Multilocus genotypes are initially defined by naive string matching, but this definition does not take into account missing data or genotyping error, casting these as unique genotypes. Defining multilocus genotypes by genetic distance allows you to incorporate genotypes that have missing data o genotyping error into their parent clusters.
1 2 3 4 5 6 7  mlg.filter(pop, threshold = 0, missing = "asis", memory = FALSE,
algorithm = "farthest_neighbor", distance = "diss.dist", threads = 1L,
stats = "MLGs", ...)
mlg.filter(pop, missing = "asis", memory = FALSE,
algorithm = "farthest_neighbor", distance = "diss.dist", threads = 1L,
...) < value

pop 
a 
threshold 
a number indicating the minimum distance two MLGs must be separated by to be considered different. Defaults to 0, which will reflect the original (naive) MLG definition. 
missing 
any method to be used by 
memory 
whether this function should remember the last distance matrix it generated. TRUE will attempt to reuse the last distance matrix if the other parameters are the same. (default) FALSE will ignore any stored matrices and not store any it generates. 
algorithm 
determines the type of clustering to be done.

distance 
a character or function defining the distance to be applied
to pop. Defaults to 
threads 
(unused) Previously, this was the maximum number of parallel threads to be used within this function. Default is 1 indicating that this function will run serially. Any other number will result in a warning. 
stats 
a character vector specifying which statistics should be returned (details below). Choices are "MLG", "THRESHOLDS", "DISTANCES", "SIZES", or "ALL". If choosing "ALL" or more than one, a named list will be returned. 
... 
any parameters to be passed off to the distance method. 
value 
the threshold at which genotypes should be collapsed. 
This function will take in any distance matrix or function and collapse multilocus genotypes below a given threshold. If you use this function as the assignment method (mlg.filter(myData, distance = myDist) < 0.5), the distance function or matrix will be remembered by the object. This means that if you define your own distance matrix or function, you must keep it in memory to further utilize mlg.filter.
Default, a vector of collapsed multilocus genotypes. Otherwise, any combination of the following:
a numeric vector defining the multilocus genotype cluster of each individual in the dataset. Each genotype cluster is separated from every other genotype cluster by at least the defined threshold value, as calculated by the selected algorithm.
A numeric vector representing the thresholds beyond which clusters of multilocus genotypes were collapsed.
A square matrix representing the distances between each cluster.
The sizes of the multilocus genotype clusters in order.
mlg.vector
makes use of mlg.vector
grouping prior to
applying the given threshold. Genotype numbers returned by
mlg.vector
represent the lowest numbered genotype (as returned by
mlg.vector
) in in each new multilocus genotype. Therefore
mlg.filter
and mlg.vector
return the same vector when
threshold is set to 0 or less.
filter_stats
,
cutoff_predictor
,
mll
,
genclone
,
snpclone
,
diss.dist
,
bruvo.dist
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95  data(partial_clone)
pc < as.genclone(partial_clone, threads = 1L) # convert to genclone object
# Basic Use 
# Show MLGs at threshold 0.05
mlg.filter(pc, threshold = 0.05, distance = "nei.dist", threads = 1L)
pc # 26 mlgs
# Set MLGs at threshold 0.05
mlg.filter(pc, distance = "nei.dist", threads = 1L) < 0.05
pc # 25 mlgs
## Not run:
# The distance definition is persistant
mlg.filter(pc) < 0.1
pc # 24 mlgs
# But you can still change the definition
mlg.filter(pc, distance = "diss.dist", percent = TRUE) < 0.1
pc
# Choosing a threshold 
# Thresholds for collapsing multilocus genotypes should not be arbitrary. It
# is important to consider what threshold is suitable. One method of choosing
# a threshold is to find a gap in the distance distribution that represents
# clonal groups. You can look at this by analyzing the distribution of all
# possible thresholds with the function "cutoff_predictor".
# For this example, we'll use Bruvo's distance to predict the cutoff for
# P. infestans.
data(Pinf)
Pinf
# Repeat lengths are necessary for Bruvo's distance
(pinfreps < fix_replen(Pinf, c(2, 2, 6, 2, 2, 2, 2, 2, 3, 3, 2)))
# Now we can collect information of the thresholds. We can set threshold = 1
# because we know that this will capture the maximum possible distance:
(thresholds < mlg.filter(Pinf, distance = bruvo.dist, stats = "THRESHOLDS",
replen = pinfreps, threshold = 1))
# We can use these thresholds to find an appropriate cutoff
(pcut < cutoff_predictor(thresholds))
mlg.filter(Pinf, distance = bruvo.dist, replen = pinfreps) < pcut
Pinf
# This can also be visualized with the "filter_stats" function.
# Special case: threshold = 0 
# It's important to remember that a threshold of 0 is equal to the original
# MLG definition. This example will show a data set that contains genotypes
# with missing data that share all alleles with other genotypes except for
# the missing one.
data(monpop)
monpop # 264 mlg
mlg.filter(monpop) < 0
nmll(monpop) # 264 mlg
# In order to merge these genotypes with missing data, we should set the
# threshold to be slightly higher than 0. We will use the smallest fraction
# the computer can store.
mlg.filter(monpop) < .Machine$double.eps ^ 0.5
nmll(monpop) # 236 mlg
# Custom distance 
# Custom genetic distances can be used either in functions from other
# packages or userdefined functions
data(Pinf)
Pinf
mlg.filter(Pinf, distance = function(x) dist(tab(x))) < 3
Pinf
mlg.filter(Pinf) < 4
Pinf
# genlight / snpclone objects 
set.seed(999)
gc < as.snpclone(glSim(100, 0, n.snp.struc = 1e3, ploidy = 2))
gc # 100 mlgs
mlg.filter(gc) < 0.25
gc # 82 mlgs
## End(Not run)

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