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*The author of this computation has been verified* R Software Module: /rwasp_arimabackwardselection.wasp (opens new window with default values) Title produced by software: ARIMA Backward Selection Date of computation: Fri, 11 Dec 2009 10:25:03 -0700   Cite this page as follows: Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst.htm/, Retrieved Wed, 22 May 2013 19:18:53 +0000   Original text written by user:   IsPrivate? No (this computation is public)   User-defined keywords:   System-generated keywords (parent): t1260457743507lh3bg5rk9vdf (pk = 65451) Estimated Impact 39   Dataseries X: » Textfile « » CSV « » Stem and Leaf « » Histogram « » Kernel Density « » Harrell-Davis Quantiles « » Central Tendency « » Variability « 255 280.2 299.9 339.2 374.2 393.5 389.2 381.7 375.2 369 357.4 352.1 346.5 342.9 340.3 328.3 322.9 314.3 308.9 294 285.6 281.2 280.3 278.8 274.5 270.4 263.4 259.9 258 262.7 284.7 311.3 322.1 327 331.3 333.3 321.4 327 320 314.7 316.7 314.4 321.3 318.2 307.2 301.3 287.5 277.7 274.4 258.8 253.3 251 248.4 249.5 246.1 244.5 243.6 244 240.8 249.8 248 259.4 260.5 260.8 261.3 259.5 256.6 257.9 256.5 254.2 253.3 253.8 255.5 257.1 257.3 253.2 252.8 252 250.7 252.2 250 251 253.4 251.2 255.6 261.1 258.9 259.9 261.2 264.7 267.1 266.4 267.7 268.6 267.5 268.5 268.5 270.5 270.9 270.1 269.3 269.8 270.1 264.9 263.7 264.8 263.7 255.9 276.2 360.1 380.5 373.7 369.8 366.6 359.3 345.8 326.2 324.5 328.1 327.5 324.4 316.5 310.9 301.5 291.7 290.4 287.4 277.7 281.6 288 276 272.9 283 283.3 276.8 284.5 282.7 281.2 287.4 283.1 284 285.5 289.2 292.5 296.4 305.2 303.9 311.5 316.3 316.7 322.5 317.1 309.8 303.8 290.3 293.7 291.7 296.5 289.1 288.5 293.8 297.7 305.4 302.7 302.5 303 294.5 294.1 294.5 297.1 289.4 292.4 287.9 286.6 280.5 272.4 269.2 270.6 267.3 262.5 266.8 268.8 263.1 261.2 266 262.5 265.2 261.3 253.7 249.2 239.1 236.4 235.2 245.2 246.2 247.7 251.4 253.3 254.8 250 249.3 241.5 243.3 248 253 252.9 251.5 251.6 253.5 259.8 334.1 448 445.8 445 448.2 438.2 439.8 423.4 410.8 408.4 406.7 405.9 402.7 405.1 399.6 386.5 381.4 375.2 357.7 359 355 352.7 344.4 343.8 338 339 333.3 334.4 328.3 330.7 330 331.6 351.2 389.4 410.9 442.8 462.8 466.9 461.7 439.2 430.3 416.1 402.5 397.3 403.3 395.9 387.8 378.6 377.1 370.4 362 350.3 348.2 344.6 343.5 342.8 347.6 346.6 349.5 342.1 342 342.8 339.3 348.2 333.7 334.7 354 367.7 363.3 358.4 353.1 343.1 344.6 344.4 333.9 331.7 324.3 321.2 322.4 321.7 320.5 312.8 309.7 315.6 309.7 304.6 302.5 301.5 298.8 291.3 293.6 294.6 285.9 297.6 301.1 293.8 297.7 292.9 292.1 287.2 288.2 283.8 299.9 292.4 293.3 300.8 293.7 293.1 294.4 292.1 291.9 282.5 277.9 287.5 289.2 285.6 293.2 290.8 283.1 275 287.8 287.8 287.4 284 277.8 277.6 304.9 294 300.9 324 332.9 341.6 333.4 348.2 344.7 344.7 329.3 323.5 323.2 317.4 330.1 329.2 334.9 315.8 315.4 319.6 317.3 313.8 315.8   Output produced by software: Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 ARIMA Parameter Estimation and Backward Selection Iteration ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 Estimates ( 1 ) 0.4714 -0.1016 0.1174 -0.0726 0.0063 -0.0956 -0.0141 0.0017 0.0381 -0.0615 0.0029 (p-val) (0 ) (0.0833 ) (0.0474 ) (0.2214 ) (0.9163 ) (0.1108 ) (0.815 ) (0.9775 ) (0.5249 ) (0.3025 ) (0.9575 ) Estimates ( 2 ) 0.4714 -0.1017 0.1174 -0.0728 0.0065 -0.0959 -0.0134 0 0.0388 -0.0618 0.0031 (p-val) (0 ) (0.0821 ) (0.047 ) (0.2196 ) (0.9125 ) (0.1058 ) (0.8059 ) (NA ) (0.4741 ) (0.295 ) (0.9546 ) Estimates ( 3 ) 0.4712 -0.1016 0.1174 -0.0728 0.0063 -0.0958 -0.0135 0 0.0386 -0.0604 0 (p-val) (0 ) (0.0823 ) (0.0471 ) (0.2191 ) (0.9154 ) (0.1058 ) (0.8046 ) (NA ) (0.4755 ) (0.2624 ) (NA ) Estimates ( 4 ) 0.4708 -0.1009 0.1166 -0.0702 0 -0.0933 -0.014 0 0.0385 -0.0604 0 (p-val) (0 ) (0.0825 ) (0.0465 ) (0.1928 ) (NA ) (0.0842 ) (0.7952 ) (NA ) (0.476 ) (0.2624 ) (NA ) Estimates ( 5 ) 0.4721 -0.101 0.1173 -0.0713 0 -0.0989 0 0 0.038 -0.0616 0 (p-val) (0 ) (0.0821 ) (0.045 ) (0.1847 ) (NA ) (0.0457 ) (NA ) (NA ) (0.4816 ) (0.252 ) (NA ) Estimates ( 6 ) 0.4718 -0.1031 0.115 -0.0707 0 -0.0952 0 0 0 -0.046 0 (p-val) (0 ) (0.0757 ) (0.049 ) (0.1885 ) (NA ) (0.0531 ) (NA ) (NA ) (NA ) (0.348 ) (NA ) Estimates ( 7 ) 0.4715 -0.1026 0.1177 -0.0672 0 -0.0958 0 0 0 0 0 (p-val) (0 ) (0.0775 ) (0.044 ) (0.2113 ) (NA ) (0.0519 ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 8 ) 0.4667 -0.0958 0.0884 0 0 -0.1011 0 0 0 0 0 (p-val) (0 ) (0.0985 ) (0.099 ) (NA ) (NA ) (0.04 ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 9 ) 0.4609 -0.0559 0 0 0 -0.094 0 0 0 0 0 (p-val) (0 ) (0.2899 ) (NA ) (NA ) (NA ) (0.0562 ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 10 ) 0.4366 0 0 0 0 -0.0948 0 0 0 0 0 (p-val) (0 ) (NA ) (NA ) (NA ) (NA ) (0.0544 ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 11 ) 0.4419 0 0 0 0 0 0 0 0 0 0 (p-val) (0 ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 12 ) NA NA NA NA NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 13 ) NA NA NA NA NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 14 ) NA NA NA NA NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 15 ) NA NA NA NA NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 16 ) NA NA NA NA NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 17 ) NA NA NA NA NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 18 ) NA NA NA NA NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 19 ) NA NA NA NA NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 20 ) NA NA NA NA NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimates ( 21 ) NA NA NA NA NA NA NA NA NA NA NA (p-val) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) (NA ) Estimated ARIMA Residuals Value 0.254999840129496 22.5044750036747 8.56666662771422 30.5224425186224 17.7574029835131 4.29599552355775 -11.6967564104387 -3.23382517886057 -1.3582203395938 0.363289924781157 -5.57531773752589 1.59376998313809 -3.69385227442251 -1.86624221154045 -1.64456945586153 -11.4526930140069 -1.26091474164383 -6.74499560813354 -2.17643243331952 -12.8838361522639 -2.14168358217944 -1.87046307725319 0.508960961751882 -1.92237013143057 -4.15707307840262 -3.63529234373198 -6.00640711665477 -0.861174356807055 -0.457347594503176 5.38727105814723 19.5405131043552 16.6069251456254 -1.47618585191458 -0.146684270651747 1.98071964374680 0.568335608514019 -10.6875339659314 13.3167766852150 -8.42091884471262 -1.77953733352012 4.7214249729181 -2.98352756439783 6.77597977179363 -5.58140968107278 -10.3102510268844 -1.60024022024908 -11.0346762136322 -3.99346348653688 1.63243972026879 -14.4532195827589 0.267593529950204 -0.458219069875355 -2.90413962006073 1.30602813826920 -4.19305875208141 -1.59455923558826 -0.72289670018273 0.574867852086356 -3.62110455264951 10.5012827267068 -6.05139007083937 12.0341347439427 -3.96214318012437 -0.142299936365134 0.0656719856086738 -1.16508461175118 -2.28482519204198 3.64675039128031 -1.86325285284317 -1.66037124905191 0.151495802842817 0.722267692048007 1.20679919716125 0.981081698292542 -0.631221091288239 -4.4053519562481 1.30459048277018 -0.57797477518946 -0.789589774432585 2.21921198863623 -2.83588525719867 1.57176092898328 1.92551666590845 -3.32359205303203 5.23720003276861 3.72132028654715 -4.80965833750361 2.05523929659222 1.09095576969378 2.72390820340979 1.28914647423488 -1.22635406951514 1.39703512765425 0.42726717916446 -1.36966753200971 1.81201868806608 -0.209044230306233 1.93364022405365 -0.349887340344139 -0.889305672917828 -0.555028878217911 0.944050449621102 0.0817182689389142 -5.14136967879006 1.10804987500495 1.54803641060801 -1.65605955227312 -7.27238035170387 23.7336349085304 74.5448033833175 -16.3414340879630 -15.6016149793778 -1.03564810548448 -2.23684000112524 -3.97856341876542 -2.35939358094083 -11.7724797909141 6.21200603411711 3.97243913390685 -2.47498743939468 -3.53009958616695 -7.82644894638594 -4.00922237573161 -7.11640406798512 -5.35502460832708 2.92144212084384 -2.72634650700343 -9.13922708502753 7.60378737501503 3.806285506444 -15.7230430208308 2.01552196156638 11.1689476928090 -5.02884786269072 -6.26125028693571 11.1443804553037 -6.29913481742108 -1.00806477594227 7.81232196040889 -6.97825356118074 2.16102496762392 1.83705041949958 2.87451538295466 1.54251567026279 3.04709859052122 6.68976387405303 -5.05643875474465 8.30973202064393 1.83287650358733 -1.38266567443975 5.99509336685207 -7.09783089698362 -5.06579688844033 -2.09260915909044 -10.4255807636347 9.33152661061939 -2.93447762766027 5.1612086526585 -10.1875422816269 2.0617715401645 4.28214239830851 1.90853256249147 5.80780313787665 -5.60650019470921 0.277203716297436 0.530432884470429 -8.21584342746746 3.68050817973995 1.30458292025844 2.16941547935818 -8.85402493750252 6.40893849830286 -6.61548766571497 0.626615707580754 -5.4945476272718 -5.1904837132538 -0.393793492219515 3.08140211856113 -4.33778740662609 -3.48258015889695 5.81722657065444 -0.645100294504516 -6.87648589999907 0.721131285989316 5.31663163428527 -6.05054308181866 4.63561074109811 -4.88912198788336 -6.43776067328628 -1.36223707972536 -7.68042595681803 1.37749208770288 0.234680483522965 10.1541574028457 -4.08611218863956 0.636837978222815 2.08767803959111 0.0287560543547727 0.556769806059776 -4.50684839395012 1.49030429811003 -7.35220605662946 5.55595382026971 4.09430516003428 3.09035124791052 -2.73785577424309 -1.42270342973413 -0.0282486564308044 2.02698307764976 5.9160879176074 72.0236485882466 81.453854796326 -52.0572978876145 0.169919584661216 3.72937016155208 -10.7997650952742 13.0092508395253 -6.30081799612901 -5.64891851702669 3.02485987880135 -0.348888715151986 -1.00583891362544 -2.69906974242491 2.24228832804863 -7.7422282761272 -10.9264201901437 0.457821897931524 -4.04936608711529 -15.0966655105968 9.167379818955 -5.08893074033716 -1.79562195850667 -7.77938240472781 2.43571872008994 -7.19705632138488 3.65530766420926 -6.51576218181549 3.37037247027303 -7.36705715169802 5.00615731099157 -2.29759045264859 2.00039410340889 18.3611402850415 29.74763579032 4.24499769939860 22.7414047961881 6.00726578235458 -4.47958975456731 -5.13183646820397 -16.6085222238934 2.96087101610163 -7.29047539755811 -5.50480523939791 1.12594177254795 7.77717166743429 -12.1523735710082 -5.71314753161306 -7.00999141172116 1.22710820456729 -6.53811314241801 -4.90622672424109 -8.73438454960586 2.23991509945114 -3.55537378483785 0.329428943754976 -0.85493804715179 4.30927711212962 -4.20466087328958 3.13748413428323 -9.00731288787841 3.0262899717888 0.777296570265946 -3.39421230606587 10.3331724375045 -18.1104957411102 6.62865256933969 18.8539565698854 5.35016492497965 -10.7127183108062 -2.13540361534467 -4.53543439448919 -7.59141397082891 7.69526844374246 0.443910421766134 -10.8298058992382 1.91939792065915 -6.94199868692471 -0.817427179528806 2.69554625246388 -1.24283609053134 -1.88980221570932 -7.38468314128465 -0.439978973091456 6.95946772481665 -8.36196481061319 -2.59063534942516 0.0127140409153412 -0.81317426495292 -2.55731554564005 -5.76196054072238 5.01490785436914 -0.487574330490077 -9.33564278996124 15.4033024405401 -1.86375164262307 -9.53896971685276 7.3049525373159 -6.40779782235336 0.470747402853817 -3.44159297519934 3.47095984413050 -5.52860112556243 18.3905979850387 -14.9837102037998 4.0983862219781 6.64257445246568 -10.2794262859934 2.08248198940521 3.08821292403883 -3.5785301001838 0.889415674812085 -8.60168970815062 -1.16938118350669 11.5513121178083 -2.3677696524731 -4.56019714943142 9.1526685276553 -6.60899930340815 -7.08832621855396 -3.82838441439492 16.4973234990597 -5.92929116288849 0.320477567417242 -3.45289384696707 -5.44564176419402 1.73881605777888 28.6007485954430 -22.8181825159368 11.6206218651629 19.7653931996173 -1.77237399054815 4.79562525112755 -9.4100708585567 17.3465038782381 -9.30702144793804 3.71784472365658 -14.5562828486824 1.74783453201519 1.45471070493767 -4.26599569849816 14.9002692005773 -6.44435596895232 4.63299204509082 -22.1382498776520 7.9099222225575 3.82478724129368 -2.92961060588715 -2.58122374904985 4.06833029299077   Charts produced by software: http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/1l5ul1260552296.