1 FRESA.CAD ARCENE Benchmark

1.1 ARCENE


trainLabeled <- read.delim("./Arcene/ARCENE/trainSet.txt")
validLabeled <- read.delim("./Arcene/ARCENE/arcene_valid.txt")

trainLabeled$Labels <-  1*(trainLabeled$Labels > 0)
validLabeled$Labels <-  1*(validLabeled$Labels > 0)

sum(trainLabeled$Labels)
sum(validLabeled$Labels)

arcene <- rbind(trainLabeled,validLabeled)
arcene <- as.data.frame(arcene)

ExperimentName <- "ARCENE_V75"
bswimsReps <- 10;
theData <- arcene;
theOutcome <- "Labels";
reps <- 60;
fraction <- 0.75;

BSWiMSFileName <- paste(ExperimentName,"FRESAMethod.RDATA",sep = "_")
CVFileName <- paste(ExperimentName,"CVMethod.RDATA",sep = "_")

1.2 Benchmarking


BSWiMSMODEL <- BSWiMS.model(formula = paste(theOutcome," ~ 1"),data = theData,NumberofRepeats = 1)


save(BSWiMSMODEL,file = BSWiMSFileName)

load(file = BSWiMSFileName)

par(mfrow = c(2,2),cex=0.6);
cp <- BinaryBenchmark(theData,theOutcome,reps,fraction)

save(cp,file = CVFileName)
par(mfrow = c(1,1),cex=1.0);


load(file = CVFileName)

1.3 Results

1.3.1 Classifier Results


hm <- heatMaps(Outcome = "Outcome",data = cp$testPredictions,title = "Heat Map",Scale = TRUE,hCluster = "col",cexRow = 0.25,cexCol = 0.75,srtCol = 45)


#The Times
par(mfrow = c(2,1),cex=1.0);
pander::pander(cp$cpuElapsedTimes)

BSWiMS	RF	RPART	LASSO	SVM	KNN	ENS
46.35	62.75	6.062	2.064	0.04	0.01983	111.2

par(mfrow = c(1,1),cex=1.0);
learningTime <- -1*cp$cpuElapsedTimes
pr <- plot(cp)

1.3.2 Radar Plots

op <- par(no.readonly = TRUE)

library(fmsb)
par(mfrow = c(1,2),xpd = TRUE,pty = "s",mar = c(1,1,1,1))

mNames <- names(cp$cpuElapsedTimes)

classRanks <- c(pr$minMaxMetrics$BER[1],pr$minMaxMetrics$ACC[2],pr$minMaxMetrics$AUC[2],pr$minMaxMetrics$SEN[2],pr$minMaxMetrics$SPE[2],min(cp$cpuElapsedTimes))
classRanks <- rbind(classRanks,c(pr$minMaxMetrics$BER[2],0,0,0,0,max(cp$cpuElapsedTimes)))
classRanks <- as.data.frame(rbind(classRanks,cbind(t(pr$metrics[c("BER","ACC","AUC","SEN","SPE"),mNames]),cp$cpuElapsedTimes)))
colnames(classRanks) <- c("BER","ACC","AUC","SEN","SPE","CPU")

classRanks$BER <- -classRanks$BER
classRanks$CPU <- -classRanks$CPU

colors_border = c( rgb(1.0,0.0,0.0,1.0), rgb(0.0,1.0,0.0,1.0) , rgb(0.0,0.0,1.0,1.0), rgb(0.2,0.2,0.0,1.0), rgb(0.0,1.0,1.0,1.0), rgb(1.0,0.0,1.0,1.0), rgb(0.0,0.0,0.0,1.0) )
colors_in = c( rgb(1.0,0.0,0.0,0.05), rgb(0.0,1.0,0.0,0.05) , rgb(0.0,0.0,1.0,0.05),rgb(1.0,1.0,0.0,0.05), rgb(0.0,1.0,1.0,0.05) , rgb(1.0,0.0,1.0,0.05), rgb(0.0,0.0,0.0,0.05) )
radarchart(classRanks,axistype = 0,maxmin = T,pcol = colors_border,pfcol = colors_in,plwd = c(6,2,2,2,2,2,2),plty = 1, cglcol = "grey", cglty = 1,axislabcol = "black",cglwd = 0.8, vlcex  = 0.5 ,title = "Prediction Model")

