Evaluates the terms in formula on each of the networks joined
using Networks function, and returns either a weighted
sum or an lm-style linear model for the ERGM
coefficients KrCo22tergm.multi. Its syntax follows that of lm closely,
with sensible defaults.
The default formula (~1) sums the specified network
statistics. If lm refers to any network attributes for which some
networks have missing values, the term will stop with an
error. This can be avoided by pre-filtering with subset, which
controls which networks are affected by the term.
# binary: N(formula, lm=~1, subset=TRUE, weights=1, contrasts=NULL, offset=0, label=NULL,
# .NetworkID=".NetworkID", .NetworkName=".NetworkName")# valued: N(formula, lm=~1, subset=TRUE, weights=1, contrasts=NULL, offset=0, label=NULL,
# .NetworkID=".NetworkID", .NetworkName=".NetworkName")
Optional strings indicating the vertex attributes used to distinguish and name the networks; intended to be used by term developers.
a one-sided ergm()-style formula with the terms to be evaluated
a one-sided lm()-style formula whose RHS specifies the network-level predictors for the terms in the ergm() formula formula.
see lm().
A constant, a vector of length equal to the number of networks, or a matrix whose number of rows is the number of networks and whose number of columns is the number of free parameters of the ERGM. It can be specified in lm as well.
reserved for future use; attempting to change it will cause an error: at this time, there is no way to assign sampling weights to networks.
An optional parameter which will add a label to model parameters to help identify the term (which may have similar predictors but, say, a different network subset) in the output or a function that wraps the names.
By default, an N(formula, lm) term will add \(p \times q\) free
parameters, where \(p\) is the number of free parameters
(possibly curved) of the ERGM specified by formula, and \(q\)
is the number of parameters specified by the lm formula. That is,
there would be one parameter for each combination of an ERGM
parameter and a linear model parameter, in an ERGM-major order
(i.e., for each ERGM parameter, the linear model parameters will be
enumerated). For example, the term gwesp() has two free
parameters: its coefficient and its decay rate. We can specify a
model in which they depend on \(\log(n)\) as N(~gwesp, ~log(n)), resulting in the following 4 parameters, with the
intercept for the linear model being implicit:
#> [1] "N(1)~gwesp" "N(log(n))~gwesp" "N(1)~gwesp.decay"
#> [4] "N(log(n))~gwesp.decay"
If a different linear model is desired for different ERGM terms
(e.g., some are to be affected by network size while others are
not), multiple N() terms can be specified. This covers most such
cases, but not all. For example, suppose that for the above model,
we wish for its coefficient to depend on log(n) but for the decay
parameter not to. In this case, one can use the offset()
decorator with partial offsetting. Then, specifying
offset(N(~gwesp(), ~log(n)), 4), we get:
#> [1] "N(1)~gwesp" "N(log(n))~gwesp"
#> [3] "N(1)~gwesp.decay" "offset(N(log(n))~gwesp.decay)"
Then, setting the corresponding offset.coef = 0 will fix the
coefficient of log(n) for the decay parameter at 0, while
allowing a constant decay parameter to be estimated.
ergmTerm for index of model terms currently visible to the package.
ergm:::.formatTermKeywords("ergmTerm", "N", "subsection")
vignette("Goeyvaerts_reproduction") for a demonstration.