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Type: Package
Title: Efficient Inference on High-Dimensional Linear Model with Missing Outcomes
Version: 0.2
Description: A statistically and computationally efficient debiasing method for conducting valid inference on the high-dimensional linear regression function with missing outcomes. The reference paper is Zhang, Giessing, and Chen (2023) <doi:10.48550/arXiv.2309.06429>.
URL: https://github.com/zhangyk8/Debias-Infer/
BugReports: https://github.com/zhangyk8/Debias-Infer/issues
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: CVXR, caret, stats
Suggests: MASS, glmnet
Maintainer: Yikun Zhang <yikunzhang@foxmail.com>
NeedsCompilation: no
Packaged: 2023-10-09 18:15:24 UTC; yikun
Author: Yikun Zhang ORCID iD [aut, cre], Alexander Giessing ORCID iD [aut], Yen-Chi Chen ORCID iD [aut]
Repository: CRAN
Date/Publication: 2023-10-09 19:30:05 UTC

The proposed debiasing (primal) program.

Description

This function implements our proposed debiasing (primal) program that solves for the weights for correcting the Lasso pilot estimate.

Usage

DebiasProg(X, x, Pi, gamma_n = 0.1)

Arguments

X

The input design n*d matrix.

x

The current query point, which is a 1*d array.

Pi

An n*n diagonal matrix with (estimated) propensity scores as its diagonal entries.

gamma_n

The regularization parameter "\gamma/n". (Default: gamma_n=0.1.)

Value

The estimated weights by our debiasing program, which is a n-dim vector.

Author(s)

Yikun Zhang, yikunzhang@foxmail.com

References

Zhang, Y., Giessing, A. and Chen, Y.-C. (2023) Efficient Inference on High-Dimensional Linear Model with Missing Outcomes. https://arxiv.org/abs/2309.06429.

Fu, A., Narasimhan, B. and Boyd, S. (2017) CVXR: An R Package for Disciplined Convex Optimization. Journal of Statistical Software 94 (14): 1–34. doi: 10.18637/jss.v094.i14.

Examples


  require(MASS)
  require(glmnet)
  d = 1000
  n = 900

  Sigma = array(0, dim = c(d,d)) + diag(d)
  rho = 0.1
  for(i in 1:(d-1)){
    for(j in (i+1):d){
      if ((j < i+6) | (j > i+d-6)){
        Sigma[i,j] = rho
        Sigma[j,i] = rho
      }
    }
  }
  sig = 1

  ## Current query point
  x_cur = rep(0, d)
  x_cur[c(1, 2, 3, 7, 8)] = c(1, 1/2, 1/4, 1/2, 1/8)
  x_cur = array(x_cur, dim = c(1,d))

  ## True regression coefficient
  s_beta = 5
  beta_0 = rep(0, d)
  beta_0[1:s_beta] = sqrt(5)

  ## Generate the design matrix and outcomes
  X_sim = mvrnorm(n, mu = rep(0, d), Sigma)
  eps_err_sim = sig * rnorm(n)
  Y_sim = drop(X_sim %*% beta_0) + eps_err_sim

  obs_prob = 1 / (1 + exp(-1 + X_sim[, 7] - X_sim[, 8]))
  R_sim = rep(1, n)
  R_sim[runif(n) >= obs_prob] = 0

  ## Estimate the propensity scores via the Lasso-type generalized linear model
  zeta = 5*sqrt(log(d)/n)/n
  lr1 = glmnet(X_sim, R_sim, family = "binomial", alpha = 1, lambda = zeta,
               standardize = TRUE, thresh=1e-6)
  prop_score = drop(predict(lr1, newx = X_sim, type = "response"))

  ## Estimate the debiasing weights
  w_obs = DebiasProg(X_sim, x_cur, Pi=diag(prop_score), gamma_n = 0.1)

The proposed debiasing (primal) program with cross-validation.

Description

This function implements our proposed debiasing program that selects the tuning parameter "\gamma/n" by cross-validation and returns the final debiasing weights.

Usage

DebiasProgCV(X, x, prop_score, gamma_lst = NULL, cv_fold = 5, cv_rule = "1se")

Arguments

X

The input design n*d matrix.

x

The current query point, which is a 1*d array.

prop_score

An n-dim numeric vector with (estimated) propensity scores as its entries.

gamma_lst

A numeric vector with candidate values for the regularization parameter "\gamma/n". (Default: gamma_lst=NULL. Then, gamma_lst contains 41 equally spacing value between 0.001 and max(abs(x)).)

cv_fold

The number of folds for cross-validation on the dual program. (Default: cv_fold=5.)

cv_rule

The criteria/rules for selecting the final value of the regularization parameter "\gamma/n" in the dual program. (Default: cv_rule="1se". The candidate choices include "1se", "minfeas", and "mincv".)

