| Title: | Full Pattern Summation of X-Ray Powder Diffraction Data |
|---|---|
| Description: | Full pattern summation of X-ray powder diffraction data as described in Chipera and Bish (2002) <doi:10.1107/S0021889802017405> and Butler and Hillier (2021) <doi:10.1016/j.cageo.2020.104662>. Derives quantitative estimates of crystalline and amorphous phase concentrations in complex mixtures. |
| Authors: | Benjamin Butler [aut, cre], Stephen Hillier [aut], Dylan Beaudette [ctb], Dennis Eberl [ctb] |
| Maintainer: | Benjamin Butler <[email protected]> |
| License: | GPL-2 | file LICENSE |
| Version: | 1.3.0.999 |
| Built: | 2025-03-06 03:35:21 UTC |
| Source: | https://github.com/benmbutler/powdr |
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.
afps( lib, smpl, harmonise, solver, obj, refs, std, force, std_conc, omit_std, closed, normalise, tth_align, align, manual_align, shift, tth_fps, lod, amorphous, amorphous_lod, weighting, skip_nnls, ... )afps( lib, smpl, harmonise, solver, obj, refs, std, force, std_conc, omit_std, closed, normalise, tth_align, align, manual_align, shift, tth_fps, lod, amorphous, amorphous_lod, weighting, skip_nnls, ... )
lib |
A |
smpl |
A data frame. First column is 2theta, second column is counts |
harmonise |
logical parameter defining whether to harmonise the |
solver |
The optimisation routine to be used. One of |
obj |
The objective function to minimise. One of |
refs |
A character string of reference pattern IDs or names from the specified library.
The IDs or names supplied must be present within the |
std |
The phase ID (e.g. "QUA.1") to be used as internal
standard. Must match an ID provided in the |
force |
An optional string of phase ID's or names specifying which phases should be forced to
remain throughout the automated full pattern summation. The ID's or names supplied must be present
within the |
std_conc |
The concentration of the internal standard (if known) in weight percent. If
unknown then either omit the argument from the function call of use |
omit_std |
A logical parameter to be used when the |
closed |
A logical parameter to be used when the |
normalise |
deprecated. Please use the |
tth_align |
A vector defining the minimum and maximum 2theta values to be used during
alignment (e.g. |
align |
The maximum shift that is allowed during initial 2theta alignment (degrees). Default = 0.1. |
manual_align |
A logical operator denoting whether to optimise the alignment within the
negative/position 2theta range defined in the |
shift |
A single numeric value denoting the maximum (positive or negative) shift, in degrees 2theta, that is allowed during the shifting of selected phases. Default = 0. |
tth_fps |
A vector defining the minimum and maximum 2theta values to be used during
automated full pattern summation (e.g. |
lod |
Optional parameter used to define the limit of detection (in weight percent) of the internal standard
(i.e. the phase provided in the |
amorphous |
A character string of any phase IDs that should be treated as amorphous. These must
match phases present in |
amorphous_lod |
Optional parameter used to exclude amorphous phases if they are below this specified limit (percent). Must be between 0 and 100. Default = 0. |
weighting |
an optional 2 column data frame specifying the 2theta values in the first
column and a numeric weighting vector in the second column that specifies areas of the pattern
to either emphasise (values > 1) or omit (values = 0) when minimising the objective function
defined in the |
skip_nnls |
an optional logical argument defining whether the non-negative least squares (NNLS)
step is skipped. Default |
... |
Other parameters passed to methods e.g. |
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. Details provided in Butler and Hillier (2021).
a powdRafps object with components:
tth |
a vector of the 2theta scale of the fitted data |
fitted |
a vector of the count intensities of fitted XRPD pattern |
measured |
a vector of the count intensities of original XRPD measurement (aligned) |
residuals |
a vector of the residuals (measured minus fitted) |
phases |
a dataframe of the phases used to produce the fitted pattern |
phases_grouped |
the phases dataframe grouped and summed by phase_name |
obj |
named vector of the objective parameters summarising the quality of the fit |
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 |
Butler, B. M., Hillier, S., 2021.powdR: An R package for quantitative mineralogy using full pattern summation of X-ray powder diffraction data. Comp. Geo. 147, 104662. doi:10.1016/j.cageo.2020.104662
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.
#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$Mix1, std = "CORUNDUM", std_conc = 20, align = 0.3, lod = 1) ## End(Not run)#Load the minerals library data(minerals) # Load the soils data data(soils) ## Not run: afps_sand <- afps(lib = minerals, smpl = soils$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)
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.
