Graphics Reference
In-Depth Information
Call:
lm(formula
=
heightIn ~ ageYear, data
=
data)
Coefficients:
(Intercept)
ageYear
30.658
2.301
attr(,
"split_type"
)
[
1
]
"data.frame"
attr(,
"split_labels"
)
sex
1
f
2
m
Now that we have the list of model objects, we can run
predictvals()
to get predicted values
from each model, using the
ldply()
function:
predvals
<-
ldply(models,
.
fun
=
predictvals, xvar
=
"ageYear"
, yvar
=
"heightIn"
)
predvals
sex ageYear heightIn
f
11.58000 57.96250
f
11.63980 58.03478
f
11.69960 58.10707
...
m
17.38040 70.64912
m
17.44020 70.78671
m
17.50000 70.92430
Finally, we can plot the data with the predicted values (
Figure 5-24
):
ggplot(heightweight, aes(x
=
ageYear, y
=
heightIn, colour
=
sex))
+
geom_point()
+
geom_line(data
=
predvals)
