fitDVboostmodel: Fitting a DVboost model based on annotated SVs

In Liuy12/DVboost: Adaptive Variant Filtering using generalized boosted regression models

Fitting a DVboost model based on annotated SVs

Description

Internal function called by runDVboostwrapper

Usage

fitDVboostmodel(input.mtx, is.known.variant, fitting.verbose = FALSE,
  min.N.known.var = 50)

Arguments

input.mtx	annotated SVs loaded and formatted via loadVariants
is.known.variant	numeric vector of 0/1 in the same order as input.mtx indicating whether a SV is known(1) or novel(0)
fitting.verbose	logical value, if TRUE, it will print out progress and performance indicators. Default to FALSE
min.N.known.var	minimum number of known SVs needed to build training model. Default to 50

Value

DV.res which is essentially a gbm.object with several additional fields:

• fitted.values: converted to probability based on fit field

• ID: supplied IDs for SVs

• is.known.variant: 0/1 indicating whether the SV is known(1) or novel(0)

• DVboost.Q.score: Q scores for SVs

See Also

runDVboostwrapper

Examples

data(ExampleData, package='DVboost')
sample <- 'NA12878'
outdir <- getwd()
tmp.mtx.DEL <- ExampleData[ExampleData$SVType == 'DEL',]
truth.vec <- tmp.mtx.DEL$CNVMAP == 1 | tmp.mtx.DEL$CNVR ==1
is.semi.truth.vec <- as.numeric(truth.vec)
DV.fit.res1 <- fitDVboostmodel(input.mtx=tmp.mtx.DEL, is.known.variant = is.semi.truth.vec)

Liuy12/DVboost documentation built on May 25, 2022, 6:17 a.m.