我正在使用betareg软件包进行Beta回归，但收到以下错误：
optim中的错误（par =开始，fn = loglikfun，gr = gradfun，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。具体来说，这些betareg.fit行使用lm.wfit生成起始值。
事实证明，我的数据集的一个起始值以NA的形式返回。我不确定为什么会这样，因为在{/ {1}}的数据/输入中没有缺失值。
lm.wfit## 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,
0.0549927641099855, 0.0386658431130327, 0.0256367439122648, 0.0166402535657686,
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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,
0.071608040201005, 0.0416666666666667, 0.268330928934329, 0.0422895357985838,
0.127678318597993, 0.037029330162505, 0.0328938677375888, 0.10183299389002,
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0.0770505385252693, 0.174605316421536, 0.0842012497997116, 0.068774108570891,
0.137089781654799, 0.00986436498150432, 0.0812065297585365, 0.0904255319148936,
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0.0709677419354839, 0.0640070144673389, 0.106259964391839, 0.0315146661646867,
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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0.0443828016643551, 0.0975012248897599, 0.00964630225080386,
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0.197810150080232, 0.0363158937772493, 0.0415070411371503, 0.109979633401222,
0.0285551113649343, 0.0348520911254675, 0.0730930274895321, 0.119239631336406,
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0.0805254140491148, 0.101811822995094, 0.167819649250561, 0.142857142857143,
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0.126714801444043, 0.0475465770701011, 0.195590169850639, 0.204512967122728,
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0.450549450549451, 0.0741477272727273, 0.371069078965643, 0.0344827586206897,
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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,
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0.573885848318999, 0.0979737591064492, 0.05517578125, 0.43828037308911,
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0.0013271400132714, 0.0275678987134981, 0.0806451612903226, 0.00146134736226801,
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0.0273133797449057, 0.00974025974025974, 0.0176592797783934,
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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",
"7722", "7700", "6638", "8176", "5745", "6", "9481", "2233",
"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")))
：引起问题的不适用betareg最初，我认为这可能与使用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
（3.1-0）的CRAN版本有关。但是我通过betareg更新到rforge版本（3.2-0），仍然有相同的问题。
如果我从公式中删除有问题的预测变量，则devtools::install_github("rforge/betareg/pkg")运行良好；但是，预测变量是必需的。
答案 0 :(得分：4)
NA / glm / lm / lm.fit / .lm.fit的 lm.wfit系数表示模型矩阵秩不足。它们只是0，标准错误为0（即固定为0）。
我很高兴您已经进行了许多调试工作并找到了错误的根源，但是直接给我们提供模型矩阵x对于我们进行调查没有多大帮助。如果您向我们展示模型公式和数据框，那就太好了。
无论如何，我（有些痛苦）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
第x1至x8列（如果全部包含在内）在截距上具有共线性问题（奇怪；这些列不是虚拟列，因此它们不是来自因子变量） 。如果您不想删除其中任何一个，请改为放置拦截。