# MPR

##### Most Parsimonious Reconstruction

This function does ancestral character reconstruction by parsimony as described in Hanazawa et al. (1995) and modified by Narushima and Hanazawa (1997).

- Keywords
- models

##### Usage

`MPR(x, phy, outgroup)`

##### Arguments

- x
- a vector of integers.
- phy
- an object of class
`"phylo"`

; the tree must be unrooted and fully dichotomous. - outgroup
- an integer or a character string giving the tip of
`phy`

used as outgroup.

##### Details

Hanazawa et al. (1995) and Narushima and Hanazawa (1997) used Farris's (1970) and Swofford and Maddison's (1987) framework to reconstruct ancestral states using parsimony. The character is assumed to take integer values. The algorithm finds the sets of values for each node as intervals with lower and upper values.

It is recommended to root the tree with the outgroup before the
analysis, so plotting the values with `nodelabels`

is
simple.

##### Value

- a matrix of integers with two columns named ``lower'' and ``upper'' giving the lower and upper values of the reconstructed sets for each node.

##### References

Farris, J. M. (1970) Methods for computing Wagner trees.
*Systematic Zoology*, **19**, 83--92.

Hanazawa, M., Narushima, H. and Minaka, N. (1995) Generating most
parsimonious reconstructions on a tree: a generalization of the
Farris--Swofford--Maddison method. *Discrete Applied
Mathematics*, **56**, 245--265.

Narushima, H. and Hanazawa, M. (1997) A more efficient algorithm for
MPR problems in phylogeny. *Discrete Applied Mathematics*,
**80**, 231--238.

Swofford, D. L. and Maddison, W. P. (1987) Reconstructing ancestral
character states under Wagner parsimony. *Mathematical
Biosciences*, **87**, 199--229.

##### See Also

##### Examples

```
## the example in Narushima and Hanazawa (1997):
tr <- read.tree(text = "(((i,j)c,(k,l)b)a,(h,g)e,f)d;")
x <- c(1, 3, 0, 6, 5, 2, 4)
names(x) <- letters[6:12]
(o <- MPR(x, tr, "f"))
plot(tr)
nodelabels(paste("[", o[, 1], ",", o[, 2], "]", sep = ""))
tiplabels(x[tr$tip.label], adj = -2)
## some random data:
x <- rpois(30, 1)
tr <- rtree(30, rooted = FALSE)
MPR(x, tr, "t1")
```

*Documentation reproduced from package ape, version 3.2, License: GPL (>= 2)*