问题描述
我正在使用 mgcv 包运行游戏。这是我的模型代码。我正在使用方法 ="ML"，因为我希望使用 MuMIn 包和 dredge() 在此模型上进行模型选择，而您不能使用 REML 进行模型比较。
mDEG_nb <- bam(deg ~ s(SE_score) + s(ri) + TL + species + sex + season + year +
s(code,bs = 're') + s(station,bs = 're'),family=nb(),data=node_dat,method = "ML")
th1 <- mDEG_nb$family$getTheta(TRUE) #extracts theta
mDEG_nb <- bam(deg ~ s(SE_score) + s(r)i + TL + species + sex + season + year +
s(code,family=nb(th1),method = "ML",na.action = "na.fail")
但是使用 gam.check() 我可以看到我有一个平滑残差的问题。
Method: ML Optimizer: outer newton
full convergence after 7 iterations.
Gradient range [-0.001295518,0.002610357]
(score 82388.55 & scale 1).
Hessian positive definite,eigenvalue range [0.001293007,29.20119].
Model rank = 224 / 224
Basis dimension (k) checking results. Low p-value (k-index<1) may
indicate that k is too low,especially if edf is close to k'.
k' edf k-index p-value
s(SE_score) 9.00 1.00 0.90 0.59
s(ri) 9.00 8.98 0.34 <2e-16 ***
s(code) 134.00 100.15 NA NA
s(station) 63.00 54.46 NA NA
为了解决这个问题，我尝试增加 K 的值，但是我不断收到此错误，表明当我使用大于 12 的 K 值时，估计 theta 存在问题。
Warning messages:
1: In estimate.theta(theta,family,G$y,linkinv(eta),scale = scale1,:
step failure in theta estimation
2: In estimate.theta(theta,:
step failure in theta estimation
此外，无论我增加多少K，我仍然有剩余问题。但这可能是由于未计算新的 theta 值时出错。
这个警告有问题吗？我怎样才能改变我的模型，使这个平滑器的残差不再是一个问题？如果残差不是随机的，这将如何影响我的模型结果？我认为他们不值得信任？
如果您需要，我的数据集示例如下。
> dput(head(node_dat))
structure(list(station = structure(c(17L,49L,23L,25L,9L),.Label = c("BE01","BE02","BEUWM01","BL01","BL02","GCB01","GCB02","GCB03","NI01","NI01b","NI03","PB01","PB02","PB03","PB04","PB05","PB06","PB07","PB08","PB09","PB10","PB11","PB12","PB13","PB14","PB15","PB16","PB17","PB18","PB19","PB20","PB21","PB22","PB23","PB24","PB25","PB26","PB27","PB28","PB29","PB30","PB4G01","PB4G02","PBUWM01","PBUWM02","SA01","SA02","SA02b","SA03","SA04","SA05","SA06","SA07","SA11","SAUWM01","SB01","SB02/AR02","SB03/AR05","SB04/AR06","VB01","VB02","VB03","VB04"),class = "factor"),monthyear = structure(c(27L,17L,38L,4L,19L,29L),.Label = c("2014/01","2014/02","2014/03","2014/04","2014/05","2014/06","2014/07","2014/08","2014/09","2014/10","2014/11","2014/12","2015/01","2015/02","2015/03","2015/04","2015/05","2015/06","2015/07","2015/08","2015/09","2015/10","2015/11","2015/12","2016/01","2016/02","2016/03","2016/04","2016/05","2016/06","2016/07","2016/08","2016/09","2016/10","2016/11","2016/12","2017/01","2017/02","2017/03","2017/04","2017/05","2017/06","2017/07","2017/08","2017/09","2017/10","2017/11","2017/12","2018/01","2018/02","2018/03","2018/04","2018/05","2018/06","2018/07","2018/08","2018/09","2018/10","2018/11","2018/12"),code = structure(c(99L,204L,183L,146L,135L),.Label = c("2390","13573","13574","13575","13576","19318","19319","19321","19322","19506","19514","19519","19520","19524","25537","25540","25541","25543","25546","25549","25552","25553","27583","27585","27586","27591","27592","27593","27594","27595","27596","27597","27600","27601","27605","27607","27608","27613","27614","27617","27619","27620","27621","27626","27627","27629","27630","27631","27632","28608","28611","28612","28618","28625","28628","28629","28631","28632","28633","28638","28641","28644","28662","28672","28674","52978","54815","54846","54852","54860","54863","54865","54866","54868","54877","54882","54883","54884","54886","54890","54892","54895","54896","54901","54904","54914","54919","54920","54922","54925","54931","54932","54938","54952","54954","54955","54958","54959","54962","59950","59953","59954","59955","59957","59958","59959","59960","59961","59962","59964","59966","59969","59970","59971","59972","59973","59975","59976","59979","59981","59988","2388","12950","12952","12956","12958","12960","12962","12964","12966","12968","13577","14203","19320","19523","25534","25535","25536","25539","25542","25544","25545","25547","25548","25550","27584","27588","27589","27590","27598","27599","27602","27603","27604","27606","27609","27610","27611","27615","27616","27618","27622","27624","27625","28624","28627","28637","28639","28642","28660","28670","34176","34177","34178","34179","52975","52977","54817","54821","54822","54825","54845","54849","54880","54887","54889","54893","54898","54899","54905","54911","54912","54915","54933","54947","54957","54961","59951","59963","59968","59978","59991","59992","59993","59994","59995"
),species = structure(c(1L,2L,1L,2L),.Label = c("Grey Reef Shark","Silvertip Shark"),deg = c(0,0),gs = c(0,btw = c(0,ud = c(0,ri = c(0,SE_score = c(0.35,0.39,0.18,0.23,0.36,0.42
),region = structure(c(5L,6L,5L,4L),.Label = c("Benares","Blenheim","Grand Chagos Bank","Nelsons Island","Peros Banhos","Saloman","Speakers Bank","Victory Bank"),date = structure(c(1456790400,1430434800,1485907200,1396306800,1435705200,1462057200),class = c("POSIXct","POSIXt"),tzone = ""),month = c(3,5,2,4,7,5),season = structure(c(2L,1L),.Label = c("dry.season","wet.season"),year = structure(c(3L,3L),.Label = c("2014","2015","2016","2017","2018"),sex = structure(c(1L,.Label = c("F","M"),TL = c(117,157,137,108,94,137),TL_stand = c(0.353383458646617,0.654135338345865,0.503759398496241,0.285714285714286,0.180451127819549,0.503759398496241),btw_stand = c(0,0)),na.action = structure(c(`59` = 59L,`91` = 91L,`119` = 119L,`144` = 144L,`715` = 715L,`754` = 754L,`780` = 780L,`803` = 803L,`2116` = 2116L,`2452` = 2452L,`2489` = 2489L,`2504` = 2504L,`2544` = 2544L,`3070` = 3070L,`3092` = 3092L,`3126` = 3126L,`3151` = 3151L,`4464` = 4464L,`4800` = 4800L,`4842` = 4842L,`4862` = 4862L,`4893` = 4893L,`6181` = 6181L,`8221` = 8221L,`10073` = 10073L,`11232` = 11232L,`11603` = 11603L,`11639` = 11639L,`11663` = 11663L,`11688` = 11688L,`12266` = 12266L,`12288` = 12288L,`12322` = 12322L,`12347` = 12347L,`13660` = 13660L,`14023` = 14023L,`14045` = 14045L,`14075` = 14075L,`14104` = 14104L,`15417` = 15417L,`15780` = 15780L,`15795` = 15795L,`15837` = 15837L,`15877` = 15877L,`17138` = 17138L,`17164` = 17164L,`17194` = 17194L,`17219` = 17219L,`18532` = 18532L,`18895` = 18895L,`18917` = 18917L,`18951` = 18951L,`18976` = 18976L,`20289` = 20289L,`20652` = 20652L,`20674` = 20674L,`20704` = 20704L,`20729` = 20729L,`22055` = 22055L,`22409` = 22409L,`22435` = 22435L,`22461` = 22461L,`22490` = 