## S3 method for class 'powdRlib' afps( lib, smpl, harmonise, solver, obj, refs, std, force, std_conc, omit_std, closed, normalise, tth_align, align, manual_align, shift, tth_fps, lod, amorphous, amorphous_lod, weighting, skip_nnls, ... )## S3 method for class 'powdRlib' afps( lib, smpl, harmonise, solver, obj, refs, std, force, std_conc, omit_std, closed, normalise, tth_align, align, manual_align, shift, tth_fps, lod, amorphous, amorphous_lod, weighting, skip_nnls, ... )
lib |
A |
smpl |
A data frame. First column is 2theta, second column is counts |
harmonise |
logical parameter defining whether to harmonise the |
solver |
The optimisation routine to be used. One of |
obj |
The objective function to minimise. One of |
refs |
A character string of reference pattern IDs or names from the specified library.
The IDs or names supplied must be present within the |
std |
The phase ID (e.g. "QUA.1") to be used as internal
standard. Must match an ID provided in the |
force |
An optional string of phase ID's or names specifying which phases should be forced to
remain throughout the automated full pattern summation. The ID's or names supplied must be present
within the |
std_conc |
The concentration of the internal standard (if known) in weight percent. If
unknown then either omit the argument from the function call of use |
omit_std |
A logical parameter to be used when the |
closed |
A logical parameter to be used when the |
normalise |
deprecated. Please use the |
tth_align |
A vector defining the minimum and maximum 2theta values to be used during
alignment (e.g. |
align |
The maximum shift that is allowed during initial 2theta alignment (degrees). Default = 0.1. |
manual_align |
A logical operator denoting whether to optimise the alignment within the
negative/position 2theta range defined in the |
shift |
A single numeric value denoting the maximum (positive or negative) shift, in degrees 2theta, that is allowed during the shifting of selected phases. Default = 0. |
tth_fps |
A vector defining the minimum and maximum 2theta values to be used during
automated full pattern summation (e.g. |
lod |
Optional parameter used to define the limit of detection (in weight percent) of the internal standard
(i.e. the phase provided in the |
amorphous |
A character string of any phase IDs that should be treated as amorphous. These must
match phases present in |
amorphous_lod |
Optional parameter used to exclude amorphous phases if they are below this specified limit (percent). Must be between 0 and 100. Default = 0. |
weighting |
an optional 2 column data frame specifying the 2theta values in the first
column and a numeric weighting vector in the second column that specifies areas of the pattern
to either emphasise (values > 1) or omit (values = 0) when minimising the objective function
defined in the |
skip_nnls |
an optional logical argument defining whether the non-negative least squares (NNLS)
step is skipped. Default |
... |
other arguments |
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. Details provided in Butler and Hillier (2021).
a powdRafps object with components:
tth |
a vector of the 2theta scale of the fitted data |
fitted |
a vector of the count intensities of fitted XRPD pattern |
measured |
a vector of the count intensities of original XRPD measurement (aligned) |
residuals |
a vector of the residuals (measured minus fitted) |
phases |
a dataframe of the phases used to produce the fitted pattern |
phases_grouped |
the phases dataframe grouped and summed by phase_name |
obj |
named vector of the objective parameters summarising the quality of the fit |
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 |
Butler, B. M., Hillier, S., 2021.powdR: An R package for quantitative mineralogy using full pattern summation of X-ray powder diffraction data. Comp. Geo. 147, 104662. doi:10.1016/j.cageo.2020.104662
Bish, D.L., Post, J.E., 1989. Modern powder diffraction. Mineralogical Society of America.
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.
#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$Mix1, std = "CORUNDUM", std_conc = 20, align = 0.3, lod = 1) ## End(Not run)#Load the minerals library data(minerals) # Load the soils data data(soils) ## Not run: afps_sand <- afps(lib = minerals, smpl = soils$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)
A powdRlib object of 21 pure reference patterns and associated reference
intensity ratios for a range of common soil minerals. Data were collected on a Bruker D2
Phaser using Cu K-alpha radiation. All patterns have been normalised to 10,000
counts and reference intensity ratios transformed so that all are relative
to that of corundum.
afsisafsis
A powdRlib object of 3 components
A dataframe of all the count intensities of all reference patterns. Column names denote the unique phase ID of each reference pattern
A vector of the 2theta scale for all reference patterns in the library
A dataframe the phase IDs, names and reference intensity ratios (RIR)
afsis reference patternsA data frame detailing the original codes associated with the afsis reference
patterns prior to their addition to powdR.
afsis_codesafsis_codes
An 2 column data frame. First column contains the phase IDs from afsis$phase_id
and the second column the original IDs prior to the inclusion in powdR.
