farmer1

Responses for one SRCV and one MRCV from a survey of Kansas farmers. This data was first examined by Loughin and Scherer (1998) and subsequently examined by papers such as Agresti and Liu (1999) and Bilder, Loughin, and Nettleton (2000).

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

MRCV

Methods for Analyzing Multiple Response Categorical Variables
        (MRCVs)

farmer1 function

The data frame contains the following 6 columns:

Column 1, labeled <code>Ed</code>, corresponds to the respondent's highest attained level of education.  A total of r = 5 levels of education are represented in the data.
<ul><li><code>1</code>:</li></ul> {High school}
<item><code>2</code>:</item> {Vocational school}
<item><code>3</code>:</item> {2-Year college}
<item><code>4</code>:</item> {4-Year college}
<item><code>5</code>:</item> {Other}

format

itemize

item

Loughin, T. M. and Scherer, P. N. (1998)  Testing for association in contingency tables with multiple column responses.  <em>Biometrics</em>, <b>54</b>, 630--637.

farmer1: Data for One SRCV and One MRCV from the Kansas Farmer Survey

Description

Usage

Arguments

format

itemize

item

source

References