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mid-110502.Rt
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mid-110502.Rt
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R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R est un logiciel libre livr'e sans AUCUNE GARANTIE.
Vous pouvez le redistribuer sous certaines conditions.
Tapez 'license()' ou 'licence()' pour plus de d'etails.
R est un projet collaboratif avec de nombreux contributeurs.
Tapez 'contributors()' pour plus d'information et
'citation()' pour la facon de le citer dans les publications.
Tapez 'demo()' pour des d'emonstrations, 'help()' pour l'aide
en ligne ou 'help.start()' pour obtenir l'aide au format HTML.
Tapez 'q()' pour quitter R.
[1] "fr_FR/fr_FR/fr_FR/C/fr_FR/C"
[Sauvegarde de la session précédente restaurée]
Started working: Mon May 2 13:32:35 2011
> rm(list=ls())
>
> source("10code.R")
> sim3 <- sim.Y.finite(100,3,5,tfun=theta.216,xfun=sim.X,lambda=c(1,2,-3))
Erreur dans sim.Y.finite(100, 3, 5, tfun = theta.216, xfun = sim.X, lambda = c(1, :
impossible de trouver la fonction "%do%"
>
> source("10code.R")
> library(foreach)
Le chargement a nécessité le package : iterators
Le chargement a nécessité le package : codetools
foreach: simple, scalable parallel programming from REvolution Computing
Use REvolution R for scalability, fault tolerance and more.
http://www.revolution-computing.com
>
> sim3 <- sim.Y.finite(100,3,5,tfun=theta.216,xfun=sim.X,lambda=c(1,2,-3))
>
> modl <- gen.k(sim3,17)
> k <- which.min(sapply(modl,KZ.k))-1
> bb <- prep.nls.finite(sim3,5)
>
> summary(nls(that~theta.216(index,lambda),data=bb,start=list(lambda=c(1,1,1))))
Formula: that ~ theta.216(index, lambda)
Parameters:
Estimate Std. Error t value Pr(>|t|)
lambda1 0.97809 0.04659 20.995 1.56e-12 ***
lambda2 2.14968 0.36224 5.934 2.74e-05 ***
lambda3 -3.11303 0.35654 -8.731 2.89e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.09437 on 15 degrees of freedom
Number of iterations to convergence: 1
Achieved convergence tolerance: 7.506e-08
> object.size(sim3)
8016 bytes
> length(sim3)
[1] 7
> length(sim3$X)
[1] 300
> length(sim3$x)
[1] 318
> sim3$theta
[,1]
[1,] 0.000000
[2,] 1.250000
[3,] 1.333333
[4,] 1.312500
[5,] 1.280000
[6,] 1.250000
[7,] 1.224490
[8,] 1.203125
[9,] 1.185185
[10,] 1.170000
[11,] 1.157025
[12,] 1.145833
[13,] 1.136095
[14,] 1.127551
[15,] 1.120000
[16,] 1.113281
[17,] 1.107266
[18,] 1.101852
> plot(sim3$theta)
>
> sim3.100 <- foreach(i=1:100,.combine=c) %do% {
+ sim.Y.finite(100,3,5,tfun=theta.216,xfun=sim.X,lambda=c(1,2,-3))
+ }
>
> pnls3.100.1 <- lapply(sim3.100,prep.nls.finite,k=1)
Erreur dans UseMethod("reflow") :
pas de méthode pour 'reflow' applicable pour un objet de classe "c('double', 'numeric')"
>
> sim3.100 <- foreach(i=1:100,.combine=c) %do% {
+ list(sim.Y.finite(100,3,5,tfun=theta.216,xfun=sim.X,lambda=c(1,2,-3)))
+ }
>
