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)An object of a {manynet}-consistent class:
adjacency or incidence matrix from {base} R
edgelist data.frame from {base} R or tbl/tbl_df from {tibble}
stocnet stocnet, from the {manynet} package
igraph igraph, from the {igraph} package
network network, from the {network} package
tidygraph tbl_graph, from the {tidygraph} package
A tidygraph object modified as explained in the function description, details, or section.
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
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
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