simdata30nodes {envlpaster} | R Documentation |

## A generated aster data set with 30 nodes

### Description

Simulated data for an aster analysis. Loads 7 objects.

### Usage

`data(simdata30nodes)`

### Format

The data frame with records for 3000 organisms over 10 years.
The dataset corresponding to our aster analysis. The following four descriptions explain the elements of this dataset.

- u
Indicates survival for each of the 10 years.

- w
Counts offspring for each of the 10 years.

- v
Indicates if `w > 0`

for each of the 10 years.

- z
A covariate of potential interest, 10 in total.

- variables
Character vector giving the names of the variables in
the graph.

- root
The root data. For `aster.default`

an
`nind`

by `nnode`

matrix, for `aster.formula`

an `nind * nnode`

vector.

- modmat
An `nind`

by `nnode`

by `ncoef`

three-dimensional array, the model matrix. `aster.formula`

constructs such a modmat from its formula, the data frame data,
and the variables in the environment of the formula.

- formula
Necessary for changing to class `aster.formula`

.

- xlevels
Necessary for changing to class `aster.formula`

.

- terms
Necessary for changing to class `aster.formula`

.

- simdata30nodes.asterdata
An object of class `asterdata`

corresponding to `simdata30nodes`

.

### Details

This object contains an aster data set in wide form, an
object of class `asterdata`

corresponding to the
original data set, and vectors specifying the graphical
structure of the aster model.

There are 3000 simulated individuals in this aster analysis.
Our data is generated in two parts. The first part follows
Technical report 671 (TR 671) on Charlie Geyer's Aster
Models for Life History Analysis webpage. For our data,
`nind = 3000`

, `ntime = 10`

, `psurv = 0.95`

, `prepr = 0.7`

, `mpois = 1`

,
and the seed is set at `set.seed(13)`

which is different
from the original simulation setup.

We follow the model construction in TR 671 through `out6`

.
We then generate a new dataset from the aster model where
the components of the submodel mean-value parameter vector
`\tau`

corresponding to Darwinian fitness is in the
space spanned by the first, second, and fourth eigenvectors
of Fisher information.

### References

Geyer, C. J. and Shaw, R. G. (2009).
Model Selection in Estimation of Fitness Landscapes. Technical Report No. 671. School of Statistics, University of Minnesota.
http://conservancy.umn.edu/handle/11299/56219.

[Package

*envlpaster* version 0.1-2

Index]