| Jointness | R Documentation |
Jointness computes the joint inclusion probabilitiy of two given
covariates as well as the jointness measurements of Ley and Steel (2007)
Jointness(x, covariates = "All")
x |
An object of class |
covariates |
It can be either "All"(default) or a vector contaning the name of two covariates. |
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.
Gonzalo Garcia-Donato and Anabel Forte
Maintainer: <anabel.forte@uv.es>
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.
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.
## 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)
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