plotMPPI: Plots the marginal posterior probability of inclusion (MPPI)...
In R2GUESS: Wrapper Functions for GUESS

Description Usage Arguments Value Author(s) Examples

The plotMPPI function plots the marginal posterior probability of inclusion (MPPI) of each predictor.

1 2	plotMPPI(x, threshold.model = 0.01, threshold.variable = 0.1, Figure = TRUE, cutoff = TRUE, useMC = FALSE)

`x`	an object of class `ESS`.
`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 `FDR.permutation`.
`Figure`	if `TRUE` (by default) will generate the MPPI plot. If `FALSE` only information on the selected predictors will provided.
`cutoff`	if `TRUE` (by default) will plot an horizontal line representing the cut-off value indicating by the argument `threshold.variable`. If `FALSE` the cut-off value is not plotted.
`useMC`	if `TRUE`, use simple Monte Carlo estimation for the MPPI across all visited models.

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>`threshold.variable` and belonging to the best models.
`var.MPI`	predictors which have a MPPI greater than `threshold.variable`.

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

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)