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

In netdiffuseR: Analysis of Diffusion and Contagion Processes on Networks

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(...)

## 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(), ...)

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\times 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.

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),

• 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.

Value

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."

Author(s)

George G. Vega Yon

References

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

See Also

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

Examples

# 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. ------------------------------------
 str(classify(brfarmersDiffNet))
 ans <- cbind(
 as.data.frame(classify(brfarmersDiffNet)), brfarmersDiffNet$toa
 )
 head(ans)

# Creating a mosaic plot with the medical innovations -----------------------
x <- classify(medInnovationsDiffNet)
plot(x)

netdiffuseR documentation built on Aug. 30, 2023, 5:07 p.m.