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

Natively built for computing Moran's I on `dgCMatrix`

objects, this
routine allows computing the I on large sparse matrices (graphs). Part of
its implementation was based on `ape::Moran.I`

,
which computes the I for dense matrices.

1 |

`x` |
Numeric vector of size |

`w` |
Numeric matrix of size |

`normalize.w` |
Logical scalar. When TRUE normalizes rowsums to one (or zero). |

`alternative` |
Character String. Specifies the alternative hypothesis that
is tested against the null of no autocorrelation; must be of one |

In the case that the vector `x`

is close to constant (degenerate random
variable), the statistic becomes irrelevant, and furthermore, the standard error
tends to be undefined (`NaN`

).

A list of class `diffnet_moran`

with the following elements:

`observed` |
Numeric scalar. Observed correlation index. |

`expected` |
Numeric scalar. Expected correlation index equal to |

`sd` |
Numeric scalar. Standard error under the null. |

`p.value` |
Numeric scalar. p-value of the specified |

George G. Vega Yon

Moran's I. (2015, September 3). In Wikipedia, The Free Encyclopedia. Retrieved 06:23, December 22, 2015, from https://en.wikipedia.org/w/index.php?title=Moran%27s_I&oldid=679297766

Other statistics: `bass`

,
`classify_adopters`

,
`cumulative_adopt_count`

, `dgr`

,
`ego_variance`

, `exposure`

,
`hazard_rate`

, `infection`

,
`struct_equiv`

, `threshold`

,
`vertex_covariate_dist`

Other Functions for inference: `bootnet`

,
`struct_test`

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netdiffuseR documentation built on June 7, 2018, 5:05 p.m.

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