Description Usage Arguments Details Value References See Also Examples

Stand-alone estimation of exchangeable variance matrix based on residuals and design matrix.

1 2 |

`e` |
Optional vector of residuals, of length |

`X` |
Optional matrix of covariates from regression, must have |

`directed` |
Optional logical indicator of whether input data is for a directed network, default is |

`nodes` |
Optional |

`type` |
Optional string indicating whether the ‘meat’ in the sandwich variance estimator is estimated using exchangeable theory (see Marrs et. al. (2017)) or using dyadic clustering (Fafchamps and Gubert (2007)). |

`tmax` |
Optional numeric of third dimension of relational data array, default is |

`fit` |
Optional fitted model object. One of either |

This function takes *X* and *e* values computes the variance-covariance matrix of *\hat{β}* that resulted in the residuals *e = Y - X \hat{β}* assuming that the errors are exchangeable, as based on Marrs et. al. (2017) when `type = "exchangeable"`

. When `type = "dyadic clustering"`

, the theory from Fafchamps and Gubert (2007) is implemented.

A an object of class `vhat`

containing summary information:

`vhat` |
Estimated variance-covariance matrix of cofficient estimates |

`phi` |
Vector of variance-covariance parameter estimates. |

`corrected` |
Logical of whether variance-covariance matrix was corrected from negative definite to positive semi-definite. |

`type` |
See inputs. |

`tmax` |
See inputs. |

Marrs, F. W., Fosdick, B. K., & McCormick, T. H., (2017). Standard errors for regression on relational data with exchangeable errors. arXiv preprint arXiv:1701.05530.

Fafchamps, M., & Gubert, F. (2007). Risk sharing and network formation. American Economic Review, 97(2), 75-79.

1 2 3 4 5 6 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.