早上好，
我正在研究有关PM 2.5的一些时空数据。
我想应用一个随机森林的版本，该版本在观察结果中明确说明空间依赖性，如“随机森林的空间依赖数据” 。
这是r软件包：
The要点是，我想考虑时空依赖性，而不仅仅是空间依赖性。
从理论上讲，如果我可以输入函数，以估计时空协方差矩阵q（将是nt x nt矩阵），那么拟合可以如alghoritm的原始版本中运行。
我不知道如何修改函数rfgls_estimate，以便能够输入协方差矩阵q用于种植树木。
有建议吗？
非常感谢您
Good morning,
I'm working on some spatio temporal data concerning PM 2.5.
I want to apply a version of random forest which explicitly accounts for spatial dependence in the observations, as introduced in "Random Forest for spatially dependent data" https://www.tandfonline.com/doi/abs/10.1080/01621459.2021.1950003#:~:text=Spatial%20linear%20mixed%2Dmodels%2C%20consisting,the%20covariate%20effect%20is%20nonlinear.
Here is the R package: https://www.google.com/url?sa=t&source=web&rct=j&url=https://cran.r-project.org/web/packages/RandomForestsGLS/vignettes/RandomForestsGLS_user_guide.pdf&ved=2ahUKEwjF6JiRhf73AhUQyxoKHbkBB-8QFnoECAUQAQ&usg=AOvVaw31g4t0m-Uoz1Wy-2ysDvla
The point is that I want to account for spatio temporal dependence, not just spatial dependence.
Theoretically if I could provide in input to the function the estimation of the spatio temporal covariance matrix Q (which will be a NT x NT matrix) then the fitting could run as in the original version of the alghoritm.
I have no idea about how to modify the function RFGLS_estimate to be able to provide in input the covariance matrix Q to be used to grow the trees.
Any suggestion?
Thank you very much in advance
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您是正确的，从概念上讲，RFGLS方法可以与任何输入协方差（或逆协方差矩阵Q）一起使用。但是，当前的Randomforestsgls r包装只能对某些类别的空间或时间依赖家族进行建模，而不能对时空依赖性进行建模。这是因为某些空间协方差（例如最近的邻居高斯工艺）和时间协方差（如自回旋模型）产生稀疏的逆协方差矩阵，这些矩阵有助于大大加速RFGLS算法。我们目前正在开发允许使用用户指定的协方差模型的扩展名。
You are correct that conceptually the RFGLS method can work with any input covariance (or inverse covariance matrix Q). However, currently the RandomForestsGLS R-package can only model certain classes of spatial or temporal dependence families but not spatio-temporal dependence. This is because certain spatial covariances (like the Nearest Neighbor Gaussian Process) and temporal covariances (like autoregressive model) yield sparse inverse covariance matrices that help considerably speed up the RFGLS algorithm. We are currently working on an extension that allows use of an user-specified covariance model.