COPY_biglasso_main | R Documentation |
Fit solution paths for linear or logistic regression models penalized by lasso (alpha = 1) or elastic-net (1e-4 < alpha < 1) over a grid of values for the regularization parameter lambda.
COPY_biglasso_main( X, y.train, ind.train, ind.col, covar.train, family = c("gaussian", "binomial"), alphas = 1, K = 10, ind.sets = NULL, nlambda = 200, lambda.min.ratio = if (n > p) 1e-04 else 0.001, nlam.min = 50, n.abort = 10, base.train = NULL, pf.X = NULL, pf.covar = NULL, eps = 1e-05, max.iter = 1000, dfmax = 50000, lambda.min = if (n > p) 1e-04 else 0.001, power_scale = 1, power_adaptive = 0, return.all = FALSE, warn = TRUE, ncores = 1 )
family |
Either "gaussian" (linear) or "binomial" (logistic). |
alphas |
The elastic-net mixing parameter that controls the relative contribution from the lasso (l1) and the ridge (l2) penalty. The penalty is defined as α||β||_1 + (1-α)/2||β||_2^2.
|
K |
Number of sets used in the Cross-Model Selection and Averaging
(CMSA) procedure. Default is |
ind.sets |
Integer vectors of values between |
nlambda |
The number of lambda values. Default is |
lambda.min.ratio |
The smallest value for lambda, as a fraction of
lambda.max. Default is |
nlam.min |
Minimum number of lambda values to investigate. Default is |
n.abort |
Number of lambda values for which prediction on the validation
set must decrease before stopping. Default is |
base.train |
Vector of base predictions. Model will be learned starting from these predictions. This can be useful if you want to previously fit a model with large-effect variables that you don't want to penalize. |
pf.X |
A multiplicative factor for the penalty applied to each coefficient.
If supplied, |
pf.covar |
Same as |
eps |
Convergence threshold for inner coordinate descent.
The algorithm iterates until the maximum change in the objective after any
coefficient update is less than |
max.iter |
Maximum number of iterations. Default is |
dfmax |
Upper bound for the number of nonzero coefficients. Default is
|
lambda.min |
This parameter has been renamed |
power_scale |
When using lasso (alpha = 1), penalization to apply that
is equivalent to scaling genotypes dividing by (standard deviation)^power_scale.
Default is 1 and corresponding to standard scaling. Using 0 would correspond
to using unscaled variables and using 0.5 is Pareto scaling. If you e.g. use
|
power_adaptive |
Multiplicative penalty factor to apply to variables
in the form of 1 / m_j^power_adaptive, where m_j is the marginal statistic
for variable j. Default is 0, which effectively disables this option.
If you e.g. use |
return.all |
Deprecated. Now always return all models. |
warn |
Whether to warn if some models may not have reached a minimum.
Default is |
The objective function for linear regression (family = "gaussian"
) is
\frac{1}{2n}\textrm{RSS} + \textrm{penalty},
for logistic regression
(family = "binomial"
) it is
-\frac{1}{n} loglike + \textrm{penalty}.
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