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inst/doc/benchmarking-selectboost-networks.R
In SelectBoost: A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets
## ----setup, include = FALSE---------------------------------------------------
#file.edit(normalizePath("~/.Renviron"))
LOCAL <- identical(Sys.getenv("LOCAL"), "TRUE")
#LOCAL=TRUE
knitr::opts_chunk$set(purl = LOCAL)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- eval = FALSE------------------------------------------------------------
# devtools::install_github("fbertran/SelectBoost", ref = "doMC")
## ----loadresults, cache= FALSE, eval = LOCAL----------------------------------
library(SelectBoost)
data(results_simuls_reverse_engineering_v3)
colgrey=grey(.05,NULL)
## ----ranges, cache= FALSE, eval = LOCAL---------------------------------------
rangeCPy_S=range(sensitivity_C,sensitivity_PL,sensitivity_PL2,sensitivity_PL2_W,sensitivity_PL2_tW,sensitivity_PSel,sensitivity_PSel_W,sensitivity_PSel.5,sensitivity_PSel.e2,sensitivity_PSel.5.e2,sensitivity_robust,sensitivity_PB,predictive_positive_value_PB_095_075,predictive_positive_value_PB_075_075,sensitivity_PB_W)
rangeCPy_PPV=range(predictive_positive_value_C,predictive_positive_value_PL,predictive_positive_value_PL2,predictive_positive_value_PL2_W,predictive_positive_value_PL2_tW,predictive_positive_value_PSel,predictive_positive_value_PSel_W,predictive_positive_value_PSel.5,predictive_positive_value_PSel.e2,predictive_positive_value_PSel.5.e2,predictive_positive_value_robust,predictive_positive_value_PB,predictive_positive_value_PB_095_075,predictive_positive_value_PB_075_075,predictive_positive_value_PB_W)
rangeCPy_F=range(F_score_C,F_score_PL,F_score_PL2,F_score_PL2_W,F_score_PL2_tW,F_score_PSel,F_score_PSel_W,F_score_PSel.5,F_score_PSel.e2,F_score_PSel.5.e2,F_score_PB,F_score_PB_095_075,F_score_PB_075_075,F_score_PB_W)
rangeCPx=range(test.seq_C,test.seq_PL,test.seq_PL2,test.seq_PL2_W,test.seq_PL2_tW,test.seq_PSel,test.seq_PSel_W,test.seq_PSel.5,test.seq_PSel.e2,test.seq_PSel.5.e2,test.seq_robust,test.seq_PB,test.seq_PB_095_075,test.seq_PB_075_075,test.seq_PB_W)
## ----artgraphs1, cache= FALSE, fig.width=6, eval = LOCAL----------------------
layout(matrix(1:6,nrow=2))
matplot(t(test.seq_PL2),t(sensitivity_PL2),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Lasso2",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PL2,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PL2_W),t(sensitivity_PL2_W),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Lasso2_W",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PL2_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel),t(sensitivity_PSel),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel_W),t(sensitivity_PSel_W),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection_W",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PSel_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB),t(sensitivity_PB),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PB,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_W),t(sensitivity_PB_W),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost_W",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PB_W,col=grey(.05,NULL),lty=3)
## ----artgraphs2, cache= FALSE, fig.width=6, fig.keep='none', eval = LOCAL-----
layout(matrix(1:6,nrow=2))
matplot(t(test.seq_robust),t(sensitivity_robust),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Robust",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_robust,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB),t(sensitivity_PB),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PB,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_095_075),t(sensitivity_PB_095_075),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost_095_075",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PB_095_075,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_075_075),t(sensitivity_PB_075_075),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost_075_075",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PB_075_075,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_W),t(sensitivity_PB_W),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost_W",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PB_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel),t(sensitivity_PSel),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
## ----artgraphs3, cache= FALSE, fig.width=6, eval = LOCAL----------------------
layout(matrix(1:6,nrow=2))
matplot(t(test.seq_PL2),t(predictive_positive_value_PL2),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Lasso2",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PL2,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PL2_W),t(predictive_positive_value_PL2_W),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Lasso2_W",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PL2_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel),t(predictive_positive_value_PSel),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel_W),t(predictive_positive_value_PSel_W),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection_W",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PSel_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB),t(predictive_positive_value_PB),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PB,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_W),t(predictive_positive_value_PB_W),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost_Weighted",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PB_W,col=grey(.05,NULL),lty=3)
## ----artgraphs4, cache= FALSE, fig.width=6, fig.keep='none', eval = LOCAL-----
layout(matrix(1:6,nrow=2))
matplot(t(test.seq_robust),t(predictive_positive_value_robust),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Robust",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_robust,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB),t(predictive_positive_value_PB),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PB,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_095_075),t(predictive_positive_value_PB_095_075),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost_095_075",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PB_095_075,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_075_075),t(predictive_positive_value_PB_075_075),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost_075_075",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PB_075_075,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_W),t(predictive_positive_value_PB_W),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost_Weighted",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PB_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel),t(predictive_positive_value_PSel),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
## ----artgraphs5, cache= FALSE, fig.width=6, eval = LOCAL----------------------
layout(matrix(1:6,nrow=2))
matplot(t(test.seq_PL2),t(F_score_PL2),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Lasso2",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PL2,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PL2_W),t(F_score_PL2_W),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Lasso2_W",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PL2_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel),t(F_score_PSel),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel_W),t(F_score_PSel_W),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection_W",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PSel_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB),t(F_score_PB),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PB,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_W),t(F_score_PB_W),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost_Weighted",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PB_W,col=grey(.05,NULL),lty=3)
## ----artgraphs6, cache= FALSE, fig.width=6, fig.keep='none', eval = LOCAL-----
layout(matrix(1:6,nrow=2))
matplot(t(test.seq_robust),t(F_score_robust),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Robust",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_robust,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB),t(F_score_PB),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PB,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_095_075),t(F_score_PB_095_075),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost_095_075",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PB_095_075,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_075_075),t(F_score_PB_075_075),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost_075_075",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PB_075_075,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_W),t(F_score_PB_W),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost_Weighted",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PB_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel),t(F_score_PSel),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
