simdata30nodes: A generated aster data set with 30 nodes
In envlpaster: Enveloping the Aster Model
Description
Usage
Format
Details
References
Description
Simulated data for an aster analysis. Loads 7 objects.
Usage
1
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
τ 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.
envlpaster documentation built on May 2, 2019, 2:10 a.m.
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Description Usage Format Details References
Description
Simulated data for an aster analysis. Loads 7 objects.
Usage
1 |
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 > 0for 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.defaultannindbynnodematrix, foraster.formulaannind * nnodevector.- modmat
An
nindbynnodebyncoefthree-dimensional array, the model matrix.aster.formulaconstructs 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
asterdatacorresponding tosimdata30nodes.
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
τ 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.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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