我用nls拟合一个分段函数，然后用confint()获取参数的CI，代码如下

<br />
f.pl<-function(x,a,b,c){<br />
  ifelse(x>c,a+b*c,a+b*x)<br />
}<br />
fit.V3<-nls(NTR~f.pl(FTSW,a,b,c),<br />
            data=V3,<br />
            start=list(a=0.1,b=1.5,c=0.3),<br />
            trace = T, control = list(warnOnly = T,minFactor = 1/2048))<br />
confint(fit.V3)<br />

但是结果参数都只有一个置信上界或

2.5% 97.5%

a 0.1378599 NA

b NA 1.557623

c 0.5312437 NA

有人遇到过这种情况吗？我有多个不同的数据集但是就这一个是这种情况。

数据集V3

[data]

NTR FTSW

0.974360778 1

1.114397501 1

1.040693963 1

1.201367677 1

1.006790335 1

1.055434671 1

1.043642105 1

1.145352988 1

1.045116175 1

1.216108385 1

0.866753612 1

1.182204757 1

1.080493874 1

1.011212548 1

0.888891367 0.985875767

0.965270181 0.984838457

1.013994597 0.986107011

1.066669641 0.983118255

1.077204649 0.98588843

1.032430862 0.985726532

1.008727092 0.986830602

1.165435348 0.985398031

0.987657075 0.984808879

1.166752224 0.983585683

0.853335713 0.987152876

1.262884179 0.984710996

1.056134632 0.986727987

1.033747738 0.987070022

0.953137022 0.971452381

1.025575435 0.970138179

0.965845515 0.970954602

1.029387983 0.966340079

1.026846285 0.968987603

0.962032967 0.970097482

1.042096477 0.972582355

1.179348208 0.968766444

0.969658063 0.968739287

1.171723112 0.965957701

0.862906717 0.972994822

1.197140099 0.966429007

1.023033736 0.972206631

1.053534121 0.972274055

0.887726067 0.955426398

0.94829357 0.953953834

1.004202034 0.955998977

0.965854562 0.949561903

1.036815305 0.952293388

0.933241291 0.955006678

0.97947329 0.957329663

1.09272377 0.951326768

0.931449353 0.952391155

1.088781506 0.947613458

0.806730471 0.958159453

1.119244451 0.9484711

0.933958066 0.957630955

0.983773941 0.956648761

1.018091545 0.902497917

0.96462035 0.900888434

1.088673521 0.900859948

1.127172781 0.893738219

1.086534674 0.892520661

1.159255498 0.903095907

1.142144716 0.906493555

1.289725213 0.894027663

1.050174261 0.896704662

1.304697147 0.887168978

0.883344134 0.908977692

1.454416492 0.888935489

1.11861739 0.910445599

1.219143236 0.904909999

0.877392558 0.892326759

1.007815776 0.891843652

0.936675839 0.890843615

1.055242401 0.882822048

1.078955713 0.882024793

0.960389151 0.892291131

1.078955713 0.896560704

1.090812369 0.882695633

0.877392558 0.886184436

1.019672432 0.875032331

0.829965933 0.899954117

1.090812369 0.875972243

0.948532495 0.900975937

0.936675839 0.894166431

0.831304588 0.876514455

0.976453008 0.874796944

0.778523344 0.875297637

0.936867075 0.864386768

1.082015495 0.86322314

0.897281142 0.876143771

0.950062386 0.879633935

0.989648319 0.865406299

0.923671764 0.870329105

1.108406117 0.857921649

0.686156168 0.884659923

1.055624873 0.858433735

0.989648319 0.886490793

1.279945159 0.879276223

0.894330638 0.863052629

0.901952774 0.85995628

0.874004942 0.863687349

0.932441319 0.849679972

0.99595912 0.846280992

0.912115622 0.862587963

0.942604167 0.866241327

1.062017633 0.851311734

0.848597821 0.855330819

1.0340698 0.84120889

0.782539308 0.873298521

1.049314072 0.843182858

1.026447664 0.872910971

1.011203392 0.860993309

0.94310859 0.787837347

0.931132608 0.788761206

0.880234684 0.795993467

0.745504885 0.773660335

0.934126603 0.765289256

0.835324751 0.791021271

0.964066559 0.797232194

1.02993446 0.772758025

0.787420823 0.783767569

1.02993446 0.760231591

0.706582944 0.806004064

1.035922451 0.764450206

1.068856402 0.799760995

1.035922451 0.785976817

0.821073441 0.720528216

0.853679245 0.726390309

0.850715081 0.738138812

0.841822589 0.722082979

0.948532495 0.700826446

0.821073441 0.735402588

1.007815776 0.737337475

1.075991549 0.708110952

0.779575144 0.727416867

1.055242401 0.691788862

0.76179016 0.754440779

1.037457416 0.698490163

1.04042158 0.735121041

0.990030792 0.720761474

0.856448933 0.661338917

0.894198748 0.668632052

0.783308666 0.681661649

0.832855298 0.663255795

0.981495196 0.634710744

0.870605114 0.680182605

1.042838646 0.674094604

