Modifying missing tie data

Source: R/manip_miss.R

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 data object of the same class as the function was given.

References

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.

See also

Other modifications: add_nodes(), add_ties(), as(), from, reformat, split(), to_levels, to_paths, to_project, to_scope

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    1    1   1     0    0
#> Jane      0   0     1    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