> pnls3.100.1 <- lapply(sim3.100,prep.nls.finite,k=1)
>
> nls3.100.1 <- lapply(pnls3.100.1,function(l){
+ nls(that~theta.216(index,lambda),data=ll,start=list(lambda=c(1,1,1)))
+ })
Erreur dans nls(that ~ theta.216(index, lambda), data = ll, start = list(lambda = c(1, :
objet 'll' introuvable
>
> nls3.100.1 <- lapply(pnls3.100.1,function(l){
+ nls(that~theta.216(index,lambda),data=l,start=list(lambda=c(1,1,1)))
+ })
> sapply(nls3.100.1,coef)
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
lambda1 2.807342 -0.2975058 2.2844731 2.757006 2.649933 2.574415 2.933872
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
lambda1 3.292736 3.155881 2.404853 3.814145 1.681560 2.168881 1.626061
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
lambda1 2.113308 3.341194 0.3285328 1.821054 1.6802482 3.364167 3.752815
[,22] [,23] [,24] [,25] [,26] [,27]
lambda1 2.605169 3.644633 2.613716 3.002841 1.017974 3.314847
[,28] [,29] [,30] [,31] [,32] [,33]
lambda1 -0.2465612 4.272331 2.275681 0.2847683 3.071479 2.799839
[,34] [,35] [,36] [,37] [,38] [,39]
lambda1 1.504225 2.1108115 2.502616 3.149863 1.235721 2.2077015
[,40] [,41] [,42] [,43] [,44] [,45]
lambda1 1.5015429 4.059516 3.0507606 2.978861 0.6688254 1.680002
[,46] [,47] [,48] [,49] [,50] [,51] [,52]
lambda1 2.879374 1.408860 2.727460 1.3094730 1.7704324 3.285514 1.306380
[,53] [,54] [,55] [,56] [,57] [,58]
lambda1 2.2699844 2.2912758 2.774768 1.9526682 1.521225 2.659223
[,59] [,60] [,61] [,62] [,63] [,64]
lambda1 1.5785228 2.48255259 0.5718911 1.836362 3.534656 2.271403
[,65] [,66] [,67] [,68] [,69] [,70]
lambda1 2.618980 2.041506 3.093451 0.3363113 2.410879 4.438019
[,71] [,72] [,73] [,74] [,75] [,76] [,77]
lambda1 1.8902459 2.441062 0.658837 3.675728 2.1929628 1.918372 3.039867
[,78] [,79] [,80] [,81] [,82] [,83] [,84]
lambda1 0.6585282 2.392274 2.919128 2.842958 2.954411 1.8038108 2.965654
[,85] [,86] [,87] [,88] [,89] [,90]
lambda1 3.164056 1.5124079 0.5592688 3.135304 2.896683 1.579585
[,91] [,92] [,93] [,94] [,95] [,96]
lambda1 2.3621079 2.796158 2.2445254 2.403737 3.153235 0.1870515
[,97] [,98] [,99] [,100]
lambda1 3.808404 3.458006 2.411589 1.902224
[getOption("max.print") est atteint -- 2 lignes omises ]]
> dim(sapply(nls3.100.1,coef))
[1] 3 100
> aa <- sapply(nls3.100.1,coef)
> aa[1,]
[1] 2.8073420 -0.2975058 2.2844731 2.7570060 2.6499326 2.5744147
[7] 2.9338718 3.2927362 3.1558812 2.4048534 3.8141451 1.6815599
[13] 2.1688814 1.6260607 2.1133084 3.3411939 0.3285328 1.8210544
[19] 1.6802482 3.3641667 3.7528153 2.6051688 3.6446328 2.6137161
[25] 3.0028409 1.0179740 3.3148475 -0.2465612 4.2723314 2.2756806
[31] 0.2847683 3.0714789 2.7998387 1.5042249 2.1108115 2.5026163
[37] 3.1498634 1.2357209 2.2077015 1.5015429 4.0595163 3.0507606
[43] 2.9788613 0.6688254 1.6800019 2.8793741 1.4088601 2.7274599
[49] 1.3094730 1.7704324 3.2855140 1.3063796 2.2699844 2.2912758
