fitSegBIC: Fit segmented regression models on a feature/gene

View source: R/fitSegBIC.R

fitSegBICR Documentation

Fit segmented regression models on a feature/gene

Description

fits segmented regression models

Usage

fitSegBIC(Data, maxK = 2, tVectIn = NULL, minNumInSeg = 5,
  pvalCut = 0.1, numTry = 5, keepFit = FALSE)

Arguments

Data

a matrix of normalized expression measurements. Rows are genes/features and columns are samples.

maxK

maximum number of breakpoints to consider. For each gene, trendy will fit maxK + 1 models containing 0 -> maxK breakpoints (1 -> (maxK + 1) segments). The model with the lowest BIC value will be selected (unless forceRsq = TRUE, see below).

tVectIn

a numerical vector indicating the time-points or the order of samples. If it is NULL (default), then the time/order will be assumed to be equaly spaced from 1:N (N is number of samples).

minNumInSeg

minimum number of samples required to be within a segment. If a breakpoint model has a segment with fewer than minNumInSeg point in any segment, then the model is not considered valid.

pvalCut

p-value cutoff. If the p-value of a segment is greater than PvalCut, then the segment will be called as 'no change'.

numTry

the number of different seeds to try. If all numTry runs fail, then the linear regression (no breakpoints, one segment) model will be returned.

keepFit

whether to report the fitted object (default is FALSE).

Value

Trend: direction of each sample; -1: down, 0: no change, 1: up Slope: fitted slopes, Slope.Trend: sign of fitted slopes, Slope.Pvalue: p value of each segment, Breakpoint: estimated breakpoints, Fitted.Values: fitted values AdjustedR2: adjusted r value of the model Fit: fit object

Author(s)

Rhonda Bacher and Ning Leng


rhondabacher/Trendy documentation built on Oct. 26, 2023, 3:46 a.m.