legend("topleft",legend = rownames(classRanks[-c(1,2),]),bty = "n",pch = 20,col = colors_in,text.col = colors_border,cex = 0.5,pt.cex = 2)


filnames <- c("BSWiMS","LASSO_MIN","RF","IDI","tStudent","kendall","mRMR.classic")

filterRanks <- c(pr$minMaxMetrics$BER[1],pr$minMaxMetrics$ACC[2],pr$minMaxMetrics$AUC[2],pr$minMaxMetrics$SEN[2],pr$minMaxMetrics$SPE[2],max(cp$jaccard),min(cp$featsize));

filterRanks <- rbind(filterRanks,c(pr$minMaxMetrics$BER[2],0,0,0,0,min(cp$jaccard),max(cp$featsize)));

filterRanks <- as.data.frame(rbind(filterRanks,cbind(t(pr$metrics_filter[c("BER","ACC","AUC","SEN","SPE"),filnames]),cp$jaccard[filnames],cp$featsize[filnames])));
colnames(filterRanks) <- c("BER","ACC","AUC","SEN","SPE","Jaccard","SIZE")
filterRanks$BER <- -filterRanks$BER
filterRanks$SIZE <- -filterRanks$SIZE

colors_border = c( rgb(1.0,0.0,0.0,1.0), rgb(0.0,1.0,0.0,1.0) , rgb(0.0,0.0,1.0,1.0), rgb(0.2,0.2,0.0,1.0), rgb(0.0,1.0,1.0,1.0), rgb(1.0,0.0,1.0,1.0), rgb(0.0,0.0,0.0,1.0) )
colors_in = c( rgb(1.0,0.0,0.0,0.05), rgb(0.0,1.0,0.0,0.05) , rgb(0.0,0.0,1.0,0.05),rgb(1.0,1.0,0.0,0.05), rgb(0.0,1.0,1.0,0.05) , rgb(1.0,0.0,1.0,0.05), rgb(0.0,0.0,0.0,0.05) )
radarchart(filterRanks,axistype = 0,maxmin = T,pcol = colors_border,pfcol = colors_in,plwd = c(6,2,2,2,2,2,2),plty = 1, cglcol = "grey", cglty = 1,axislabcol = "black",cglwd = 0.8, vlcex  = 0.6,title = "Filter Method" )


legend("topleft",legend = rownames(filterRanks[-c(1,2),]),bty = "n",pch = 20,col = colors_in,text.col = colors_border,cex = 0.5,pt.cex = 2)


detach("package:fmsb", unload=TRUE)

par(mfrow = c(1,1))
par(op)

1.3.3 Feature Analysis



rm <- rowMeans(cp$featureSelectionFrequency)
selFrequency <- cp$featureSelectionFrequency[rm > 0.1,]
gplots::heatmap.2(as.matrix(selFrequency),trace = "none",mar = c(10,10),main = "Features",cexRow = 0.25,cexCol = 0.5)



topFeat <- min(ncol(BSWiMSMODEL$bagging$formulaNetwork),30);
gplots::heatmap.2(BSWiMSMODEL$bagging$formulaNetwork[1:topFeat,1:topFeat],trace="none",mar = c(10,10),main = "B:SWiMS Formula Network",cexRow = 0.5,cexCol = 0.5)

pander::pander(summary(BSWiMSMODEL$bagging$bagged.model,caption="Colon",round = 3))

coefficients:

