GitHub - fbertran/SelectBoost: A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets (original) (raw)

SelectBoost

A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets

Frédéric Bertrand and Myriam Maumy-Bertrand

The SelectBoost package implements SelectBoost: a general algorithm to enhance the performance of variable selection methods https://doi.org/10.1093/bioinformatics/btaa855, F. Bertrand, I. Aouadi, N. Jung, R. Carapito, L. Vallat, S. Bahram, M. Maumy-Bertrand (2015),

With the growth of big data, variable selection has become one of the major challenges in statistics. Although many methods have been proposed in the literature their performance in terms of recall and precision are limited in a context where the number of variables by far exceeds the number of observations or in a high correlated setting.

Results: This package implements a new general algorithm which improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. Our algorithm can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.

This website and these examples were created by F. Bertrand and M. Maumy-Bertrand.

Installation

You can install the released version of SelectBoost from CRAN with:

install.packages("SelectBoost")

You can install the development version of SelectBoost from github with:

devtools::install_github("fbertran/SelectBoost")

If you are a Linux/Unix or a Macos user, you can install a version of SelectBoost with support for doMC from github with:

devtools::install_github("fbertran/SelectBoost", ref = "doMC")

Examples

First example: Simulated dataset

Simulating data

Create a correlation matrix for two groups of variable with an intragroup correlation value of corgroupcor_groupcorgroup.

library(SelectBoost) group<-c(rep(1:2,5)) cor_group<-c(.8,.4) C<-simulation_cor(group,cor_group) C #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 1.0 0.0 0.8 0.0 0.8 0.0 0.8 0.0 0.8 0.0 #> [2,] 0.0 1.0 0.0 0.4 0.0 0.4 0.0 0.4 0.0 0.4 #> [3,] 0.8 0.0 1.0 0.0 0.8 0.0 0.8 0.0 0.8 0.0 #> [4,] 0.0 0.4 0.0 1.0 0.0 0.4 0.0 0.4 0.0 0.4 #> [5,] 0.8 0.0 0.8 0.0 1.0 0.0 0.8 0.0 0.8 0.0 #> [6,] 0.0 0.4 0.0 0.4 0.0 1.0 0.0 0.4 0.0 0.4 #> [7,] 0.8 0.0 0.8 0.0 0.8 0.0 1.0 0.0 0.8 0.0 #> [8,] 0.0 0.4 0.0 0.4 0.0 0.4 0.0 1.0 0.0 0.4 #> [9,] 0.8 0.0 0.8 0.0 0.8 0.0 0.8 0.0 1.0 0.0 #> [10,] 0.0 0.4 0.0 0.4 0.0 0.4 0.0 0.4 0.0 1.0

Simulate predictor dataset witn N=100N=100N=100 observations.

N<-100 X<-simulation_X(N,C) head(X) #> [,1] [,2] [,3] [,4] [,5] [,6] #> [1,] -1.5321046 -1.4697218 -1.0037681 0.27502077 -0.9495274 -2.0829749 #> [2,] 0.2144978 -1.0002400 0.5538293 0.09626492 0.2995111 0.1413112 #> [3,] -0.3969052 1.6329261 0.4687922 0.40198830 -0.3523466 1.6470825 #> [4,] -0.8909846 0.2583202 -0.5393219 0.46851301 -0.2908467 0.5266052 #> [5,] -0.8543071 -0.6790359 -0.2004707 -2.95957734 0.0948163 -1.8173478 #> [6,] -1.4011010 -0.8882664 -1.3439198 0.06230701 -0.9166235 1.2120350 #> [,7] [,8] [,9] [,10] #> [1,] -1.50111614 1.1691130 -0.08175974 -1.561240 #> [2,] -0.60316385 0.1585346 0.46090450 0.293033 #> [3,] -0.33970295 0.2716709 0.47533410 1.656613 #> [4,] -0.05853370 1.7014325 -0.50885571 1.635436 #> [5,] -0.01191556 -1.6059544 0.13570387 -1.464627 #> [6,] -1.50579752 0.3249712 -1.07033488 0.117601 suppsuppsupp set the predictors that will be used to simulate the response (=true predictors). minBminBminB and maxBmaxBmaxB set the minimum and maximum absolute value for a beta\betabeta coefficient used in the model for the (true) predictors. stnstnstn is a scaling factor for the noise in the response.

supp<-c(1,1,1,0,0,0,0,0,0,0) minB<-1 maxB<-2 stn<-500 DATA_exemple<-simulation_DATA(X,supp,minB,maxB,stn) str(DATA_exemple) #> List of 6 #> $ X : num [1:100, 1:10] -1.532 0.214 -0.397 -0.891 -0.854 ... #> $ Y : num [1:100] -3.535 -2.042 1.768 -0.181 -2.121 ... #> $ support: num [1:10] 1 1 1 0 0 0 0 0 0 0 #> $ beta : num [1:10] 1.37 1.73 -1.09 0 0 ... #> $ stn : num 500 #> $ sigma : num [1, 1] 0.0795 #> - attr(*, "class")= chr "simuls"

Selectboost analysis

By default fastboost performs B=100B=100B=100 resamplings of the model. As a result, we get a matrix with the proportions of selection of each variable at a given resampling level c_0c_0c0. The resampling are designed to take into account the correlation structure of the predictors. The correlation used by default is the Pearson Correlation but any can be passed through the corrfunc argument. The c0c_0c_0 value sets the minimum level for which correlations between two predictors are kept in the resampling process. The correlation structure is used to group the variables. Two groups functions group_func_1, grouping by thresholding the correlation matrix, and group_func_2, grouping using community selection, are available but any can be provided using the group argument of the function. The func argument is the variable selection function that should be used to assess variable memberships. It defaults to lasso_msgps_AICc but many others, for instance for lasso, elastinet, logistic glmnet and network inference with the Cascade package, are provided:

lasso_cv_glmnet_bin_min(X, Y)
lasso_cv_glmnet_bin_1se(X, Y)
lasso_glmnet_bin_AICc(X, Y)
lasso_glmnet_bin_BIC(X, Y)
lasso_cv_lars_min(X, Y)
lasso_cv_lars_1se(X, Y)
lasso_cv_glmnet_min(X, Y)
lasso_cv_glmnet_min_weighted(X, Y, priors)
lasso_cv_glmnet_1se(X, Y)
lasso_cv_glmnet_1se_weighted(X, Y, priors)
lasso_msgps_Cp(X, Y, penalty = "enet")
lasso_msgps_AICc(X, Y, penalty = "enet")
lasso_msgps_GCV(X, Y, penalty = "enet")
lasso_msgps_BIC(X, Y, penalty = "enet")
enetf_msgps_Cp(X, Y, penalty = "enet", alpha = 0.5)
enetf_msgps_AICc(X, Y, penalty = "enet", alpha = 0.5)
enetf_msgps_GCV(X, Y, penalty = "enet", alpha = 0.5)
enetf_msgps_BIC(X, Y, penalty = "enet", alpha = 0.5)
lasso_cascade(M, Y, K, eps = 10^-5, cv.fun)

User defined functions can alse be specified in the func argument. See the vignette for an example of use with adaptative lasso.

Default steps for c_0c_0c_0

quantile(abs(cor(DATA_exemple$X))[abs(cor(DATA_exemple$X))!=1],(0:10)/10) #> 0% 10% 20% 30% 40% 50% #> 4.141445e-05 1.077611e-02 3.821167e-02 5.018753e-02 7.621041e-02 1.289050e-01 #> 60% 70% 80% 90% 100% #> 2.981748e-01 4.616924e-01 7.858471e-01 8.150396e-01 8.363768e-01

