Description Usage Arguments Value Author(s) References
Fit a SAOM regression model based on a static, structural network and a set of covariates. The typical application is a diffusion model, where the network is defined by geographic adjacency, and the dependent variable is binary or categorical.
1 |
formula |
an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. Contains dependent variable and covariates, excluding the diffusion effect. |
data |
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which spatialSAOM is called. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
network |
square connection matrix of all observations. The dimension must match the number of observations in the data frame. |
diffusion |
a list of covariates through which diffusion takes place, if not through the dependent variable. |
rateFix |
rate at which behavioral changes are fixed to guarantee estimation. |
maxRounds |
maximum number of iterations of the SAOM algorithm. |
method |
the method to be used; for fitting, currently only "avAlt" and "avSim" are supported. |
projname |
name under which temporary output is saved by the SAOM implementation. |
... |
not used. |
Returns an object of class sienaFit
.
Johan A. Elkink and Thomas U. Grund
Elkink, Johan A. and Thomas U. Grund. 2019. "Modelling Diffusion through Statistical Network Analysis: A Simulation Study." Arxiv 1903.08648.
Snijders, Tom A.B. and Christian E.G. Steglich. 2015. "Representing Micro–Macro Linkages by Actor-based Dynamic Network Models." Sociological Methods & Research 44(2):222– 271.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.