Package 'synthesizer'

Create a function that generates synthetic data

Description

Create a function that accepts a non-negative integer n, and that returns synthetic data sampled from the emperical (multivariate) distribution of x.

Usage

make_synthesizer(x, ...)

## S3 method for class 'numeric'
make_synthesizer(x, ...)

## S3 method for class 'integer'
make_synthesizer(x, ...)

## S3 method for class 'logical'
make_synthesizer(x, ...)

## S3 method for class 'factor'
make_synthesizer(x, ...)

## S3 method for class 'character'
make_synthesizer(x, ...)

## S3 method for class 'ts'
make_synthesizer(x, ...)

## S3 method for class 'data.frame'
make_synthesizer(x, rankcor = 1, ...)

Arguments

x	[vector|data.frame] Template data to be synthesized.
...	arguments passed to other methods
rankcor	[numeric] in $(0,1]$ The correlations between the ranks of the real data and synthetic data. Either a single number or a vector of the form c("variable1"=x1,...). Only used if x is a data frame.

Value

A function accepting a single integer argument: the number of synthesized values or records to return.

See Also

Other synthesis: synthesize()

Examples

synth <- make_synthesizer(cars$speed)
synth(10)


synth <- make_synthesizer(iris)
synth(6)
synth(150)
synth(250)

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Compute the pMSE metric between synthetic and real data

Description

The propensity mean squared error is defined as $\frac{1}{N}\sum_{i=1}^N(p_i-c)^2$, where $c$ is the number of synthetic records, divided by the sum of the number of synthetic and real records.

Usage

pmse(synth, real, model = c("lr", "rf"), nrep = NULL)

Arguments

synth	[data.frame] Synthesized data.
real	[real] Data to compare with the synthesized data.
model	[character] Model used to compute propensity scores. Options are "lr": logistic regression, and "rf": random forest.
nrep	[integer] Number of model repetitions to average the pMSE value over. Ignored for lr.

Value

[numeric] scalar.

Examples

scars <- synthesize(cars)
pmse(scars, cars)

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Create synthetic version of a dataset

Description

Create n values or records based on the emperical (multivariate) distribution of y. For data frames it is possible to decorrelate synthetic from the original variables by lowering the value for the rankcor parameter.

Usage

synthesize(x, n = NROW(x), rankcor = 1)

Arguments

x	[vector|data.frame] data to synthesize.
n	[integer] Number of values or records to synthesize.
rankcor	[numeric] in $[0,1]$. Either a single rank correlation value that is applied to all variables, or a vector of the form c(variable1=ut1lity1,...). Variables not explicitly mentioned will have rankcor=1. See also the note below.

Value

A data object of the same type and structure as x.

Note

The utility of a synthetic variable is lowered by decorelating the rank correlation between the real and synthetic data. If rankcor=1, the synthetic data will ordered such that it has the same rank order as the original data. If rankcor=0, no such reordering will take place. For values between 0 and 1, blocks of data are randomly selected and randomly permuted iteratively until the rank correlation between original and synthetic data drops below the parameter.

See Also

Other synthesis: make_synthesizer()

Examples

synthesize(cars$speed,10)
synthesize(cars)
synthesize(cars,25)

s1 <- synthesize(iris, rankcor=1)
s2 <- synthesize(iris, rankcor=0.5)
s3 <- synthesize(iris, rankcor=c("Species"=0.5))

oldpar <- par(mfrow=c(2,2), pch=16, las=1)
plot(Sepal.Length ~ Sepal.Width, data=iris, col=iris$Species, main="Iris")
plot(Sepal.Length ~ Sepal.Width, data=s1, col=s1$Species, main="Synthetic Iris")
plot(Sepal.Length ~ Sepal.Width, data=s2, col=s2$Species, main="Low utility Iris")
plot(Sepal.Length ~ Sepal.Width, data=s3, col=s3$Species, main="Low utility Species")
par(oldpar)

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