netlogit {sna} | R Documentation |
netlogit
performs a logistic regression of the network variable in y
on the network variables in set x
. The resulting fits (and coefficients) are then tested against the indicated null hypothesis.
netlogit(y, x, intercept=TRUE, mode="digraph", diag=FALSE, nullhyp=c("qap", "qapspp", "qapy", "qapx", "qapallx", "cugtie", "cugden", "cuguman", "classical"), tol=1e-7, reps=1000)
y |
dependent network variable. NA s are allowed, and the data should be dichotomous. |
x |
the stack of independent network variables. Note that NA s are permitted, as is dichotomous data. |
intercept |
logical; should an intercept term be fitted? |
mode |
string indicating the type of graph being evaluated. "digraph" indicates that edges should be interpreted as directed; "graph" indicates that edges are undirected. mode is set to "digraph" by default. |
diag |
boolean indicating whether or not the diagonal should be treated as valid data. Set this true if and only if the data can contain loops. diag is FALSE by default. |
nullhyp |
string indicating the particular null hypothesis against which to test the observed estimands. |
tol |
tolerance parameter for qr.solve . |
reps |
integer indicating the number of draws to use for quantile estimation. (Relevant to the null hypothesis test only – the analysis itself is unaffected by this parameter.) Note that, as for all Monte Carlo procedures, convergence is slower for more extreme quantiles. By default, reps =1000. |
netlogit
is primarily a front-end to the built-in glm.fit
routine. netlogit
handles vectorization, sets up glm
options, and deals with null hypothesis testing; the actual fitting is taken care of by glm.fit
.
Logistic network regression using is directly analogous to standard logistic regression elementwise on the appropriately vectorized adjacency matrices of the networks involved. As such, it is often a more appropriate model for fitting dichotomous response networks than is linear network regression.
Because of the frequent presence of row/column/block autocorrelation in network data, classical hull hypothesis tests (and associated standard errors) are generally suspect. Further, it is sometimes of interest to compare fitted parameter values to those arising from various baseline models (e.g., uniform random graphs conditional on certain observed statistics). The tests supported by netlogit
are as follows:
classical
cug
cugtest
) controlling for order.cugden
cugtie
qap
qaptest
); currently identical to qapspp
. qapallx
qapspp
qapx
qapy
Note that interpretation of quantiles for single coefficients can be complex in the presence of multicollinearity or third variable effects. Although qapspp
is known to be robust to these conditions in the OLS case, there are no equivalent results for logistic regression. Caution is thus advised. Reasonable printing and summarizing of netlogit
objects is provided by print.netlogit
and summary.netlogit
, respectively. No plot methods exist at this time.
An object of class netlogit
Carter T. Butts buttsc@uci.edu
Butts, C.T., and Carley, K.M. (2001). ``Multivariate Methods for Interstructural Analysis.'' CASOS working paper, Carnegie Mellon University.
#Create some input graphs x<-rgraph(20,4) #Create a response structure y.l<-x[1,,]+4*x[2,,]+2*x[3,,] #Note that the fourth graph is #unrelated y.p<-apply(y.l,c(1,2),function(a){1/(1+exp(-a))}) y<-rgraph(20,tprob=y.p) #Fit a netlogit model nl<-netlogit(y,x,reps=100) #Examine the results summary(nl)