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finding interpolated values along an empirical parametric curve

Hi Michael,

I can get what appears to be a good interpolation with a regression spline
in a multivariate LM, playing around with the tuning parameter to leave 1
residual df. Try this:

library(splines)
mod <- lm(cbind(log.det, norm.beta) ~ bs(lambda, df=4), data=pd)
summary(mod)

x <- data.frame(lambda=seq(min(pd$lambda), max(pd$lambda), length=100))
fit <- predict(mod, newdata=x)
points(fit[, "norm.beta"], fit[, "log.det"], pch=16, cex=0.5)

x.2 <- data.frame(lambda=c(lambda.HKB, lambda.LW))
fit.2 <- predict(mod, x.2)
points(fit.2[, "norm.beta"], fit.2[, "log.det"], pch=15, col="green")

That doesn't solve the problem of calculating the normal, however.

I hope this helps,
 John