result.boost.raw = fastboost(DATA_exemple$X, DATA_exemple$Y) result.boost.raw #> 1 2 3 4 5 6 7 8 9 10 #> c0 = 1 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 #> c0 = 0.836 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 #> c0 = 0.811 1.00 1.00 1.00 0.46 0.99 0.89 0.43 1.00 0.43 1.00 #> c0 = 0.786 0.38 1.00 0.38 0.03 0.90 0.46 0.33 1.00 0.34 1.00 #> c0 = 0.449 0.47 1.00 0.46 0.36 0.37 0.91 0.46 1.00 0.38 1.00 #> c0 = 0.298 0.46 0.87 0.48 0.85 0.43 0.94 0.46 0.99 0.50 0.95 #> c0 = 0.129 0.41 0.95 0.41 0.94 0.38 0.91 0.49 0.94 0.35 0.91 #> c0 = 0.076 0.38 0.88 0.49 0.91 0.37 0.93 0.37 0.94 0.39 0.96 #> c0 = 0.051 0.45 0.94 0.37 0.89 0.34 0.95 0.38 0.94 0.51 0.91 #> c0 = 0.038 0.40 0.92 0.48 0.89 0.46 0.91 0.40 0.90 0.41 0.94 #> c0 = 0.013 0.42 0.71 0.85 0.95 0.34 0.98 0.32 0.97 0.31 0.95 #> c0 = 0 0.39 0.69 0.83 0.95 0.36 0.98 0.40 0.95 0.33 0.82 #> c0 = 0 0.33 0.38 0.40 0.35 0.35 0.26 0.44 0.37 0.31 0.33 #> attr(,"c0.seq") #> 100% 90% 80% 70% 60% 50% 40% 30% #> 1.000000 0.836377 0.811485 0.785847 0.449464 0.298175 0.128905 0.076210 0.050791 #> 20% 10% 0%
#> 0.038212 0.012626 0.000041 0.000000 #> attr(,"c0lim") #> [1] TRUE #> attr(,"steps.seq") #> [1] 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 #> attr(,"typeboost") #> [1] "fastboost" #> attr(,"limi_alea") #> [1] NA #> attr(,"B") #> [1] 100 #> attr(,"class") #> [1] "selectboost"

Applying a non increasing post-processing step to the results improves the performance of the algorithm.

result.boost = force.non.inc(result.boost.raw) result.boost #> 1 2 3 4 5 6 7 8 9 10 #> 1.00 1.00 1.00 0 0 0 0 0 0 0 #> c0 = 0.836 1.00 1.00 1.00 0 0 0 0 0 0 0 #> c0 = 0.811 1.00 1.00 1.00 0 0 0 0 0 0 0 #> c0 = 0.786 0.38 1.00 0.38 0 0 0 0 0 0 0 #> c0 = 0.449 0.38 1.00 0.38 0 0 0 0 0 0 0 #> c0 = 0.298 0.37 0.87 0.38 0 0 0 0 0 0 0 #> c0 = 0.129 0.32 0.87 0.31 0 0 0 0 0 0 0 #> c0 = 0.076 0.29 0.80 0.31 0 0 0 0 0 0 0 #> c0 = 0.051 0.29 0.80 0.19 0 0 0 0 0 0 0 #> c0 = 0.038 0.24 0.78 0.19 0 0 0 0 0 0 0 #> c0 = 0.013 0.24 0.57 0.19 0 0 0 0 0 0 0 #> c0 = 0 0.21 0.55 0.17 0 0 0 0 0 0 0 #> c0 = 0 0.15 0.24 0.00 0 0 0 0 0 0 0 #> attr(,"c0.seq") #> 100% 90% 80% 70% 60% 50% 40% 30% #> 1.000000 0.836377 0.811485 0.785847 0.449464 0.298175 0.128905 0.076210 0.050791 #> 20% 10% 0%
#> 0.038212 0.012626 0.000041 0.000000 #> attr(,"c0lim") #> [1] TRUE #> attr(,"steps.seq") #> [1] 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 #> attr(,"typeboost") #> [1] "fastboost" #> attr(,"limi_alea") #> [1] NA #> attr(,"B") #> [1] 100 #> attr(,"class") #> [1] "fastboost"

Comparing true and selected predictors

We can compute, for all the c0c_0c0 values and for a selection threshold varying from 111 to 0.50.50.5 by 0.050.050.05 steps, the recall (sensitivity), the precision (positive predictive value), as well as several Fscores ($F_1$ harmonic mean of recall and precision, F1/2F_{1/2}F1/2 and F_2F_2F_2 two weighted harmonic means of recall and precision).

All_res=NULL #Here are the cutoff level tested for(lev in 20:10/20){ F_score=NULL for(u in 1:nrow(result.boost)){ F_score<-rbind(F_score,SelectBoost::compsim(DATA_exemple,result.boost[u,], level=lev)[1:5]) } All_res <- abind::abind(All_res,F_score,along=3) }

For a selection threshold equal to 0.900.900.90, all the c_0c_0c_0 values and the 5 criteria.

matplot(1:nrow(result.boost),All_res[,,3],type="l",ylab="criterion value", xlab="c0 value",xaxt="n",lwd=2) axis(1, at=1:length(attr(result.boost,"c0.seq")),
labels=round(attr(result.boost,"c0.seq"),3)) legend(x="topright",legend=c("recall (sensitivity)", "precision (positive predictive value)","non-weighted Fscore", "F1/2 weighted Fscore","F2 weighted Fscore"),lty=1:5,col=1:5,lwd=2)

Fscores for all selection thresholds and all the c_0c_0c_0 values.

matplot(1:nrow(result.boost),All_res[,3,],type="l",ylab="Fscore", xlab="c0 value",xaxt="n",lwd=2,col=1:11,lty=1:11) axis(1, at=1:length(attr(result.boost,"c0.seq")), labels=round(attr(result.boost,"c0.seq"),3)) legend(x="topright",legend=(20:11)/20,lty=1:11,col=1:11,lwd=2, title="Threshold")

Complete Selectboost analysis

What is the maximum number of steps ?

all.cors=unique(abs(cor(DATA_exemple$X))[abs(cor(DATA_exemple$X))!=1]) length(all.cors) #> [1] 45

With such datasets, we can perform all the 45 steps for the Selectboost analysis. We switch to community analysis from the igraph package as the grouping variable function.

groups.seq.f2=lapply(sort(unique(c(1,all.cors,0)),decreasing=TRUE), function(c0) if(c0!=1){lapply(group_func_2(cor(DATA_exemple$X),c0)$communities,sort)} else {lapply(group_func_2(cor(DATA_exemple$X),c0),sort)}) names(groups.seq.f2)<-sort(unique(c(1,all.cors,0)),decreasing=TRUE) groups.seq.f2[[1]] #> [[1]] #> [1] 1 #> #> [[2]] #> [1] 2 #> #> [[3]] #> [1] 3 #> #> [[4]] #> [1] 4 #> #> [[5]] #> [1] 5 #> #> [[6]] #> [1] 6 #> #> [[7]] #> [1] 7 #> #> [[8]] #> [1] 8 #> #> [[9]] #> [1] 9 #> #> [[10]] #> [1] 10

