二项式重复数据中的glmmadaptive
我正在使用R中的GLMMADAPTIVE进行重复的二项式数据
。 。
所以我运行了这个模型。我的模型是:固定效果(时间*治疗+持续时间),随机效应仅具有随机截距。
fm <- mixed_model(fixed = Outcome ~ Time * Treatment + Duration,
random = ~ 1 | ID,
data = data2,
family = binomial(),
nAGQ = 15,
iter_EM=0,
max_coef_value = 50,
initial_values = list(betas = rep(0, 5)),
na.action=na.exclude)
这样做有警告:
> Hessian matrix at convergence is not positive value: unstable solution.
然后我使用摘要(FM)
,它在我的SE和P值中显示nas
。
有人知道如何解决吗?我被卡住了1个月以上.......
I am doing repeated binomial data with GLMMadaptive in R. Outcomes were collected at 3 different time points(collected at baseline, 12 weeks, 24 weeks)and data looks like:
original data set
I want to calculate the marginal probability of this outcome, and I have missing data (MAR). So I run this model. My model is: fixed effect(time*Treatment+Duration), Random effect only has random intercept.
fm <- mixed_model(fixed = Outcome ~ Time * Treatment + Duration,
random = ~ 1 | ID,
data = data2,
family = binomial(),
nAGQ = 15,
iter_EM=0,
max_coef_value = 50,
initial_values = list(betas = rep(0, 5)),
na.action=na.exclude)
There is a warning by doing this:
> Hessian matrix at convergence is not positive value: unstable solution.
And then I use summary(fm)
, which showed NAs
in my SE and P-value.
Is anyone know how to fix it? I have been stuck for more than 1 month.......
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