cv.plasso {plasso}

Cross-Validated Lasso and Post-Lasso

Description

cv.plasso uses the glmnet package to estimate the coefficient paths and cross-validates least squares Lasso AND Post-Lasso.

Usage

cv.plasso(x, y, w = NULL, kf = 10, parallel = FALSE, ...)

Arguments

x

Matrix of covariates (number of observations times number of covariates matrix)

y

Vector of outcomes

w

Vector of weights

kf

Number of folds in k-fold cross-validation

parallel

Set as TRUE for parallelized cross-validation. Default is FALSE.

...

Pass glmnet options

Value

cv.plasso object (using a list structure) including the base glmnet object and cross-validation results (incl. optimal Lambda values) for both Lasso and Post-Lasso model.

call

the call that produced this

lasso_full

base glmnet object

kf

number of folds in k-fold cross-validation

cv_MSE_lasso

cross-validated MSEs of Lasso model (for every iteration of k-fold cross-validation)

cv_MSE_plasso

cross-validated MSEs of Post-Lasso model (for every iteration of k-fold cross-validation)

mean_MSE_lasso

averaged cross-validated MSEs of Lasso model

mean_MSE_plasso

averaged cross-validated MSEs of Post-Lasso model

ind_min_l

index of MSE optimal lambda value for Lasso model

ind_min_pl

index of MSE optimal lambda value for Post-Lasso model

lambda_min_l

MSE optimal lambda value for Lasso model

lambda_min_pl

MSE optimal lambda value for Post-Lasso model

names_l

Names of active variables for MSE optimal Lasso model

names_pl

Names of active variables for MSE optimal Post-Lasso model

coef_min_l

Coefficients for MSE optimal Lasso model

coef_min_pl

Coefficients for MSE optimal Post-Lasso model

x

Input matrix of covariates

y

Matrix of outcomes

w

Matrix of weights

Examples

# load toeplitz data
data(toeplitz)
# extract target and features from data
y = as.matrix(toeplitz[,1])
X = toeplitz[,-1]
# fit cv.plasso to the data
p.cv = plasso::cv.plasso(X,y)
# get basic summary statistics
print(summary(p.cv, default=FALSE))
# plot cross-validated MSE curves and number of active coefficients
plot(p.cv, legend_pos="bottomleft")
# get coefficients at MSE optimal lambda value for both Lasso and Post-Lasso model
coef(p.cv)
# get coefficients at MSE optimal lambda value according to 1-standard-error rule
coef(p.cv, se_rule=-1)
# predict fitted values along whole lambda sequence 
pred = predict(p.cv, s="all")
head(pred$plasso)