Description Usage Arguments Details Value Author(s) References Examples

View source: R/funs.randomized.R

Compute p-values and confidence intervals based on selecting an active set with the randomized lasso, at a fixed value of the tuning parameter lambda and using Gaussian randomization.

1 2 3 4 5 6 7 | ```
randomizedLassoInf(rand_lasso_soln,
targets=NULL,
level=0.9,
sampler=c("norejection", "adaptMCMC"),
nsample=10000,
burnin=2000,
opt_samples=NULL)
``` |

`rand_lasso_soln` |
A randomized lasso solution as returned by |

`targets` |
If not NULL, should be a list with entries
For example, this cross-covariance could be estimated by jointly bootstrapping the target of interest and the above vector. |

`level` |
Level for confidence intervals. |

`sampler` |
Which sampler to use – default is a no-rejection sampler. Otherwise use MCMC from the adaptMCMC package. |

`nsample` |
Number of samples of optimization variables to sample. |

`burnin` |
How many samples of optimization variable to discard (should be less than nsample). |

`opt_samples` |
Optional sample of optimization variables. If not NULL then no MCMC will be run. |

This function computes selective p-values and confidence intervals for a randomized version of the lasso, given a fixed value of the tuning parameter lambda.

`targets` |
A list with entries |

`pvalues` |
P-values testing hypotheses that each specific target is 0. |

`ci` |
Confidence interval for parameters determined by |

Jelena Markovic, Jonathan Taylor

Jelena Markovic and Jonathan Taylor (2016). Bootstrap inference after using multiple queries for model selection. arxiv.org:1612.07811

Xiaoying Tian and Jonathan Taylor (2015). Selective inference with a randomized response. arxiv.org:1507.06739

Xiaoying Tian, Snigdha Panigrahi, Jelena Markovic, Nan Bi and Jonathan Taylor (2016). Selective inference after solving a convex problem. arxiv.org:1609.05609

1 2 3 4 5 6 7 8 9 10 11 12 13 14 |

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