filter_stats function

Utilize all algorithms of mlg.filter

This function is a wrapper to mlg.filter. It will calculate all of the stats for mlg.filter utilizing all of the algorithms.

filter_stats(
  x,
  distance = bitwise.dist,
  threshold = 1e+06 + .Machine$double.eps^0.5,
  stats = "All",
  missing = "ignore",
  plot = FALSE,
  cols = NULL,
  nclone = NULL,
  hist = "Scott",
  threads = 1L,
  ...
)

Arguments

• x: a genind, genclone, genlight, or snpclone object

• distance: a distance function or matrix

• threshold: a threshold to be passed to mlg.filter

  (Default: 1e6)

• stats: what statistics should be calculated.

• missing: how to treat missing data with mlg.filter

• plot: If the threshold is a maximum threshold, should the statistics be plotted (Figure 2)

• cols: the colors to use for each algorithm (defaults to set1 of RColorBrewer).

• nclone: the number of multilocus genotypes you expect for the data. This will draw horizontal line on the graph at the value nclone and then vertical lines showing the cutoff thresholds for each algorithm.

• hist: if you want a histogram to be plotted behind the statistics, select a method here. Available methods are "sturges", "fd", or "scott" (default) as documented in hist. If you don't want to plot the histogram, set hist = NULL.

• threads: (unused) Previously the number of threads to be used. As of poppr version 2.4.1, this is by default set to 1.

• ...: extra parameters passed on to the distance function.

Returns

a list of results from mlg.filter from the three algorithms. (returns invisibly if plot = TRUE)

Note

This function originally appeared in tools:::Rd_expr_doi("10.5281/zenodo.17424")

Examples

# Basic usage example: Bruvo's Distance --------------------------------
data(Pinf)
pinfreps <- fix_replen(Pinf, c(2, 2, 6, 2, 2, 2, 2, 2, 3, 3, 2))
bres <- filter_stats(Pinf, distance = bruvo.dist, replen = pinfreps, plot = TRUE, threads = 1L)
print(bres) # shows all of the statistics

# Use these results with cutoff_filter()
print(thresh <- cutoff_predictor(bres$farthest$THRESHOLDS))
mlg.filter(Pinf, distance = bruvo.dist, replen = pinfreps) <- thresh
Pinf 

# Different distances will give different results -----------------------
nres <- filter_stats(Pinf, distance = nei.dist, plot = TRUE, threads = 1L, missing = "mean")
print(thresh <- cutoff_predictor(nres$farthest$THRESHOLDS))
mlg.filter(Pinf, distance = nei.dist, missing = "mean") <- thresh
Pinf

References

ZN Kamvar, JC Brooks, and NJ Grünwald. 2015. Supplementary Material for Frontiers Plant Genetics and Genomics 'Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality'. DOI: tools:::Rd_expr_doi("10.5281/zenodo.17424")

Kamvar ZN, Brooks JC and Grünwald NJ (2015) Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. Front. Genet. 6:208. doi: tools:::Rd_expr_doi("10.3389/fgene.2015.00208")

See Also

mlg.filter cutoff_predictor

bitwise.dist diss.dist

Author(s)

Zhian N. Kamvar, Jonah C. Brooks