R glmnet在使用预测时产生错误
我似乎对R glmnet在使用预测时产生错误,r,glmnet,lasso-regression,variable-selection,R,Glmnet,Lasso Regression,Variable Selection,我似乎对glmnet有问题。我想运行常规套索回归，以了解10个变量（Dim1，Dim2…）中的哪一个对预测我的连续变量ptScores贡献最大。 所有变量都是连续的，validInd是一个data.frame，trainingData和validationData都是长度为95的字符向量 运行以下代码： lambda_seq <- 10^seq(2, -2, by = -.1) trainingData = sample(rownames(validInd), 190/2) validat
glmnetDim1Dim2ptScoresvalidIndtrainingDatavalidationDatalambda_seq <- 10^seq(2, -2, by = -.1)
trainingData = sample(rownames(validInd), 190/2)
validationData = subjects$ID[subjects$ID %in% trainingData]
LASSO = cv.glmnet(data.matrix(validInd[trainingData,-11]), validInd[trainingData,11], lambda = lambda_seq)
plot(LASSO)
predictLASSO = predict.glmnet(object = LASSO, s = "lambda.min",
newx = data.matrix(validInd[validationData,-11]))
ID Name Year Dim#1 Dim#2 Dim#3 Dim#4 Dim#5 Dim#6 Dim#7 Dim#8 Dim#9 Dim#10 ptScore
X004 y7 X004 y7 14.2594361560831 1.76025391802766 -0.668570729483714 -1.64432433500694 -3.11101507888161 -2.93744325366336 2.39088933956153 -0.632863861457362 -2.01542044174992 0.397373751034953 0.205293413285948
X006 y7 X006 y7 7.36769839988443 1.40189322331541 -1.51699477009677 -2.11631615238169 2.44537900196125 0.281302344948175 1.04396856774743 -1.21825370453226 9.62767430413176E-02 -4.34274219927117E-02 0.402603441521971
X010 y7 X010 y7 -2.19534888488862 -5.71552997812972 -2.54798524356995 -5.46532854493649 2.0349871802497 1.86977495475099 1.0102785949972 -0.648213341255023 1.19509421829095 -0.474244031626319 0.255199305410578
X011 y7 X011 y7 4.47733296227365 4.41612174858369E-02 1.04199108489624 0.507709900129822 -1.49702171420879 0.562924762828593 -2.09921985421718 -1.10136884350998 -1.14148162529434 -1.87562935354984 0.541577519851939
X012 y7 X012 y7 -6.3214287141386 1.75365412184414 -0.623570056714132 2.62771487690855 -0.755944893491157 1.11946631740662 0.377908536769657 -0.375206246075601 -0.158315945250462 1.60467668360538 0.723807672288481
X015 y7 X015 y7 4.43675650235116 0.125831659587608 -1.63660536215786 0.704457166310484 -0.485788420367662 -1.23623362303915 -2.94185073254018 0.371005042652702 0.390480922397501 -0.751322935004845 0.566289988658306
X016 y7 X016 y7 2.22481961161996 -0.495782079794814 -0.936901673888438 3.46441249413796 -0.299713046858658 1.35032843918957E-02 -0.446639771849217 -2.34040678094425 0.413652502415691 0.352777159566364 0.643054677259255
X022 y7 X022 y7 10.4159916313874 0.363807261537086 2.21572539303282 2.39473567103644 -8.21247405890959E-02 0.121713032667271 -0.172976208049391 0.399694378320414 0.813322631516825 -1.34474965111697 0.177545698165701
X023 y7 X023 y7 -4.8775402107091 -4.12501756265517 -2.27881222191454 -1.86202209268234 -0.502096138361181 1.7091075290976 -0.377341826579625 -0.361068200236776 -1.04929174692168 -1.83743917666367 0.441999945490185
X024 y7 X024 y7 -13.6186193549591 4.53794647377105 -1.1640424147117 0.853074420261857 3.00276105179634 2.2621696100755 1.54863273082483 1.46874484773545 0.642079320801509 -1.17894488952068 0.740868185192386
X025 y7 X025 y7 -8.28369499974165 0.795185031920821 3.36606773135854 1.30335423715504 1.31846438526884 1.92128352973198 0.119247908530787 1.98937148422595 0.98667359874469 0.742089792025553 0.638259141472873
