使用Betareg时，OpTim"；提供的非限定值出错 [英] "non-finite value supplied by optim" error when using betareg
本文介绍了使用Betareg时，OpTim"；提供的非限定值出错的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在使用betareg程序包进行测试版回归，但收到以下错误：
OpTim中的错误(par=启动，fn=logLike，gr=gradun，方法=方法，： OpTim提供的非限定值
我可以将此错误追溯到为optim创建初始值。具体地说，这些betareg.fit行使用lm.wfit生成起始值。
NA。我不确定为什么会这样，因为lm.wfit的数据/输入中没有缺失值。
查看NA的可重现示例
## data -- a sample of 100 obs from my actual data
nobs <- 100L
w <- rep(1, nobs)
offset <- rep(0, nobs)
y <- stats::rbeta(nobs, 0.75, 1.658)
x <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0165928242550604,
0.0984749494334759, 0.05517578125, 0.0185352577155742, 0.168701442841287,
0.0514759697487192, 0.026507054296708, 0.0188496858385694, 0.108620689655172,
0.0722387772757858, 0.0272373540856031, 0.0538907902524382, 0.0295235311312482,
0.0318257956448911, 0.231788079470199, 0.0674772036474164, 0.14846108458939,
0.0969908238068386, 0.0441553321506012, 0.154121863799283, 0,
0.110460389247421, 0.0292207792207792, 0.0522853185595568, 0.205288796102992,
0.00961124552835874, 0.0546908714289824, 0.0268199233716475,
0.0253164556962025, 0.181780542384243, 0.0551724137931034, 0.128842504743833,
0.0751429349305745, 0.217853751187085, 0.0510314875135722, 0.108407709439207,
0.04, 0.0638009815535624, 0.128329297820823, 0.0398115958281933,
0.0513258247605534, 0.0520833333333333, 0.0956239870340357, 0.0742899497995351,
0.144527098831031, 0.0723209169054441, 0.140116763969975, 0.172426847735821,
0.00830471112933819, 0.0548386400835806, 0.0372010221576987,
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0.0135106607557526, 0.0581090909090909, 0.0321364452423698, 0.0141176470588235,
0.0203003337041157, 0.0948080795499367, 0.0202898550724638, 0.0443828016643551,
0.105830475257227, 0.0482315112540193, 0.0394736842105263, 0,
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0.0356999429308195, 0.0268438884545218, 0.0748295057905382, 0.0556640625,
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0.0732353773706287, 0.022508038585209, 0.0368509212730318, 0.101545253863135,
0.0158054711246201, 0.152565574210159, 0.123442866663249, 0.0672186083185492,
0.129032258064516, 0, 0.104565780781544, 0.0551948051948052,
0.03601108033241, 0.160055671537926, 0.02201309207499, 0.0668891510112489,
0.0421455938697318, 0.0632911392405063, 0.234027661399237, 0.0206896551724138,
0.0950664136622391, 0.0936564116526001, 0.183475783475783, 0.0466883821932682,
0.088748974363268, 0.0422641509433962, 0.0467084108986292, 0.0920096852300242,
0.0401480318492767, 0.05103760198652, 0.0208333333333333, 0.0470016207455429,
0.0887666928515318, 0.075451647183847, 0.0310601719197708, 0.0928685551212356,
0.148991255923013, 0.0204541959296663, 0.0689569784090949, 0.0356901206750445,
0.0680173661360347, 0.0508956145768993, 0.0320699343321964, 0.0293185419968304,
0.0659340659340659, 0.00284090909090909, 0.0402373780415312,
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0, 0.00778642936596218, 0.0745676859521289, 0.0191304347826087,
0.0443828016643551, 0.0975012248897599, 0.00964630225080386,
0, 0, 0.0678391959798995, 0.05, 0.167973405256225, 0.0427812745869394,
0.197810150080232, 0.0363158937772493, 0.0415070411371503, 0.109979633401222,
0.0285551113649343, 0.0348520911254675, 0.0730930274895321, 0.119239631336406,
0.087821043910522, 0.251855350155175, 0.0668162153501042, 0.0731018910527801,
0.0945505662261226, 0.0530209617755857, 0.0879397164898608, 0.0531914893617021,
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0.0387096774193548, 0.0791319596668128, 0.0898373582199164, 0.0641540015091306,
0.0308596309526326, 0.0853097037616193, 0.145443642937691, 0.134765625,
0.0735152424185233, 0.207547169811321, 0.0712368870456209, 0.1626763574177,
0.0128081198646689, 0.096551724137931, 0.130923771988849, 0.0525291828793774,
0.230005661202748, 0.0833089739842151, 0.123953098827471, 0.344370860927152,
0.0419452887537994, 0.188428900927962, 0.237145640026657, 0.184407648334319,
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0.157088122605364, 0.10126582278481, 0.260575112627044, 0.0413793103448276,
0.198292220113852, 0.177511570922951, 0.296296296296296, 0.139522258414767,
