# NOT RUN {
# 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, color = TRUE)
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 {
# For the mlg.table, you can also choose to display the number of MLGs across
# populations in the background
mlg.table(Aeut, background = TRUE)
mlg.table(Aeut, background = TRUE, color = TRUE)
# 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, ]
# }
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