MRCV
Methods for Analyzing Multiple Response Categorical Variables (MRCVs)
Description
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. Statisical methods implemented are described in Bilder et al. (2000) <doi:10.1080/03610910008813665>, Bilder and Loughin (2004) <doi:10.1111/j.0006-341X.2004.00147.x>, Bilder and Loughin (2007) <doi:10.1080/03610920600974419>, and Koziol and Bilder (2014) <https://journal.r-project.org/articles/RJ-2014-014/>.
Downloads
502
Last 30 days
7465th
1.6K
Last 90 days
5.7K
Last year
Trend: +2.2% (30d vs prior 30d)
11
Last 30 days
26
Last 90 days
103
Last year
Trend: -21.4% (30d vs prior 30d)
CRAN Check Status
Show all 13 flavors
| Flavor | Status |
|---|---|
| r-devel-linux-x86_64-debian-clang | OK |
| r-devel-linux-x86_64-debian-gcc | OK |
| r-devel-linux-x86_64-fedora-clang | OK |
| r-devel-linux-x86_64-fedora-gcc | OK |
| r-devel-windows-x86_64 | OK |
| r-oldrel-macos-arm64 | OK |
| r-oldrel-macos-x86_64 | OK |
| r-oldrel-windows-x86_64 | OK |
| r-patched-linux-x86_64 | OK |
| r-release-linux-x86_64 | OK |
| r-release-macos-arm64 | OK |
| r-release-macos-x86_64 | OK |
| r-release-windows-x86_64 | OK |
Check History
OK 13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Jun 9, 2026
ERROR 12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Jun 8, 2026
examples
Running examples in ‘MRCV-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: MI.test
> ### Title: Test for Marginal Independence
> ### Aliases: MI.test MI.stat
>
> ### ** Examples
>
...[truncated]...
nd-Order Rao-Scott Adjusted Results:
X^2_S.adj = 36.17
df.adj = 6.78
p.adj < 0.0001
>
> # Test for MMI using the marginal logit model approach
> library(geepack)
Error in library(geepack) : there is no package called ‘geepack’
Execution halted