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(476000 + ,113000 + ,363000 + ,475000 + ,110000 + ,364000 + ,470000 + ,107000 + ,363000 + ,461000 + ,103000 + ,358000 + ,455000 + ,98000 + ,357000 + ,456000 + ,98000 + ,357000 + ,517000 + ,137000 + ,380000 + ,525000 + ,148000 + ,378000 + ,523000 + ,147000 + ,376000 + ,519000 + ,139000 + ,380000 + ,509000 + ,130000 + ,379000 + ,512000 + ,128000 + ,384000 + ,519000 + ,127000 + ,392000 + ,517000 + ,123000 + ,394000 + ,510000 + ,118000 + ,392000 + ,509000 + ,114000 + ,396000 + ,501000 + ,108000 + ,392000 + ,507000 + ,111000 + ,396000 + ,569000 + ,151000 + ,419000 + ,580000 + ,159000 + ,421000 + ,578000 + ,158000 + ,420000 + ,565000 + ,148000 + ,418000 + ,547000 + ,138000 + ,410000 + ,555000 + ,137000 + ,418000 + ,562000 + ,136000 + ,426000 + ,561000 + ,133000 + ,428000 + ,555000 + ,126000 + ,430000 + ,544000 + ,120000 + ,424000 + ,537000 + ,114000 + ,423000 + ,543000 + ,116000 + ,427000 + ,594000 + ,153000 + ,441000 + ,611000 + ,162000 + ,449000 + ,613000 + ,161000 + ,452000 + ,611000 + ,149000 + ,462000 + ,594000 + ,139000 + ,455000 + ,595000 + ,135000 + ,461000 + ,591000 + ,130000 + ,461000 + ,589000 + ,127000 + ,463000 + ,584000 + ,122000 + ,462000 + ,573000 + ,117000 + ,456000 + ,567000 + ,112000 + ,455000 + ,569000 + ,113000 + ,456000 + ,621000 + ,149000 + ,472000 + ,629000 + ,157000 + ,472000 + ,628000 + ,157000 + ,471000 + ,612000 + ,147000 + ,465000 + ,595000 + ,137000 + ,459000 + ,597000 + ,132000 + ,465000 + ,593000 + ,125000 + ,468000 + ,590000 + ,123000 + ,467000 + ,580000 + ,117000 + ,463000 + ,574000 + ,114000 + ,460000 + ,573000 + ,111000 + ,462000 + ,573000 + ,112000 + ,461000 + ,620000 + ,144000 + ,476000 + ,626000 + ,150000 + ,476000 + ,620000 + ,149000 + ,471000 + ,588000 + ,134000 + ,453000 + ,566000 + ,123000 + ,443000 + ,557000 + ,116000 + ,442000 + ,561000 + ,117000 + ,444000 + ,549000 + ,111000 + ,438000 + ,532000 + ,105000 + ,427000 + ,526000 + ,102000 + ,424000 + ,511000 + ,95000 + ,416000 + ,499000 + ,93000 + ,406000 + ,555000 + ,124000 + ,431000 + ,565000 + ,130000 + ,434000 + ,542000 + ,124000 + ,418000 + ,527000 + ,115000 + ,412000 + ,510000 + ,106000 + ,404000 + ,514000 + ,105000 + ,409000 + ,517000 + ,105000 + ,412000 + ,508000 + ,101000 + ,406000 + ,493000 + ,95000 + ,398000 + ,490000 + ,93000 + ,397000 + ,469000 + ,84000 + ,385000 + ,478000 + ,87000 + ,390000 + ,528000 + ,116000 + ,413000 + ,534000 + ,120000 + ,413000 + ,518000 + ,117000 + ,401000 + ,506000 + ,109000 + ,397000 + ,502000 + ,105000 + ,397000 + ,516000 + ,107000 + ,409000 + ,528000 + ,109000 + ,419000 + ,533000 + ,109000 + ,424000 + ,536000 + ,108000 + ,428000 + ,537000 + ,107000 + ,430000 + ,524000 + ,99000 + ,424000 + ,536000 + ,103000 + ,433000 + ,587000 + ,131000 + ,456000 + ,597000 + ,137000 + ,459000 + ,581000 + ,135000 + ,446000 + ,564000 + ,124000 + ,441000 + ,558000 + ,118000 + ,439000 + ,575000 + ,121000 + ,454000 + ,580000 + ,121000 + ,460000 + ,575000 + ,118000 + ,457000 + ,563000 + ,113000 + ,451000 + ,552000 + ,107000 + ,444000 + ,537000 + ,100000 + ,437000 + ,545000 + ,102000 + ,443000 + ,601000 + ,130000 + ,471000 + ,604000 + ,136000 + ,469000 + ,586000 + ,133000 + ,454000 + ,564000 + ,120000 + ,444000 + ,549000 + ,112000 + ,436000 + ,551000 + ,109000 + ,442000 + ,556000 + ,110000 + ,446000 + ,548000 + ,106000 + ,442000 + ,540000 + ,102000 + ,438000 + ,531000 + ,98000 + ,433000 + ,521000 + ,92000 + ,428000 + ,519000 + ,92000 + ,426000 + ,572000 + ,120000 + ,452000 + ,581000 + ,127000 + ,455000 + ,563000 + ,124000 + ,439000 + ,548000 + ,114000 + ,434000 + ,539000 + ,108000 + ,431000 + ,541000 + ,106000 + ,435000 + ,562000 + ,111000 + ,450000 + ,559000 + ,110000 + ,449000 + ,546000 + ,104000 + ,442000 + ,536000 + ,100000 + ,437000 + ,528000 + ,96000 + ,431000 + ,530000 + ,98000 + ,433000 + ,582000 + ,122000 + ,460000 + ,599000 + ,134000 + ,465000 + ,584000 + ,133000 + ,451000 + ,571000 + ,125000 + ,447000) + ,dim=c(3 + ,130) + ,dimnames=list(c('Totaal' + ,'jongerdan25jaar' + ,'vanaf25jaar') + ,1:130)) > y <- array(NA,dim=c(3,130),dimnames=list(c('Totaal','jongerdan25jaar','vanaf25jaar'),1:130)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Totaal jongerdan25jaar vanaf25jaar 1 476000 113000 363000 2 475000 110000 364000 3 470000 107000 363000 4 461000 103000 358000 5 455000 98000 357000 6 456000 98000 357000 7 517000 137000 380000 8 525000 148000 378000 9 523000 147000 376000 10 519000 139000 380000 11 509000 130000 379000 12 512000 128000 384000 13 519000 127000 392000 14 517000 123000 394000 15 510000 118000 392000 16 509000 114000 396000 17 501000 108000 392000 18 507000 111000 396000 19 569000 151000 419000 20 580000 159000 421000 21 578000 158000 420000 22 565000 148000 418000 23 547000 138000 410000 24 555000 137000 418000 25 562000 136000 426000 26 561000 133000 428000 27 555000 126000 430000 28 544000 120000 424000 29 537000 114000 423000 30 543000 116000 427000 31 594000 153000 441000 32 611000 162000 449000 33 613000 161000 452000 34 611000 149000 462000 35 594000 139000 455000 36 595000 135000 461000 37 591000 130000 461000 38 589000 127000 463000 39 584000 122000 462000 40 573000 117000 456000 41 567000 112000 455000 42 569000 113000 456000 43 621000 149000 472000 44 629000 157000 472000 45 628000 157000 471000 46 612000 147000 465000 47 595000 137000 459000 48 597000 132000 465000 49 593000 125000 468000 50 590000 123000 467000 51 580000 117000 463000 52 574000 114000 460000 53 573000 111000 462000 54 573000 112000 461000 55 620000 144000 476000 56 626000 150000 476000 57 620000 149000 471000 58 588000 134000 453000 59 566000 123000 443000 60 557000 116000 442000 61 561000 117000 444000 62 549000 111000 438000 63 532000 105000 427000 64 526000 102000 424000 65 511000 95000 416000 66 499000 93000 406000 67 555000 124000 431000 68 565000 130000 434000 69 542000 124000 418000 70 527000 115000 412000 71 510000 106000 404000 72 514000 105000 409000 73 517000 105000 412000 74 508000 101000 406000 75 493000 95000 398000 76 490000 93000 397000 77 469000 84000 385000 78 478000 87000 390000 79 528000 116000 413000 80 534000 120000 413000 81 518000 117000 401000 82 506000 109000 397000 83 502000 105000 397000 84 516000 107000 409000 85 528000 109000 419000 86 533000 109000 424000 87 536000 108000 428000 88 537000 107000 430000 89 524000 99000 424000 90 536000 103000 433000 91 587000 131000 456000 92 597000 137000 459000 93 581000 135000 446000 94 564000 124000 441000 95 558000 118000 439000 96 575000 121000 454000 97 580000 121000 460000 98 575000 118000 457000 99 563000 113000 451000 100 552000 107000 444000 101 537000 100000 437000 102 545000 102000 443000 103 601000 130000 471000 104 604000 136000 469000 105 586000 133000 454000 106 564000 120000 444000 107 549000 112000 436000 108 551000 109000 442000 109 556000 110000 446000 110 548000 106000 442000 111 540000 102000 438000 112 531000 98000 433000 113 521000 92000 428000 114 519000 92000 426000 115 572000 120000 452000 116 581000 127000 455000 117 563000 124000 439000 118 548000 114000 