This is a vignette to explain those model details.
We have the dataset with on predict variable y
and $p$ predictors $x_1,...,x_p$. This package trys to find sufficient conditions in the datasets in the forms:
$$y \geq \sum_{i \in I} \beta_i x_i$$
step 1: we generate the dataset.
set.seed(1) library(magrittr) d <- estcondmin::gen_dat(n = 100, beta = c(1,1, 0, 0, 0)) knitr::kable(head(data.frame(y= d$y, d$X)))
step 2: We estimate the relationship
estcondmin::estcondmin(y = d$y, X = d$X, lambda = 0.3)
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