Modifying missing tie data

These functions offer tools for imputing missing tie data. Currently two options are available:

• na_to_zero() replaces any missing values with zeros, which are the modal value in sparse social networks.

• na_to_mean() replaces missing values with the average non-missing value.

na_to_zero(.data)

na_to_mean(.data)

Arguments

.data

An object of a {manynet}-consistent class:

• matrix (adjacency or incidence) from {base} R

• edgelist, a data frame from {base} R or tibble from {tibble}

• igraph, from the {igraph} package

• network, from the {network} package

• tbl_graph, from the {tidygraph} package

Value

A tidygraph object modified as explained in the function description, details, or section.

References

On missing data

Krause, Robert, Mark Huisman, Christian Steglich, and Tom A.B. Snijders. 2020. "Missing data in cross-sectional networks: An extensive comparison of missing data treatment methods". Social Networks, 62: 99-112. doi:10.1016/j.socnet.2020.02.004

See also

Other modifications: modif_correlation, modif_direction, modif_from, modif_labels, modif_levels, modif_paths, modif_permutation, modif_plexity, modif_project, modif_scope, modif_split, modif_weight

Examples

missTest <- ison_adolescents |> 
   add_tie_attribute("weight", c(1,NA,NA,1,1,1,NA,NA,1,1)) |> 
   as_matrix()
missTest
#>       Betty Sue Alice Jane Dale Pam Carol Tina
#> Betty     0   1     0    0    0   0     0    0
#> Sue       1   0    NA    0    1  NA     0    0
#> Alice     0  NA     0   NA    1  NA     0    0
#> Jane      0   0    NA    0    1   0     0    0
#> Dale      0   1     1    1    0   0     0    0
#> Pam       0  NA    NA    0    0   0     1    0
#> Carol     0   0     0    0    0   1     0    1
#> Tina      0   0     0    0    0   0     1    0
na_to_zero(missTest)
#>       Betty Sue Alice Jane Dale Pam Carol Tina
#> Betty     0   1     0    0    0   0     0    0
#> Sue       1   0     0    0    1   0     0    0
#> Alice     0   0     0    0    1   0     0    0
#> Jane      0   0     0    0    1   0     0    0
#> Dale      0   1     1    1    0   0     0    0
#> Pam       0   0     0    0    0   0     1    0
#> Carol     0   0     0    0    0   1     0    1
#> Tina      0   0     0    0    0   0     1    0
na_to_mean(missTest)
#>       Betty Sue Alice Jane Dale Pam Carol Tina
#> Betty     0   1     0    0    0   0     0    0
#> Sue       1   0     0    0    1   0     0    0
#> Alice     0   0     0    0    1   0     0    0
#> Jane      0   0     0    0    1   0     0    0
#> Dale      0   1     1    1    0   0     0    0
#> Pam       0   0     0    0    0   0     1    0
#> Carol     0   0     0    0    0   1     0    1
#> Tina      0   0     0    0    0   0     1    0