# Load the data set
data(Aeut)
# Investigate the number of multilocus genotypes.
amlg <- mlg(Aeut)
amlg # 119
# show the multilocus genotype vector
avec <- mlg.vector(Aeut)
avec
# Get a table
atab <- mlg.table(Aeut, plot = FALSE)
atab
# See where multilocus genotypes cross populations
acrs <- mlg.crosspop(Aeut) # MLG.59: (2 inds) Athena Mt. Vernon
# See which individuals belong to each MLG
aid <- mlg.id(Aeut)
aid["59"] # individuals 159 and 57
## Not run:
#
# # A simple example. 10 individuals, 5 genotypes.
# mat1 <- matrix(ncol=5, 25:1)
# mat1 <- rbind(mat1, mat1)
# mat <- matrix(nrow=10, ncol=5, paste(mat1,mat1,sep="/"))
# mat.gid <- df2genind(mat, sep="/")
# mlg(mat.gid)
# mlg.vector(mat.gid)
# mlg.table(mat.gid)
#
# # Now for a more complicated example.
# # Data set of 1903 samples of the H3N2 flu virus genotyped at 125 SNP loci.
# data(H3N2)
# mlg(H3N2, quiet=FALSE)
#
# H.vec <- mlg.vector(H3N2)
#
# # Changing the population vector to indicate the years of each epidemic.
# pop(H3N2) <- other(H3N2)$x$country
# H.tab <- mlg.table(H3N2, plot=FALSE, total=TRUE)
#
# # Show which genotypes exist accross populations in the entire dataset.
# res <- mlg.crosspop(H3N2, quiet=FALSE)
#
# # Let's say we want to visualize the multilocus genotype distribution for the
# # USA and Russia
# mlg.table(H3N2, sublist=c("USA", "Russia"), bar=TRUE)
#
# # An exercise in subsetting the output of mlg.table and mlg.vector.
# # First, get the indices of each MLG duplicated across populations.
# inds <- mlg.crosspop(H3N2, quiet=FALSE, indexreturn=TRUE)
#
# # Since the columns of the table from mlg.table are equal to the number of
# # MLGs, we can subset with just the columns.
# H.sub <- H.tab[, inds]
#
# # We can also do the same by using the mlgsub flag.
# H.sub <- mlg.table(H3N2, mlgsub=inds)
#
# # We can subset the original data set using the output of mlg.vector to
# # analyze only the MLGs that are duplicated across populations.
# new.H <- H3N2[H.vec %in% inds, ]
#
# ## End(Not run)
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