Is their any way to co-relate age with BMI ?
the metabolism decreases with age, for example a woman who eats the same as she did at the age of 20 (and few years) will start to put 6 kilos weight per decade if she eats the same and have the same lifestyle after this age. I am a little bit sexist here as I do not know for men !!! I can check this.
If you look at constant intake and no change in lifestyle you can predict the weight gain and therefore the BMI as on the top your muscle mass will decrease after around 45-50 years old, and muscles burn more calories and are heavier… so there there is catch as well, if you are a body builder or very sporty your BMI will be high but with no fat !!! to correct this we come to the problem of measurements. But in a nutshell yes on average (good distribution ah ah ) the BMI could be predicted with age.
Hope it answers your question a bit.
what i was trying to ask is whther we can create any frmula or any code to co-relate BMI with age in R ?
Thanks @Lesaffrea …i got ur point… But i wat i wanted to know is there any way to do this thing in R ?
R is very versatile, you can do the EDA in R first after few graphic you will have a fair idea of which relation you have between you independent variables and the BMI. Of course to do F(age) -> BMI will not be sufficient for a good model. One issue if you aim for high standard will be the power, do you have enough data? A lot of papers are written, but when you dig there is not enough power to come to valid conclusion, it has more to do with design than R.
Cut it short yes you can do in R, catch ask the right question first.
Hope this help a little,
well if you take some R default data set and this one from India … I was wrong I have attache the pdf story I can not upload the markdown. Package MASS data set Pima.tr2 BMI.pdf (272.0 KB)
PS: Overview markdown
library(MASS) library(GGally) library(dplyr)
IS BMI really increasing with age? Yes nromally but let look at the Prima data set of MASS !! The conclusion will be different.
data(Pima.tr2) PimaV <-Pima.tr2 %>% dplyr::select(glu:age) ggpairs(PimaV, diag=list(continous="density"), axisLabels='show')