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netdiffuseR (version 1.17.0)

classify_adopters: Classify adopters accordingly to Time of Adoption and Threshold levels.

Description

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.

Usage

classify_adopters(...)
classify(...)
"classify_adopters"(graph, include_censored = FALSE, ...)
"classify_adopters"(graph, toa, t0 = NULL, t1 = NULL, expo = NULL, include_censored = FALSE, ...)
"ftable"(x, as.pcent = TRUE, digits = 2, ...)
"as.data.frame"(x, row.names = NULL, optional = FALSE, ...)
"plot"(x, y = NULL, ftable.args = list(), table.args = list(), ...)

Arguments

...
Further arguments passed to the method.
graph
A dynamic graph.
include_censored
Logical scalar, passed to threshold.
toa
Integer vector of length $n$ with times of adoption.
t0
Integer scalar passed to threshold and toa_mat.
t1
Integer scalar passed to toa_mat.
expo
Numeric matrix of size $n*T$ with network exposures.
x
A diffnet_adopters class object.
as.pcent
Logical scalar. When TRUE returns a table with percentages instead.
digits
Integer scalar. Passed to round.
row.names
Passed to as.data.frame.
optional
Passed to as.data.frame.
y
Ignored.
ftable.args
List of arguments passed to ftable.
table.args
List of arguments passed to table.

Value

A list of class diffnet_adopters with the following elements: with the following elements:

Details

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)<="" code="">,
  • Early Majority: mean(toa) - sd(toa) < toa[i] <= mean(toa)="" <="" code="">,
  • Late Majority: mean(toa) < toa[i] <= mean(toa)="" +="" sd(toa)="" <="" code="">, 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)<="" code="">,
  • Low Thresh.: mean(thr) - sd(thr) < thr[i] <= mean(thr)="" <="" code="">,
  • High Thresh.: mean(thr) < thr[i] <= mean(thr)="" +="" sd(thr)="" <="" code="">, 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.

References

Valente, T. W. (1995). "Network models of the diffusion of innovations" (2nd ed.). Cresskill N.J.: Hampton Press.

See Also

Other statistics: cumulative_adopt_count, dgr, ego_variance, exposure, hazard_rate, infection, moran, struct_equiv, threshold, vertex_covariate_dist

Examples

Run this code
# 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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