| chemVI {RFPM} | R Documentation |
Chemical Variable Importance for Floating Percentile Model Benchmarks
Description
Generate statistics describing the relative importance of chemicals among benchmarks generated by FPM
Usage
chemVI(data, paramList, ...)
Arguments
data |
data.frame containing, at a minimum, chemical concentrations as columns and a logical |
paramList |
character vector naming columns of |
... |
additional arguments passed to |
Details
The purpose of chemVI is to inform the user about the relative influence of each chemical over the sediment quality benchmarks generated by FPM.
Three statistics are generated: chemDensity, MADP, dOR, dFM, and dMCC. The chemDensity statistic (which is also generated by FPM)
describes how little a particular chemical's value increased within the floating percentile model algorithm.
Low chemDensity (close to 0) means that the value was able to increase substantially within the algorithm without triggering one or more of the criteria for
stopping the algorithm (see ?FPM), whereas high chemDensity (close to 1) indicates the final benchmark for that chemical did not float (increase)
much before being locked in. In other words, low chemDensity might be interpreted as relatively low importance. We caution against using this
metric in isolation, as it is the more difficult to interpret of the three.
The MADP statistic (or mean absolute difference percent) is calculated by sequentially dropping each chemical from consideration, recalculating the benchmarks
for the remaining chemicals, and then determining how much each benchmark changed (as a percent of the original value). Thus, the MADP
is a measure of a chemical's influence over other benchmarks. The dOR statistic is the difference between the overall reliability
of benchmarks with all chemicals versus without each chemical. dFM and dMCC are similar to the dOR statistic, but for the Fowlkes-Mallows Index
and Matthew's Correlation Coefficient. In any case, larger positive values indicate a greater impact of a chemical
on the overall predictive performance of floating percentile model benchmarks. Small values (close to 0) indicate low influence. Larger negative values indicate that
the chemical actually adversely impacts toxicity predictions. If there are chemicals with negative values, consider reevaluting the data without the associated chemical
or using optimFPM or cvFPM to optimize the overall reliability prior to running FPM and chemVI.
Value
data.frame with 2 columns
See Also
chemSig, chemSigSelect, optimFPM, cvFPM, FPM
Examples
paramList = c("Cd", "Cu", "Fe", "Mn", "Ni", "Pb", "Zn")
chemVI(h.tristate, paramList, testType = "np")
chemVI(h.tristate, paramList, testType = "p")