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/1l5ul1260552296.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/20x8j1260552296.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/20x8j1260552296.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/3x5jw1260552296.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/3x5jw1260552296.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/48krm1260552296.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/48krm1260552296.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/5caa11260552296.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/5caa11260552296.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/6rzp81260552296.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/6rzp81260552296.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/7wn0h1260552296.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260552333qlfd6zwzk61enst/7wn0h1260552296.ps (opens in new window) Click here to open pdf file.   Parameters (Session): par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 0 ;   Parameters (R input): par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 0 ;   R code (references can be found in the software module): library(lattice) if (par1 == 'TRUE') par1 <- TRUE if (par1 == 'FALSE') par1 <- FALSE par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter par3 <- as.numeric(par3) #degree of non-seasonal differencing par4 <- as.numeric(par4) #degree of seasonal differencing par5 <- as.numeric(par5) #seasonal period par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial par6 <- 11 par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial armaGR <- function(arima.out, names, n){ try1 <- arima.out$coef try2 <- sqrt(diag(arima.out$var.coef)) try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names))) dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv')) try.data.frame[,1] <- try1 for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i] try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2] try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5) vector <- rep(NA,length(names)) vector[is.na(try.data.frame[,4])] <- 0 maxi <- which.max(try.data.frame[,4]) continue <- max(try.data.frame[,4],na.rm=TRUE) > .05 vector[maxi] <- 0 list(summary=try.data.frame,next.vector=vector,continue=continue) } arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){ nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3] coeff <- matrix(NA, nrow=nrc*2, ncol=nrc) pval <- matrix(NA, nrow=nrc*2, ncol=nrc) mylist <- rep(list(NULL), nrc) names <- NULL if(order[1] > 0) names <- paste('ar',1:order[1],sep='') if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') ) if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep='')) if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep='')) arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML') mylist[[1]] <- arima.out last.arma <- armaGR(arima.out, names, length(series)) mystop <- FALSE i <- 1 coeff[i,] <- last.arma[[1]][,1] pval [i,] <- last.arma[[1]][,4] i <- 2 aic <- arima.out$aic while(!mystop){ mylist[[i]] <- arima.out arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector) aic <- c(aic, arima.out$aic) last.arma <- armaGR(arima.out, names, length(series)) mystop <- !last.arma$continue coeff[i,] <- last.arma[[1]][,1] pval [i,] <- last.arma[[1]][,4] i <- i+1 } list(coeff, pval, mylist, aic=aic) } arimaSelectplot <- function(arimaSelect.out,noms,choix){ noms <- names(arimaSelect.out[[3]][[1]]$coef) coeff <- arimaSelect.out[[1]] k <- min(which(is.na(coeff[,1])))-1 