434000 119 539000 108000 431000 120 541000 106000 435000 121 562000 111000 450000 122 559000 110000 449000 123 546000 104000 442000 124 536000 100000 437000 125 528000 96000 431000 126 530000 98000 433000 127 582000 122000 460000 128 599000 134000 465000 129 584000 133000 451000 130 571000 125000 447000 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) jongerdan25jaar vanaf25jaar 1346.8859 0.9927 0.9989 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -1135.41 -111.29 1.63 155.64 1179.38 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.347e+03 6.733e+02 2.0 0.0476 * jongerdan25jaar 9.927e-01 2.759e-03 359.8 <2e-16 *** vanaf25jaar 9.989e-01 1.650e-03 605.3 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 519.3 on 127 degrees of freedom Multiple R-squared: 0.9998, Adjusted R-squared: 0.9998 F-statistic: 3.673e+05 on 2 and 127 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,] 0.757665802 0.484668396 0.2423342 [2,] 0.637551052 0.724897897 0.3624489 [3,] 0.507123017 0.985753965 0.4928770 [4,] 0.634686213 0.730627574 0.3653138 [5,] 0.519935186 0.960129628 0.4800648 [6,] 0.422250653 0.844501307 0.5777493 [7,] 0.340335126 0.680670251 0.6596649 [8,] 0.262137621 0.524275242 0.7378624 [9,] 0.193767469 0.387534939 0.8062325 [10,] 0.139788523 0.279577045 0.8602115 [11,] 0.336651304 0.673302608 0.6633487 [12,] 0.540274729 0.919450542 0.4597253 [13,] 0.461823176 0.923646353 0.5381768 [14,] 0.431600168 0.863200335 0.5683998 [15,] 0.488942355 0.977884710 0.5110576 [16,] 0.479501682 0.959003364 0.5204983 [17,] 0.515810237 0.968379525 0.4841898 [18,] 0.578966874 0.842066252 0.4210331 [19,] 0.541583628 0.916832743 0.4584164 [20,] 0.499092992 0.998185984 0.5009070 [21,] 0.445960508 0.891921015 0.5540395 [22,] 0.540210609 0.919578783 0.4597894 [23,] 0.484344422 0.968688844 0.5156556 [24,] 0.423661613 0.847323225 0.5763384 [25,] 0.366090053 0.732180105 0.6339099 [26,] 0.364471467 0.728942933 0.6355285 [27,] 0.367391460 0.734782920 0.6326085 [28,] 0.352460770 0.704921539 0.6475392 [29,] 0.317231385 0.634462770 0.6827686 [30,] 0.272391466 0.544782931 0.7276085 [31,] 0.346772604 0.693545207 0.6532274 [32,] 0.302006686 0.604013371 0.6979933 [33,] 0.374064212 0.748128424 0.6259358 [34,] 0.330193125 0.660386249 0.6698069 [35,] 0.284101930 0.568203861 0.7158981 [36,] 0.239618101 0.479236203 0.7603819 [37,] 0.199373210 0.398746419 0.8006268 [38,] 0.179867218 0.359734436 0.8201328 [39,] 0.163909741 0.327819483 0.8360903 [40,] 0.146462642 0.292925284 0.8535374 [41,] 0.124558191 0.249116381 0.8754418 [42,] 0.170506945 0.341013890 0.8294931 [43,] 0.142426687 0.284853375 0.8575733 [44,] 0.116278112 0.232556224 0.8837219 [45,] 0.093327140 0.186654280 0.9066729 [46,] 0.073293409 0.146586818 0.9267066 [47,] 0.056591570 0.113183140 0.9434084 [48,] 0.043025554 0.086051107 0.9569744 [49,] 0.032252335 0.064504671 0.9677477 [50,] 0.025733657 0.051467314 0.9742663 [51,] 0.020819707 0.041639414 0.9791803 [52,] 0.016554368 0.033108737 0.9834456 [53,] 0.055407536 0.110815073 0.9445925 [54,] 0.042500637 0.085001273 0.9574994 [55,] 0.083554404 0.167108809 0.9164456 [56,] 0.065517116 0.131034231 0.9344829 [57,] 0.050619604 0.101239208 0.9493804 [58,] 0.038787754 0.077575508 0.9612122 [59,] 0.029468122 0.058936244 0.9705319 [60,] 0.022518694 0.045037388 0.9774813 [61,] 0.017286355 0.034572709 0.9827136 [62,] 0.012558824 0.025117649 0.9874412 [63,] 0.039002735 0.078005470 0.9609973 [64,] 0.029520277 0.059040554 0.9704797 [65,] 0.021906499 0.043812998 0.9780935 [66,] 0.016242150 