lars.glm: Function to fit least angle regression path of solution for...
In covTest: Computes covariance test for adaptive linear modelling

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/funcs.R

Function to fit least angle regression path of solution for Ll-penalized (lasso) logistic regression and the Cox proportional hazards model.

1	lars.glm(x, y, status = NULL, family = c("binomial", "cox"), standardize=TRUE,frac.arclength=.1)

`x`	N by p matrix of predictors
`y`	N-vector of outcome values
`status`	Optional N-vector of censoring indicators for Cox Proportioanl hazards model. 1=failed; 0=censored.
`family`	"binomial" or "cox"
`standardize`	Should predictor be standardized? Default TRUE
`frac.arclength`	Step length parameter for `glmpath`. See help file for `glmpath` for details

This function estimates the least angle regression path of solution for Ll-penalized (lasso) logistic regression and the Cox proportional hazards model, using the R functions glmpath and coxpath written by Park and Hastie. These latter functions use the predictor-corrector strategy devised by Park and Hastie (2007). An additional L2 penalty can be added for stability.

`beta`	Matrix of estimated coefficients, with LAR steps in the rows.
`a0`	Estimate of intercept
`lambda0`	Raw values of lambda used
`lambda`	Values of lambda multiplied by sdx, the standard deviation of each predictor
`lambda2`	Value of lambda2 (L2 penalty parameter)
`act`	Actions (predictor added) at each step
`maxp`	Maximum number of predictors entered
`family`	family used- "binomial" or "cox"
`call`	Call to lars.glm
`pathobj`	Result of call to glmpath or coxpath

Rob Tibshirani

Park, M.Y. and Hastie, T. (2007) 1l regularization path algorithm for generalized linear models. JRSSB B 69(4), 659-677

covTest, predict.glm.Rd

x=matrix(rnorm(100*10),ncol=10)
x=scale(x,TRUE,TRUE)/sqrt(99)

#logistic
y=5*x[,2]+rnorm(100)
y=1*(y>0)
a=lars.glm(x,y,family="binomial")


# Cox model
y=6*x[,2]+rnorm(100)+10
status=sample(c(0,1),size=length(y),replace=TRUE)
a=lars.glm(x,y,status=status,family="cox")