Bootstrap the index of association

This function will perform the index of association on a bootstrapped data set multiple times to create a distribution, showing the variation of the index due to repeat observations.

Usage

boot.ia(gid, how = "partial", reps = 999, quiet = FALSE, ...)

Arguments

gid: a genind or genclone object
how: method of bootstrap. The default how = "partial" will include all the unique genotypes and sample with replacement from the unique genotypes until the total number of individuals has been reached. Using how = "full" will randomly sample with replacement from the data as it is. Using how = "psex" will sample from the full data set after first weighting the samples via the probability of encountering the nth occurence of a particular multilocus genotype. See psex() for details.
reps: an integer specifying the number of replicates to perform. Defaults to 999.
quiet: a logical. If FALSE, a progress bar will be displayed. If TRUE, the progress bar is suppressed.
...: options passed on to psex()

Value

a data frame with the index of association and standardized index of association in columns. Number of rows represents the number of reps.

Note

This function is experimental. Please do not use this unless you know what you are doing.

Examples

data(Pinf)
boot.ia(Pinf, reps = 99)
#>           Ia      rbarD
#> 1  0.4819019 0.05194235
#> 2  0.6471303 0.07089416
#> 3  0.5075382 0.05548256
#> 4  0.4484793 0.04779991
#> 5  0.4723701 0.05180478
#> 6  0.5795416 0.06215560
#> 7  0.6201807 0.06798984
#> 8  0.5199299 0.05675760
#> 9  0.5554505 0.06058716
#> 10 0.5996637 0.06538732
#> 11 0.5989807 0.06480046
#> 12 0.5095099 0.05555006
#> 13 0.5304545 0.05815529
#> 14 0.5448810 0.05946702
#> 15 0.5558104 0.06089201
#> 16 0.5105514 0.05586222
#> 17 0.5018430 0.05481066
#> 18 0.4981589 0.05440054
#> 19 0.5492266 0.05985564
#> 20 0.6106282 0.06596382
#> 21 0.4955819 0.05407274
#> 22 0.5957244 0.06530788
#> 23 0.4660049 0.05091427
#> 24 0.5924443 0.06467981
#> 25 0.6357892 0.06990272
#> 26 0.6097687 0.06713849
#> 27 0.4804808 0.05260730
#> 28 0.5917740 0.06472826
#> 29 0.5760935 0.06316190
#> 30 0.5506424 0.06025225
#> 31 0.4829750 0.05206509
#> 32 0.5844528 0.06411806
#> 33 0.5152625 0.05615920
#> 34 0.4324953 0.04714766
#> 35 0.4456071 0.04864187
#> 36 0.4670333 0.05109920
#> 37 0.5668780 0.06208478
#> 38 0.6399688 0.07008245
#> 39 0.7040248 0.07722015
#> 40 0.5463752 0.05906408
#> 41 0.5964468 0.06562497
#> 42 0.5860739 0.06409211
#> 43 0.5748121 0.06292909
#> 44 0.5079590 0.05481726
#> 45 0.5288720 0.05788804
#> 46 0.5471259 0.06000560
#> 47 0.5485903 0.06011772
#> 48 0.5127772 0.05628946
#> 49 0.5009626 0.05485145
#> 50 0.6240469 0.06839995
#> 51 0.5739781 0.06284737
#> 52 0.4804929 0.05207227
#> 53 0.4706100 0.05074339
#> 54 0.5848158 0.06333332
#> 55 0.6571567 0.07197002
#> 56 0.5469144 0.05948287
#> 57 0.5537667 0.05977034
#> 58 0.4502435 0.04824133
#> 59 0.5026848 0.05503491
#> 60 0.5180611 0.05668011
#> 61 0.5486374 0.05877376
#> 62 0.5414529 0.05932712
#> 63 0.5713421 0.06249268
#> 64 0.4346541 0.04754724
#> 65 0.5747481 0.06132894
#> 66 0.5696405 0.06212929
#> 67 0.5047758 0.05518573
#> 68 0.5197619 0.05689543
#> 69 0.4612496 0.04921734
#> 70 0.6133986 0.06618885
#> 71 0.5100138 0.05572065
#> 72 0.5181444 0.05652484
#> 73 0.5223402 0.05710837
#> 74 0.5856362 0.06410488
#> 75 0.4882622 0.05356209
#> 76 0.6005801 0.06557054
#> 77 0.5169951 0.05652175
#> 78 0.4195476 0.04593809
#> 79 0.5202460 0.05698222
#> 80 0.5581146 0.06114942
#> 81 0.4827087 0.05264719
#> 82 0.5238842 0.05744573
#> 83 0.6006422 0.06586924
#> 84 0.4537650 0.04884950
#> 85 0.5718191 0.06235611
#> 86 0.4802257 0.05260593
#> 87 0.4711562 0.05155444
#> 88 0.5366242 0.05882522
#> 89 0.6795960 0.07296641
#> 90 0.5174513 0.05657002
#> 91 0.5505765 0.06013948
#> 92 0.4696297 0.05137168
#> 93 0.5595995 0.06124075
#> 94 0.4504844 0.04923967
#> 95 0.4512368 0.04942074
#> 96 0.4494671 0.04883154
#> 97 0.5624981 0.06171292
#> 98 0.6124019 0.06690649
#> 99 0.4900166 0.05284311