htlr_fit: Fit a HTLR Model (Internal API)
In HTLR: Bayesian Logistic Regression with Heavy-Tailed Priors

htlr_fit

R Documentation

Fit a HTLR Model (Internal API)

Description

This function trains linear logistic regression models with HMC in restricted Gibbs sampling. It also makes predictions for test cases if X_ts are provided.

Usage

htlr_fit(
  X_tr,
  y_tr,
  fsel = 1:ncol(X_tr),
  stdzx = TRUE,
  ptype = c("t", "ghs", "neg"),
  sigmab0 = 2000,
  alpha = 1,
  s = -10,
  eta = 0,
  iters_h = 1000,
  iters_rmc = 1000,
  thin = 1,
  leap_L = 50,
  leap_L_h = 5,
  leap_step = 0.3,
  hmc_sgmcut = 0.05,
  initial_state = "lasso",
  keep.warmup.hist = FALSE,
  silence = TRUE,
  rep.legacy = TRUE,
  alpha.rda = 0.2,
  lasso.lambda = seq(0.05, 0.01, by = -0.01),
  X_ts = NULL,
  predburn = NULL,
  predthin = 1
)

Arguments

`X_tr`	Input matrix, of dimension nobs by nvars; each row is an observation vector.
`y_tr`	Vector of response variables. Must be coded as non-negative integers, e.g., 1,2,...,C for C classes, label 0 is also allowed.
`fsel`	Subsets of features selected before fitting, such as by univariate screening.
`stdzx`	Logical; if `TRUE`, the original feature values are standardized to have `mean = 0` and `sd = 1`.
`ptype`	The prior to be applied to the model. Either "t" (student-t, default), "ghs" (horseshoe), or "neg" (normal-exponential-gamma).
`sigmab0`	The `sd` of the normal prior for the intercept.
`alpha`	The degree freedom of t/ghs/neg prior for coefficients.
`s`	The log scale of priors (logw) for coefficients.
`eta`	The `sd` of the normal prior for logw. When it is set to 0, logw is fixed. Otherwise, logw is assigned with a normal prior and it will be updated during sampling.
`iters_h`	A positive integer specifying the number of warmup (aka burnin).
`iters_rmc`	A positive integer specifying the number of iterations after warmup.
`thin`	A positive integer specifying the period for saving samples.
`leap_L`	The length of leapfrog trajectory in sampling phase.
`leap_L_h`	The length of leapfrog trajectory in burnin phase.
`leap_step`	The stepsize adjustment multiplied to the second-order partial derivatives of log posterior.
`hmc_sgmcut`	The coefficients smaller than this criteria will be fixed in each HMC updating step.
`initial_state`	The initial state of Markov Chain; can be a previously fitted `fithtlr` object, or a user supplied initial state vector, or a character string matches the following: "lasso" - (Default) Use Lasso initial state with `lambda` chosen by cross-validation. Users may specify their own candidate `lambda` values via optional argument `lasso.lambda`. Further customized Lasso initial states can be generated by `lasso_deltas`. "bcbcsfrda" - Use initial state generated by package `BCBCSF` (Bias-corrected Bayesian classification). Further customized BCBCSF initial states can be generated by `bcbcsf_deltas`. WARNING: This type of initial states can be used for continuous features such as gene expression profiles, but it should not be used for categorical features such as SNP profiles. "random" - Use random initial values sampled from N(0, 1).
`keep.warmup.hist`	Warmup iterations are not recorded by default, set `TRUE` to enable it.
`silence`	Setting it to `FALSE` for tracking MCMC sampling iterations.
`rep.legacy`	Logical; if `TRUE`, the output produced in `HTLR` versions up to legacy-3.1-1 is reproduced. The speed would be typically slower than non-legacy mode on multi-core machine.
`alpha.rda`	A user supplied alpha value for `bcbcsf_deltas` when setting up BCBCSF initial state. Default: 0.2.
`lasso.lambda`	- A user supplied lambda sequence for `lasso_deltas` when setting up Lasso initial state. Default: {.01, .02, ..., .05}. Will be ignored if `rep.legacy` is set to `TRUE`.
`X_ts`	Test data which predictions are to be made.
`predburn, predthin`	For prediction base on `X_ts` (when supplied), `predburn` of Markov chain (super)iterations will be discarded, and only every `predthin` are used for inference.

Value

A list of fitting results. If X_ts is not provided, the list is an object with S3 class htlr.fit.

References

Longhai Li and Weixin Yao (2018). Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection. Journal of Statistical Computation and Simulation 2018, 88:14, 2827-2851.

HTLR documentation built on Oct. 22, 2022, 5:05 p.m.