nlme：使用CSH协方差模型拟合混合模型

我正在尝试使用nlme软件包在R中使用重复测量（MMRM）模型拟合混合模型 .

数据的结构如下：每个患者属于三组之一（grp）并被分配到治疗组（trt） . 在6次就诊（访视）期间测量患者结果（y） .

我想在不同的访问中使用具有异构方差的复合对称模型（如SAS的PROC MIXED的CSH类型，https://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm#statug_mixed_sect020.htm） .

为此，我使用lme中的相关参数将相关结构设置为CS（corCompSymm）和权重参数，因此方差是访问的函数 .

我也试过添加访问corCompSymm本身的form参数 .

我遇到的问题：无论我是否在调用 lme 中设置权重参数，我都会得到相同的结果（换句话说，我似乎得到了CS模型而不是CSH模型） .

执行下面的代码，您会注意到模型参数估计的协方差矩阵的对角线是相同的，无论使用什么模型表明重量参数被忽略 .

remove(list = objects())
library(nlme)

set.seed(55)

npatients     = 200; 
nvisits       = 6;

#---
# Generate some data:
subject_table = data.frame(subject = sprintf("S%03d", 1:npatients),
                           trt     = sample(x = c("P", "D"),       replace = T, size = npatients),
                           grp     = sample(x = c("A", "B", "C"),  replace = T, size = npatients))
subject_table = merge(subject_table, 
                      data.frame(visit.number = 1:6))
subject_table = transform(subject_table, 
                          visit = sprintf("V%02d", visit.number),
                          y     = rnorm(nrow(subject_table), mean = 0, sd = visit.number^2))
subject_table = transform(subject_table, 
                          visit   = factor(visit),
                          subject = factor(subject, ordered = T, levels =     sort(unique(as.character(subject)))),
                          grp     = factor(grp),
                          trt     = factor(trt))
#---
# Fit MMRM model to data using nlme
cs_model       = lme(y ~ trt*visit*grp,                              # fixed     effects 
                     random      = ~1|subject,                       # random effects 
                     data        = subject_table,                    # data
                     correlation = corCompSymm(form=~1|subject))     # CS correlation matrix within patient

csh_model_v1   = lme(y ~ trt*visit*grp,                              # fixed effects 
                     random      = ~1|subject,                       # random effects 
                     data        = subject_table,                    # data
                     weights     = varIdent(~1|visit),               # different "weight" within each visit (I think)
                     correlation = corCompSymm(form=~1|subject))     # CS correlation matrix within patient

csh_model_v2   = lme(y ~ trt*visit*grp,                              # fixed effects 
                     random      = ~1|subject,                       # random effects 
                     data        = subject_table,                    # data
                     weights     = varIdent(~visit|subject),         # different "weight" within each visit (I think)
                     correlation = corCompSymm(form=~1|subject))     # CS correlation matrix within patient

csh_model_v3   = lme(y ~ trt*visit*grp,                              # fixed effects 
                     random      = ~1|subject,                       # random effects 
                     data        = subject_table,                    # data
                     correlation = corCompSymm(form=~visit|subject)) # CS correlation matrix within patient

diag(vcov(cs_model))
diag(vcov(csh_model_v1))
diag(vcov(csh_model_v2))
diag(vcov(csh_model_v3))

问题：如何让nlme适应不同访问的不同方差参数？