0.032484300 0.9837579 [67,] 0.011903788 0.023807576 0.9880962 [68,] 0.008606127 0.017212253 0.9913939 [69,] 0.015343011 0.030686021 0.9846570 [70,] 0.011707589 0.023415178 0.9882924 [71,] 0.009025055 0.018050111 0.9909749 [72,] 0.007763851 0.015527703 0.9922361 [73,] 0.009921763 0.019843525 0.9900782 [74,] 0.025622230 0.051244461 0.9743778 [75,] 0.051941765 0.103883530 0.9480582 [76,] 0.039529305 0.079058611 0.9604707 [77,] 0.030191473 0.060382946 0.9698085 [78,] 0.023608180 0.047216359 0.9763918 [79,] 0.018268445 0.036536890 0.9817316 [80,] 0.013787837 0.027575675 0.9862122 [81,] 0.010242922 0.020485843 0.9897571 [82,] 0.007478518 0.014957035 0.9925215 [83,] 0.005386963 0.010773927 0.9946130 [84,] 0.007824156 0.015648311 0.9921758 [85,] 0.005551929 0.011103859 0.9944481 [86,] 0.003957932 0.007915863 0.9960421 [87,] 0.022825440 0.045650881 0.9771746 [88,] 0.018085423 0.036170845 0.9819146 [89,] 0.032833256 0.065666512 0.9671667 [90,] 0.069989864 0.139979728 0.9300101 [91,] 0.055151951 0.110303902 0.9448480 [92,] 0.079024787 0.158049574 0.9209752 [93,] 0.061046598 0.122093196 0.9389534 [94,] 0.110958165 0.221916329 0.8890418 [95,] 0.174495316 0.348990631 0.8255047 [96,] 0.142542385 0.285084769 0.8574576 [97,] 0.113376484 0.226752968 0.8866235 [98,] 0.100733091 0.201466183 0.8992669 [99,] 0.102091077 0.204182155 0.8979089 [100,] 0.120033193 0.240066386 0.8799668 [101,] 0.091055843 0.182111686 0.9089442 [102,] 0.167096961 0.334193922 0.8329030 [103,] 0.128053422 0.256106844 0.8719466 [104,] 0.095405415 0.190810829 0.9045946 [105,] 0.069518162 0.139036325 0.9304818 [106,] 0.050026620 0.100053240 0.9499734 [107,] 0.036083284 0.072166569 0.9639167 [108,] 0.044561118 0.089122235 0.9554389 [109,] 0.072547225 0.145094450 0.9274528 [110,] 0.049556081 0.099112163 0.9504439 [111,] 0.082167720 0.164335441 0.9178323 [112,] 0.058467200 0.116934401 0.9415328 [113,] 0.041091096 0.082182191 0.9589089 [114,] 0.029798323 0.059596646 0.9702017 [115,] 0.019377611 0.038755222 0.9806224 [116,] 0.044501932 0.089003865 0.9554981 [117,] 0.024156118 0.048312237 0.9758439 [118,] 0.012303106 0.024606212 0.9876969 [119,] 0.019977812 0.039955623 0.9800222 > postscript(file="/var/wessaorg/rcomp/tmp/1u4lw1353431758.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2mb3w1353431758.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3bj5f1353431758.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/44zer1353431758.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5ydsf1353431758.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 = 130 Frequency = 1 1 2 3 4 5 -105.8241095 873.3922938 -149.6785558 -184.6329856 -222.3219839 6 7 8 9 10 777.6780161 89.0353253 -832.8521093 157.5515634 103.6534741 11 12 13 14 15 36.7281782 27.8281616 29.6680988 2.7190541 -36.1135707 16 17 18 19 20 -1060.7753624 890.7956855 -82.7025856 -764.0362019 296.7236460 21 22 23 24 25 288.2709452 -787.1070516 -869.3468070 132.4931302 134.3330674 26 27 28 29 30 114.6930971 -934.1831707 15.1006243 -29.8974485 -10.7047939 31 32 33 34 35 275.7417286 350.6724096 346.7942146 270.5215864 189.4254574 36 37 38 39 40 -832.9490815 130.5055466 -889.1344237 73.1765779 29.7694473 41 42 43 44 45 -7.9195510 0.5331498 281.9578508 340.4304459 339.2868195 46 47 48 49 50 259.3343169 -820.6181856 149.6982011 101.9655598 86.2037845 51 52 53 54 55 37.7748325 12.4167300 -7.2232403 -1.0577924 249.9869847 56 57 58 59 60 293.8414310 280.8142244 1150.5928326 58.7567499 -993.5503973 61 62 63 64 65 16.0459300 -34.6702750 -91.1046122 -116.4627147 -176.7752469 66 67 68 69 70 -202.8296601 52.3423070 1099.6276326 37.4751632 -35.1682650 71 72 73 74 75 -110.0989460 -111.6898882 -108.2590088 855.6429350 -197.3605229 76 77 78 79 80 -213.1222981 -292.6274849 735.0178705 -1026.7155641 1002.5207334 81 82 83 84 85 -33.1300071 -96.1771079 -125.4134055 -97.0717394 -71.0173262 86 87 88 89 90 -65.2991939 -68.0337626 -73.0555841 861.6100622 -98.8610023 91 92 93 94 95 132.0964888 1179.3818144 149.8965219 -936.2214286 1017.6368722 96 97 98 99 100 56.7184920 -936.4197493 38.2221482 -1005.1849824 942.9551862 101 102 103 104 105 -116.2137197 -94.7338122 141.9418110 -816.4909955 -855.5726154 106 107 108 109 110 37.9731532 970.3515465 -44.7139180 -32.8303378 -66.6411411 111 112 113 114 115 -100.4519444 -135.4063742 815.0210473 812.7337944 47.1221647 116 117 118 119 120 -898.2834352 61.4913185 -17.3175576 -64.6028832 -74.6465262 121 122 123 124 125 979.0532424 -29.3994585 -81.2592899 -1116.2137197 847.6882241 126 127 128 129 130 -1135.4063742 70.8893251 164.3163499 140.9965053 -922.0505956 > postscript(file="/var/wessaorg/rcomp/tmp/6glhv1353431758.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 = 130 Frequency = 1 lag(myerror, k = 1) myerror 0 -105.8241095 NA 1 873.3922938 -105.8241095 2 -149.6785558 873.3922938 3 -184.6329856 -149.6785558 4 -222.3219839 -184.6329856 5 777.6780161 -222.3219839 6 89.0353253 777.6780161 7 -832.8521093 89.0353253 8 157.5515634 -832.8521093 9 103.6534741 157.5515634 10 36.7281782 103.6534741 11 27.8281616 36.7281782 12 29.6680988 27.8281616 13 2.7190541 29.6680988 14 -36.1135707 2.7190541 15 -1060.7753624 -36.1135707 16 890.7956855 -1060.7753624 17 -82.7025856 890.7956855 18 -764.0362019 -82.7025856 19 296.7236460 -764.0362019 20 288.2709452 296.7236460 21 -787.1070516 288.2709452 22 -869.3468070 -787.1070516 23 132.4931302 -869.3468070 24 134.3330674 132.4931302 25 114.6930971 134.3330674 26 -934.1831707 114.6930971 27 15.1006243 -934.1831707 28 -29.8974485 15.1006243 29 -10.7047939 -29.8974485 30 275.7417286 -10.7047939 31 350.6724096 275.7417286 32 346.7942146 350.6724096 33 270.5215864 346.7942146 34 189.4254574 270.5215864 35 -832.9490815 189.4254574 36 130.5055466 -832.9490815 37 -889.1344237 130.5055466 38 73.1765779 -889.1344237 39 29.7694473 73.1765779 40 -7.9195510 29.7694473 41 0.5331498 -7.9195510 42 281.9578508 0.5331498 43 340.4304459 281.9578508 44 339.2868195 340.4304459 45 259.3343169 339.2868195 46 -820.6181856 259.3343169 47 149.6982011 -820.6181856 48 101.9655598 149.6982011 49 86.2037845 101.9655598 50 37.7748325 86.2037845 51 12.4167300 37.7748325 52 -7.2232403 12.4167300 53 -1.0577924 -7.2232403 54 249.9869847 -1.0577924 55 293.8414310 249.9869847 56 280.8142244 293.8414310 57 1150.5928326 280.8142244 58 58.7567499 1150.5928326 59 -993.5503973 58.7567499 60 16.0459300 -993.5503973 61 -34.6702750 16.0459300 62 -91.1046122 -34.6702750 63 -116.4627147 -91.1046122 64 -176.7752469 -116.4627147 65 -202.8296601 -176.7752469 66 52.3423070 -202.8296601 67 1099.6276326 52.3423070 68 37.4751632 1099.6276326 69 -35.1682650 37.4751632 70 -110.0989460 -35.1682650 71 -111.6898882 -110.0989460 72 -108.2590088 -111.6898882 73 855.6429350 -108.2590088 74 -197.3605229 855.6429350 75 -213.1222981 -197.3605229 76 -292.6274849 -213.1222981 77 735.0178705 -292.6274849 78 -1026.7155641 735.0178705 79 1002.5207334 -1026.7155641 80 -33.1300071 1002.5207334 81 -96.1771079 -33.1300071 82 -125.4134055 -96.1771079 83 -97.0717394 -125.4134055 84 -71.0173262 -97.0717394 85 -65.2991939 -71.0173262 