A data frame containing an example re-grouping structure for the afsis
reference library, which results in a slightly coarser description of clay
minerals and Fe/Ti-(hydr)oxides in powdRfps or powdRafps objects
when used with regroup().
afsis_regroupafsis_regroup
A data frame with three columns:
the phase IDs present in afsis$phases$phase_id
.
The phase names that constitute the first regrouping structure.
The phase names that constitute the second regrouping structure
See ?align_xy.XY and align_xy.multiXY for
method-specific details.
align_xy(x, std, xmin, xmax, xshift, ...)align_xy(x, std, xmin, xmax, xshift, ...)
x |
an |
std |
a dataframe of the chosen standard that each sample will be aligned to (column 1 = 2theta, column 2 = counts) |
xmin |
the minimum 2theta value used during alignment |
xmax |
the maximum 2theta value used during alignment |
xshift |
the maximum (positive and negative) 2theta shift that is allowed during alignment |
... |
other arguments |
an XY or multiXY object.
# Load soils xrd data data(soils) #Load minerals library data(minerals) ## Not run: #Create a standard quartz pattern to align to quartz <- data.frame(tth = minerals$tth, counts = minerals$xrd$QUA.1) #Plot the main quartz peak prior to alignment plot(soils, wavelength = "Cu", xlim = c(26,27), normalise = TRUE) #align data aligned <- align_xy(soils, std = quartz, xmin = 10, xmax = 60, xshift = 0.2) #replot data plot(aligned, wavelength = "Cu", xlim = c(26,27), normalise = TRUE) #Alternatively try with a single XY object unaligned <- as_multi_xy(list("quartz" = quartz, "sandstone" = soils$sandstone)) plot(unaligned, wav = "Cu", xlim = c(26,27), normalise = TRUE) sandstone_a <- align_xy(soils$sandstone, std = quartz, xmin = 10, xmax = 60, xshift = 0.3) aligned <- as_multi_xy(list("quartz" = quartz, "sandstone" = sandstone_a)) plot(aligned, wav = "Cu", xlim = c(26,27), normalise = TRUE) ## End(Not run)# Load soils xrd data data(soils) #Load minerals library data(minerals) ## Not run: #Create a standard quartz pattern to align to quartz <- data.frame(tth = minerals$tth, counts = minerals$xrd$QUA.1) #Plot the main quartz peak prior to alignment plot(soils, wavelength = "Cu", xlim = c(26,27), normalise = TRUE) #align data aligned <- align_xy(soils, std = quartz, xmin = 10, xmax = 60, xshift = 0.2) #replot data plot(aligned, wavelength = "Cu", xlim = c(26,27), normalise = TRUE) #Alternatively try with a single XY object unaligned <- as_multi_xy(list("quartz" = quartz, "sandstone" = soils$sandstone)) plot(unaligned, wav = "Cu", xlim = c(26,27), normalise = TRUE) sandstone_a <- align_xy(soils$sandstone, std = quartz, xmin = 10, xmax = 60, xshift = 0.3) aligned <- as_multi_xy(list("quartz" = quartz, "sandstone" = sandstone_a)) plot(aligned, wav = "Cu", xlim = c(26,27), normalise = TRUE) ## End(Not run)
align_xy.multiXY takes a multiXY object and aligns
each of the XY data frames within it to a given standard.
An optimisation routine is used that computes a suitable
linear shift. After all samples have been aligned,
the function harmonises the data to a single 2theta scale.
## S3 method for class 'multiXY' align_xy(x, std, xmin, xmax, xshift, ...)## S3 method for class 'multiXY' align_xy(x, std, xmin, xmax, xshift, ...)
x |
a |
std |
a dataframe of the chosen standard that each sample will be aligned to (column 1 = 2theta, column 2 = counts) |
xmin |
the minimum 2theta value used during alignment |
xmax |
the maximum 2theta value used during alignment |
xshift |
the maximum (positive and negative) 2theta shift that is allowed during alignment |
... |
other arguments |
a multiXY object.
