在chol.default（K）获取错误：订单5领先的未成年人不正定与betareg

Question

我试图使用，以适应betaregression模型betaregpackage对这些数据的betaregfunction：

df <- data.frame(category=c("c1","c1","c1","c1","c1","c1","c2","c2","c2","c2","c2","c2","c3","c3","c3","c3","c3","c3","c4","c4","c4","c4","c4","c4","c5","c5","c5","c5","c5","c5"), 
       value=c(6.6e-18,0.0061,0.015,1.1e-17,4.7e-17,0.0032,0.29,0.77,0.64,0.59,0.39,0.72,0.097,0.074,0.073,0.08,0.06,0.11,0.034,0.01,0.031,0.041,4.7e-17,0.025,0.58,0.14,0.24,0.29,0.55,0.15),stringsAsFactors = F) 

df$category <- factor(df$category,levels=c("c1","c2","c3","c4","c5")) 

使用这个命令：

library(betareg) 
fit <- betareg(value ~ category, data = df) 

而且我得到这个error：

Error in chol.default(K) : 
    the leading minor of order 5 is not positive definite 
In addition: Warning message: 
In sqrt(wpp) : NaNs produced 
Error in chol.default(K) : 
    the leading minor of order 5 is not positive definite 
In addition: Warning messages: 
1: In betareg.fit(X, Y, Z, weights, offset, link, link.phi, type, control) : 
    failed to invert the information matrix: iteration stopped prematurely 
2: In sqrt(wpp) : NaNs produced 

是否有任何解决方案或者beta回归是否无法适用于这些数据？

Answer 1

拟合beta类分布到类别1中的数据将是非常具有挑战性的，三个观测值基本为零。四舍五入为：0.00000,0.00000,0.00000,0.00320,0.00610,0.01500。我不清楚这个类别是否应该与其他类别相同。

在类别4中，还有另一个数值为零的观察结果，尽管其他观察值稍大一些：0.00000,0.01000,0.02500,0.03100,0.03400,0.04100。

省略类别1至少允许估计没有数字问题的模型。另一个问题是，渐近推断是否可以很好地近似每组六个观测值的两个参数。尽管精确度在群组中似乎不一样。

betareg(value ~ category | 1, data = df, subset = category != "c1") 
## Call: 
## betareg(formula = value ~ category | 1, data = df, subset = category != 
##  "c1") 
## 
## Coefficients (mean model with logit link): 
## (Intercept) categoryc3 categoryc4 categoryc5 
##  0.2634  -2.2758  -4.4627  -1.0206 
## 
## Phi coefficients (precision model with log link): 
## (Intercept) 
##  2.312 
betareg(value ~ category | category, data = df, subset = category != "c1") 
## Call: 
## betareg(formula = value ~ category | category, data = df, subset = category != 
##  "c1") 
## 
## Coefficients (mean model with logit link): 
## (Intercept) categoryc3 categoryc4 categoryc5 
##  0.2566  -2.6676  -4.0601  -0.9784 
## 
## Phi coefficients (precision model with log link): 
## (Intercept) categoryc3 categoryc4 categoryc5 
##  2.0849  3.5619  -0.2308  -0.1376