1d:T7f6,# Binary segmentation, normal change in mean, cross-validation for model selection Efficient implementation of binary segmentation for change in mean, with automatic model selection via cross-validation. ```r binseg_normal_cv(data.vec, max.segments = length(data.vec), position.vec = seq_along(data.vec), n.validation.sets = 100L, prop.validation = 0.5) ``` ## Arguments - `data.vec`: Vector of numeric data to segment. - `max.segments`: Maximum number of segments to compute, default=length(`data.vec`). - `position.vec`: integer vector of positions at which data are measured, default=1:length(`data.vec`). - `n.validation.sets`: Number of validation sets. - `prop.validation`: Proportion of validation set. ## Author(s) Toby Dylan Hocking [aut, cre] ## Examples ```r data.table::setDTthreads(1) seg.mean.vec <- 1:5 data.mean.vec <- rep(seg.mean.vec, each=20) set.seed(1) n.data <- length(data.mean.vec) data.vec <- rnorm(n.data, data.mean.vec, 0.2) plot(data.vec) (fit <- binsegRcpp::binseg_normal_cv(data.vec)) seg.dt <- coef(fit) model.color <- "red" seg.dt[, segments(start.pos, mean, end.pos, mean, col=model.color)] seg.dt[start>1, abline(v=start.pos, col=model.color)] ## plot method shows number of times selected. plot(fit) if(requireNamespace("neuroblastoma")){ data(neuroblastoma, package="neuroblastoma", envir=environment()) library(data.table) profiles.dt <- data.table(neuroblastoma$profiles) one.chrom <- profiles.dt[profile.id=="4" & chromosome=="2"] fit <- one.chrom[, binsegRcpp::binseg_normal_cv( logratio, position.vec=position)] selected.segs <- coef(fit) if(require(ggplot2)){ ggplot()+ geom_point(aes( position, logratio), data=one.chrom)+ geom_segment(aes( start.pos, mean, xend=end.pos, yend=mean), data=selected.segs, color=model.color)+ geom_vline(aes( xintercept=start.pos), data=selected.segs[start>1], color=model.color) } } ```1e

binseg_normal_cv function