# Load soils xrd data data(soils) #Load minerals library data(minerals) ## Not run: #Create a standard quartz pattern to align to quartz <- data.frame(tth = minerals$tth, counts = minerals$xrd$QUA.1) #Plot the main quartz peak prior to alignment plot(soils, wavelength = "Cu", xlim = c(26,27), normalise = TRUE) #align data aligned <- align_xy(soils, std = quartz, xmin = 10, xmax = 60, xshift = 0.2) #replot data plot(aligned, wavelength = "Cu", xlim = c(26,27), normalise = TRUE) ## End(Not run)# Load soils xrd data data(soils) #Load minerals library data(minerals) ## Not run: #Create a standard quartz pattern to align to quartz <- data.frame(tth = minerals$tth, counts = minerals$xrd$QUA.1) #Plot the main quartz peak prior to alignment plot(soils, wavelength = "Cu", xlim = c(26,27), normalise = TRUE) #align data aligned <- align_xy(soils, std = quartz, xmin = 10, xmax = 60, xshift = 0.2) #replot data plot(aligned, wavelength = "Cu", xlim = c(26,27), normalise = TRUE) ## End(Not run)
align_xy.XY takes an XY object and aligns
it to a given standard. An optimisation routine is used
that computes a suitable linear shift.
## S3 method for class 'XY' align_xy(x, std, xmin, xmax, xshift, ...)## S3 method for class 'XY' align_xy(x, std, xmin, xmax, xshift, ...)
x |
an |
std |
a dataframe of the chosen standard that each sample is aligned to (column 1 = 2theta, column 2 = counts) |
xmin |
the minimum 2theta value used during alignment |
xmax |
the maximum 2theta value used during alignment |
xshift |
the maximum (positive and negative) 2theta shift that is allowed during alignment |
... |
other arguments |
an XY object.
# Load soils xrd data data(soils) #Load minerals library data(minerals) ## Not run: #Create a standard quartz pattern to align to quartz <- data.frame(tth = minerals$tth, counts = minerals$xrd$QUA.1) unaligned <- as_multi_xy(list("quartz" = quartz, "sandstone" = soils$sandstone)) plot(unaligned, wav = "Cu", xlim = c(26,27), normalise = TRUE) sandstone_a <- align_xy(soils$sandstone, std = quartz, xmin = 10, xmax = 60, xshift = 0.3) aligned <- as_multi_xy(list("quartz" = quartz, "sandstone" = sandstone_a)) plot(aligned, wav = "Cu", xlim = c(26,27), normalise = TRUE) ## End(Not run)# Load soils xrd data data(soils) #Load minerals library data(minerals) ## Not run: #Create a standard quartz pattern to align to quartz <- data.frame(tth = minerals$tth, counts = minerals$xrd$QUA.1) unaligned <- as_multi_xy(list("quartz" = quartz, "sandstone" = soils$sandstone)) plot(unaligned, wav = "Cu", xlim = c(26,27), normalise = TRUE) sandstone_a <- align_xy(soils$sandstone, std = quartz, xmin = 10, xmax = 60, xshift = 0.3) aligned <- as_multi_xy(list("quartz" = quartz, "sandstone" = sandstone_a)) plot(aligned, wav = "Cu", xlim = c(26,27), normalise = TRUE) ## End(Not run)
as_multi_xy takes a list or data frame of XRPD data and ensures
that the data meet various requirements to create a multiXY object.
Once a multiXY object has been created, it can easily be plotted using
the associated plot.multiXY method.
as_multi_xy(x, ...)as_multi_xy(x, ...)
x |
a list or data frame of XRPD data |
... |
other arguments |
a multiXY object.
#EXAMPLE 1 #load soils data data(soils) #extract first two samples from the list soils <- soils[c(1:2)] #convert to multiXY soils <- as_multi_xy(soils) #EXAMPLE 2 #load the soils data data(soils) #Convert to data frame soils_df <- multi_xy_to_df(soils, tth = TRUE) #Convert back to multiXY object soils2 <- as_multi_xy(soils_df)#EXAMPLE 1 #load soils data data(soils) #extract first two samples from the list soils <- soils[c(1:2)] #convert to multiXY soils <- as_multi_xy(soils) #EXAMPLE 2 #load the soils data data(soils) #Convert to data frame soils_df <- multi_xy_to_df(soils, tth = TRUE) #Convert back to multiXY object soils2 <- as_multi_xy(soils_df)
as_multi_xy.data.frame takes a data frame of XRPD data from multiple
samples and ensures that it meets various requirements to create a multiXY object.
