Summary of BayesMfp object {bfp} | R Documentation |
Calculate and print the summary of a BayesMfp object
Description
Calculate and print the summary of a BayesMfp
object,
using S3 methods for the class.
Usage
## S3 method for class 'BayesMfp'
summary(object, level=0.95, table=TRUE,
shrinkage=NULL, ...)
## S3 method for class 'summary.BayesMfp'
print(x, ...)
Arguments
object |
a valid |
x |
a return value of |
level |
credible level for coefficients HPD intervals (default: 0.95) |
table |
should a data.frame of the models be included? (default) |
shrinkage |
shrinkage factor used, where |
... |
only used by |
Value
summary.BayesMfp
returns a list with S3 class
summary.BayesMfp
, where the arguments “call”,
“numVisited”, “termNames”,
“shiftScaleMax”, “inclusionProbs”, “chainlength”
(only for model sampling results) are copied from the attributes of
the BayesMfp
object, please see its help page for
details.
The other elements are:
dataframe |
the model overview as data.frame (only if
|
localInclusionProbs |
local variable inclusion probability estimates |
nModels |
number of models contained in |
If there are multiple models in object
, the list element
postProbs
contains the exact (for exhaustively explored model
spaces) or estimated (if model sampling has been done) posterior model
probabilities.
If object
contains only one FP model, then this one is
summarized in more detail:
level |
used credible level for coefficients HPD intervals |
shrinkage |
used shrinkage factor |
summaryMat |
matrix with posterior summaries of the single
coefficients: “mode” gives the posterior mode,
“HPDlower” and “HPDupper” give the boundaries of the HPD
intervals with specified credible |
sigma2Sum |
posterior summary for the regression variance: again mode, and lower and upper HPD bounds are given in a rowvector. |
Note
Note that if you extract the summary of a single model with these
functions, you ignore the uncertainty about the shrinkage factor
t=g/(g+1) by plugging in the number shrinkage
. If you want to
incorporate this uncertainty, you must run BmaSamples
on
this model and call the corresponding method
summary.BmaSamples
.
Author(s)
Daniel Saban\'es Bov\'e
See Also
Examples
## generate a BayesMfp object
set.seed(19)
x1 <- rnorm(n=15)
x2 <- rbinom(n=15, size=20, prob=0.5)
x3 <- rexp(n=15)
y <- rt(n=15, df=2)
test <- BayesMfp(y ~ bfp (x2, max = 4) + uc (x1 + x3), nModels = 100,
method="exhaustive")
## summary of multiple models:
summary(test)
## summary of just one model (no. 10):
summary(test[10])
## internal structure is usually not interesting:
str(summary(test[10]))