Retrieve alter's attributes (network effects)

Description

For a given set of vertices V, retrieves each vertex's alter's attributes. This function enables users to calculate exposure on variables other than the attribute that is diffusing. Further, it enables the specification of alternative functions to use to characterize ego's personal network including calculating the mean, maximum, minimum, median, or sum of the alters' attributes. These measures may be static or dynamic over the interval of diffusion and they may be binary or valued.

Usage

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egonet_attrs(graph, attrs, V = NULL, direction = "outgoing",
  fun = function(x) x, as.df = FALSE, self = getOption("diffnet.self"),
  self.attrs = FALSE, valued = getOption("diffnet.valued"))

Arguments

graph

Any class of accepted graph format (see netdiffuseR-graphs).

attrs

If graph is static, Numeric matrix with n rows, otherwise a list of numeric matrices with n rows.

V

Integer vector. Set of vertices from which the attributes will be retrieved.

direction

Character scalar. Either "outgoing", "incomming".

fun

Function. Applied to each

as.df

Logical scalar. When TRUE returns a data.frame instead of a list (see details).

self

Logical scalar. When TRUE allows loops (self edges).

self.attrs

Logical scalar. When TRUE ego's attributes are included in the output as the first row.

valued

Logical scalar. When TRUE weights will be considered. Otherwise non-zero values will be replaced by ones.

Details

By indexing inner/outer edges, this function retrieves ego network attributes for all v in V, which by default is the complete set of vertices in the graph.

When as.df=TRUE the function returns a data.frame of size (|V| * T)*k where T is the number of time periods and k is the number of columns generated by the function.

The function can be used to create network effects as those in the RSiena package. The difference here is that the definition of the statistic directly relies on the user. For example, in the RSiena package, the dyadic covariate effect 37. covariate (centered) main effect (X)

s_i37(x) = sum(x[ij] * (w[ij] - mean(w)))

Which, having a diffnet object with attributes named x and w, can be calculated as

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    egonet_attrs(diffnet, as.df=TRUE, fun=function(dat) {
     sum(dat[, "x"]*(dat[, "w"] - mean(dat[, "w"])))
    })
    

Furthermore, we could use the median centered instead, for example

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    egonet_attrs(diffnet, as.df=TRUE, fun=function(dat) {
     sum(dat[, "x"]*(dat[, "w"] - median(dat[, "w"])))
    })
    

Where for each i, dat will be a matrix with as many rows as individuals in his egonetwork. Such matrix holds the column names of the attributes in the network.

Value

A list with ego alters's attributes. By default, if the graph is static, the output is a list of length length(V) with matrices having the following columns:

value

Either the corresponding value of the tie.

id

Alter's id

...

Further attributes contained in attrs

On the other hand, if graph is dynamic, the output is list of length T of lists of length length(V) with data frames having the following columns:

value

The corresponding value of the adjacency matrix.

id

Alter's id

per

Time id

...

Further attributes contained in attrs

Author(s)

George G. Vega Yon

See Also

Other data management functions: as_diffnet, edgelist_to_adjmat, isolated, survey_to_diffnet

Examples

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# Creating a random graph
set.seed(1001)
diffnet <- rdiffnet(150, 20, seed.graph="small-world")

# Adding attributes
indeg <- dgr(diffnet, cmode="indegree")
head(indeg)
diffnet[["indegree"]] <- indeg

# Retrieving egonet's attributes (vertices 1 and 20)
egonet_attrs(diffnet, V=c(1,20))

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