## ----graphs, cache= FALSE, fig.width=6, eval=FALSE----------------------------
# layout(matrix(1:6,nrow=2))
# matplot(t(test.seq_C),t(sensitivity_C),type="l",xlab="cutoff",ylab="Sensitivity",main="Cascade",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_C,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL),t(sensitivity_PL),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Lasso",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PL,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2_W),t(sensitivity_PL2_W),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Lasso2_W",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PL2_W,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel),t(sensitivity_PSel),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel_W),t(sensitivity_PSel_W),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection_W",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PSel_W,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2_tW),t(sensitivity_PL2_tW),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Lasso2_wrongW",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PL2_tW,col=grey(.05,NULL),lty=3)
## ----graphs2, cache= FALSE, fig.width=6, eval=FALSE---------------------------
# layout(matrix(1:6,nrow=2))
# matplot(t(test.seq_PSel),t(sensitivity_PSel),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel.5),t(sensitivity_PSel.5),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection_.5",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PSel.5,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel.e2),t(sensitivity_PSel.e2),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection.e2",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PSel.e2,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel.5.e2),t(sensitivity_PSel.5.e2),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection_.5.e2",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PSel.5.e2,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2),t(sensitivity_PL2),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Lasso2",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PL2,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PB),t(sensitivity_PB),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PB,col=grey(.05,NULL),lty=3)
## ----graphs3, cache= FALSE, fig.width=6, eval=FALSE---------------------------
# layout(matrix(1:6,nrow=2))
# matplot(t(test.seq_C),t(predictive_positive_value_C),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Cascade",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_C,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL),t(predictive_positive_value_PL),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Lasso",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PL,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2_W),t(predictive_positive_value_PL2_W),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Lasso2_W",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PL2_W,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel),t(predictive_positive_value_PSel),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel_W),t(predictive_positive_value_PSel_W),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection_W",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PSel_W,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2_tW),t(predictive_positive_value_PL2_tW),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Lasso2_wrongW",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PL2_tW,col=grey(.05,NULL),lty=3)
## ----graphs4, cache= FALSE, fig.width=6, eval=FALSE---------------------------
# layout(matrix(1:6,nrow=2))
# matplot(t(test.seq_PSel),t(predictive_positive_value_PSel),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel.5),t(predictive_positive_value_PSel.5),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection.5",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PSel.5,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel.e2),t(predictive_positive_value_PSel.e2),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel.5.e2),t(predictive_positive_value_PSel.5.e2),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection.5.e2",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PSel.5.e2,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2),t(predictive_positive_value_PL2),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Lasso2",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PL2,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PB),t(predictive_positive_value_PB),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PB,col=grey(.05,NULL),lty=3)
## ----graphs5, cache= FALSE, fig.width=6, eval=FALSE---------------------------
# layout(matrix(1:6,nrow=2))
# matplot(t(test.seq_C),t(F_score_C),type="l",xlab="cutoff",ylab="Fscore",main="Cascade",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_C,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL),t(F_score_PL),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Lasso",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PL,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2_W),t(F_score_PL2_W),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Lasso2_W",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PL2_W,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel),t(F_score_PSel),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel_W),t(F_score_PSel_W),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection_W",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PSel_W,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2_tW),t(F_score_PL2_tW),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Lasso2_wrongW",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PL2_tW,col=grey(.05,NULL),lty=3)
## ----graphs6, cache= FALSE, fig.width=6, eval=FALSE---------------------------
# layout(matrix(1:6,nrow=2))
# matplot(t(test.seq_PSel),t(F_score_PSel),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection",ylim=rangeCPy_F,col=grey(.25,NULL))
# abline(v=nv_PSel,lty=3,col=grey(.05,NULL))
# matplot(t(test.seq_PSel.5),t(F_score_PSel.5),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection.5",ylim=rangeCPy_F,col=grey(.25,NULL))
# abline(v=nv_PSel.5,lty=3,col=grey(.05,NULL))
# matplot(t(test.seq_PSel.e2),t(F_score_PSel.e2),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection.e2",ylim=rangeCPy_F,col=grey(.25,NULL))
# abline(v=nv_PSel.e2,lty=3,col=grey(.05,NULL))
# matplot(t(test.seq_PSel.5.e2),t(F_score_PSel.5.e2),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection.5.e2",ylim=rangeCPy_F,col=grey(.25,NULL))
# abline(v=nv_PSel.5.e2,lty=3,col=grey(.05,NULL))
# matplot(t(test.seq_PL2),t(F_score_PL2),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Lasso2",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PL2,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PB),t(F_score_PB),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PB,col=grey(.05,NULL),lty=3)
## ----code, cache= FALSE, eval=FALSE-------------------------------------------
# library(Cascade)
# if(exists("M")){rm(M)}
# BBB=1
# NNN=100
# {
# sensitivity_C<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PL<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PL2<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PL2_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PL2_tW<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PSel<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PSel_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PSel.5<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PSel.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PSel.5.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_robust<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PB<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PB_095_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PB_075_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PB_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_C<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PL<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PL2<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PL2_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PL2_tW<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PSel<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PSel_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PSel.5<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PSel.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PSel.5.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_robust<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PB<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PB_095_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PB_075_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PB_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_C<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PL<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PL2<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PL2_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PL2_tW<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PSel<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PSel_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PSel.5<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PSel.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PSel.5.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_robust<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PB<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PB_095_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PB_075_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PB_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# #Here are the cutoff level tested
# test.seq_C<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PL<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PL2<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PL2_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PL2_tW<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PSel<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PSel_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PSel.5<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PSel.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PSel.5.