有序回归的SPSS分析与解释

2017-05-30 MedSci MedSci原创

等级回归分析对应的英文为“ordinal regression”，也称有序回归，以等级变量做因变量建立模型来预测危险发生的概率，因变量中各个类别要按不同程度的顺序取值。第一步：调用界面：分析---回归---有序选择变量做因变量、因子、协变量。通过频数描述知，等级越高，概率即数目越多，所以选则“补充对数-对数”即complementary log-

等级回归分析对应的英文为“ordinal regression”，也称有序回归，以等级变量做因变量建立模型来预测危险发生的概率，因变量中各个类别要按不同程度的顺序取值。

第一步：在SPSS中调用界面：分析---回归---有序

选择变量做因变量、因子、协变量。

通过频数描述知，等级越高，概率即数目越多，所以选则“补充对数-对数”即complementary log-log。

其中不少知识在以前的文章讲到过的。

单元格信息：最好不要选择，因信息太多，表格输出太大而无法显示。

平行性检验：检验因变量中各类别的斜率是否相等即各类别的参数是否相同，此选项仅对有位置分量（Location component）的模型有效。

“估计响应概率”：“模型估计出的观察单位属于因变量中类别的概率”。

“包含多项常量”：“实际似然比值，含常数项”。

“位置”：“默认的是主效应模型”。

“度量”：“定义模型中的标度分量”，所选择的变量必须是位置分量所选的变量中的一部分，设置标度分量的目的是改善模型，否则没必要做选择，此处不做选择。

结果解释：

由于数据量很大，图片只是一部分的截图。

注意观察警告的情况，便于正确理解结果，比如下面的“拟合度”中的显著性，不少数据都是缺失的，有时两种检验的显著性可能不一样，就是因为太多数据缺失造成的。

“模型拟合信息”：显著性的计算概率可知，小于显著性水平0.05。

“案例处理摘要”：对原始数据的初步统计分析，表格形式如同频数描述一样。

“伪R方”：值越大，模型拟合的越好。

“拟合度”：显著性值越大，说明拟合的越好。有时因为数据缺失，“伪R方”比“拟合度”更有意义。

“参数估计值”：通过观察显著性P值，看出变量的显著性，P值大的变量说明不显著，剔除掉，或者重新选择模型。

“平行线检验”：由显著性概率知，斜率相同。

最后的预测类别，预测类别概率，实际类别概率都保存在数据窗口中。

如果平行线检验时，P值非常小，可能有2个原因：
（1）连接函数选择不准确。解决方法：在Options子对话框的最下方就是link下拉列表，可以选择以上5种连接函数，通过不同的连接函数选择消除该效应。
（2）系数的确在随着分割点发生变化。解决方法：使用无序多分类的Logistic回归分析multinomial logistic

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2020-02-25 静锅

请问自变量需要设置哑变量吗

91 0
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2017-06-10 1e0d2fb7m34(暂无匿称)

越来越喜欢读梅斯的文章了，增长了不少知识

76 0
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2017-05-31 sxlw

自变量是选入因子还是协变量框？有没有规定？比方说，是否是这样：连续变量选入协变量，分类变量选入因子框（factor(s))。例子中把性别选入了协变量框，自变量选入哪个框究竟有什么规定呢？还是无所谓

76 0
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2017-05-30 jaysppr

感谢梅斯医学！！

84 0
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2017-05-30 1e0f8808m18(暂无匿称)

好文章，学习了，感谢分享。

74 0
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2017-05-30 楠博One

学习谢谢分享

39 0

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