The goal of latentnetmediate is to โฆ
You can install the development version of latentnetmediate from GitHub with:
# install.packages("devtools")
devtools::install_github("alexpghayes/latentnetmediate")
Estimate Latent Network Mediation Effects via Spectral Embeddings
Home Page: https://alexpghayes.github.io/latentnetmediate/
License: Other
The goal of latentnetmediate is to โฆ
You can install the development version of latentnetmediate from GitHub with:
# install.packages("devtools")
devtools::install_github("alexpghayes/latentnetmediate")
estimatr::lm_robust()
is not a drop-replacement for lm()
, in the sense that it sometimes gives NaN for covariance estimates when lm()
does not. Need to investigate this and determine a fix, but for now just planning to drop lm()
back in wherever there was an lm_robust()
call. As a consequence, standard errors may not be heteroscedasticity robust until I figure out how to handle this.
https://www.stats.ox.ac.uk/~snijders/siena/Glasgow_data.html -- adolescent smoking data, used in \cite{di_maria_networks_2022}
attr = "weight" in igraph::as_adjacency_matrix
Programming around nodelm
is somehow broken. Messy code dump below, not anything at all close to a reprex.
library(targets)
library(netmediate)
data_block2 <- tar_read(data_block2, 1)[[1]]
mediator_block2_models <- tar_read(mediator_block2_models, 1)[[1]]
length(data_block2)
purrr::map2(data_block2, mediator_block2_models, ~fit_mediator(graph = .x, k = .y$k))
fit_mediator <- function(graph, k) {
nodelm_robust(US(A, k) ~ . - name - 1, graph = graph)
}
fit_mediator(data_block2[[1]], mediator_block2_models[[1]]$k)
k <- 5
nodelm_robust(US(A, k) ~ . - name - 1, graph = data_block2[[1]])
fit_mediator(data_block2[[1]], k)
fit_mediator <- function(graph, k) {
formula <- as.formula(glue::glue("US(A, {k}) ~ . - name - 1"))
nodelm_robust(formula, graph = graph)
}
fit_mediator <- function(graph, k) {
netmediate::US # hack for {targets} to track dependencies in formulas
formula <- as.formula(glue::glue("US(A, {k}) ~ . - name - 1"))
nodelm_robust(formula, graph = graph)
}
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