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 nonnegative 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" (studentt, default), "ghs" (horseshoe), or "neg" (normalexponentialgamma). 
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 secondorder 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 HyperLasso Priors for Highdimensional Feature Selection. Journal of Statistical Computation and Simulation 2018, 88:14, 28272851.
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