penC: Solve the penalized logistic regression.
In CSTE: Covariate Specific Treatment Effect (CSTE) Curve
penC R Documentation
Solve the penalized logistic regression.
Description
Solve the penalized logistic regression.
Usage
penC(x, y, off, beta, lam, pen)
Arguments
x
samples of covariates which is a n*p matrix.
y
samples of binary outcome which is a n*1 vector.
off
offset in logistic regression.
beta
initial estimates.
lam
value of the lasso penalty parameter \lambda for \beta_1 and \beta_2.
pen
1: MCP estimator; 2: SCAD estimator.
Value
A numeric vector, estimate of beta
CSTE documentation built on April 4, 2025, 12:27 a.m.
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| penC | R Documentation |
Solve the penalized logistic regression.
Description
Solve the penalized logistic regression.
Usage
penC(x, y, off, beta, lam, pen)
Arguments
x |
samples of covariates which is a |
y |
samples of binary outcome which is a |
off |
offset in logistic regression. |
beta |
initial estimates. |
lam |
value of the lasso penalty parameter |
pen |
1: MCP estimator; 2: SCAD estimator. |
Value
A numeric vector, estimate of beta
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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