# R: Prepare network data for estimation with missing data

prepare_data {missSBM} | R Documentation

## Prepare network data for estimation with missing data

### Description

This function puts together the adjacency matrix of a network and an optional list of covariates into a single `sampledNetwork` object, ready to use for inference with the `estimate` function of the missSBM package.

### Usage

    prepare_data(adjacencyMatrix, covariates = NULL,
      similarity = missSBM:::l1_similarity)

### Arguments

`adjacencyMatrix` | The adjacency matrix of the network (NAs allowed)
`covariates` | An optional list with M entries (the M covariates). If the covariates are node-centred, each entry of `covariates` must be a size-N vector; if the covariates are dyad-centred, each entry of `covariates` must be N x N matrix.
`similarity` | An optional R x R -> R function to compute similarities between node covariates. Default is `l1_similarity`, that is, -abs(x-y). Only relevant when the `covariates` is a list of size-N vectors.

### Value

Returns an R6 object with class `sampledNetwork`.

### See Also

`estimate` and `sampledNetwork`.

### Examples

    data(war)
    adj_beligerent <- war$beligerent %>% igraph::as_adj(sparse = FALSE)
    sampledNet_war_nocov <- prepare_data(adj_beligerent)
    sampledNet_war_withcov <- prepare_data(adj_beligerent, list(military_power = war$beligerent$power))

[Package missSBM version 0.2.0 Index]