KnnDistCVStepwise: Stepwise K-Nearest Neighbour correct cross-validation with...
In ArdernHB/KnnDist: Kernal Nearest Neighbour classification with distance inputs

Description Usage Arguments Details Value Author(s)

View source: R/KNNCrossValidation_functions.R

This function takes a square matrix of distances among specimens of known group membership and returns the results of a leave-one-out correct cross validation identification exercise for each incremental increase in k. The results of the analyses can be plotted to visualise the change in correct identification given changes in k.

KnnDistCVStepwise(
  DistMat,
  GroupMembership,
  Kmax,
  Equal = TRUE,
  EqualIter = 100,
  SampleSize = NA,
  TieBreaker = c("Random", "Remove", "Report"),
  Verbose = FALSE,
  PrintProg = TRUE,
  PlotResults = TRUE
)

`DistMat`	is a square matrix of pairwise distances among all reference specimens.
`GroupMembership`	a character or factor vector in the same order as the distance data to denote group membership.
`Kmax`	This sets the maximum K that K will increase to stepwise.
`Equal`	indicates where groups should be sampled to equal sample size
`EqualIter`	sets the number of iterations resampling to equal sample size will be carried out.
`SampleSize`	is the sample number that groups will be subsampled to if `Equal` is set to TRUE. The default is set to NA and will therefore use the smallest sample size of the groups provided.
`TieBreaker`	is the method used to break ties if there is no majority resulting from K. Three methods are available('Random', 'Remove' and 'Report'): Random randomly returns one of tied classifications; Remove returns 'UnIDed' for the classification; Report returns a the multiple classifications as a single character string with tied classifications separated by '_'. NOTE: for correct cross-validation proceedures the results of both Report will be considered an incorrect identification even if one of the multiple reported classifications is correct.
`Verbose`	determines whether the cross-validation results for each reference specimen is returned. Note that if this is set to TRUE and Equal is set to TRUE the funtion will return a list with the results of each iteration which will slow the process dramatically and take a lot of local memory.
`PrintProg`	Only used when resampling to equal sample size is used (i.e. `Equal = TRUE`) and shows what iteration the function is on. Set to FALSE to ignore.
`PlotResults`	logical when set to TRUE the results are plotted. When `Equal = TRUE` a polygon is plotted marking the 5th and 9th percentile.

The function also provides functionality to resample unequal groups to equal sample size a set number of times.

This function applies both a weighted approach and an unweighted appraoch and returns both results.

When the PrintProg is set to TRUE, the progress function of the svMisc package is used.

Returns a matrix of the leave-one-out classifications for all the specimens along with their known classification for both weighted and unweighted approaches.

Ardern Hulme-Beaman

ArdernHB/KnnDist documentation built on Feb. 5, 2021, 5:09 a.m.