Table continues below
	Estimate	lower	OR	upper	u.Accuracy	r.Accuracy
V2134	-0.001704	0.9981	0.9983	0.9985	0.675	0.75
V8685	-0.002132	0.9976	0.9979	0.9982	0.56	0.76
V9818	-0.002505	0.9969	0.9975	0.9981	0.57	0.735
V2556	0.000729	1.001	1.001	1.001	0.71	0.715
V2294	0.001176	1.001	1.001	1.002	0.69	0.775
V2804	0.004186	1.003	1.004	1.005	0.675	0.82
V6959	-0.0009317	0.9988	0.9991	0.9993	0.58	0.745
V5417	-0.002602	0.9966	0.9974	0.9982	0.57	0.815
V9635	-0.002057	0.9973	0.9979	0.9986	0.495	0.735
V8156	-0.001134	0.9985	0.9989	0.9992	0.58	0.78
V6239	-0.003003	0.996	0.997	0.998	0.655	0.78
V2256	-0.001324	0.9982	0.9987	0.9991	0.585	0.74
V6594	0.0007884	1.001	1.001	1.001	0.665	0.765
V9395	-0.001996	0.9974	0.998	0.9987	0.555	0.76
V9215	-0.002468	0.9967	0.9975	0.9983	0.58	0.81
V9275	-0.004022	0.9947	0.996	0.9973	0.565	0.84
V34	-0.00129	0.9983	0.9987	0.9991	0.57	0.745
V5761	-0.0007928	0.9989	0.9992	0.9995	0.56	0.725
V2750	-0.008007	0.9893	0.992	0.9948	0.56	0.855
V4580	-0.004844	0.9935	0.9952	0.9968	0.56	0.79
V4290	-0.001773	0.9976	0.9982	0.9989	0.665	0.72
V5391	-0.0007635	0.999	0.9992	0.9995	0.685	0.8
V6584	-0.00164	0.9978	0.9984	0.9989	0.535	0.75
V7728	-0.003072	0.9958	0.9969	0.998	0.565	0.83
V22	0.005036	1.003	1.005	1.007	0.605	0.825
V7748	-0.003928	0.9947	0.9961	0.9975	0.66	0.805
V7319	-0.001464	0.998	0.9985	0.9991	0.48	0.78
V86	-0.001604	0.9978	0.9984	0.999	0.675	0.705
V9585	-0.005611	0.9923	0.9944	0.9965	0.685	0.85
V7272	-0.001276	0.9982	0.9987	0.9992	0.66	0.78
V3170	-0.005465	0.9925	0.9946	0.9966	0.56	0.765
V6511	0.0004981	1	1	1.001	0.635	0.79
V4584	-0.0009363	0.9987	0.9991	0.9994	0.51	0.77
V4194	-0.004057	0.9943	0.996	0.9976	0.655	0.825
V5069	0.005116	1.003	1.005	1.007	0.56	0.81
V8055	-0.00185	0.9974	0.9982	0.9989	0.55	0.81
V256	-0.002022	0.9971	0.998	0.9988	0.505	0.815
V4352	-0.003606	0.9949	0.9964	0.9979	0.62	0.855
V1046	-0.007007	0.9901	0.993	0.9959	0.56	0.88
V6111	-0.003491	0.9951	0.9965	0.998	0.54	0.815
V6146	0.0005967	1	1.001	1.001	0.615	0.805
V2515	-0.001835	0.9974	0.9982	0.9989	0.555	0.77
V3591	0.0006718	1	1.001	1.001	0.625	0.78
V130	-0.00722	0.9898	0.9928	0.9959	0.56	0.79
V9432	0.0003485	1	1	1	0.695	0.785
V1936	0.0008359	1	1.001	1.001	0.69	0.805
V2747	0.0005391	1	1.001	1.001	0.54	0.78
V2242	0.0006586	1	1.001	1.001	0.58	0.795
V533	-0.001313	0.9981	0.9987	0.9993	0.67	0.77
V3206	-0.002486	0.9964	0.9975	0.9986	0.65	0.79
V762	0.002817	1.002	1.003	1.004	0.59	0.81
V7857	-0.001865	0.9973	0.9981	0.999	0.675	0.76
