This document provides some examples of how to specify nodal
attributes and their transformations in ergm terms. For the
R help on the topic, see ?nodal_attributes and for help on
implementing terms that use this interface, see
API?nodal_attributes.
It is sometimes desirable to specify a transformation of a nodal
attribute as a covariate in a model term. Most ergm terms
now support a new Tidyverse-inspired user interface to do so. Arguments
using this interface are typically called attr,
attrs, by, or on and are
interpreted depending on their type:
Extract the vertex attribute with this name.
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.
The function is called on the LHS network, expected to return a vector or matrix of appropriate dimension. (Shorter vectors and matrix columns will be recycled as needed.)
Borrowing the interface from , the expression on the right hand side 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 . For example, in the example below, would return the absolute difference of each actor’s “Grade” attribute from its network-wide mean, divided by the network size.
Use as is, checking only for correct length and type, with optional attribute indicating the predictor’s name.
Any of these arguments may also be wrapped in
COLLAPSE_SMALLEST(attr, n, into), a convenience function
that will transform the attribute by collapsing the smallest
n categories into one, naming it according to the
into argument. Note that into must be of the
same type (numeric, character, etc.) as the vertex attribute in
question. This is compatible with using magrittr’s pipes
for improved readability, i.e.,
attr %>% COLLAPSE_SMALLEST(n, into). This is illustrated
in the next section.
Then, taking faux.mesa.high dataset’s actor attribute
Grade, representing the grade of the student, we can
evaluate, equivalently, the linear effect of grade on overall activity
of an actor:
## nodecov.Grade
## 3491## nodecov.Grade
## 3491## nodecov.nw%v%"Grade"
## 3491Taking advantage of nodecov’s new ability to take
matrix-valued arguments, we might also evaluate a polynomial effect of
Grade:
## Warning: In term 'nodecov' in package 'ergm': Attribute specification
## '~cbind(Grade, Grade^2)' is a matrix with some column names set and others
## not; you may need to set them manually. See example(nodal_attributes) for
## more information.## nodecov.cbind(Grade,Grade^2).1 nodecov.cbind(Grade,Grade^2).2
## 3491 31123Notice the Warning. This is because the way cbind()
assigns column names, the name of the second column will be blank unless
we set it directly, in which case it can be anything:
## x
## [1,] 1 1
## [2,] 2 4## x x2
## [1,] 1 1
## [2,] 2 4## x x^2
## [1,] 1 1
## [2,] 2 4As the warning suggested, we can ensure that all columns have names, in which case they are not replaced with numbers:
## nodecov.Grade nodecov.Grade2
## 3491 31123General functions, such as stats::poly(), can also be
used:
## nodecov.poly(Grade,2).1 nodecov.poly(Grade,2).2
## -2.412818 4.974174We can even pass a random nodal covariate. Notice that setting an attribute “name” gives it a label:
randomcov <- structure(I(rbinom(network.size(faux.mesa.high),1,0.5)), name="random")
summary(faux.mesa.high~nodefactor(I(randomcov)))## nodefactor.random.1
## 197For categorical attributes, to select which levels are of interest and their ordering, use the argument . Selection of nodes (from the appropriate vector of nodal indices) is likewise handled as the selection of levels, using the argument . These arguments are interpreted as follows:
Use the given list of levels as is.
Used for indexing of a list of all possible levels (typically, unique
values of the attribute) in default order (typically lexicographic). 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().
Retain all possible levels; usually equivalent to passing .
Use as is.
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.
The expression on the right hand side of the formula is evaluated in an environment in which the network itself is accessible as , the list of unique values of the attribute as or as , and the attribute vector itself as . Its return value is interpreted as above.
Note that or often has a default that is sensible for the term in question.
Returning to the faux.mesa.high example, and treating
Grade as a categorical variable, we can use a number of
combinations:
## nodefactor.Grade.8 nodefactor.Grade.9 nodefactor.Grade.10
## 75 65 36
## nodefactor.Grade.11 nodefactor.Grade.12
## 49 28## nodefactor.Grade.7 nodefactor.Grade.8 nodefactor.Grade.9
## 153 75 65
## nodefactor.Grade.10 nodefactor.Grade.11 nodefactor.Grade.12
## 36 49 28##
## 7 8 9 10 11 12
## 62 40 42 25 24 12## nodefactor.Grade.8 nodefactor.Grade.9 nodefactor.Grade.10
## 75 65 36
## nodefactor.Grade.11 nodefactor.Grade.12
## 49 28# Collapse the smallest two grades (11 and 12) into a new category, 99.
library(magrittr) # For the %>% operator.
