Your question is much too broad for this list. It is not clear from
your question how much statistics you know and what you have already
done. (It also raises the concern that this may be a homework
assignment.)
Have you tried
ox.lm <- lm(vo2 ~ speed * stride * mass, data = DATA)
But you really need a mixed model which would require you to
understand the above in detail and then use lmer() to fit mixed
models as in the lme4 package.
On Mar 18, 2007, at 2:07 PM, pasarah wrote:
> We are two french students and we have a problem concerning an
> exercize.
> We don't know how to resolve it.
> It would be fantastic if someone can help us.
> Thanks.
>
> Description:
> This study examined how the metabolic cost of locomotion varied
> with speed, stride frequency and body mass. Cost was determined
> by measuring oxygen consumption (’vo2’), analyzing the oxygen
> content in air inhaled and exhaled by the subjects through a mask.
> The rate of oxgen consumption was measured for three ’subject’s
> locomoting at all combinations of low and high levels of the three
> factors - running ’speed’, ’stride’ frequency, and ’mass’
> distribution in the leg. The first factor was set using a
> treadmill, the second by synchronizing the subjects’ pace with a
> metronome, and the third by varying the positions of weights
> strapped onto the legs or waist. In addition to these three
> design factors, other variables were measured by filming each
> trial with a high-speed motion camera. Ignore these other
> measurements for this problem.
> Format:
> Running data frame with 24 observations on 13 variables.
> [,1] subject factor subject identifier
> [,2] order ordered order of treatment
> [,3] speed factor running speed
> [,4] stride numeric stride frequency
> [,5] mass numeric attached mass in g
> [,6] vo2 numeric oxygen consumption
> [,7] tm numeric time
> [,8] etot numeric etot
> [,9] cn numeric cn
> [,10] ar numeric ar
> [,11] ecn numeric ecn
> [,12] ahm numeric ahm
> [,13] vhm numeric vhm
>
> DATA
> subject order speed stride mass vo2 tm etot cn ar ecn ahm vhm
> 1 1 4 2.68 138 W 2838 71.8 221.14 0.381 0.053 0.260 15.2 13.7
> 2 1 8 2.68 138 L 2905 71.8 221.14 0.356 0.084 0.220 16.4 13.6
> 3 1 7 2.68 146 W 2630 71.8 221.14 0.359 0.056 0.225 15.0 12.7
> 4 1 3 2.68 146 L 2696 71.8 221.14 0.351 0.061 0.225 14.2 12.4
> 5 1 1 3.13 138 W 3100 71.8 270.69 0.335 0.099 0.285 13.5 14.2
> 6 1 6 3.13 138 L 3343 71.8 270.69 0.313 0.119 0.230 16.6 14.7
> 7 1 2 3.13 146 W 3001 71.8 270.69 0.344 0.065 0.225 12.0 13.5
> 8 1 5 3.13 146 L 3079 71.8 270.69 0.314 0.095 0.200 12.1 13.3
> 9 2 2 2.68 138 W 2782 76.8 236.54 0.403 0.038 0.240 13.6 11.1
> 10 2 8 2.68 138 L 2763 76.8 236.54 0.370 0.071 0.210 15.8 12.2
> 11 2 3 2.68 146 W 2582 76.8 236.54 0.363 0.048 0.215 14.3 11.0
> 12 2 5 2.68 146 L 2619 76.8 236.54 0.343 0.073 0.215 15.0 12.3
> 13 2 6 3.13 138 W 3277 76.8 289.54 0.364 0.076 0.230 11.7 11.7
> 14 2 1 3.13 138 L 3461 76.8 289.54 0.350 0.090 0.210 11.6 12.2
> 15 2 7 3.13 146 W 3116 76.8 289.54 0.341 0.071 0.215 12.3 11.6
> 16 2 4 3.13 146 L 3154 76.8 289.54 0.316 0.099 0.215 14.6 12.8
> 17 3 4 2.68 138 W 2665 69.8 214.98 0.316 0.120 0.245 16.7 14.5
> 18 3 7 2.68 138 L 2759 69.8 214.98 0.311 0.114 0.230 15.5 13.5
> 19 3 3 2.68 146 W 2504 69.8 214.98 0.289 0.124 0.225 19.0 14.2
> 20 3 5 2.68 146 L 2537 69.8 214.98 0.290 0.118 0.240 16.7 12.3
> 21 3 2 3.13 138 W 3077 69.8 263.15 0.294 0.138 0.250 14.4 15.4
> 22 3 1 3.13 138 L 3117 69.8 263.15 0.284 0.141 0.230 15.0 14.4
> 23 3 6 3.13 146 W 2816 69.8 263.15 0.271 0.137 0.210 14.6 13.7
> 24 3 8 3.13 146 L 2966 69.8 263.15 0.286 0.128 0.250 14.7 13.4
_____________________________
Professor Michael Kubovy
University of Virginia
Department of Psychology
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