Description Usage Arguments Value Examples
Fit a generalized linear model via penalized maximum likelihood, with joint optimization of full quantile normalization.
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x |
The input matrix, each row is a sample, each column a feature. |
y |
The response variable. Quantitative for |
family |
The response type. For |
penalty |
The penalty type. |
lambda |
The scaling of the penalty (default |
intercept |
Should intercept(s) be fitted (default= |
f_init |
The initial distribution for the quantile transformation (default is a Gaussian cdf) |
maxiter |
Maximum number of times the optimization loop (once in the
quantile distribution and once in the model) is performed (default
|
eps |
Stopping criterion: the loop will stop when the norm of the
difference between the quantile distribution after two successive
optimization is smaller than |
use.glmnet |
Whether the |
opts |
List of parameters, which must include: |
toto
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