| bodyfat | R Documentation |
Percentage of body fat measurements from individuals assisted in a public hospital in Curitiba, Paraná, Brazil.
bodyfat
A data frame with 298 observations and 9 variables:
ARMS: arms fat percentage.
LEGS: legs fat percentage.
BODY: body fat percentage.
ANDROID: android fat percentage.
GYNECOID: gynoid fat percentage.
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).
Josmar Mazucheli jmazucheli@gmail.com
Bruna Alves pg402900@uem.br
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.
data(bodyfat, package = "vasicekreg")
bodyfat$BMI <- bodyfat$BMI / 100
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 = VASIM(mu.link = "logit", sigma.link = "logit")
)
## Not run:
## Quantile regression models for different tau levels
fittaus <- lapply(c(0.10, 0.25, 0.50, 0.75, 0.90), function(Tau) {
tau <<- Tau
gamlss(
ARMS ~ AGE + BMI + SEX + IPAQ,
data = bodyfat,
family = VASIQ(mu.link = "logit", sigma.link = "logit")
)
})
sapply(fittaus, summary)
## End(Not run)
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