Cleveland: Cleveland Heart Disease Dataset
In glmbayes: Bayesian Generalized Linear Models (IID Samples)

Cleveland

R Documentation

Cleveland Heart Disease Dataset

Description

A cleaned version of the Cleveland heart disease dataset from the UCI Machine Learning Repository. This version contains only complete cases and includes a derived binary outcome variable hd indicating the presence ("Yes") or absence ("No") of heart disease.

Usage

data("Cleveland")

Format

A data frame with 297 observations and 15 variables:

age: Age in years (numeric).
sex: Sex (0 = female, 1 = male).
cp: Chest pain type (numeric code 1–4).
trestbps: Resting blood pressure (mm Hg).
chol: Serum cholesterol (mg/dl).
fbs: Fasting blood sugar > 120 mg/dl (1 = true, 0 = false).
restecg: Resting electrocardiographic results (numeric code).
thalach: Maximum heart rate achieved.
exang: Exercise-induced angina (1 = yes, 0 = no).
oldpeak: ST depression induced by exercise relative to rest.
slope: Slope of the peak exercise ST segment.
ca: Number of major vessels colored by fluoroscopy (0–3).
thal: Thalassemia status (numeric code).
num: Original UCI disease score (0–4).
hd: Binary heart disease indicator: "No" (num = 0) or "Yes" (num > 0).

Source

UCI Machine Learning Repository: Heart Disease Data Set. https://archive.ics.uci.edu/dataset/45/heart+disease

Examples

############################### Start of Cleveland dataset example ####################

data("Cleveland")
head(Cleveland)
summary(Cleveland)

# OpenCL-accelerated Bayesian logistic regression example
# This only runs off CRAN when OpenCL is available
if (identical(Sys.getenv("NOT_CRAN"), "true") && has_opencl()) {
  ps <- Prior_Setup(
    hd ~ age + sex + cp + trestbps + chol +
      fbs + restecg + thalach + exang + oldpeak + slope + ca + thal,
    family = binomial(logit),
    data = Cleveland
  )

  fit <- glmb(
    hd ~ age + sex + cp + trestbps + chol +
      fbs + restecg + thalach + exang + oldpeak + slope + ca + thal,
    family       = binomial(link = "logit"),
    pfamily      = dNormal(mu = ps$mu, Sigma = ps$Sigma),
    data         = Cleveland,
    n            = 1000,
    Gridtype     = 2,
    use_parallel = TRUE,
    use_opencl   = TRUE,
    verbose      = FALSE
  )
  summary(fit)
} else {
  message("Skipping OpenCL example (CRAN check or OpenCL not built).")
}
###############################################################################
## End of Cleveland dataset example
###############################################################################

glmbayes documentation built on Aug. 5, 2026, 1:07 a.m.