Description Usage Arguments Value Author(s) Examples
Ridge.permut
1 2 |
X |
an matrix object with n*p size |
y |
an vector of size p |
lambda1 |
The l1 penalty to used. |
lambda2 |
The l2 penalty to use in case of elatic-net for screening. |
alpha |
The α mixing parameter to use in case of sparse group lasso for screening or if glmnet is used for screening. |
beta |
a vector of size p representing the weight of variable. A variable with weight equal to zero signify will not appear in the ordinary least square regression. No constraint to the number of variables with a non-zero weight. |
iter |
Number of iterations used to simulate the null hypothesis law. Bigger is better. Default is 1000. |
group |
a vector of size p representing the group index for each variables. Default is 1:ncol(X) which represent the particular case whit no group. Warning : this vector must be in ascending group order and variables ordered in this sense. |
penalty |
a string of characters to determine the adaptive ridge specific penalty to be used. This should be one of "quadrupen" to elastic-net by quadrupen package, "glmnet" to elastic-net by glmnet package, "grplasso" to group lasso by grplasso package and "SGL" to sparse group lasso by SGL package at screening. By default is "quadrupen". |
lawWithOne |
a boolean to indicate if one distribution under the null hypothesis for all groups of variables could be used instead of one distribution for each of them. Warning : It is an approximation and that work only if all groups have the same size. Default is FALSE. |
An list with ridge estimates, F statistic an F statistics under H0 and p-value for each variables or group significancy. Contains equally pval.gamma obtaines with the gamma law approximation.
JM BECU
1 | # see ridgeAdap help
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