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?
Thanks,
Rachel Hayes
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