[55] 2.7747684 1.9526682 1.5212249 2.6592234 1.5785228 2.4825526
[61] 0.5718911 1.8363623 3.5346561 2.2714032 2.6189797 2.0415058
[67] 3.0934510 0.3363113 2.4108788 4.4380195 1.8902459 2.4410620
[73] 0.6588370 3.6757280 2.1929628 1.9183715 3.0398667 0.6585282
[79] 2.3922744 2.9191284 2.8429578 2.9544113 1.8038108 2.9656545
[85] 3.1640555 1.5124079 0.5592688 3.1353041 2.8966832 1.5795854
[91] 2.3621079 2.7961581 2.2445254 2.4037366 3.1532345 0.1870515
[97] 3.8084044 3.4580065 2.4115891 1.9022240
> aa[2,]
[1] -4.8625877 9.3478800 -2.1905219 -6.5977009 -4.8899038 -4.1422075
[7] -6.0559445 -10.2927055 -8.3259382 -6.2718205 -8.2313527 1.0873896
[13] -4.0731430 1.3203297 -4.2513849 -8.0474018 6.3434960 -2.0430347
[19] -1.7683752 -7.3595083 -9.6924478 -3.9014131 -7.1232502 -3.6730484
[25] -7.7430122 1.3885688 -10.0754817 9.9955215 -12.0669593 -3.6487361
[31] 7.8599436 -6.4634280 -7.4758121 1.2428934 -0.9309741 -4.9221087
[37] -6.2089465 1.3378756 -1.9941454 0.9741672 -9.5769090 -4.4900366
[43] -7.1834037 2.9148191 1.1004744 -7.8217230 1.4136433 -5.1997387
[49] 0.7837986 -1.2805635 -6.9939668 4.1206422 0.8257351 -2.6739401
[55] -6.1094092 -0.9512113 3.3984666 -4.6728664 -2.5648518 -1.9535749
[61] 5.2145315 -1.5300596 -10.1057311 -3.9039722 -4.2205780 2.9942820
[67] -8.4984592 4.1930913 -4.1705851 -11.0575740 -1.9595903 -3.5402764
[73] 6.5064376 -9.4230098 -2.9940057 1.0323852 -6.3231967 3.7957840
[79] -4.2294707 -5.9855134 -5.4182558 -5.3651142 -0.9347848 -4.7538484
[85] -6.8795999 -1.9394072 6.0025473 -8.6440018 -6.8910272 1.2683922
[91] -3.5490903 -6.5799520 -1.2329437 -6.1789972 -8.9818787 4.4341472
[97] -8.1338512 -6.2720262 -3.0123563 2.0789068
> aa[3,]
[1] 1.86675232 -9.59475032 -0.50062010 5.51277372 2.10642052
[6] 1.86525420 4.14103257 7.38854621 6.35068980 3.60543981
[11] 4.33935731 -2.61014060 2.20392981 -3.01702910 1.64418082
[16] 4.43018496 -7.32487517 1.09753079 0.18596954 4.42742866
[21] 6.25277314 1.43501017 2.78305837 1.55820419 6.94864941
[26] -2.10694153 7.53625814 -11.22257464 9.69938904 2.68120842
[31] -9.13334881 4.20682394 4.72906904 -2.81606169 -1.18559479
[36] 3.46628621 2.81656736 -2.47398803 0.32628342 -2.58009008
[41] 7.01280350 0.87211512 5.37579782 -3.86668993 -3.31920671
[46] 5.39455548 -3.52605685 2.92577991 -2.40296915 -0.45717051
[51] 4.63298451 -6.82470544 -4.60051500 -0.34685030 3.45401652
[56] -1.33724006 -5.43347011 3.84292735 0.97212755 -0.02444125
[61] -7.32841220 0.16393201 8.04762692 1.80462889 2.70679425
[66] -5.21037015 4.82828793 -5.38500558 3.05656704 6.25911110
[71] -0.10204268 1.25317744 -6.90767888 6.72398562 0.73045390
[76] -3.01221406 4.20347457 -3.88745386 2.42692191 3.20944898
[81] 3.05665066 2.96357923 -0.45974011 1.68798129 2.57300937
[86] 0.27870349 -6.82087751 7.01889019 4.79293677 -2.75639663
[91] 0.24749387 3.85211159 -0.98450716 4.30392635 6.67135676
[96] -5.90534704 4.83082702 3.45818989 1.06692000 -4.74819207
