Description Usage Arguments Details Value Auxiliary functions Author(s) See Also Examples
View source: R/diffnetclass.R
diffnet
objects contain difussion networks. With adjacency
matrices and time of adoption (toa) vector as its main components, most of the
package's functions have methods for this class of objects.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47  as_diffnet(graph, toa, t0 = min(toa, na.rm = TRUE), t1 = max(toa, na.rm =
TRUE), vertex.dyn.attrs = NULL, vertex.static.attrs = NULL,
id.and.per.vars = NULL, graph.attrs = NULL,
undirected = getOption("diffnet.undirected"),
self = getOption("diffnet.self"),
multiple = getOption("diffnet.multiple"), name = "Diffusion Network",
behavior = "Unspecified")
diffnet.attrs(graph, element = c("vertex", "graph"), attr.class = c("dyn",
"static"), as.df = FALSE)
diffnet.attrs(graph, element = "vertex", attr.class = "static") < value
diffnet.toa(graph)
diffnet.toa(graph, i) < value
## S3 method for class 'diffnet'
plot(x, y = NULL, t = 1, displaylabels = FALSE,
vertex.col = c("blue", "grey"), gmode = ifelse(x$meta$undirected, "graph",
"digraph"), vertex.cex = "degree", edge.col = "gray",
mode = "fruchtermanreingold", layout.par = NULL,
main = "Diffusion network in time %d", ...)
## S3 method for class 'diffnet'
print(x, ...)
## S3 method for class 'diffnet'
summary(object, slices = NULL, no.print = FALSE,
skip.moran = FALSE, valued = getOption("diffnet.valued", FALSE),
mode = "out", ...)
nodes(graph)
diffnetLapply(graph, FUN, ...)
## S3 method for class 'diffnet'
str(object, ...)
## S3 method for class 'diffnet'
dimnames(x)
## S3 method for class 'diffnet'
t(x)
## S3 method for class 'diffnet'
dim(x)

graph 
A dynamic graph (see 
toa 
Numeric vector of size n. Times of adoption. 
t0 
Integer scalar. Passed to 
t1 
Integer scalar. Passed to 
vertex.dyn.attrs 
Vertices dynamic attributes (see details). 
vertex.static.attrs 
Vertices static attributes (see details). 
id.and.per.vars 
A character vector of length 2. Optionally specified to check the order of the rows in the attribute data. 
graph.attrs 
Graph dynamic attributes (not supported yet). 
undirected 
Logical scalar. When 
self 
Logical scalar. When 
multiple 
Logical scalar. When 
name 
Character scalar. Name of the diffusion network (descriptive). 
behavior 
Character scalar. Name of the behavior been analyzed (innovation). 
element 
Character vector/scalar. Indicates what to retrieve/alter. 
attr.class 
Character vector/scalar. Indicates the class of the attribute, either dynamic ( 
as.df 
Logical scalar. When TRUE returns a data.frame. 
value 
In the case of 
i 
Indices specifying elements to replace. See 
x 
A 
y 
Ignored. 
t 
Integer scalar indicating the time slice to plot. 
displaylabels 
Logical scalar. When TRUE, 
vertex.col 
Character scalar/vector. Color of the vertices. 
gmode 
Character scalar. Passed to 
vertex.cex 
Numeric scalar/vector. Size of the vertices. 
edge.col 
Character scalar/vector. Color of the edges. 
mode 
Character scalar. In the case of 
layout.par 
Layout parameters (see details). 
main 
Character. A title template to be passed to sprintf. 
... 
In the case of 
object 
A 
slices 
Either an integer or character vector. While integer vectors are used as indexes, character vectors are used jointly with the time period labels. 
no.print 
Logical scalar. When TRUE suppress screen messages. 
skip.moran 
Logical scalar. When TRUE Moran's I is not reported (see details). 
valued 
Logical scalar. When 
FUN 
a function to be passed to lapply 
diffnet
objects hold both, static and dynamic vertex attributes. When
creating diffnet
objects, these can be specified using the arguments
vertex.static.attrs
and vertex.dyn.attrs
; depending on whether
the attributes to specify are static or dynamic, netdiffuseR currently
supports the following objects:
Class  Dimension  Check sorting 
Static attributes  
matrix  with n rows  id 
data.frame  with n rows  id 
vector  of length n   
Dynamic attributes  
matrix  with n * T rows  id , per 
data.frame  with n * T rows  id , per 
vector  of length n*T   
list  of length T with matrices or data.frames of n rows  id , per 
The last column, Check sorting, lists the variables that
the user should specify if he wants the function to check the order of the rows
of the attributes (notice that this is not possible for the case of vectors).
By providing the name of the vertex id variable, id
, and the time period
id variable, per
, the function makes sure that the attribute data is
presented in the right order. See the example below. If the user does not
provide the names of the vertex id and time period variables then the function
does not check the way the rows are sorted, further it assumes that the data
is in the correct order.
Plotting is done via the function gplot
,
and its layout via gplot.layout
, both from
the (sna) package.
vertex.cex
can either be a numeric scalar, a numeric vector or a character
scalar taking any of the following values "degree"
, "indegree"
, or
"outdegree"
. The later will be passed to dgr
to calculate
degree of the selected slice and will be normalized as
vertex.cex = d/[max(d)  min(d)]* 2 + .5
where d=sqrt(dgr(graph))
.
In the case of the summary
method, Moran's I is calculated over the
cumulative adoption matrix using as weighting matrix the inverse of the geodesic
distance matrix. All this via moran
. For each time period t
,
this is calculated as:
1 
Where C[,t]
is the tth column of the cumulative adoption matrix,
G^(1)
is the elementwise inverse of the geodesic matrix at time t
,
and moran
is netdiffuseR's moran's I routine. When skip.moran=TRUE
Moran's I is not reported. This can be useful when the graph is particuarly
large (tens of thousands of vertices) as when doing so geodesic distances are
not calculated, which avoids allocating a square matrix of size n on
the memory. As a difference from the adjacency matrices, the matrix with the
geodesic distances can't be stored as a sparse matrix (saving space).
A list of class diffnet
with the following elements:
graph 
A list of length T. Containing sparse square matrices of size n
and class 
toa 
An integer vector of size T with times of adoption. 
adopt, cumadopt 
Numeric matrices of size n*T as those returned
by 
vertex.static.attrs 
If not NULL, a data frame with n rows with vertex static attributes. 
vertex.dyn.attrs 
A list of length T with data frames containing vertex attributes throught time (dynamic). 
graph.attrs 
A data frame with T rows. 
meta 
A list of length 9 with the following elements:

