# Estimate a within-block network model. Function to estimate the within-block model. Both pseudo-maximum likelihood and monte carlo approximate maximum likelihood estimators are implemented. ```r est_within( formula, network, seed = NULL, method = "MPLE", add_intercepts = TRUE, clustering_with_features = FALSE, return_network = FALSE, ... ) ``` ## Arguments - `formula`: An `formula` object of the form `y ~ `, where `y` is a `network` object. The network object must contain block information as a vertex attribute with the name 'block'. For the details on the possible ``, see `ergmTerm` and Morris, Handcock and Hunter (2008). The `L-ergmTerm` is supported to enable size-dependent coefficients. - `network`: a network object with one vertex attribute called 'block' representing which node belongs to which block - `seed`: seed value (integer) for the random number generator - `method`: If "MPLE" (the default), then the maximum pseudolikelihood estimator is returned. If "MLE", then an approximate maximum likelihood estimator is returned. - `add_intercepts`: Boolean value to indicate whether adequate intercepts should be added to the provided formula so that the model in the first stage of the estimation is a nested model of the estimated model in the second stage of the estimation - `clustering_with_features`: Boolean value to indicate if the clustering was carried out making use of the covariates or not (only important if `add_intercepts = TRUE`) - `return_network`: Boolean value to indicate if the network object should be returned in the output. This is needed if the user wants to use, e.g., the `gof` function as opposed to the `gof.bigergm` function. - `...`: Additional arguments, to be passed to the `ergm` function ## Returns 'ergm' object of the estimated model. ## Examples ```r adj <- c( c(0, 1, 0, 0, 1, 0), c(1, 0, 1, 0, 0, 1), c(0, 1, 0, 1, 1, 0), c(0, 0, 1, 0, 1, 1), c(1, 0, 1, 1, 0, 1), c(0, 1, 0, 1, 1, 0) ) adj <- matrix(data = adj, nrow = 6, ncol = 6) rownames(adj) <- as.character(1001:1006) colnames(adj) <- as.character(1001:1006) # Use non-consecutive block names block <- c(70, 70, 70, 70, 95, 95) g <- network::network(adj, matrix.type = "adjacency", directed = FALSE) g %v% "block" <- block g %v% "vertex.names" <- 1:length(g %v% "vertex.names") est <- est_within( formula = g ~ edges, network = g, parallel = FALSE, verbose = 0, initial_estimate = NULL, seed = NULL, method = "MPLE", add_intercepts = FALSE, clustering_with_features = FALSE ) ``` ## References Morris M, Handcock MS, Hunter DR (2008). Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects. Journal of Statistical Software, 24.

est_within function