Value

A list that contains three elements.

w_obs

The final estimated weights by our debiasing program.

ll_obs

The final value of the solution to our debiasing dual program.

gamma_n_opt

The final value of the tuning parameter "\gamma/n" selected by cross-validation.

Author(s)

Yikun Zhang, yikunzhang@foxmail.com

References

Zhang, Y., Giessing, A. and Chen, Y.-C. (2023) Efficient Inference on High-Dimensional Linear Model with Missing Outcomes. https://arxiv.org/abs/2309.06429.

Examples


  require(MASS)
  require(glmnet)
  d = 1000
  n = 900

  Sigma = array(0, dim = c(d,d)) + diag(d)
  rho = 0.1
  for(i in 1:(d-1)){
    for(j in (i+1):d){
      if ((j < i+6) | (j > i+d-6)){
        Sigma[i,j] = rho
        Sigma[j,i] = rho
      }
    }
  }
  sig = 1

  ## Current query point
  x_cur = rep(0, d)
  x_cur[c(1, 2, 3, 7, 8)] = c(1, 1/2, 1/4, 1/2, 1/8)
  x_cur = array(x_cur, dim = c(1,d))

  ## True regression coefficient
  s_beta = 5
  beta_0 = rep(0, d)
  beta_0[1:s_beta] = sqrt(5)

  ## Generate the design matrix and outcomes
  X_sim = mvrnorm(n, mu = rep(0, d), Sigma)
  eps_err_sim = sig * rnorm(n)
  Y_sim = drop(X_sim %*% beta_0) + eps_err_sim

  obs_prob = 1 / (1 + exp(-1 + X_sim[, 7] - X_sim[, 8]))
  R_sim = rep(1, n)
  R_sim[runif(n) >= obs_prob] = 0

  ## Estimate the propensity scores via the Lasso-type generalized linear model
  zeta = 5*sqrt(log(d)/n)/n
  lr1 = glmnet(X_sim, R_sim, family = "binomial", alpha = 1, lambda = zeta,
               standardize = TRUE, thresh=1e-6)
  prop_score = drop(predict(lr1, newx = X_sim, type = "response"))

  ## Estimate the debiasing weights with the tuning parameter selected by cross-validations.
  deb_res = DebiasProgCV(X_sim, x_cur, prop_score, gamma_lst = c(0.1, 0.5, 1),
                         cv_fold = 5, cv_rule = '1se')

Coordinate descent algorithm for solving the dual form of our debiasing program.

Description

This function implements the coordinate descent algorithm for the debiasing dual program. More details can be found in Appendix A of our paper.

Usage

DualCD(
  X,
  x,
  Pi = NULL,
  gamma_n = 0.05,
  ll_init = NULL,
  eps = 1e-09,
  max_iter = 5000
)

Arguments

X

The input design n*d matrix.

x

The current query point, which is a 1*d array.

Pi

An n*n diagonal matrix with (estimated) propensity scores as its diagonal entries.

gamma_n

The regularization parameter "\gamma/n". (Default: gamma_n=0.05.)

ll_init

The initial value of the dual solution vector. (Default: ll_init=NULL. Then, the vector with all-one entries is used.)

eps

The tolerance value for convergence. (Default: eps=1e-9.)

max_iter

The maximum number of coordinate descent iterations. (Default: max_iter=5000.)

Value

The solution vector to our dual debiasing program.

Author(s)

Yikun Zhang, yikunzhang@foxmail.com

References

Zhang, Y., Giessing, A. and Chen, Y.-C. (2023) Efficient Inference on High-Dimensional Linear Model with Missing Outcomes. https://arxiv.org/abs/2309.06429.

