dimarConstructReferenceData: dimarConstructReferenceData
In kreutz-lab/DIMAR: Data-driven selection of an imputation algorithm in R
View source: R/dimarConstructReferenceData.R
dimarConstructReferenceData R Documentation
dimarConstructReferenceData
Description
Constructs reference dataset: Take data with least #MVs, enlarge data by proteins with least #MVs and normalize to all proteins
Usage
dimarConstructReferenceData(mtx, cut = 0.2)
Arguments
mtx
Quantitative matrix
cut
minimum number of proteins kept for the reference data
Value
Reference dataset
Examples
mtx <- matrix(rnorm(1000),nrow=100)
mtx[sample(c(1:1000),100)] <- NA
coef <- dimarLearnPattern(mtx)
ref <- dimarConstructReferenceData(mtx)
sim <- dimarAssignPattern(ref, coef, mtx)
kreutz-lab/DIMAR documentation built on Aug. 19, 2024, 11:07 a.m.
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View source: R/dimarConstructReferenceData.R
| dimarConstructReferenceData | R Documentation |
dimarConstructReferenceData
Description
Constructs reference dataset: Take data with least #MVs, enlarge data by proteins with least #MVs and normalize to all proteins
Usage
dimarConstructReferenceData(mtx, cut = 0.2)
Arguments
mtx |
Quantitative matrix |
cut |
minimum number of proteins kept for the reference data |
Value
Reference dataset
Examples
mtx <- matrix(rnorm(1000),nrow=100)
mtx[sample(c(1:1000),100)] <- NA
coef <- dimarLearnPattern(mtx)
ref <- dimarConstructReferenceData(mtx)
sim <- dimarAssignPattern(ref, coef, mtx)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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