probabilityKM: Probability of Variables and Models Calculated with a...
In ClementCalenge/ungulateCollisions: Identifying the Factors Affecting Car-Ungulate Collisions
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
Description
Probability of Variables and Models Calculated with a Kuo-Mallik Approach
Usage
1
Arguments
x
an object of class mcmc.list, returned by the
function coda.samples from the package rjags. The
model string used to fit the model should have been generated by
the function prepareFit.
Details
probabilityKM calculates the probability of
inclusion of the variables, as well as the probability that each
possible model is true, using a Kuo-Mallik approach. The model used
should have been fitted with the package rjags, using the model
string returned by the function prepareFit.
Value
a list with two elements containing: (i) variables:
a data.frame containing the probability of inclusion of each
variable in the model, (ii) models: a data.frame containing
the probability that each possible model (i.e. a given combination
of the variables used in the model) is true. In both cases, the
variables and models are sorted by decreasing value of probability.
Author(s)
Clement Calenge, clement.calenge@oncfs.gouv.fr
See Also
Examples
1
2
data("modelRedDeer")
probabilityKM(modelRedDeer)
ClementCalenge/ungulateCollisions documentation built on May 6, 2019, 12:05 p.m.
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Description Usage Arguments Details Value Author(s) See Also Examples
Description
Probability of Variables and Models Calculated with a Kuo-Mallik Approach
Usage
1 |
Arguments
x |
an object of class |
Details
probabilityKM calculates the probability of
inclusion of the variables, as well as the probability that each
possible model is true, using a Kuo-Mallik approach. The model used
should have been fitted with the package rjags, using the model
string returned by the function prepareFit.
Value
a list with two elements containing: (i) variables:
a data.frame containing the probability of inclusion of each
variable in the model, (ii) models: a data.frame containing
the probability that each possible model (i.e. a given combination
of the variables used in the model) is true. In both cases, the
variables and models are sorted by decreasing value of probability.
Author(s)
Clement Calenge, clement.calenge@oncfs.gouv.fr
See Also
Examples
1 2 | data("modelRedDeer")
probabilityKM(modelRedDeer)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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