Package index • ncvreg

Model fitting

ncvfit(): Direct interface for nonconvex penalized regression (non-pathwise)

ncvreg(): Fit an MCP- or SCAD-penalized regression path

ncvsurv(): Fit an MCP- or SCAD-penalized survival model

std(): Standardizes a design matrix

Cross-validation

assign_fold(): Assign folds for cross-validation

cv.ncvreg() cv.ncvsurv(): Cross-validation for ncvreg/ncvsurv

plot(<cv.ncvreg>): Plots the cross-validation curve from a cv.ncvreg object

summary(<cv.ncvreg>) print(<summary.cv.ncvreg>): Summarizing cross-validation-based inference

AUC(<cv.ncvsurv>): AUC for cv.ncvsurv objects

Plotting and extracting model features

logLik(<ncvreg>) logLik(<ncvsurv>): Extract Log-Likelihood

predict(<cv.ncvreg>) coef(<cv.ncvreg>) predict(<cv.ncvsurv>) predict(<ncvreg>) coef(<ncvreg>): Model predictions based on a fitted ncvreg object.

predict(<ncvsurv>): Model predictions based on a fitted ncvsurv object.

plot(<ncvreg>): Plot coefficients from a ncvreg object

plot(<ncvsurv.func>): Plot survival curve for ncvsurv model

residuals(<ncvreg>): Extract residuals from a ncvreg or ncvsurv fit

Inference

local_mfdr(): Estimate local mFDR for all features

mfdr(): Marginal false discovery rates

plot(<mfdr>): Plot marginal false discovery rate curves

perm.ncvreg(): Permutation fitting for ncvreg

permres(): Permute residuals for a fitted ncvreg model

summary(<ncvreg>) print(<summary.ncvreg>): Summary method for ncvreg objects

boot_ncvreg(): Hybrid Bootstrap Confidence Intervals

Data sets

Heart: Risk factors associated with heart disease

Lung: VA lung cancer data set

Prostate: Factors associated with prostate specific antigen