lasso: Fit a model using a design matrix

In tkdmah/iilasso: Independently Interpretable Lasso

Description

Fit a model using a design matrix

Usage

lasso(X, y, family = "gaussian", impl = "cpp", lambda.min.ratio = 1e-04,
  nlambda = 100, lambda = NULL, warm = "lambda", ...)

Arguments

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

Value

lasso model

beta	coefficients
beta_standard	standardized coefficients
a0	intercepts
lambda	regularization parameters
alpha	alpha defined above
delta	delta defined above
family	family

Examples

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)

tkdmah/iilasso documentation built on May 17, 2019, 6:38 a.m.