Body Fat Proportions Dataset

Body fat proportions from individuals assisted in a public hospital in Curitiba, Paraná, Brazil.

Usage

bodyfat

Format

A data frame with 298 observations and 10 variables. The five body-fat responses are proportions in \((0,1)\) (for example, 0.163 represents 16.3 percent):

• ID: individual identifier.

• ARMS: arms fat proportion.

• LEGS: legs fat proportion.

• BODY: body fat proportion.

• ANDROID: android fat proportion.

• GYNECOID: gynoid fat proportion.

• AGE: age of individuals.

• BMI: body mass index.

• SEX: 1 for female and 2 for male.

• IPAQ: physical activity level according to IPAQ (0 = sedentary, 1 = insufficiently active, 2 = active).

References

Mazucheli, J., Alves, B., Korkmaz, M. Ç., and Leiva, V. (2022). Vasicek quantile and mean regression models for bounded data: New formulation, mathematical derivations, and numerical applications. Mathematics, 10, 1389.

Mazucheli, J., Leiva, V., Alves, B., and Menezes, A. F. B. (2021). A new quantile regression for modeling bounded data under a unit Birnbaum-Saunders distribution with applications in medicine and politics. Symmetry, 13(4), 1–21.

Petterle, R. R., Bonat, W. H., Scarpin, C. T., Jonasson, T., and Borba, V. Z. C. (2020). Multivariate quasi-beta regression models for continuous bounded data. The International Journal of Biostatistics, 17(1), 39–53.

Author

Josmar Mazucheli jmazucheli@gmail.com

Bruna Alves pg402900@uem.br

Examples

data(bodyfat, package = "vasicekreg")

bodyfat$AGE <- bodyfat$AGE - mean(bodyfat$AGE)
bodyfat$BMI <- bodyfat$BMI - mean(bodyfat$BMI)
bodyfat$SEX <- as.factor(bodyfat$SEX)
bodyfat$IPAQ<- as.factor(bodyfat$IPAQ)

library(gamlss)

## Mean regression model
fitmean <- gamlss(
  ARMS ~ AGE + BMI + SEX + IPAQ,
  data = bodyfat,
  family = NVASIM(mu.link = "logit", sigma.link = "logit")
)
#> GAMLSS-RS iteration 1: Global Deviance = -667.3347 
#> GAMLSS-RS iteration 2: Global Deviance = -908.7608 
#> GAMLSS-RS iteration 3: Global Deviance = -911.2209 
#> GAMLSS-RS iteration 4: Global Deviance = -911.2213 

if (FALSE) { # \dontrun{
## Median regression with the normal kernel
fit_normal <- gamlss(
  ARMS ~ AGE + BMI + SEX + IPAQ,
  data = bodyfat,
  family = NVASIQ(
    quantile = 0.50,
    mu.link = "logit",
    sigma.link = "logit"
  )
)

## Median regression with the logistic kernel
fit_logistic <- gamlss(
  ARMS ~ AGE + BMI + SEX + IPAQ,
  data = bodyfat,
  family = LVASIQ(
    quantile = 0.50,
    mu.link = "logit",
    sigma.link = "logit"
  )
)

## Median regression with the hyperbolic-secant kernel
fit_hsk <- gamlss(
  ARMS ~ AGE + BMI + SEX + IPAQ,
  data = bodyfat,
  family = HVASIQ(
    quantile = 0.50,
    mu.link = "logit",
    sigma.link = "logit"
  )
)

summary(fit_normal)
summary(fit_logistic)
summary(fit_hsk)
} # }