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Legacy name for assignment evaluation

Source: R/assignment_ngs_legacy.R
assignment_ngs.Rd

`assignment_ngs()` is the former name of [evaluate_assignment()]. New code should use the more descriptive name.

Usage

assignment_ngs(
  data,
  strata = NULL,
  pop.levels = NULL,
  assignment.analysis = c("gsi_sim", "adegenet"),
  markers.sampling = c("ranked", "random"),
  marker.number = "all",
  thl = 1,
  iteration.method = 10,
  subsample = NULL,
  iteration.subsample = 1,
  verbose = TRUE,
  parallel.core = parallel::detectCores() - 1,
  ...
)

Arguments

data

Several input format are accepted. assigner uses genometranslator read_genome to import the data. See function documentation for more details.

strata

Optional strata data or filename passed to readers that support it. Default: strata = NULL.

pop.levels

(optional, string) This refers to the levels in a factor. In this case, the id of the pop. Use this argument to have the pop ordered your way instead of the default alphabetical or numerical order. e.g. pop.levels = c("QUE", "ONT", "ALB") instead of the default pop.levels = c("ALB", "ONT", "QUE"). White spaces in population names are replaced by underscore. Default: pop.levels = NULL.

assignment.analysis

(character) Assignment analysis conducted with assignment.analysis = "gsi_sim" or assignment.analysis = "adegenet". See Details section below for installing gsi_sim.

markers.sampling

(character) 2 options for markers selection:

  1. markers.sampling == "random" the subset of markers are selected randomly, this is the classic Leave-One-Out (LOO) assignment.

  2. markers.sampling == "ranked" the subset of markers are first ranked based on an overall decreasing Fst values. The Fst is computed with fst_WC84 function, that uses a fast implementation of Weir and Cockerham 1984 Fst/Theta equations. This selection method is used during Training-Holdout-Leave One Out (thl) assignment. How many markers are selected is controlled with argument thl.

marker.number

(Integer or string of number or "all") The assignment analysis can use all your markers (default) or a subset of your markers. e.g. To test 500, 1000, 2000 and all the markers: marker.number = c(500, 1000, 2000, "all"). To use only 500 makers marker.number = 500. How those markers are sampled is determined with the argument markers.sampling, next. Default = marker.number = "all".

thl

(character, integer, proportion) For markers.sampling = "ranked" only. Several options are available:

  1. thl = 1: Only 1 individual sample is used as holdout. This individual is not participating in the markers ranking. For each marker number, the analysis will be repeated with all the indiviuals in the data set (e.g. 500 individuals, 500 times 500, 1000, 2000 markers). This is the default.

  2. proportion: e.g. thl = 0.15, 15 percent of the individuals, in each strata, are chosen randomly as holdout individuals.

  3. thl = "all": all individuals are used for ranking (not good) and the argument iteration.method = 1 is set by default. This is for testing only.

Different lists of holdout individuals are generated when the argument iteration.method (bootstrap) is used.

iteration.method

(integer) With random markers selection method, the iterations argument = the number of iterations to repeat marker resampling. Default: iteration.method = 10.

With marker.number = c(500, 1000) and default iterations setting, 500 markers will be randomly chosen 10 times and 1000 markers will be randomly chosen 10 times.

Notes: If all the markers are used, with marker.number = "all" or in a series of marker number groupings marker.number = c(200, 500, "all"), the number of iteration is automatically set to 1. The remaining groupings are unaffected.

With ranked makers selection method, using thl = 1, the analysis will be repeated for each individuals in the data set for every marker.number selected. With a proportion argument thl = 0.15, 15 percent of individuals in each populations are chosen randomly as holdout individuals and this process is reapeated the number of times chosen by the iteration.method value.

subsample

(Integer or Character, optional) This argument subsample individuals. With subsample = 36, 36 individuals in each populations are chosen randomly to represent the dataset. This integer as to be smaller than the population with min sample size, if higher, the minimum sample size found in the data will be used as default. In doubt, use subsample = "min", the function will use the smallest population sample size found in the data. The number of times this process is repeated is controlled by the argument iteration.subsample. Default: subsample = NULL (no subsampling).

iteration.subsample

(Integer) The number of iterations to repeat subsampling of individuals. With subsample = 20 and iteration.subsample = 10, 20 individuals/populations will be randomly chosen 10 times. Default: iteration.subsample = 1.

verbose

(optional, logical) verbose = TRUE to be chatty during execution. Default: verbose = FALSE.

parallel.core

Number of processor cores passed to readers that support parallel processing. Default: parallel.core = parallel::detectCores() - 1.

...

Additional arguments passed to [evaluate_assignment()].

Value

The value returned by [evaluate_assignment()].