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

Function to make predictions from lars.glm fit

1 2 |

`object` |

Result of call to lars.glm

`x` |
N by p matrix of predictors |

`lambda` |
Value of L1- regularization parameter at which predictions are desired |

`time` |
Optional N-vector of survival times, required for Cox Proportional hazards model. |

`status` |
Optional N-vector of censoring indicators, required for Cox Proportional hazards model. 1=failed; 0=censored. |

`...` |
additional arguments (not used) |

Vector of predicted values, on the linear predictor scale.

Rob Tibshirani

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

lars.glm, covTest

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
#logistic
x=matrix(rnorm(100*10),ncol=10)
x=scale(x,TRUE,TRUE)/sqrt(99)
y=4*x[,2]+rnorm(100)
y=1*(y>0)
a=lars.glm(x,y,family="binomial")
yhat=predict.lars.glm(a,x,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")
#yhat=predict.lars.glm(a,x,family="cox")
``` |

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