X026 y7 X026 y7 -9.126960905357 1.08725723261223 -0.573951041914609 1.67782607044232 0.198336623247584 -2.86953122246331 1.68852114521665 0.549962201982335 -0.380286483868369 1.01628826947139 0.759959459159294
X028 y7 X028 y7 -5.25213145411614 -1.96999472344059 1.42004682865149 -0.91455749550075 0.53138842064767 0.372108093120868 0.999674017662924 0.983440963089232 -0.45271465928228 1.52238785156272 0.672605306141995
X029 y7 X029 y7 -3.93494816396007 6.84260030634352 4.84132536930456 -0.703242523684553 1.27001300421836 2.88402236252198 -1.41616084473683 0.18558627899548 -0.255520180512287 -0.11825893347637 0.656308969574663
X031 y7 X031 y7 -6.16292963856133 4.85060509570318 4.2126847620166 -2.43553872291154 -0.43435465583943 1.37553276359068 -0.579643934018511 1.17488164948438 1.32392129023095 2.71520764572902 0.718541909152817
X032 y7 X032 y7 -1.7152551073983 -0.234688383679724 1.26286864412284 -1.86322100220324 1.63507032962317 1.94284519738854 -0.367428571302029 -1.62696732853019 -1.20990245118527 1.05664015163771 0.583917996518399
X033 y7 X033 y7 -10.9148039117832 -3.36356028164647 -1.34182187001758 -2.07743121838056 3.3930211353689 -1.69081847936732 1.50845514067627 0.649001132628532 1.8237842283369 1.06407445448196 0.711772373386066
X034 y7 X034 y7 -10.4794967653808 7.01058540479745 2.12270784102553 1.92426221678289 -3.81951360261083 1.98320703989698 0.344382033696651 1.69608467068158 7.70807524085422E-02 0.281493387449773 0.746483819273614
X035 y7 X035 y7 -0.38702071945627 -0.037845940584369 0.253390462384328 2.45809313523985 1.3912538136 0.396616621403596 -0.652711117741324 -2.02482113921603 0.735140206294811 0.114276526211146 0.694123571039207
X036 y7 X036 y7 -3.34295132184417 -4.41772971639855 -2.42496546304556 1.43539718446744 0.24081427494968 1.27215110035618 1.57122124745865 -9.07926785309553E-02 -0.500599334998908 0.3526156338948 0.482638053394283
X037 y7 X037 y7 1.36505270190872 0.154232922168672 3.72787626189391 -3.58555329515882 -0.258287182557876 3.77695923237513 2.06363274503012 -7.44424212333302E-02 -0.243026580519433 0.754189664133906 0.570798239999777
X038 y7 X038 y7 -7.57715353844738 1.49530333294752 -1.3618206776225 0.285673183897211 -2.00257652679547 -2.9316605063682 -5.78101977945745E-02 1.21490778203858 -1.41511921486559 0.910320527049355 0.717900142262124
X040 y7 X040 y7 -3.71657538478269 2.9915033382211 -2.37358232145532 -1.74140110557328 0.879562060220561 -0.186006811914471 -3.03422383938489 7.09245387496289E-02 1.69266042104312 9.11003346977334E-02 0.664458698220819
X041 y7 X041 y7 3.56870600204357 1.89948021381691 -1.18807030156106 -5.11225314209496 -1.72892843911463 0.836946514430054 0.825742473828449 2.18107132564707 -1.41175755455589 -3.15021775639787E-02 0.434972906089723
X046 y7 X046 y7 -3.2070611126168 1.9658134244394 -4.96699587273018 2.58507257721916 0.677391573876038 -0.794142281429037 1.76204196290053 -1.24528928618193 1.30435133802695 -0.348545696553259 0.731839649682648
