# yule.cov

##### Fits the Yule Model With Covariates

This function fits by maximum likelihood the Yule model with covariates, that is a birth-only model where speciation rate is determined by a generalized linear model.

- Keywords
- models

##### Usage

`yule.cov(phy, formula, data = NULL)`

##### Details

The model fitted is a generalization of the Yule model where the speciation rate is determined by:

$$\ln\frac{\lambda_i}{1 - \lambda_i} = \beta_1 x_{i1} + \beta_2 x_{i2} + \dots + \alpha$$

where $\lambda_i$ is the speciation rate for species i,
$x_{i1}, x_{i2}, \dots$ are species-specific
variables, and $\beta_1, \beta_2, \dots, \alpha$
are parameters to be estimated. The term on the left-hand side above
is a logit function often used in generalized linear models for
binomial data (see `family`

). The above model can
be written in matrix form:

$$\mathrm{logit} \lambda_i = x_i' \beta$$

The standard-errors of the parameters are computed with the second derivatives of the log-likelihood function. (See References for other details on the estimation procedure.)

The function needs three things:

- a phylogenetic tree which may contain multichotomies;
- a formula which specifies the predictors of the model described
above: this is given as a standardRformula and has no response (no
left-hand side term), for instance:
`~ x + y`

, it can include interactions (`~ x + a * b`

) (see`formula`

for details); - the predictors specified in the formula must be accessible to
the function (either in the global space, or though the
`data`

option); they can be numeric vectors or factors. The length and the order of these data are important: the number of values (length) must be equal to the number of tips of the tree + the number of nodes. The order is the following: first the values for the tips in the same order than for the labels, then the values for the nodes sequentially from the root to the most terminal nodes (i.e. in the order given by`phy$edge`

).

The user must obtain the values for the nodes separately.

Note that the method in its present implementation assumes that the
change in a species trait is more or less continuous between two nodes
or between a node and a tip. Thus reconstructing the ancestral values
with a Brownian motion model may be consistent with the present
method. This can be done with the function `ace`

.

##### Value

A NULL value is returned, the results are simply printed. The output
includes the deviance of the null (intercept-only) model and a
likelihood-ratio test of the fitted model against the null model.
Note that the deviance of the null model is different from the one
returned by `yule`

because of the different parametrizations.

##### References

Paradis, E. (2005) Statistical analysis of diversification with
species traits. *Evolution*, **59**, 1--12.

##### See Also

`branching.times`

, `diversi.gof`

,
`diversi.time`

, `ltt.plot`

,
`birthdeath`

, `bd.ext`

, `yule`

##### Examples

```
### a simple example with some random data
data(bird.orders)
x <- rnorm(45) # the tree has 23 tips and 22 nodes
### the standard-error for x should be as large as
### the estimated parameter
yule.cov(bird.orders, ~ x)
### another example with a tree that has a multichotomy
data(bird.families)
y <- rnorm(272) # 137 tips + 135 nodes
yule.cov(bird.families, ~ y)
```

*Documentation reproduced from package ape, version 3.0-2, License: GPL (>= 2)*