V3708	-0.001677	0.9976	0.9983	0.9991	0.54	0.775
V7976	0.0004925	1	1	1.001	0.595	0.77
V8502	-0.002102	0.9969	0.9979	0.9989	0.665	0.795
V7220	-0.002444	0.9965	0.9976	0.9987	0.68	0.79
V5801	0.0008284	1	1.001	1.001	0.705	0.855
V6508	-0.001492	0.9978	0.9985	0.9992	0.695	0.875
V6163	-0.001404	0.998	0.9986	0.9992	0.49	0.805
V1184	-0.001079	0.9984	0.9989	0.9994	0.555	0.82
V4301	-0.003449	0.995	0.9966	0.9982	0.56	0.775
V5005	0.0009395	1.001	1.001	1.001	0.695	0.825
V6688	-0.001667	0.9975	0.9983	0.9991	0.555	0.775
V4070	0.00118	1.001	1.001	1.002	0.69	0.875
V2064	-0.004154	0.9939	0.9959	0.9978	0.56	0.875
V3222	-0.0008748	0.9987	0.9991	0.9996	0.565	0.755
V729	-0.000856	0.9987	0.9991	0.9996	0.57	0.82
V7891	0.0009756	1	1.001	1.001	0.685	0.805
V5473	-0.00123	0.9982	0.9988	0.9994	0.665	0.78
V5400	-0.004823	0.9928	0.9952	0.9976	0.635	0.82
V819	-0.001041	0.9984	0.999	0.9995	0.525	0.8
V5489	-0.0005666	0.9991	0.9994	0.9997	0.65	0.865
V8749	-0.002053	0.9969	0.9979	0.999	0.56	0.805
V5672	0.002274	1.001	1.002	1.003	0.595	0.86
V8650	0.00273	1.001	1.003	1.004	0.6	0.87
V9213	-0.005285	0.9921	0.9947	0.9974	0.56	0.77
V7402	-0.004599	0.9931	0.9954	0.9977	0.56	0.87
V3557	-0.0003388	0.9995	0.9997	0.9998	0.695	0.78
V7513	-0.004291	0.9935	0.9957	0.9979	0.63	0.85
V1451	0.001211	1.001	1.001	1.002	0.555	0.835
V4069	-0.0007956	0.9988	0.9992	0.9996	0.585	0.775
V2358	0.001014	1	1.001	1.002	0.585	0.79
V9965	-0.001354	0.9979	0.9986	0.9994	0.535	0.825
V1248	-0.001476	0.9978	0.9985	0.9993	0.58	0.77
V9070	-0.002689	0.9959	0.9973	0.9987	0.575	0.78
V4406	-0.0005463	0.9992	0.9995	0.9997	0.59	0.82
V1967	-0.0005941	0.9991	0.9994	0.9997	0.69	0.81
V3161	0.0008534	1	1.001	1.001	0.585	0.795
V872	-0.00108	0.9983	0.9989	0.9995	0.675	0.795
V5321	-0.004448	0.9932	0.9956	0.9979	0.56	0.85
V167	-0.001165	0.9982	0.9988	0.9995	0.51	0.82
V9735	0.0002626	1	1	1	0.685	0.8
V1975	-0.003744	0.9942	0.9963	0.9983	0.68	0.86
V4564	-0.001317	0.998	0.9987	0.9994	0.56	0.84
V5466	-0.2421	0.6864	0.785	0.8978	0.56	0.855
V9947	0.003156	1.001	1.003	1.005	0.56	0.82
V4542	-0.0006869	0.9989	0.9993	0.9997	0.56	0.815
V3365	-0.001061	0.9983	0.9989	0.9995	0.685	0.845
V7435	0.000534	1	1.001	1.001	0.55	0.77
V782	-0.001145	0.9982	0.9989	0.9995	0.695	0.78
V1787	-0.00218	0.9965	0.9978	0.9991	0.685	0.825
V5	-0.001349	0.9978	0.9987	0.9995	0.665	0.775
V6164	-0.001531	0.9975	0.9985	0.9994	0.48	0.79
V4198	-0.001401	0.9977	0.9986	0.9995	0.635	0.84
V723	-0.002311	0.9961	0.9977	0.9993	0.65	0.81

