fit.LASSO

Interface for fitting survival models with the lasso, conforming to the requirements of argument <code>fit.fun</code> in <code>peperr</code> call.

models

regression

survival

Designed for prediction error estimation
through resampling techniques, possibly accelerated by parallel
execution on a compute cluster. Newly developed model fitting
routines can be easily incorporated. Methods used in the package are detailed in
Porzelius Ch., Binder H. and Schumacher M. (2009) <doi:10.1093/bioinformatics/btp062>
and were used, for instance, in
Porzelius Ch., Schumacher M.and Binder H. (2011) <doi:10.1007/s00180-011-0236-6>.

Frederic Bertrand

peperr

Parallelised Estimation of Prediction Error

fit.LASSO function

<dl> <dt>response</dt>
<dd>response. Could be numeric vector for linear regression, <code>Surv</code> object for Cox regression or a binary vector for logistic regression.</dd> <dt>x</dt>
<dd><code>n*p</code> matrix of covariates.</dd> <dt>cplx</dt>
<dd>LASSO penalty. <code>lambda1</code> of <code>penalized</code> call.</dd> <dt>...</dt>
<dd>additional arguments passed to <code>penalized</code> call.</dd></dl>

Arguments

Interface function for fitting a generalised linear model with the lasso — fit.LASSO

<dl>

 <dt>response</dt>
<dd>response. Could be numeric vector for linear regression, <code>Surv</code> object for Cox regression or a binary vector for logistic regression.</dd>

 <dt>x</dt>
<dd><code>n*p</code> matrix of covariates.</dd>

 <dt>cplx</dt>
<dd>LASSO penalty. <code>lambda1</code> of <code>penalized</code> call.</dd>

 <dt>...</dt>
<dd>additional arguments passed to <code>penalized</code> call.</dd>

</dl>

fit.LASSO: Interface function for fitting a generalised linear model with the lasso

Description

Usage

Value

Arguments

Details

See Also