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_robust<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PB<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PB_095_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PB_075_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PB_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# nv_C<-rep(0,NNN)
# nv_PL<-rep(0,NNN)
# nv_PL2<-rep(0,NNN)
# nv_PL2_W<-rep(0,NNN)
# nv_PL2_tW<-rep(0,NNN)
# nv_PSel<-rep(0,NNN)
# nv_PSel_W<-rep(0,NNN)
# nv_PSel.5<-rep(0,NNN)
# nv_PSel.e2<-rep(0,NNN)
# nv_PSel.5.e2<-rep(0,NNN)
# nv_robust<-rep(0,NNN)
# nv_PB<-rep(0,NNN)
# nv_PB_095_075<-rep(0,NNN)
# nv_PB_075_075<-rep(0,NNN)
# nv_PB_W<-rep(0,NNN)
#
# #We change F matrices
# T<-4
# F<-array(0,c(T-1,T-1,T*(T-1)/2))
#
# for(i in 1:(T*(T-1)/2)){diag(F[,,i])<-1}
# F[,,2]<-F[,,2]*0.2
# F[2,1,2]<-1
# F[3,2,2]<-1
# F[,,4]<-F[,,2]*0.3
# F[3,1,4]<-1
# F[,,5]<-F[,,2]
#
# TFshape=Patterns::CascadeFshape(ngrp = 4,sqF = 4)
# TF=Patterns::CascadeFinit(ngrp = 4,sqF = 4)
# # TF[,,1]
# TF[,,2]<-cbind(rbind(rep(0,3),F[,,1]),rep(0,4))
# TF[,,3]<-cbind(rbind(rep(0,3),F[,,2]),rep(0,4))
# TF[,,4]<-cbind(rbind(rep(0,3),F[,,3]),rep(0,4))
# # TF[,,5]
# # TF[,,6]
# TF[,,7]<-cbind(rbind(rep(0,3),F[,,4]),rep(0,4))
# TF[,,8]<-cbind(rbind(rep(0,3),F[,,5]),rep(0,4))
# # TF[,,9]
# # TF[,,10]
# # TF[,,11]
# TF[,,12]<-cbind(rbind(rep(0,3),F[,,6]),rep(0,4))
# # TF[,,13]
# # TF[,,14]
# # TF[,,15]
# # TF[,,16]
# }
#
# #We set the seed to make the results reproducible
# set.seed(1)
# for(iii in BBB:NNN){
# #We create a random scale free network
# if(!file.exists(paste(paste("Net",iii,sep="_"),".RData",sep=""))){
# Net<-Cascade::network_random(
# nb=100,
# time_label=rep(1:4,each=25),
# exp=1,
# init=1,
# regul=round(rexp(100,1))+1,
# min_expr=0.1,
# max_expr=2,
# casc.level=0.4
# )
# Net@F<-F
# assign(paste("Net",iii,sep="_"),Net);rm(Net)
# save(list=paste("Net",iii,sep="_"),file=paste(paste("Net",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net",iii,sep="_"),".RData",sep=""))
# }
#
# #We simulate gene expression according to the network Net
# if(!file.exists(paste(paste("M",iii,sep="_"),".RData",sep=""))){
# assign(paste("M",iii,sep="_"),Cascade::gene_expr_simulation(
# network=get(paste("Net",iii,sep="_")),
# time_label=rep(1:4,each=25),
# subject=5,
# level_peak=200))
# save(list=paste("M",iii,sep="_"),file=paste(paste("M",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("M",iii,sep="_"),".RData",sep=""))
# }
# }
#
# #We infer the new network
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_C",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_C",iii,sep="_"),Cascade::inference(get(paste("M",iii,sep="_"))))
# save(list=paste("Net_inf_C",iii,sep="_"),file=paste(paste("Net_inf_C",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_C",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PL",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PL",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="LASSO"))
# save(list=paste("Net_inf_PL",iii,sep="_"),file=paste(paste("Net_inf_PL",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PL",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PL2",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PL2",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="LASSO2"))
# save(list=paste("Net_inf_PL2",iii,sep="_"),file=paste(paste("Net_inf_PL2",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PL2",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# Temp_Weights_Net<-slot(get(paste("Net",iii,sep="_")),"network")
# Temp_Weights_Net[Temp_Weights_Net!=0]=.1
# Temp_Weights_Net[Temp_Weights_Net==0]=1000
# assign(paste("Weights_Net",iii,sep="_"),Temp_Weights_Net)
# if(!file.exists(paste(paste("Net_inf_PL2_W",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PL2_W",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="LASSO2",priors=get(paste("Weights_Net",iii,sep="_"))))
# save(list=c(paste("Net_inf_PL2_W",iii,sep="_"),paste("Weights_Net",iii,sep="_")),file=paste(paste("Net_inf_PL2_W",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PL2_W",iii,sep="_"),".RData",sep=""))
# }
# rm(Temp_Weights_Net)
# }
# for(iii in BBB:NNN){
# Temp_Weights_Net<-slot(get(paste("Net",iii,sep="_")),"network")
# Temp_Weights_Net[Temp_Weights_Net!=0]=.1
# Temp_Weights_Net[Temp_Weights_Net==0]=1000
# assign(paste("Weights_Net",iii,sep="_"),Temp_Weights_Net)
# if(!file.exists(paste(paste("Net_inf_PL2_tW",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PL2_tW",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="LASSO2",priors=t(get(paste("Weights_Net",iii,sep="_")))))
# save(list=c(paste("Net_inf_PL2_tW",iii,sep="_"),paste("Weights_Net",iii,sep="_")),file=paste(paste("Net_inf_PL2_tW",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PL2_tW",iii,sep="_"),".RData",sep=""))
# }
# rm(Temp_Weights_Net)
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PSel",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PSel",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="stability.c060",use.Gram=FALSE,error.stabsel=0.05,pi_thr.stabsel=0.9,mc.cores=1,intercept.stabpath=FALSE))
# save(list=paste("Net_inf_PSel",iii,sep="_"),file=paste(paste("Net_inf_PSel",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PSel",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# Temp_Weights_Net<-slot(get(paste("Net",iii,sep="_")),"network")
# Temp_Weights_Net[Temp_Weights_Net!=0]=.1
# Temp_Weights_Net[Temp_Weights_Net==0]=1000
# assign(paste("Weights_Net",iii,sep="_"),Temp_Weights_Net)
# if(!file.exists(paste(paste("Net_inf_PSel_W",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PSel_W",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="stability.c060.weighted",use.Gram=FALSE,error.stabsel=0.05,pi_thr.stabsel=0.9,mc.cores=2,intercept.stabpath=FALSE,priors=get(paste("Weights_Net",iii,sep="_"))))
# save(list=c(paste("Net_inf_PSel_W",iii,sep="_"),paste("Weights_Net",iii,sep="_")),file=paste(paste("Net_inf_PSel_W",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PSel_W",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PSel.5",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PSel.5",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="stability.c060",use.Gram=FALSE,error.stabsel=0.05,pi_thr.stabsel=0.51,mc.cores=1,intercept.stabpath=FALSE))
# save(list=paste("Net_inf_PSel.5",iii,sep="_"),file=paste(paste("Net_inf_PSel.5",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PSel.5",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PSel.e2",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PSel.e2",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="stability.c060",use.Gram=FALSE,error.stabsel=0.2,pi_thr.stabsel=0.9,mc.cores=1,intercept.stabpath=FALSE))
# save(list=paste("Net_inf_PSel.e2",iii,sep="_"),file=paste(paste("Net_inf_PSel.e2",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PSel.e2",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PSel.5.e2",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PSel.5.e2",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="stability.c060",use.Gram=FALSE,error.stabsel=0.2,pi_thr.stabsel=0.51,mc.cores=1,intercept.stabpath=FALSE))
# save(list=paste("Net_inf_PSel.5.e2",iii,sep="_"),file=paste(paste("Net_inf_PSel.5.e2",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PSel.5.e2",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_robust",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_robust",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="robust"))
# save(list=paste("Net_inf_robust",iii,sep="_"),file=paste(paste("Net_inf_robust",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_robust",iii,sep="_"),".RData",sep=""))
# }
# }
#
# # vmf.lme<-function (x, tol = 1e-07)
# # {
# # dm <- dim(x)
# # p <- dm[2]
# # n <- dm[1]
# # Apk <- function(p, k) besselI(k, p/2, expon.scaled = TRUE)/besselI(k, p/2 - 1, expon.scaled = TRUE)
# # m1 <- Rfast::colsums(x)
# # R <- sqrt(sum(m1^2))/n
# # m <- m1/n/R
# # k1 <- R * (p - R^2)/(1 - R^2)
# # if (k1 < 1e+05) {
# # apk <- Apk(p, k1)
# # k2 <- k1 - (apk - R)/(1 - apk^2 - (p - 1)/k1 * apk)
# # while (abs(k2 - k1) > tol) {
# # k1 <- k2
# # if (k1 < 1e+05) {apk <- Apk(p, k1)} else {k2<-k1;break}
# # k2 <- k1 - (apk - R)/(1 - apk^2 - (p - 1)/k1 * apk)
# # }
# # k <- k2
# # }
# # else k <- k1
# # loglik <- n * (p/2 - 1) * log(k) - 0.5 * n * p * log(2 * pi) - n * (log(besselI(k, p/2 - 1, expon.scaled = TRUE)) + k) + k * n * R
# # list(loglik = loglik, mu = m, kappa = k)
# # }
#
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PB",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PB",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="selectboost.weighted"))
# save(list=paste("Net_inf_PB",iii,sep="_"),file=paste(paste("Net_inf_PB",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PB",iii,sep="_"),".RData",sep=""))
# }
# }
# #Default values
# #,steps.seq=.95
# #,limselect=.75
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PB_c095_limsel075",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PB_c095_limsel075",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="selectboost.weighted"
# ,steps.seq=.95
# ,limselect=.75
# ))
# save(list=paste("Net_inf_PB_c095_limsel075",iii,sep="_"),file=paste(paste("Net_inf_PB_c095_limsel075",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PB_c095_limsel075",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PB_c075_limsel075",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PB_c075_limsel075",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="selectboost.weighted"
# ,steps.seq=.75
# ,limselect=.75
# ))
# save(list=paste("Net_inf_PB_c075_limsel075",iii,sep="_"),file=paste(paste("Net_inf_PB_c075_limsel075",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PB_c075_limsel075",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# Temp_Weights_Net<-slot(get(paste("Net",iii,sep="_")),"network")
# Temp_Weights_Net[Temp_Weights_Net!