我可以在广义加法 Model (GAM) 中将泊松分布用作连续非负数据的族吗？
[英]Can I use poisson distribution as family in Generalized Additive Model (GAM) for continuous, non-negative data?
问题描述
I am building a GAM with a data set which distribution resembles poisson-distributed data.
我正在构建一个 GAM，其数据集的分布类似于泊松分布数据。 However, my data is continuous, ie, it contains information on tree volumes in cubic meters.但是，我的数据是连续的，即它包含以立方米为单位的树木体积信息。 So, when doing the GAM code in R (with mgcv library) can I use poisson as the family?那么，在做R中的GAM代码（带mgcv库）时，我可以使用泊松作为族吗？ Or should I choose something else since the data is not count data?或者我应该选择别的东西，因为数据不是计数数据？ I indeed found some threads discussing similar issues but they didn't provide an answer.我确实发现了一些讨论类似问题的线程，但他们没有提供答案。
My simplified example code with only one explanatory variable:我的简化示例代码只有一个解释变量：
gam_volumes <- gam(volumes_m3 ~ s(age, k=10), data=training, family=poisson)
1 个解决方案
解决方案1
0 2023-02-01 12:53:15
I would use a Gamma distribution with log link for this;为此，我会使用带有日志链接的 Gamma 分布； this distribution will look like a Poisson (right skewed) but it is a continuous distribution.该分布看起来像泊松分布（右偏），但它是连续分布。 You can't have 0s in the Gamma but that's OK as a 0 volume tree isn't an observable tree. Gamma 中不能有 0，但这没关系，因为 0 卷树不是可观察的树。
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