result.boost.45.raw = fastboost(DATA_exemple$X, DATA_exemple$Y, B=100, steps.seq=sort(unique(all.cors),decreasing=TRUE)) result.boost.45.raw #> 1 2 3 4 5 6 7 8 9 10 #> c0 = 1 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 #> c0 = 0.836 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 #> c0 = 0.829 1.00 1.00 1.00 0.76 1.00 0.55 0.17 0.96 1.00 1.00 #> c0 = 0.829 0.95 1.00 1.00 0.46 1.00 0.74 0.77 1.00 0.52 0.99 #> c0 = 0.819 0.99 1.00 1.00 0.49 0.99 0.89 0.59 1.00 0.68 0.99 #> c0 = 0.815 1.00 1.00 1.00 0.34 1.00 0.88 0.59 1.00 0.64 0.99 #> c0 = 0.806 1.00 1.00 1.00 0.33 0.99 0.91 0.41 1.00 0.46 0.99 #> c0 = 0.796 1.00 1.00 1.00 0.38 0.93 0.85 0.47 1.00 0.54 1.00 #> c0 = 0.791 0.97 1.00 0.99 0.22 0.94 0.86 0.51 1.00 0.44 1.00 #> c0 = 0.79 0.93 1.00 1.00 0.24 0.96 0.89 0.46 1.00 0.55 1.00 #> c0 = 0.785 0.38 1.00 0.46 0.01 0.43 0.16 0.48 1.00 0.51 1.00 #> c0 = 0.543 0.49 1.00 0.45 0.01 0.39 0.31 0.43 1.00 0.56 1.00 #> c0 = 0.487 0.46 1.00 0.38 0.00 0.38 0.36 0.47 1.00 0.47 0.35 #> c0 = 0.475 0.45 1.00 0.43 0.47 0.46 0.34 0.48 1.00 0.35 1.00 #> c0 = 0.462 0.40 1.00 0.42 0.40 0.39 0.92 0.42 1.00 0.41 0.99 #> c0 = 0.401 0.45 1.00 0.39 0.41 0.40 0.99 0.38 1.00 0.44 0.98 #> c0 = 0.359 0.51 0.91 0.40 0.99 0.30 0.82 0.38 0.99 0.45 0.99 #> c0 = 0.31 0.42 0.96 0.46 0.95 0.40 0.97 0.39 0.99 0.43 0.96 #> c0 = 0.304 0.48 0.90 0.53 0.84 0.51 0.96 0.46 1.00 0.45 0.95 #> c0 = 0.294 0.45 0.90 0.42 0.90 0.49 0.95 0.44 0.99 0.42 0.95 #> c0 = 0.284 0.50 0.92 0.44 0.95 0.33 0.89 0.34 0.91 0.37 0.89 #> c0 = 0.131 0.39 0.95 0.42 0.94 0.49 0.90 0.38 0.90 0.37 0.89 #> c0 = 0.13 0.48 0.94 0.38 0.92 0.43 0.89 0.40 0.95 0.35 0.94 #> c0 = 0.129 0.45 0.94 0.47 0.93 0.41 0.95 0.51 0.94 0.43 0.90 #> c0 = 0.114 0.38 0.91 0.47 0.92 0.39 0.93 0.43 0.90 0.45 0.92 #> c0 = 0.111 0.40 0.86 0.44 0.91 0.42 0.93 0.39 0.94 0.39 0.92 #> c0 = 0.096 0.46 0.97 0.44 0.91 0.41 0.92 0.37 0.95 0.40 0.90 #> c0 = 0.083 0.48 0.89 0.39 0.92 0.42 0.88 0.38 0.95 0.51 0.94 #> c0 = 0.065 0.49 0.89 0.39 0.96 0.46 0.88 0.44 0.93 0.41 0.92 #> c0 = 0.063 0.41 0.89 0.38 0.93 0.40 0.94 0.46 0.92 0.34 0.95 #> c0 = 0.056 0.44 0.94 0.38 0.93 0.43 0.87 0.47 0.91 0.30 0.92 #> c0 = 0.053 0.45 0.91 0.54 0.92 0.41 0.91 0.32 0.91 0.41 0.93 #> c0 = 0.05 0.38 0.96 0.46 0.94 0.48 0.93 0.34 0.89 0.40 0.95 #> c0 = 0.046 0.40 0.96 0.43 0.95 0.44 0.93 0.40 0.89 0.52 0.93 #> c0 = 0.044 0.35 0.91 0.39 0.95 0.41 0.93 0.45 0.90 0.40 0.91 #> c0 = 0.043 0.38 0.93 0.39 0.94 0.41 0.92 0.41 0.93 0.42 0.97 #> c0 = 0.04 0.39 0.90 0.46 0.94 0.45 0.90 0.42 0.96 0.44 0.90 #> c0 = 0.03 0.35 0.95 0.42 0.89 0.41 0.93 0.42 0.92 0.41 0.94 #> c0 = 0.02 0.32 0.78 0.92 0.96 0.31 0.97 0.30 0.87 0.23 1.00 #> c0 = 0.018 0.31 0.85 0.87 0.98 0.35 0.94 0.31 0.95 0.32 0.97 #> c0 = 0.015 0.39 0.76 0.87 0.92 0.30 0.94 0.29 0.95 0.31 0.98 #> c0 = 0.011 0.24 0.86 0.87 0.93 0.19 0.99 0.41 0.96 0.28 0.85 #> c0 = 0.009 0.48 0.74 0.84 0.97 0.30 0.99 0.47 0.92 0.34 0.81 #> c0 = 0.008 0.43 0.83 0.82 0.99 0.26 0.94 0.34 0.95 0.27 0.85 #> c0 = 0.008 0.36 0.83 0.81 1.00 0.20 0.98 0.37 0.97 0.28 0.86 #> c0 = 0 0.50 0.85 0.89 0.99 0.31 0.98 0.38 0.93 0.28 0.92 #> c0 = 0 0.34 0.36 0.39 0.35 0.31 0.35 0.51 0.25 0.33 0.35 #> attr(,"c0.seq") #> [1] 1.000000 0.836377 0.829162 0.828827 0.819400 0.815040 0.806154 0.796102 #> [9] 0.790728 0.790366 0.784717 0.543037 0.487104 0.474899 0.461692 0.400552 #> [17] 0.358610 0.309558 0.304284 0.294102 0.284316 0.130908 0.129817 0.128905 #> [25] 0.113625 0.110504 0.095538 0.083468 0.065324 0.063235 0.056491 0.053206 #> [33] 0.050188 0.045647 0.044071 0.043374 0.040275 0.029958 0.020424 0.017876 #> [41] 0.015400 0.010776 0.009051 0.007821 0.007701 0.000041 0.000000 #> attr(,"c0lim") #> [1] TRUE #> attr(,"steps.seq") #> [1] 0.000000e+00 8.363768e-01 8.291616e-01 8.288270e-01 8.194005e-01 #> [6] 8.150396e-01 8.061535e-01 7.961019e-01 7.907282e-01 7.903658e-01 #> [11] 7.847174e-01 5.430366e-01 4.871037e-01 4.748994e-01 4.616924e-01 #> [16] 4.005519e-01 3.586096e-01 3.095578e-01 3.042840e-01 2.941020e-01 #> [21] 2.843163e-01 1.309077e-01 1.298174e-01 1.289050e-01 1.136253e-01 #> [26] 1.105037e-01 9.553777e-02 8.346778e-02 6.532436e-02 6.323546e-02 #> [31] 5.649128e-02 5.320573e-02 5.018753e-02 4.564702e-02 4.407068e-02 #> [36] 4.337394e-02 4.027507e-02 2.995808e-02 2.042441e-02 1.787624e-02 #> [41] 1.539992e-02 1.077611e-02 9.050604e-03 7.820714e-03 7.700949e-03 #> [46] 4.141445e-05 1.000000e+00 #> attr(,"typeboost") #> [1] "fastboost" #> attr(,"limi_alea") #> [1] NA #> attr(,"B") #> [1] 100 #> attr(,"class") #> [1] "selectboost"

Applying a non increasing post-processing step to the results improves the performance of the algorithm.

