debiased_lasso: Doing statistical inference on l1-penalized multinomial...

In pemultinom: L1-Penalized Multinomial Regression with Statistical Inference

Doing statistical inference on l1-penalized multinomial regression via debiased Lasso (or desparisified Lasso).

Description

Doing statistical inference on l1-penalized multinomial regression via debiased Lasso (or desparisified Lasso). This function implements the algorithm described in Tian et al. (2024), which is an extension of the original debiased Lasso (Van de Geer et al. 2014; Zhang and Zhang. 2014; Javanmard, A. and Montanari, A. (2014)) to the multinomial case.

Usage

debiased_lasso(
  x,
  y,
  ref = NULL,
  beta = NULL,
  nfolds = 5,
  ncores = 1,
  nlambda = 50,
  max_iter = 200,
  tol = 0.001,
  lambda.choice = "lambda.min",
  alpha = 0.05,
  method = c("nodewise_reg", "LP"),
  info = TRUE,
  LP.parameter = list(eta = NULL, split.ratio = 0.5),
  lambda_min_ratio = 0.01
)

Arguments

x	the design/predictor matrix, each row of which is an observation vector.
y	the response variable. Can be of one type from factor/integer/character.
ref	the reference level. Default = NULL, which sets the same reference level as used in obtaining beta. Even when the user inputs ref manually, it should be always the same reference level as used in obtaining beta.
beta	the beta estimate from l1-penalized multinomial regression. Should be in the same format as beta.min or beta.1se in output of function cv.pemultinom. The user is recommended to directly pass beta.min or beta.1se from the output of function cv.pemultinom to parameter beta.
nfolds	the number of cross-validation folds to use. Cross-validation is used to determine the best tuning parameter lambda in the nodewise regression, i.e., Algorithm 2 in Tian et al. (2024). Default = 5.
ncores	the number of cores to use for parallel computing. Default = 1.
nlambda	the number of penalty parameter candidates in the cross-validation procedure. Cross-validation is used to determine the best tuning parameter lambda in the nodewise regression, i.e., Algorithm 2 in Tian et al. (2024). Default = 100.
max_iter	the maximum iteration rounds in each iteration of the coordinate descent algorithm. Default = 200.
tol	convergence threshold (tolerance level) for coordinate descent. Default = 1e-3.
lambda.choice	the choice of the tuning parameter lambda used in the nodewise regression. It can only be either "lambda.min" or "lambda.1se". The interpretation is the same as in the 'cv.pemultinom' function. Default = "lambda.min".
alpha	significance level used in the output confidence interval. Has to be a number between 0 and 1. Default = 0.05.
method	which method to estimate the Hessian inverse matrix. Can be either "nodewise_reg" or "LP". nodewise_reg: the method presented in the main text of Tian et al. (2024). LP: the method presented in Section A of the supplements of Tian et al. (2024). Warning: This method is not well implemented as 'eta' parameter has to be set as fixed and currently cannot be automatically tuned.
info	whether to print the information or not. Default = TRUE.
LP.parameter	If method = 'LP', then this parameter will be used. It has to be a list of two components like 'list(eta = NULL, split.ratio = 0.5)'. Here is the interpretation: eta: This is the parameter used in the LP method which is the righthand side of the contraints. Default 'eta' = 'NULL', which will be set as 2\sqrt{\log(p)/n} where p is the number of features and n is the total sample size. split.ratio: The split ratio used in the LP method. The data will be splitted into two parts by class based on 'split.ratio'. The first part will be used to fit the Lasso estimator, and the second part will be used to fit the Hessian inverse through the LP method. Default 'split.ratio' = 0.5.
lambda_min_ratio	the ratio between lambda.min and lambda.max used for the Lasso problem in the nodewise regression (for estimating the Hessian inverse), where lambda.max is automatically determined by the code and the lambda sequence will be determined by 'exp(seq(log(lambda.max), log(lambda.min), len = nlambda))'.

Value

A list of data frames, each of which contains the inference results for each class (v.s. the reference class). In the data frame, each row represents a variable. The columns include:

beta	the debiased point estimate of the coefficient
p_value	p value of each variable
CI_lower	lower endpoint of the confidence interval for each coefficient
CI_upper	upper endpoint of the confidence interval for each coefficient
std_dev	the estimated standard deviation of each component of beta estimate

References

Tian, Y., Rusinek, H., Masurkar, A. V., & Feng, Y. (2024). L1‐Penalized Multinomial Regression: Estimation, Inference, and Prediction, With an Application to Risk Factor Identification for Different Dementia Subtypes. Statistics in Medicine, 43(30), 5711-5747.

Van de Geer, S., Bühlmann, P., Ritov, Y. A., & Dezeure, R. (2014). On asymptotically optimal confidence regions and tests for high-dimensional models. The Annals of Statistics, 42(3), 1166-1202.

Zhang, C. H., & Zhang, S. S. (2014). Confidence intervals for low dimensional parameters in high dimensional linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(1), 217-242.

Javanmard, A., & Montanari, A. (2014). Confidence intervals and hypothesis testing for high-dimensional regression. The Journal of Machine Learning Research, 15(1), 2869-2909.

See Also

cv.pemultinom, predict_pemultinom.

Examples

# generate data from a logistic regression model with n = 100, p = 50, and K = 3
set.seed(0, kind = "L'Ecuyer-CMRG")
n <- 100
p <- 50
K <- 3

Sigma <- outer(1:p, 1:p, function(x,y) {
  0.9^(abs(x-y))
})
R <- chol(Sigma)
s <- 3
beta_coef <- matrix(0, nrow = p+1, ncol = K-1)
beta_coef[1+1:s, 1] <- c(3, 3, 3)
beta_coef[1+1:s+s, 2] <- c(3, 3, 3)

x <- matrix(rnorm(n*p), ncol = p) %*% R
y <- sapply(1:n, function(j){
  prob_i <- c(sapply(1:(K-1), function(k){
    exp(sum(x[j, ]*beta_coef[-1, k]))
  }), 1)
  prob_i <- prob_i/sum(prob_i)
  sample(1:K, size = 1, replace = TRUE, prob = prob_i)
})

# fit the l1-penalized multinomial regression model
fit <- cv.pemultinom(x, y, ncores = 2)
beta <- fit$beta.min

# run the debiasing approach
fit_debiased <- debiased_lasso(x, y, beta = beta, ncores = 2)

pemultinom documentation built on April 4, 2025, 1:48 a.m.