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.

moran(x, w, normalize.w = TRUE, alternative = "two.sided")

x | Numeric vector of size \(n\). |
---|---|

w | Numeric matrix of size \(n\times n\). Weights. It can be
either a object of class |

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 |

A list of class `diffnet_moran`

with the following elements:

Numeric scalar. Observed correlation index.

Numeric scalar. Expected correlation index equal to \(-1/(N-1)\).

Numeric scalar. Standard error under the null.

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

.

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`

).

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()`

if (require("ape")) { # Generating a small random graph set.seed(123) graph <- rgraph_ba(t = 4) w <- approx_geodesic(graph) x <- rnorm(5) # Computing Moran's I moran(x, w) # Comparing with the ape's package version ape::Moran.I(x, as.matrix(w)) }#>#> #>#>#> #>#> $observed #> [1] -0.1427118 #> #> $expected #> [1] -0.25 #> #> $sd #> [1] 0.1768444 #> #> $p.value #> [1] 0.5440623 #>