result.boost.45 = force.non.inc(result.boost.45.raw) result.boost.45 #> 1 2 3 4 5 6 7 8 9 10 #> 1.00 1.00 1.00 0 0 0 0 0 0 0 #> c0 = 0.836 1.00 1.00 1.00 0 0 0 0 0 0 0 #> c0 = 0.829 1.00 1.00 1.00 0 0 0 0 0 0 0 #> c0 = 0.829 0.95 1.00 1.00 0 0 0 0 0 0 0 #> c0 = 0.819 0.95 1.00 1.00 0 0 0 0 0 0 0 #> c0 = 0.815 0.95 1.00 1.00 0 0 0 0 0 0 0 #> c0 = 0.806 0.95 1.00 1.00 0 0 0 0 0 0 0 #> c0 = 0.796 0.95 1.00 1.00 0 0 0 0 0 0 0 #> c0 = 0.791 0.92 1.00 0.99 0 0 0 0 0 0 0 #> c0 = 0.79 0.88 1.00 0.99 0 0 0 0 0 0 0 #> c0 = 0.785 0.33 1.00 0.45 0 0 0 0 0 0 0 #> c0 = 0.543 0.33 1.00 0.44 0 0 0 0 0 0 0 #> c0 = 0.487 0.30 1.00 0.37 0 0 0 0 0 0 0 #> c0 = 0.475 0.29 1.00 0.37 0 0 0 0 0 0 0 #> c0 = 0.462 0.24 1.00 0.36 0 0 0 0 0 0 0 #> c0 = 0.401 0.24 1.00 0.33 0 0 0 0 0 0 0 #> c0 = 0.359 0.24 0.91 0.33 0 0 0 0 0 0 0 #> c0 = 0.31 0.15 0.91 0.33 0 0 0 0 0 0 0 #> c0 = 0.304 0.15 0.85 0.33 0 0 0 0 0 0 0 #> c0 = 0.294 0.12 0.85 0.22 0 0 0 0 0 0 0 #> c0 = 0.284 0.12 0.85 0.22 0 0 0 0 0 0 0 #> c0 = 0.131 0.01 0.85 0.20 0 0 0 0 0 0 0 #> c0 = 0.13 0.01 0.84 0.16 0 0 0 0 0 0 0 #> c0 = 0.129 0.00 0.84 0.16 0 0 0 0 0 0 0 #> c0 = 0.114 0.00 0.81 0.16 0 0 0 0 0 0 0 #> c0 = 0.111 0.00 0.76 0.13 0 0 0 0 0 0 0 #> c0 = 0.096 0.00 0.76 0.13 0 0 0 0 0 0 0 #> c0 = 0.083 0.00 0.68 0.08 0 0 0 0 0 0 0 #> c0 = 0.065 0.00 0.68 0.08 0 0 0 0 0 0 0 #> c0 = 0.063 0.00 0.68 0.07 0 0 0 0 0 0 0 #> c0 = 0.056 0.00 0.68 0.07 0 0 0 0 0 0 0 #> c0 = 0.053 0.00 0.65 0.07 0 0 0 0 0 0 0 #> c0 = 0.05 0.00 0.65 0.00 0 0 0 0 0 0 0 #> c0 = 0.046 0.00 0.65 0.00 0 0 0 0 0 0 0 #> c0 = 0.044 0.00 0.60 0.00 0 0 0 0 0 0 0 #> c0 = 0.043 0.00 0.60 0.00 0 0 0 0 0 0 0 #> c0 = 0.04 0.00 0.57 0.00 0 0 0 0 0 0 0 #> c0 = 0.03 0.00 0.57 0.00 0 0 0 0 0 0 0 #> c0 = 0.02 0.00 0.40 0.00 0 0 0 0 0 0 0 #> c0 = 0.018 0.00 0.40 0.00 0 0 0 0 0 0 0 #> c0 = 0.015 0.00 0.31 0.00 0 0 0 0 0 0 0 #> c0 = 0.011 0.00 0.31 0.00 0 0 0 0 0 0 0 #> c0 = 0.009 0.00 0.19 0.00 0 0 0 0 0 0 0 #> c0 = 0.008 0.00 0.19 0.00 0 0 0 0 0 0 0 #> c0 = 0.008 0.00 0.19 0.00 0 0 0 0 0 0 0 #> c0 = 0 0.00 0.19 0.00 0 0 0 0 0 0 0 #> c0 = 0 0.00 0.00 0.00 0 0 0 0 0 0 0 #> attr(,"c0.seq") #> [1] 1.000000 0.836377 0.829162 0.828827 0.819400 0.815040 0.806154 0.796102 #> [9] 0.790728 0.790366 0.784717 0.543037 0.487104 0.474899 0.461692 0.400552 #> [17] 0.358610 0.309558 0.304284 0.294102 0.284316 0.130908 0.129817 0.128905 #> [25] 0.113625 0.110504 0.095538 0.083468 0.065324 0.063235 0.056491 0.053206 #> [33] 0.050188 0.045647 0.044071 0.043374 0.040275 0.029958 0.020424 0.017876 #> [41] 0.015400 0.010776 0.009051 0.007821 0.007701 0.000041 0.000000 #> attr(,"c0lim") #> [1] TRUE #> attr(,"steps.seq") #> [1] 0.000000e+00 8.363768e-01 8.291616e-01 8.288270e-01 8.194005e-01 #> [6] 8.150396e-01 8.061535e-01 7.961019e-01 7.907282e-01 7.903658e-01 #> [11] 7.847174e-01 5.430366e-01 4.871037e-01 4.748994e-01 4.616924e-01 #> [16] 4.005519e-01 3.586096e-01 3.095578e-01 3.042840e-01 2.941020e-01 #> [21] 2.843163e-01 1.309077e-01 1.298174e-01 1.289050e-01 1.136253e-01 #> [26] 1.105037e-01 9.553777e-02 8.346778e-02 6.532436e-02 6.323546e-02 #> [31] 5.649128e-02 5.320573e-02 5.018753e-02 4.564702e-02 4.407068e-02 #> [36] 4.337394e-02 4.027507e-02 2.995808e-02 2.042441e-02 1.787624e-02 #> [41] 1.539992e-02 1.077611e-02 9.050604e-03 7.820714e-03 7.700949e-03 #> [46] 4.141445e-05 1.000000e+00 #> attr(,"typeboost") #> [1] "fastboost" #> attr(,"limi_alea") #> [1] NA #> attr(,"B") #> [1] 100 #> attr(,"class") #> [1] "fastboost"

Comparing true and selected predictors

Due to the effect of the correlated resampling, the proportion of selection for a variable may increase, especially if it is a variable that is often discarded. Hence, one should force those proportions of selection to be non-increasing. It is one of the results of the summarysummarysummary function for the selectboostselectboostselectboost class.

dec.result.boost.45 <- summary(result.boost.45)$selectboost_result.dec #> Error in summary(result.boost.45)$selectboost_result.dec: $ operator is invalid for atomic vectors dec.result.boost.45 #> Error in eval(expr, envir, enclos): objet 'dec.result.boost.45' introuvable

Let's compute again, for all the c0c_0c0 values, the recall (sensitivity), precision (positive predictive value), and several Fscores ($F_1$ harmonic mean of recall and precision, F1/2F_{1/2}F1/2 and F_2F_2F_2 two weighted harmonic means of recall and precision).

All_res.45=NULL #Here are the cutoff level tested for(lev.45 in 20:10/20){ F_score.45=NULL for(u.45 in 1:nrow(dec.result.boost.45 )){ F_score.45<-rbind(F_score.45,SelectBoost::compsim(DATA_exemple, dec.result.boost.45[u.45,],level=lev.45)[1:5]) } All_res.45 <- abind::abind(All_res.45,F_score.45,along=3) } #> Error in nrow(dec.result.boost.45): objet 'dec.result.boost.45' introuvable

For a selection threshold equal to 0.900.900.90, all the c_0c_0c_0 values and the 5 criteria.

matplot(1:nrow(dec.result.boost.45),All_res.45[,,3],type="l", ylab="criterion value",xlab="c0 value",xaxt="n",lwd=2) #> Error in nrow(dec.result.boost.45): objet 'dec.result.boost.45' introuvable axis(1, at=1:length(attr(result.boost.45,"c0.seq")), labels=round(attr(result.boost.45,"c0.seq"),3)) #> Error in axis(1, at = 1:length(attr(result.boost.45, "c0.seq")), labels = round(attr(result.boost.45, : plot.new has not been called yet legend(x="topright",legend=c("recall (sensitivity)", "precision (positive predictive value)","non-weighted Fscore", "F1/2 weighted Fscore","F2 weighted Fscore"), lty=1:5,col=1:5,lwd=2) #> Error in (function (s, units = "user", cex = NULL, font = NULL, vfont = NULL, : plot.new has not been called yet

Fscores for all selection thresholds and all the c_0c_0c_0 values.

matplot(1:nrow(dec.result.boost.45),All_res.45[,3,],type="l", ylab="Fscore",xlab="c0 value",xaxt="n",lwd=2,col=1:11,lty=1:11) #> Error in nrow(dec.result.boost.45): objet 'dec.result.boost.45' introuvable axis(1, at=1:length(attr(result.boost.45,"c0.seq")), labels=round(attr(result.boost.45,"c0.seq"),3)) #> Error in axis(1, at = 1:length(attr(result.boost.45, "c0.seq")), labels = round(attr(result.boost.45, : plot.new has not been called yet legend(x="topright",legend=(20:11)/20,lty=1:11,col=1:11,lwd=2, title="Threshold") #> Error in (function (s, units = "user", cex = NULL, font = NULL, vfont = NULL, : plot.new has not been called yet

Confidence indices.