1.087666551 0.639893257

0.72432458 0.671066164

1.014526284 0.620958596

0.681856038 0.698289235

0.976776469 0.631767577

0.99801074 0.671567474

0.979135832 0.657807935

0.789724606 0.583773158

0.837663161 0.592623789

0.749355298 0.616329181

0.820001588 0.59013609

0.865417061 0.548760331

0.825047752 0.606622411

0.97643266 0.591878871

0.968863415 0.553258663

0.686278252 0.605287967

0.973909579 0.535405185

0.676185925 0.63514606

0.966340333 0.552844289

1.006709642 0.594977276

0.951201842 0.579587221

0.864158542 0.516891387

0.901389862 0.526041353

0.785776814 0.557884173

0.836724937 0.522816248

0.97389296 0.477892562

0.799493617 0.541434922

1.0483556 0.519893603

0.938621182 0.48109449

0.713273717 0.547008913

0.850441739 0.458606076

0.695637828 0.576591142

0.905308948 0.479830715

0.954297528 0.522732622

0.950378441 0.508528885

0.795262494 0.422658604

0.766096388 0.433788581

0.804984529 0.478973572

0.727208246 0.434368333

0.785540458 0.375206612

0.81081775 0.460100074

0.913871325 0.420379085

0.878871998 0.391077201

0.668876034 0.469017827

0.803040122 0.372256819

0.74470791 0.499027726

0.874983184 0.391756901

0.927482175 0.43455431

0.90414929 0.417114315

0.592711823 0.335263574

0.634452092 0.354772075

0.682453402 0.397504772

0.659496254 0.35689873

0.540536486 0.291735537

0.663670281 0.376971074

0.701236523 0.332955116

0.649061186 0.306133955

0.672018335 0.395311964

0.544710513 0.290085752

0.640713133 0.415346632

0.626104038 0.305970718

0.770107967 0.348186641

0.715845617 0.329469419

0.535390378 0.274578517

0.604590495 0.293805026

0.684716946 0.333156227

0.668327445 0.29144313

0.478937651 0.23822314

0.655580055 0.313577737

0.622801052 0.270456279

0.580916771 0.247688491

0.557243047 0.326319849

0.579095715 0.238156821

0.956054247 0.348270664

0.562706214 0.24877993

0.692001169 0.281373916

0.626443164 0.264819527

0.420874348 0.211756661

0.415312131 0.22722259

0.508015732 0.25916524

0.426436564 0.215423494

0.352273683 0.183884298

0.522848308 0.241811694

0.43199878 0.206841391

0.374522547 0.187739608

0.528410524 0.260755914

0.352273683 0.174887089

0.098265817 0.233564203

0.344857395 0.189873418

0.407895843 0.212569483

0.419020276 0.199981152

0.359226453 0.163251352

0.431071744 0.182299501

0.491329084 0.205246276

0.398625483 0.167781759

0.296651522 0.144628099

0.465835594 0.18559496

0.349956093 0.163501423

0.370814403 0.149778246

0.391672713 0.199691464

0.285063572 0.137084419

0.419483794 0.221984313

0.310557063 0.154415129

0.389355123 0.172735338

0.338368143 0.157383847

0.280976283 0.130130986

0.355903292 0.144997293

0.391493621 0.163527953

0.382127745 0.132154028

0.267864056 0.118181818

0.363395992 0.145525587

0.355903292 0.135414148

0.249132304 0.11970984

0.380254569 0.163481316

0.280976283 0.112612165

0.438323001 0.182437895

0.292215334 0.12886991

0.395239971 0.142316537

0.325932488 0.129865234

0.302125165 0.098079019

0.316177498 0.106892887

0.425083081 0.122399984

0.274020499 0.089897881

0.298612082 0.088636364

0.375899915 0.106851664

0.353064873 0.100072543

0.363604123 0.094715478

0.33901254 0.119986287

0.27577704 0.082767951

0.400491498 0.131311586

0.333742915 0.0991307

0.432109248 0.10411197

0.342525623 0.097069079

0.263807838 0.061326097

0.321754068 0.070793975

0.30650506 0.074778125

0.257708234 0.057584357

0.247033929 0.053512397

0.333953274 0.064190738

0.317179365 0.062684846

0.300405457 0.055814478

0.304980159 0.07863387

0.234834723 0.051531008

0.320229167 0.081496209

0.263807838 0.062909867

0.276007044 0.059570153

0.30650506 0.060314768

0.181360151 0.024359495

0.225904749 0.02847803

0.284767254 0.035224433

0.173405758 0.022578039

0.154315216 0.020041322

0.163860487 0.020533062

0.205223329 0.023995089

0.159087852 0.018792754

0.265676712 0.035781625

0.167042244 0.020890949

0.259313198 0.035613625

0.249767927 0.029929846

0.23545002 0.026797516

0.189314543 0.022429554

[/data]
</p>