Description Usage Arguments Value Examples
Fit a model using a design matrix
1 2 3 4 5 |
X |
matrix of explanatory variables |
y |
vector of objective variable |
family |
family of regression: "gaussian" (default) or "binomial" |
impl |
implementation language of optimization (only "cpp" is supported) |
lambda.min.ratio |
ratio of max lambda and min lambda (ignored if lambda is specified) |
nlambda |
the number of lambda (ignored if lambda is specified) |
lambda |
lambda sequence |
min_eig_th |
threshold of the minimum eigenvalue in the PSD matrix problem. |
use |
method to calculate correlation matrix from missing data (default "pairwise.complete.obs") |
positify |
method for solving PSD matrix |
weight_power |
weighting power (default 0 meaning no-weighting) |
eig_tol |
tol parameter in eigs_sym function |
eig_maxitr |
maxitr parameter in eigs_sym |
mu |
augmented Lagrangian parameter |
verbose |
whether output verbose warnings and messages (default FALSE) |
... |
parameters for optimization |
lasso model
beta |
coefficients |
beta_standard |
standardized coefficients |
a0 |
intercepts |
lambda |
regularization parameters |
family |
family |
1 2 3 4 5 6 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.