HDSI_model: Get the performance of HDSI model on any given dataset

In rahiuhn/HDSI: What the Package Does (One Line, Title Case)

Description

The functions takes a dataframe list as an input and evalaute the performance of different HDSI modeling techniques like LASSO, ALASSO, RIDGE, ARIDGE, Forward, and Regression

Usage

HDSI_model(
  model = "all",
  inputdf = df,
  outvar = "y",
  seed = 1,
  bootstrap = T,
  effectsize = "large",
  k = 7,
  cint = 0.95,
  sd_level = 1,
  model_tech = "reg",
  para = HDSI_para_control(interactions = T, int_term = 2, intercept = T, out_type =
    "continuous", perf_metric = c("beta", "mp", "mp_beta")),
  covariate = c(1, "X8_"),
  min_max = c("min", "ci", "quartile")
)

Arguments

model	determines the HDSI models that need to be run
inputdf	is the dataframe containing the training and the test datasets
outvar	is the outcome variable in the dataset.
para	are the hyperparameters used to tweak the model settings. It includes interactions, interaction level, number of latent factors for PLS, intercept term and datatype.
covariate	takes the covariate which needs to be controlled during the feature selection process. This is functional only for forward selection approach.

Value

The performance of the different models alongwith feature selection by the models

rahiuhn/HDSI documentation built on Dec. 22, 2021, 12:01 p.m.