I have applied the same linear model to several different subsets of a
dataset. I recently read that in R, code should never be repeated. I feel my
code as it currently stands has a lot of repetition, which could be
condensed into fewer lines. I will use the mtcars dataset to replicate what
I have done. My question is: how can I use fewer lines of code (for example
using a for loop, a function or plyr) to achieve the same output as below?
data(mtcars)
# Apply the same model to the dataset but choosing different combinations of
dependent (DV) and independent (IV) variables in each case:
lm.mpg= lm(mpg~cyl+disp+hp, data=mtcars)
lm.drat = lm(drat~wt+qsec, data=mtcars)
lm.gear = lm(gear~carb+hp, data=mtcars)
# Plot residuals against fitted values for each model
plot(lm.mpg$fitted,lm.mpg$residuals, main = "lm.mpg")
plot(lm.drat$fitted,lm.drat$residuals, main = "lm.drat")
plot(lm.gear$fitted,lm.gear$residuals, main = "lm.gear")
# Plot residuals against IVs for each model
plotResIV <- function (IV,lmResiduals)
{
lapply(IV, function (x) plot(x,lmResiduals))
}
plotResIV(lm.mpg$model[,-1],lm.mpg$residuals)
plotResIV(lm.drat$model[,-1],lm.drat$residuals)
plotResIV(lm.gear$model[,-1],lm.gear$residuals)
Many thanks
Ross Ahmed
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