tnam-terms: Terms used in (Temporal) Network Autocorrelation Models (tnam)

Description

The function tnam is used to fit (temporal) network autocorrelation models. The function tnamdata can be used alternatively to create a data frame containing all the data ready for estimation. This may be useful when a non-standard model should be estimated, like a tobit model or a model with zero inflation, for example. Both functions accept a formula containing several model terms. The model terms are themselves functions which can be called separately. For example, one model term is called netlag. This model term can be part of the formula handed over to the tnam function, or netlag can be called directly in order to create a single variable. This help page describes the different model terms available in (temporal) network autocorrelation models. See the tnam help page for details on the model.

Usage

attribsim(y, attribute, match = FALSE, lag = 0, 
    normalization = c("no", "row", "column"), center = FALSE, 
    coefname = NULL)
centrality(networks, type = c("indegree", "outdegree", "freeman", 
    "betweenness", "flow", "closeness", "eigenvector", 
    "information", "load", "bonpow"), directed = TRUE, lag = 0, 
    rescale = FALSE, center = FALSE, coefname = NULL, ...)
cliquelag(y, networks, k.min = 2, k.max = Inf, directed = TRUE, 
    lag = 0, normalization = c("no", "row", "column"), 
    center = FALSE, coefname = NULL)
clustering(networks, directed = TRUE, lag = 0, center = FALSE, 
    coefname = NULL, ...)
covariate(y, lag = 0, exponent = 1, center = FALSE, 
    coefname = NULL)
degreedummy(networks, deg = 0, type = c("indegree", "outdegree", 
    "freeman"), reverse = FALSE, directed = TRUE, lag = 0, 
    center = FALSE, coefname = NULL, ...)
interact(x, y, lag = 0, center = FALSE, coefname = NULL)
netlag(y, networks, lag = 0, pathdist = 1, decay = pathdist^-1, 
    normalization = c("no", "row", "column", "complete"), 
    reciprocal = FALSE, center = FALSE, coefname = NULL, ...)
structsim(y, networks, lag = 0, method = c("euclidean", 
    "minkowski", "jaccard", "binary", "hamming"), center = FALSE, 
    coefname = NULL, ...)
weightlag(y, networks, lag = 0, normalization = c("no", "row", 
    "column"), center = FALSE, coefname = NULL)

Arguments

attribute

A vector, list of vectors or data frame with the same dimensions as y. Based on this attribute, the similarity between nodes i and j will be calculated, and the resulting similarity matrix is used to weight the y variable.

center

Should the model term be centered? That is, should the mean of the variable be subtracted from the actual value at each time step?

coefname

An additional name that is used as part of the coefficient label for easier identification in the summary output of the model.

decay

For each value in pathdist, the decay argument specifies the relative importance. By default, a geometric decay is used, that is, the behavior of nodes at path distance 2 is counted only half as much as the behavior of adjacent n

deg

The degree (e.g., deg = 2) or degree range (e.g., deg = 1:3).

directed

Is the input matrix or network a directed network?

exponent

The exponent of a covariate. For example, exponent = 2 creates a squared variable. This may be helpful for modeling non-linear effects or for modeling a quadratic behavior shape.

k.max

Maximal clique size.

k.min

Minimal clique size.

lag

The temporal lag. The default value 0 means there is no lag. A value of 1 would specify a single-period lag, that is, current behavior is modeled conditional on previous influence. A value of 2 would specify a two-period lag, that is, current behavior is

match

If match = FALSE, a similarity matrix is computed by subtracting node j's attribute value from node i's attribute value, standardizing the resulting distance between 0 and 1, and converting it into a similarity by subtracting it from 1. This

method

The distance function used for computing structural similarity. Possible values are "euclidean", "minkowski", "jaccard", "binary", and "hamming".

networks

The network(s) for computing the peer influence, also known as the weight matrix. This can be a matrix or a network object (for a single time step) or a list of matrices or network objects (for multiple time steps).

normalization

Possible values: "no" for switching off normalization, "row" for row normalization of the weight matrix, "column" for column normalization of the weight matrix, and "complete" for complete normalization.

pathdist

An integer or a vector of integers. For example, if pathdist = 1 is used, this computes the sum of the behavior of adjacent nodes. If pathdist = 2 is specified, this computes the effect of indirect paths of length 2 ("friends of

reciprocal

If reciprocal = TRUE is specified, only the behavior of nodes to which a reciprocal relation exists is counted (that is, a link in both directions).

rescale

Should the centrality index be rescaled between 0 and 1?

reverse

Reverse the selection of degrees. For example, when deg = 0 and reverse = FALSE are specified, resulting values of 1 indicate that a node has no connections, whereas the combination deg = 0 and reverse = TRUE

type

The type of centrality measure. Possible values are "indegree", "outdegree", "freeman", "betweenness", "flow", "closeness", "eigenvector", "information"

x

A variable that should be interacted with y. Either a vector or a list of vectors or another model term (this is the preferred way).

y

The outcome or behavior variable. Either a vector (for a single time step) or a list of vectors with named elements in each vector (for multiple time steps) or a data frame with row names where each column is one time step (for multiple time steps).

...

Additional arguments to be handed over to subroutines.

`References`

Leenders, Roger Th. A. J. (2002):
Modeling Social Influence through Network Autocorrelation: 
Constructing the Weight Matrix. Social Networks 24: 21--47.
http://dx.doi.org/10.1016/S0378-8733(01)00049-1.
Daraganova, Galina and Garry Robins (2013):
Autologistic Actor Attribute Models. In: Lusher, Dean, Johan 
Koskinen and Garry Robins, "Exponential Random Graph Models for 
Social Networks: Theory, Methods, and Applications", 
Cambridge University Press, chapter 9: 102--114.
Hays, Jude C., Aya Kachi and Robert J. Franzese Jr. (2010):
A Spatial Model Incorporating Dynamic, Endogenous Network 
Interdependence: A Political Science Application.
Statistical Methodology 7: 406--428.
http://dx.doi.org/10.1016/j.stamet.2009.11.005

`See Also`

xergm-package tnam tnamdata preprocess knecht