> hist(aa[3,])
> hist(aa[2,])
> hist(aa[1,])
> summary(nls3.100.1[[1]])
Formula: that ~ theta.216(index, lambda)
Parameters:
Estimate Std. Error t value Pr(>|t|)
lambda1 2.807 1.227 2.288 0.106
lambda2 -4.863 5.728 -0.849 0.458
lambda3 1.867 4.762 0.392 0.721
Residual standard error: 0.7087 on 3 degrees of freedom
Number of iterations to convergence: 1
Achieved convergence tolerance: 1.273e-08
> sapply(pnls3.100.1,function(l)l$that)
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
X1 -0.2135241 -0.6388087 -0.4088824 1.6668964 -0.1300558 0.2158977 0.9863364
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
X1 0.37205963 1.121452 -0.2923112 -0.1350445 0.1083986 0.3253778 -0.1732296
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
X1 -0.5029270 -0.24514809 -0.6916221 0.8443703 0.1237352 0.4287346 0.33793324
[,22] [,23] [,24] [,25] [,26] [,27] [,28]
X1 0.08640577 -0.6947342 0.4790484 2.2499152 0.2750173 0.79992845 -1.600369
[,29] [,30] [,31] [,32] [,33] [,34] [,35]
X1 1.9145515 1.2231328 -1.09584258 0.8530263 0.1008517 -0.1152858 -0.0355171
[,36] [,37] [,38] [,39] [,40] [,41] [,42]
X1 1.021908 -0.2459776 0.03728353 0.4604444 -0.1654907 1.5399538 -0.5996733
[,43] [,44] [,45] [,46] [,47] [,48] [,49]
X1 1.1928146 -0.3826023 -0.5871910 0.42876972 -0.7711324 0.4670136 -0.3474820
[,50] [,51] [,52] [,53] [,54] [,55] [,56]
X1 -0.003444798 0.9572784 -1.4251320 -1.592916 -0.7327941 0.09586702 -0.4104687
[,57] [,58] [,59] [,60] [,61] [,62] [,63]
X1 -0.6231445 1.7781655 -0.005390182 0.4495144 -1.5909580 0.4262666 1.5005897
[,64] [,65] [,66] [,67] [,68] [,69] [,70]
X1 0.1277330 1.0963446 -0.2720114 -0.5994718 -0.8682305 1.291718 -0.3648736
[,71] [,72] [,73] [,74] [,75] [,76] [,77]
X1 -0.1669288 0.1197707 0.11637897 0.9967722 -0.1323034 -0.1281955 0.9099961
[,78] [,79] [,80] [,81] [,82] [,83] [,84]
X1 0.4783156 0.6365032 0.15257625 0.4809867 0.4882474 0.3321348 -0.1322165
[,85] [,86] [,87] [,88] [,89] [,90] [,91]
X1 -1.1616853 -0.1703646 -0.3638085 1.52643231 0.8015634 0.06942399 -0.9810860
[,92] [,93] [,94] [,95] [,96] [,97] [,98]
X1 0.10714561 -0.02556766 0.5063733 0.8585134 -1.3434476 0.4825202 0.6535340
[,99] [,100]
X1 0.45681871 -0.8228965
[getOption("max.print") est atteint -- 5 lignes omises ]]
> aa <- sapply(pnls3.100.1,function(l)l$that)
> dim(aa)
[1] 6 100
> apply(aa,1,mean)
X1 X2 X3 X4 X5 X6
0.1356855 1.2325300 1.2640188 1.4051358 1.2956972 2.3821176
> sapply(sim3.100,function(l)l$theta)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
[1,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
[1,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32]
[1,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40]
[1,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48]
[1,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56]
[1,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[,57] [,58] [,59] [,60] [,61] [,62] [,63] [,64]
