Description Usage Format Details Source Examples

For 71 healthy female subjects, body fat measurements and several anthropometric measurements are available for predictive modelling of body fat.

1 | ```
data("bodyfat")
``` |

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.

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.

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")

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