frequency_matching | R Documentation |
A method to select unbalanced groupd in a cohort.
frequency_matching (data,label,times=5,seed=1234)
data |
a data.frame of data. |
label |
a classification of the groups. |
times |
The ratio between the two groups. |
seed |
a single number for random number generation. |
The function returns a list with 2 items or 4 items (if a test data set is present):
data |
the data after the frequency matching. |
label |
the label after the frequency matching. |
selection |
the rows selected for the frequency matching. |
Stefano Cacciatore
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
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) # Analysis without matching rbind(A,B,C,D) # The order is important. Right is more important than left in the vector # So, Ethnicity will be more important than Age var=c("Age","BMI","Gleason score") t=frequency_matching(clinical[,var],clinical[,"Hospital"],times=1) newdata=clinical[t$selection,] hosp.new=newdata[,"Hospital"] gender.new=newdata[,"Gender"] GS.new=newdata[,"Gleason score"] BMI.new=newdata[,"BMI"] age.new=newdata[,"Age"] A=categorical.test("Gender",gender.new,hosp.new) B=categorical.test("Gleason score",GS.new,hosp.new) C=continuous.test("BMI",BMI.new,hosp.new,digits=2) D=continuous.test("Age",age.new,hosp.new,digits=1) # Analysis with matching rbind(A,B,C,D)
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