First compute the highest c_0c_0c_0 value for which the proportion of selection is under the threshold thrthrthr. In that analysis, we set thr=1thr=1thr=1.

thr=1 index.last.c0=apply(dec.result.boost.45>=thr,2,which.min)-1 #> Error in apply(dec.result.boost.45 >= thr, 2, which.min): objet 'dec.result.boost.45' introuvable index.last.c0 #> Error in eval(expr, envir, enclos): objet 'index.last.c0' introuvable

Define some colorRamp ranging from blue (high confidence) to red (low confidence).

jet.colors <- colorRamp(rev(c( "blue", "#007FFF", "#FF7F00", "red", "#7F0000")))

rownames(dec.result.boost.45)[index.last.c0] #> Error in rownames(dec.result.boost.45): objet 'dec.result.boost.45' introuvable attr(result.boost.45,"c0.seq")[index.last.c0] #> Error in eval(expr, envir, enclos): objet 'index.last.c0' introuvable confidence.indices = c(0,1-attr(result.boost.45,"c0.seq"))[index.last.c0+1] #> Error in eval(expr, envir, enclos): objet 'index.last.c0' introuvable confidence.indices #> Error in eval(expr, envir, enclos): objet 'confidence.indices' introuvable barplot(confidence.indices,col=rgb(jet.colors(confidence.indices), maxColorValue = 255), names.arg=colnames(result.boost.45), ylim=c(0,1)) #> Error in barplot(confidence.indices, col = rgb(jet.colors(confidence.indices), : objet 'confidence.indices' introuvable

First compute the highest c_0c_0c_0 value for which the proportion of selection is under the threshold thrthrthr. In that analysis, we set thr=1thr=1thr=1.

thr=.9 index.last.c0=apply(dec.result.boost.45>=thr,2,which.min)-1 #> Error in apply(dec.result.boost.45 >= thr, 2, which.min): objet 'dec.result.boost.45' introuvable index.last.c0 #> Error in eval(expr, envir, enclos): objet 'index.last.c0' introuvable

Second example: biological network data

Simulating data using real data

The loop should be used to generate at least 100 datasets and then average the results.

require(CascadeData) data(micro_S) data(micro_US) micro_US<-Cascade::as.micro_array(micro_US,c(60,90,240,390),6) micro_S<-Cascade::as.micro_array(micro_S,c(60,90,240,390),6) S<-Cascade::geneSelection(list(micro_S,micro_US),list("condition",c(1,2),1),-1) rm(micro_S);data(micro_S) Sel<-micro_S[S@name,]

supp<-c(1,1,1,1,1,rep(0,95)) minB<-1 maxB<-2 stn<-5

set.seed(3141) for(i in 1:1){ X<-t(as.matrix(Sel[sample(1:1300 ,100),])) Xnorm<-t(t(X)/sqrt(diag(t(X)%*%X))) assign(paste("DATA_exemple3_nb_",i,sep=""),simulation_DATA(Xnorm,supp,minB,maxB,stn)) }

all.cors.micro=unique(abs(cor(DATA_exemple3_nb_1$X))[abs(cor( DATA_exemple3_nb_1$X))!=1]) length(unique(all.cors.micro)) #> [1] 4950 quantile(all.cors.micro,.90) #> 90% #> 0.6938712

top10p.all.cors.micro=all.cors.micro[all.cors.micro>=quantile(all.cors.micro,.90)] c0seq.top10p.all.cors.micro=quantile(top10p.all.cors.micro,rev( seq(0,length(top10p.all.cors.micro),length.out = 50)/495)) c0seq.top10p.all.cors.micro #> 100% 97.95918% 95.91837% 93.87755% 91.83673% 89.79592% 87.7551% 85.71429% #> 0.9486685 0.9184348 0.8993626 0.8867508 0.8783368 0.8688498 0.8597920 0.8517712 #> 83.67347% 81.63265% 79.59184% 77.55102% 75.5102% 73.46939% 71.42857% 69.38776% #> 0.8441046 0.8370590 0.8315722 0.8248607 0.8193079 0.8124198 0.8084936 0.8038357 #> 67.34694% 65.30612% 63.26531% 61.22449% 59.18367% 57.14286% 55.10204% 53.06122% #> 0.7967669 0.7920303 0.7885399 0.7842243 0.7803654 0.7783504 0.7750129 0.7711674 #> 51.02041% 48.97959% 46.93878% 44.89796% 42.85714% 40.81633% 38.77551% 36.73469% #> 0.7687731 0.7663441 0.7606838 0.7577961 0.7553123 0.7524554 0.7493711 0.7456580 #> 34.69388% 32.65306% 30.61224% 28.57143% 26.53061% 24.4898% 22.44898% 20.40816% #> 0.7431055 0.7403313 0.7377508 0.7345842 0.7313349 0.7296512 0.7264820 0.7246836 #> 18.36735% 16.32653% 14.28571% 12.2449% 10.20408% 8.163265% 6.122449% 4.081633% #> 0.7229066 0.7198827 0.7158667 0.7122053 0.7076771 0.7044341 0.7009353 0.6991250 #> 2.040816% 0% #> 0.6955766 0.6939670