[1,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72]
[1,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80]
[1,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[,81] [,82] [,83] [,84] [,85] [,86] [,87] [,88]
[1,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96]
[1,] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
[,97] [,98] [,99] [,100]
[1,] 0.000000 0.000000 0.000000 0.000000
[getOption("max.print") est atteint -- 17 lignes omises ]]
> apply(sapply(sim3.100,function(l)l$theta),1,sd)
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> sim3.100.1[[1]]$theta
Erreur : objet 'sim3.100.1' introuvable
> sim3.100[[1]]$theta
[,1]
[1,] 0.000000
[2,] 1.250000
[3,] 1.333333
[4,] 1.312500
[5,] 1.280000
[6,] 1.250000
[7,] 1.224490
[8,] 1.203125
[9,] 1.185185
[10,] 1.170000
[11,] 1.157025
[12,] 1.145833
[13,] 1.136095
[14,] 1.127551
[15,] 1.120000
[16,] 1.113281
[17,] 1.107266
[18,] 1.101852
> aa <- sapply(pnls3.100.1,function(l)l$that)
> apply(aa,1,mean)
X1 X2 X3 X4 X5 X6
0.1356855 1.2325300 1.2640188 1.4051358 1.2956972 2.3821176
> apply(aa,1,mean)-sim3.100[[1]]$theta
[,1]
[1,] 0.13568554
[2,] -0.01746995
[3,] -0.06931452
[4,] 0.09263584
[5,] 0.01569724
[6,] 1.13211759
[7,] -1.08880425
[8,] 0.02940505
[9,] 0.07883363
[10,] 0.23513584
[11,] 0.13867245
[12,] 1.23628425
[13,] -1.00040913
[14,] 0.10497903
[15,] 0.14401881
[16,] 0.29185459
[17,] 0.18843081
[18,] 1.28026574
> aa
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
X1 -0.2135241 -0.6388087 -0.4088824 1.6668964 -0.1300558 0.2158977 0.9863364
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
X1 0.37205963 1.121452 -0.2923112 -0.1350445 0.1083986 0.3253778 -0.1732296
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
X1 -0.5029270 -0.24514809 -0.6916221 0.8443703 0.1237352 0.4287346 0.33793324
[,22] [,23] [,24] [,25] [,26] [,27] [,28]
X1 0.08640577 -0.6947342 0.4790484 2.2499152 0.2750173 0.79992845 -1.600369
[,29] [,30] [,31] [,32] [,33] [,34] [,35]
X1 1.9145515 1.2231328 -1.09584258 0.8530263 0.1008517 -0.1152858 -0.0355171
[,36] [,37] [,38] [,39] [,40] [,41] [,42]
X1 1.021908 -0.2459776 0.03728353 0.4604444 -0.1654907 1.5399538 -0.5996733
[,43] [,44] [,45] [,46] [,47] [,48] [,49]
X1 1.1928146 -0.3826023 -0.5871910 0.42876972 -0.7711324 0.4670136 -0.3474820
[,50] [,51] [,52] [,53] [,54] [,55] [,56]
X1 -0.003444798 0.9572784 -1.4251320 -1.592916 -0.7327941 0.09586702 -0.4104687
[,57] [,58] [,59] [,60] [,61] [,62] [,63]
X1 -0.6231445 1.7781655 -0.005390182 0.4495144 -1.5909580 0.4262666 1.5005897
[,64] [,65] [,66] [,67] [,68] [,69] [,70]
X1 0.1277330 1.0963446 -0.2720114 -0.5994718 -0.8682305 1.291718 -0.3648736
[,71] [,72] [,73] [,74] [,75] [,76] [,77]
X1 -0.1669288 0.1197707 0.11637897 0.9967722 -0.1323034 -0.1281955 0.9099961
[,78] [,79] [,80] [,81] [,82] [,83] [,84]
