simulate_g2: Simulate g2
In inbreedR: Analysing Inbreeding Based on Genetic Markers

Description Usage Arguments Details Value Author(s) Examples

This function can be used to simulate genotype data, draw subsets of loci and calculate the respective g2 values. Every subset of markers is drawn independently to give insights into the variation and precision of g2 calculated from a given number of markers and individuals.

1 2	simulate_g2(n_ind = NULL, H_nonInb = 0.5, meanF = 0.2, varF = 0.03, subsets = NULL, reps = 100, type = c("msats", "snps"), CI = 0.95)

`n_ind`	number of individuals to sample from the population
`H_nonInb`	true genome-wide heteorzygosity of a non-inbred individual
`meanF`	mean realized inbreeding f
`varF`	variance in realized inbreeding f
`subsets`	a vector specifying the sizes of marker-subsets to draw. Specifying `subsets = c(2, 5, 10, 15, 20)` would draw marker sets of 2 to 20 markers. The minimum number of markers to calculate g2 is 2.
`reps`	number of resampling repetitions
`type`	specifies g2 formula. Type "snps" for large datasets and "msats" for smaller datasets.
`CI`	Confidence intervals to calculate (default to 0.95)

The simulate_g2 function simulates genotypes from which subsets of loci can be sampled independently. These simulations can be used to evaluate the effects of the number of individuals and loci on the precision and magnitude of g2. The user specifies the number of simulated individuals (n_ind), the subsets of loci (subsets) to be drawn, the heterozygosity of non-inbred individuals (H_nonInb) and the distribution of f among the simulated individuals. The f values of the simulated individuals are sampled randomly from a beta distribution with mean (meanF) and variance (varF) specified by the user (e.g. as in wang2011). This enables the simulation to mimic populations with known inbreeding characteristics, or to simulate hypothetical scenarios of interest. For computational simplicity, allele frequencies are assumed to be constant across all loci and the simulated loci are unlinked. Genotypes (i.e. the heterozygosity/homozygosity status at each locus) are assigned stochastically based on the f values of the simulated individuals. Specifically, the probability of an individual being heterozygous at any given locus (H) is expressed as H = H0(1-f) , where H0 is the user-specified heterozygosity of a non-inbred individual and f is an individual's inbreeding coefficient drawn from the beta distribution.

simulate_g2 returns an object of class "inbreed". The functions 'print' and 'plot' are used to print a summary and to plot the g2 values with means and confidence intervals

An 'inbreed' object from simulate_g2 is a list containing the following components:

`call`	function call.
`estMat`	matrix with all r2(h,f) estimates. Each row contains the values for a given subset of markers
`true_g2`	"true" g2 value based on the assigned realized inbreeding values
`n_ind`	specified number of individuals
`subsets`	vector specifying the marker sets
`reps`	repetitions per subset
`H_nonInb`	true genome-wide heteorzygosity of a non-inbred individual
`meanF`	mean realized inbreeding f
`varF`	variance in realized inbreeding f
`min_val`	minimum g2 value
`max_val`	maximum g2 value
`all_CI`	confidence intervals for all subsets
`all_sd`	standard deviations for all subsets

Marty Kardos (marty.kardos@ebc.uu.se) & Martin A. Stoffel (martin.adam.stoffel@gmail.com)

data(mouse_msats)
genotypes <- convert_raw(mouse_msats)
sim_g2 <- simulate_g2(n_ind = 10, H_nonInb = 0.5, meanF = 0.2, varF = 0.03,
                      subsets = c(4,6,8,10), reps = 100, 
                      type = "msats")
plot(sim_g2)

[1] "done with subsampling of 4 loci"
[1] "done with subsampling of 6 loci"
[1] "done with subsampling of 8 loci"
[1] "done with subsampling of 10 loci"