afps: Automated full pattern summation

Description Usage Arguments Details Value References Examples

View source: R/afps.R

Description

afps returns estimates of phase concentrations using automated full pattern summation of X-ray powder diffraction data. It is designed for high-throughput cases involving mineral quantification from large reference libraries. For more details see ?afps.powdRlib.

Usage

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afps(lib, ...)

Arguments

lib

A powdRlib object representing the reference library. Created using the powdRlib constructor function.

...

Other parameters passed to methods e.g. afps.powdRlib

Details

Applies automated full pattern summation to an XRPD measurement to quantify phase concentrations. Requires a powdRlib library of reference patterns with reference intensity ratios in order to derive mineral concentrations.

Value

a list with components:

tth

a vector of the 2theta scale of the fitted data

fitted

a vector of the fitted XRPD pattern

measured

a vector of the original XRPD measurement (aligned)

residuals

a vector of the residuals (fitted vs measured)

phases

a dataframe of the phases used to produce the fitted pattern

phases_grouped

the phases dataframe grouped by phase_name and summed

rwp

the Rwp of the fitted vs measured pattern

weighted_pure_patterns

a dataframe of reference patterns used to produce the fitted pattern. All patterns have been weighted according to the coefficients used in the fit

coefficients

a named vector of coefficients used to produce the fitted pattern

inputs

a list of input arguments used in the function call

References

Chipera, S.J., Bish, D.L., 2013. Fitting Full X-Ray Diffraction Patterns for Quantitative Analysis: A Method for Readily Quantifying Crystalline and Disordered Phases. Adv. Mater. Phys. Chem. 03, 47-53. doi:10.4236/ampc.2013.31A007

Chipera, S.J., Bish, D.L., 2002. FULLPAT: A full-pattern quantitative analysis program for X-ray powder diffraction using measured and calculated patterns. J. Appl. Crystallogr. 35, 744-749. doi:10.1107/S0021889802017405

Eberl, D.D., 2003. User's guide to RockJock - A program for determining quantitative mineralogy from powder X-ray diffraction data. Boulder, CA.

Examples

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#Load the minerals library
data(minerals)

# Load the soils data
data(soils)

## Not run: 
afps_sand <-  afps(lib = minerals,
                 smpl = soils$sandstone,
                 std = "QUA.2",
                 align = 0.2,
                 lod = 0.2,
                 amorphous = "ORG",
                 amorphous_lod = 1)

afps_lime <- afps(lib = minerals,
                smpl = soils$limestone,
                std = "QUA.2",
                align = 0.2,
                lod = 0.2,
                amorphous = "ORG",
                amorphous_lod = 1)

afps_granite <- afps(lib = minerals,
                   smpl = soils$granite,
                   std = "QUA.2",
                   align = 0.2,
                   lod = 0.2,
                   amorphous = "ORG",
                   amorphous_lod = 1)

#Alternatively run all 3 at once using lapply

afps_soils <- lapply(soils, afps,
                     lib = minerals,
                     std = "QUA.2",
                     align = 0.2,
                     lod = 0.2,
                     amorphous = "ORG",
                     amorphous_lod = 1)

#Automated quantification using the rockjock library

data(rockjock)
data(rockjock_mixtures)

#This takes a few minutes to run
rockjock_a1 <- afps(lib = rockjock,
                    smpl = rockjock_mixtures$Mix1,
                    std = "CORUNDUM",
                    align = 0.3,
                    lod = 1)

#Quantifying the same sample but defining the internal standard
#concentration (also takes a few minutes to run):
rockjock_a1s <- afps(lib = rockjock,
                     smpl = rockjock_mixtures$Mix1,
                     std = "CORUNDUM",
                     std_conc = 20,
                     align = 0.3,
                     lod = 1)


## End(Not run)

benmbutler/powdR documentation built on Sept. 16, 2020, 5:03 a.m.