If the computer model output has an associated time or spatial history then the computer program will generate a graph of the coefficients over time or space for each input-variable, output-variable combination of interest, thus indicating the importance of each input over time or space. Relationships between the coefficients and differences in their interpretations are identified. The coefficients provide alternative measures of the relative contribution (importance) of each of the various inputs to the observed output variations. These coefficients can be calculated on either the original observations or moreВ В» on the ranks of the original observations. The present computer program calculates the partial correlation coefficients and/or the standardized regression coefficients from the multivariate input to, and output from, a computer model. In particular, this program is most useful in analyzing input-output relationships when the input has been selected using the Latin hypercube sampling program developed at Sandia (Iman and Shortencarier, 1984). The computer program is designed to be used in conjunction with sensitivity analyses of complex computer models. Soil Science Identifiers URN: urn:nbn:se:kth:diva-278939 DOI: 10.3390/soilsystems4020026 ISI: 000551259000006 Scopus ID: 2-s2.0-85086821627 OAI: oai:DiVA.This document is for users of a computer program developed by the authors at Sandia National Laboratories. XANES, linear combination fitting, uncertainty, phosphorus, Latin hypercube sampling, energy calibration National Category

Place, publisher, year, edition, pagesMDPI, 2020. The probabilistic approach, in which at least 100 spectral variants are analyzed, improves our ability to identify the most likely P compounds present in a soil sample, and a procedure for this is suggested in the paper. These results highlight the necessity of careful energy calibration and the use of frequent calibration checks. Uncertainties in the calibrated energy were important for the LCF weights, particularly for organic P, which changed with up to 2.7 percentage units per 0.01 eV error in energy. Use of the probabilistic method resulted in uncertainties ranging from 2 to 11 percentage units. Moreover, adsorbed phytates and monetite were well described by other standards, which puts into question their use as end-members in LCF. This supports an approach in which the LCF standards are grouped. Many of the LCF standards, particularly within the same standard groups, were strongly correlated. We present a probabilistic approach for refining the calculation of LCF weights based on Latin hypercube sampling of normalized XANES spectra, where the contributions of energy calibration and normalization to fit uncertainty were considered. Here, the co-variance between 34 standards was determined and their significance for LCF was discussed. Further, there are few reports to what extent the LCF standards represent unique end-members. Despite the rapid growth of such applications, the uncertainties of the fitted weights are still poorly known. 4, no 2, article id 26 Article in journal (Refereed) Published Abstract ДЄ common technique to quantitatively estimate P speciation in soil samples is to apply linear combination fitting (LCF) to normalized P K-edge X-ray absorption near-edge structure (XANES) spectra. Show others and affiliations 2020 (English) In: Soil systems, ISSN 2571-8789, Vol.

Latin hypercube sampling with weight