weighted_anchor_regression {AnchorRegression}

weighted_anchor_regression

Description

Estimates weighted Anchor Regression coefficients

Usage

weighted_anchor_regression(
  data_x_list,
  data_anchor_list,
  gamma,
  target_variable,
  anchor_model_pre = NULL,
  test_split = 0.4,
  lambda = 0
)

Arguments

data_x_list

list containing coefficient dataframes for different environments

data_anchor_list

list containing anchor dataframes for different environments

gamma

is the regularization parameter for the Anchor Regression

target_variable

is the target variable name contained in the x dataframe

anchor_model_pre

is the pre estimated model for the Anchor Regression. In case of NULL a new model is estimated.

test_split

is desired test/train split for the estimation

lambda

penalization coefficient for Anchor Shrinkage. Initially set to 0.

Value

A list estimated coefficients with names, weights and the raw coefficient matrix

Examples

   environments <- 10 # number of observed environments

   # populate list with generated data of x and anchor
   data_x_list <- c()
   data_anchor_list <- c()
   for(e in 1:environments){
     x <- as.data.frame(matrix(data = rnorm(100),nrow = 100,ncol = 10))
     anchor <- as.data.frame(matrix(data = rnorm(200),nrow = 100,ncol = 2))
     colnames(anchor) <- c('X1','X2')
     data_x_list[[e]] <- x
     data_anchor_list[[e]]  <- anchor
   }

   # estimate model
   gamma <- 2
   target_variable <- 'V2'
   weighted_anchor_regression(data_x_list,
                              data_anchor_list,
                              gamma,
                              target_variable,
                              anchor_model_pre=NULL,
                              test_split=0.4,
                              lambda=0)