Description Usage Arguments Value Author(s) References See Also Examples
Computes the Quantile Universal Threshold for GLM-lasso.
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y |
response variable. Quantitative for family= |
X |
input matrix, of dimension n x p; each row is an observation vector. |
family |
response type (see above). Default is |
alpha.level |
level, such that quantile τ=(1- |
M |
number of Monte Carlo Simulations to estimate the distribution Λ. Default is 1000. |
qut.standardize |
standardize matrix X with a quantile-based standardization. Default is TRUE. |
intercept |
should intercept(s) be fitted (default=TRUE) or set to zero (FALSE). |
no.penalty |
unpenalized subset of covariates. |
offset |
a vector of length |
bootstrap |
set TRUE if it is desired to bootstrap matrix X when computing the Quantile Universal Threshold (Random scenario). Default is TRUE. |
beta0 |
coefficients of the unpenalized covariates for generating the null data for the Quantile Universal Threshold. By default is |
method |
objective function for the zero thresholding. Select |
fixbeta0 |
used when |
lambda |
value of the Quantile Universal Threshold. |
Xnew |
standardized matrix X; |
scale.factor |
scale factor for |
lambda.max |
smallest lambda that sets the lasso estimates to the zero vector. |
beta0 |
estimated value of the intercept when family is not |
Jairo Diaz
C. Giacobino, J. Diaz, S. Sardy, N. Hengartner. Quantile universal threshold for model selection. 2016 Jianqing Fan, Shaojun Guo and Ning Hao. Variance estimation using refitted cross-validation in ultrahigh dimensional regression. Journal of the Royal Statistical Society: Series B. 2012 Stephen Reid, Robert Tibshirani, and Jerome Friedman. A Study of Error Variance Estimation in Lasso Regression. 2013
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