X1 0.4783156 0.6365032 0.15257625 0.4809867 0.4882474 0.3321348 -0.1322165
[,85] [,86] [,87] [,88] [,89] [,90] [,91]
X1 -1.1616853 -0.1703646 -0.3638085 1.52643231 0.8015634 0.06942399 -0.9810860
[,92] [,93] [,94] [,95] [,96] [,97] [,98]
X1 0.10714561 -0.02556766 0.5063733 0.8585134 -1.3434476 0.4825202 0.6535340
[,99] [,100]
X1 0.45681871 -0.8228965
[getOption("max.print") est atteint -- 5 lignes omises ]]
> hist(aa[6,])
> sd(aa[6,])
[1] 0.8931407
> mean(aa[6,])
[1] 2.382118
> median(aa[6,])
[1] 2.358554
> bb <- lapply(sim3.100,prep.nls.finite,k=0)
> cc <- sapply(bb,function(l)l$that)
> apply(cc,1,mean)
X1 X2 X3
0.1486333 1.2364522 2.5700803
> bb <- lapply(sim3.100,prep.nls.finite,k=5)
> cc <- sapply(bb,function(l)l$that)
> apply(cc,1,mean)
X1 X2 X3 X4 X5
-0.0004105123 1.2490140069 1.3294415656 1.3215652846 1.2798333793
X6 X7 X8 X9 X10
1.2452970770 1.2395502435 1.1799299368 1.1837817933 1.1909850155
X11 X12 X13 X14 X15
1.1398107886 1.1669273537 1.1266831388 1.1228182403 1.1408204693
X16 X17 X18
1.1212435234 1.0970365025 1.1070235231
> bb <- lapply(sim3.100,prep.nls.finite,k=6)
> cc <- sapply(bb,function(l)l$that)
> apply(cc,1,mean)
X1 X2 X3 X4 X5 X6
0.0062574115 1.2436190713 1.3356591195 1.3213514163 1.2767852239 1.2450886533
X7 X8 X9 X10 X11 X12
1.2393694271 1.1807219802 1.1810647639 1.1826192538 1.1453966333 1.1651267746
X13 X14 X15 X16 X17 X18
1.1286610493 1.1230123331 1.1392502630 1.1190851366 1.1003419606 1.1020958219
X19 X20 X21
0.0001132790 0.0083657905 0.0083327360
>
> kk <- lapply(sim3.100,gen.k,kmax=17)
> modl3.100 <- kk
> length(kk)
[1] 100
> length(kk[[1]])
[1] 18
> kk[[1]]
[[1]]
Call:
lm(formula = Y ~ . - 1, data = reflow(object, k))
Coefficients:
X1 X2 X3
-0.04051 1.16987 2.47549
[[2]]
Call:
lm(formula = Y ~ . - 1, data = reflow(object, k))
Coefficients:
X1 X2 X3 X4 X5 X6
-0.2135 0.9908 1.5345 1.4099 1.0978 2.8953
[[3]]
Call:
lm(formula = Y ~ . - 1, data = reflow(object, k))
Coefficients:
X1 X2 X3 X4 X5 X6 X7 X8
-0.5013 1.0038 0.6088 1.6258 0.9808 1.8391 1.6397 1.3524
X9
2.6431
[[4]]
Call:
lm(formula = Y ~ . - 1, data = reflow(object, k))
Coefficients:
X1 X2 X3 X4 X5 X6 X7 X8
-0.0959 0.8870 0.7455 1.2861 1.0298 1.0034 1.6223 1.2342
X9 X10 X11 X12
1.7383 1.4228 1.2429 2.4379
[[5]]
Call:
lm(formula = Y ~ . - 1, data = reflow(object, k))
Coefficients:
X1 X2 X3 X4 X5 X6 X7 X8
-0.2654 1.1145 0.9912 1.4977 0.9169 1.1464 1.3377 1.2644
X9 X10 X11 X12 X13 X14 X15
1.0654 1.4163 1.1542 1.7226 1.0212 1.0528 1.9549
[[6]]
Call:
lm(formula = Y ~ . - 1, data = reflow(object, k))
Coefficients:
X1 X2 X3 X4 X5 X6 X7 X8
-0.1815 1.2599 1.2840 1.2580 1.0732 1.3496 1.2504 1.0869
X9 X10 X11 X12 X13 X14 X15 X16
1.1410 1.2169 1.0778 1.1407 1.1992 1.0558 1.1235 1.1527
X17 X18
1.2978 1.2659
[[7]]
Call:
lm(formula = Y ~ . - 1, data = reflow(object, k))
Coefficients:
X1 X2 X3 X4 X5 X6 X7 X8
-0.1765 1.2512 1.2727 1.2308 1.0823 1.3383 1.2882 1.0486
X9 X10 X11 X12 X13 X14 X15 X16
