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nodal_attributes

Specifying nodal attributes and their levels

Description

This document describes the ways to specify nodal attributes or functions of nodal attributes and which levels for categorical factors to include. For the helper functions to facilitate this, see nodal_attributes-API.

Usage

LARGEST(l, a)

SMALLEST(l, a)

COLLAPSE_SMALLEST(object, n, into)

Arguments

object, l, a, n, into

COLLAPSE_SMALLEST, LARGEST, and SMALLEST are technically functions but they are generally not called in a standard fashion but rather as a part of an vertex attribute specification or a level specification as described below. The above usage examples are needed to pass R's package checking without warnings; please disregard them, and refer to the sections and examples below instead.

Specifying nodal attributes

Term nodal attribute arguments, typically called attr, attrs, by, or on are interpreted as follows:

a character string: Extract the vertex attribute with this name.
a character vector of length > 1: Extract the vertex attributes and paste them together, separated by dots if the term expects categorical attributes and (typically) combine into a covariate matrix if it expects quantitative attributes.
a function: The function is called on the LHS network and additional arguments to ergm_get_vattr(), expected to return a vector or matrix of appropriate dimension. (Shorter vectors and matrix columns will be recycled as needed.)
a formula: The expression on the RHS of the formula is evaluated in an environment of the vertex attributes of the network, expected to return a vector or matrix of appropriate dimension. (Shorter vectors and matrix columns will be recycled as needed.) Within this expression, the network itself accessible as either . or .nw. For example, nodecov(~abs(Grade-mean(Grade))/network.size(.)) would return the absolute difference of each actor's "Grade" attribute from its network-wide mean, divided by the network size.
an AsIs object created by I(): Use as is, checking only for correct length and type, with optional attribute "name" indicating the predictor's name.

Any of these arguments may also be wrapped in or piped through COLLAPSE_SMALLEST(attr, n, into) or, attr %>% COLLAPSE_SMALLEST(n, into), a convenience function that will transform the attribute by collapsing the smallest n categories into one, naming it into. Note that into must be of the same type (numeric, character, etc.) as the vertex attribute in question.

Specifying categorical attribute levels and their ordering

For categorical attributes, to select which levels are of interest and their ordering, use the argument levels. Selection of nodes (from the appropriate vector of nodal indices) is likewise handled as the selection of levels, using the argument nodes. These arguments are interpreted as follows:

an expression wrapped in I(): Use the given list of levels as is.
a numeric or logical vector: Used for indexing of a list of all possible levels (typically, unique values of the attribute) in default older (typically lexicographic), i.e., sort(unique(attr))[levels]. In particular, levels=TRUE will retain all levels. Negative values exclude. Another special value is LARGEST, which will refer to the most frequent category, so, say, to set such a category as the baseline, pass levels=-LARGEST. In addition, LARGEST(n) will refer to the n largest categories. SMALLEST works analogously. Note that if there are ties in frequencies, they will be broken arbitrarily. To specify numeric or logical levels literally, wrap in I().
NULL: Retain all possible levels; usually equivalent to passing TRUE.
a character vector: Use as is.
a function: The function is called on the list of unique values of the attribute, the values of the attribute themselves, and the network itself, depending on its arity. Its return value is interpreted as above.
a formula: The expression on the RHS of the formula is evaluated in an environment in which the network itself is accessible as .nw, the list of unique values of the attribute as . or as .levels, and the attribute vector itself as .attr. Its return value is interpreted as above.

Note that levels or nodes often has a default that is sensible for the term in question.

Examples

library(magrittr) # for %>%

data(faux.mesa.high)

# Activity by grade with a baseline grade excluded:
summary(faux.mesa.high~nodefactor(~Grade))
# Retain all levels:
summary(faux.mesa.high~nodefactor(~Grade, levels=TRUE)) # or levels=NULL
# Use the largest grade as baseline (also Grade 7):
summary(faux.mesa.high~nodefactor(~Grade, levels=-LARGEST))
# Activity by grade with no baseline smallest two grades (11 and
# 12) collapsed into a new category, labelled 0:
table(faux.mesa.high %v% "Grade")
summary(faux.mesa.high~nodefactor((~Grade) %>% COLLAPSE_SMALLEST(2, 0),
                                  levels=TRUE))

# Mixing between lower and upper grades:
summary(faux.mesa.high~mm(~Grade>=10))
# Mixing between grades 7 and 8 only:
summary(faux.mesa.high~mm("Grade", levels=I(c(7,8))))
# or
summary(faux.mesa.high~mm("Grade", levels=1:2))
# or using levels2 (see ? mm) to filter the combinations of levels,
summary(faux.mesa.high~mm("Grade",
        levels2=~sapply(.levels,
                        function(l)
                          l[[1]]%in%c(7,8) && l[[2]]%in%c(7,8))))

# Activity by grade with a random covariate. Note that setting an attribute "name" gives it a name:
randomcov <- structure(I(rbinom(network.size(faux.mesa.high),1,0.5)), name="random")
summary(faux.mesa.high~nodefactor(I(randomcov)))

ergm

Fit, Simulate and Diagnose Exponential-Family Models for Networks

v3.11.0

GPL-3 + file LICENSE

Authors

Mark S. Handcock [aut], David R. Hunter [aut], Carter T. Butts [aut], Steven M. Goodreau [aut], Pavel N. Krivitsky [aut, cre] (<https://orcid.org/0000-0002-9101-3362>), Martina Morris [aut], Li Wang [ctb], Kirk Li [ctb], Skye Bender-deMoll [ctb], Chad Klumb [ctb], Michał Bojanowski [ctb], Ben Bolker [ctb]

Initial release

2020-10-14