Table continues below
	full.Accuracy	u.AUC	r.AUC	full.AUC	IDI	NRI
V2134	0.875	0.6879	0.7463	0.8762	0.2862	1.078
V8685	0.875	0.5	0.7589	0.8762	0.237	1.084
V9818	0.835	0.5747	0.733	0.8369	0.2347	1.05
V2556	0.855	0.7045	0.7127	0.8511	0.2209	1.128
V2294	0.83	0.6843	0.7784	0.8336	0.1903	1.2
V2804	0.895	0.6636	0.8198	0.8941	0.1637	1.099
V6959	0.815	0.569	0.7382	0.8117	0.1709	0.8263
V5417	0.875	0.5613	0.8141	0.8738	0.1689	0.892
V9635	0.835	0.47	0.7305	0.8344	0.1495	0.8068
V8156	0.855	0.586	0.7804	0.8511	0.1497	0.6818
V6239	0.83	0.6774	0.7768	0.8336	0.1447	1.148
V2256	0.795	0.5905	0.7508	0.7963	0.1483	0.6023
V6594	0.835	0.6656	0.767	0.8344	0.1328	0.9156
V9395	0.855	0.5722	0.7601	0.8511	0.1357	0.8279
V9215	0.845	0.586	0.8145	0.8458	0.1459	0.9984
V9275	0.89	0.5678	0.8389	0.8908	0.1354	1.034
V34	0.82	0.5674	0.7528	0.8247	0.1297	0.5536
V5761	0.795	0.5499	0.7313	0.7999	0.144	0.474
V2750	0.895	0.5	0.8547	0.8941	0.1215	1.032
V4580	0.835	0.5	0.7821	0.8344	0.1035	0.9903
V4290	0.81	0.6692	0.7135	0.806	0.1276	0.9838
V5391	0.845	0.6956	0.8007	0.8458	0.1178	0.8685
V6584	0.815	0.5325	0.7463	0.8141	0.1248	0.6753
V7728	0.895	0.5386	0.8287	0.8941	0.131	1
V22	0.895	0.5706	0.8206	0.8941	0.1258	1.045
V7748	0.875	0.6684	0.8028	0.8738	0.1183	1.209
V7319	0.845	0.4554	0.7792	0.8458	0.1069	0.7159
V86	0.795	0.683	0.7037	0.7999	0.1141	0.7955
V9585	0.845	0.7041	0.8502	0.8458	0.1055	1.151
V7272	0.795	0.683	0.7817	0.7999	0.0924	0.8847
V3170	0.8	0.5	0.7646	0.7995	0.07861	0.5341
V6511	0.815	0.6193	0.7869	0.8117	0.09844	0.5974
V4584	0.81	0.4968	0.7679	0.806	0.1124	0.7776
V4194	0.85	0.6798	0.8267	0.8515	0.1022	1.174
V5069	0.845	0.5	0.8121	0.8458	0.09829	1.128
V8055	0.85	0.5361	0.8145	0.8515	0.1073	0.8328
V256	0.83	0.4935	0.8141	0.8336	0.09637	0.6542
V4352	0.89	0.6266	0.8535	0.8908	0.08608	0.9399
V1046	0.89	0.5	0.8807	0.8908	0.06775	0.974
V6111	0.875	0.554	0.8117	0.8738	0.08499	0.9083
V6146	0.875	0.599	0.804	0.8762	0.08022	0.5844
V2515	0.815	0.5686	0.7666	0.8117	0.07655	0.3766
V3591	0.82	0.6067	0.7877	0.8247	0.09777	0.6997
V130	0.82	0.5	0.7967	0.8247	0.06357	0.6688
V9432	0.835	0.6912	0.7898	0.8369	0.1015	0.6688
V1936	0.85	0.6818	0.8076	0.8478	0.106	0.8994
V2747	0.795	0.5114	0.7853	0.7963	0.097	0.7208
V2242	0.8	0.5593	0.8036	0.8007	0.09945	0.6039
V533	0.82	0.6907	0.7703	0.8247	0.06407	0.5179
V3206	0.85	0.6741	0.7881	0.8478	0.08146	0.9269
V762	0.845	0.5548	0.8121	0.8458	0.09084	0.4205
V7857	0.815	0.683	0.7601	0.8141	0.08208	0.7776
