在chol.default（K）獲取錯誤：訂單5領先的未成年人不正定與betareg

Question

我試圖使用，以適應betaregression模型betaregpackage對這些數據的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")) 

使用這個命令：

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

而且我得到這個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 

是否有任何解決方案或者beta迴歸是否無法適用於這些數據？

Answer 1

擬合beta類分佈到類別1中的數據將是非常具有挑戰性的，三個觀測值基本爲零。四捨五入爲：0.00000,0.00000,0.00000,0.00320,0.00610,0.01500。我不清楚這個類別是否應該與其他類別相同。

在類別4中，還有另一個數值爲零的觀察結果，儘管其他觀察值稍大一些：0.00000,0.01000,0.02500,0.03100,0.03400,0.04100。

省略類別1至少允許估計沒有數字問題的模型。另一個問題是，漸近推斷是否可以很好地近似每組六個觀測值的兩個參數。儘管精確度在羣組中似乎不一樣。

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