Description Usage Arguments Value References
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.
1 2 3 4 5 6 7 | 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", silence = TRUE, pre.legacy = TRUE,
alpha.rda = 0.2, lasso.lambda = seq(0.05, 0.01, by = -0.01),
X_ts = NULL, predburn = NULL, predthin = 1)
|
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 |
ptype |
The prior to be applied to the model. Either "t" (student-t, default), "ghs" (horseshoe), or "neg" (normal-exponential-gamma). |
sigmab0 |
The |
alpha |
The degree freedom of t/ghs/neg prior for coefficients. |
s |
The log scale of priors (logw) for coefficients. |
eta |
The |
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
|
silence |
Setting it to |
pre.legacy |
Logical; if |
alpha.rda |
A user supplied alpha value for |
lasso.lambda |
- A user supplied lambda sequence for |
X_ts |
Test data which predictions are to be made. |
predburn, predthin |
For prediction base on |
A list of fitting results. If X_ts
is not provided, the list is an object
with S3 class htlrfit
.
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.
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