Once a multiXY object has been created, it can easily be plotted using
the associated plot.multiXY method.
## S3 method for class 'data.frame' as_multi_xy(x, ...)## S3 method for class 'data.frame' as_multi_xy(x, ...)
x |
a data frame of XRPD data, with the first column as the 2theta axis and subsequent columns of count intensities. |
... |
other arguments |
a multiXY object.
#load the soils data data(soils) #Convert to data frame soils_df <- multi_xy_to_df(soils, tth = TRUE) #Convert back to multiXY object soils2 <- as_multi_xy(soils_df)#load the soils data data(soils) #Convert to data frame soils_df <- multi_xy_to_df(soils, tth = TRUE) #Convert back to multiXY object soils2 <- as_multi_xy(soils_df)
as_multi_xy.list takes a list of XRPD data and ensures that they meet
various requirements to create a multiXY object. These requirements
include that each item in the list contains 2 columns of numeric data in a
data frame. as_multi_xy.list also checks that all names are unique.
Once a multiXY object has been created, it can easily be plotted using
the associated plot.multiXY method.
## S3 method for class 'list' as_multi_xy(x, ...)## S3 method for class 'list' as_multi_xy(x, ...)
x |
a list of XRPD data frames (column 1 = 2theta, column 2 = counts) |
... |
other arguments |
a multiXY object.
#' #load soils data data(soils) #extract first two samples from the list soils <- soils[c(1:2)] #convert to multiXY soils <- as_multi_xy(soils)#' #load soils data data(soils) #extract first two samples from the list soils <- soils[c(1:2)] #convert to multiXY soils <- as_multi_xy(soils)
as_xy takes a data frame of XY XRPD data and ensures that it
meets the criteria for an XY object. These requirements
include that the data contains 2 columns of numeric data in a dataframe.
Once an XY object has been created, it can easily be plotted using the
associated plot.XY method.
as_xy(x)as_xy(x)
x |
a data frame (column 1 = 2theta, column 2 = counts) |
an XY object.
# Load soils xrd data data(rockjock_mixtures) xy <- as_xy(rockjock_mixtures$Mix1) class(xy) ## Not run: plot(xy, wavelength = "Cu") plot(xy, wavelength = "Cu", interactive = TRUE) ## End(Not run)# Load soils xrd data data(rockjock_mixtures) xy <- as_xy(rockjock_mixtures$Mix1) class(xy) ## Not run: plot(xy, wavelength = "Cu") plot(xy, wavelength = "Cu", interactive = TRUE) ## End(Not run)
bkg fits a background to X-Ray Powder Diffraction data
bkg(xrd, lambda, hwi, it, int)bkg(xrd, lambda, hwi, it, int)
xrd |
an xy data frame of the data to fit a background to. First column is the 2theta scale, second column is count intensities |
lambda |
second derivative penalty for primary smoothing. Default = 0.5. |
hwi |
Half width of local windows. Default = 25. |
it |
Number of iterations in suppression loop. Default = 50. |
int |
Number of buckets to divide the data into. Default = |
A wrapper for the baseline.fillPeaks in the baseline package.
a powdRbkg object consisting of of 3 vectors
tth |
The 2theta axis of the measurement |
counts |
The count intensities of the measurement |
background |
The count intensities of the fitted background |
data(soils) ## Not run: fit_bkg <- bkg(soils$granite) plot(bkg) ## End(Not run)data(soils) ## Not run: fit_bkg <- bkg(soils$granite) plot(bkg) ## End(Not run)
powdRfps or
powdRafps objectclose_quant closes the quantitative data within a powdRfps or
powdRafps object (derived from fps() and afps(), respectively) by
ensuring that the composition sums to 100 percent. See also ?close_quant.powdRfps and
?close_quant.powdRafps.
close_quant(x, ...)close_quant(x, ...)
