Find nearest matches from reference set of profiles

Source: R/rsp.match.R

rsp_match_profile compares a supplied species (re)SPECIATE profile (or similar data set) and a reference set of supplied profiles and attempt to identify nearest matches on the basis of similarity.

rsp_match_profile(
  rsp,
  ref,
  matches = 10,
  rescale = 5,
  min.n = 8,
  method = "pd",
  test.rsp = FALSE
)

Arguments

rsp

A respeciate object or similar data.frame containing a species profile to be compared with profiles in ref. If rsp contains more than one profile, these are averaged (using rsp_average_profile), and the average compared.

ref

A respeciate object, a data.frame containing a multiple species profiles, to be used as reference library when identifying nearest matches for rsp.

matches

Numeric (default 10), the maximum number of profile matches to report.

rescale

Numeric (default 5), the data scaling method to apply before comparing rsp and profiles in ref: options 0 to 5 handled by rsp_rescale.

min.n

numeric (default 8), the minimum number of paired species measurements in two profiles required for a match to be assessed. See also rsp_cor_species.

method

Character (default 'pd'), the similarity measure to use, current options 'pd', the Pearson's Distance (1 - Pearson's correlation coefficient), or 'sid', the Standardized Identity Distance (See References).

test.rsp

Logical (default FALSE). The match process self-tests by adding rsp to ref, which should generate a perfect fit=0 score. Setting test.rsp to TRUE retains this as an extra record.

Value

rsp_match_profile returns a fit report: a data.frame of up to n fit reports for the nearest matches to rsp from the reference profile data set, ref.

References

Distance metrics are based on recommendations by Belis et al (2015) and as implemented in Mooibroek et al (2022):

Belis, C.A., Pernigotti, D., Karagulian, F., Pirovano, G., Larsen, B.R., Gerboles, M., Hopke, P.K., 2015. A new methodology to assess the performance and uncertainty of source apportionment models in intercomparison exercises. Atmospheric Environment, 119, 35–44. https://doi.org/10.1016/j.atmosenv.2015.08.002.

Mooibroek, D., Sofowote, U.M. and Hopke, P.K., 2022. Source apportionment of ambient PM10 collected at three sites in an urban-industrial area with multi-time resolution factor analyses. Science of The Total Environment, 850, p.157981. http://dx.doi.org/10.1016/j.scitotenv.2022.157981.