This data was prepared using nucleotide data from 5 different chimp species. I then chose 10 arbitrary loci and compared them and found their PID and percent dis-similarity as well. I found these numbers for each pair of comparissons that could be made of the species availible. These results where then plotted in a table.

Preliminaries

#install.packages("ape")
#install.packages("phangorn")

library(ape)
library(phangorn)

Build the matrix.

Be sure to add the nrow = … statemetn.

five_sim_mat <- matrix(c(1.0,     0.0,  0.0,  0.0,  0.0,        
                         0.8,   1.0,    0.0,  0.0,  0.0,                
                         0.7,   0.7,    1.0,    0.0,  0.0,      
                         0.3,     0.3,  0.4,    1.0,    0.0,        
                         0.6,   0.6,    0.5,    0.4,    1.0),
                         nrow=5, byrow = T)

Name things

row.names(five_sim_mat) <-c("M_E", "B", "G", "T", "M")
colnames(five_sim_mat)  <-c("M_E", "B", "G", "T", "M")

Turn into a distance matrix. This is 2 steps and requires the as.dist() command

five_dist_mat<- 1-five_sim_mat

five_dist_mat2 <- as.dist(five_dist_mat)

Neighbor-Joining tree with nj()

five_nj <- nj(five_dist_mat2)

Plot unrooted NJ tree

plot(five_nj, "unrooted")

Plot rooted NJ tree

plot(five_nj)

Build UPGMA tree

five_upgma <- phangorn::upgma(five_dist_mat2)

Plot UPGMA tree

plot(five_upgma)

Compare rooted NJ and UPGMA plots

par(mfrow=c(1,2))
plot(five_nj)
plot(five_upgma)

Build WPGMA tree

five_wpgma <- wpgma(five_dist_mat2)
plot(wpgma(five_dist_mat))

Compare rooted WPGMA and UPGMA plots

par(mfrow=c(1,2))
plot(five_upgma)
plot(five_wpgma)

Build Minimum evolution tree

plot(fastme.ols(five_dist_mat2))