Description Usage Arguments Value Author(s) Examples
The plotMPPI
function plots the marginal posterior
probability of inclusion (MPPI) of each predictor.
1 2 |
x |
an object of class |
threshold.model |
either an integer representing the number of model to be retained in the list of best models, or a value defining the minimal model posterior probability for inclusion. |
threshold.variable |
threshold probability for selecting the
most relevant predictors. This threshold can be calibrated by
controlling the FDR
using |
Figure |
if |
cutoff |
if |
useMC |
if |
The plotMPPI
function returns information
on the best models (i.e. those satisfying the
threshold.model
criterion) and on the most relevant
predictors.
(above threshold.variable
).
Rank |
the rank on the models selected. |
nVisits |
number of times each model has been visited along the run. |
ModSize |
number of predictors in each of the best models. |
logCondPost |
the log conditional posterior for each model. |
Jeffries |
Jeffrie's scale value for each model. |
postProb |
posterior probability of each model. |
modelName |
list of predictors in each of the best models. |
modelPosInX |
position (in the predictor matrix) of the constituents of the best models. |
var.TOP.MPI |
predictors with MPPI> |
var.MPI |
predictors which have a MPPI greater than
|
Benoit Liquet, b.liquet@uq.edu.au,
Marc Chadeau-Hyam m.chadeau@imperial.ac.uk,
Leonardo Bottolo l.bottolo@imperial.ac.uk,
Gianluca Campanella g.campanella11@imperial.ac.uk
1 2 3 4 5 | modelY_Hopx <- example.as.ESS.object()
# To get a large plot
# dev.new(width=13,height=6)
MPPI.Hopx <- plotMPPI(modelY_Hopx,threshold.model=20,threshold.variable=0.45)
print(MPPI.Hopx)
|
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