This data set illustrates the relationship between body measurements and
body fat in 252 males aged between 21 and 81 years.
Usage
data(fat)
Format
A data frame with 252 rows and 26 variables.
Obs
observation number.
Perc.body.fat.Brozek
percent body fat using Brozek's
equation, i.e., 457/Density - 414.2.
Perc.body.fat.Siri
percent body fat using Siri's
equation, i.e., 495/Density - 450.
Density
density (frac{g}{cm^3}).
Age
age (years).
Weight
weight (lbs).
Height
height (inches).
Adiposity.index
adiposity index computed as Weight/Height^2 (frac{kg}{m^2}).
Fat.free.weight
fat free weight computed as (1 -
Brozek's percent body fat) * Weight (lbs).
Neck.circ
neck circumference (cm).
Chest.circ
chest circumference (cm).
Abdomen.circ
abdomen circumference (cm) measured at
the umbilicus and level with the iliac crest.
Hip.circ
hip circumference (cm).
Thigh.circ
thigh circumference (cm).
Knee.circ
knee circumference (cm).
Ankle.circ
ankle circumference (cm).
Biceps.circ
extended biceps circumference (cm).
Forearm.circ
forearm circumference (cm).
Wrist.circ
wrist circumference (cm).
inv.Density
inverse of density (frac{cm^3}{g}).
z1
log of weight divided by log of height (allometric measure).
z2
abdomen circumference divided by chest
circumference (beer gut factor).
z3
index based on knee, wrist, and ankle circumference
relative to height (frac{(Knee.circ * Wrist.circ * Ankle.circ)^(1/3)}{Height}).
z4
fleshiness index based on biceps, thigh, forearm,
knee, wrist, and ankle circumference (frac{Biceps.circ *
Thigh.circ * Forearm.circ}{Knee.circ * Wrist.circ *
Ankle.circ}).
z5
age standardized to zero mean and unit variance.
z6
square of standardized age.
Details
Burnham and Anderson (2002, p. 268) use this data set to show model
selection uncertainty in the context of all possible combinations of
explanatory variables. The data are originally from Penrose et
al. (1985) who used only the first 143 cases of the 252 observations in
the data set. Johnson (1996) later used these data as an example of
multiple regression. Note that observation number 42 originally had an
erroneous height of 29.5 inches and that this value was changed to 69.5
inches.
Burnham and Anderson (2002, p. 274) created six indices based on the
original measurements (i.e., z1 – z6). Although Burnham and Anderson
(2002) indicate that the fleshiness index (z4) involved the cubic
root in the equation, the result table for the full model on p. 276
suggests that the index did not include the cubic root for z4.
The latter is the version of z4 used in the data set here.
Source
Burnham, K. P., Anderson, D. R. (2002) Model Selection and
Multimodel Inference: a practical information-theoretic
approach. Second edition. Springer: New York.
Johnson, J. W. (1996). Fitting percentage of body fat to simple body
measurements. Journal of Statistics Education4 [online].
Penrose, K., Nelson, A., Fisher, A. (1985) Generalized body composition
prediction equation for men using simple measurement techniques
Medicine and Science in Sports and Exercise17, 189.
Examples
data(fat)
str(fat)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(AICcmodavg)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AICcmodavg/fat.Rd_%03d_medium.png", width=480, height=480)
> ### Name: fat
> ### Title: Fat Data and Body Measurements
> ### Aliases: fat
> ### Keywords: datasets
>
> ### ** Examples
>
> data(fat)
> str(fat)
'data.frame': 252 obs. of 26 variables:
$ Obs : int 1 2 3 4 5 6 7 8 9 10 ...
$ Perc.body.fat.Brozek: num 12.6 6.9 24.6 10.9 27.8 20.6 19 12.8 5.1 12 ...
$ Perc.body.fat.Siri : num 12.3 6.1 25.3 10.4 28.7 20.9 19.2 12.4 4.1 11.7 ...
$ Density : num 1.07 1.09 1.04 1.08 1.03 ...
$ Age : int 23 22 22 26 24 24 26 25 25 23 ...
$ Weight : num 154 173 154 185 184 ...
$ Height : num 67.8 72.2 66.2 72.2 71.2 ...
$ Adiposity.index : num 23.7 23.4 24.7 24.9 25.6 26.5 26.2 23.6 24.6 25.8 ...
$ Fat.free.weight : num 135 161 116 165 133 ...
$ Neck.circ : num 36.2 38.5 34 37.4 34.4 39 36.4 37.8 38.1 42.1 ...
$ Chest.circ : num 93.1 93.6 95.8 101.8 97.3 ...
$ Abdomen.circ : num 85.2 83 87.9 86.4 100 94.4 90.7 88.5 82.5 88.6 ...
$ Hip.circ : num 94.5 98.7 99.2 101.2 101.9 ...
$ Thigh.circ : num 59 58.7 59.6 60.1 63.2 66 58.4 60 62.9 63.1 ...
$ Knee.circ : num 37.3 37.3 38.9 37.3 42.2 42 38.3 39.4 38.3 41.7 ...
$ Ankle.circ : num 21.9 23.4 24 22.8 24 25.6 22.9 23.2 23.8 25 ...
$ Biceps.circ : num 32 30.5 28.8 32.4 32.2 35.7 31.9 30.5 35.9 35.6 ...
$ Forearm.circ : num 27.4 28.9 25.2 29.4 27.7 30.6 27.8 29 31.1 30 ...
$ Wrist.circ : num 17.1 18.2 16.6 18.2 17.7 18.8 17.7 18.8 18.2 19.2 ...
$ inv.Density : num 0.934 0.921 0.96 0.93 0.967 ...
$ z1 : num 1.2 1.2 1.2 1.22 1.22 ...
$ z2 : num 0.915 0.887 0.918 0.849 1.028 ...
$ z3 : num 0.355 0.348 0.376 0.345 0.367 ...
$ z4 : num 3.7 3.26 2.79 3.7 3.14 ...
$ z5 : num -1.74 -1.82 -1.82 -1.5 -1.66 ...
$ z6 : num 3.02 3.3 3.3 2.25 2.75 ...
>
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> dev.off()
null device
1
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