.Rhistory

library(pegas)
data(woodmouse)
data(woodmouse)
?woodmouse
alview(woodmouse[1:5, 1:25])
alview(woodmouse[1:7, 1:100])
alview(woodmouse[1:5, 1:75])
image(woodmouse)
quart()
quartz()
alview(woodmouse[1:5, 1:75])
image(woodmouse)
image(woodmouse, "n", "blue")
image(woodmouse, "n", "blue")
image(woodmouse[1:2, 1:100])
grid(100, 2, col = "black")
(sp <- site.spectrum(woodmouse))
plot(sp)
(sp <- site.spectrum(woodmouse[, 1:500]))
plot(sp)
(sp <- site.spectrum(woodmouse[2:7, 50:800]))
plot(sp)
(sp <- site.spectrum(woodmouse[1:10, 50:800]))
plot(sp)
(sp <- site.spectrum(woodmouse[, 500:800]))
plot(sp)
h <- haplotype(woodmouse)
net <- haploNet(h)
plot(net)
nuc.div(woodmouse, pairwise.deletion = TRUE)
seg.sites(woodmouse)
length(seg.sites(woodmouse)
)
theta.s(woodmouse, variance = TRUE)
tajima.test(woodmouse)
seg.sites(woodmouse[,800:])
woodmouse
seg.sites(woodmouse[,800:965])
nuc.div(woodmouse[,800:965], pairwise.deletion = TRUE)
tajima.test(woodmouse[,800:965])
q()
# Chunk 1: setup
knitr::opts_chunk$set(echo = TRUE)
options(digits  = 3)
# Chunk 2: install pegas
#install.packages("pegas")
# Chunk 3: load_libs
library(pegas)
# Chunk 4: load_woodmouse
data(woodmouse)
# Chunk 5: sum_woodmouse
print(woodmouse)
str(woodmouse)
alview(woodmouse[1:5, 1:50])
image(woodmouse)
grid(ncol(woodmouse), nrow(woodmouse),
col = "lightgrey")
image(woodmouse[1:2, 1:100])
grid(ncol(woodmouse[1:2, 1:100]), nrow(woodmouse[1:2, 1:100]), col = "grey")
image(woodmouse[1:3, 1:100])
grid(ncol(woodmouse[1:3, 1:100]), nrow(woodmouse[1:3, 1:100]), col = "grey")
image(woodmouse[1:4, 1:100])
grid(ncol(woodmouse[1:4, 1:100]), nrow(woodmouse[1:4, 1:100]), col = "grey")