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

Given an n-vector y and the model y=m+e, and an m by n "irreducible" matrix amat, test the null hypothesis that the vector m is in the null space of amat.

1 | ```
doubconetest(y, amat, nsim = 1000)
``` |

`y` |
a vector of length n |

`amat` |
an m by n "irreducible" matrix |

`nsim` |
number of simulations to approximate null distribution – default is 1000, but choose more if a more precise p-value is desired |

The matrix amat defines a polyhedral convex cone of vectors x such that amat%*%x>=0, and also the opposite cone amat%*%x<=0. The linear space C is those x such that amat%*%x=0. The function provides a p-value for the null hypothesis that m=E(y) is in C, versus the alternative that it is in one of the two cones defined by amat.

`pval` |
The p-value for the test |

`p0` |
The least-squares fit under the null hypothesis |

`p1` |
The least-squares fit to the "positive" cone |

`p2` |
The least-squares fit to the "negative" cone |

Mary C Meyer and Bodhisattva Sen

TBA, Meyer, M.C. (1999) An Extension of the Mixed Primal-Dual Bases Algorithm to the Case of More Constraints than Dimensions, Journal of Statistical Planning and Inference, 81, pp13-31.

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