Package {opencesp}

Type:	Package
Title:	Generation and Evaluation of Synthetic Tabular Datasets
Version:	0.4.0
Maintainer:	Rémy Chapelle <remy.chapelle@universite-paris-saclay.fr>
Description:	Various tools developed as part of the Open-CESP (Centre de recherche en Epidémiologie et Santé des Populations) initiative to generate and evaluate synthetic datasets for statistical disclosure control. This includes tools to investigate the risk-utility tradeoff achievable with given synthesis methods, as well as statistical tools to estimate (conditional) probability distributions. The main eventual aim is to help researchers and statisticians disseminate open research data.
License:	GPL-3
URL:	https://opencesp.vjf.inserm.fr/en
Encoding:	UTF-8
NeedsCompilation:	yes
Imports:	stats, cluster, rpart, parallel, fastmap, PCAmixdata, randomForest, mice
Suggests:	gbm
RoxygenNote:	8.0.0
Language:	en-US
Packaged:	2026-05-27 22:04:49 UTC; remyc
Author:	Rémy Chapelle [aut, cre], Centre de recherche en Epidémiologie et Santé des Populations [cph]
Repository:	CRAN
Date/Publication:	2026-06-01 08:30:09 UTC

opencesp: Generation and Evaluation of Synthetic Tabular Datasets

Description

Various tools developed as part of the Open-CESP (Centre de recherche en Epidémiologie et Santé des Populations) initiative to generate and evaluate synthetic datasets for statistical disclosure control. This includes tools to investigate the risk-utility tradeoff achievable with given synthesis methods, as well as statistical tools to estimate (conditional) probability distributions. The main eventual aim is to help researchers and statisticians disseminate open research data.

Author(s)

Maintainer: Rémy Chapelle remy.chapelle@universite-paris-saclay.fr (ORCID)

Authors:

• Rémy Chapelle remy.chapelle@universite-paris-saclay.fr (ORCID)

Other contributors:

• Centre de recherche en Epidémiologie et Santé des Populations [copyright holder]

See Also

Useful links:

• https://opencesp.vjf.inserm.fr/en

Average squared differences between empirical distributions

Description

ASDED returns an empirical distributions-based metric, as proposed in doi:10.29012/jpc.v1i1.568 (see equation 5 in this paper).

Usage

ASDED(orig, synth)

Arguments

Value

The ASDED metric.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

ASDED(orig, synth)

Cross-classification metric in the real-synthetic order

Description

CCM_RS returns the CrCl-RS metric, as described in doi:10.1186/s12874-020-00977-1. This metric is calculated as the average, over categorical variables, of the ratio of the classification accuracy on the original hold-out data to the classification accuracy on the synthetic data. The classifiers used are CARTs. The training and hold-out sets respectively correspond to the first and second half of the original data.

Usage

CCM_RS(orig, synth)

Arguments

Value

The CrCl-RS metric.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

CCM_RS(orig, synth)

Cross-classification metric in the synthetic-real order

Description

CCM_SR returns the CrCl-SR metric, as described in doi:10.1186/s12874-020-00977-1.

Usage

CCM_SR(orig, synth)

Arguments

Value

The CrCl-SR metric.

See Also

CCM_RS()

Examples

data(iris)

orig <- iris

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

CCM_SR(orig, synth)

Cross-regression metric in the real-synthetic order

Description

CRM_RS returns a cross-regression metric, adapted from the CrCl-RS metric described in doi:10.1186/s12874-020-00977-1. This metric is calculated as the average, over numeric variables, of the ratio between the mean squared prediction error obtained on the original hold-out data and the mean squared prediction error obtained on the synthetic data. Regressions are performed using CART. The training and hold-out sets respectively correspond to the first and second half of the original data.

Usage

CRM_RS(orig, synth)

Arguments

Value

The cross-regression metric.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

CRM_RS(orig, synth)

Cross-regression metric in the synthetic-real order

Description

CRM_SR returns a cross-regression metric, adapted from the CrCl-SR metric described in doi:10.1186/s12874-020-00977-1.

Usage

CRM_SR(orig, synth)

Arguments

Value

The cross-regression metric.