V3708	0.835	0.554	0.7772	0.8344	0.07206	0.5877
V7976	0.8	0.5751	0.7764	0.7995	0.0939	0.5633
V8502	0.85	0.6692	0.7926	0.8515	0.07854	1.034
V7220	0.85	0.6887	0.793	0.8478	0.05969	0.7062
V5801	0.89	0.7062	0.8547	0.8908	0.06615	0.75
V6508	0.895	0.7045	0.8726	0.8941	0.07967	0.9659
V6163	0.85	0.4752	0.8088	0.8478	0.07836	0.6786
V1184	0.845	0.5199	0.821	0.8458	0.07752	1.034
V4301	0.795	0.5	0.7772	0.7963	0.05748	0.4659
V5005	0.845	0.696	0.8267	0.8458	0.07879	0.6461
V6688	0.815	0.5722	0.7723	0.8141	0.07394	0.4659
V4070	0.895	0.683	0.8738	0.8941	0.08022	0.9773
V2064	0.895	0.5718	0.8774	0.8941	0.06936	1.054
V3222	0.8	0.569	0.7593	0.8007	0.06797	0.4545
V729	0.83	0.5625	0.8186	0.8336	0.06545	0.6234
V7891	0.85	0.6871	0.8113	0.8515	0.07532	0.7435
V5473	0.815	0.668	0.7744	0.8117	0.07191	0.6494
V5400	0.85	0.6607	0.8186	0.8515	0.0659	0.7289
V819	0.8	0.5211	0.8019	0.8007	0.06816	0.6023
V5489	0.89	0.6388	0.8649	0.8908	0.0585	0.7159
V8749	0.845	0.5	0.8052	0.8458	0.05735	0.4334
V5672	0.89	0.5629	0.858	0.8908	0.06616	0.4497
V8650	0.895	0.5686	0.8681	0.8941	0.05488	0.6997
V9213	0.8	0.5	0.7691	0.8007	0.04509	0.414
V7402	0.89	0.5	0.8693	0.8908	0.06297	1.166
V3557	0.81	0.7021	0.7756	0.806	0.06857	0.6282
V7513	0.845	0.655	0.849	0.8458	0.04981	0.7273
V1451	0.875	0.5101	0.832	0.8762	0.06029	0.862
V4069	0.8	0.5905	0.7784	0.7995	0.05914	0.3263
V2358	0.855	0.5588	0.7869	0.8511	0.06436	0.664
V9965	0.85	0.5373	0.8255	0.8478	0.06039	0.737
V1248	0.81	0.5946	0.7666	0.806	0.05768	0.2094
V9070	0.845	0.5864	0.7804	0.8458	0.05201	0.5211
V4406	0.83	0.5816	0.8259	0.8336	0.06009	0.4903
V1967	0.835	0.6964	0.8072	0.8344	0.05704	0.7468
V3161	0.835	0.5601	0.7975	0.8369	0.07767	0.6412
V872	0.795	0.6952	0.7938	0.7963	0.04499	0.4156
V5321	0.875	0.5	0.849	0.8738	0.05966	0.9018
V167	0.875	0.4907	0.821	0.8762	0.05721	0.6169
V9735	0.815	0.6847	0.7983	0.8141	0.05636	0.6364
V1975	0.89	0.6997	0.8592	0.8908	0.05371	0.5779
V4564	0.875	0.5	0.8377	0.8738	0.05281	0.8417
V5466	0.875	0.5	0.8523	0.8738	0.04637	1.144
V9947	0.85	0.5	0.8247	0.8515	0.04956	0.4253
V4542	0.85	0.5	0.8129	0.8478	0.04916	0.6834
V3365	0.875	0.6859	0.8446	0.8738	0.0556	0.8791
V7435	0.835	0.5142	0.7703	0.8369	0.04955	0.5795
V782	0.8	0.7143	0.778	0.7995	0.03793	0.1104
V1787	0.845	0.7041	0.8206	0.8458	0.0465	0.763
V5	0.8	0.6838	0.7723	0.8007	0.03853	0.1867
V6164	0.8	0.4785	0.7906	0.7995	0.043	0.3782
V4198	0.875	0.6437	0.8401	0.8762	0.03432	0.3409
V723	0.835	0.6729	0.8097	0.8369	0.03443	0.3864

