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我正在使用 R 中的包序数对基于 1 - 5 李克特量表的因变量运行序数逻辑回归，并试图弄清楚如何测试比例赔率假设。

我当前的模型是y ~ x1 + x2 + x3 + x4 + x2*x3 + (1|ID) + (1|form)x1 和 x2 是二分法，x3 和 x4 是连续变量。（92 个科目，4 个表格）。

据我所知，
最新版本的 clmm 中没有实现 -"nominal"。
-clmm2（旧版本）不接受多个随机变量
-nominal_test() 似乎只适用于 clm2（完全没有随机效应）

对于不同的 dv（只有一个随机项且没有交互），我使用过：

m1 <- clmm2 (y ~ x1 + x2 + x3, random = ID, Hess = TRUE, data = d
m1.nom <- clmm2 (y ~ x1 + x2, random = ID, Hess = TRUE, nominal = ~x3, data = d)
m2.nom <- clmm2 (y ~ x2+ x3, random = ID, Hess = TRUE, nominal = ~ x1, data = d)
m3.nom <- clmm2 (y ~ x1+ x3, random = ID, Hess = TRUE, nominal = ~ x2, data = d)

anova (m1.nom, m1)
anova (m2.nom, m1)
anova (m3.nom, m1)  # (as well as considering the output in summary (m#.nom)

但我不确定如何修改这种方法来处理当前模型（2 个随机项和固定效应的相互作用），我也不确定这实际上是否是首先测试比例赔率假设的正确方法。（包教程中的例子只有2个固定效果。）

我对其他方法（无论是其他软件包、软件还是图形方法）持开放态度，可以让我对此进行测试。有什么建议么？

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Even in the case of the most basic ordinal logistic regression models, the diagnostic tests for the proportional odds assumption are known to frequently reject the null hypothesis that the coefficients are the same across the levels of the ordered factor. The statistician Frank Harrell suggests here a general graphical method for examining the proportional odds assumption, which is probably your best bet. In this approach you'd just graph the linear predictions from a logit model (with random effects) for each level of the outcome and one predictor variable at a time.

于 2014-05-08T19:10:31.250 回答