searchLinesAtGivenSlope: Search Points Forming Lines At Given Slope

Description

searchLinesAtGivenSlope searchs among set of points (2-dim) those forming line(s) with user-defined slope ('coeff'), ie search optimal (slope-) offset parameter(s) for (regression) line(s) with given slope ('coef'). Note: larger data-sets : segment residuals to 'coeff' & select most homogenous

Usage

searchLinesAtGivenSlope(
  dat,
  coeff = 1.5,
  filtExtr = c(0, 1),
  minMaxDistThr = NULL,
  lmCompare = TRUE,
  indexPoints = TRUE,
  displHist = FALSE,
  displScat = FALSE,
  bestCluByDistRat = TRUE,
  neighbDiLim = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Value

This functions returns a matrix of line-characteristics (or if indexPoints is TRUE then list (line-characteristics & index & lm-results)

Arguments

dat: matrix or data.frame, main input
coeff: (numeric) slope to consider
filtExtr: (integer) lower & upper quantile values, remove points with extreme deviation to offset=0, (if single value: everything up to or after will be used)
minMaxDistThr: (logical) optional minumum and maximum distance threshold
lmCompare: (logical) add'l fitting of linear regression to best results, return offset AND slope based on lm fit
indexPoints: (logical) return results as list with element 'index' specifying retained points
displHist: (logical) display histogram of residues
displScat: (logical) display (simple) scatter plot
bestCluByDistRat: (logical) initial selection of decent clusters based on ratio overallDist/averNeighbDist (or by CV & cor)
neighbDiLim: (numeric) additional threshold for (trimmed mean) neighbour-distance
silent: (logical) suppress messages
debug: (logical) for bug-tracking: more/enhanced messages
callFrom: (character) allow easier tracking of messages produced

Details

Note: The package MASS is required when using as lmCompare=TRUE. For larger data the function will try using the package NbClust (available from CRAN) if installed.

Examples

Run this code

set.seed(2016); ra1 <- runif(300)
 dat1 <- cbind(x=round(c(1:100+ra1[1:100]/5,4*ra1[1:50]),1),
  y=round(c(1:100+ra1[101:200]/5, 4*ra1[101:150]), 1))
(li1 <- searchLinesAtGivenSlope(dat1, coeff=1))