x |
A |
... |
other arguments |
a powdRfps or powdRafps object with components:
tth |
a vector of the 2theta scale of the fitted data |
fitted |
a vector of the fitted XRPD pattern |
measured |
a vector of the original XRPD measurement (aligned and harmonised) |
residuals |
a vector of the residuals (fitted vs measured) |
phases |
a dataframe of the phases used to produce the fitted pattern and their concentrations |
phases_grouped |
the phases dataframe grouped by phase_name and concentrations summed |
obj |
named vector of the objective parameters summarising the quality of the fit |
weighted_pure_patterns |
a dataframe of reference patterns used to produce the fitted pattern. All patterns have been weighted according to the coefficients used in the fit |
coefficients |
a named vector of coefficients used to produce the fitted pattern |
inputs |
a list of input arguments used in the function call |
## Not run: data(rockjock) data(rockjock_mixtures) rockjock_1 <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", align = 0.3, std_conc = 20) sum(rockjock_1$phases$phase_percent) rockjock_1c <- close_quant(rockjock_1) sum(rockjock_1c$phases$phase_percent) rockjock_a1 <- afps(lib = rockjock, smpl = rockjock_mixtures$Mix1, std = "CORUNDUM", align = 0.3, lod = 1, std_conc = 20) sum(rockjock_a1$phases$phase_percent) rockjock_a1c <- close_quant(rockjock_a1) sum(rockjock_a1c$phases$phase_percent) ## End(Not run)## Not run: data(rockjock) data(rockjock_mixtures) rockjock_1 <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", align = 0.3, std_conc = 20) sum(rockjock_1$phases$phase_percent) rockjock_1c <- close_quant(rockjock_1) sum(rockjock_1c$phases$phase_percent) rockjock_a1 <- afps(lib = rockjock, smpl = rockjock_mixtures$Mix1, std = "CORUNDUM", align = 0.3, lod = 1, std_conc = 20) sum(rockjock_a1$phases$phase_percent) rockjock_a1c <- close_quant(rockjock_a1) sum(rockjock_a1c$phases$phase_percent) ## End(Not run)
powdRafps objectclose_quant closes the quantitative data within a powdRafps object
(derived from afps()) by ensuring that the composition sums to 100 percent.
## S3 method for class 'powdRafps' close_quant(x, ...)## S3 method for class 'powdRafps' close_quant(x, ...)
x |
A |
... |
other arguments |
a powdRafps object with components:
tth |
a vector of the 2theta scale of the fitted data |
fitted |
a vector of the fitted XRPD pattern |
measured |
a vector of the original XRPD measurement (aligned and harmonised) |
residuals |
a vector of the residuals (fitted vs measured) |
phases |
a dataframe of the phases used to produce the fitted pattern and their concentrations |
phases_grouped |
the phases dataframe grouped by phase_name and concentrations summed |
obj |
named vector of the objective parameters summarising the quality of the fit |
weighted_pure_patterns |
a dataframe of reference patterns used to produce the fitted pattern. All patterns have been weighted according to the coefficients used in the fit |
coefficients |
a named vector of coefficients used to produce the fitted pattern |
inputs |
a list of input arguments used in the function call |
## Not run: data(rockjock) data(rockjock_mixtures) rockjock_a1 <- afps(lib = rockjock, smpl = rockjock_mixtures$Mix1, std = "CORUNDUM", align = 0.3, lod = 1, std_conc = 20) sum(rockjock_a1$phases$phase_percent) rockjock_a1c <- close_quant(rockjock_a1) sum(rockjock_a1c$phases$phase_percent) ## End(Not run)## Not run: data(rockjock) data(rockjock_mixtures) rockjock_a1 <- afps(lib = rockjock, smpl = rockjock_mixtures$Mix1, std = "CORUNDUM", align = 0.3, lod = 1, std_conc = 20) sum(rockjock_a1$phases$phase_percent) rockjock_a1c <- close_quant(rockjock_a1) sum(rockjock_a1c$phases$phase_percent) ## End(Not run)
powdRfps objectclose_quant closes the quantitative data within a powdRfps object
(derived from fps()) by ensuring that the composition sums to 100 percent.
## S3 method for class 'powdRfps' close_quant(x, ...)## S3 method for class 'powdRfps' close_quant(x, ...)
x |
A |
... |
other arguments |
a powdRfps object with components:
tth |
a vector of the 2theta scale of the fitted data |
fitted |
a vector of the fitted XRPD pattern |
measured |
a vector of the original XRPD measurement (aligned and harmonised) |
residuals |
a vector of the residuals (fitted vs measured) |
phases |
a dataframe of the phases used to produce the fitted pattern and their concentrations |
phases_grouped |
the phases dataframe grouped by phase_name and concentrations summed |
obj |
named vector of the objective parameters summarising the quality of the fit |
weighted_pure_patterns |
a dataframe of reference patterns used to produce the fitted pattern. All patterns have been weighted according to the coefficients used in the fit |
coefficients |
a named vector of coefficients used to produce the fitted pattern |
inputs |
a list of input arguments used in the function call |
## Not run: data(rockjock) data(rockjock_mixtures) rockjock_1 <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", align = 0.3, std_conc = 20) sum(rockjock_1$phases$phase_percent) rockjock_1c<- close_quant(rockjock_1) sum(rockjock_1c$phases$phase_percent) ## End(Not run)## Not run: data(rockjock) data(rockjock_mixtures) rockjock_1 <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", align = 0.3, std_conc = 20) sum(rockjock_1$phases$phase_percent) rockjock_1c<- close_quant(rockjock_1) sum(rockjock_1c$phases$phase_percent) ## End(Not run)
delta computes the absolute difference between a measured and fitted pattern.