result.boost.micro_nb1 = fastboost(DATA_exemple3_nb_1$X, DATA_exemple3_nb_1$Y, B=100, steps.seq=c0seq.top10p.all.cors.micro) result.boost.micro_nb1 #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 #> c0 = 1 1.00 1.00 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 #> c0 = 0.949 1.00 1.00 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 #> c0 = 0.918 1.00 1.00 1.00 0.91 1.00 0.03 0.03 0.05 0.00 0.00 0.01 0.69 0.43 0.01 #> c0 = 0.899 1.00 1.00 1.00 0.92 1.00 0.01 0.00 0.06 0.07 0.00 0.00 0.57 0.24 0.01 #> c0 = 0.887 1.00 1.00 0.64 0.80 1.00 0.07 0.01 0.16 0.10 0.14 0.09 0.76 0.30 0.03 #> c0 = 0.878 1.00 1.00 0.55 0.80 1.00 0.05 0.02 0.11 0.11 0.11 0.10 0.62 0.33 0.02 #> c0 = 0.869 1.00 1.00 0.51 0.69 1.00 0.09 0.03 0.08 0.12 0.12 0.48 0.54 0.34 0.01 #> c0 = 0.86 1.00 1.00 0.59 0.69 1.00 0.12 0.06 0.12 0.13 0.19 0.54 0.76 0.37 0.30 #> c0 = 0.852 1.00 1.00 0.57 0.75 1.00 0.14 0.05 0.15 0.12 0.20 0.42 0.77 0.36 0.32 #> c0 = 0.844 1.00 1.00 0.61 0.76 1.00 0.08 0.03 0.11 0.18 0.23 0.39 0.72 0.38 0.34 #> 15 16 17 18 19 20 21 22 23 24 25 26 27 28 #> c0 = 1 1.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 1.00 0.00 #> c0 = 0.949 1.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 1.00 0.00 #> c0 = 0.918 0.86 0.07 0.07 0.84 1.00 0.24 0.00 0.20 0.02 0.02 0.88 0.11 0.05 0.38 #> c0 = 0.899 0.91 0.03 0.06 0.96 1.00 0.39 0.10 0.19 0.01 0.02 0.88 0.07 0.05 0.19 #> c0 = 0.887 0.81 0.04 0.11 0.96 1.00 0.37 0.10 0.27 0.05 0.00 0.87 0.09 0.01 0.39 #> c0 = 0.878 0.82 0.07 0.08 0.96 1.00 0.27 0.17 0.26 0.17 0.03 0.86 0.18 0.06 0.44 #> c0 = 0.869 0.75 0.05 0.09 0.95 0.98 0.29 0.14 0.17 0.17 0.03 0.83 0.11 0.08 0.49 #> c0 = 0.86 0.91 0.09 0.12 0.91 1.00 0.30 0.14 0.34 0.20 0.08 0.78 0.22 0.12 0.39 #> c0 = 0.852 0.97 0.14 0.22 0.87 0.99 0.16 0.18 0.21 0.22 0.05 0.76 0.18 0.10 0.38 #> c0 = 0.844 0.93 0.19 0.12 0.89 1.00 0.18 0.21 0.30 0.19 0.10 0.67 0.23 0.16 0.45 #> 29 30 31 32 33 34 35 36 37 38 39 40 41 42 #> c0 = 1 1.00 0.00 1.00 0.00 0.00 1.00 1.00 1.00 0.00 0.00 1.00 0.00 0.00 0.00 #> c0 = 0.949 1.00 0.00 1.00 0.00 0.00 1.00 1.00 1.00 0.00 0.00 1.00 0.00 0.00 0.00 #> c0 = 0.918 1.00 0.05 0.84 0.00 0.37 1.00 1.00 0.99 0.00 0.00 0.89 0.00 0.00 0.00 #> c0 = 0.899 1.00 0.02 0.93 0.00 0.37 1.00 1.00 0.99 0.00 0.00 0.90 0.00 0.00 0.00 #> c0 = 0.887 1.00 0.01 0.87 0.52 0.63 1.00 1.00 0.97 0.02 0.02 0.80 0.09 0.01 0.00 #> c0 = 0.878 0.99 0.03 0.81 0.52 0.58 1.00 1.00 0.98 0.02 0.00 0.84 0.09 0.00 0.00 #> c0 = 0.869 0.99 0.02 0.86 0.46 0.52 1.00 0.99 1.00 0.02 0.01 0.85 0.12 0.01 0.00 #> c0 = 0.86 0.99 0.05 0.82 0.44 0.59 0.99 0.99 0.97 0.02 0.01 0.70 0.13 0.00 0.05 #> c0 = 0.852 1.00 0.09 0.81 0.47 0.65 1.00 1.00 0.98 0.24 0.02 0.72 0.17 0.00 0.07 #> c0 = 0.844 0.97 0.10 0.82 0.28 0.60 0.98 1.00 0.99 0.30 0.04 0.74 0.15 0.02 0.09 #> 43 44 45 46 47 48 49 50 51 52 53 54 55 56 #> c0 = 1 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 0.00 #> c0 = 0.949 0.00 0.00 1.00 0.00 1.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 1.00 0.00 #> c0 = 0.918 0.11 0.92 0.25 0.00 0.42 0.01 0.17 0.05 0.96 0.00 0.00 0.15 1.00 0.73 #> c0 = 0.899 0.09 0.90 0.29 0.00 0.28 0.04 0.05 0.01 0.84 0.00 0.00 0.03 1.00 0.44 #> c0 = 0.887 0.14 0.78 0.26 0.02 0.41 0.03 0.14 0.04 0.87 0.02 0.00 0.04 1.00 0.24 #> c0 = 0.878 0.20 0.88 0.55 0.00 0.36 0.12 0.16 0.04 0.77 0.02 0.00 0.17 1.00 0.33 #> c0 = 0.869 0.29 0.64 0.39 0.09 0.26 0.04 0.16 0.03 0.88 0.00 0.00 0.23 1.00 0.24 #> c0 = 0.86 0.35 0.39 0.37 0.16 0.28 0.15 0.12 0.14 0.81 0.05 0.01 0.13 1.00 0.28 #> c0 = 0.852 0.29 0.28 0.39 0.27 0.21 0.10 0.21 0.02 0.84 0.01 0.00 0.10 1.00 0.20 #> c0 = 0.844 0.30 0.39 0.40 0.19 0.20 0.12 0.12 0.04 0.75 0.05 0.03 0.04 1.00 0.28 #> 57 58 59 60 61 62 63 64 65 66 67 68 69 70 #> c0 = 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 1.00 1.00 0.00 1.00 #> c0 = 0.949 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 1.00 1.00 0.00 1.00 #> c0 = 0.918 0.08 0.05 0.02 0.17 0.00 0.03 0.04 0.57 0.06 0.27 1.00 1.00 0.04 0.09 #> c0 = 0.899 0.07 0.04 0.01 0.19 0.00 0.00 0.03 0.68 0.06 0.20 1.00 1.00 0.04 0.11 #> c0 = 0.887 0.06 0.06 0.08 0.23 0.00 0.03 0.06 0.55 0.05 0.14 1.00 1.00 0.04 0.15 #> c0 = 0.878 0.23 0.21 0.04 0.22 0.00 0.04 0.06 0.47 0.04 0.23 1.00 1.00 0.09 0.22 #> c0 = 0.869 0.24 0.11 0.11 0.22 0.00 0.08 0.06 0.53 0.08 0.17 1.00 1.00 0.07 0.24 #> c0 = 0.86 0.34 0.16 0.10 0.14 0.00 0.11 0.10 0.48 0.15 0.17 1.00 1.00 0.13 0.24 #> c0 = 0.852 0.32 0.23 0.10 0.25 0.00 0.11 0.13 0.42 0.16 0.33 0.99 1.00 0.10 0.28 #> c0 = 0.844 0.28 0.18 0.10 0.24 0.00 0.10 0.16 0.46 0.13 0.23 0.99 1.00 0.06 0.37 #> 71 72 73 74 75 76 77 78 79 80 81 82 83 84 #> c0 = 1 0.00 1.00 1.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 #> c0 = 0.949 0.00 1.00 1.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 1.00 #> c0 = 0.918 0.00 0.06 1.00 0.07 0.00 0.04 0.02 0.79 0.00 0.16 0.03 0.00 0.00 1.00 #> c0 = 0.899 0.00 0.05 1.00 0.03 0.00 0.01 0.00 0.59 0.00 0.17 0.05 0.00 0.00 0.99 #> c0 = 0.887 0.05 0.10 1.00 0.04 0.00 0.04 0.00 0.84 0.00 0.15 0.21 0.00 0.00 1.00 #> c0 = 0.878 0.02 0.09 1.00 0.05 0.03 0.03 0.01 0.68 0.00 0.11 0.18 0.00 0.00 0.99 #> c0 = 0.869 0.07 0.08 0.99 0.08 0.04 0.08 0.01 0.57 0.00 0.14 0.17 0.00 0.02 1.00 #> c0 = 0.86 0.08 0.10 0.98 0.11 0.04 0.12 0.01 0.70 0.00 0.17 0.19 0.00 0.02 0.99 #> c0 = 0.852 0.04 0.12 1.00 0.09 0.06 0.03 0.00 0.66 0.00 0.15 0.15 0.00 0.12 0.98 #> c0 = 0.844 0.07 0.15 0.96 0.06 0.02 0.10 0.13 0.70 0.00 0.12 0.17 0.17 0.12 1.00 #> 85 86 87 88 89 90 91 92 93 94 95 96 97 98 #> c0 = 1 0.00 1.00 0.00 0.00 0.00 0.00 1.00 0.00 1.00 1.00 0.00 0.00 0.00 1.00 #> c0 = 0.949 0.00 1.00 0.00 0.00 0.00 0.00 1.00 0.00 1.00 1.00 0.00 0.00 0.00 1.00 #> c0 = 0.918 0.26 0.07 0.03 0.71 0.01 0.63 0.31 0.00 0.71 0.98 0.00 0.22 0.02 0.85 #> c0 = 0.899 0.37 0.05 0.03 0.74 0.03 0.79 0.12 0.00 0.60 0.89 0.02 0.17 0.04 0.84 #> c0 = 0.887 0.45 0.10 0.09 0.61 0.05 0.41 0.31 0.00 0.60 0.89 0.00 0.15 0.03 0.82 #> c0 = 0.878 0.38 0.08 0.08 0.67 0.05 0.19 0.30 0.00 0.58 0.89 0.02 0.13 0.06 0.81 #> c0 = 0.869 0.45 0.07 0.17 0.87 0.08 0.14 0.43 0.02 0.36 0.93 0.02 0.11 0.05 0.62 #> c0 = 0.86 0.34 0.10 0.21 0.35 0.07 0.20 0.38 0.02 0.36 0.83 0.04 0.26 0.06 0.69 #> c0 = 0.852 0.25 0.14 0.18 0.33 0.10 0.17 0.48 0.03 0.45 0.81 0.01 0.22 0.05 0.72 #> c0 = 0.844 0.26 0.10 0.19 0.32 0.13 0.24 0.42 0.03 0.43 0.87 0.06 0.18 0.10 0.78 #> 99 100 #> c0 = 1 1.00 0.00 #> c0 = 0.949 1.00 0.00 #> c0 = 0.918 1.00 0.00 #> c0 = 0.899 1.00 0.11 #> c0 = 0.887 1.00 0.17 #> c0 = 0.878 1.00 0.14 #> c0 = 0.869 1.00 0.20 #> c0 = 0.86 1.00 0.20 #> c0 = 0.852 1.00 0.17 #> c0 = 0.844 1.00 0.24 #> [ getOption("max.print") est atteint -- 42 lignes omises ] #> attr(,"c0.seq") #> [1] 1.000000 0.948669 0.918435 0.899363 0.886751 0.878337 0.868850 0.859792 #> [9] 0.851771 0.844105 0.837059 0.831572 0.824861 0.819308 0.812420 0.808494 #> [17] 0.803836 0.796767 0.792030 0.788540 0.784224 0.780365 0.778350 0.775013 #> [25] 0.771167 0.768773 0.766344 0.760684 0.757796 0.755312 0.752455 0.749371 #> [33] 0.745658 0.743105 0.740331 0.737751 0.734584 0.731335 0.729651 0.726482 #> [41] 0.724684 0.722907 0.719883 0.715867 0.712205 0.707677 0.704434 0.700935 #> [49] 0.699125 0.695577 0.693967 0.000000 #> attr(,"c0lim") #> [1] TRUE #> attr(,"steps.seq") #> [1] 0.0000000 0.9486685 0.9184348 0.8993626 0.8867508 0.8783368 0.8688498 #> [8] 0.8597920 0.8517712 0.8441046 0.8370590 0.8315722 0.8248607 0.8193079 #> [15] 0.8124198 0.8084936 0.8038357 0.7967669 0.7920303 0.7885399 0.7842243 #> [22] 0.7803654 0.7783504 0.7750129 0.7711674 0.7687731 0.7663441 0.7606838 #> [29] 0.7577961 0.7553123 0.7524554 0.7493711 0.7456580 0.7431055 0.7403313 #> [36] 0.7377508 0.7345842 0.7313349 0.7296512 0.7264820 0.7246836 0.7229066 #> [43] 0.7198827 0.7158667 0.7122053 0.7076771 0.7044341 0.7009353 0.6991250 #> [50] 0.6955766 0.6939670 1.0000000 #> attr(,"typeboost") #> [1] "fastboost" #> attr(,"limi_alea") #> [1] NA #> attr(,"B") #> [1] 100 #> attr(,"class") #> [1] "selectboost"

