ergm-references {ergm.count} | R Documentation |

This page describes the possible reference measures (baseline distributions)
for modeling count data found in the `ergm.count`

package.

Each of these is specified on the RHS of a one-sided formula passed as
the `reference`

argument to `ergm`

.
See the `ergm`

documentation for a complete
description of how reference measures are specified.

Poisson- and geometric-reference ERGMs have an unbouded sample
space. This means that the parameter space may be constrained in
complex ways that depend on the terms used in the model. At this
time `ergm`

has no way to detect when a parameter
configuration had strayed outside of the parameter space, but it may
be noticeable on a runtime trace plot (activated via
`MCMC.runtime.traceplot`

control parameter), when the simulated
values keep climbing upwards. (See Krivitsky (2012) for a further
discussion.)

A possible remedy if this appears to occur is to try lowering the
control parameter `MCMLE.steplength`

.

`MCMLE.trustregion`

Because Monte Carlo MLE's approximation to the likelihood becomes less
accurate as the estimate moves away from the one used for the
sample, `ergm`

limits how far the optimization can move
the estimate for every iteration: the log-likelihood may not change
by more than `MCMLE.trustregion`

control
parameter, which defaults to 20. This is an adequate value for
binary ERGMs, but because each dyad in a valued ERGM contains more
information, this number may be too small, resulting in
unnecessarily many iterations needed to find the MLE.

Automatically setting `MCMLE.trustregion`

is
work in progress, but, in the meantime, you may want to set it to a
high number (e.g., 1000).

Reference measures currently available are:

`Poisson`

*Poisson-reference ERGM:*Specifies each dyad's baseline distribution to be Poisson with mean 1:*h(y)=∏_{i,j} 1/y_{i,j}!*, with the support of*y_{i,j}*being natural numbers (and*0*). Using`valued ERGM terms`

that are “generalized” from their binary counterparts, with form`"sum"`

(see previous link for the list) produces Poisson regression. Using`CMP`

induces a Conway-Maxwell-Poisson distribution that is Poisson when its coefficient is*0*and geometric when its coefficient is*1*.Three proposal functions are currently implemented, two of them designed to improve mixing for sparse networks. They can can be selected via the

`MCMC.prop.weights=`

control parameter. The sparse proposals work by proposing a jump to 0. Both of them take an optional proposal argument`p0`

(i.e.,`MCMC.prop.args=list(p0=...)`

) specifying the probability of such a jump. However, the way in which they implement it are different:`"random"`

Select a dyad

*(i,j)*at random, and draw the proposal*y_{i,j}^\star \sim \mathrm{Poisson}_{\ne y_{i,j}}(y_{i,j}+0.5)*(a Poisson distribution with mean slightly higher than the current value and conditional on not proposing the current value).`"0inflated"`

As

`"random"`

but, with probability`p0`

, propose a jump to 0 instead of a Poisson jump (if not already at 0). If`p0`

is not given, defaults to the "surplus" of 0s in the observed network, relative to Poisson.`"TNT"`

(the default)As

`"0inflated"`

but instead of selecting a dyad at random, select a tie with probability`p0`

, and a random dyad otherwise, as with the binary TNT. Currently,`p0`

defaults to 0.2.

`Geometric`

*Geometric-reference ERGM:*Specifies each dyad's baseline distribution to be uniform on the natural numbers (and*0*):*h(y)=1*. In itself, this "distribution" is improper, but in the presence of`sum`

, a geometric distribution is induced. Using`CMP`

(in addition to`sum`

) induces a Conway-Maxwell-Poisson distribution that is geometric when its coefficient is*0*and Poisson when its coefficient is*-1*.`Binomial(trials)`

*Binomial-reference ERGM:*Specifies each dyad's baseline distribution to be binomial with`trials`

trials and success probability of*0.5*:*h(y)=∏_{i,j}{{\code{trials}}\choose{y_{i,j}}}*. Using`valued ERGM terms`

that are “generalized” from their binary counterparts, with form`"sum"`

(see previous link for the list) produces logistic regression.

Krivitsky PN (2012). Exponential-Family Random Graph Models for Valued Networks. *Electronic Journal of Statistics*, 2012, 6, 1100-1128. doi: 10.1214/12-EJS696

Shmueli G, Minka TP, Kadane JB, Borle S, and Boatwright P (2005). A Useful Distribution for Fitting Discrete Data: Revival of the Conway–Maxwell–Poisson Distribution. *Journal of the Royal Statistical Society: Series C*, 54(1): 127-142.

ergm, network, %v%, %n%, sna, summary.ergm, print.ergm

[Package *ergm.count* version 4.0.2 Index]