coeff <- coeff[1:k,] pval <- arimaSelect.out[[2]][1:k,] aic <- arimaSelect.out$aic[1:k] coeff[coeff==0] <- NA n <- ncol(coeff) if(missing(choix)) choix <- k layout(matrix(c(1,1,1,2, 3,3,3,2, 3,3,3,4, 5,6,7,7),nr=4), widths=c(10,35,45,15), heights=c(30,30,15,15)) couleurs <- rainbow(75)[1:50]#(50) ticks <- pretty(coeff) par(mar=c(1,1,3,1)) plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA) points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA) title('aic',line=2) par(mar=c(3,0,0,0)) plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1)) rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)), xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)), ytop = rep(1,50), ybottom= rep(0,50),col=couleurs,border=NA) axis(1,ticks) rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0) text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2) par(mar=c(1,1,3,1)) image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks)) for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) { if(pval[j,i]<.01) symb = 'green' else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange' else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red' else symb = 'black' polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5), c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5), col=symb) if(j==choix) { rect(xleft=i-.5, xright=i+.5, ybottom=k-j+1.5, ytop=k-j+.5, lwd=4) text(i, k-j+1, round(coeff[j,i],2), cex=1.2, font=2) } else{ rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5) text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1) } } axis(3,1:n,noms) par(mar=c(0.5,0,0,0.5)) plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8)) cols <- c('green','orange','red','black') niv <- c('0','0.01','0.05','0.1') for(i in 0:3){ polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i), c(.4 ,.7 , .4 , .4), col=cols[i+1]) text(2*i,0.5,niv[i+1],cex=1.5) } text(8,.5,1,cex=1.5) text(4,0,'p-value',cex=2) box() residus <- arimaSelect.out[[3]][[choix]]$res par(mar=c(1,2,4,1)) acf(residus,main='') title('acf',line=.5) par(mar=c(1,2,4,1)) pacf(residus,main='') title('pacf',line=.5) par(mar=c(2,2,4,1)) qqnorm(residus,main='') title('qq-norm',line=.5) qqline(residus) residus } if (par2 == 0) x <- log(x) if (par2 != 0) x <- x^par2 (selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5))) bitmap(file='test1.png') resid <- arimaSelectplot(selection) dev.off() resid bitmap(file='test2.png') acf(resid,length(resid)/2, main='Residual Autocorrelation Function') dev.off() bitmap(file='test3.png') pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function') dev.off() bitmap(file='test4.png') cpgram(resid, main='Residual Cumulative Periodogram') dev.off() bitmap(file='test5.png') hist(resid, main='Residual Histogram', xlab='values of Residuals') dev.off() bitmap(file='test6.png') densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test7.png') qqnorm(resid, main='Residual Normal Q-Q Plot') qqline(resid) dev.off() ncols <- length(selection[[1]][1,]) nrows <- length(selection[[2]][,1])-1 load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Iteration', header=TRUE) for (i in 1:ncols) { a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE) } a<-table.row.end(a) for (j in 1:nrows) { a<-table.row.start(a) mydum <- 'Estimates (' mydum <- paste(mydum,j) mydum <- paste(mydum,')') a<-table.element(a,mydum, header=TRUE) for (i in 1:ncols) { a<-table.element(a,round(selection[[1]][j,i],4)) } a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'(p-val)', header=TRUE) for (i in 1:ncols) { mydum <- '(' mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='') mydum <- paste(mydum,')') a<-table.element(a,mydum) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Value', 1,TRUE) a<-table.row.end(a) for (i in (par4*par5+par3):length(resid)) { a<-table.row.start(a) a<-table.element(a,resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab')