Hayes, Rachel M wrote:
Hi all,
I have a vector of proportions (post_op_prw) such that
>summary(amb$post_op_prw)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0000 0.0000 0.3985 0.9134 0.9962 1.0000
summary(cut2(amb$post_op_prw,0.0001))
[0.0000,0.0001) [0.0001,0.9962] NA's
1904 1672 1
I want to use post_op_prw as a predictor variable in an OLS model. I
decided to fit it using a restricted cubic spline. But, I'm seeing
behavior I don't understand. See below:
rcspline.eval(amb$post_op_prw,nk = 3, knots.only = T)
[1] 0.0000000 0.6147927 0.9092937 0.9667178
Warning message:
In rcspline.eval(amb$post_op_prw, nk = 3, knots.only = T) :
could not obtain 3 knots with default algorithm.
Used alternate algorithm to obtain 4 knots
rcspline.eval(amb$post_op_prw,nk = 4, knots.only = T)
[1] 0.0000000 0.8476793 0.9783558
rcspline.eval(amb$post_op_prw,nk = 5, knots.only = T)
[1] 0.0000000 0.9012711 0.9783558
Why are the 4 and 5 knot spline requests returning a spline with 3
knots? I get the best model results using rcs(amb$post_op_prw,3). I'm
kind of new to using splines. Does the fact that observations are
clustered at the ends make the spline fit questionable?
Yes, or at least it makes the choice of knots questionable. For that
type of variable with many ties I tend to use a quadratic effect
(pol(x,2) in Design or rms packages).
Frank
Thanks,
Rachel Hayes
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