neMarker | R Documentation |
'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.
neMarker(M, maxNe=100, maxMarker=1000, nSamples=5)
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. |
A vector with allele coverage based on different number of individuals
Giovanny Covarrubias-Pazaran
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
The core functions of the package mmec
####=========================================####
#### 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)
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