summary(faux.mesa.high~nodefactor((~Grade) %>% COLLAPSE_SMALLEST(2, 99)))## nodefactor.Grade.8 nodefactor.Grade.9 nodefactor.Grade.10
## 75 65 36
## nodefactor.Grade.99
## 77## mm[Grade>=10=FALSE,Grade>=10=TRUE] mm[Grade>=10=TRUE,Grade>=10=TRUE]
## 27 43## mm[Grade=7,Grade=8] mm[Grade=8,Grade=8]
## 0 33## mm[Grade=7,Grade=8] mm[Grade=8,Grade=8]
## 0 33# 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))))## mm[Grade=7,Grade=7] mm[Grade=7,Grade=8] mm[Grade=8,Grade=8]
## 75 0 33Generally, levels2= selects from among the combinations
of levels selected by levels=. Here are some examples,
using the attribute Sex (which as two levels):
# Here is the full list of combinations of sexes in an undirected network:
summary(faux.mesa.high~mm("Sex", levels2=TRUE))## mm[Sex=F,Sex=F] mm[Sex=F,Sex=M] mm[Sex=M,Sex=M]
## 82 71 50## mm[Sex=F,Sex=M]
## 71## mm[Sex=F,Sex=M]
## 71## mm[Sex=F,Sex=M]
## 71## mm[Sex=F,Sex=F] mm[Sex=M,Sex=M]
## 82 50# We can select via a mixing matrix: (Network is undirected and
# attributes are the same on both sides, so we can use either M or
# its transpose.)
(M <- matrix(c(FALSE,TRUE,FALSE,FALSE),2,2))## [,1] [,2]
## [1,] FALSE FALSE
## [2,] TRUE FALSE## mm[Sex=F,Sex=M] mm[Sex=F,Sex=M]
## 71 71## mm[Sex=F,Sex=M]
## 71# Or, select by specific attribute value combinations, though note the
# names 'row' and 'col' and the order for undirected networks:
summary(faux.mesa.high~mm("Sex",
levels2 = I(list(list(row="M",col="M"),
list(row="M",col="F"),
list(row="F",col="M")))))## Warning: In term 'mm' in package 'ergm': Selected cells '[M,F]' are
## redundant (below the diagonal) in the mixing matrix and will have count 0.## mm[Sex=M,Sex=M] mm[Sex=M,Sex=F] mm[Sex=F,Sex=M]
## 50 0 71The attributes of the mm() term can be a two-sided
formula with different attributes:
## mm[Grade=7,Race=Black] mm[Grade=8,Race=Black] mm[Grade=9,Race=Black]
## 1 6 5
## mm[Grade=10,Race=Black] mm[Grade=11,Race=Black] mm[Grade=12,Race=Black]
## 4 7 3
## mm[Grade=7,Race=Hisp] mm[Grade=8,Race=Hisp] mm[Grade=9,Race=Hisp]
## 92 15 28
## mm[Grade=10,Race=Hisp] mm[Grade=11,Race=Hisp] mm[Grade=12,Race=Hisp]
## 10 19 14
## mm[Grade=7,Race=NatAm] mm[Grade=8,Race=NatAm] mm[Grade=9,Race=NatAm]
## 37 53 25
## mm[Grade=10,Race=NatAm] mm[Grade=11,Race=NatAm] mm[Grade=12,Race=NatAm]
## 16 15 10
## mm[Grade=7,Race=Other] mm[Grade=8,Race=Other] mm[Grade=9,Race=Other]
## 0 0 1
## mm[Grade=10,Race=Other] mm[Grade=11,Race=Other] mm[Grade=12,Race=Other]
## 0 0 0
## mm[Grade=7,Race=White] mm[Grade=8,Race=White] mm[Grade=9,Race=White]
## 23 1 6
## mm[Grade=10,Race=White] mm[Grade=11,Race=White] mm[Grade=12,Race=White]
## 6 8 1# It is possible to have collapsing functions in the formula; note
# the parentheses around "~Race": this is because a formula
# operator (~) has lower precedence than pipe (|>):
summary(faux.mesa.high~mm(Grade~(~Race) %>% COLLAPSE_SMALLEST(3,"BWO"), levels2=TRUE))## mm[Grade=7,Race=BWO] mm[Grade=8,Race=BWO] mm[Grade=9,Race=BWO]
## 24 7 12
## mm[Grade=10,Race=BWO] mm[Grade=11,Race=BWO] mm[Grade=12,Race=BWO]
## 10 15 4
## mm[Grade=7,Race=Hisp] mm[Grade=8,Race=Hisp] mm[Grade=9,Race=Hisp]
## 92 15 28
## mm[Grade=10,Race=Hisp] mm[Grade=11,Race=Hisp] mm[Grade=12,Race=Hisp]
## 10 19 14
## mm[Grade=7,Race=NatAm] mm[Grade=8,Race=NatAm] mm[Grade=9,Race=NatAm]
## 37 53 25
## mm[Grade=10,Race=NatAm] mm[Grade=11,Race=NatAm] mm[Grade=12,Race=NatAm]
## 16 15 10