See Also

CRM_RS()

Examples

data(iris)

orig <- iris

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

CRM_SR(orig, synth)

Generalized Correct Attribution Probability (GCAP)

Description

GCAP returns the GCAP for a given row of a dataframe, as described in doi:10.48550/arXiv.2310.06571.

Usage

GCAP(row, data, keys, rad_keys, targets, rad_targets)

Arguments

Value

The computed probability.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

target_row <- orig[1, ]

keys <- colnames(orig)[1:3]
targets <- colnames(orig)[4:5]

radii_k <- sapply(Filter(is.numeric, orig[, keys]), function(x) {
  d <- diff(sort(x))
  mean(pmin(c(Inf, d), c(d, Inf)))
})

radii_t <- sapply(Filter(is.numeric, orig[, targets]), function(x) {
  d <- diff(sort(x))
  mean(pmin(c(Inf, d), c(d, Inf)))
})

GCAP(target_row, synth, keys, radii_k, targets, radii_t)

Generalized Targeted Correct Attribution Probability (GTCAP)

Description

GTCAP computes the GTCAP for a synthetic version of a dataframe, as described in doi:10.48550/arXiv.2310.06571.

Usage

GTCAP(orig, synth, keys, rad_keys, targets, rad_targets, n_cores = 1)

Arguments

Value

A list with the following elements:

mean

The standardized mean GCAP for target synthetic rows.

ind

A vector containing standardized GCAP values, one for each target synthetic row.

Examples

data(iris)

orig <- iris[1:25, ] # we use only a sample of the data because GTCAP is computationally demanding

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

target_row <- orig[1, ]

keys <- colnames(orig)[1:3]
targets <- colnames(orig)[4:5]

radii_k <- sapply(Filter(is.numeric, orig[, keys]), function(x) {
  d <- diff(sort(x))
  mean(pmin(c(Inf, d), c(d, Inf)))
})

radii_t <- sapply(Filter(is.numeric, orig[, targets]), function(x) {
  d <- diff(sort(x))
  mean(pmin(c(Inf, d), c(d, Inf)))
})

GTCAP(orig, synth, keys, radii_k, targets, radii_t)$mean

Log-cluster metric

Description

LCM returns a log-cluster metric, as described in doi:10.1186/s12874-020-00977-1. This metric is based on cluster analysis as proposed initially in doi:10.29012/jpc.v1i1.568. Here we perform cluster analysis based on Gower's distance to account for mixed data types (see for example doi:10.48550/arXiv.2101.02481.

Usage

LCM(orig, synth, n_clusters)

Arguments

Value

The log-cluster metric.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

LCM(orig, synth, 3)

Pairwise correlation difference adapted for categorical data

Description

PCD_cat returns a metric adapted from the pairwise correlation difference suggested in doi:10.1186/s12874-020-00977-1. Contrary to the original proposal, the matrices from which the Frobenius norm is returned contain Cramer's V rather than Pearson correlation coefficients. Consequently, the metric is suitable to nominal data, and is computed on subsets of the input dataframes represented by all their categorical variables.

Usage

PCD_cat(orig, synth)

Arguments

Value

The adapted pairwise correlation difference.

Examples

data(iris)

# categorize numeric variables for the example
iris[] <- lapply(iris, function(x) if(is.numeric(x)) cut(x, 2) else x)

orig <- iris

# simple synthesis by sampling with replacement
synth <- iris[sample(1:nrow(orig), nrow(orig), replace = TRUE), ]

PCD_cat(orig, synth)

Pairwise correlation difference

Description

PCD_num returns the pairwise correlation difference, as defined in doi:10.1186/s12874-020-00977-1. The metric is computed on subsets of the input dataframes represented by all their numeric variables.

Usage

PCD_num(orig, synth)

Arguments

Value

The pairwise correlation difference.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling with replacement
synth <- iris[sample(1:nrow(orig), nrow(orig), replace = TRUE), ]

PCD_num(orig, synth)

Adaptive proportion of matches

Description

adaptive_matches_prop returns the proportion of rows in an original dataframe that can be found in a synthetic dataframe.

Usage

adaptive_matches_prop(orig, synth)

Arguments

Details

Matching is based on Gower's distance and is adaptive: an original row is counted as matched when its nearest synthetic row is closer than its nearest other original row.

