R 如何在二项混合效应模型或GEE中指定抽样权重（逆概率权重）

我正在研究为混合效应逻辑回归指定与数据帧（not）中每个数据点相关的逆概率权重，用于与动物可用GPS位置的比较。我的问题与这篇文章密切相关：（），但对于混合效应模型。svyglm函数指定正确的权重，但调查包不允许随机效应，lme4使用分析权重。我研究了coxme包作为替代方案，但帮助文件表明，权重是按照lm指定的，lm使用分析权重

在r中是否实现了一个包/函数来指定混合效果的采样权重，或者使用coxme包指定采样权重的方法

示例数据：

data2 <- structure(list(Use = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0), Status = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), AnimalID = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5), St.A1k = c(0.029627, 0.043414, 
0.113816, 0.000000, 0.020241, 0.000000,0.000000, 0.007334, 0.000000, 
0.046055, 0.028454, 0.042828, 0.018480, 0.106776, 0.018480, 0.046641, 
0.033148, 0.039308, 0.035494, 0.000000, 0.004987, 0.051335, 0.046935, 
0.018774, 0.000000, 0.043708, 0.014667, 0.080375, 0.000000, 0.015254, 
0.000000, 0.053388, 0.055148, 0.036668, 0.006160, 0.016720, 0.029041, 
0.057788, 0.023174, 0.022294, 0.031388, 0.043414, 0.005573, 0.000000, 
0.024054,0.000000, 0.000000, 0.074215, 0.021121, 0.016720, 0.028454, 
0.042828, 0.018480, 0.106776, 0.018480, 0.046641, 0.033148, 0.039308, 
0.035494, 0.000000, 0.000000, 0.053388, 0.055148, 0.036668, 0.006160, 
0.016720, 0.029041, 0.057788, 0.023174, 0.022294,0.031388, 0.043414, 
0.005573, 0.000000, 0.024054, 0.000000, 0.000000, 0.074215, 0.021121, 
0.016720, 0.029627, 0.043414, 0.113816, 0.000000, 0.020241, 0.000000, 
0.000000, 0.007334, 0.000000, 0.046055, 0.029627, 0.043414, 0.113816, 
0.000000, 0.020241, 0.000000, 0.000000, 0.007334, 0.000000, 0.046055), 
InvWeight = c(1.332636, 1.248722, 1.248722, 1.248722, 1.179661, 1, 1, 1, 1, 
1, 1.060296, 1.060296, 1.249593, 1.248595, 1.248626, 1, 1, 1, 1, 1, 
1.294132, 1.740839, 1.740839, 2.377546, 2.377546, 1, 1, 1, 1, 1, 
2.378091,2.378091, 2.378091, 2.378091, 1.060295, 1, 1, 1, 1, 1, 1.060296, 
1.060296, 1.249593, 1.248595, 1.248626, 1, 1, 1, 1, 1, 2.378091, 2.378091, 
2.378091, 2.378091, 1.060295, 1, 1, 1, 1, 1, 2.378091, 2.378091, 2.378091, 
2.378091, 1.060295, 1, 1, 1, 1, 1, 1.294132,1.740839, 1.740839, 2.377546, 
2.377546, 1, 1, 1, 1, 1, 1.332636, 1.248722, 1.248722, 1.248722, 1.179661, 
1, 1, 1, 1, 1, 1.060296,1.060296, 1.249593, 1.248595, 1.248626, 1, 1, 1, 1, 
1)),.Names = c("Use", "Status", "AnimalID", "St.A1k", "InvWeight"), class = 
c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -100L))

---

data2您最终找到问题的答案了吗？您可以将IPW应用于geepack中的GEE，并将cox模型作为汇总逻辑回归（）应用。我发现了你的问题，因为我试图找到为什么IPW“不能”用于混合模型-GEE是建议的，但并不是基于我所能找到的任何可引用的证据。
des2 <- svydesign(id = ~1,  weights = ~InvWeight, data = data2)
glm.sampling.weights <- svyglm(Use ~ St.A1k, family = binomial, design=des2)
summary(glm.sampling.weights)

glm.w <- glm(Use ~ St.A1k, family = binomial, weight=InvWeight, data=data2)
summary(glm.w)

cox.w <- coxme(Surv(Status,Use) ~ St.A1k + (1|AnimalID), weight=InvWeight, 
data=data2)
summary(cox.w)