0.138297694201175, 0.0645283018867925, 0.12692502961584, 0.162227602905569,
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0.120879120879121, 0.0133522727272727, 0.0824038693376326, 0.0344827586206897,
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0.0233592880978865, 0.158352535758029, 0.0527536231884058, 0.104022191400832,
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0.10678391959799, 0.0583333333333333, 0.233080348742395, 0.127753737214791,
0.233930088412044, 0.13375941339675, 0.13934317947634, 0.338085539714868,
0.0805254140491148, 0.101811822995094, 0.167819649250561, 0.142857142857143,
0.227009113504557, 0.254081770341384, 0.155103348822304, 0.161162856336438,
0.124957303134607, 0.155363748458693, 0.214043708410037, 0.111702127659574,
0.126714801444043, 0.0475465770701011, 0.195590169850639, 0.204512967122728,
0.158064516129032, 0.306298407131375, 0.191432758458608, 0.179792327044226,
0.072245355202807, 0.286421683606985, 0.19797912900281, 0.32275390625,
0.4288140812333, 0.180910099889012, 0.165406196633325, 0.539974348011971,
0.0396326727887869, 0.205172413793103, 0.528357204652504, 0.0953307392996109,
0.456736831270425, 0.312189418298743, 0.440536013400335, 0.322295805739514,
0.185410334346505, 0.289871801748646, 0.277182549853899, 0.478218016952494,
0.186379928315412, 0.730769230769231, 0.312846181208395, 0.573051948051948,
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0.314176245210728, 0.151898734177215, 0.321921116708234, 0.191379310344828,
0.352371916508539, 0.384971413013885, 0.245204178537512, 0.314060803474484,
0.177397478189521, 0.128301886792453, 0.418175664240988, 0.0532687651331719,
0.263429404508243, 0.351343561546648, 0.520833333333333, 0.160453808752026,
0.40037131675589, 0.0701381509032944, 0.139484240687679, 0.208627671654089,
0.302720922280299, 0.464089813912954, 0.325967367919772, 0.509502256498519,
0.357452966714906, 0.387770228536133, 0.389464763566684, 0.401743264659271,
0.450549450549451, 0.0741477272727273, 0.371069078965643, 0.0344827586206897,
0.351488283723876, 0.394836363636364, 0.10394973070018, 0.0164705882352941,
0.0661846496106785, 0.247721658259119, 0.271552795031056, 0.375866851595007,
0.326800587947085, 0.090032154340836, 0.302631578947368, 0, 0.293969849246231,
0.0916666666666667, 0.262623094775136, 0.37765538945712, 0.437246326652613,
0.553230281411019, 0.553700229860763, 0.327902240325866, 0.18275271273558,
0.23291397483849, 0.407002852114813, 0.115207373271889, 0.569179784589892,
0.306166509243017, 0.536532606954014, 0.426756985605419, 0.214233164297538,
0.515413070283601, 0.401348069939186, 0.23936170212766, 0.406859205776173,
0.301968017675986, 0.46031385697156, 0.417500510516643, 0.280645161290323,
0.545082566125968, 0.308916785607088, 0.40121177121836, 0.110259770455074,
0.573885848318999, 0.0979737591064492, 0.05517578125, 0.43828037308911,
0, 0.0195169553549646, 0.106028217186832, 0.0224746254229096,
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0.172457222550019, 0.257446045009484, 0.141730731322689, 0.129032258064516,
0, 0.218723004217694, 0.116883116883117, 0.0207756232686981,
0.00150777081883554, 0.598457747628731, 0.310768434074628, 0.0344827586206897,
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0.175878028859243, 0.0207027540360874, 0.342562432138979, 0.0380448433496877,
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0.381862921002717, 0.310497634940381, 0.266713526509752, 0.221418234442836,
0.376899320568252, 0.147387273053657, 0.412044374009509, 0.137362637362637,
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0.00933572710951526, 0, 0.000278086763070078, 0.0862344564156858,
0.598343685300207, 0.208044382801664, 0.171974522292994, 0.0418006430868167,
0.0921052631578947, 0, 0.268844221105528, 0.0333333333333333,
0.0333061531706705, 0.330841856805665, 0, 0.228289734443123,
0.226188274459035, 0.0712830957230143, 0.0177041690462593, 0.0492786710059746,
0.246950664482068, 0.00576036866359447, 0.0306545153272577, 0,
0.0744271751321904, 0.198701665255433, 0.268701227042224, 0.196054254007398,
0.0790648033408067, 0.0531914893617021, 0.00144404332129964,
0.620702802934266, 0.28496257232331, 0.197569940780069, 0.170967741935484,
