MRCV: Methods for Analyzing Multiple Response Categorical Variables (MRCVs)
The MRCV package provides functions for analyzing the association between one single response categorical variable (SRCV) and one multiple response categorical variable (MRCV), or between two or three MRCVs. A modified Pearson chi-square statistic can be used to test for marginal independence for the one or two MRCV case, or a more general loglinear modeling approach can be used to examine various other structures of association for the two or three MRCV case. Bootstrap- and asymptotic-based standardized residuals and model-predicted odds ratios are available, in addition to other descriptive information.
- Natalie Koziol and Chris Bilder
- Date of publication
- 2014-09-04 06:55:56
- Natalie Koziol <firstname.lastname@example.org>
- GPL (>= 3)
- Perform MRCV Model Comparison Tests
- Data for One SRCV and One MRCV from the Kansas Farmer Survey
- Data for Two MRCVs from the Kansas Farmer Survey
- Data for Three MRCVs from the Kansas Farmer Survey
- Model the Association Among Two or Three MRCVs
- Create an Item Response Table or Data Frame
- Create a Marginal Table
- Test for Marginal Independence
- Methods for Analyzing Multiple Response Categorical Variables
- Calculate Observed and Model-Predicted Odds Ratios for MRCV...
- Control Printed Display of MRCV Regression Modeling Objects
- Control Printed Display of Objects of Class "MMI"
- Control Printed Display of Objects of Class "SPMI"
- Calculate Standardized Pearson Residuals for MRCV Data
- Steller Sea Lion Scat Data of Riemer, Wright, and Brown...
- Summarize Two or Three MRCV Model Fit Information
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