continuous.test {KODAMA}

Continuous Information

Description

Summarization of the continuous information.

Usage

continuous.test (name,
                 x,    
                 y,
                 digits = 3,
                 scientific = FALSE, 
                 range = c("IQR","95%CI"), 
                 logchange = FALSE,
                 pos=2, 
                 method=c("non-parametric","parametric"),
                 total.column=FALSE, ...) 

Arguments

name

the name of the feature.

x

the information to summarize.

y

the classification of the cohort.

digits

how many significant digits are to be used.

scientific

either a logical specifying whether result should be encoded in scientific format.

range

the range to be visualized.

logchange

either a logical specifying whether log2 of fold change should be visualized.

pos

a value indicating the position of range to be visualized. 1 for column, 2 for row.

method

a character string indicating which test method is to be computed. "non-parametric" (default), or "parametric".

total.column

option to visualize the total (by default = "FALSE")

...

further arguments to be passed to or from methods.

Value

The function returns a table with the summarized information and the relative p-value. For non-parametric method, if the number of group is equal to two, the p-value is computed using the Wilcoxon rank-sum test, Kruskal-Wallis test otherwise. For parametric method, if the number of group is equal to two, the p-value is computed using the Student's t-Test, ANOVA one-way 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

correlation.test, categorical.test, txtsummary

Examples

data(clinical)

hosp=clinical[,"Hospital"]
gender=clinical[,"Gender"]
GS=clinical[,"Gleason score"]
BMI=clinical[,"BMI"]
age=clinical[,"Age"]

A=categorical.test("Gender",gender,hosp)
B=categorical.test("Gleason score",GS,hosp)

C=continuous.test("BMI",BMI,hosp,digits=2)
D=continuous.test("Age",age,hosp,digits=1)

rbind(A,B,C,D)