regression  R Documentation 
This function provides an implementation of the multiple regression quadratic assignment procedure (MRQAP) for both onemode and twomode network linear models. It offers several advantages:
it works with combined graph/network objects such as igraph and network objects by constructing the various dependent and independent matrices for the user.
it uses a more intuitive formulabased system for specifying the model, with several ways to specify how nodal attributes should be handled.
it can handle categorical variables (factors/characters) and interactions intuitively, naming the reference variable where appropriate.
it relies on {furrr}
for parallelising
and {progressr}
for reporting progress to the user,
which can be useful when many simulations are required.
results are {broom}
compatible,
with tidy()
and glance()
reports to facilitate comparison
with results from different models.
Note that a t or zvalue is always used as the test statistic,
and properties of the dependent network
– modes, directedness, loops, etc –
will always be respected in permutations and analysis.
network_reg(
formula,
.data,
method = c("qap", "qapy"),
times = 1000,
strategy = "sequential",
verbose = FALSE
)
formula 
A formula describing the relationship being tested. Several additional terms are available to assist users investigate the effects they are interested in. These include:

.data 
An object of a

method 
A method for establishing the null hypothesis. Note that "qap" uses Dekker et al's (2007) double semipartialling technique, whereas "qapy" permutes only the $y$ variable. "qap" is the default. 
times 
Integer indicating number of simulations used for quantile estimation.
(Relevant to the null hypothesis test only 
the analysis itself is unaffected by this parameter.)
Note that, as for all Monte Carlo procedures, convergence is slower for more
extreme quantiles.
By default, 
strategy 
If 
verbose 
Whether the function should report on its progress.
By default FALSE.
See 
Krackhardt, David. 1988. “Predicting with Networks: Nonparametric Multiple Regression Analysis of Dyadic Data.” Social Networks 10(4):359–81. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/03788733(88)900044")}.
Dekker, David, David Krackhard, and Tom A. B. Snijders. 2007. “Sensitivity of MRQAP tests to collinearity and autocorrelation conditions.” Psychometrika 72(4): 563581. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s1133600790161")}.
vignette("p7linearmodel")
Other models:
tests
networkers < ison_networkers %>% to_subgraph(Discipline == "Sociology")
model1 < network_reg(weight ~ alter(Citations) + sim(Citations),
networkers, times = 20)
# Should be run many more `times` for publicationready results
tidy(model1)
glance(model1)
plot(model1)
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