Description Usage Arguments Details Value Author(s) References Examples
Standardizes feature matrix including categorical features and fits a group lasso model
1 
eqn 

dat 

lambda 
Penalty parameter (scalar) 
model 
an object of class 
nonpen 

standardize 
logical. If true, the design matrix of the continuous features will be centered and standardized to unit norm 
... 
additional arguments to be passed to the 
Design matrices of the categorical features and interactions between categorical features are centered and standardized by columnwise scaling. After fitting a group lasso model to the standardized desgin matrix, coefficients are rescaled and centered to the original scale of the data. Interactions between categorical and continuous features are standardized by a singular value decomposition.
A dataframe containing the coefficients of the fitted group lasso model that have been rescaled to the original scale of the data is returned. Coefficients of interaction terms for which no observations are included in dat are returned as NA.
Felicitas Detmer, fdetmer@gmu.edu
Detmer, Felicitas J., and Martin Slawski. "A Note on Coding and Standardization of Categorical Variables in (Sparse) Group Lasso Regression." arXiv preprint arXiv:1805.06915 (2018).
1 2 3 4 5 6 7 8 9 10 11 12 13  data(dattest)
#set datatype of categorical features to factor=
dattest$X1cut=as.factor(dattest$X1cut)
dattest$X2cut=as.factor(dattest$X2cut)
dattest$X3cut=as.factor(dattest$X3cut)
table(dattest[,c("X1cut", "X2cut", "X3cut")])
#fit group lasso models
coefs1=fit_grp(y~X1cut * X2cut +X1cut * X3cut +X2cut * X3cut, dattest, lambda=0.5, model=LinReg())
coefs2=fit_grp(y~X1cut * X2cut +X1cut * X3cut +X2cut * X3cut, dattest, lambda=0.5, model=LinReg(),
nonpen=~X1cut)

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