	z.IDI	z.NRI	Frequency
V2134	9.429	9.081	0.05
V8685	8.296	9.033	0.05
V9818	7.962	8.708	0.05
V2556	7.818	9.809	0.05
V2294	6.846	10.68	0.05
V2804	6.813	9.231	0.05
V6959	6.564	6.459	0.05
V5417	6.436	7.088	0.05
V9635	6.351	6.203	0.05
V8156	6.13	5.565	0.05
V6239	6.118	10.37	0.05
V2256	6.069	4.544	0.05
V6594	6.064	7.282	0.05
V9395	6.027	6.628	0.05
V9215	5.979	8.341	0.05
V9275	5.949	8.709	0.05
V34	5.817	4.194	0.05
V5761	5.787	3.438	0.05
V2750	5.7	8.516	0.05
V4580	5.669	8.392	0.05
V4290	5.582	8.047	0.05
V5391	5.508	7.042	0.05
V6584	5.485	5.254	0.05
V7728	5.462	8.106	0.05
V22	5.425	8.749	0.05
V7748	5.423	10.97	0.05
V7319	5.252	5.627	0.05
V86	5.248	6.22	0.05
V9585	5.152	10.14	0.05
V7272	5.098	7.751	0.05
V3170	5.096	5.438	0.05
V6511	5.044	4.399	0.05
V4584	4.926	6.066	0.05
V4194	4.837	10.5	0.05
V5069	4.829	9.589	0.05
V8055	4.795	6.422	0.05
V256	4.751	4.882	0.05
V4352	4.731	7.709	0.05
V1046	4.721	8.603	0.05
V6111	4.66	7.456	0.05
V6146	4.66	4.289	0.05
V2515	4.653	2.985	0.05
V3591	4.642	5.286	0.05
V130	4.606	6.97	0.05
V9432	4.593	5.051	0.05
V1936	4.584	7.06	0.05
V2747	4.559	5.425	0.05
V2242	4.555	4.45	0.05
V533	4.538	3.92	0.05
V3206	4.434	7.641	0.05
V762	4.416	3.29	0.05
V7857	4.403	6.066	0.05
V3708	4.379	4.749	0.05
V7976	4.375	4.124	0.05
V8502	4.323	8.709	0.05
V7220	4.317	5.468	0.05
V5801	4.287	5.77	0.05
V6508	4.281	7.869	0.05
V6163	4.252	5.179	0.05
V1184	4.238	8.709	0.05
V4301	4.202	4.442	0.05
V5005	4.197	4.848	0.05
V6688	4.125	3.662	0.05
V4070	4.115	7.857	0.05
V2064	4.075	9.213	0.05
V3222	4.037	3.393	0.05
V729	3.989	4.649	0.05
V7891	3.975	5.627	0.05
V5473	3.93	4.832	0.05
V5400	3.904	6.49	0.05
V819	3.903	4.544	0.05
V5489	3.892	5.627	0.05
V8749	3.891	3.313	0.05
V5672	3.873	3.366	0.05
V8650	3.871	5.286	0.05
V9213	3.855	4.619	0.05
V7402	3.853	10.58	0.05
V3557	3.851	4.705	0.05
V7513	3.848	6.086	0.05
V1451	3.828	6.742	0.05
V4069	3.789	2.514	0.05
V2358	3.771	4.971	0.05
V9965	3.752	5.69	0.05
V1248	3.752	1.603	0.05
V9070	3.727	4.287	0.05
V4406	3.702	3.661	0.05
V1967	3.696	5.842	0.05
V3161	3.693	4.789	0.05
V872	3.681	3.179	0.05
V5321	3.659	7.821	0.05
V167	3.637	4.55	0.05
V9735	3.635	4.738	0.05
V1975	3.618	4.263	0.05
V4564	3.612	6.892	0.05
V5466	3.533	10.7	0.05
V9947	3.526	3.077	0.05
V4542	3.48	5.248	0.05
V3365	3.427	6.979	0.05
V7435	3.368	4.254	0.05
V782	3.314	0.7831	0.05
V1787	3.312	6.387	0.05
V5	3.176	1.326	0.05
V6164	3.103	2.808	0.05
V4198	2.958	2.715	0.05
V723	2.87	2.833	0.05

