Using R for Introductory Statistics : John Verzani

Linear regression 289Coefficients:(Intercept) x y1.684 0.881 1.076The output of lm() stores much more than is seen initially (which is just the formula andthe estimates for the coefficients). It is recommended that the return value be stored.Afterward, the different extractor functions can be used to view the results.■ Example 10.5: Finding the regression estimates for baby’s birth weightFitting the birth-weight model is straightforward. The basic model formula iswt ~ gestation+age+ht+wt1+dage+dht+dwtWe’ve seen with this data set that the variables have some missing values that are codednot with NA but with very large values that are obvious when plotted, but not when weblindly use the functions. In particular, gestation should be less than 350 days, mother’sage and height less than 99, and weight less than 999, etc. We can avoid these cases byusing the subset= argument as illustrated. Recall that we combine logical expressionswith & for “and” and | for “or.”> res.lm = lm(wt ~ gestation + age + ht + wt1 + dage +dht + dwt ,+ data = babies,+ subset= gestation < 350 & age < 99 & ht < 99 & wt1 res.lmCall:…Coefficients:(Intercept) gestation age ht wt1−105.4576 0.4625 0.1384 1.2161 0.0289dage dht dwt0.0590 −0.0663 0.0782A residual plot (not shown) shows nothing too unusual:> plot(fitted(res.lm), resid(res.lm))The diagnostic plots found with plot (res. lm) indicate that observation 261 might be aproblem. Looking at babies [261,], it appears that this case is an outlier, as it has a veryshort gestation period. It could be handled separately.The subset= argument is very useful, though repeated uses may make us wish that wecould use it just once prior to modeling. In this case the subset () function is available.Using update() with model formulasWhen comparing models, we may be interested in adding or subtracting a term andrefitting. Rather than typing in the entire model formula again, R provides a way to add