I have a number of melting curves, for which I want to determine the slope of the steepest part between the minimum (valley) and maximum (peak) using R code (the slope in the inflection point corresponds to the melting point). The solutions I can imagine are either to determine the slope in every point and then find the maximum positive value, or by fitting a 4-parameter Weibull-type curve using the drc package to determine the inflection point (basically corresponding to the 50% response point between minimum and maximum). In the latter case the tricky part is that this fitting has to be restricted for each curve to the temperature range between the minimum (valley) and maximum (peak) fluorescence response. These temperature ranges are different for each curve.
Grateful for any feedback!
The diff function accomplishes the equivalent of numerical differentiation on equally spaced values (up to a constant factor) so finding maximum (or minimum) values can be used to identify location of steepest ascent (or descent):
z <- exp(-seq(0,3, by=0.1)^2 )
plot(z)
plot(diff(z))
z[ which(abs(diff(z))==max(abs(diff(z))) )]
# [1] 0.6126264
# could have also tested for min() instead of max(abs())
plot(z)
abline( v = which(abs(diff(z))==max(abs(diff(z))) ) )
abline( h = z[which(abs(diff(z))==max(abs(diff(z))) ) ] )
With an x-difference of 1, the slope is just the difference at that point:
diff(z) [ which(abs(diff(z))==max(abs(diff(z))) ) ]
[1] -0.08533397
... but I question whether that is really of much interest. I would have thought that getting the index (which would be the melting point subject to an offset) would be the value of interest.
来源：https://stackoverflow.com/questions/20430233/determining-slopes-with-r-code