Examples


  require(MASS)
  require(glmnet)
  d = 1000
  n = 900

  Sigma = array(0, dim = c(d,d)) + diag(d)
  rho = 0.1
  for(i in 1:(d-1)){
    for(j in (i+1):d){
      if ((j < i+6) | (j > i+d-6)){
        Sigma[i,j] = rho
        Sigma[j,i] = rho
      }
    }
  }
  sig = 1

  ## Current query point
  x_cur = rep(0, d)
  x_cur[c(1, 2, 3, 7, 8)] = c(1, 1/2, 1/4, 1/2, 1/8)
  x_cur = array(x_cur, dim = c(1,d))

  ## True regression coefficient
  s_beta = 5
  beta_0 = rep(0, d)
  beta_0[1:s_beta] = sqrt(5)

  ## Generate the design matrix and outcomes
  X_sim = mvrnorm(n, mu = rep(0, d), Sigma)
  eps_err_sim = sig * rnorm(n)
  Y_sim = drop(X_sim %*% beta_0) + eps_err_sim

  obs_prob = 1 / (1 + exp(-1 + X_sim[, 7] - X_sim[, 8]))
  R_sim = rep(1, n)
  R_sim[runif(n) >= obs_prob] = 0

  ## Estimate the propensity scores via the Lasso-type generalized linear model
  zeta = 5*sqrt(log(d)/n)/n
  lr1 = glmnet(X_sim, R_sim, family = "binomial", alpha = 1, lambda = zeta,
               standardize = TRUE, thresh=1e-6)
  prop_score = drop(predict(lr1, newx = X_sim, type = "response"))

  ## Solve the debiasing dual program
  ll_cur = DualCD(X_sim, x_cur, Pi = diag(prop_score), gamma_n = 0.1, ll_init = NULL,
                  eps=1e-9, max_iter = 5000)

The objective function of the debiasing dual program.

Description

This function computes the objective function value of the debiasing dual program.

Usage

DualObj(X, x, Pi, ll_cur, gamma_n = 0.05)

Arguments

X

The input design n*d matrix.

x

The current query point, which is a 1*d array.

Pi

An n*n diagonal matrix with (estimated) propensity scores as its diagonal entries.

ll_cur

The current value of the dual solution vector.

gamma_n

The regularization parameter "\gamma/n". (Default: gamma_n=0.1.)

Value

The value of the objective function of our dual debiasing program.

Author(s)

Yikun Zhang, yikunzhang@foxmail.com

References

Zhang, Y., Giessing, A. and Chen, Y.-C. (2023) Efficient Inference on High-Dimensional Linear Model with Missing Outcomes. https://arxiv.org/abs/2309.06429.

Examples


  require(MASS)
  require(glmnet)
  d = 1000
  n = 900

  Sigma = array(0, dim = c(d,d)) + diag(d)
  rho = 0.1
  for(i in 1:(d-1)){
    for(j in (i+1):d){
      if ((j < i+6) | (j > i+d-6)){
        Sigma[i,j] = rho
        Sigma[j,i] = rho
      }
    }
  }
  sig = 1

  ## Current query point
  x_cur = rep(0, d)
  x_cur[c(1, 2, 3, 7, 8)] = c(1, 1/2, 1/4, 1/2, 1/8)
  x_cur = array(x_cur, dim = c(1,d))

  ## True regression coefficient
  s_beta = 5
  beta_0 = rep(0, d)
  beta_0[1:s_beta] = sqrt(5)

  ## Generate the design matrix and outcomes
  X_sim = mvrnorm(n, mu = rep(0, d), Sigma)
  eps_err_sim = sig * rnorm(n)
  Y_sim = drop(X_sim %*% beta_0) + eps_err_sim

  obs_prob = 1 / (1 + exp(-1 + X_sim[, 7] - X_sim[, 8]))
  R_sim = rep(1, n)
  R_sim[runif(n) >= obs_prob] = 0

  ## Estimate the propensity scores via the Lasso-type generalized linear model
  zeta = 5*sqrt(log(d)/n)/n
  lr1 = glmnet(X_sim, R_sim, family = "binomial", alpha = 1, lambda = zeta,
               standardize = TRUE, thresh=1e-6)
  prop_score = drop(predict(lr1, newx = X_sim, type = "response"))

  ## Solve the debiasing dual program and estimate the dual objective function value
  ll_cur = DualCD(X_sim, x_cur, Pi = diag(prop_score), gamma_n = 0.1, ll_init = NULL,
                  eps=1e-9, max_iter = 5000)
  dual_val = DualObj(X_sim, x_cur, Pi=diag(prop_score), ll_cur=ll_cur, gamma_n=0.1)

The soft-thresholding function

Description

This function implements the soft-threshold operator S_{\lambda}(x)=sign(x)\cdot (x-\lambda)_+.

Usage

SoftThres(theta, lamb)

Arguments

theta

The input numeric vector.

lamb

The thresholding parameter.

Value

The resulting vector after soft-thresholding.

Author(s)

Yikun Zhang, yikunzhang@foxmail.com

Examples

a = c(1,2,4,6)
SoftThres(theta=a, lamb=3)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.