multi_analysis: Continuous Information

In KODAMA: Knowledge Discovery by Accuracy Maximization

Continuous Information

Description

Summarization of the continuous information.

Usage

multi_analysis  (data, 
                 y, 
                 FUN=c("continuous.test","correlation.test"), ...)                 

Arguments

data	the matrix containing the continuous values. Each row corresponds to a different sample. Each column corresponds to a different variable.
y	the classification of the cohort.
FUN	function to be considered. Choices are "continuous.test" and "correlation.test"
...	further arguments to be passed to or from methods.

Value

The function returns a table with the summarized information. If the number of group is equal to two, the p-value is computed using the Wilcoxon rank-sum test, Kruskal-Wallis test otherwise.

Author(s)

Stefano Cacciatore

References

Cacciatore S, Luchinat C, Tenori L
Knowledge discovery by accuracy maximization.
Proc Natl Acad Sci U S A 2014;111(14):5117-22. doi: 10.1073/pnas.1220873111. Link

Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA
KODAMA: an updated R package for knowledge discovery and data mining.
Bioinformatics 2017;33(4):621-623. doi: 10.1093/bioinformatics/btw705. Link

See Also

categorical.test,continuous.test,correlation.test, txtsummary

Examples

data(clinical)


multi_analysis(clinical[,c("BMI","Age")],clinical[,"Hospital"],FUN="continuous.test")

KODAMA documentation built on Jan. 12, 2023, 5:08 p.m.