Adopters are classified as in Valente (1995). In general, this is done depending on the distance in terms of standard deviations from the mean of Time of Adoption and Threshold.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  classify_adopters(...)
classify(...)
## S3 method for class 'diffnet'
classify_adopters(graph, include_censored = FALSE, ...)
## Default S3 method:
classify_adopters(graph, toa, t0 = NULL, t1 = NULL,
expo = NULL, include_censored = FALSE, ...)
## S3 method for class 'diffnet_adopters'
ftable(x, as.pcent = TRUE, digits = 2, ...)
## S3 method for class 'diffnet_adopters'
as.data.frame(x, row.names = NULL,
optional = FALSE, ...)
## S3 method for class 'diffnet_adopters'
plot(x, y = NULL, ftable.args = list(),
table.args = list(), ...)

... 
Further arguments passed to the method. 
graph 
A dynamic graph. 
include_censored 
Logical scalar, passed to 
toa 
Integer vector of length n with times of adoption. 
t0 
Integer scalar passed to 
t1 
Integer scalar passed to 
expo 
Numeric matrix of size n*T with network exposures. 
x 
A 
as.pcent 
Logical scalar. When 
digits 
Integer scalar. Passed to 
row.names 
Passed to 
optional 
Passed to 
y 
Ignored. 
ftable.args 
List of arguments passed to 
table.args 
List of arguments passed to 
Classifies (only) adopters according to time of adoption and threshold as described in Valente (1995). In particular, the categories are defined as follow:
For Time of Adoption, with toa
as the vector of times of adoption:
Early Adopters: toa[i] <= mean(toa)  sd(toa)
,
Early Majority: mean(toa)  sd(toa) < toa[i] <= mean(toa)
,
Late Majority: mean(toa) < toa[i] <= mean(toa) + sd(toa)
, and
Laggards: mean(toa) + sd(toa) < toa[i]
.
For Threshold levels, with thr
as the vector of threshold levels:
Very Low Thresh.: thr[i] <= mean(thr)  sd(thr)
,
Low Thresh.: mean(thr)  sd(thr) < thr[i] <= mean(thr)
,
High Thresh.: mean(thr) < thr[i] <= mean(thr) + sd(thr)
, and
Very High. Thresh.: mean(thr) + sd(thr) < thr[i]
.
By default threshold levels are not computed for left censored data. These
will have a NA
value in the thr
vector.
The plot method, plot.diffnet_adopters
, is a wrapper for the
plot.table
method. This generates a
mosaicplot
plot.
A list of class diffnet_adopters
with the following elements:
toa 
A factor vector of length n with 4 levels: "Early Adopters", "Early Majority", "Late Majority", and "Laggards" 
thr 
A factor vector of length n with 4 levels: "Very Low Thresh.", "Low Thresh.", "High Thresh.", and "Very High Thresh." 
George G. Vega Yon
Valente, T. W. (1995). "Network models of the diffusion of innovations" (2nd ed.). Cresskill N.J.: Hampton Press.
Other statistics: cumulative_adopt_count
,
dgr
, ego_variance
,
exposure
, hazard_rate
,
infection
, moran
,
struct_equiv
, threshold
,
vertex_covariate_dist
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  # Classifying brfarmers 
x < brfarmersDiffNet
diffnet.toa(x)[x$toa==max(x$toa, na.rm = TRUE)] < NA
out < classify_adopters(x)
# This is one way
round(
with(out, ftable(toa, thr, dnn=c("Time of Adoption", "Threshold")))/
nnodes(x[!is.na(x$toa)])*100, digits=2)
# This is other
ftable(out)
# Can be coerced into a data.frame, e.g. 
## Not run:
View(classify(brfarmersDiffNet))
cbind(as.data.frame(classify(brfarmersDiffNet)), brfarmersDiffNet$toa)
## End(Not run)
# Creating a mosaic plot with the medical innovations 
x < classify(medInnovationsDiffNet)
plot(x)

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