X048 y7 X048 y7 -8.76908227910536 -2.58866919749497 -3.47359591232635 2.39032746268097 -0.435285448011596 -0.611678098455047 -0.387857745557412 7.5630308277455E-04 2.82150278577457 -1.05165750982245 0.554033843744992
X050 y7 X050 y7 0.475793940378902 0.532527233132171 1.09069683661794 -0.273929007898906 3.56606669789417 -5.32586521203006E-03 -2.06199829748345 0.916218309187297 -0.511609127771478 -0.829866398097593 0.586056512557182
X052 y7 X052 y7 0.437961232041306 -0.525296154559809 4.12777505698584 0.928000030902302 1.50933898151927 0.536752855000547 -1.35328387942828 -3.45343510641525E-02 0.342800240728446 -1.36024230292128 0.503553558040187
X053 y7 X053 y7 -6.35893956287975 -0.481951608723444 4.49223165394607 -0.170823560932413 1.07820528949514 1.90426407261065 1.6976781544118 1.60663679082296 0.377536827292087 -4.32712770127625E-02 0.694829758520922
X054 y7 X054 y7 -1.6173741934411 -0.78544603543625 -1.5586277454018 0.276715960571991 -1.37115552595115 -1.43527489189538 -0.944340962417392 -0.215524444567615 -1.44942417414991 1.11503418539154 0.635810834838745
X058 y7 X058 y7 -0.533953659527016 -3.59419670524299 -6.21847389392613E-02 3.12248184904771 -0.970549462282703 1.73600706110163 0.716076363110235 -2.03157502654338 -0.185983613882669 -1.13023192445807 0.544695504576695
X060 y7 X060 y7 -9.33029002765201 -7.33684242872651 0.251727648993351 1.61228307501029 1.58272329429705 -1.0149446684102 -0.271252146648448 1.44859865101697 0.401334428833948 1.56787478926467 0.591928043482019
X062 y7 X062 y7 4.97202858939999 -0.405994077513881 -4.7355765771677 0.371412533877246 -1.33579080581324 0.341680698379248 0.373782667456135 -1.36795891418662 0.201812628898999 -0.509864200796645 0.412265678072685
X064 y7 X064 y7 1.56303493507485 1.05410634364062 -1.11670857011548 -2.77203704570765 -1.32949528606372 -1.61433165655895 1.32918153646877 -0.312768874242464 -0.688931739007988 0.734214203780031 0.453251813757181
X065 y7 X065 y7 5.05828924636548 0.908758251845031 3.62690379671223 0.164838132208794 0.221854014751618 0.643668333644285 -0.382838756002362 0.676441843706409 1.7103682845196 0.853707219236365 0.32293905373328
X066 y7 X066 y7 -2.40603983747625 7.318117319931 -8.68510001944344E-02 -0.397226292933707 0.152339697912322 2.09248669870129 -0.26977707763143 -1.62178289672658 0.848545563744191 -1.38664444002672 0.700043422915476
X067 y7 X067 y7 -6.62949725610905 1.68523719992851 0.851585358021073 0.951786423099745 -0.616021491459863 -2.82842995392253 1.16159509426505 0.493196821437061 -2.39793328950165 -1.38433590850038 0.601160811740337
X069 y7 X069 y7 1.47542719023891 13.0641832135398 -0.581357473415856 -0.714399303360872 -1.79962725645702 -1.81831420473663 0.610502113121055 -2.706236099573 0.347757363707449 0.293755342189694 0.754695271073973
X070 y7 X070 y7 -1.72139117485005 -3.59264407072097 -1.12987545996396 2.78410751589509 1.18420494996691 -1.66990029841491 -1.31210755888876 -1.64519705582319 -1.17749982138072 -0.789854195043032 0.596281826903686
X072 y7 X072 y7 5.31181586621353 -1.76062594156829 0.978811817696108 0.805416187256626 -0.101536944556724 0.733573760323421 -0.685405370241293 -0.807975536080938 -0.63928589312435 -0.776631166896858 0.342870418080616
