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.
Value
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.
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)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(R2GUESS)
Loading required package: fields
Loading required package: spam
Loading required package: grid
Spam version 1.3-0 (2015-10-24) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.
Attaching package: 'spam'
The following objects are masked from 'package:base':
backsolve, forwardsolve
Loading required package: maps
# maps v3.1: updated 'world': all lakes moved to separate new #
# 'lakes' database. Type '?world' or 'news(package="maps")'. #
Loading required package: MCMCpack
Loading required package: coda
Loading required package: MASS
##
## Markov Chain Monte Carlo Package (MCMCpack)
## Copyright (C) 2003-2016 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
##
## Support provided by the U.S. National Science Foundation
## (Grants SES-0350646 and SES-0350613)
##
Loading required package: mixOmics
Loading required package: lattice
Loading required package: ggplot2
Loaded mixOmics 6.0.0
Visit http://www.mixOmics.org for more details about our methods.
Any bug reports or comments? Notify us at mixomics at math.univ-toulouse.fr or https://bitbucket.org/klecao/package-mixomics/issues
Thank you for using mixOmics!
Attaching package: 'mixOmics'
The following object is masked from 'package:maps':
map
Loading required package: mvtnorm
Loading required package: snowfall
Loading required package: snow
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/R2GUESS/plotMPPI.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotMPPI
> ### Title: Plots the marginal posterior probability of inclusion (MPPI) for
> ### each predictor
> ### Aliases: plotMPPI
>
> ### ** Examples
>
> modelY_Hopx <- example.as.ESS.object()
The run is ok
You can now analyse the results
> # 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)
$Rank
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
$nVisits
[1] 16 0 9 53 11 43 10 9 12 2 7 6 2 9 15 4 14 6 12 3
$ModeSize
[1] 6 1 3 2 4 5 2 6 3 6 6 7 4 7 2 4 5 3 6 2
$logCondPost
[1] -88.00595 -89.06327 -89.56516 -89.60842 -89.89529 -90.10727 -90.47748
[8] -90.92658 -90.95458 -91.06575 -91.08671 -91.16273 -91.57089 -91.79191
[15] -91.79864 -91.85612 -91.96068 -91.96761 -92.10514 -92.12596
$jeffries
[1] 20.716117 8.356013 13.240837 10.751275 15.450697 17.619289 10.373849
[8] 19.447705 12.637420 19.387262 19.378160 21.464294 14.722996 21.191042
[15] 9.800077 14.599121 16.814363 12.197464 18.935861 9.657920
$postProb
[1] 0.341911258 0.118775578 0.071905085 0.068860637 0.051687266 0.041813997
[7] 0.028876514 0.018428998 0.017920194 0.016034634 0.015702073 0.014552717
[13] 0.009675703 0.007756974 0.007704992 0.007274570 0.006552323 0.006507092
[19] 0.005670976 0.005554128
$modelName
[1] "D3Mit16 D6Cebrp40s27 D10Rat33 Dcp1 D11Mit4 D14Mit3"
[2] "D14Mit3"
[3] "D2Cebr204s17 D13Mit3 D14Mit3"
[4] "D2Cebr204s17 D14Mit3"
[5] "D2Rat136 D3Rat109 D6Cebrp97s14 D14Mit3"
[6] "D2Cebr204s17 D10Rat102 D11Mit4 D13Mit3 D14Mit3"
[7] "D14Mit3 D16Rat72"
[8] "D3Mit16 D6Rat80 D10Rat33 Dcp1 D11Mit4 D14Mit3"
[9] "D2Cebr204s17 D14Mit3 D16Rat72"
[10] "D2Cebr204s17 D4Rat152 D10Rat102 D11Mit4 D13Mit3 D14Mit3"
[11] "D2Cebr204s17 D8Rat56 D10Rat102 D11Mit4 D13Mit3 D14Mit3"
[12] "D2Cebr204s17 D4Rat152 D7Cebr69s5 D10Rat102 D11Mit4 D13Mit3 D14Mit3"
[13] "D2Cebr204s17 D5Rat39 D13Mit3 D14Mit3"
[14] "D3Mit16 D3Cebrp113s9 D6Cebrp40s27 D10Rat33 Dcp1 D11Mit4 D14Mit3"
[15] "Pthlh D14Mit3"
[16] "D2Cebr204s17 D10Rat33 D13Mit3 D14Mit3"
[17] "D3Mit16 D6Cebrp40s27 D10Rat33 D11Mit4 D14Mit3"
[18] "D2Cebr204s17 D6Cebrp97s14 D14Mit3"
[19] "D2Cebr204s17 D10Rat102 Dcp1 D11Mit4 D13Mit3 D14Mit3"
[20] "D11Mit4 D14Mit3"
$modelPosInX
[1] "171 305 490 517 527 616" "616"
[3] "102 596 616" "102 616"
[5] "101 161 319 616" "102 498 527 596 616"
[7] "616 654" "171 304 490 517 527 616"
[9] "102 616 654" "102 216 498 527 596 616"
[11] "102 402 498 527 596 616" "102 216 399 498 527 596 616"
[13] "102 296 596 616" "171 178 305 490 517 527 616"
[15] "262 616" "102 490 596 616"
[17] "171 305 490 527 616" "102 319 616"
[19] "102 498 517 527 596 616" "527 616"
$var.TOP.MPI
[1] "D2Cebr204s17" "D11Mit4" "D14Mit3"
$var.MPI
[1] "D2Cebr204s17" "D11Mit4" "D14Mit3"
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.