The summary function computes applies a non increasing post-processing step to the results to improve the performance of the algorithm. The results are store int the selectboost_result.dec entry of the summary.

dec.result.boost.micro_nb1 <- summary(result.boost.micro_nb1)$selectboost_result.dec dec.result.boost.micro_nb1 #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 #> 1.00 1.00 1.00 1.00 1.00 0 0 0 0 0 0 1.00 0 0 1.00 0 0 1.00 #> c0 = 0.949 1.00 1.00 1.00 1.00 1.00 0 0 0 0 0 0 1.00 0 0 1.00 0 0 1.00 #> c0 = 0.918 1.00 1.00 1.00 0.91 1.00 0 0 0 0 0 0 0.69 0 0 0.86 0 0 0.84 #> c0 = 0.899 1.00 1.00 1.00 0.91 1.00 0 0 0 0 0 0 0.57 0 0 0.86 0 0 0.84 #> c0 = 0.887 1.00 1.00 0.64 0.79 1.00 0 0 0 0 0 0 0.57 0 0 0.76 0 0 0.84 #> c0 = 0.878 1.00 1.00 0.55 0.79 1.00 0 0 0 0 0 0 0.43 0 0 0.76 0 0 0.84 #> c0 = 0.869 1.00 1.00 0.51 0.68 1.00 0 0 0 0 0 0 0.35 0 0 0.69 0 0 0.83 #> c0 = 0.86 1.00 1.00 0.51 0.68 1.00 0 0 0 0 0 0 0.35 0 0 0.69 0 0 0.79 #> c0 = 0.852 1.00 1.00 0.49 0.68 1.00 0 0 0 0 0 0 0.35 0 0 0.69 0 0 0.75 #> c0 = 0.844 1.00 1.00 0.49 0.68 1.00 0 0 0 0 0 0 0.30 0 0 0.65 0 0 0.75 #> 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 #> 1.00 0 0 0 0 0 1.00 0 1.00 0 1.00 0 1.00 0 0 1.000000e+00 #> c0 = 0.949 1.00 0 0 0 0 0 1.00 0 1.00 0 1.00 0 1.00 0 0 1.000000e+00 #> c0 = 0.918 1.00 0 0 0 0 0 0.88 0 0.05 0 1.00 0 0.84 0 0 1.000000e+00 #> c0 = 0.899 1.00 0 0 0 0 0 0.88 0 0.05 0 1.00 0 0.84 0 0 1.000000e+00 #> c0 = 0.887 1.00 0 0 0 0 0 0.87 0 0.01 0 1.00 0 0.78 0 0 1.000000e+00 #> c0 = 0.878 1.00 0 0 0 0 0 0.86 0 0.01 0 0.99 0 0.72 0 0 1.000000e+00 #> c0 = 0.869 0.98 0 0 0 0 0 0.83 0 0.01 0 0.99 0 0.72 0 0 1.000000e+00 #> c0 = 0.86 0.98 0 0 0 0 0 0.78 0 0.01 0 0.99 0 0.68 0 0 9.900000e-01 #> c0 = 0.852 0.97 0 0 0 0 0 0.76 0 0.00 0 0.99 0 0.67 0 0 9.900000e-01 #> c0 = 0.844 0.97 0 0 0 0 0 0.67 0 0.00 0 0.96 0 0.67 0 0 9.700000e-01 #> 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 #> 1.00 1.00 0 0 1.00 0 0 0 0 0 1.00 0 1.00 0 0 0 1.00 0 0 #> c0 = 0.949 1.00 1.00 0 0 1.00 0 0 0 0 0 1.00 0 1.00 0 0 0 1.00 0 0 #> c0 = 0.918 1.00 0.99 0 0 0.89 0 0 0 0 0 0.25 0 0.42 0 0 0 0.96 0 0 #> c0 = 0.899 1.00 0.99 0 0 0.89 0 0 0 0 0 0.25 0 0.28 0 0 0 0.84 0 0 #> c0 = 0.887 1.00 0.97 0 0 0.79 0 0 0 0 0 0.22 0 0.28 0 0 0 0.84 0 0 #> c0 = 0.878 1.00 0.97 0 0 0.79 0 0 0 0 0 0.22 0 0.23 0 0 0 0.74 0 0 #> c0 = 0.869 0.99 0.97 0 0 0.79 0 0 0 0 0 0.06 0 0.13 0 0 0 0.74 0 0 #> c0 = 0.86 0.99 0.94 0 0 0.64 0 0 0 0 0 0.04 0 0.13 0 0 0 0.67 0 0 #> c0 = 0.852 0.99 0.94 0 0 0.64 0 0 0 0 0 0.04 0 0.06 0 0 0 0.67 0 0 #> c0 = 0.844 0.99 0.94 0 0 0.64 0 0 0 0 0 0.04 0 0.05 0 0 0 0.58 0 0 #> 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 #> 0 1.00 0 0 0 0 0 0 0 0 1.00 0 0 1.00 1.00 0 1.00 0 1.00 #> c0 = 0.949 0 1.00 0 0 0 0 0 0 0 0 1.00 0 0 1.00 1.00 0 1.00 0 1.00 #> c0 = 0.918 0 1.00 0 0 0 0 0 0 0 0 0.57 0 0 1.00 1.00 0 0.09 0 0.06 #> c0 = 0.899 0 1.00 0 0 0 0 0 0 0 0 0.57 0 0 1.00 1.00 0 0.09 0 0.05 #> c0 = 0.887 0 1.00 0 0 0 0 0 0 0 0 0.44 0 0 1.00 1.00 0 0.09 0 0.05 #> c0 = 0.878 0 1.00 0 0 0 0 0 0 0 0 0.36 0 0 1.00 1.00 0 0.09 0 0.04 #> c0 = 0.869 0 1.00 0 0 0 0 0 0 0 0 0.36 0 0 1.00 1.00 0 0.09 0 0.03 #> c0 = 0.86 0 1.00 0 0 0 0 0 0 0 0 0.31 0 0 1.00 1.00 0 0.09 0 0.03 #> c0 = 0.852 0 1.00 0 0 0 0 0 0 0 0 0.25 0 0 0.99 1.00 0 0.09 0 0.03 #> c0 = 0.844 0 1.00 0 0 0 0 0 0 0 0 0.25 0 0 0.99 1.00 0 0.09 0 0.03 #> 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 #> 1.00 0 0 0 0 1.00 0 0 0 0 0 1.00 0 1.00 0 0 0 0 1.00 0 #> c0 = 0.949 1.00 0 0 0 0 1.00 0 0 0 0 0 1.00 0 1.00 0 0 0 0 1.00 0 #> c0 = 0.918 1.00 0 0 0 0 0.79 0 0 0 0 0 1.00 0 0.07 0 0 0 0 0.31 0 #> c0 = 0.899 1.00 0 0 0 0 0.59 0 0 0 0 0 0.99 0 0.05 0 0 0 0 0.12 0 #> c0 = 0.887 1.00 0 0 0 0 0.59 0 0 0 0 0 0.99 0 0.05 0 0 0 0 0.12 0 #> c0 = 0.878 1.00 0 0 0 0 0.43 0 0 0 0 0 0.98 0 0.03 0 0 0 0 0.11 0 #> c0 = 0.869 0.99 0 0 0 0 0.32 0 0 0 0 0 0.98 0 0.02 0 0 0 0 0.11 0 #> c0 = 0.86 0.98 0 0 0 0 0.32 0 0 0 0 0 0.97 0 0.02 0 0 0 0 0.06 0 #> c0 = 0.852 0.98 0 0 0 0 0.28 0 0 0 0 0 0.96 0 0.02 0 0 0 0 0.06 0 #> c0 = 0.844 0.94 0 0 0 0 0.28 0 0 0 0 0 0.96 0 0.00 0 0 0 0 0.00 0 #> 93 94 95 96 97 98 99 100 #> 1.00 1.00 0 0 0 1.00 1.00 0 #> c0 = 0.949 1.00 1.00 0 0 0 1.00 1.00 0 #> c0 = 0.918 0.71 0.98 0 0 0 0.85 1.00 0 #> c0 = 0.899 0.60 0.89 0 0 0 0.84 1.00 0 #> c0 = 0.887 0.60 0.89 0 0 0 0.82 1.00 0 #> c0 = 0.878 0.58 0.89 0 0 0 0.81 1.00 0 #> c0 = 0.869 0.36 0.89 0 0 0 0.62 1.00 0 #> c0 = 0.86 0.36 0.79 0 0 0 0.62 1.00 0 #> c0 = 0.852 0.36 0.77 0 0 0 0.62 1.00 0 #> c0 = 0.844 0.34 0.77 0 0 0 0.62 1.00 0 #> [ getOption("max.print") est atteint -- 42 lignes omises ]

