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.

Value

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).

Author

George G. Vega Yon

Examples

data(medInnovationsDiffNet)
summary(medInnovationsDiffNet)
#> Diffusion network summary statistics
#> Name     : Medical Innovation
#> Behavior : Adoption of Tetracycline
#> -----------------------------------------------------------------------------
#>  Period   Adopters   Cum Adopt. (%)   Hazard Rate   Density   Moran's I (sd)  
#> -------- ---------- ---------------- ------------- --------- ---------------- 
#>        1         11        11 (0.09)             -      0.02  0.07 (0.03) **  
#>        2          9        20 (0.16)          0.08      0.02  0.04 (0.03)     
#>        3          9        29 (0.23)          0.09      0.02 -0.03 (0.03)     
#>        4         11        40 (0.32)          0.11      0.02 -0.02 (0.03)     
#>        5         11        51 (0.41)          0.13      0.02 -0.06 (0.03)     
#>        6         11        62 (0.50)          0.15      0.02 -0.02 (0.03)     
#>        7         13        75 (0.60)          0.21      0.02 -0.00 (0.03)     
#>        8          7        82 (0.66)          0.14      0.02  0.01 (0.03)     
#>        9          4        86 (0.69)          0.09      0.02  0.01 (0.03)     
#>       10          1        87 (0.70)          0.03      0.02  0.01 (0.03)     
#>       11          5        92 (0.74)          0.13      0.02  0.02 (0.03)     
#>       12          3        95 (0.76)          0.09      0.02  0.02 (0.03)     
#>       13          3        98 (0.78)          0.10      0.02  0.01 (0.03)     
#>       14          4       102 (0.82)          0.15      0.02  0.04 (0.03)     
#>       15          4       106 (0.85)          0.17      0.02  0.05 (0.03) *   
#>       16          2       108 (0.86)          0.11      0.02  0.03 (0.03)     
#>       17          1       109 (0.87)          0.06      0.02  0.02 (0.03)     
#>       18         16       125 (1.00)          1.00      0.02               -  
#> -----------------------------------------------------------------------------
#>  Left censoring  : 0.09 (11)
#>  Right centoring : 0.00 (0)
#>  # of nodes      : 125
#> 
#>  Moran's I was computed on contemporaneous autocorrelation using 1/geodesic
#>  values. Significane levels  *** <= .01, ** <= .05, * <= .1.