Simple ways to interpret effects in modeling ordinal categorical data

被引：31

作者：

Agresti, Alan ^{[1
]}

Tarantola, Claudia ^{[2
]}

机构：

[1] Univ Florida, Dept Stat, Gainesville, FL 32611 USA

[2] Univ Pavia, Dept Econ & Management, I-27100 Pavia, Italy

来源：

STATISTICA NEERLANDICA | 2018年 / 72卷 / 03期

关键词：

cumulative link models; cumulative logits; marginal effects; multiple correlation; proportional odds; R-squared; stochastic ordering; REGRESSION;

D O I：

10.1111/stan.12130

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We survey effect measures for models for ordinal categorical data that can be simpler to interpret than the model parameters. For describing the effect of an explanatory variable while adjusting for other explanatory variables, we present probability-based measures, including a measure of relative size and partial effect measures based on instantaneous rates of change. We also discuss summary measures of predictive power that are analogs of R-squared and multiple correlation for quantitative response variables. We illustrate the measures for an example and provide R code for implementing them.

引用

页码：210 / 223

页数：14

共 26 条

[1] APPLYING R2-TYPE MEASURES TO ORDERED CATEGORICAL-DATA [J].