1.1324 1.1938 1.0800 1.1383 1.1746 1.0584 1.1036 1.2065
X17 X18 X19 X20 X21
1.3127 1.1965 0.1102 0.1296 -0.2002
[[8]]
Call:
lm(formula = Y ~ . - 1, data = reflow(object, k))
Coefficients:
X1 X2 X3 X4 X5 X6 X7 X8
-0.19059 1.23342 1.28116 1.21683 1.10368 1.31606 1.34155 1.05364
X9 X10 X11 X12 X13 X14 X15 X16
1.12040 1.16147 1.12019 1.13779 1.19590 1.04498 1.10827 1.22838
X17 X18 X19 X20 X21 X22 X23 X24
1.31738 1.21143 0.03996 0.12193 -0.13024 -0.06567 -0.20025 0.18360
[[9]]
Call:
lm(formula = Y ~ . - 1, data = reflow(object, k))
Coefficients:
X1 X2 X3 X4 X5 X6 X7 X8
-0.22760 1.24690 1.28921 1.23496 1.10756 1.30296 1.33665 1.06629
X9 X10 X11 X12 X13 X14 X15 X16
1.11890 1.14689 1.11326 1.11618 1.22418 1.02304 1.10113 1.23007
X17 X18 X19 X20 X21 X22 X23 X24
1.33010 1.19016 0.05764 0.11382 -0.09597 -0.06783 -0.19053 0.22774
X25 X26 X27
-0.07550 -0.01405 -0.08837
[[10]]
Call:
lm(formula = Y ~ . - 1, data = reflow(object, k))
Coefficients:
X1 X2 X3 X4 X5 X6 X7
-0.153713 1.253117 1.270360 1.175415 1.157070 1.346095 1.345685
X8 X9 X10 X11 X12 X13 X14
1.094094 1.120092 1.136609 1.101456 1.196306 1.091597 0.992584
X15 X16 X17 X18 X19 X20 X21
1.104476 1.298743 1.237293 1.221688 0.044791 0.170803 -0.134242
X22 X23 X24 X25 X26 X27 X28
-0.054549 -0.215860 0.256234 0.030637 -0.004709 -0.088087 -0.069855
X29 X30
0.166688 -0.304760
[[11]]
^C ^C
>
> k3.100 <- lapply(modl3.100,function(l)which.min(sapply(l,KZ.k))-1)
> k3.100
[[1]]
[1] 5
[[2]]
[1] 5
[[3]]
[1] 11
[[4]]
[1] 5
[[5]]
[1] 5
[[6]]
[1] 5
[[7]]
[1] 5
[[8]]
[1] 5
[[9]]
[1] 5
[[10]]
[1] 5
[[11]]
[1] 5
[[12]]
[1] 5
[[13]]
[1] 5
[[14]]
[1] 5
[[15]]
[1] 5
[[16]]
[1] 5
[[17]]
[1] 5
[[18]]
[1] 5
[[19]]
[1] 5
[[20]]
[1] 5
[[21]]
[1] 5
[[22]]
[1] 5
[[23]]
[1] 5
[[24]]
[1] 5
[[25]]
[1] 6
[[26]]
[1] 5
[[27]]
[1] 5
[[28]]
[1] 5
[[29]]
[1] 5
[[30]]
[1] 5
[[31]]
[1] 6
[[32]]
[1] 5
[[33]]
[1] 5
[[34]]
[1] 5
[[35]]
[1] 5
[[36]]
[1] 5
[[37]]
[1] 5
[[38]]
[1] 5
[[39]]
[1] 5
[[40]]
[1] 5
[[41]]
[1] 5
[[42]]
[1] 5
[[43]]
[1] 5
[[44]]
[1] 5
[[45]]
[1] 5
[[46]]
[1] 5
[[47]]
[1] 6
[[48]]
[1] 5
[[49]]
[1] 5
[[50]]
[1] 5
[[51]]
[1] 6
[[52]]
[1] 5
[[53]]
[1] 5
[[54]]
[1] 5
[[55]]
[1] 6
[[56]]
[1] 5
[[57]]
[1] 5
[[58]]
[1] 5
[[59]]
[1] 5
[[60]]
[1] 5
[[61]]
[1] 5
[[62]]
[1] 6
[[63]]
[1] 5
[[64]]
[1] 5
[[65]]
[1] 5
[[66]]
[1] 5
[[67]]
[1] 5
[[68]]
[1] 5
[[69]]
[1] 5
[[70]]
[1] 5
[[71]]
[1] 6
[[72]]
[1] 5
[[73]]
[1] 5
[[74]]
[1] 5
[[75]]
[1] 5
[[76]]
[1] 7
[[77]]
[1] 7
[[78]]
[1] 5
[[79]]
[1] 5
[[80]]
[1] 5
[[81]]
[1] 8
[[82]]
[1] 9
[[83]]
[1] 5
[[84]]
[1] 5
[[85]]
[1] 5
[[86]]
[1] 5
[[87]]
[1] 5
[[88]]
[1] 5
[[89]]
[1] 5
[[90]]
[1] 10
[[91]]
[1] 5
[[92]]
[1] 5
[[93]]
[1] 5
[[94]]
[1] 5
[[95]]
[1] 5
[[96]]
[1] 5
[[97]]
[1] 5
[[98]]
[1] 5
[[99]]
[1] 5
[[100]]
[1] 5
> sapply(k3.100,function(l)l[[1]])
[1] 5 5 11 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6
[26] 5 5 5 5 5 6 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 5 5 5
