In rmsharp/stepwiser: Demonstrates effects of using stepwise variable selection

knitr::opts_chunk$set(eval = FALSE)

```r library(stepwiser)

pkgs <- list("stringi", "rmsutilityr", "stepwiser") lapply(pkgs, library, character.only = TRUE) file <- get_dated_filename("dk_and_net_sim.csv") sims <- 20 directions <- c("both", "forward") betas <- c(1, 2, 3, -1, -2, -3) n_values <- c(100, 250, 500, 1000) n_values <- 100 rho_values <- c(0, sqrt(0.1), sqrt(0.2), sqrt(0.4))

rho_values <- 0

predictors <- c(12, 24)

predictors <- 24 MFWER <- 0.15 alpha_values <- c(0.5, 0.15, 1 - (1 - MFWER)^(1 / predictors), 0.05)

alpha_values <- 0.15

alpha_values <- 0.05 weight <- 1 nlambda <- 100 sim_object <- dk_sim(file = file, directions = directions, betas, n_values, alpha_values, rho_values, predictors, weight = weight, nlambda = nlambda, sims = sims, rnorm, 0, 0.2) sim_wide_df <- sim_object_to_df(sim_object) write.csv(sim_wide_df, file = get_dated_filename( stri_c("simulation_", nrow(sim_wide_df), ".csv")))

rho <- sim_wide_df$rho

p <- sim_wide_df$p

alpha <- sim_wide_df$alpha

table(sim_wide_df$noise_min_step[sim_wide_df$p == 24 & rho > 0.6])

table(sim_wide_df$noise_min_lasso[sim_wide_df$p == 24 & rho > 0.6])

table(sim_wide_df$noise_min_ridge[sim_wide_df$p == 24 & rho > 0.6])

table(sim_wide_df$noise_min_net[sim_wide_df$p == 24 & rho > 0.6])

table(sim_wide_df$noise_median_step[sim_wide_df$p == 24 & rho > 0.6])

table(sim_wide_df$noise_median_lasso[sim_wide_df$p == 24 & rho > 0.6])

table(sim_wide_df$noise_median_ridge[sim_wide_df$p == 24 & rho > 0.6])

table(sim_wide_df$noise_median_net[sim_wide_df$p == 24 & rho > 0.6])

````

rmsharp/stepwiser documentation built on May 26, 2019, 9:33 a.m.