Value

The proportion.

Examples

data(iris)

orig <- iris

synth <- iris[sample(1:nrow(orig), nrow(orig), replace = TRUE), ]

adaptive_matches_prop(orig, synth)

Avatarization of a dataset

Description

avatarize returns a dataframe containing avatars of original observations, as described in doi:10.1038/s41746-023-00771-5.

Usage

avatarize(data, k, npc)

Arguments

Details

Avatarization being a stochastic procedure, this function does not necessarily give the same result for successive calls with the same parameters. The distance metric used in this implementation of the Avatar method is the Euclidean distance.

Value

The avatar dataframe.

Examples

data(iris)

orig <- iris[sapply(iris, is.numeric)]

synth <- avatarize(orig, k = 5, npc = 2)

plot(orig$Sepal.Width, orig$Sepal.Length)
points(synth$Sepal.Width, synth$Sepal.Length, col = "red")

Membership F1 metric

Description

cor_F1 returns a corrected F1 membership disclosure metric, as described in doi:10.1093/jamiaopen/ooac083.

Usage

cor_F1(orig_train, orig_ho, synth, m, h, N, t_prop = nrow(orig_train)/N)

Arguments

Value

The membership disclosure metric, or NA if the F1 score is undefined.

Examples

data(iris)

orig <- iris
ids_train <- sample(1:nrow(orig), round(nrow(orig) * 0.8))
orig_train <- orig[ids_train, ]
orig_ho <- orig[-ids_train, ]

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig_train, function(x) {
                          x[sample(length(x), nrow(orig_train), replace = TRUE)]
                       }))

cor_F1(orig_train, orig_ho, synth, m = 20, h = 2, N = 1000)

Distance to the closest record

Description

dcr returns the median distance between each synthetic unit and its nearest original neighbor. This type of metric was notably suggested in doi:10.14778/3231751.3231757. Here, Gower's distance is used to account for mixed data types; see, for example, doi:10.48550/arXiv.2101.02481.

Usage

dcr(orig, synth, method = c("auto", "euclidean", "gower"))

Arguments

Value

The median distance.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

dcr(orig, synth)

Dependency order in a data set

Description

dep_order returns the dependency order of variables determined by hierarchical clustering.

Usage

dep_order(
  df,
  blocks = rep(1, ncol(df)),
  score_func,
  bf_first = TRUE,
  n_bf = 1,
  n_cores = 1
)

Arguments

Value

A numeric vector representing the order of dependency.

Examples

data(iris)

iris <- iris[sapply(iris, is.numeric)]

score_lin <- function(x, y) {
  if(is.null(x)) resids <- y-mean(y)
  else {
    df <- cbind.data.frame(y, x)
    fit <- lm(y ~ ., data = df)
    resids <- fit$residuals
  }
  sd_hat <- sd(resids)
  return(sum(dnorm(resids, sd = sd_hat, log = TRUE)))
}

dep_order(iris, score_func = score_lin)

Precision of numeric variables

Description

get_prec returns the number of decimal places found in a numeric vector.

Usage

get_prec(vec, max = TRUE)

Arguments

Value

Either a single integer or an integer vector.

Examples

data(iris)

sapply(iris, get_prec)

Estimated Hellinger distance

Description

hellinger_distance returns an estimate of the Hellinger distance between original and synthetic data, as suggested (for example) in doi:10.1109/ACCESS.2022.3144765. Missing values in the input vectors are ignored.

Usage

hellinger_distance(orig, synth)

Arguments

Value

The estimated Hellinger distance.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling with replacement
synth <- iris[sample(1:nrow(orig), nrow(orig), replace = TRUE), ]

hellinger_distance(orig$Species, synth$Species)

Random-forest imputation

Description

impute_rf imputes missing values in a dataframe using the random-forest method from mice.

Usage

impute_rf(df, ...)

Arguments

Value

A dataframe with imputed values.

Examples

data(airquality)

colSums(is.na(airquality))

airquality <- impute_rf(airquality)

colSums(is.na(airquality))

Independent blocks of variables

Description

ind_blocks finds approximately independent blocks of variables in a dataset by hierarchical clustering.

