Dylan Beaudette wrote:
Hi,
I have fit a series of ols() models, by group, in this manner:
l <- ols(y ~ rcs(x, 4))
... where the series of 'x' values in each group is the same, however knots
are not always identical between groups. The result is a table of 'coefs'
derived from the ols objects, by group:
group Intercept top top' top''
1 6.864 0.01 2.241 -2.65
2 6.836 0.047 -0.556 0.606
3 5.877 -0.019 0.084 -0.175
4 6.021 -0.003 0.121 -0.128
5 7.164 0.014 0.031 -0.096
I would like to describe groups of relationships, based on the coefficients,
however I am not sure if they are directly comparable. In addition, I would
like to regress these coefs on another set of variables, with the aim of
predicting a series of RCS coefficients along external gradients. In essence,
I am hoping to use RCS coefficients to summarize y ~ rcs(x), in a way that
can then me modeled like this: [y ~ rcs(x)] ~ z.
Is this interpretation of RCS coefficients even possible? If not, would
forcing knot locations make it a possibility? Or, would modeling both knots
and RCS coefs with external variables lead to sensible predictions?
Cheers,
Dylan
Dylan,
It is possible to interpret rcs coefficients. But it is not possible to
equate coefficients across fits using different know locations. My
suggestion is either to specify the same knots (e.g., rcs(x, c(2, 4, 6,
8)) across fits or to compare the fitted relationships (predictions)
rather than the coefficients.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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