See equation for Delta in section 2.1 of Butler and Hillier (2021).
delta(measured, fitted, weighting)delta(measured, fitted, weighting)
measured |
a vector of count intensities for a measured pattern |
fitted |
a vector of count intensities for a fitted pattern |
weighting |
an optional weighting vector of the same length as those specified
in |
a single numeric value
Butler, B.M., Hillier, S., 2021. powdR: An R package for quantitative mineralogy using full pattern summation of X-ray powder diffraction data. Computers and Geosciences. 147, 104662. doi:10.1016/j.cageo.2020.104662
# Load soils xrd data data(soils) #Load minerals library data(minerals) ## Not run: #Produce a fit fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) delta(measured = fps_sand$measured, fitted = fps_sand$fitted) ## End(Not run)# Load soils xrd data data(soils) #Load minerals library data(minerals) ## Not run: #Produce a fit fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) delta(measured = fps_sand$measured, fitted = fps_sand$fitted) ## End(Not run)
extract_xy is a wrapper for read_xyData of the rxylib package,
which extracts the xy data from various proprietary formats of X-ray powder
diffraction data using the xylib C++ library. For more information see
?rxylib and ?rxylib::read_xyData.
extract_xy(files)extract_xy(files)
files |
path of the file(s) to be imported. |
If only one path is supplied then an XY data frame with 2 columns is returned, the first being the 2theta axis and the second being the count intensities. If more than one path is supplied then a multiXY list is returned, with each item in the list being an XY data frame as already described.
#load example RAW file file <- system.file("extdata/D5000/RAW/D5000_1.RAW", package = "powdR") raw1 <- extract_xy(file) #Load multiple RAW files files <- dir(system.file("extdata/D5000/RAW", package = "powdR"), full.names = TRUE) raw_list <- extract_xy(files) class(raw_list) ## Not run: plot(raw_list, wavelength = "Cu") plot(raw_list, wavelength = "Cu", interactive = TRUE) ## End(Not run)#load example RAW file file <- system.file("extdata/D5000/RAW/D5000_1.RAW", package = "powdR") raw1 <- extract_xy(file) #Load multiple RAW files files <- dir(system.file("extdata/D5000/RAW", package = "powdR"), full.names = TRUE) raw_list <- extract_xy(files) class(raw_list) ## Not run: plot(raw_list, wavelength = "Cu") plot(raw_list, wavelength = "Cu", interactive = TRUE) ## End(Not run)
fps returns estimates of phase concentrations using full pattern
summation of X-ray powder diffraction data.
fps( lib, smpl, harmonise, solver, obj, refs, std, force, std_conc, omit_std, normalise, closed, tth_align, align, manual_align, tth_fps, shift, remove_trace, weighting, ... )fps( lib, smpl, harmonise, solver, obj, refs, std, force, std_conc, omit_std, normalise, closed, tth_align, align, manual_align, tth_fps, shift, remove_trace, weighting, ... )
lib |
A |
smpl |
A data frame. First column is 2theta, second column is counts |
harmonise |
logical parameter defining whether to harmonise the |
solver |
The optimisation routine to be used. One of |
obj |
The objective function to minimise when "BFGS", "Nelder-Mead",
or "CG" are used as the |
refs |
A character string of reference pattern IDs or names from the specified library.
