resi_boot: Internal lassoenet function
In MokyoZhou/lassoenet: An Interactive Implementation of Penalised Regressions

Description Usage Arguments Value Details Author(s)

Internal lassoenet function

resi_boot(x = x, y = y, B.rep = B.rep, significance = significance,
  best.lambda = best.lambda, best.coef = best.coef, bestgamma = bestgamma,
  ridgelambda = ridgelambda, parallel = parallel, method = 1,
  dataa = data, xx.indices = c(1, 2, 3))

`x`	A `model.matrix` of the predictors.
`y`	A vector of response values for the model fitting.
`B.rep`	The number of residual bootstrappings to do.
`significance`	The significance level of the confidence intervals e.g. 100(1-α)%.
`best.lambda`	The optimum lambda selected from either the "OLS" or "ridge" method.
`best.coef`	A vector of coefficients from either the "OLS" or "ridge" method for constructing inital weights.
`bestgamma`	The optimum gamma selected from either the "OLS" or "ridge" method.
`ridgelambda`	The optimum lambda for the ridge regression (If the method "ridge" has been selecteed).
`parallel`	Parallelisation
`method`	1 indicates "OLS", 2 indicates "ridge".
`dataa`	Your full `data.frame`
`xx.indices`	Locations of the predictors within dataa.

The 100(1-α)% confidence intervals for the point estimates from using residual bootstrapping.

These are not intended for use by users. This function takes in outputs from one of OLS.approach or OLS.robust or ridge.approach or ridge.robust and computes the corresponding 100(1-α)% confidence intervals for the parameters. The return from this function will enter the funtion Adlasso for futher wrapping.

Mokyo Zhou

MokyoZhou/lassoenet documentation built on May 20, 2019, 11:38 a.m.