sensitivity.lasso: Perturbation of weights

Description Usage Arguments Value

View source: R/sensitivity_lasso.R

Description

Sensitivity analysis of predictors using perturbed weights

Usage

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sensitivity.lasso(x, y, wt = NULL, ts = NULL, method = lasso_cd,
  k = 5, n_it = 10, df = NULL, nsim = 50, rel_err = 1)

Arguments

x

predictors

y

response

wt

weights for the coefficients of weighted LASSO. Defaults to NULL

ts

stepsize for proximal gradient and sub-gradient method (use opt_ts() to generate stepsize). Defaults to NULL

method

lasso optimization function. Three different methods are available to use. method = c(lasso_cd, lasso_sg, lasso_pg). Defaults to lasso_cd

k

number of fold. Default value is 5.

n_it

number of iteration for lasso_cd method. Default value is 10.

df

Degree of freedom. Number of desired variables to be zero. Defaults to NULL

nsim

number of perturbed weights. Default value is 50

rel_err

Relative purturbed error. Default value is 1

Value

The function returns the summary of sensitivity analysis.


tathagatabasu/bootlasso documentation built on Aug. 9, 2019, 1:07 a.m.