具有随机效应的多项 Logit 不会使用 mbgit 收敛
我想估计随机效应 (RE) 多项 Logit 模型。
我一直在应用 mclogit 包 中的 mblogit。然而,一旦我将 RE 引入模型中,它就无法收敛。 有解决方法吗?
例如,我尝试调整mbgit的拟合过程并增加最大迭代次数(maxit),但没有成功正确编写控制函数的语法。这是正确的方法吗?如果是这样,您能否建议我如何将其实施到我的模型中,到目前为止,该模型如下所示:
meta.mblogit <- mblogit(Migration ~ ClimateHazard4 , weights = logNsquare,
data = meta.df, subset= Panel==1, random = ~1|StudyID,
)
在这里,两个变量(迁移和气候危险4)都是因子变量。
或者您是否可以推荐我另一种方法来估计 RE 多项式 logit? 非常感谢!
I would like to estimate a random effects (RE) multinomial logit model.
I have been applying mblogit from the mclogit package. However, once I introduce RE into my model, it fails to converge.
Is there a workaround this?
For instance, I tried to adjust the fitting process of mblogit and increase the maximal number of iterations (maxit), but did not succeed to correctly write the syntax for the control function. Would this be the right approach? And if so, could you advise me how to implement it into my model which so far looks as follows:
meta.mblogit <- mblogit(Migration ~ ClimateHazard4 , weights = logNsquare,
data = meta.df, subset= Panel==1, random = ~1|StudyID,
)
Here, both variables (Migration and ClimateHazard4) are factor variables.
Or is there an alternative approach you could recommend me for an estimation of RE multinomial logit?
Thank you very much!
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