lasso {iilasso} | R Documentation |
Fit a model using a design matrix
lasso(X, y, family = "gaussian", impl = "cpp", lambda.min.ratio = 1e-04, nlambda = 100, lambda = NULL, warm = "lambda", ...)
X |
matrix of explanatory variables |
y |
vector of objective variable |
family |
family of regression: "gaussian" (default) or "binomial" |
impl |
implementation language of optimization: "cpp" (default) or "r" |
lambda.min.ratio |
ratio of max lambda and min lambda (ignored if lambda is specified) |
nlambda |
the number of lambda (ignored if lambda is specified) |
lambda |
lambda sequence |
warm |
warm start direction: "lambda" (default) or "delta" |
... |
parameters for optimization |
lasso model
beta |
coefficients |
beta_standard |
standardized coefficients |
a0 |
intercepts |
lambda |
regularization parameters |
alpha |
alpha defined above |
delta |
delta defined above |
family |
family |
X <- matrix(c(1,2,3,5,4,7,6,8,9,10), nrow=5, ncol=2) b <- matrix(c(-1,1), nrow=2, ncol=1) e <- matrix(c(0,-0.1,0.1,-0.1,0.1), nrow=5, ncol=1) y <- as.numeric(X %*% b + e) fit <- lasso(X, y) pr <- predict_lasso(fit, X) plot_lasso(fit)