summary.diffnet function

Summary of diffnet objects

## S3 method for class 'diffnet'
summary(
  object,
  slices = NULL,
  no.print = FALSE,
  skip.moran = FALSE,
  valued = getOption("diffnet.valued", FALSE),
  ...
)

Arguments

• object: An object of class diffnet.
• 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 TRUE weights will be considered. Otherwise non-zero values will be replaced by ones.
• ...: Further arguments to be passed to approx_geodesic.

Returns

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.

Details

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:

m = moran(C[,t], G^(-1))

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).

Examples

data(medInnovationsDiffNet)
summary(medInnovationsDiffNet)

See Also

Other diffnet methods: %*%(), as.array.diffnet(), c.diffnet(), diffnet-arithmetic, diffnet-class, diffnet_index, plot.diffnet()

Author(s)

George G. Vega Yon