R: Prediction of Body Fat by Skinfold Thickness, Circumferences,...
bodyfat
R Documentation
Prediction of Body Fat by Skinfold Thickness, Circumferences, and
Bone Breadths
Description
For 71 healthy female subjects, body fat measurements and several
anthropometric measurements are available for predictive modelling
of body fat.
Usage
data("bodyfat")
Format
A data frame with 71 observations on the following 10 variables.
age
age in years.
DEXfat
body fat measured by DXA, response variable.
waistcirc
waist circumference.
hipcirc
hip circumference.
elbowbreadth
breadth of the elbow.
kneebreadth
breadth of the knee.
anthro3a
sum of logarithm of three anthropometric measurements.
anthro3b
sum of logarithm of three anthropometric measurements.
anthro3c
sum of logarithm of three anthropometric measurements.
anthro4
sum of logarithm of three anthropometric measurements.
Details
Garcia et al. (2005) report on the development of predictive regression equations
for body fat content by means of common anthropometric
measurements which were obtained for 71 healthy German women.
In addition, the women's body composition was measured by
Dual Energy X-Ray Absorptiometry (DXA). This reference method
is very accurate in measuring body fat but finds little applicability
in practical environments, mainly because of high costs and the
methodological efforts needed. Therefore, a simple regression equation
for predicting DXA measurements of body fat is of special interest for the practitioner.
Backward-elimination was applied to select
important variables from the available anthropometrical measurements, and
Garcia (2005) report a final linear model utilizing
hip circumference, knee breadth and a compound covariate which is defined as
the sum of log chin skinfold, log triceps skinfold and log subscapular skinfold.
Source
Ada L. Garcia, Karen Wagner, Torsten Hothorn, Corinna Koebnick,
Hans-Joachim F. Zunft and Ulrike Trippo (2005),
Improved prediction of body fat by measuring skinfold
thickness, circumferences, and bone breadths. Obesity Research,
13(3), 626–634.
Peter Buehlmann and Torsten Hothorn (2007),
Boosting algorithms: regularization, prediction and model fitting.
Statistical Science, 22(4), 477–505.
Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid
(2012). Model-based Boosting in R: A Hands-on Tutorial Using the R
Package mboost. Computational Statistics. http://dx.doi.org/10.1007/s00180-012-0382-5
Available as vignette via: vignette(package = "mboostDevel", "mboost_tutorial")
Examples
data("bodyfat", package = "TH.data")
### final model proposed by Garcia et al. (2005)
fmod <- lm(DEXfat ~ hipcirc + anthro3a + kneebreadth, data = bodyfat)
coef(fmod)