755.82644 [81,] 95612.08143 -7483.33425 [82,] 3828.12820 95612.08143 [83,] -7056.93095 3828.12820 [84,] -31412.71422 -7056.93095 [85,] 43116.17194 -31412.71422 [86,] -12249.16988 43116.17194 [87,] 69767.18023 -12249.16988 [88,] -9322.66596 69767.18023 [89,] 1454.22131 -9322.66596 [90,] 8472.91941 1454.22131 [91,] -1787.38223 8472.91941 [92,] 7941.78823 -1787.38223 [93,] 35598.28150 7941.78823 [94,] 31729.10954 35598.28150 [95,] -12587.29756 31729.10954 [96,] -59240.79833 -12587.29756 [97,] -38308.96649 -59240.79833 [98,] 5706.79843 -38308.96649 [99,] -17951.59253 5706.79843 [100,] 8923.66238 -17951.59253 [101,] -39324.61294 8923.66238 [102,] 8771.89832 -39324.61294 [103,] -13125.69231 8771.89832 [104,] -3571.82759 -13125.69231 [105,] -31304.20133 -3571.82759 [106,] -13080.98786 -31304.20133 [107,] 13559.51637 -13080.98786 [108,] -80094.22838 13559.51637 [109,] 25861.38671 -80094.22838 [110,] -18882.99075 25861.38671 [111,] 3163.18136 -18882.99075 [112,] -13581.28923 3163.18136 [113,] -6271.75478 -13581.28923 [114,] -3040.58983 -6271.75478 [115,] 16269.21990 -3040.58983 [116,] -5287.92776 16269.21990 [117,] -16696.34445 -5287.92776 [118,] 3743.40938 -16696.34445 [119,] -16026.16351 3743.40938 [120,] -10458.09316 -16026.16351 [121,] 23870.27101 -10458.09316 [122,] 26567.97497 23870.27101 [123,] -13182.64573 26567.97497 [124,] -10723.12971 -13182.64573 [125,] -15704.77908 -10723.12971 [126,] 14257.40432 -15704.77908 [127,] -24049.52380 14257.40432 [128,] 19425.18080 -24049.52380 [129,] -25742.34387 19425.18080 [130,] -13094.26730 -25742.34387 [131,] -5558.00231 -13094.26730 [132,] -26324.81184 -5558.00231 [133,] 65113.59306 -26324.81184 [134,] 57322.51248 65113.59306 [135,] 15384.45607 57322.51248 [136,] 76069.32111 15384.45607 [137,] 10170.73718 76069.32111 [138,] -26141.43369 10170.73718 [139,] 10582.66670 -26141.43369 [140,] 39227.36579 10582.66670 [141,] 71927.63382 39227.36579 [142,] -3588.34560 71927.63382 [143,] 130223.24447 -3588.34560 [144,] 29615.38172 130223.24447 [145,] -8576.62834 29615.38172 [146,] -30382.75457 -8576.62834 [147,] -8497.73224 -30382.75457 [148,] -27446.70328 -8497.73224 [149,] -24942.08991 -27446.70328 [150,] -27348.70328 -24942.08991 [151,] -26991.70328 -27348.70328 [152,] -27446.70328 -26991.70328 [153,] -27446.70328 -27446.70328 [154,] 12890.30905 -27446.70328 [155,] -9687.48439 12890.30905 [156,] -27446.70328 -9687.48439 [157,] -27243.70328 -27446.70328 [158,] -26810.84972 -27243.70328 [159,] -14123.86433 -26810.84972 [160,] -21721.62434 -14123.86433 [161,] 30400.16303 -21721.62434 [162,] -26477.70328 30400.16303 [163,] 15540.27418 -26477.70328 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -77024.54714 -31875.49563 2 -22255.76693 -77024.54714 3 6489.08346 -22255.76693 4 -23103.48897 6489.08346 5 -42743.65079 -23103.48897 6 48428.10654 -42743.65079 7 -4520.59453 48428.10654 8 69.84104 -4520.59453 9 275.36212 69.84104 10 -33420.15561 275.36212 11 12519.29194 -33420.15561 12 -10296.33245 12519.29194 13 46245.98713 -10296.33245 14 9314.56428 46245.98713 15 34805.69020 9314.56428 16 -13234.76071 34805.69020 17 55391.44633 -13234.76071 18 -34929.72688 55391.44633 19 13831.60521 -34929.72688 20 -11510.46468 13831.60521 21 96636.04979 -11510.46468 22 -11002.77124 96636.04979 23 -19015.96265 -11002.77124 24 7738.70659 -19015.96265 25 -6162.62433 7738.70659 26 -10546.76617 -6162.62433 27 -15978.40724 -10546.76617 28 7690.32093 -15978.40724 29 -17005.63530 7690.32093 30 -29245.60911 -17005.63530 31 11360.69109 -29245.60911 32 -20454.23703 11360.69109 33 14690.22825 -20454.23703 34 -21058.67359 14690.22825 35 39952.84987 -21058.67359 36 26290.03500 39952.84987 37 -7651.21685 26290.03500 38 -17232.52091 -7651.21685 39 17075.00664 -17232.52091 40 39154.18809 17075.00664 41 17227.98173 39154.18809 42 -39456.64062 17227.98173 43 -2196.60612 -39456.64062 44 -18801.85883 -2196.60612 45 161998.57938 -18801.85883 46 -36304.37223 161998.57938 47 12749.85247 -36304.37223 48 -1954.61022 12749.85247 49 -7740.60968 -1954.61022 50 -23472.80085 -7740.60968 51 -26521.12794 -23472.80085 52 55626.34444 -26521.12794 53 -364.63538 55626.34444 54 305.49943 -364.63538 55 8938.13168 305.49943 56 -13665.60327 8938.13168 57 -35834.29311 -13665.60327 58 -25571.57228 -35834.29311 59 37072.86104 -25571.57228 60 -11535.41696 37072.86104 61 -47027.92744 -11535.41696 62 33809.31769 -47027.92744 63 32532.36491 33809.31769 64 -38703.85487 32532.36491 65 -41429.74605 -38703.85487 66 -8018.81870 -41429.74605 67 -9420.59950 -8018.81870 68 13789.69796 -9420.59950 69 -35535.71301 13789.69796 70 -5733.05533 -35535.71301 71 5702.90420 -5733.05533 72 -13824.01770 5702.90420 73 21779.58031 -13824.01770 74 -10960.76909 21779.58031 75 39192.76275 -10960.76909 76 -9620.23026 39192.76275 77 -17509.82982 -9620.23026 78 -15225.59208 -17509.82982 79 10755.82644 -15225.59208 80 -7483.33425 10755.82644 81 95612.08143 -7483.33425 82 3828.12820 95612.08143 83 -7056.93095 3828.12820 84 -31412.71422 -7056.93095 85 43116.17194 -31412.71422 86 -12249.16988 43116.17194 87 69767.18023 -12249.16988 88 -9322.66596 69767.18023 89 1454.22131 -9322.66596 90 8472.91941 1454.22131 91 -1787.38223 8472.91941 92 7941.78823 -1787.38223 93 35598.28150 7941.78823 94 31729.10954 35598.28150 95 -12587.29756 31729.10954 96 -59240.79833 -12587.29756 97 -38308.96649 -59240.79833 98 5706.79843 -38308.96649 99 -17951.59253 5706.79843 100 8923.66238 -17951.59253 101 -39324.61294 8923.66238 102 8771.89832 -39324.61294 103 -13125.69231 8771.89832 104 -3571.82759 -13125.69231 105 -31304.20133 -3571.82759 106 -13080.98786 -31304.20133 107 13559.51637 -13080.98786 108 -80094.22838 13559.51637 109 25861.38671 -80094.22838 110 -18882.99075 25861.38671 111 3163.18136 -18882.99075 112 -13581.28923 3163.18136 113 -6271.75478 -13581.28923 114 -3040.58983 -6271.75478 115 16269.21990 -3040.58983 116 -5287.92776 16269.21990 117 -16696.34445 -5287.92776 118 3743.40938 -16696.34445 119 -16026.16351 3743.40938 120 -10458.09316 -16026.16351 121 23870.27101 -10458.09316 122 26567.97497 23870.27101 123 -13182.64573 26567.97497 124 -10723.12971 -13182.64573 125 -15704.77908 -10723.12971 126 14257.40432 -15704.77908 127 -24049.52380 14257.40432 128 19425.18080 -24049.52380 129 -25742.34387 19425.18080 130 -13094.26730 -25742.34387 131 -5558.00231 -13094.26730 132 -26324.81184 -5558.00231 133 65113.59306 -26324.81184 134 57322.51248 65113.59306 135 15384.45607 57322.51248 136 76069.32111 15384.45607 137 10170.73718 76069.32111 138 -26141.43369 10170.73718 139 10582.66670 -26141.43369 140 39227.36579 10582.66670 141 71927.63382 39227.36579 142 -3588.34560 71927.63382 143 130223.24447 -3588.34560 144 29615.38172 130223.24447 145 -8576.62834 29615.38172 146 -30382.75457 -8576.62834 147 -8497.73224 -30382.75457 148 -27446.70328 -8497.73224 149 -24942.08991 -27446.70328 150 -27348.70328 -24942.08991 151 -26991.70328 -27348.70328 152 -27446.70328 -26991.70328 153 -27446.70328 -27446.70328 154 12890.30905 -27446.70328 155 -9687.48439 12890.30905 156 -27446.70328 -9687.48439 157 -27243.70328 -27446.70328 158 -26810.84972 -27243.70328 159 -14123.86433 -26810.84972 160 -21721.62434 -14123.86433 161 30400.16303 -21721.62434 162 -26477.70328 30400.16303 163 15540.27418 -26477.70328 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/735p71321907957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8d4yz1321907957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9uaib1321907957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10dm0p1321907957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11yq9v1321907957.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12hzaj1321907957.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13i6db1321907957.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14acsd1321907957.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15hhkv1321907957.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/16mjuh1321907957.tab") + } > > try(system("convert tmp/1lpbj1321907957.ps tmp/1lpbj1321907957.png",intern=TRUE)) character(0) > try(system("convert tmp/2h34o1321907957.ps tmp/2h34o1321907957.png",intern=TRUE)) character(0) > try(system("convert tmp/3bbqk1321907957.ps tmp/3bbqk1321907957.png",intern=TRUE)) character(0) > try(system("convert tmp/4xrpe1321907957.ps tmp/4xrpe1321907957.png",intern=TRUE)) character(0) > try(system("convert tmp/51jwu1321907957.ps tmp/51jwu1321907957.png",intern=TRUE)) character(0) > try(system("convert tmp/6s3zh1321907957.ps tmp/6s3zh1321907957.png",intern=TRUE)) character(0) > try(system("convert tmp/735p71321907957.ps tmp/735p71321907957.png",intern=TRUE)) character(0) > try(system("convert tmp/8d4yz1321907957.ps tmp/8d4yz1321907957.png",intern=TRUE)) character(0) > try(system("convert tmp/9uaib1321907957.ps tmp/9uaib1321907957.png",intern=TRUE)) character(0) > try(system("convert tmp/10dm0p1321907957.ps tmp/10dm0p1321907957.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.925 0.544 5.534