Description
Usage
Arguments
Value
Author(s)
References
See Also
View source: R/control.ergm.ego.R
Constructs and checks the list of control parameters for estimation by
ergm.ego
.
 (
ppopsize = ("auto", "samp", "pop"),
ppopsize.mul = 1,
ppop.wt = ("round", "sample"),
stats.wt = ("data", "ppop"),
stats.est = ("survey", "asymptotic", "bootstrap", "jackknife", "naive"),
boot.R = 10000,
ignore.max.alters = ,
ergm = control.ergm(),
)

ppopsize, ppopsize.mul 
Parameters to determine the size
N' of the pseudopopulation network. ppopsize can be
 "auto"
If the popsize (N) argument is
specified and is different from 1, as if "pop" ; otherwise,
as "samp" .
 "samp"
set N' based on the sample size:
N'=S \times \code{popsize.mul}
 "pop"
set N' based on the population size:
N'=N \times \code{popsize.mul}
 a number
set N' directly (popsize.mul
ignored)
 a
network object use the specified network as the
pseudopopulation network directly; use at your own risk
 a data frame
use the specified data frame as the
pseudopopulation; use at your own risk
The default is to use the same pseudopopulation size as the sample size,
but, particularly if there are sampling weights in the data, it should be
bigger.
Note that depending on ppop.wt , this may only be an approximate
target specification, with the actual constructed pseudopopulation network
being slightly bigger or smaller.

ppop.wt 
Because each ego must be represented in the pseuodopopulation
network an integral number of times, if the sample is weighted (or the
target N' calculated from ppopsize and ppopsize.mul is
not a multiple of the sample size), it may not be possible, for a finite
N' to represent each ego exactly according to its relative weight,
and ppop.wt controls how the fractional egos are allocated:
 "round"
(default) Rather than treating ppopsize as
a hard setting, calculate N' w_i / w_\cdot for each ego i and
round it to the nearest integer. Then, the N' actually used will be
the sum of these rounded freqencies.
 "sample"
Resample in proportion to w_i.

stats.wt 
Weight assigned to each ego's contribution to the ERGM's
sufficient statistic:
 "data"
(default) Use weights
N' w_i / w_\cdot for each ego i as in the data.
 "ppop"
Use weights ultimately used in the
pseudopopulation network.

stats.est, boot.R 
Method to be used to estimate the ERGM's sufficient
statistics and their variance:
 "survey"
Variance estimator returned by survey::svymean() , appropriate to the design of the dataset.
 "asymptotic"
Delta method, as derived by Krivitsky and
Morris (2015), assuming the ego weights are sampled alongside the
egos.
 (default)
Delta method, as derived by Krivitsky and Morris
(2015), assuming the ego weights are sampled alongside the egos.
 "bootstrap"
Nonparametric bootstrap with bias correction,
resampling egos, using R replications.
 "jackknife"
Jackknife with bias correction.
 "naive"
"Naive" estimator, assuming that weights are
fixed.

ignore.max.alters 
if TRUE , ignores any constraints on the
number of nominations.

ergm 
Control parameters for the ergm call
to fit the model, constructed by control.ergm .

... 
Not used at this time.

A list with arguments as components.
Pavel N. Krivitsky
Pavel N. Krivitsky and Martina Morris. Inference for Social Network Models
from EgocentricallySampled Data, with Application to Understanding
Persistent Racial Disparities in HIV Prevalence in the US. Thechnical
Report. National Institute for Applied Statistics Research Australia,
University of Wollongong, 2015(0515).
http://niasra.uow.edu.au/publications/UOW190187.html
control.ergm
statnet/ergm.ego documentation built on April 26, 2021, 4:46 a.m.