compar.ou | R Documentation |

This function fits an Ornstein–Uhlenbeck model giving a phylogenetic tree, and a continuous character. The user specifies the node(s) where the optimum changes. The parameters are estimated by maximum likelihood; their standard-errors are computed assuming normality of these estimates.

compar.ou(x, phy, node = NULL, alpha = NULL)

`x` |
a numeric vector giving the values of a continuous character. |

`phy` |
an object of class |

`node` |
a vector giving the number(s) of the node(s) where the
parameter ‘theta’ (the trait optimum) is assumed to change. The
node(s) can be specified with their labels if |

`alpha` |
the value of |

The Ornstein–Uhlenbeck (OU) process can be seen as a generalization
of the Brownian motion process. In the latter, characters are assumed
to evolve randomly under a random walk, that is change is equally
likely in any direction. In the OU model, change is more likely
towards the direction of an optimum (denoted *theta*) with
a strength controlled by a parameter denoted *alpha*.

The present function fits a model where the optimum parameter
*theta*, is allowed to vary throughout the tree. This is
specified with the argument `node`

: *theta* changes
after each node whose number is given there. Note that the optimum
changes *only* for the lineages which are descendants of this
node.

Hansen (1997) recommends to not estimate *alpha* together
with the other parameters. The present function allows this by giving
a numeric value to the argument `alpha`

. By default, this
parameter is estimated, but this seems to yield very large
standard-errors, thus validating Hansen's recommendation. In practice,
a “poor man estimation” of *alpha* can be done by
repeating the function call with different values of `alpha`

, and
selecting the one that minimizes the deviance (see Hansen 1997 for an
example).

If `x`

has names, its values are matched to the tip labels of
`phy`

, otherwise its values are taken to be in the same order
than the tip labels of `phy`

.

The user must be careful here since the function requires that both
series of names perfectly match, so this operation may fail if there
is a typing or syntax error. If both series of names do not match, the
values in the `x`

are taken to be in the same order than the tip
labels of `phy`

, and a warning message is issued.

an object of class `"compar.ou"`

which is list with the following
components:

`deviance` |
the deviance (= -2 * loglik). |

`para` |
a data frame with the maximum likelihood estimates and their standard-errors. |

`call` |
the function call. |

The inversion of the variance-covariance matrix in the likelihood
function appeared as somehow problematic. The present implementation
uses a Cholevski decomposition with the function
`chol2inv`

instead of the usual function
`solve`

.

Emmanuel Paradis

Hansen, T. F. (1997) Stabilizing selection and the comparative
analysis of adaptation. *Evolution*, **51**, 1341–1351.

`ace`

, `compar.lynch`

,
`corBrownian`

, `corMartins`

, `pic`

data(bird.orders) ### This is likely to give you estimates close to 0, 1, and 0 ### for alpha, sigma^2, and theta, respectively: compar.ou(x <- rnorm(23), bird.orders) ### Much better with a fixed alpha: compar.ou(x, bird.orders, alpha = 0.1) ### Let us 'mimick' the effect of different optima ### for the two clades of birds... x <- c(rnorm(5, 0), rnorm(18, 5)) ### ... the model with two optima: compar.ou(x, bird.orders, node = 25, alpha = .1) ### ... and the model with a single optimum: compar.ou(x, bird.orders, node = NULL, alpha = .1) ### => Compare both models with the difference in deviances ## which follows a chi^2 with df = 1.

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