POPInf (version 1.0.0)

optim_est: Gradient descent for obtaining estimator

Description

optim_est function for gradient descent for obtaining estimator

Usage

optim_est(
  X_lab,
  X_unlab,
  Y_lab,
  Yhat_lab,
  Yhat_unlab,
  w,
  theta,
  quant = NA,
  method,
  step_size = 0.1,
  max_iterations = 500,
  convergence_threshold = 1e-06
)

Value

estimator

Arguments

X_lab

Array or DataFrame containing observed covariates in labeled data.

X_unlab

Array or DataFrame containing observed or predicted covariates in unlabeled data.

Y_lab

Array or DataFrame of observed outcomes in labeled data.

Yhat_lab

Array or DataFrame of predicted outcomes in labeled data.

Yhat_unlab

Array or DataFrame of predicted outcomes in unlabeled data.

w

weights vector POP-Inf linear regression (d-dimensional, where d equals the number of covariates).

theta

parameter theta

quant

quantile for quantile estimation

method

indicates the method to be used for M-estimation. Options include "mean", "quantile", "ols", "logistic", and "poisson".

step_size

step size for gradient descent

max_iterations

maximum of iterations for gradient descent

convergence_threshold

convergence threshold for gradient descent