86 -68.0337626 -65.2991939 87 -73.0555841 -68.0337626 88 861.6100622 -73.0555841 89 -98.8610023 861.6100622 90 132.0964888 -98.8610023 91 1179.3818144 132.0964888 92 149.8965219 1179.3818144 93 -936.2214286 149.8965219 94 1017.6368722 -936.2214286 95 56.7184920 1017.6368722 96 -936.4197493 56.7184920 97 38.2221482 -936.4197493 98 -1005.1849824 38.2221482 99 942.9551862 -1005.1849824 100 -116.2137197 942.9551862 101 -94.7338122 -116.2137197 102 141.9418110 -94.7338122 103 -816.4909955 141.9418110 104 -855.5726154 -816.4909955 105 37.9731532 -855.5726154 106 970.3515465 37.9731532 107 -44.7139180 970.3515465 108 -32.8303378 -44.7139180 109 -66.6411411 -32.8303378 110 -100.4519444 -66.6411411 111 -135.4063742 -100.4519444 112 815.0210473 -135.4063742 113 812.7337944 815.0210473 114 47.1221647 812.7337944 115 -898.2834352 47.1221647 116 61.4913185 -898.2834352 117 -17.3175576 61.4913185 118 -64.6028832 -17.3175576 119 -74.6465262 -64.6028832 120 979.0532424 -74.6465262 121 -29.3994585 979.0532424 122 -81.2592899 -29.3994585 123 -1116.2137197 -81.2592899 124 847.6882241 -1116.2137197 125 -1135.4063742 847.6882241 126 70.8893251 -1135.4063742 127 164.3163499 70.8893251 128 140.9965053 164.3163499 129 -922.0505956 140.9965053 130 NA -922.0505956 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 873.3922938 -105.8241095 [2,] -149.6785558 873.3922938 [3,] -184.6329856 -149.6785558 [4,] -222.3219839 -184.6329856 [5,] 777.6780161 -222.3219839 [6,] 89.0353253 777.6780161 [7,] -832.8521093 89.0353253 [8,] 157.5515634 -832.8521093 [9,] 103.6534741 157.5515634 [10,] 36.7281782 103.6534741 [11,] 27.8281616 36.7281782 [12,] 29.6680988 27.8281616 [13,] 2.7190541 29.6680988 [14,] -36.1135707 2.7190541 [15,] -1060.7753624 -36.1135707 [16,] 890.7956855 -1060.7753624 [17,] -82.7025856 890.7956855 [18,] -764.0362019 -82.7025856 [19,] 296.7236460 -764.0362019 [20,] 288.2709452 296.7236460 [21,] -787.1070516 288.2709452 [22,] -869.3468070 -787.1070516 [23,] 132.4931302 -869.3468070 [24,] 134.3330674 132.4931302 [25,] 114.6930971 134.3330674 [26,] -934.1831707 114.6930971 [27,] 15.1006243 -934.1831707 [28,] -29.8974485 15.1006243 [29,] -10.7047939 -29.8974485 [30,] 275.7417286 -10.7047939 [31,] 350.6724096 275.7417286 [32,] 346.7942146 350.6724096 [33,] 270.5215864 346.7942146 [34,] 189.4254574 270.5215864 [35,] -832.9490815 189.4254574 [36,] 130.5055466 -832.9490815 [37,] -889.1344237 130.5055466 [38,] 73.1765779 -889.1344237 [39,] 29.7694473 73.1765779 [40,] -7.9195510 29.7694473 [41,] 0.5331498 -7.9195510 [42,] 281.9578508 0.5331498 [43,] 340.4304459 281.9578508 [44,] 339.2868195 340.4304459 [45,] 259.3343169 339.2868195 [46,] -820.6181856 259.3343169 [47,] 149.6982011 -820.6181856 [48,] 101.9655598 149.6982011 [49,] 86.2037845 101.9655598 [50,] 37.7748325 86.2037845 [51,] 12.4167300 37.7748325 [52,] -7.2232403 12.4167300 [53,] -1.0577924 -7.2232403 [54,] 249.9869847 -1.0577924 [55,] 293.8414310 249.9869847 [56,] 280.8142244 293.8414310 [57,] 1150.5928326 280.8142244 [58,] 58.7567499 1150.5928326 [59,] -993.5503973 58.7567499 [60,] 16.0459300 -993.5503973 [61,] -34.6702750 16.0459300 [62,] -91.1046122 -34.6702750 [63,] -116.4627147 -91.1046122 [64,] -176.7752469 -116.4627147 [65,] -202.8296601 -176.7752469 [66,] 52.3423070 -202.8296601 [67,] 1099.6276326 52.3423070 [68,] 37.4751632 1099.6276326 [69,] -35.1682650 37.4751632 [70,] -110.0989460 -35.1682650 [71,] -111.6898882 -110.0989460 [72,] -108.2590088 -111.6898882 [73,] 855.6429350 -108.2590088 [74,] -197.3605229 855.6429350 [75,] -213.1222981 -197.3605229 [76,] -292.6274849 -213.1222981 [77,] 735.0178705 -292.6274849 [78,] -1026.7155641 735.0178705 [79,] 1002.5207334 -1026.7155641 [80,] -33.1300071 1002.5207334 [81,] -96.1771079 -33.1300071 [82,] -125.4134055 -96.1771079 [83,] -97.0717394 -125.4134055 [84,] -71.0173262 -97.0717394 [85,] -65.2991939 -71.0173262 [86,] -68.0337626 -65.2991939 [87,] -73.0555841 -68.0337626 [88,] 861.6100622 -73.0555841 [89,] -98.8610023 861.6100622 [90,] 132.0964888 -98.8610023 [91,] 1179.3818144 132.0964888 [92,] 149.8965219 1179.3818144 [93,] -936.2214286 149.8965219 [94,] 1017.6368722 -936.2214286 [95,] 56.7184920 1017.6368722 [96,] -936.4197493 56.7184920 [97,] 38.2221482 -936.4197493 [98,] -1005.1849824 38.2221482 [99,] 942.9551862 -1005.1849824 [100,] -116.2137197 942.9551862 [101,] -94.7338122 -116.2137197 [102,] 141.9418110 -94.7338122 [103,] -816.4909955 141.9418110 [104,] -855.5726154 -816.4909955 [105,] 37.9731532 -855.5726154 [106,] 970.3515465 37.9731532 [107,] -44.7139180 970.3515465 [108,] -32.8303378 -44.7139180 [109,] -66.6411411 -32.8303378 [110,] -100.4519444 -66.6411411 [111,] -135.4063742 -100.4519444 [112,] 815.0210473 -135.4063742 [113,] 812.7337944 815.0210473 [114,] 47.1221647 812.7337944 [115,] -898.2834352 47.1221647 [116,] 61.4913185 -898.2834352 [117,] -17.3175576 61.4913185 [118,] -64.6028832 -17.3175576 [119,] -74.6465262 -64.6028832 [120,] 979.0532424 -74.6465262 [121,] -29.3994585 979.0532424 [122,] -81.2592899 -29.3994585 [123,] -1116.2137197 -81.2592899 [124,] 847.6882241 -1116.2137197 [125,] -1135.4063742 847.6882241 [126,] 70.8893251 -1135.4063742 [127,] 164.3163499 70.8893251 [128,] 140.9965053 164.3163499 [129,] -922.0505956 140.9965053 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 873.3922938 -105.8241095 2 -149.6785558 873.3922938 3 -184.6329856 -149.6785558 4 -222.3219839 -184.6329856 5 777.6780161 -222.3219839 6 89.0353253 777.6780161 7 -832.8521093 89.0353253 8 157.5515634 -832.8521093 9 103.6534741 157.5515634 10 36.7281782 103.6534741 11 27.8281616 36.7281782 12 29.6680988 27.8281616 13 2.7190541 29.6680988 14 -36.1135707 2.7190541 15 -1060.7753624 -36.1135707 16 890.7956855 -1060.7753624 17 -82.7025856 890.7956855 18 -764.0362019 -82.7025856 19 296.7236460 -764.0362019 20 288.2709452 296.7236460 21 -787.1070516 288.2709452 22 -869.3468070 -787.1070516 23 132.4931302 -869.3468070 24 134.3330674 132.4931302 25 114.6930971 134.3330674 26 -934.1831707 114.6930971 27 15.1006243 -934.1831707 28 -29.8974485 15.1006243 29 -10.7047939 -29.8974485 30 275.7417286 -10.7047939 31 350.6724096 275.7417286 32 346.7942146 350.6724096 33 270.5215864 346.7942146 34 189.4254574 270.5215864 35 -832.9490815 189.4254574 36 130.5055466 -832.9490815 37 -889.1344237 130.5055466 38 73.1765779 -889.1344237 39 29.7694473 73.1765779 40 -7.9195510 29.7694473 41 0.5331498 -7.9195510 42 281.9578508 0.5331498 43 340.4304459 281.9578508 44 339.2868195 340.4304459 45 259.3343169 339.2868195 46 -820.6181856 259.3343169 47 149.6982011 -820.6181856 48 101.9655598 149.6982011 49 86.2037845 101.9655598 50 37.7748325 86.2037845 51 12.4167300 37.7748325 52 -7.2232403 12.4167300 53 -1.0577924 -7.2232403 54 249.9869847 -1.0577924 55 293.8414310 249.9869847 56 280.8142244 293.8414310 57 1150.5928326 280.8142244 58 58.7567499 1150.5928326 59 -993.5503973 58.7567499 60 16.0459300 -993.5503973 61 -34.6702750 16.0459300 62 -91.1046122 -34.6702750 63 -116.4627147 -91.1046122 64 -176.7752469 -116.4627147 65 -202.8296601 -176.7752469 66 