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

View source: R/diffnet-methods.r

Summary of diffnet objects

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`object` |
An object of class |

`slices` |
Either an integer or character vector. While integer vectors are used as indexes, character vectors are used jointly with the time period labels. |

`no.print` |
Logical scalar. When TRUE suppress screen messages. |

`skip.moran` |
Logical scalar. When TRUE Moran's I is not reported (see details). |

`valued` |
Logical scalar. When |

`...` |
Further arguments to be passed to |

Moran's I is calculated over the
cumulative adoption matrix using as weighting matrix the inverse of the geodesic
distance matrix. All this via `moran`

. For each time period `t`

,
this is calculated as:

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Where `C[,t]`

is the t-th column of the cumulative adoption matrix,
`G^(-1)`

is the element-wise inverse of the geodesic matrix at time `t`

,
and `moran`

is netdiffuseR's moran's I routine. When `skip.moran=TRUE`

Moran's I is not reported. This can be useful for both: reducing computing
time and saving memory as geodesic distance matrix can become large. Since
version `1.18.0`

, geodesic matrices are approximated using `approx_geodesic`

which, as a difference from `geodist`

from the
sna package, and `distances`

from the
igraph package returns a matrix of class `dgCMatrix`

(more
details in `approx_geodesic`

).

A data frame with the following columns:

`adopt` |
Integer. Number of adopters at each time point. |

`cum_adopt` |
Integer. Number of cumulative adopters at each time point. |

`cum_adopt_pcent` |
Numeric. Proportion of comulative adopters at each time point. |

`hazard` |
Numeric. Hazard rate at each time point. |

`density` |
Numeric. Density of the network at each time point. |

`moran_obs` |
Numeric. Observed Moran's I. |

`moran_exp` |
Numeric. Expected Moran's I. |

`moran_sd` |
Numeric. Standard error of Moran's I under the null. |

`moran_pval` |
Numeric. P-value for the observed Moran's I. |

George G. Vega Yon

Other diffnet methods: `%*%`

,
`as.array.diffnet`

, `c.diffnet`

,
`diffnet-arithmetic`

,
`diffnet-class`

, `diffnet_index`

,
`plot.diffnet`

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