=0]=.1
# Temp_Weights_Net[Temp_Weights_Net==0]=1000
# assign(paste("Weights_Net",iii,sep="_"),Temp_Weights_Net)
# if(!file.exists(paste(paste("Net_inf_PB_W",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PB_W",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="selectboost.weighted",priors=get(paste("Weights_Net",iii,sep="_"))))
# save(list=paste("Net_inf_PB_W",iii,sep="_"),file=paste(paste("Net_inf_PB_W",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PB_W",iii,sep="_"),".RData",sep=""))
# }
# }
#
# # ,fitfun="stability.c060.weighted"
# # ,fitfun="LASSO2.weighted"
#
# #Comparing true and inferred networks
# #Here are the cutoff level tested
# for(iii in BBB:NNN){
# test.seq_C[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_C",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PL[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PL",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PL2[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PL2",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PL2_W[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PL2_W",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PL2_tW[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PL2_tW",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PSel[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PSel",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PSel_W[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PSel_W",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PSel.5[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PSel.5",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PSel.e2[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PSel.e2",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PSel.5.e2[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PSel.5.e2",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_robust[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_robust",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PB[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PB",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PB_095_075[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PB_c095_limsel075",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PB_075_075[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PB_c075_limsel075",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PB_W[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PB_W",iii,sep="_")),"network")*0.9)),length.out=200)
# }
#
# for(iii in BBB:NNN){
# cat(iii,"\n")
# u<-0
# cat("Net_inf_C")
# for(i in test.seq_C[iii,]){
# u<-u+1
# sensitivity_C[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_C",iii,sep="_")),i)[1]
# predictive_positive_value_C[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_C",iii,sep="_")),i)[2]
# F_score_C[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_C",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PL")
# for(i in test.seq_PL[iii,]){
# u<-u+1
# sensitivity_PL[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL",iii,sep="_")),i)[1]
# predictive_positive_value_PL[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL",iii,sep="_")),i)[2]
# F_score_PL[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PL2")
# for(i in test.seq_PL2[iii,]){
# u<-u+1
# sensitivity_PL2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2",iii,sep="_")),i)[1]
# predictive_positive_value_PL2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2",iii,sep="_")),i)[2]
# F_score_PL2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PL2_W")
# for(i in test.seq_PL2_W[iii,]){
# u<-u+1
# sensitivity_PL2_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2_W",iii,sep="_")),i)[1]
# predictive_positive_value_PL2_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2_W",iii,sep="_")),i)[2]
# F_score_PL2_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2_W",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PL2_tW")
# for(i in test.seq_PL2_tW[iii,]){
# u<-u+1
# sensitivity_PL2_tW[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2_tW",iii,sep="_")),i)[1]
# predictive_positive_value_PL2_tW[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2_tW",iii,sep="_")),i)[2]
# F_score_PL2_tW[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2_tW",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PSel")
# for(i in test.seq_PSel[iii,]){
# u<-u+1
# sensitivity_PSel[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel",iii,sep="_")),i)[1]
# predictive_positive_value_PSel[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel",iii,sep="_")),i)[2]
# F_score_PSel[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PSel_W")
# for(i in test.seq_PSel_W[iii,]){
# u<-u+1
# sensitivity_PSel_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel_W",iii,sep="_")),i)[1]
# predictive_positive_value_PSel_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel_W",iii,sep="_")),i)[2]
# F_score_PSel_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel_W",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PSel.5")
# for(i in test.seq_PSel.5[iii,]){
# u<-u+1
# sensitivity_PSel.5[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.5",iii,sep="_")),i)[1]
# predictive_positive_value_PSel.5[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.5",iii,sep="_")),i)[2]
# F_score_PSel.5[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.5",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PSel.e2")
# for(i in test.seq_PSel.e2[iii,]){
# u<-u+1
# sensitivity_PSel.e2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.e2",iii,sep="_")),i)[1]
# predictive_positive_value_PSel.e2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.e2",iii,sep="_")),i)[2]
# F_score_PSel.e2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.e2",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PSel.5.e2")
# for(i in test.seq_PSel.5.e2[iii,]){
# u<-u+1
# sensitivity_PSel.5.e2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.5.e2",iii,sep="_")),i)[1]
# predictive_positive_value_PSel.5.e2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.5.e2",iii,sep="_")),i)[2]
# F_score_PSel.5.e2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.5.e2",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_robust")
# for(i in test.seq_robust[iii,]){
# u<-u+1
# sensitivity_robust[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_robust",iii,sep="_")),i)[1]
# predictive_positive_value_robust[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_robust",iii,sep="_")),i)[2]
# F_score_robust[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_robust",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PB")
# for(i in test.seq_PB[iii,]){
# u<-u+1
# sensitivity_PB[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB",iii,sep="_")),i)[1]
# predictive_positive_value_PB[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB",iii,sep="_")),i)[2]
# F_score_PB[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PB_095_075")
# for(i in test.seq_PB_095_075[iii,]){
# u<-u+1
# sensitivity_PB_095_075[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_c095_limsel075",iii,sep="_")),i)[1]
# predictive_positive_value_PB_095_075[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_c095_limsel075",iii,sep="_")),i)[2]
# F_score_PB_095_075[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_c095_limsel075",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PB_075_075")
# for(i in test.seq_PB[iii,]){
# u<-u+1
# sensitivity_PB_075_075[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_c075_limsel075",iii,sep="_")),i)[1]
# predictive_positive_value_PB_075_075[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_c075_limsel075",iii,sep="_")),i)[2]
# F_score_PB_075_075[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_c075_limsel075",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PB_W\n")
# for(i in test.seq_PB_W[iii,]){
# u<-u+1
# sensitivity_PB_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_W",iii,sep="_")),i)[1]
# predictive_positive_value_PB_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_W",iii,sep="_")),i)[2]
# F_score_PB_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_W",iii,sep="_")),i)[3]
# }
# #Corresponding Fscore evolution
# nv_C[iii]=test.seq_C[iii,which.max(F_score_C[iii,])]
# nv_PL[iii]=test.seq_PL[iii,which.max(F_score_PL[iii,])]
# nv_PL2[iii]=test.seq_PL2[iii,which.max(F_score_PL2[iii,])]
# nv_PL2_W[iii]=test.seq_PL2[iii,which.max(F_score_PL2_W[iii,])]
# nv_PL2_tW[iii]=test.seq_PL2[iii,which.max(F_score_PL2_tW[iii,])]
# nv_PSel[iii]=test.seq_PSel[iii,which.max(F_score_PSel[iii,])]
# nv_PSel_W[iii]=test.seq_PSel[iii,which.max(F_score_PSel_W[iii,])]
# nv_PSel.5[iii]=test.seq_PSel.5[iii,which.max(F_score_PSel.5[iii,])]
# nv_PSel.e2[iii]=test.seq_PSel.e2[iii,which.max(F_score_PSel.e2[iii,])]
# nv_PSel.5.e2[iii]=test.seq_PSel.5.e2[iii,which.max(F_score_PSel.5.e2[iii,])]
# nv_robust[iii]=test.seq_robust[iii,which.max(F_score_robust[iii,])]
# nv_PB[iii]=test.seq_PB[iii,which.max(F_score_PB[iii,])]
# nv_PB_095_075[iii]=test.seq_PB_095_075[iii,which.max(F_score_PB_095_075[iii,])]
# nv_PB_075_075[iii]=test.seq_PB_075_075[iii,which.max(F_score_PB_075_075[iii,])]
# nv_PB_W[iii]=test.seq_PB_W[iii,which.max(F_score_PB_W[iii,])]
# }
#
# save(
# sensitivity_C,
# sensitivity_PL,
# sensitivity_PL2,
# sensitivity_PL2_W,
# sensitivity_PL2_tW,
# sensitivity_PSel,
# sensitivity_PSel_W,
# sensitivity_PSel.5,
# sensitivity_PSel.e2,
# sensitivity_PSel.5.e2,
# sensitivity_robust,
# sensitivity_PB,
# sensitivity_PB_095_075,
# sensitivity_PB_075_075,
# sensitivity_PB_W,
# predictive_positive_value_C,
# predictive_positive_value_PL,
# predictive_positive_value_PL2,
# predictive_positive_value_PL2_W,
# predictive_positive_value_PL2_tW,
# predictive_positive_value_PSel,
# predictive_positive_value_PSel_W,
# predictive_positive_value_PSel.5,
# predictive_positive_value_PSel.e2,
# predictive_positive_value_PSel.5.e2,
# predictive_positive_value_robust,
# predictive_positive_value_PB,
# predictive_positive_value_PB_095_075,
# predictive_positive_value_PB_075_075,
# predictive_positive_value_PB_W,
# F_score_C,
# F_score_PL,
# F_score_PL2,
# F_score_PL2_W,
# F_score_PL2_tW,
# F_score_PSel,
# F_score_PSel_W,
# F_score_PSel.5,
# F_score_PSel.e2,
# F_score_PSel.5.e2,
# F_score_robust,
# F_score_PB,
# F_score_PB_095_075,
# F_score_PB_075_075,
# F_score_PB_W,
# #Here are the cutoff level tested
# test.seq_C,
# test.seq_PL,
# test.seq_PL2,
# test.seq_PL2_W,
# test.seq_PL2_tW,
# test.seq_PSel,
# test.seq_PSel_W,
# test.seq_PSel.5,
# test.seq_PSel.e2,
# test.seq_PSel.5.e2,
# test.seq_robust,
# test.seq_PB,
# test.seq_PB_095_075,
# test.seq_PB_075_075,
# test.seq_PB_W,
# nv_C,
# nv_PL,
# nv_PL2,
# nv_PL2_W,
# nv_PL2_tW,
# nv_PSel,
# nv_PSel_W,
# nv_PSel.5,
# nv_PSel.e2,
# nv_PSel.5.e2,
# nv_robust,
# nv_PB,
# nv_PB_095_075,
# nv_PB_075_075,
# nv_PB_W,file="results_simuls_reverse_engineering_v3.RData",
# compress = "xz")
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## ----setup, include = FALSE---------------------------------------------------
#file.edit(normalizePath("~/.Renviron"))
LOCAL <- identical(Sys.getenv("LOCAL"), "TRUE")
#LOCAL=TRUE
knitr::opts_chunk$set(purl = LOCAL)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- eval = FALSE------------------------------------------------------------
# devtools::install_github("fbertran/SelectBoost", ref = "doMC")
## ----loadresults, cache= FALSE, eval = LOCAL----------------------------------
library(SelectBoost)
data(results_simuls_reverse_engineering_v3)
colgrey=grey(.05,NULL)
## ----ranges, cache= FALSE, eval = LOCAL---------------------------------------
rangeCPy_S=range(sensitivity_C,sensitivity_PL,sensitivity_PL2,sensitivity_PL2_W,sensitivity_PL2_tW,sensitivity_PSel,sensitivity_PSel_W,sensitivity_PSel.5,sensitivity_PSel.e2,sensitivity_PSel.5.e2,sensitivity_robust,sensitivity_PB,predictive_positive_value_PB_095_075,predictive_positive_value_PB_075_075,sensitivity_PB_W)
rangeCPy_PPV=range(predictive_positive_value_C,predictive_positive_value_PL,predictive_positive_value_PL2,predictive_positive_value_PL2_W,predictive_positive_value_PL2_tW,predictive_positive_value_PSel,predictive_positive_value_PSel_W,predictive_positive_value_PSel.5,predictive_positive_value_PSel.e2,predictive_positive_value_PSel.5.e2,predictive_positive_value_robust,predictive_positive_value_PB,predictive_positive_value_PB_095_075,predictive_positive_value_PB_075_075,predictive_positive_value_PB_W)
rangeCPy_F=range(F_score_C,F_score_PL,F_score_PL2,F_score_PL2_W,F_score_PL2_tW,F_score_PSel,F_score_PSel_W,F_score_PSel.5,F_score_PSel.e2,F_score_PSel.5.e2,F_score_PB,F_score_PB_095_075,F_score_PB_075_075,F_score_PB_W)
rangeCPx=range(test.seq_C,test.seq_PL,test.seq_PL2,test.seq_PL2_W,test.seq_PL2_tW,test.seq_PSel,test.seq_PSel_W,test.seq_PSel.5,test.seq_PSel.e2,test.seq_PSel.5.e2,test.seq_robust,test.seq_PB,test.seq_PB_095_075,test.seq_PB_075_075,test.seq_PB_W)
## ----artgraphs1, cache= FALSE, fig.width=6, eval = LOCAL----------------------
layout(matrix(1:6,nrow=2))
matplot(t(test.seq_PL2),t(sensitivity_PL2),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Lasso2",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PL2,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PL2_W),t(sensitivity_PL2_W),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Lasso2_W",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PL2_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel),t(sensitivity_PSel),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel_W),t(sensitivity_PSel_W),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection_W",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PSel_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB),t(sensitivity_PB),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PB,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_W),t(sensitivity_PB_W),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost_W",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PB_W,col=grey(.05,NULL),lty=3)
## ----artgraphs2, cache= FALSE, fig.width=6, fig.keep='none', eval = LOCAL-----
layout(matrix(1:6,nrow=2))
matplot(t(test.seq_robust),t(sensitivity_robust),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Robust",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_robust,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB),t(sensitivity_PB),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PB,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_095_075),t(sensitivity_PB_095_075),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost_095_075",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PB_095_075,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_075_075),t(sensitivity_PB_075_075),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost_075_075",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PB_075_075,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_W),t(sensitivity_PB_W),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost_W",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PB_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel),t(sensitivity_PSel),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection",ylim=rangeCPy_S,col=grey(.05,NULL))
abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
## ----artgraphs3, cache= FALSE, fig.width=6, eval = LOCAL----------------------
layout(matrix(1:6,nrow=2))
matplot(t(test.seq_PL2),t(predictive_positive_value_PL2),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Lasso2",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PL2,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PL2_W),t(predictive_positive_value_PL2_W),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Lasso2_W",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PL2_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel),t(predictive_positive_value_PSel),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel_W),t(predictive_positive_value_PSel_W),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection_W",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PSel_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB),t(predictive_positive_value_PB),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PB,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_W),t(predictive_positive_value_PB_W),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost_Weighted",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PB_W,col=grey(.05,NULL),lty=3)
## ----artgraphs4, cache= FALSE, fig.width=6, fig.keep='none', eval = LOCAL-----
layout(matrix(1:6,nrow=2))
matplot(t(test.seq_robust),t(predictive_positive_value_robust),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Robust",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_robust,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB),t(predictive_positive_value_PB),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PB,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_095_075),t(predictive_positive_value_PB_095_075),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost_095_075",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PB_095_075,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_075_075),t(predictive_positive_value_PB_075_075),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost_075_075",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PB_075_075,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_W),t(predictive_positive_value_PB_W),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost_Weighted",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PB_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel),t(predictive_positive_value_PSel),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection",ylim=rangeCPy_PPV,col=grey(.05,NULL))
abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
## ----artgraphs5, cache= FALSE, fig.width=6, eval = LOCAL----------------------
layout(matrix(1:6,nrow=2))
matplot(t(test.seq_PL2),t(F_score_PL2),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Lasso2",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PL2,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PL2_W),t(F_score_PL2_W),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Lasso2_W",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PL2_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel),t(F_score_PSel),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel_W),t(F_score_PSel_W),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection_W",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PSel_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB),t(F_score_PB),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PB,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_W),t(F_score_PB_W),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost_Weighted",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PB_W,col=grey(.05,NULL),lty=3)
## ----artgraphs6, cache= FALSE, fig.width=6, fig.keep='none', eval = LOCAL-----
layout(matrix(1:6,nrow=2))
matplot(t(test.seq_robust),t(F_score_robust),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Robust",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_robust,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB),t(F_score_PB),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PB,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_095_075),t(F_score_PB_095_075),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost_095_075",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PB_095_075,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_075_075),t(F_score_PB_075_075),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost_075_075",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PB_075_075,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PB_W),t(F_score_PB_W),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost_Weighted",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PB_W,col=grey(.05,NULL),lty=3)
matplot(t(test.seq_PSel),t(F_score_PSel),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection",ylim=rangeCPy_F,col=grey(.05,NULL))
abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
