使用 glmmTMB 建模计数数据时的错误和警告 [英] Error and warning when modeling count data using glmmTMB

问题描述

我正在尝试使用 glmmTMB 用计数数据拟合零膨胀泊松随机效应模型.但是，我收到了一些错误和警告.

head(data)计算时间学习1 0 259 12 0 199 13 0 571 14 0 927 15 7 254 16 0 877 1字符串(数据)'data.frame': 959 obs.3个变量:$ 计数:int 0 0 0 0 7 0 0 0 0 0 ...$时间:int 259 199 571 927 254 877 555 158 1014 705 ...$ study : 因子 w/10 个级别 "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...

时间是一个抵消项，而学习是我必须添加的随机效应.

错误 1

如果我添加一个随机效应，那么下面的随机效应模型报告同样的错误

f1<-glmmTMB(count~1+offset(time)+1|study, data = data, family = poisson, ziformula = ~1|study)f2<-glmmTMB(count~1+offset(time)+1|study, data = subset(data, study=="1"), family = poisson, ziformula = ~1|study)nlminb(start = par,objective = fn,gradient = gr, control = control$optCtrl) 中的 NA/NaN 函数评估错误:梯度函数必须返回长度为 4 的数字向量

错误 2

我想我需要尝试一个更简单的模型，因此我放弃了随机效应.不幸的是，我收到了一个不同的错误.

f3<-glmmTMB(count~1+offset(time), data = data, family = poisson, ziformula = ~1)f4<-glmmTMB(count~1+offset(time), data = subset(data, study=="1"), family = poisson, ziformula = ~1)nlminb(start = par,objective = fn,gradient = gr, control = control$optCtrl) 中的 NA/NaN 函数评估错误:NA/NaN 梯度评估

警告 1

好的.我想知道如何获得没有任何错误的输出.因此，我放弃了偏移项而不是随机效应，从而获得了警告而不是错误.

f5<-glmmTMB(count~1+1|study, data = data, family = poisson, ziformula = ~1|study)f6<-glmmTMB(count~1+1|study, data = subset(data, study=="1"), family = poisson, ziformula = ~1|study)Cholmod 警告矩阵非正定"在文件 ../Supernodal/t_cholmod_super_numeric.c，第 729 行Cholmod 警告矩阵非正定"在文件 ../Supernodal/t_cholmod_super_numeric.c，第 729Cholmod 警告矩阵文件非正定"../Supernodal/t_cholmod_super_numeric.c，文件 ../Supernodal/t_cholmod_super_numeric.c，第 729Cholmod 行警告矩阵非正定"，文件 ../Supernodal/t_cholmod_super_line 729Cholmod 警告矩阵非正定"文件 ../Supernodal/t_cholmod_super_numeric.c 中的 729Cholmod 警告矩阵非正定"，第 729Cholmod 文件 ../Supernodal/t_cholmod_super_numeric.c 中的第 729Cholmod 警告矩阵非正定"，第 729Cholmod 警告矩阵非正定"文件../Supernodal/t_cholmod_super_numeric.c，第 729 行

良好"适合，既没有错误也没有警告

经过一系列改造后，我发现如果我删除两个随机效应项和偏移项中的至少一个，模型将不会报告错误或警告.

f7<-glmmTMB(count~1+1|study, data = data, family = poisson, ziformula = ~1)f8<-glmmTMB(count~1, data = data, family = poisson, ziformula = ~1|study)

问题

是什么问题导致了这些错误和警告?如果可能，我怎样才能拟合 ZIP 混合效果模型并且既不出现错误也不收到警告?

示例

我有一个可重现的例子，它产生了警告 1

count.ex<-rpois(500, 0.2)study.ex<-as.factor(sample(1:5, 500, replace = TRUE))time.ex<-rexp(500, 150)fit.ex<-glmmTMB(count.ex~1+offset(time.ex)+1|study.ex, family = poisson, ziformula = ~1|study.ex)Cholmod 警告矩阵非正定"在文件 ../Supernodal/t_cholmod_super_numeric.c，第 729 行Cholmod 警告矩阵非正定"在文件 ../Supernodal/t_cholmod_super_numeric.c，第 729Cholmod 警告矩阵文件非正定"../Supernodal/t_cholmod_super_numeric.c，第729行模型收敛问题；非正定 Hessian 矩阵.见小插图('疑难解答')

解决方案

如果没有可重现的示例，这将相当困难！

• 你的模型有一个明显的问题:你已经在你的模型中指定了 offset(time)，你几乎肯定想使用 offset(log(time)).您的 time 值很大(我可以在您的 str() 中看到的值范围为 500-1000；偏移量输入 predicted 值模型为 exp(offset)，要么大得离谱(如果 offset )或无穷大.>

• 你应该小心保护带括号的随机效应项(例如你使用~1 + 1|study 而不是~1 + (1|study)code>; 我不认为它会导致您在这里展示的示例中出现问题，但通常管道 (|) 运算符的优先级较低，这意味着例如 ~1+a+b|study 等价于 ~(1+a+b)|study ...

