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.
|Author||Natalie Koziol and Chris Bilder|
|Date of publication||2014-09-04 06:55:56|
|Maintainer||Natalie Koziol <firstname.lastname@example.org>|
|License||GPL (>= 3)|
anova.genloglin: Perform MRCV Model Comparison Tests
farmer1: Data for One SRCV and One MRCV from the Kansas Farmer Survey
farmer2: Data for Two MRCVs from the Kansas Farmer Survey
farmer3: Data for Three MRCVs from the Kansas Farmer Survey
genloglin: Model the Association Among Two or Three MRCVs
item.response.table: Create an Item Response Table or Data Frame
marginal.table: Create a Marginal Table
MI.test: Test for Marginal Independence
MRCV-package: Methods for Analyzing Multiple Response Categorical Variables
predict.genloglin: Calculate Observed and Model-Predicted Odds Ratios for MRCV...
print.genloglin: Control Printed Display of MRCV Regression Modeling Objects
print.MMI: Control Printed Display of Objects of Class "MMI"
print.SPMI: Control Printed Display of Objects of Class "SPMI"
residuals.genloglin: Calculate Standardized Pearson Residuals for MRCV Data
sealion: Steller Sea Lion Scat Data of Riemer, Wright, and Brown...
summary.genloglin: Summarize Two or Three MRCV Model Fit Information