## ----graphs, cache= FALSE, fig.width=6, eval=FALSE----------------------------
# layout(matrix(1:6,nrow=2))
# matplot(t(test.seq_C),t(sensitivity_C),type="l",xlab="cutoff",ylab="Sensitivity",main="Cascade",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_C,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL),t(sensitivity_PL),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Lasso",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PL,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2_W),t(sensitivity_PL2_W),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Lasso2_W",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PL2_W,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel),t(sensitivity_PSel),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel_W),t(sensitivity_PSel_W),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection_W",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PSel_W,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2_tW),t(sensitivity_PL2_tW),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Lasso2_wrongW",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PL2_tW,col=grey(.05,NULL),lty=3)
## ----graphs2, cache= FALSE, fig.width=6, eval=FALSE---------------------------
# layout(matrix(1:6,nrow=2))
# matplot(t(test.seq_PSel),t(sensitivity_PSel),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel.5),t(sensitivity_PSel.5),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection_.5",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PSel.5,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel.e2),t(sensitivity_PSel.e2),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection.e2",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PSel.e2,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel.5.e2),t(sensitivity_PSel.5.e2),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Stability_Selection_.5.e2",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PSel.5.e2,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2),t(sensitivity_PL2),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Lasso2",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PL2,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PB),t(sensitivity_PB),type="l",xlab="cutoff",ylab="Sensitivity",main="Patterns_Select_Boost",ylim=rangeCPy_S,col=grey(.05,NULL))
# abline(v=nv_PB,col=grey(.05,NULL),lty=3)
## ----graphs3, cache= FALSE, fig.width=6, eval=FALSE---------------------------
# layout(matrix(1:6,nrow=2))
# matplot(t(test.seq_C),t(predictive_positive_value_C),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Cascade",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_C,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL),t(predictive_positive_value_PL),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Lasso",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PL,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2_W),t(predictive_positive_value_PL2_W),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Lasso2_W",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PL2_W,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel),t(predictive_positive_value_PSel),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel_W),t(predictive_positive_value_PSel_W),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection_W",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PSel_W,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2_tW),t(predictive_positive_value_PL2_tW),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Lasso2_wrongW",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PL2_tW,col=grey(.05,NULL),lty=3)
## ----graphs4, cache= FALSE, fig.width=6, eval=FALSE---------------------------
# layout(matrix(1:6,nrow=2))
# matplot(t(test.seq_PSel),t(predictive_positive_value_PSel),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel.5),t(predictive_positive_value_PSel.5),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection.5",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PSel.5,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel.e2),t(predictive_positive_value_PSel.e2),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel.5.e2),t(predictive_positive_value_PSel.5.e2),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Stability_Selection.5.e2",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PSel.5.e2,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2),t(predictive_positive_value_PL2),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Lasso2",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PL2,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PB),t(predictive_positive_value_PB),type="l",xlab="cutoff",ylab="Predictive Positive Value",main="Patterns_Select_Boost",ylim=rangeCPy_PPV,col=grey(.05,NULL))
# abline(v=nv_PB,col=grey(.05,NULL),lty=3)
## ----graphs5, cache= FALSE, fig.width=6, eval=FALSE---------------------------
# layout(matrix(1:6,nrow=2))
# matplot(t(test.seq_C),t(F_score_C),type="l",xlab="cutoff",ylab="Fscore",main="Cascade",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_C,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL),t(F_score_PL),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Lasso",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PL,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2_W),t(F_score_PL2_W),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Lasso2_W",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PL2_W,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel),t(F_score_PSel),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PSel,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PSel_W),t(F_score_PSel_W),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection_W",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PSel_W,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PL2_tW),t(F_score_PL2_tW),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Lasso2_wrongW",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PL2_tW,col=grey(.05,NULL),lty=3)
## ----graphs6, cache= FALSE, fig.width=6, eval=FALSE---------------------------
# layout(matrix(1:6,nrow=2))
# matplot(t(test.seq_PSel),t(F_score_PSel),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection",ylim=rangeCPy_F,col=grey(.25,NULL))
# abline(v=nv_PSel,lty=3,col=grey(.05,NULL))
# matplot(t(test.seq_PSel.5),t(F_score_PSel.5),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection.5",ylim=rangeCPy_F,col=grey(.25,NULL))
# abline(v=nv_PSel.5,lty=3,col=grey(.05,NULL))
# matplot(t(test.seq_PSel.e2),t(F_score_PSel.e2),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection.e2",ylim=rangeCPy_F,col=grey(.25,NULL))
# abline(v=nv_PSel.e2,lty=3,col=grey(.05,NULL))
# matplot(t(test.seq_PSel.5.e2),t(F_score_PSel.5.e2),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Stability_Selection.5.e2",ylim=rangeCPy_F,col=grey(.25,NULL))
# abline(v=nv_PSel.5.e2,lty=3,col=grey(.05,NULL))
# matplot(t(test.seq_PL2),t(F_score_PL2),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Lasso2",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PL2,col=grey(.05,NULL),lty=3)
# matplot(t(test.seq_PB),t(F_score_PB),type="l",xlab="cutoff",ylab="Fscore",main="Patterns_Select_Boost",ylim=rangeCPy_F,col=grey(.05,NULL))
# abline(v=nv_PB,col=grey(.05,NULL),lty=3)
## ----code, cache= FALSE, eval=FALSE-------------------------------------------
# library(Cascade)
# if(exists("M")){rm(M)}
# BBB=1
# NNN=100
# {
# sensitivity_C<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PL<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PL2<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PL2_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PL2_tW<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PSel<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PSel_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PSel.5<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PSel.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PSel.5.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_robust<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PB<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PB_095_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PB_075_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# sensitivity_PB_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_C<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PL<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PL2<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PL2_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PL2_tW<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PSel<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PSel_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PSel.5<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PSel.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PSel.5.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_robust<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PB<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PB_095_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PB_075_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# predictive_positive_value_PB_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_C<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PL<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PL2<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PL2_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PL2_tW<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PSel<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PSel_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PSel.5<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PSel.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PSel.5.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_robust<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PB<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PB_095_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PB_075_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# F_score_PB_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# #Here are the cutoff level tested
# test.seq_C<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PL<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PL2<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PL2_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PL2_tW<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PSel<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PSel_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PSel.5<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PSel.