如果我使用

 ~count.ex~1+offset(log(time.ex))+(1|study.ex)

在您的示例中，我没有收到任何警告.

I am trying to fit zero inflated poisson random effects model with count data using glmmTMB. However, I received some errors and warnings.

head(data)

  count time study    
1     0  259     1 
2     0  199     1 
3     0  571     1 
4     0  927     1 
5     7  254     1 
6     0  877     1 

str(data)

 'data.frame':  959 obs. of  3 variables:
 $ count : int  0 0 0 0 7 0 0 0 0 0 ...
 $ time  : int  259 199 571 927 254 877 555 158 1014 705 ...
 $ study : Factor w/ 10 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...

time is an offset term, and study is a random effect I have to add.

Error 1

If I add a random effect, then the following random-effect models report the same error

f1<-glmmTMB(count~1+offset(time)+1|study, data = data, family = poisson, ziformula = ~1|study)

f2<-glmmTMB(count~1+offset(time)+1|study, data = subset(data, study=="1"), family = poisson, ziformula = ~1|study)

NA/NaN function evaluationError in nlminb(start = par, objective = fn, gradient = gr, control = control$optCtrl) : 
 gradient function must return a numeric vector of length 4

Error 2

I thought I need to try a simpler model, and thus I dropped the random-effect. Unfortunately, I received a different error.

f3<-glmmTMB(count~1+offset(time), data = data, family = poisson, ziformula = ~1)

f4<-glmmTMB(count~1+offset(time), data = subset(data, study=="1"), family = poisson, ziformula = ~1)

NA/NaN function evaluationError in nlminb(start = par, objective = fn, gradient = gr, control = control$optCtrl) : 
  NA/NaN gradient evaluation

Warning 1

OK. I wonder how I can get an output without any error. So, I dropped the offset term instead of the random-effect, and thus obtained a warning instead of an error.

f5<-glmmTMB(count~1+1|study, data = data, family = poisson, ziformula = ~1|study)

f6<-glmmTMB(count~1+1|study, data = subset(data, study=="1"), family = poisson, ziformula = ~1|study)

Cholmod warning 'matrix not positive definite' at file ../Supernodal/t_cholmod_super_numeric.c, line 729Cholmod warning 'matrix not positive definite' at file ../Supernodal/t_cholmod_super_numeric.c, line 729Cholmod warning 'matrix not positive definite' at file ../Supernodal/t_cholmod_super_numeric.c, line 729Cholmod warning 'matrix not positive definite' at file ../Supernodal/t_cholmod_super_numeric.c, line 729Cholmod warning 'matrix not positive definite' at file ../Supernodal/t_cholmod_super_numeric.c, line 729Cholmod warning 'matrix not positive definite' at file ../Supernodal/t_cholmod_super_numeric.c, line 729Cholmod warning 'matrix not positive definite' at file ../Supernodal/t_cholmod_super_numeric.c, line 729Cholmod warning 'matrix not positive definite' at file ../Supernodal/t_cholmod_super_numeric.c, line 729

"Good" fit with neither error nor warning

After a bunch of refits, I find that if I drop at least one of two random-effect terms and the offset term, models will report neither an error or a warning.

f7<-glmmTMB(count~1+1|study, data = data, family = poisson, ziformula = ~1)

f8<-glmmTMB(count~1, data = data, family = poisson, ziformula = ~1|study)

Question

What issues caused these errors and warining? If possible, how could I fit a ZIP mixed effect model and receive neither error nor warning?

Example

I have a reproducible example which produced the warning 1

count.ex<-rpois(500, 0.2)
study.ex<-as.factor(sample(1:5, 500, replace = TRUE))
time.ex<-rexp(500, 150)

fit.ex<-glmmTMB(count.ex~1+offset(time.ex)+1|study.ex, family = poisson, ziformula = ~1|study.ex)

Cholmod warning 'matrix not positive definite' at file ../Supernodal/t_cholmod_super_numeric.c, line 729Cholmod warning 'matrix not positive definite' at file ../Supernodal/t_cholmod_super_numeric.c, line 729Cholmod warning 'matrix not positive definite' at file ../Supernodal/t_cholmod_super_numeric.c, line 729Model convergence problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')

解决方案

This is rather difficult without a reproducible example!

• There is one obvious problem with your model: you've specified offset(time) in your model, you almost certainly want to use offset(log(time)). Your time values are large (the values I can see in your str() range from 500-1000; the offset enters the predicted value in the model as exp(offset), which is either ridiculously large (if offset < log(.Machine$double.xmax) == 709.78) or infinite.

• You should be careful to protect random-effect terms with parentheses (e.g. you use ~1 + 1|study rather than ~1 + (1|study); I don't think it causes problems in the examples you show here, but in general the pipe (|) operator has low precedence, which means that e.g. ~1+a+b|study is equivalent to ~(1+a+b)|study ...

If I use

 ~count.ex~1+offset(log(time.ex))+(1|study.ex)

in your example, I don't get any warnings.

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