Description Usage Arguments Details Value Examples
Fitting Elastic Net with random intercept
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y |
response vector. Should be standardized before input (use scale). |
X |
Design matrix belonging to fixed effects coefficients (beta). Should be standardized before input (use scale). |
batch |
factor with batch effect names (vector for each observations) |
lambda |
numeric, penalty levels for fixed effects betas |
alpha |
numeric, penalty levels for fixed effects betas (balancing LASSO/RIDGE erros) |
rho |
proportion of variation explained by batch effect |
beta |
numeric, initial values of the beta coefficients (using glmnet or marginal estimates if not provided) |
glmnetPenalty |
boolean, whether to use the original peanalty (FALSE) or the glmnet penalty (TRUE) |
glmnetWarmup |
boolean, whether to use glmnet beta-estimates as warmup (if not marginals are used) |
maxit |
maximum number of iterations (i.e. forloops) in the coordinate decent algorithm |
toler |
tolerance level of beta changes for each iterations (similar to 'thresh' in glmnet) |
verbose |
boolean, show progress Default: FALSE |
The model is fitted using the coordinate decent algorithm (exact solutions) The extended model includes the rho parameter (proportion of total variation) as argument. Calls a algorithm (iterate) in C which uses exact solutions of marginal beta's based on formula x = sgn(c/a)(|c/a| - b/a)_+, where ax + b*sgn(x) = c The function assume that the response y and design matrix X are centralized (no intercept returned from function)
fitted beta values
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