sommer (version 4.3.5)

neMarker: Effective population size based on marker matrix

Description

`neMarker` uses a marker matrix to approximate the effective population size (Ne) by discovering how many individuals are needed to sample all possible alleles in a population.

Usage

neMarker(M, maxNe=100, maxMarker=1000, nSamples=5)

Value

$S3

A vector with allele coverage based on different number of individuals

Arguments

M

marker matrix coded in a numerical faashion (any allele dosage is fine).

maxNe

maximum number of effective population size to be calculated.

maxMarker

maximum number of markers to use for the analysis.

nSamples

number of individuals to sample for the Ne calculation.

Author

Giovanny Covarrubias-Pazaran

References

Not based on any theory published yet but in a solid intuition on what is really important for a breeding program when we ask what is the effective population size

See Also

The core functions of the package mmec

Examples

  
####=========================================####
#### For CRAN time limitations most lines in the 
#### examples are silenced with one '#' mark, 
#### remove them and run the examples
####=========================================####

# data(DT_cpdata) # Madison cranberries
# DT <- DT_cpdata
# GT <- GT_cpdata
# MP <- MP_cpdata
# M <- GT
# # run the function
# ne <- neMarker(M, maxNe = 30, nSamples = 10)
# ################
# data(DT_technow) # maize
# M <- Md_technow # dent
# M <- (M*2) - 1
# M <- M + 1
# # run the function
# ne <- neMarker(M, maxNe = 60, nSamples = 10)
# ##
# M <- Mf_technow # flint
# M <- (M*2) - 1
# M <- M + 1
# # run the function
# ne <- neMarker(M, maxNe = 60, nSamples = 10)
# ################
# data(DT_wheat) # cimmyt wheat
# M <- GT_wheat + 1
# # run the function
# ne <- neMarker(M, maxNe = 60, nSamples = 10)
# ###############
# data(DT_rice) # Zhao rice
# M <- atcg1234(GT_rice)$M
# # run the function
# ne <- neMarker(M,  maxNe = 60, nSamples = 10)
# ###############
# data(DT_polyploid) # endelman potatoes
# M <- atcg1234(data=GT_polyploid, ploidy=4)$M
# # run the function
# ne <- neMarker(M,  maxNe = 60, nSamples = 10)
# 
# library(ggplot2) #For making plots
# ggplot(ne,aes(x=Ne,y=allelesCovered))+
#   geom_ribbon(aes(x=Ne,ymin=allelesCovered-allelesCoveredSe,
#                   ymax=allelesCovered+allelesCoveredSe),
#                   alpha=0.2,linetype=0)+
#   geom_line(linewidth=1)+
#   guides(alpha=FALSE)+
#   theme_bw()+ 
#   scale_x_continuous("Individual number")+
#   scale_y_continuous("Allele coverage")  + 
#                geom_hline(yintercept = 0.95) + 
#                geom_hline(yintercept = 0.975)