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0.0469890478271147, 0.340425531914894, 0.216967509025271, 7.16946330049502e-05,
0.0013271400132714, 0.0275678987134981, 0.0806451612903226, 0.00146134736226801,
0.0792675274278758, 0.0146813198032706, 0.472691340280273, 0.00295369646425159,
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0.0182250396196513, 0.010989010989011, 0.0190340909090909, 0.000341111490123878,
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0.000332402389386587, 0.00658331296056997, 0.00745354298550133,
0.106451612903226, 0.00401870524623703, 0.120851102817492, 0.0428155973269565,
0.222051954091015, 0.00773173486230562, 0.0590151610073926, 0.00927734375,
0.00420172240819286, 0, 0.0102464015613564, 0.0269345874305259,
0.00869985500241663, 0.0344827586206897, 0.00288378352398347,
0.00778210116731518, 0.0201551684207818, 0.0423852674656533,
0.0050251256281407, 0, 0.00547112462006079, 0.0142509252802661,
0.00779207463987287, 0.019712201852947, 0.043010752688172, 0,
0.0273133797449057, 0.00974025974025974, 0.0176592797783934,
0, 0.0310305927058439, 0.022756696107483, 0.00383141762452107,
0, 0, 0.0137931034482759, 0.0187855787476281, 0.0283147291042744,
0.00455840455840456, 0.0485884907709012, 0.0202783033875316,
0.0505660377358491, 0.0424775765781012, 0, 0.0069530111023887,
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0.0493989553698703, 0.00252254856234439, 0.0520984081041968,
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1, 1, 0), .Dim = c(100L, 35L), .Dimnames = list(c("2801", "2316",
"382", "8062", "2687", "2731", "8019", "5652", "8429", "3479",
"7753", "9001", "2188", "8121", "8478", "5817", "1528", "2460",
"3946", "3531", "3421", "2802", "1975", "3639", "2894", "5897",
"9331", "9490", "7135", "5858", "7724", "9414", "9095", "6601",
"5064", "7111", "3593", "7322", "9522", "7116", "6922", "5172",
"2458", "5199", "1387", "3878", "6119", "8722", "6378", "4661",
"6109", "3682", "5751", "9390", "7915", "5268", "1029", "5953",
"242", "2912", "8798", "9607", "9768", "2222", "8260", "851",
"4205", "1823", "5063", "4189", "7541", "608", "6849", "7220",
"2889", "6770", "7064", "646", "4919", "1404", "120", "9716",
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"341", "228", "1543", "553", "9709", "9493", "881", "7647", "6039",
"2925"), c("(Intercept)", "x 1", "x 2", "x 3", "x 4", "x 5",
"x 6", "x 7", "x 8", "x 9", "x 10", "x 11", "x 12", "x 13", "x 14",
"x 15", "x 16", "x 17", "x 18", "x 19", "x 20", "x 21", "x 22",
"x 23", "x 24", "x 25", "x 26", "x 27", "x 28", "x 29", "x 30",
"x 31", "x 32", "x 33", "x 34")))
内部betareg：导致问题的NA
linkfun <- function(mu) {.Call(stats:::C_logit_link, mu)}
auxreg_test <- lm.wfit(x, linkfun(y), w, offset)
# problem:
(beta <- auxreg_test$coefficients)
is.na(beta['x 8'])
> beta['x 8']
x 8
NA
我最初认为这可能与使用betareg(3.1-0)的cran版本有关。但我通过devtools::install_github("rforge/betareg/pkg")更新到rforge版本(3.2-0)，仍然有相同的问题。
如果从公式中删除有问题的预测值，betareg运行正常；但是，预测值是必需的。
推荐答案
NA系数来自glm/lm/lm.fit/.lm.fit/lm.wfit暗示模型矩阵是秩亏的。它们仅为0，标准误差为0(即固定为0)。
x对我们来说提供的信息较少。如果你给我们展示一下模型公式和数据框就好了。
不管怎样，我已经(费了些力气)从您的模型矩阵中发现了共线性问题。
rowSums(, x[, 2:9])
#2801 2316 382 8062 2687 2731 8019 5652 8429 3479 7753 9001 2188 8121 8478 5817
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#1528 2460 3946 3531 3421 2802 1975 3639 2894 5897 9331 9490 7135 5858 7724 9414
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#9095 6601 5064 7111 3593 7322 9522 7116 6922 5172 2458 5199 1387 3878 6119 8722
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#6378 4661 6109 3682 5751 9390 7915 5268 1029 5953 242 2912 8798 9607 9768 2222
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#8260 851 4205 1823 5063 4189 7541 608 6849 7220 2889 6770 7064 646 4919 1404
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
# 120 9716 7722 7700 6638 8176 5745 6 9481 2233 341 228 1543 553 9709 9493
# 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
# 881 7647 6039 2925
# 1 1 1 1
列x1到x8，如果全部包含，则与截取有共线性问题(奇怪；这些列不是哑列，所以它们不是来自因子变量)。如果您不想删除其中任何一个，请删除截取。
这篇关于使用Betareg时，OpTim"；提供的非限定值出错的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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