我正在尝试将数据拟合为零膨胀的负二项式模型,但是当在摘要中计算SE时,我的3个独立变量之一(暴露)似乎正在导致生成NaN(请参见zeroinfl调用的结尾)。功能。我还尝试过运行负二项式障碍模型,并且遇到了类似问题。
str(eggTreat)
'data.frame': 455 obs. of 4 variables:
$ Exposure : Factor w/ 2 levels "C","E": 2 2 2 2 2 2 2 2 2 2 ...
$ hi_lo : Factor w/ 2 levels "hi","lo": 2 2 2 2 2 2 2 2 2 2 ...
$ Egg_count: int 0 0 0 0 0 0 0 0 0 0 ...
$ Food : Factor w/ 2 levels "1.5A5YS","5ASMQ": 2 2 2 2 2 2 2 2 2 2 ...
mod.zeroinfl <- zeroinfl(Egg_count ~ Food+Exposure+hi_lo | Food+Exposure+hi_lo, data=eggTreat,
+ dist="negbin")
> summary(mod.zeroinfl)
Call:
zeroinfl(formula = Egg_count ~ Food + Exposure + hi_lo | Food + Exposure + hi_lo, data = eggTreat, dist = "negbin")
Pearson residuals:
Min 1Q Median 3Q Max
-0.65632 -0.47163 -0.28588 0.02976 9.00804
Count model coefficients (negbin with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04435 0.14393 -0.308 0.7580
Food -1.12486 0.22267 -5.052 4.38e-07 ***
Exposure -2.34990 0.38684 -6.075 1.24e-09 ***
hi_lo -0.44893 0.19524 -2.299 0.0215 *
Log(theta) -0.24387 0.22639 -1.077 0.2814
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.830e+01 NA NA NA
Food -5.768e+00 5.628e+04 0 1
Exposure 4.612e-01 NA NA NA
hi_lo -7.477e+00 9.963e+05 0 1
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Theta = 0.7836
Number of iterations in BFGS optimization: 21
Log-likelihood: -350.2 on 9 Df
Warning message:
In sqrt(diag(object$vcov)) : NaNs produced
function (object, ...)
{
object$residuals <- residuals(object, type = "pearson")
kc <- length(object$coefficients$count)
kz <- length(object$coefficients$zero)
se <- sqrt(diag(object$vcov))
这个问题通常是由完全分离引起的; 使用此搜索词或搜索Hauck-Donner效应,将向您显示问题是,预测变量的某种线性组合可以完美地将零和非零分开(因为零通胀中的预测变量都是分类的,这将转换为所有值均为零或非零的类别的组合。
我来看一下with(eggTreat, table(eggcount>0, Food, Exposure, hi_lo))
(以使表最容易阅读的顺序排列参数)。
典型症状包括:
|beta|>10
);在这种情况下,您的截距为-18.3,1e-8
在基线类别中给出了0的预测零概率(其他两个值也很大,尽管不如截距那么极端)Food
,hi_lo
),导致z值有效为零,p值有效为1NA
您所看到的值有多种解决方案可以解决此问题:
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.830e+01 NA NA NA
Food -5.768e+00 5.628e+04 0 1
Exposure 4.612e-01 NA NA NA
hi_lo -7.477e+00 9.963e+05 0 1
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