| Type: | Package |
| Title: | Classification of Sparseness in 2-by-2 Categorical Data |
| Version: | 1.0.5 |
| Date: | 2025-09-29 |
| Maintainer: | Subbiah M <sisufive@gmail.com> |
| Description: | Provides functions for classifying sparseness in 2 x 2 categorical data where one or more cells have zero counts. The classification uses three widely applied summary measures: Risk Difference (RD), Relative Risk (RR), and Odds Ratio (OR). Helps in selecting suitable continuity corrections for zero cells in multi-centre or meta-analysis studies. Also supports sensitivity analysis and can detect phenomena such as Simpson's paradox. The methodology is based on Subbiah and Srinivasan (2008) <doi:10.1016/j.spl.2008.06.023>. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.1 |
| NeedsCompilation: | no |
| Packaged: | 2025-09-28 23:45:33 UTC; admin |
| Author: | Subbiah M [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2025-10-06 05:40:02 UTC |
Classify Sparseness in 2x2 Tables using Odds Ratio
Description
Classifies sparseness in 2x2 categorical tables where one or more cells are zero. The classification uses widely applied summary measures and a continuity correction to determine whether a table exhibits mild, moderate, or severe sparseness.
Usage
nose.or(nos, cc)
Arguments
nos |
A numeric matrix or data.frame with 4 columns representing counts in a 2x2 table: a (top-left), b (top-right), c (bottom-left), d (bottom-right). Each row corresponds to a separate 2x2 table. |
cc |
Continuity correction factor (numeric, e.g., 0.3) |
Value
A matrix combining the input counts with an extra column indicating the sparseness classification for each table.
Examples
# Simple example: one 2x2 table with a zero cell
xx <- matrix(c(0, 3, 2, 5), nrow = 1, byrow = TRUE)
nose.or(xx, cc = 0.3)
# Multiple tables example: each row is a separate 2x2 table
tables <- matrix(c(
0, 3, 2, 5,
2, 0, 1, 4,
0, 0, 2, 3
), nrow = 3, byrow = TRUE)
nose.or(tables, cc = 0.3)
Classify Sparseness in 2x2 Tables using Risk Difference
Description
Classifies sparseness in 2x2 categorical tables where one or more cells are zero. The classification uses widely applied summary measures and a continuity correction to determine whether a table exhibits mild, moderate, or severe sparseness.
Usage
nose.rd(nos, cc)
Arguments
nos |
A numeric matrix or data.frame with 4 columns representing counts in a 2x2 table: a (top-left), b (top-right), c (bottom-left), d (bottom-right). Each row corresponds to a separate 2x2 table. |
cc |
Continuity correction factor (numeric, e.g., 0.3) |
Value
A matrix combining the input counts with an extra column indicating the sparseness classification for each table.
Examples
# Simple example: one 2x2 table with a zero cell
xx <- matrix(c(0, 3, 2, 5), nrow = 1, byrow = TRUE)
nose.rd(xx, cc = 0.3)
# Multiple tables example: each row is a separate 2x2 table
tables <- matrix(c(
0, 3, 2, 5,
2, 0, 1, 4,
0, 0, 2, 3
), nrow = 3, byrow = TRUE)
nose.rd(tables, cc = 0.3)
Classify Sparseness in 2x2 Tables using Risk Ratio
Description
Classifies sparseness in 2x2 categorical tables where one or more cells are zero. The classification uses widely applied summary measures and a continuity correction to determine whether a table exhibits mild, moderate, or severe sparseness.
Usage
nose.rr(nos, cc)
Arguments
nos |
A numeric matrix or data.frame with 4 columns representing counts in a 2x2 table: a (top-left), b (top-right), c (bottom-left), d (bottom-right). Each row corresponds to a separate 2x2 table. |
cc |
Continuity correction factor (numeric, e.g., 0.3) |
Value
A matrix combining the input counts with an extra column indicating the sparseness classification for each table.
Examples
# Simple example: one 2x2 table with a zero cell
xx <- matrix(c(0, 3, 2, 5), nrow = 1, byrow = TRUE)
nose.rr(xx, cc = 0.3)
# Multiple tables example: each row is a separate 2x2 table
tables <- matrix(c(
0, 3, 2, 5,
2, 0, 1, 4,
0, 0, 2, 3
), nrow = 3, byrow = TRUE)
nose.rr(tables, cc = 0.3)