Accuracy: 0.91
tAUC: 0.9111
sensitivity: 0.9205
specificity: 0.9018
bootstrap:



hm <- heatMaps(Outcome = theOutcome,data = theData[,c(theOutcome,rownames(selFrequency))],title = "Heat Map",Scale = TRUE,hCluster = "col",cexRow = 0.25,cexCol = 0.5,srtCol = 45)


vlist <- rownames(selFrequency)
vlist <- cbind(vlist,vlist)
univ <- univariateRankVariables(variableList = vlist,formula = paste(theOutcome,"~1"),Outcome = theOutcome,data = theData,type = "LOGIT",rankingTest = "zIDI",uniType = "Binary")[,c("controlMean","controlStd","caseMean","caseStd","ROCAUC","WilcoxRes.p")]

100 : V3504 200 : V1617 300 : V473 400 : V9092 500 : V4064


cnames <- colnames(univ);
univ <- cbind(univ,rm[rownames(univ)])
colnames(univ) <- c(cnames,"Frequency")
univ <- univ[order(-univ[,5]),]
pander::pander(univ[1:topFeat,],caption = "Features",round = 4)

Features (continued below)
	controlMean	controlStd	caseMean	caseStd	ROCAUC
V5005	239.3	83.62	314.7	72.85	0.772
V4960	123.7	97.33	47.51	49.25	0.7513
V2309	112.6	89	43.06	45.32	0.7509
V8368	116	90.62	44.89	46.06	0.7508
V312	121.6	94.73	47.18	48.01	0.7499
V3365	119.1	92.52	46.26	46.93	0.7493
V9617	108.8	87.49	40.89	44.75	0.7491
V414	125.3	100.4	47.47	50.45	0.7488
V9092	123.6	132.6	33.89	63.11	0.7485
V1936	243.1	79.21	316	79.48	0.748
V3783	398.6	159.9	530.9	140.9	0.7478
V3913	401.2	163.3	531.2	161.7	0.7477
V2556	255.3	92.59	324.8	64.88	0.7475
V9735	377.8	135	484	95.31	0.7453
V9432	392.5	153.2	519.6	124	0.745
V5801	385	144.3	503.2	107	0.7443
V7891	233.4	94.59	307.9	70.05	0.7441
V376	104.8	86.18	38.53	44.25	0.7425
V8585	117.7	123	30.88	59.46	0.7424
V1748	120.2	125.8	31.7	60.63	0.7413
V1148	121.8	128.1	32.58	61.86	0.7413
V2866	100.8	84.78	36.1	43.67	0.7406
V4147	82.43	101.7	21.93	43.72	0.7403
V2227	114.6	119.7	30	58.22	0.7403
V10	104.5	109.1	27.8	53.8	0.7395
V965	91.01	106.3	24.81	47.13	0.739
V66	98.75	101.2	27.45	51.87	0.7389
V540	87.76	87.73	26.7	47.89	0.7384
V4070	234.6	95.72	311.7	81.57	0.7383
V6594	210.4	98.05	288.8	76.94	0.7377

	WilcoxRes.p	Frequency
V5005	0	0.785
V4960	0	0.1583
V2309	0	0.16
V8368	0	0.1483
V312	0	0.4767
V3365	0	0.275
V9617	0	0.1433
V414	0	0.145
V9092	0	0.1233
V1936	0	0.6783
V3783	0	0.2517
V3913	0	0.1433
V2556	0	0.59
V9735	0	0.5317
V9432	0	0.1683
V5801	0	0.385
V7891	0	0.42
V376	0	0.1183
V8585	0	0.1383
V1748	0	0.175
V1148	0	0.1417
V2866	0	0.1067
V4147	0	0.12
V2227	0	0.1383
V10	0	0.2167
V965	0	0.11
V66	0	0.1017
V540	0	0.1017
V4070	0	0.69
V6594	0	0.1567

ARCENE Benchmarks

José Tamez-Peña

Sep 28, 2018

1 FRESA.CAD ARCENE Benchmark

1.1 ARCENE

1.2 Benchmarking

1.3 Results

1.3.1 Classifier Results

1.3.2 Radar Plots

1.3.3 Feature Analysis