COPY_biglasso_main  R Documentation 
Fit solution paths for linear or logistic regression models penalized by lasso (alpha = 1) or elasticnet (1e4 < 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) 1e04 else 0.001, nlam.min = 50, n.abort = 10, base.train = NULL, pf.X = NULL, pf.covar = NULL, eps = 1e05, max.iter = 1000, dfmax = 50000, lambda.min = if (n > p) 1e04 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 elasticnet mixing parameter that controls the relative contribution from the lasso (l1) and the ridge (l2) penalty. The penalty is defined as αβ_1 + (1α)/2β_2^2.

K 
Number of sets used in the CrossModel 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 largeeffect 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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