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MRCV (version 0.3-1)

Methods for Analyzing Multiple Response Categorical Variables (MRCVs)

Description

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.

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Version

Install

install.packages('MRCV')

Monthly Downloads

469

Version

0.3-1

License

GPL (>= 3)

Maintainer

Natalie Koziol

Last Published

April 10th, 2014

Functions in MRCV (0.3-1)

MI.test

Test for Marginal Independence
sealion

Steller Sea Lion Scat Data of Riemer, Wright, and Brown (2011)
predict.genloglin

Calculate Observed and Model-Predicted Odds Ratios for MRCV Data
marginal.table

Create a Marginal Table
print.SPMI

Control Printed Display of Objects of Class "SPMI"
anova.genloglin

Perform MRCV Model Comparison Tests
farmer3

Data for Three MRCVs from the Kansas Farmer Survey
farmer2

Data for Two MRCVs from the Kansas Farmer Survey
genloglin

Model the Association Among Two or Three MRCVs
summary.genloglin

Summarize Two or Three MRCV Model Fit Information
residuals.genloglin

Calculate Standardized Pearson Residuals for MRCV Data
MRCV-package

Methods for Analyzing Multiple Response Categorical Variables
farmer1

Data for One SRCV and One MRCV from the Kansas Farmer Survey
print.MMI

Control Printed Display of Objects of Class "MMI"
print.genloglin

Control Printed Display of MRCV Regression Modeling Objects
item.response.table

Create an Item Response Table or Data Frame