Confidence indices.

First compute the highest c_0c_0c_0 value for which the proportion of selection is under the threshold thrthrthr. In that analysis, we set thr=1thr=1thr=1.

thr=1 index.last.c0.micro_nb1=apply(dec.result.boost.micro_nb1>=thr,2,which.min)-1 index.last.c0.micro_nb1 #> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 #> 13 15 4 2 16 0 0 0 0 0 0 2 0 0 2 0 0 2 6 0 #> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 #> 0 0 0 0 2 0 2 0 5 0 2 0 0 7 6 2 0 0 2 0 #> 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 #> 0 0 0 0 2 0 2 0 0 0 2 0 0 0 16 0 0 0 0 0 #> 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 #> 0 0 0 2 0 0 8 17 0 2 0 2 6 0 0 0 0 2 0 0 #> 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 #> 0 0 0 3 0 2 0 0 0 0 2 0 2 2 0 0 0 2 16 0

We have to cap the confidence index value to the 1−textrmsmallestc_01-{\textrm{smallest } c_0}1−textrmsmallestc0 that we specified in the c0c_0c0 sequence and that was actually used for resampling. As a consequence, we have to exclude the c0=0c_0=0c0=0 case since we do not know what happen between c0=mathrmquantile(cors,.9)c0=\mathrm{quantile}(cors,.9)c0=mathrmquantile(cors,.9) and c0=0c_0=0c_0=0.

index.last.c0.micro_nb1 <- pmin(index.last.c0.micro_nb1, nrow(dec.result.boost.micro_nb1)-1)

Define some colorRamp ranging from blue (high confidence) to red (low confidence).

jet.colors <-colorRamp(rev(c("blue", "#007FFF", "#FF7F00", "red", "#7F0000")))

rownames(dec.result.boost.micro_nb1)[index.last.c0.micro_nb1] #> [1] "c0 = 0.825" "c0 = 0.812" "c0 = 0.899" "c0 = 0.949" "c0 = 0.808" #> [6] "c0 = 0.949" "c0 = 0.949" "c0 = 0.949" "c0 = 0.878" "c0 = 0.949" #> [11] "c0 = 0.949" "c0 = 0.887" "c0 = 0.949" "c0 = 0.869" "c0 = 0.878" #> [16] "c0 = 0.949" "c0 = 0.949" "c0 = 0.949" "c0 = 0.949" "c0 = 0.949" #> [21] "c0 = 0.808" "c0 = 0.949" "c0 = 0.86" "c0 = 0.804" "c0 = 0.949" #> [26] "c0 = 0.949" "c0 = 0.878" "c0 = 0.949" "c0 = 0.918" "c0 = 0.949" #> [31] "c0 = 0.949" "c0 = 0.949" "c0 = 0.949" "c0 = 0.949" "c0 = 0.808" attr(result.boost.micro_nb1,"c0.seq")[index.last.c0.micro_nb1] #> [1] 0.824861 0.812420 0.899363 0.948669 0.808494 0.948669 0.948669 0.948669 #> [9] 0.878337 0.948669 0.948669 0.886751 0.948669 0.868850 0.878337 0.948669 #> [17] 0.948669 0.948669 0.948669 0.948669 0.808494 0.948669 0.859792 0.803836 #> [25] 0.948669 0.948669 0.878337 0.948669 0.918435 0.948669 0.948669 0.948669 #> [33] 0.948669 0.948669 0.808494 confidence.indices.micro_nb1 = c(0,1-attr(result.boost.micro_nb1,"c0.seq"))[ index.last.c0.micro_nb1+1] confidence.indices.micro_nb1 #> [1] 0.175139 0.187580 0.100637 0.051331 0.191506 0.000000 0.000000 0.000000 #> [9] 0.000000 0.000000 0.000000 0.051331 0.000000 0.000000 0.051331 0.000000 #> [17] 0.000000 0.051331 0.121663 0.000000 0.000000 0.000000 0.000000 0.000000 #> [25] 0.051331 0.000000 0.051331 0.000000 0.113249 0.000000 0.051331 0.000000 #> [33] 0.000000 0.131150 0.121663 0.051331 0.000000 0.000000 0.051331 0.000000 #> [41] 0.000000 0.000000 0.000000 0.000000 0.051331 0.000000 0.051331 0.000000 #> [49] 0.000000 0.000000 0.051331 0.000000 0.000000 0.000000 0.191506 0.000000 #> [57] 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.051331 #> [65] 0.000000 0.000000 0.140208 0.196164 0.000000 0.051331 0.000000 0.051331 #> [73] 0.121663 0.000000 0.000000 0.000000 0.000000 0.051331 0.000000 0.000000 #> [81] 0.000000 0.000000 0.000000 0.081565 0.000000 0.051331 0.000000 0.000000 #> [89] 0.000000 0.000000 0.051331 0.000000 0.051331 0.051331 0.000000 0.000000 #> [97] 0.000000 0.051331 0.191506 0.000000 barplot(confidence.indices.micro_nb1,col=rgb(jet.colors(confidence.indices.micro_nb1), maxColorValue = 255), names.arg=colnames(result.boost.micro_nb1), ylim=c(0,1)) abline(h=)

All_micro_nb1=NULL #Here are the cutoff level tested for(lev.micro_nb1 in 20:10/20){ F_score.micro_nb1=NULL for(u.micro_nb1 in 1:nrow(dec.result.boost.micro_nb1 )){ F_score.micro_nb1<-rbind(F_score.micro_nb1,SelectBoost::compsim(DATA_exemple, dec.result.boost.micro_nb1[u.micro_nb1,],level=lev.micro_nb1)[1:5]) } All_micro_nb1 <- abind::abind(All_micro_nb1,F_score.micro_nb1,along=3) }

For a selection threshold equal to 0.900.900.90, all the c0 values and the 5 criteria.

matplot(1:nrow(dec.result.boost.micro_nb1),All_micro_nb1[,,3],type="l", ylab="criterion value",xlab="c0 value",xaxt="n",lwd=2) axis(1, at=1:length(attr(result.boost.micro_nb1,"c0.seq")), labels=round(attr(result.boost.micro_nb1,"c0.seq"),3)) legend(x="topright",legend=c("recall (sensitivity)", "precision (positive predictive value)","non-weighted Fscore", "F1/2 weighted Fscore","F2 weighted Fscore"), lty=1:5,col=1:5,lwd=2)

Fscores for all selection thresholds and all the c_0c_0c_0 values.

matplot(1:nrow(dec.result.boost.micro_nb1),All_micro_nb1[,3,],type="l", ylab="Fscore",xlab="c0 value",xaxt="n",lwd=2,col=1:11,lty=1:11)

axis(1, at=1:length(attr(result.boost.micro_nb1,"c0.seq")), labels=round(attr(result.boost.micro_nb1,"c0.seq"),3))

legend(x="bottomright",legend=(20:11)/20,lty=1:11,col=1:11,lwd=2, title="Threshold")