Perform nonlinear averaging of a temperature-dependent variable across a range of temperatures from empirically-derived data

Usage

nla_tpc_empirical(tpcs, dailytemps, tempaxis = NULL)

Arguments

tpcs: A matrix of values measured across a temperature axis (represented in tempaxis).
dailytemps: A matrix of temperatures across a 24h period, equating to each entry in tempaxis.
tempaxis: The basis temperature axis.

Value

A matrix of averaged values, where columns are temperatures, and rows are individual tpc runs.

Note

The precision of tempaxis MUST be greater than or equal to the values of dailytemps.

The width of the tpcs matrix MUST be greater than or equal to the length of tempaxis.

Input dimensionality

This function relies on carefully and appropriately-formatted inputs.

tpcs should be a 2D matrix where the rows correspond to individual tpcs or bootstrap iterations of a tpc, and columns correspond to the temperature axis in tempaxis.

dailytemps should be a 2D matrix where the rows correspond to sampling times across a 24h window (so if your temperatures are hourly, this should have 24 rows), and columns correspond to mean temperatures. So if column 5 represents 10°C, mean(dailytemps[,5]) == 10

Examples

if (FALSE) { # interactive()
nla_tpc_empirical()
}