[51] 6 5 5 5 6 5 5 5 5 5 5 6 5 5 5 5 5 5 5 5 6 5 5 5 5
[76] 7 7 5 5 5 8 9 5 5 5 5 5 5 5 10 5 5 5 5 5 5 5 5 5 5
> table(sapply(k3.100,function(l)l[[1]]))
5 6 7 8 9 10 11
87 7 2 1 1 1 1
> lapply(modl3.100,function(l)which.min(sapply(l,BIC))-1)
Erreur dans match.fun(FUN) : objet 'BIC' introuvable
> library(stats4)
> vv <- lapply(modl3.100,function(l)which.min(sapply(l,BIC))-1)
> vv <- sapply(modl3.100,function(l)which.min(sapply(l,BIC))-1)
> vv
[1] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 17 5 5 5 5 5 5 6
[26] 5 5 5 5 5 6 5 5 5 5 5 5 5 5 5 5 5 5 6 5 5 6 5 5 5
[51] 6 5 5 5 6 5 5 5 5 5 5 6 5 5 5 5 5 5 5 5 5 5 5 5 5
[76] 5 6 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
> table(vv)
vv
5 6 17
91 8 1
> theta.216(1:18,lambda=c(1,2,-3))
[,1]
[1,] 1.250000
[2,] 1.333333
[3,] 1.312500
[4,] 1.280000
[5,] 1.250000
[6,] 1.224490
[7,] 1.203125
[8,] 1.185185
[9,] 1.170000
[10,] 1.157025
[11,] 1.145833
[12,] 1.136095
[13,] 1.127551
[14,] 1.120000
[15,] 1.113281
[16,] 1.107266
[17,] 1.101852
[18,] 1.096953
> theta.216(1:18-1,lambda=c(1,2,-3))
[,1]
[1,] 0.000000
[2,] 1.250000
[3,] 1.333333
[4,] 1.312500
[5,] 1.280000
[6,] 1.250000
[7,] 1.224490
[8,] 1.203125
[9,] 1.185185
[10,] 1.170000
[11,] 1.157025
[12,] 1.145833
[13,] 1.136095
[14,] 1.127551
[15,] 1.120000
[16,] 1.113281
[17,] 1.107266
[18,] 1.101852
> theta.216(1:18-1,lambda=c(-1,2,-3))
[,1]
[1,] -2.0000000
[2,] -0.7500000
[3,] -0.6666667
[4,] -0.6875000
[5,] -0.7200000
[6,] -0.7500000
[7,] -0.7755102
[8,] -0.7968750
[9,] -0.8148148
[10,] -0.8300000
[11,] -0.8429752
[12,] -0.8541667
[13,] -0.8639053
[14,] -0.8724490
[15,] -0.8800000
[16,] -0.8867188
[17,] -0.8927336
[18,] -0.8981481
> theta.216(1:18-1,lambda=c(-0.5,0.5,-0.5))
[,1]
[1,] -0.5000000
[2,] -0.3750000
[3,] -0.3888889
[4,] -0.4062500
[5,] -0.4200000
[6,] -0.4305556
[7,] -0.4387755
[8,] -0.4453125
[9,] -0.4506173
[10,] -0.4550000
[11,] -0.4586777
[12,] -0.4618056
[13,] -0.4644970
[14,] -0.4668367
[15,] -0.4688889
[16,] -0.4707031
[17,] -0.4723183
[18,] -0.4737654
> theta.216(1:18-1,lambda=c(-0.5,10,-0.5))
[,1]
[1,] 9.00000000
[2,] 4.37500000
[3,] 2.77777778
[4,] 1.96875000
[5,] 1.48000000
[6,] 1.15277778
[7,] 0.91836735
[8,] 0.74218750
[9,] 0.60493827
[10,] 0.49500000
[11,] 0.40495868
[12,] 0.32986111
[13,] 0.26627219
[14,] 0.21173469
[15,] 0.16444444
[16,] 0.12304688
[17,] 0.08650519
[18,] 0.05401235
> theta.216(1:18-1,lambda=c(-0.5,3,-0.5))
[,1]
[1,] 2.00000000
[2,] 0.87500000
[3,] 0.44444444
[4,] 0.21875000
[5,] 0.08000000
[6,] -0.01388889
[7,] -0.08163265
[8,] -0.13281250
[9,] -0.17283951
[10,] -0.20500000
[11,] -0.23140496
[12,] -0.25347222
[13,] -0.27218935
[14,] -0.28826531
[15,] -0.30222222
[16,] -0.31445312
[17,] -0.32525952
[18,] -0.33487654
> theta.216(1:18-1,lambda=c(-0.5,3,-4))
[,1]
[1,] -1.50000000
[2,] 0.00000000
[3,] 0.05555556
[4,] 0.00000000
[5,] -0.06000000