Description Usage Arguments Details Value See Also Examples
Inference for Bayesian lasso regression models by Gibbs sampling from the Bayesian posterior distribution.
1 2 3 
X 

y 
vector of output responses 
T 
total number of MCMC samples to be collected 
beta 
initial setting of the regression coefficients. 
lambda2 
square of the initial lasso penalty parameter. 
s2 
initial variance parameter. 
rd 

ab 

icept 
if 
normalize 
if 
device 
If no external pointer is provided to function, we can provide the ID of the device to use. 
parameters 
a 9 dimensional vector of parameters to tune the GPU implementation. 
The Bayesian lasso model, hyperprior for the lasso parameter, and Gibbs Sampling algorithm implemented by this function are identical to that is described in detail in Park & Casella (2008). The GPU implementation is derived from the CPU implementation blasso from package monomvn.
lasso
returns an object of class "lasso"
, which is a
list
containing a copy of all of the input arguments as well as
of the components listed below.
mu 
a vector of 
beta 
a 
s2 
a vector of 
lambda2 
a vector of 
tau2i 
a 
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35  set.seed(0)
n_samples < 500
n_features < 40
X < matrix(rnorm(n_features * n_samples), nrow = n_samples)
y < 2 * X[,1]  3 * X[,2] + rnorm(n_samples) # only features 1 & 2 are relevant
X_train < X[1:400,]
y_train < y[1:400]
X_test < X[401:500,]
y_test < y[401:500]
# START 
# first, standardize data !!!
X_train < scale(X_train)
tmp00 < bayesCL::lasso(X = X_train,
y = y_train,
T = 500, # number of Gibbs sampling iterations
icept = T,
device=0 ) # use constant term (intercept), we do
#scale test data based on train data means and scales!!
X_test < scale(X_test,
center = attr(X_train, "scaled:center"),
scale = attr(X_train, "scaled:scale"))
p_train1 < colMeans(tmp00$beta %*% t(X_train))
p_test1 < colMeans(tmp00$beta %*% t(X_test))
plot(y_train, p_train1, col = "red", xlab = "actual", ylab = "predicted")
points(y_test, p_test1, col = "green")

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