diffnet.attrs
Allows retriving network attributes. In particular, by default
returns a list of length T with data frames with the following columns:
per
Indicating the time period to which the observation corresponds.
toa
Indicating the time of adoption of the vertex.
Further columns depending on the vertex and graph attributes.
Each vertex static attributes' are repeated T times in total so that these
can be binded (rbind
) to dynamic attributes.
When as.df=TRUE
, this convenience function is useful as it can be used
to create event history (panel data) datasets used for model fitting.
Conversely, the replacement method allows including new vertex or graph attributes either dynamic or static (see examples below).
diffnet.toa(graph)
works as an alias of graph$toa
.
The replacement method, diffnet.toa<
used as diffnet.toa(graph)<...
,
is the right way of modifying times of adoption as when doing so it
performs several checks on the time ranges, and
recalculates adoption and cumulative adoption matrices using toa_mat
.
nodes(graph)
is an alias for graph$meta$ids
.
George G. Vega Yon
Default options are listed at netdiffuseRoptions
Other data management functions: edgelist_to_adjmat
,
egonet_attrs
, isolated
,
survey_to_diffnet
Other diffnet methods: %*%
,
as.array.diffnet
, c.diffnet
,
diffnetarithmetic
,
diffnet_index
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67  # Creating a random graph
set.seed(123)
graph < rgraph_ba(t=9)
graph < lapply(1:5, function(x) graph)
# Pretty TOA
names(graph) < 2001L:2005L
toa < sample(c(2001L:2005L,NA), 10, TRUE)
# Creating diffnet object
diffnet < as_diffnet(graph, toa)
diffnet
summary(diffnet)
# Plotting slice 4
plot(diffnet, t=4)
# ATTRIBUTES 
# Retrieving attributes
diffnet.attrs(diffnet, "vertex", "static")
# Now as a data.frame (only static)
diffnet.attrs(diffnet, "vertex", "static", as.df = TRUE)
# Now as a data.frame (all of them)
diffnet.attrs(diffnet, as.df = TRUE)
# Unsorted data 
# Loading example data
data(fakesurveyDyn)
# Creating a diffnet object
fs_diffnet < survey_to_diffnet(
fakesurveyDyn, "id", c("net1", "net2", "net3"), "toa", "group",
timevar = "time", keep.isolates=TRUE, warn.coercion=FALSE)
# Now, we extract the graph data and create a diffnet object from scratch
graph < fs_diffnet$graph
ids < fs_diffnet$meta$ids
graph < Map(function(g) {
dimnames(g) < list(ids,ids)
g
}, g=graph)
attrs < diffnet.attrs(fs_diffnet, as.df=TRUE)
toa < diffnet.toa(fs_diffnet)
# Lets apply a different sorting to the data to see if it works
n < nrow(attrs)
attrs < attrs[order(runif(n)),]
# Now, recreating the old diffnet object (notice id.and.per.vars arg)
fs_diffnet_new < as_diffnet(graph, toa=toa, vertex.dyn.attrs=attrs,
id.and.per.vars = c("id", "per"))
# Now, retrieving attributes. The 'new one' will have more (repeated)
attrs_new < diffnet.attrs(fs_diffnet_new, as.df=TRUE)
attrs_old < diffnet.attrs(fs_diffnet, as.df=TRUE)
# Comparing elements!
tocompare < intersect(colnames(attrs_new), colnames(attrs_old))
all(attrs_new[,tocompare] == attrs_old[,tocompare], na.rm = TRUE) # TRUE!
# diffnetLapply 
data(medInnovationsDiffNet)
diffnetLapply(medInnovationsDiffNet, function(x, cumadopt, ...) {sum(cumadopt)})

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