Linear and logistic regression for network data

Source: R/model_regression.R

This function provides an implementation of the multiple regression quadratic assignment procedure (MRQAP) for both one-mode and two-mode 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 formula-based 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 z-value 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.

net_regression(
  formula,
  .data,
  method = c("qap", "qapy"),
  times = 1000,
  strategy = "sequential",
  verbose = FALSE
)

Arguments

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:

  • ego() constructs a matrix where the cells reflect the value of a named nodal attribute for an edge's sending node

  • alter() constructs a matrix where the cells reflect the value of a named nodal attribute for an edge's receiving node

  • same() constructs a matrix where a 1 reflects if two nodes' attribute values are the same

  • dist() constructs a matrix where the cells reflect the absolute difference between the attribute's values for the sending and receiving nodes

  • sim() constructs a matrix where the cells reflect the proportional similarity between the attribute's values for the sending and receiving nodes

  • tertius() constructs a matrix where the cells reflect some aggregate of an attribute associated with a node's other ties. Currently "mean" and "sum" are available aggregating functions. 'ego' is excluded from these calculations. See Haunss and Hollway (2023) for more on this effect.

  • dyadic covariates (other networks) can just be named

.data

A manynet-consistent network. See e.g. manynet::as_tidygraph() for more details.

method

A method for establishing the null hypothesis. Note that "qap" uses Dekker et al's (2007) double semi-partialling 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, times=1000. 1,000 - 10,000 repetitions recommended for publication-ready results.

strategy

If {furrr} is installed, then multiple cores can be used to accelerate the function. By default "sequential", but if multiple cores available, then "multisession" or "multicore" may be useful. Generally this is useful only when times > 1000. See {furrr} for more.

verbose

Whether the function should report on its progress. By default FALSE. See {progressr} for more.

References

Krackhardt, David. 1988. “Predicting with Networks: Nonparametric Multiple Regression Analysis of Dyadic Data.” Social Networks 10(4):359–81. doi:10.1016/0378-8733(88)90004-4 .

Dekker, David, David Krackhard, and Tom A. B. Snijders. 2007. “Sensitivity of MRQAP tests to collinearity and autocorrelation conditions.” Psychometrika 72(4): 563-581. doi:10.1007/s11336-007-9016-1 .

See also

Other models: test_distributions, tests

Examples

networkers <- ison_networkers %>% to_subgraph(Discipline == "Sociology")
model1 <- net_regression(weight ~ ego(Citations) + alter(Citations) + sim(Citations), 
                      networkers, times = 20)
# Should be run many more `times` for publication-ready results
tidy(model1)
#> # A tibble: 4 × 4
#>   term            estimate statistic p.value
#>   <chr>              <dbl>     <dbl>   <dbl>
#> 1 (intercept)        2.93      0.242     0.9
#> 2 ego Citations     -0.269    -1.69      0.4
#> 3 alter Citations   -0.158    -0.997     0.5
#> 4 sim Citations     39.4       3.13      0  
glance(model1)
#> # A tibble: 1 × 8
#>   r.squared adj.r.squared sigma statistic  p.value    df df.residual  nobs
#>       <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>       <int> <int>
#> 1    0.0674        0.0570  48.9      6.46 0.000309     3         268   272
plot(model1)