X073 y7 X073 y7 -6.83154542538091 -3.92242822624881 -1.30735562714719 -3.13070017851774 -1.07012896190267 1.61291603617646 -0.447759632130991 -0.214244993352655 -0.585066675773694 -0.565011919418235 0.596791110310168
X079 y7 X079 y7 6.19683396427599 -0.45015295862773 -0.589001379439875 -2.85912975635691 -1.96069465183664 -1.33320367366084 2.38154698610703 -0.138158506908214 -1.00374447348715 1.1321702238712 0.29798849247412
X080 y7 X080 y7 6.20901800903392 5.43939505549203 -3.98672804043326 -1.6983742019417 -2.17242534555216 -3.89485271347152 1.60686298747452 -1.5078090986148 -1.05199703793871 -1.5862334877556 0.502676441557364
X082 y7 X082 y7 0.6510182495572 -3.82029305675754 6.14830660856756 2.39568264624447 1.51859911006723 3.43748132085452 2.98300921884542E-03 1.02046197057199E-03 -3.25206786681409E-02 1.37307432097495 0.512226253331402
X084 y7 X084 y7 -5.05915610597868 1.28231684653798 -3.16813452113816 3.81398978942311E-02 -0.26056658322319 -0.607570108068939 -2.26895660622817 -0.280121103992718 1.1581557206668 0.70719181665203 0.758975946511367
X087 y7 X087 y7 -2.87440758130519 -1.56614425216859 -0.214685578900002 2.61868788902779 -0.549949439830095 1.99035737918547 -0.307185247663762 -2.17612427977269 -1.07670874867566 -0.302842450831714 0.639111357004194
X089 y7 X089 y7 -7.2321169776611 -3.45940607493965 -0.896630636558091 -0.624289080870102 3.37973009314874 -0.660949568507029 -1.28382758970168 1.61495582985708 -0.236546232606131 0.743689477887689 0.684182777346267
X090 y7 X090 y7 5.8759449122214 3.27805104437807 -4.07645642306576 1.31539148541223 -0.266021698028939 -0.695419677613201 0.889055163828194 2.32893377555772E-02 1.20358476072801 1.29387076400352 0.419446528984698
X092 y7 X092 y7 5.77420047603891 -1.17329563768878 0.445047009024658 1.2028311977189 -2.09516788500395 -0.503860560134249 -0.440837437962766 1.85501315350221 -0.834761299098988 0.456883238407818 0.343706388464323
X093 y7 X093 y7 9.24685096556148 2.0100787128997 1.681436650306 3.03348652975405 -1.08811689969173 -1.13694466537189 -1.27936614497546 0.348021793918123 -0.429656061648232 -2.27609340681847 0.310623710348242
X094 y7 X094 y7 13.4618107876782 -3.94822016837025 5.05159247878004 1.97556287390166 3.73953089973484 -0.646746115568399 0.965160056105702 0.676367876074884 2.28436942504023 0.537227025430724 0
X100 y7 X100 y7 -5.88798347467999 -2.25570846537281 -2.27257310819712 2.25978831635043 -1.87020337507398 -0.349201035751804 0.244248986726665 -1.55416432151377 -0.903176091406223 0.439194502863558 0.648524886958248
X102 y7 X102 y7 -0.719752186559132 8.18956111960742 2.67845648218982 -2.71471106207068 1.42202429189887 -0.524542225331085 0.447062271900674 0.686175123124667 -1.29836166579492 -0.308340551448655 0.719861271366769
X103 y7 X103 y7 13.2478766364026 0.752585450596604 2.0853104095222 -0.737709229030171 -1.17014153049354 -2.15743132778219 0.654373324873155 -0.927636291502917 -1.85545309230693 -8.19019392079715E-02 0.270433756274452
X105 y7 X105 y7 2.90005648557469 1.7193255259151 -1.21587523209629 -0.427177101578488 -0.627556210423172 -3.99113235385036 1.25742312331302 0.040912848245426 -0.785061544873709 0.493921235479174 0.602263270046018