Usage

ind_blocks(df, crit = c("dim", "n.int"), max_size = ncol(df)/2, n_cores = 1)

Arguments

Value

An integer vector representing membership of each variable to the found blocks.

Examples

data(iris)

iris <- iris[sapply(iris, is.numeric)]

blocks <- ind_blocks(iris, max_size = 2)

score_lin <- function(x, y) {
  if(is.null(x)) resids <- y-mean(y)
  else {
    df <- cbind.data.frame(y, x)
    fit <- lm(y ~ ., data = df)
    resids <- fit$residuals
  }
  sd_hat <- sd(resids)
  return(sum(dnorm(resids, sd = sd_hat, log = TRUE)))
}

dep_order(iris, score_func = score_lin, blocks = blocks)

Overlap of confidence intervals

Description

interval_overlap returns the overlap of confidence intervals constructed from original and synthetic data, as described (for example) in doi:10.1111/rssa.12358. Intervals are constructed from t distributions in case of numeric data, and with the Clopper–Pearson method for binomial intervals in case of categorical data.

Usage

interval_overlap(orig, synth, cat = NULL, conf.level = 0.95)

Arguments

Value

The computed overlap.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling with replacement
synth <- iris[sample(1:nrow(orig), nrow(orig), replace = TRUE), ]

interval_overlap(orig$Sepal.Width, synth$Sepal.Width)

Proportion of matches

Description

matches_prop returns the proportion of rows in an original dataframe that can be found in a synthetic dataframe.

Usage

matches_prop(orig, synth, method = c("exact", "gower"), thr = NULL)

Arguments

Details

Duplicates within each dataframe are treated as a single record when method = "exact". If thr = NULL, the threshold is chosen automatically as the average nearest-neighbour distance between rows of orig. It represents the typical spacing between observations in the original data.

Value

The proportion.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling with replacement
synth <- iris[sample(1:nrow(orig), nrow(orig), replace = TRUE), ]

matches_prop(orig, synth)

Mean Hellinger distance

Description

mean_hellinger returns the mean estimated Hellinger distance across all variables of original and synthetic datasets. This is close to doi:10.1093/jamia/ocaa249, but using the mean instead of the median.

Usage

mean_hellinger(orig, synth)

Arguments

Value

The average estimated Hellinger distance.

See Also

hellinger_distance()

Examples

data(iris)

orig <- iris

# simple synthesis by sampling with replacement
synth <- iris[sample(1:nrow(orig), nrow(orig), replace = TRUE), ]

mean_hellinger(orig, synth)

Outlier coverage

Description

outlier_coverage returns the outlier coverage, which is originally a privacy metric that compares two marginal distributions. Here its average value across all variables in the original data is returned .

Usage

outlier_coverage(orig, synth)

Arguments

Value

The value of the metric.

See Also

SDMetrics documentation: OutlierCoverage

Examples

data(iris)

orig <- iris

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

outlier_coverage(orig, synth)

Outlier learning factor

Description

outlier_learning_factor returns the outlier learning factor, a privacy metric which aims at quantifying the tendency of a synthesizer to produce observations that lie in low-density regions of the original data. The metric is defined as the average distance between outliers (as defined by the user) and their nearest synthetic neighbor.

Usage

outlier_learning_factor(orig, outlier_ids, synth)

Arguments

Details

The distance used is Gower's distance.

Value

The value of the metric.

Examples

data(iris)

orig <- iris[sapply(iris, is.numeric)]

# find the 5% most atypical observations by a nearest-neighbor rule
D <- as.matrix(dist(orig, method = "euclidean"))
score <- apply(D, 1, function(x) sort(x)[2])
idx_outliers <- order(score, decreasing = TRUE)[seq_len(ceiling(0.05 * nrow(orig)))]

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

outlier_learning_factor(orig, idx_outliers, synth)

Propensity score mean-squared error

Description

pMSE returns the propensity score mean-squared error obtained from an original and synthetic dataset. For each variable, numeric values in the synthetic data are replaced with the nearest value in the original data. This prevents tree-based classifiers (used to estimate the propensity scores) to produce undesirable splits based only on the marginal distributions.

Usage

pMSE(orig, synth, method = c("cart", "gbm", "glm"), formula = syn ~ ., ...)

Arguments

Value

The metric value.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

pMSE(orig, synth, method = "glm")

Heuristics for the complexity parameter of tree-based estimation of propensity scores

Description

pMSE_cp returns the mean complexity parameter obtained by tuning decision trees (with rpart::rpart()) to estimate propensity scores from a list of synthetic dataframes. Typically, this would be used to choose the complexity parameter for computing pMSE values.

Usage

pMSE_cp(orig, synth_l, xval = 25, ...)

Arguments

Value

The mean complexity value.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

cp <- pMSE_cp(orig, list(synth))

pMSE(orig, synth, method = "cart", cp = cp)

Parallel gradient boosting

Description

pgb fits a PGB model.

Usage

pgb(formula, data, pvalid = 0.2, valid_data = NULL, M = 50, ...)

Arguments

Details

Note that if valid_data is provided, then pvalid must be 0.

Value

An object of class "pgb".

Examples

data(iris)

train_data <- iris[1:100, ]
valid_data <- iris[101:nrow(iris), ]

# train with a specified validation set
fit <- pgb(Sepal.Width ~ ., data = train_data, valid_data = valid_data, pvalid = 0)

# randomly select 20% of observations to form a validation set
fit <- pgb(Sepal.Width ~ ., data = iris, pvalid = 0.2)

# train without early stopping
fit <- pgb(Sepal.Width ~ ., data = iris, pvalid = 0, ntrees = 50)

Controls for PGB fits

Description

pgb_control encapsulates the hyperparameters to be used for fitting PGB models.

Usage

pgb_control(
  ntrees = 10000,
  early_stopping_rounds = 100,
  maxdepth = 4,
  minbucket = 5,
  eta = 0.02,
  subsample = 0.5,
  maxbin = 256
)

Arguments

Value

A list with the options.

Examples

data(iris)

controls <- pgb_control(ntrees = 1) # to fit a one-tree PGB model

# the following two lines are equivalent
fit <- do.call(pgb_cvh, c(list(formula = Sepal.Width ~ ., data = iris), controls))
fit <- pgb_cvh(Sepal.Width ~ ., data = iris, ntrees = 1)

Parallel gradient boosting with cross-validation

Description

pgb_cvh fits a PGB model, with its hyperparameters determined by cross-validation

Usage

pgb_cvh(formula, data, nfolds = 5, select_h = c("greedy", "cv", "none"), ...)

Arguments

Value

An object of class "pgb".

Examples

data(iris)

fit <- pgb_cvh(Sepal.Width ~ ., data = iris)

Predict method for PGB models

Description

predict.pgb predicts quantile values from a trained PGB model.

Usage

## S3 method for class 'pgb'
predict(object, newdata, project = TRUE, ...)

Arguments

Value

A matrix of predictions, where each line corresponds to an observation and each column to a quantile level.

Examples

data(iris)

fit <- pgb_cvh(Sepal.Width ~ Petal.Length, data = iris)

preds <- predict(fit, iris)

plot(iris$Petal.Length, iris$Sepal.Width)

matlines(iris$Petal.Length[order(iris$Petal.Length)],
         preds[order(iris$Petal.Length), ], type = "l", lty = 1)

Predict conditional densities from PGB models

Description

predict_cde_pgb predicts smooth conditional densities from a trained PGB model.

Usage

predict_cde_pgb(object, newdata, y_grid)

Arguments

Value

A matrix of predictions, where each line corresponds to an observation and each column to a density value on the provided grid.

Examples

data(iris)

fit <- pgb_cvh(Sepal.Width ~ Sepal.Length, data = iris)

y_grid <- seq(from = min(iris$Sepal.Width), to = max(iris$Sepal.Width), length.out = 200)
ids_x <- c(25, 80, 125)

cde <- predict_cde_pgb(fit, iris[ids_x, ], y_grid = y_grid)

x0 <- iris[ids_x, "Sepal.Length"]
s <- diff(range(iris$Sepal.Length)) / 8 / max(cde)

plot(iris$Sepal.Length, iris$Sepal.Width, xlab = "Sepal.Length", ylab = "Sepal.Width")
for(i in seq_len(nrow(cde))) lines(x0[i] + s * cde[i, ], y_grid, lwd = 2)

Piecewise-constant conditional densities from PGB models

Description

predict_cde_pgb_raw predicts histogram-like conditional densities (i.e. with no smoothing performed) from a trained PGB model.

Usage

predict_cde_pgb_raw(object, newdata, y_grid)

Arguments

Value

A matrix of predictions, where each line corresponds to an observation and each column to a density value on the provided grid.

Examples

data(iris)

fit <- pgb_cvh(Sepal.Width ~ Sepal.Length, data = iris, select_h = "none")

y_grid <- seq(from = min(iris$Sepal.Width), to = max(iris$Sepal.Width), length.out = 200)
ids_x <- c(25, 80, 125)

cde <- predict_cde_pgb_raw(fit, iris[ids_x, ], y_grid = y_grid)

x0 <- iris[ids_x, "Sepal.Length"]
s <- diff(range(iris$Sepal.Length)) / 8 / max(cde)

plot(iris$Sepal.Length, iris$Sepal.Width, xlab = "Sepal.Length", ylab = "Sepal.Width")
for(i in seq_len(nrow(cde))) lines(x0[i] + s * cde[i, ], y_grid, lwd = 2)

Sample conditionally on vector length

Description

resample samples from a vector if it contains more than one element, and otherwise returns its only element.

Usage

resample(vec, ...)

Arguments

Value

A sampled value or vector, depending on ....

See Also

base::sample()

Examples

vec <- 1:5 # sample and resample coincide when given a vector of length > 1

set.seed(1234)
sample(vec, 1)

set.seed(1234)
resample(vec, 1)

vec <- c(5) # with vectors of length 1, sample can return values that are not in the vector

set.seed(1234)
sample(vec, 1) # sample(c(5), 1) is equivalent to sample(1:5, 1)

set.seed(1234)
resample(vec, 1) # no problem with resample

Round synthetic numeric variables to the precision observed in the original data

Description

Round synthetic numeric variables to the precision observed in the original data

Usage

round_synth(orig, synth)

Arguments

Value

A dataframe whose numeric columns are rounded with the precision observed in orig.

Examples

data(iris)

orig <- iris[sapply(iris, is.numeric)]

# synthesis by sampling from a multivariate Gaussian distribution
# with parameters estimated from the data
synth <- setNames(
  as.data.frame(
    sweep(
      matrix(rnorm(150 * ncol(orig)), 150) %*%
      chol(cov(orig) * (nrow(orig) - 1) / nrow(orig)),
      2,
      colMeans(orig),
      "+"
    )
  ),
  names(orig)
)

sapply(synth, get_prec)

synth <- round_synth(orig, synth)

sapply(synth, get_prec)

ts-AUC

Description

tsAUC computes the AUC and associated p value from a two-sample test according to doi:10.1145/3411408.3411422.

Usage

tsAUC(orig, synth, ntreeTry = 50, ntree = 500, ...)

Arguments

Value

A list with the following elements:

auc

The estimated area under the ROC curve.

p.value

The p value associated with a one-sided Wilcoxon test.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

tsAUC(orig, synth)$p.value

Univariate correct attribution probability

Description

univ_att_prob computes the univariate correct attribution probability from given parameters, as described in doi:10.48550/arXiv.2310.06571.

Usage

univ_att_prob(row, data, rad)

Arguments

Details

When the input dataframe contains only categorical variables, the value returned is the proportion of rows in the dataframe having the same combination of values as the input row. When the input dataframe also contains numeric variables, this value is weighted according to the proximity between these variables in the input row vs. each other row of the dataframe. The weights are given by radii passed as parameters.

Value

The computed probability.

Examples

data(iris)

orig <- iris

# simple synthesis by sampling from the product of the marginal distributions
synth <- as.data.frame(lapply(orig, function(x) x[sample(length(x), nrow(orig), replace = TRUE)]))

target_row <- orig[1, ]

radii <- sapply(Filter(is.numeric, orig), function(x) {
  d <- diff(sort(x))
  mean(pmin(c(Inf, d), c(d, Inf)))
})

univ_att_prob(target_row, synth, radii)