Jointness {BayesVarSel} | R Documentation |
Computation of Jointness measurements.
Description
Jointness
computes the joint inclusion probabilitiy of two given
covariates as well as the jointness measurements of Ley and Steel (2007)
Usage
Jointness(x, covariates = "All")
Arguments
x |
An object of class |
covariates |
It can be either "All"(default) or a vector contaning the name of two covariates. |
Value
An object of class jointness
is returned.
If covariates
is "All" this object is a list with three matrices
containg different jointness measurements for all pairs of covariates is
returned. In particular, for covariates i and j the jointness measurements
are:
The Joint inclusion probabilities:
P(i and j)
And the two measurements of Ley and Steel (2007)
J*= P(i and j)/P(i or j)
J*=P(i and j)/(P(i or j)-P(i and j))
If covariates
is a vector of length 2, Jointness
return a
list of four elements. The first three of them is a list of three values containing the
measurements above but just for the given pair of covariates. The fourth
element is the covariates
vector.
If method print.jointness
is used a message with the meaning of the
measurement si printed.
Author(s)
Gonzalo Garcia-Donato and Anabel Forte
Maintainer: <anabel.forte@uv.es>
References
Ley, E. and Steel, M.F.J. (2007)<DOI:10.1016/j.jmacro.2006.12.002>Jointness in Bayesian variable selection with applications to growth regression. Journal of Macroeconomics, 29(3):476-493.
See Also
Bvs
and
GibbsBvs
for performing variable selection and
obtaining an object of class Bvs
.
plot.Bvs
for different descriptive plots of the
results, BMAcoeff
for obtaining model averaged
simulations of regression coefficients and
predict.Bvs
for predictions.
Examples
## Not run:
#Analysis of Crime Data
#load data
data(UScrime)
crime.Bvs<- Bvs(formula= y ~ ., data=UScrime, n.keep=1000)
#A look at the jointness measurements:
Jointness(crime.Bvs, covariates="All")
Jointness(crime.Bvs, covariates=c("Ineq","Prob"))
#---------
#The joint inclusion probability for Ineq and Prob is: 0.65
#---------
#The ratio between the probability of including both
#covariates and the probability of including at least one of then is: 0.66
#---------
#The probability of including both covariates at the same times is 1.95 times
#the probability of including one of them alone
## End(Not run)