52.3423070 -202.8296601 67 1099.6276326 52.3423070 68 37.4751632 1099.6276326 69 -35.1682650 37.4751632 70 -110.0989460 -35.1682650 71 -111.6898882 -110.0989460 72 -108.2590088 -111.6898882 73 855.6429350 -108.2590088 74 -197.3605229 855.6429350 75 -213.1222981 -197.3605229 76 -292.6274849 -213.1222981 77 735.0178705 -292.6274849 78 -1026.7155641 735.0178705 79 1002.5207334 -1026.7155641 80 -33.1300071 1002.5207334 81 -96.1771079 -33.1300071 82 -125.4134055 -96.1771079 83 -97.0717394 -125.4134055 84 -71.0173262 -97.0717394 85 -65.2991939 -71.0173262 86 -68.0337626 -65.2991939 87 -73.0555841 -68.0337626 88 861.6100622 -73.0555841 89 -98.8610023 861.6100622 90 132.0964888 -98.8610023 91 1179.3818144 132.0964888 92 149.8965219 1179.3818144 93 -936.2214286 149.8965219 94 1017.6368722 -936.2214286 95 56.7184920 1017.6368722 96 -936.4197493 56.7184920 97 38.2221482 -936.4197493 98 -1005.1849824 38.2221482 99 942.9551862 -1005.1849824 100 -116.2137197 942.9551862 101 -94.7338122 -116.2137197 102 141.9418110 -94.7338122 103 -816.4909955 141.9418110 104 -855.5726154 -816.4909955 105 37.9731532 -855.5726154 106 970.3515465 37.9731532 107 -44.7139180 970.3515465 108 -32.8303378 -44.7139180 109 -66.6411411 -32.8303378 110 -100.4519444 -66.6411411 111 -135.4063742 -100.4519444 112 815.0210473 -135.4063742 113 812.7337944 815.0210473 114 47.1221647 812.7337944 115 -898.2834352 47.1221647 116 61.4913185 -898.2834352 117 -17.3175576 61.4913185 118 -64.6028832 -17.3175576 119 -74.6465262 -64.6028832 120 979.0532424 -74.6465262 121 -29.3994585 979.0532424 122 -81.2592899 -29.3994585 123 -1116.2137197 -81.2592899 124 847.6882241 -1116.2137197 125 -1135.4063742 847.6882241 126 70.8893251 -1135.4063742 127 164.3163499 70.8893251 128 140.9965053 164.3163499 129 -922.0505956 140.9965053 > 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/70ehv1353431758.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8h51g1353431758.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/97rvu1353431758.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10myy41353431758.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/113n2h1353431758.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/123xop1353431758.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/13uvfb1353431758.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/14dq231353431758.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/15qzsb1353431758.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/1635lp1353431758.tab") + } > > try(system("convert tmp/1u4lw1353431758.ps tmp/1u4lw1353431758.png",intern=TRUE)) character(0) > try(system("convert tmp/2mb3w1353431758.ps tmp/2mb3w1353431758.png",intern=TRUE)) character(0) > try(system("convert tmp/3bj5f1353431758.ps tmp/3bj5f1353431758.png",intern=TRUE)) character(0) > try(system("convert tmp/44zer1353431758.ps tmp/44zer1353431758.png",intern=TRUE)) character(0) > try(system("convert tmp/5ydsf1353431758.ps tmp/5ydsf1353431758.png",intern=TRUE)) character(0) > try(system("convert tmp/6glhv1353431758.ps tmp/6glhv1353431758.png",intern=TRUE)) character(0) > try(system("convert tmp/70ehv1353431758.ps tmp/70ehv1353431758.png",intern=TRUE)) character(0) > try(system("convert tmp/8h51g1353431758.ps tmp/8h51g1353431758.png",intern=TRUE)) character(0) > try(system("convert tmp/97rvu1353431758.ps tmp/97rvu1353431758.png",intern=TRUE)) character(0) > try(system("convert tmp/10myy41353431758.ps tmp/10myy41353431758.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.038 1.826 13.371