22490L,`23803` = 23803L,`24166` = 24166L,`24188` = 24188L,`24218` = 24218L,`24247` = 24247L,`25560` = 25560L,`25919` = 25919L,`25939` = 25939L,`25976` = 25976L,`25996` = 25996L,`27308` = 27308L,`27330` = 27330L,`27360` = 27360L,`27385` = 27385L,`28702` = 28702L,`29065` = 29065L,`29087` = 29087L,`29121` = 29121L,`29146` = 29146L,`30459` = 30459L,`30822` = 30822L,`30844` = 30844L,`30874` = 30874L,`30903` = 30903L,`32216` = 32216L,`32579` = 32579L,`32605` = 32605L,`32631` = 32631L,`32660` = 32660L,`33973` = 33973L,`34336` = 34336L,`34369` = 34369L,`34397` = 34397L,`34415` = 34415L,`34875` = 34875L,`34901` = 34901L,`34931` = 34931L,`34956` = 34956L,`36269` = 36269L,`36632` = 36632L,`36658` = 36658L,`36684` = 36684L,`36709` = 36709L,`38026` = 38026L,`38389` = 38389L,`38415` = 38415L,`38441` = 38441L,`38466` = 38466L,`39783` = 39783L,`40146` = 40146L,`40168` = 40168L,`40198` = 40198L,`40223` = 40223L,`41540` = 41540L,`41914` = 41914L,`41937` = 41937L,`41960` = 41960L,`41984` = 41984L,`43297` = 43297L,`43633` = 43633L,`43675` = 43675L,`43695` = 43695L,`43730` = 43730L,`45014` = 45014L,`45363` = 45363L,`45400` = 45400L,`45415` = 45415L,`45455` = 45455L,`45954` = 45954L,`45991` = 45991L,`46009` = 46009L,`46048` = 46048L,`46541` = 46541L,`46576` = 46576L,`46602` = 46602L,`46627` = 46627L,`46817` = 46817L,`46859` = 46859L,`46879` = 46879L,`46910` = 46910L,`48198` = 48198L,`48547` = 48547L,`48589` = 48589L,`48609` = 48609L,`48640` = 48640L,`49928` = 49928L,`50277` = 50277L,`50319` = 50319L,`50345` = 50345L,`50370` = 50370L,`51658` = 51658L,`52007` = 52007L,`52048` = 52048L,`52069` = 52069L,`52100` = 52100L,`53388` = 53388L,`53737` = 53737L,`53778` = 53778L,`53799` = 53799L,`53830` = 53830L,`55118` = 55118L,`55467` = 55467L,`55508` = 55508L,`55529` = 55529L,`55560` = 55560L,`56848` = 56848L,`57197` = 57197L,`57238` = 57238L,`57264` = 57264L,`57295` = 57295L,`58555` = 58555L,`58596` = 58596L,`58617` = 58617L,`58648` = 58648L,`59936` = 59936L,`60285` = 60285L,`60322` = 60322L,`60337` = 60337L,`60377` = 60377L,`60875` = 60875L,`60905` = 60905L,`60931` = 60931L,`60972` = 60972L,`61441` = 61441L,`61463` = 61463L,`61497` = 61497L,`61522` = 61522L,`62835` = 62835L,`63197` = 63197L,`63236` = 63236L,`63260` = 63260L,`63276` = 63276L,`63793` = 63793L,`64180` = 64180L,`64206` = 64206L,`64232` = 64232L,`64261` = 64261L,`65574` = 65574L,`65937` = 65937L,`65959` = 65959L,`65993` = 65993L,`66018` = 66018L,`67331` = 67331L,`67694` = 67694L,`67716` = 67716L,`67746` = 67746L,`67772` = 67772L,`69088` = 69088L,`69424` = 69424L,`69466` = 69466L,`69486` = 69486L,`69517` = 69517L,`70805` = 70805L,`72253` = 72253L,`73419` = 73419L,`73760` = 73760L,`73802` = 73802L,`73828` = 73828L,`73859` = 73859L,`75141` = 75141L,`76590` = 76590L,`77752` = 77752L,`78906` = 78906L,`79486` = 79486L,`79523` = 79523L,`79536` = 79536L,`79556` = 79556L,`80122` = 80122L,`80159` = 80159L,`80174` = 80174L,`80214` = 80214L,`82125` = 82125L
),class = "omit"),row.names = c(31669L,80335L,63799L,59674L,1051L,51949L),class = "data.frame")
解决方法
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