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PSel.5.e2<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_robust<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PB<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PB_095_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PB_075_075<-matrix(rep(NA,200*NNN),nrow=NNN)
# test.seq_PB_W<-matrix(rep(NA,200*NNN),nrow=NNN)
# nv_C<-rep(0,NNN)
# nv_PL<-rep(0,NNN)
# nv_PL2<-rep(0,NNN)
# nv_PL2_W<-rep(0,NNN)
# nv_PL2_tW<-rep(0,NNN)
# nv_PSel<-rep(0,NNN)
# nv_PSel_W<-rep(0,NNN)
# nv_PSel.5<-rep(0,NNN)
# nv_PSel.e2<-rep(0,NNN)
# nv_PSel.5.e2<-rep(0,NNN)
# nv_robust<-rep(0,NNN)
# nv_PB<-rep(0,NNN)
# nv_PB_095_075<-rep(0,NNN)
# nv_PB_075_075<-rep(0,NNN)
# nv_PB_W<-rep(0,NNN)
#
# #We change F matrices
# T<-4
# F<-array(0,c(T-1,T-1,T*(T-1)/2))
#
# for(i in 1:(T*(T-1)/2)){diag(F[,,i])<-1}
# F[,,2]<-F[,,2]*0.2
# F[2,1,2]<-1
# F[3,2,2]<-1
# F[,,4]<-F[,,2]*0.3
# F[3,1,4]<-1
# F[,,5]<-F[,,2]
#
# TFshape=Patterns::CascadeFshape(ngrp = 4,sqF = 4)
# TF=Patterns::CascadeFinit(ngrp = 4,sqF = 4)
# # TF[,,1]
# TF[,,2]<-cbind(rbind(rep(0,3),F[,,1]),rep(0,4))
# TF[,,3]<-cbind(rbind(rep(0,3),F[,,2]),rep(0,4))
# TF[,,4]<-cbind(rbind(rep(0,3),F[,,3]),rep(0,4))
# # TF[,,5]
# # TF[,,6]
# TF[,,7]<-cbind(rbind(rep(0,3),F[,,4]),rep(0,4))
# TF[,,8]<-cbind(rbind(rep(0,3),F[,,5]),rep(0,4))
# # TF[,,9]
# # TF[,,10]
# # TF[,,11]
# TF[,,12]<-cbind(rbind(rep(0,3),F[,,6]),rep(0,4))
# # TF[,,13]
# # TF[,,14]
# # TF[,,15]
# # TF[,,16]
# }
#
# #We set the seed to make the results reproducible
# set.seed(1)
# for(iii in BBB:NNN){
# #We create a random scale free network
# if(!file.exists(paste(paste("Net",iii,sep="_"),".RData",sep=""))){
# Net<-Cascade::network_random(
# nb=100,
# time_label=rep(1:4,each=25),
# exp=1,
# init=1,
# regul=round(rexp(100,1))+1,
# min_expr=0.1,
# max_expr=2,
# casc.level=0.4
# )
# Net@F<-F
# assign(paste("Net",iii,sep="_"),Net);rm(Net)
# save(list=paste("Net",iii,sep="_"),file=paste(paste("Net",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net",iii,sep="_"),".RData",sep=""))
# }
#
# #We simulate gene expression according to the network Net
# if(!file.exists(paste(paste("M",iii,sep="_"),".RData",sep=""))){
# assign(paste("M",iii,sep="_"),Cascade::gene_expr_simulation(
# network=get(paste("Net",iii,sep="_")),
# time_label=rep(1:4,each=25),
# subject=5,
# level_peak=200))
# save(list=paste("M",iii,sep="_"),file=paste(paste("M",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("M",iii,sep="_"),".RData",sep=""))
# }
# }
#
# #We infer the new network
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_C",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_C",iii,sep="_"),Cascade::inference(get(paste("M",iii,sep="_"))))
# save(list=paste("Net_inf_C",iii,sep="_"),file=paste(paste("Net_inf_C",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_C",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PL",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PL",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="LASSO"))
# save(list=paste("Net_inf_PL",iii,sep="_"),file=paste(paste("Net_inf_PL",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PL",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PL2",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PL2",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="LASSO2"))
# save(list=paste("Net_inf_PL2",iii,sep="_"),file=paste(paste("Net_inf_PL2",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PL2",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# Temp_Weights_Net<-slot(get(paste("Net",iii,sep="_")),"network")
# Temp_Weights_Net[Temp_Weights_Net!=0]=.1
# Temp_Weights_Net[Temp_Weights_Net==0]=1000
# assign(paste("Weights_Net",iii,sep="_"),Temp_Weights_Net)
# if(!file.exists(paste(paste("Net_inf_PL2_W",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PL2_W",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="LASSO2",priors=get(paste("Weights_Net",iii,sep="_"))))
# save(list=c(paste("Net_inf_PL2_W",iii,sep="_"),paste("Weights_Net",iii,sep="_")),file=paste(paste("Net_inf_PL2_W",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PL2_W",iii,sep="_"),".RData",sep=""))
# }
# rm(Temp_Weights_Net)
# }
# for(iii in BBB:NNN){
# Temp_Weights_Net<-slot(get(paste("Net",iii,sep="_")),"network")
# Temp_Weights_Net[Temp_Weights_Net!=0]=.1
# Temp_Weights_Net[Temp_Weights_Net==0]=1000
# assign(paste("Weights_Net",iii,sep="_"),Temp_Weights_Net)
# if(!file.exists(paste(paste("Net_inf_PL2_tW",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PL2_tW",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="LASSO2",priors=t(get(paste("Weights_Net",iii,sep="_")))))
# save(list=c(paste("Net_inf_PL2_tW",iii,sep="_"),paste("Weights_Net",iii,sep="_")),file=paste(paste("Net_inf_PL2_tW",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PL2_tW",iii,sep="_"),".RData",sep=""))
# }
# rm(Temp_Weights_Net)
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PSel",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PSel",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="stability.c060",use.Gram=FALSE,error.stabsel=0.05,pi_thr.stabsel=0.9,mc.cores=1,intercept.stabpath=FALSE))
# save(list=paste("Net_inf_PSel",iii,sep="_"),file=paste(paste("Net_inf_PSel",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PSel",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# Temp_Weights_Net<-slot(get(paste("Net",iii,sep="_")),"network")
# Temp_Weights_Net[Temp_Weights_Net!=0]=.1
# Temp_Weights_Net[Temp_Weights_Net==0]=1000
# assign(paste("Weights_Net",iii,sep="_"),Temp_Weights_Net)
# if(!file.exists(paste(paste("Net_inf_PSel_W",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PSel_W",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="stability.c060.weighted",use.Gram=FALSE,error.stabsel=0.05,pi_thr.stabsel=0.9,mc.cores=2,intercept.stabpath=FALSE,priors=get(paste("Weights_Net",iii,sep="_"))))
# save(list=c(paste("Net_inf_PSel_W",iii,sep="_"),paste("Weights_Net",iii,sep="_")),file=paste(paste("Net_inf_PSel_W",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PSel_W",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PSel.5",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PSel.5",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="stability.c060",use.Gram=FALSE,error.stabsel=0.05,pi_thr.stabsel=0.51,mc.cores=1,intercept.stabpath=FALSE))
# save(list=paste("Net_inf_PSel.5",iii,sep="_"),file=paste(paste("Net_inf_PSel.5",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PSel.5",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PSel.e2",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PSel.e2",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="stability.c060",use.Gram=FALSE,error.stabsel=0.2,pi_thr.stabsel=0.9,mc.cores=1,intercept.stabpath=FALSE))
# save(list=paste("Net_inf_PSel.e2",iii,sep="_"),file=paste(paste("Net_inf_PSel.e2",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PSel.e2",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PSel.5.e2",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PSel.5.e2",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="stability.c060",use.Gram=FALSE,error.stabsel=0.2,pi_thr.stabsel=0.51,mc.cores=1,intercept.stabpath=FALSE))
# save(list=paste("Net_inf_PSel.5.e2",iii,sep="_"),file=paste(paste("Net_inf_PSel.5.e2",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PSel.5.e2",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_robust",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_robust",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="robust"))
# save(list=paste("Net_inf_robust",iii,sep="_"),file=paste(paste("Net_inf_robust",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_robust",iii,sep="_"),".RData",sep=""))
# }
# }
#
# # vmf.lme<-function (x, tol = 1e-07)
# # {
# # dm <- dim(x)
# # p <- dm[2]
# # n <- dm[1]
# # Apk <- function(p, k) besselI(k, p/2, expon.scaled = TRUE)/besselI(k, p/2 - 1, expon.scaled = TRUE)
# # m1 <- Rfast::colsums(x)
# # R <- sqrt(sum(m1^2))/n
# # m <- m1/n/R
# # k1 <- R * (p - R^2)/(1 - R^2)
# # if (k1 < 1e+05) {
# # apk <- Apk(p, k1)
# # k2 <- k1 - (apk - R)/(1 - apk^2 - (p - 1)/k1 * apk)
# # while (abs(k2 - k1) > tol) {
# # k1 <- k2
# # if (k1 < 1e+05) {apk <- Apk(p, k1)} else {k2<-k1;break}
# # k2 <- k1 - (apk - R)/(1 - apk^2 - (p - 1)/k1 * apk)
# # }
# # k <- k2
# # }
# # else k <- k1
# # loglik <- n * (p/2 - 1) * log(k) - 0.5 * n * p * log(2 * pi) - n * (log(besselI(k, p/2 - 1, expon.scaled = TRUE)) + k) + k * n * R
# # list(loglik = loglik, mu = m, kappa = k)
# # }
#
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PB",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PB",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="selectboost.weighted"))
# save(list=paste("Net_inf_PB",iii,sep="_"),file=paste(paste("Net_inf_PB",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PB",iii,sep="_"),".RData",sep=""))
# }
# }
# #Default values
# #,steps.seq=.95
# #,limselect=.75
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PB_c095_limsel075",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PB_c095_limsel075",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="selectboost.weighted"
# ,steps.seq=.95
# ,limselect=.75
# ))
# save(list=paste("Net_inf_PB_c095_limsel075",iii,sep="_"),file=paste(paste("Net_inf_PB_c095_limsel075",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PB_c095_limsel075",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# if(!file.exists(paste(paste("Net_inf_PB_c075_limsel075",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PB_c075_limsel075",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="selectboost.weighted"
# ,steps.seq=.75
# ,limselect=.75
# ))
# save(list=paste("Net_inf_PB_c075_limsel075",iii,sep="_"),file=paste(paste("Net_inf_PB_c075_limsel075",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PB_c075_limsel075",iii,sep="_"),".RData",sep=""))
# }
# }
# for(iii in BBB:NNN){
# Temp_Weights_Net<-slot(get(paste("Net",iii,sep="_")),"network")
# Temp_Weights_Net[Temp_Weights_Net!=0]=.1
# Temp_Weights_Net[Temp_Weights_Net==0]=1000
# assign(paste("Weights_Net",iii,sep="_"),Temp_Weights_Net)
# if(!file.exists(paste(paste("Net_inf_PB_W",iii,sep="_"),".RData",sep=""))){
# assign(paste("Net_inf_PB_W",iii,sep="_"),Patterns::inference(get(paste("M",iii,sep="_")),Finit=TF,Fshape=TFshape,fitfun="selectboost.weighted",priors=get(paste("Weights_Net",iii,sep="_"))))
# save(list=paste("Net_inf_PB_W",iii,sep="_"),file=paste(paste("Net_inf_PB_W",iii,sep="_"),".RData",sep=""))
# } else {
# load(file=paste(paste("Net_inf_PB_W",iii,sep="_"),".RData",sep=""))
# }
# }
#
# # ,fitfun="stability.c060.weighted"
# # ,fitfun="LASSO2.weighted"
#
# #Comparing true and inferred networks
# #Here are the cutoff level tested
# for(iii in BBB:NNN){
# test.seq_C[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_C",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PL[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PL",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PL2[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PL2",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PL2_W[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PL2_W",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PL2_tW[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PL2_tW",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PSel[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PSel",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PSel_W[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PSel_W",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PSel.5[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PSel.5",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PSel.e2[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PSel.e2",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PSel.5.e2[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PSel.5.e2",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_robust[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_robust",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PB[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PB",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PB_095_075[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PB_c095_limsel075",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PB_075_075[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PB_c075_limsel075",iii,sep="_")),"network")*0.9)),length.out=200)
# test.seq_PB_W[iii,]<-seq(0,max(abs(slot(get(paste("Net_inf_PB_W",iii,sep="_")),"network")*0.9)),length.out=200)
# }
#
# for(iii in BBB:NNN){
# cat(iii,"\n")
# u<-0
# cat("Net_inf_C")
# for(i in test.seq_C[iii,]){
# u<-u+1
# sensitivity_C[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_C",iii,sep="_")),i)[1]
# predictive_positive_value_C[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_C",iii,sep="_")),i)[2]
# F_score_C[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_C",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PL")
# for(i in test.seq_PL[iii,]){
# u<-u+1
# sensitivity_PL[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL",iii,sep="_")),i)[1]
# predictive_positive_value_PL[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL",iii,sep="_")),i)[2]
# F_score_PL[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PL2")
# for(i in test.seq_PL2[iii,]){
# u<-u+1
# sensitivity_PL2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2",iii,sep="_")),i)[1]
# predictive_positive_value_PL2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2",iii,sep="_")),i)[2]
# F_score_PL2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PL2_W")
# for(i in test.seq_PL2_W[iii,]){
# u<-u+1
# sensitivity_PL2_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2_W",iii,sep="_")),i)[1]
# predictive_positive_value_PL2_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2_W",iii,sep="_")),i)[2]
# F_score_PL2_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2_W",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PL2_tW")
# for(i in test.seq_PL2_tW[iii,]){
# u<-u+1
# sensitivity_PL2_tW[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2_tW",iii,sep="_")),i)[1]
# predictive_positive_value_PL2_tW[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2_tW",iii,sep="_")),i)[2]
# F_score_PL2_tW[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PL2_tW",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PSel")
# for(i in test.seq_PSel[iii,]){
# u<-u+1
# sensitivity_PSel[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel",iii,sep="_")),i)[1]
# predictive_positive_value_PSel[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel",iii,sep="_")),i)[2]
# F_score_PSel[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PSel_W")
# for(i in test.seq_PSel_W[iii,]){
# u<-u+1
# sensitivity_PSel_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel_W",iii,sep="_")),i)[1]
# predictive_positive_value_PSel_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel_W",iii,sep="_")),i)[2]
# F_score_PSel_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel_W",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PSel.5")
# for(i in test.seq_PSel.5[iii,]){
# u<-u+1
# sensitivity_PSel.5[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.5",iii,sep="_")),i)[1]
# predictive_positive_value_PSel.5[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.5",iii,sep="_")),i)[2]
# F_score_PSel.5[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.5",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PSel.e2")
# for(i in test.seq_PSel.e2[iii,]){
# u<-u+1
# sensitivity_PSel.e2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.e2",iii,sep="_")),i)[1]
# predictive_positive_value_PSel.e2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.e2",iii,sep="_")),i)[2]
# F_score_PSel.e2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.e2",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PSel.5.e2")
# for(i in test.seq_PSel.5.e2[iii,]){
# u<-u+1
# sensitivity_PSel.5.e2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.5.e2",iii,sep="_")),i)[1]
# predictive_positive_value_PSel.5.e2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.5.e2",iii,sep="_")),i)[2]
# F_score_PSel.5.e2[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PSel.5.e2",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_robust")
# for(i in test.seq_robust[iii,]){
# u<-u+1
# sensitivity_robust[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_robust",iii,sep="_")),i)[1]
# predictive_positive_value_robust[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_robust",iii,sep="_")),i)[2]
# F_score_robust[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_robust",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PB")
# for(i in test.seq_PB[iii,]){
# u<-u+1
# sensitivity_PB[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB",iii,sep="_")),i)[1]
# predictive_positive_value_PB[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB",iii,sep="_")),i)[2]
# F_score_PB[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PB_095_075")
# for(i in test.seq_PB_095_075[iii,]){
# u<-u+1
# sensitivity_PB_095_075[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_c095_limsel075",iii,sep="_")),i)[1]
# predictive_positive_value_PB_095_075[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_c095_limsel075",iii,sep="_")),i)[2]
# F_score_PB_095_075[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_c095_limsel075",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PB_075_075")
# for(i in test.seq_PB[iii,]){
# u<-u+1
# sensitivity_PB_075_075[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_c075_limsel075",iii,sep="_")),i)[1]
# predictive_positive_value_PB_075_075[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_c075_limsel075",iii,sep="_")),i)[2]
# F_score_PB_075_075[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_c075_limsel075",iii,sep="_")),i)[3]
# }
# u<-0
# cat("Net_inf_PB_W\n")
# for(i in test.seq_PB_W[iii,]){
# u<-u+1
# sensitivity_PB_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_W",iii,sep="_")),i)[1]
# predictive_positive_value_PB_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_W",iii,sep="_")),i)[2]
# F_score_PB_W[iii,u]<-Cascade::compare(get(paste("Net",iii,sep="_")),get(paste("Net_inf_PB_W",iii,sep="_")),i)[3]
# }
# #Corresponding Fscore evolution
# nv_C[iii]=test.seq_C[iii,which.max(F_score_C[iii,])]
# nv_PL[iii]=test.seq_PL[iii,which.max(F_score_PL[iii,])]
# nv_PL2[iii]=test.seq_PL2[iii,which.max(F_score_PL2[iii,])]
# nv_PL2_W[iii]=test.seq_PL2[iii,which.max(F_score_PL2_W[iii,])]
# nv_PL2_tW[iii]=test.seq_PL2[iii,which.max(F_score_PL2_tW[iii,])]
# nv_PSel[iii]=test.seq_PSel[iii,which.max(F_score_PSel[iii,])]
# nv_PSel_W[iii]=test.seq_PSel[iii,which.max(F_score_PSel_W[iii,])]
# nv_PSel.5[iii]=test.seq_PSel.5[iii,which.max(F_score_PSel.5[iii,])]
# nv_PSel.e2[iii]=test.seq_PSel.e2[iii,which.max(F_score_PSel.e2[iii,])]
# nv_PSel.5.e2[iii]=test.seq_PSel.5.e2[iii,which.max(F_score_PSel.5.e2[iii,])]
# nv_robust[iii]=test.seq_robust[iii,which.max(F_score_robust[iii,])]
# nv_PB[iii]=test.seq_PB[iii,which.max(F_score_PB[iii,])]
# nv_PB_095_075[iii]=test.seq_PB_095_075[iii,which.max(F_score_PB_095_075[iii,])]
# nv_PB_075_075[iii]=test.seq_PB_075_075[iii,which.max(F_score_PB_075_075[iii,])]
# nv_PB_W[iii]=test.seq_PB_W[iii,which.max(F_score_PB_W[iii,])]
# }
#
# save(
# sensitivity_C,
# sensitivity_PL,
# sensitivity_PL2,
# sensitivity_PL2_W,
# sensitivity_PL2_tW,
# sensitivity_PSel,
# sensitivity_PSel_W,
# sensitivity_PSel.5,
# sensitivity_PSel.e2,
# sensitivity_PSel.5.e2,
# sensitivity_robust,
# sensitivity_PB,
# sensitivity_PB_095_075,
# sensitivity_PB_075_075,
# sensitivity_PB_W,
# predictive_positive_value_C,
# predictive_positive_value_PL,
# predictive_positive_value_PL2,
# predictive_positive_value_PL2_W,
# predictive_positive_value_PL2_tW,
# predictive_positive_value_PSel,
# predictive_positive_value_PSel_W,
# predictive_positive_value_PSel.5,
# predictive_positive_value_PSel.e2,
# predictive_positive_value_PSel.5.e2,
# predictive_positive_value_robust,
# predictive_positive_value_PB,
# predictive_positive_value_PB_095_075,
# predictive_positive_value_PB_075_075,
# predictive_positive_value_PB_W,
# F_score_C,
# F_score_PL,
# F_score_PL2,
# F_score_PL2_W,
# F_score_PL2_tW,
# F_score_PSel,
# F_score_PSel_W,
# F_score_PSel.5,
# F_score_PSel.e2,
# F_score_PSel.5.e2,
# F_score_robust,
# F_score_PB,
# F_score_PB_095_075,
# F_score_PB_075_075,
# F_score_PB_W,
# #Here are the cutoff level tested
# test.seq_C,
# test.seq_PL,
# test.seq_PL2,
# test.seq_PL2_W,
# test.seq_PL2_tW,
# test.seq_PSel,
# test.seq_PSel_W,
# test.seq_PSel.5,
# test.seq_PSel.e2,
# test.seq_PSel.5.e2,
# test.seq_robust,
# test.seq_PB,
# test.seq_PB_095_075,
# test.seq_PB_075_075,
# test.seq_PB_W,
# nv_C,
# nv_PL,
# nv_PL2,
# nv_PL2_W,
# nv_PL2_tW,
# nv_PSel,
# nv_PSel_W,
# nv_PSel.5,
# nv_PSel.e2,
# nv_PSel.5.e2,
# nv_robust,
# nv_PB,
# nv_PB_095_075,
# nv_PB_075_075,
# nv_PB_W,file="results_simuls_reverse_engineering_v3.RData",
# compress = "xz")
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