X106 y7 X106 y7 -4.60472917537252 -5.56640545961895 0.770010852917855 -3.01183314585768 -0.975570824177974 1.73607501284895 -1.28369536274259 0.137828528824118 -1.10854706826854 -0.165315956464549 0.448934375317496
X108 y7 X108 y7 6.5459572482018 -0.792755406433753 -1.95714624076072 -9.50434640321064E-02 -5.30063096031214E-02 -0.38580608118101 -4.11031240875448E-02 0.279569541388208 1.1895509027746 -0.255145074826884 0.113352115264063
X111 y7 X111 y7 -2.42539983228739 -5.41900054828541 1.17653088275733 -0.590160767698779 1.72503186163854 2.01686477861317 1.37795156556941 9.42444506993529E-02 1.63959299252878 -1.18845953874891 0.360989555160435
X112 y7 X112 y7 5.3250172913726 -3.66332040322174 3.3538680995316 2.0479624176086 1.24410020087761 -8.44328442600435E-02 -0.106295599600603 2.30338122773556 0.115211699931763 -0.330963225779836 0.303887289044354
X114 y7 X114 y7 7.24362142680027 -2.04523989830419 -1.68861850924316 1.05488084601232 -2.16757878870729 -1.56116361874208 1.15901958361976 1.51582102854526 -1.77539431197779 0.148843856238521 0.396856395957134
X115 y7 X115 y7 9.70789576557706 2.15415360808665 -0.774767736625052 -2.02622786105656 -6.83272897932898E-03 -1.25979936594413 -0.619358064016897 1.48545525633581 1.23881666328091 -0.473892068691094 0.15763059917753
X118 y7 X118 y7 5.20719333032362 -2.37368717020733 -1.32574911013336 -2.70910591219905 0.945739499835788 -0.394056048048146 -0.299532916420157 0.403463903751592 0.930547005874251 -0.686518257590447 0.192687438493111
X119 y7 X119 y7 0.599963880759262 -1.04269222058587 4.27204142269578 3.20972399672777 -1.33346444179932 -1.23379841268799 -0.514526033076736 -4.03518177966058E-02 -2.20437447323995 -0.155625307891573 0.603501689047888
X120 y7 X120 y7 5.03891337622233 -4.37958479507978 -3.75958410005882 -2.41737564483433 -2.65093584245105 6.38678877360447E-02 -3.76307051597622 -0.306951328262414 5.06385240379942E-02 1.80418407493883 0.352064145163263
X121 y7 X121 y7 2.94579461165834 -1.53944682079578 -2.18511806792739 8.52317889470527E-02 0.773374097566854 0.908527198741026 -1.06436773077894 -0.106908974206049 3.77291796895366 -1.43914893232295 0.485354296934085
好的，谢谢你提供数据。现在有点清楚了。问题是您使用的是cv.glmnet对象，而不是glmnet对象，如果您执行以下操作：
class(LASSO)
[1] "cv.glmnet"
predictLASSO = predict(object = LASSO, s = "lambda.min",
newx = data.matrix(validInd[validationData,-11]))
glmnet:：：predict.cv.glmnetpred2 = glmnet:::predict.cv.glmnet(LASSO,s="lambda.min",data.matrix(validInd[validationData,-11]))
table(pred2==predictLASSO)
TRUE
32
好的，谢谢你提供数据。现在有点清楚了。问题是您使用的是cv.glmnet对象，而不是glmnet对象，如果您执行以下操作：
class(LASSO)
[1] "cv.glmnet"
predictLASSO = predict(object = LASSO, s = "lambda.min",
newx = data.matrix(validInd[validationData,-11]))
glmnet:：：predict.cv.glmnetpred2 = glmnet:::predict.cv.glmnet(LASSO,s="lambda.min",data.matrix(validInd[validationData,-11]))
table(pred2==predictLASSO)
TRUE
32
这条线带来了问题<代码>验证数据=科目$ID[科目$ID%在%trainingData中]。。什么是主题？它应该是
validationData=setdiff（rownames（validInd），trainingData）subjectvalidIndpredict.glmnetdata.matrix（validInd[validationData，-11]）validationData=setdiff（rownames（validInd），trainingData）subjectvalidIndpredict.glmnetdata.matrix（validInd[validationData，-11]）