|
rerr() ridge_logistic() BIC_glmnetB() AICc_glmnetB()
|
AICc and BIC for glmnet logistic models |
|
net_confidence net_confidence_.5 net_confidence_thr
|
Confidence indices |
|
M Net Net_inf_C
|
Simulated Cascade network and inference |
|
SelectBoost
|
SelectBoost |
|
auto.analyze()
|
Find limits for selectboost analysis |
|
autoboost()
|
Autoboost |
|
autoboost.res.x
|
Autoboost lasso diabetes first order. |
|
autoboost.res.x.adapt
|
Autoboost adaptative lasso diabetes first order. |
|
autoboost.res.x2
|
Autoboost lasso diabetes second order. |
|
autoboost.res.x2.adapt
|
Autoboost adaptative lasso diabetes second order. |
|
boost.normalize() boost.compcorrs() boost.correlation_sign() boost.findgroups() boost.Xpass() boost.adjust() boost.random() boost.apply() boost.select()
|
Boost step by step functions |
|
fastboost()
|
Fastboost |
|
fastboost.res.x
|
Fastboost lasso diabetes first order. |
|
fastboost.res.x.adapt
|
Fastboost adaptative lasso diabetes first order. |
|
fastboost.res.x2
|
Fastboost lasso diabetes second order. |
|
fastboost.res.x2.adapt
|
Fastboost adaptative lasso diabetes second order. |
|
force.non.inc()
|
Non increasing post processinng step for selectboost analysis |
|
group_func_1()
|
Generate groups by thresholding. |
|
group_func_2()
|
Generate groups using community analysis. |
|
plot(<matrix>)
|
Miscellaneous plot functions |
|
network.confidence-class
|
Network confidence class. |
|
plot(<selectboost>)
|
Plot selectboost object |
|
plot(<summary.selectboost>)
|
Plot a summary of selectboost results |
|
plot(<network.confidence>,<ANY>)
|
plot_Selectboost_cascade |
|
test.seq_C test.seq_PL test.seq_PL2 test.seq_PL2_W test.seq_PL2_tW test.seq_PSel test.seq_PSel.5 test.seq_PSel.e2 test.seq_PSel.5.e2 test.seq_PSel_W test.seq_robust test.seq_PB test.seq_PB_095_075 test.seq_PB_075_075 test.seq_PB_W sensitivity_C sensitivity_PL sensitivity_PL2 sensitivity_PL2_W sensitivity_PL2_tW sensitivity_PSel sensitivity_PSel.5 sensitivity_PSel.e2 sensitivity_PSel.5.e2 sensitivity_PSel_W 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.5 predictive_positive_value_PSel.e2 predictive_positive_value_PSel.5.e2 predictive_positive_value_PSel_W 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.5 F_score_PSel.e2 F_score_PSel.5.e2 F_score_PSel_W F_score_robust F_score_PB F_score_PB_095_075 F_score_PB_075_075 F_score_PB_W nv_C nv_PL nv_PL2 nv_PL2_W nv_PL2_tW nv_PSel nv_PSel.5 nv_PSel.e2 nv_PSel.5.e2 nv_PSel_W nv_robust nv_PB nv_PB_095_075 nv_PB_075_075 nv_PB_W
|
Simulations for reverse-engineering |
|
selectboost()
|
Selectboost_cascade |
|
simulation_cor() simulation_X() simulation_DATA() compsim()
|
Miscellaneous simulation functions |
|
summary(<selectboost>)
|
Summarize a selectboost analysis |
|
trajC0()
|
Plot trajectories |
|
lasso_cv_glmnet_bin_min() lasso_cv_glmnet_bin_1se() lasso_glmnet_bin_AICc() lasso_glmnet_bin_BIC() lasso_cv_lars_min() lasso_cv_lars_1se() lasso_cv_glmnet_min() lasso_cv_glmnet_min_weighted() lasso_cv_glmnet_1se() lasso_cv_glmnet_1se_weighted() lasso_msgps_Cp() lasso_msgps_AICc() lasso_msgps_GCV() lasso_msgps_BIC() enetf_msgps_Cp() enetf_msgps_AICc() enetf_msgps_GCV() enetf_msgps_BIC() lasso_cascade()
|
Variable selection functions |
|
lasso_msgps_all() enet_msgps_all() alasso_msgps_all() alasso_enet_msgps_all() lasso_cv_glmnet_all_5f() spls_spls_all() varbvs_linear_all() lasso_cv_glmnet_bin_all() lasso_glmnet_bin_all() splsda_spls_all() sgpls_spls_all() varbvs_binomial_all()
|
Variable selection functions (all) |