Question

我正在尝试使用beta regression betareg function模型使用betareg package这些数据：< / p>

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"))

使用此命令：

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

我得到了这个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β回归根本无法适应这些数据？

Answer 1

将β分布拟合到类别1中的数据将是非常具有挑战性的，其中三个观察值基本上为零。舍入到五位数：0.00000,0.00000,0.00000,0.00320,0.00610,0.01500。我不清楚这个类别是否应该以与其他类似的方式建模。

在类别4中，有另一个观察值在数值上为零，尽管其他观察值稍大：0.00000,0.01000,0.02500,0.03100,0.03400,0.04100。

省略类别1至少允许在没有数字问题的情况下估计模型。渐近推断是否是来自每组六次观察的两个参数的良好近似是另一个问题。但是，不同群体的精确度似乎并不相同。

UITextView

在chol.default（K）中获取错误：第5阶段的主要次要因素与betareg无关

1 个答案: