Data_MC_contamination: Moderate-correlation dataset with contamination

In dbrobust: Robust Distance-Based Visualization and Analysis of Mixed-Type Data

Moderate-correlation dataset with contamination

Description

Synthetic dataset generated from a multivariate normal distribution with moderate correlation structure (\rho = 0.6). It contains 525 observations and 10 variables of mixed type (continuous, categorical, binary, and weights). The last 25 rows correspond to contaminated observations created by adding perturbations equal to three times the standard deviation of each quantitative variable to a subset of original units. This results in a controlled 5% contamination level. These data follow the design in \insertCiteboj2024robustificationdbrobust.

Usage

Data_MC_contamination

Format

A data frame with 525 rows and 10 variables:

V1

Continuous variable 1

V2

Continuous variable 2

V3

Continuous variable 3

V4

Continuous variable 4

V5

Categorical variable 1 (3 categories, approx. balanced)

V6

Categorical variable 2 (3 categories, approx. balanced)

V7

Categorical variable 3 (4 categories, uniform distribution)

V8

Binary variable 1 (40% zeros, 60% ones)

V9

Binary variable 2 (60% zeros, 40% ones)

w_loop

Observation weights derived from the joint distribution of V5 and V8, following a proportional frequency-based scheme.

Details

• Continuous variables were drawn directly from the multivariate normal sample.

• Binary and categorical variables were obtained by discretizing normal margins using percentile-based thresholds.

• Contaminated observations (rows 501–525) were generated by perturbing original cases with fluctuations of 3 SD.

• The weighting scheme prioritizes frequent category combinations.

References

\insertRef

boj2024robustificationdbrobust

dbrobust documentation built on Nov. 5, 2025, 6:24 p.m.