The IDs or names supplied must be present within the |
std |
The phase ID (e.g. "QUA.1") to be used as an internal
standard. Must match an ID provided in the |
force |
An optional string of phase IDs or names specifying which phases should be forced to
remain throughout the automated full pattern summation. The IDs or names supplied must be present
within the |
std_conc |
The concentration of the internal standard (if known) in weight percent. If
unknown then omit the argument from the function call or use |
omit_std |
A logical parameter to be used when the |
normalise |
deprecated. Please use the |
closed |
A logical parameter to be used when the |
tth_align |
A vector defining the minimum and maximum 2theta values to be used during
alignment (e.g. |
align |
The maximum shift that is allowed during initial 2theta alignment (degrees). Default = 0.1. |
manual_align |
A logical operator denoting whether to optimise the alignment within the
negative/position 2theta range defined in the |
tth_fps |
A vector defining the minimum and maximum 2theta values to be used during
full pattern summation (e.g. |
shift |
A single numeric value denoting the maximum (positive or negative) shift, in degrees 2theta, that is allowed during the shifting of reference patterns. Default = 0. |
remove_trace |
A single numeric value representing the limit for the concentration
of trace phases to be retained, i.e. any mineral with an estimated concentration below
|
weighting |
an optional 2 column data frame specifying the 2theta values in the first
column and a numeric weighting vector in the second column that specifies areas of the pattern
to either emphasise (values > 1) or omit (values = 0) when minimising the objective function
defined in the |
... |
Other parameters passed to methods e.g. |
Applies full pattern summation (Chipera & Bish, 2002, 2013; Eberl, 2003) 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. Details provided in Butler and Hillier (2021).
a powdRfps object 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 and harmonised) |
residuals |
a vector of the residuals (measured minus fitted) |
phases |
a dataframe of the phases used to produce the fitted pattern and their concentrations |
phases_grouped |
the phases dataframe grouped by phase_name and concentrations summed |
obj |
named vector of the objective parameters summarising the quality of the fit |
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 |
Butler, B. M., Hillier, S., 2021.powdR: An R package for quantitative mineralogy using full pattern summation of X-ray powder diffraction data. Comp. Geo. 147, 104662. doi:10.1016/j.cageo.2020.104662
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.
#Load the minerals library data(minerals) # Load the soils data data(soils) #Since the reference library is relatively small, #the whole library can be used at once to get an #estimate of the phases within each sample. ## Not run: fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) fps_lime <- fps(lib = minerals, smpl = soils$limestone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) fps_granite <- fps(lib = minerals, smpl = soils$granite, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) #Alternatively run all 3 at once using lapply fps_soils <- lapply(soils, fps, lib = minerals, std = "QUA.2", refs = minerals$phases$phase_id, align = 0.2) #Using the rockjock library: data(rockjock) data(rockjock_mixtures) rockjock_1 <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", align = 0.3) #Alternatively you can specify the internal standard #concentration if known: rockjock_1s <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", std_conc = 20, align = 0.3) ## End(Not run)#Load the minerals library data(minerals) # Load the soils data data(soils) #Since the reference library is relatively small, #the whole library can be used at once to get an #estimate of the phases within each sample. ## Not run: fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) fps_lime <- fps(lib = minerals, smpl = soils$limestone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) fps_granite <- fps(lib = minerals, smpl = soils$granite, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) #Alternatively run all 3 at once using lapply fps_soils <- lapply(soils, fps, lib = minerals, std = "QUA.2", refs = minerals$phases$phase_id, align = 0.2) #Using the rockjock library: data(rockjock) data(rockjock_mixtures) rockjock_1 <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", align = 0.3) #Alternatively you can specify the internal standard #concentration if known: rockjock_1s <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", std_conc = 20, align = 0.3) ## End(Not run)
fps_lm returns a simple fit of a given pattern using linear regression,
where coefficients may be either positive or negative. Does not return quantitative
data. For quantitative results use fps or afps.
fps_lm( lib, smpl, harmonise, refs, std, tth_align, align, manual_align, tth_fps, shift, p, ... )fps_lm( lib, smpl, harmonise, refs, std, tth_align, align, manual_align, tth_fps, shift, p, ... )
lib |
A |
smpl |
A data frame. First column is 2theta, second column is counts |
harmonise |
logical parameter defining whether to harmonise the |
refs |
A character string of reference pattern IDs or names from the specified library.
The IDs or names supplied must be present within the |
std |
The phase ID (e.g. "QUA.1") to be used as internal
standard. Must match an ID provided in the |
tth_align |
A vector defining the minimum and maximum 2theta values to be used during
alignment (e.g. |
align |
The maximum shift that is allowed during initial 2theta alignment (degrees). Default = 0.1. |
manual_align |
A logical operator denoting whether to optimise the alignment within the
negative/position 2theta range defined in the |
tth_fps |
A vector defining the minimum and maximum 2theta values to be used during
full pattern summation (e.g. |
shift |
A single numeric value denoting the maximum (positive or negative) shift, in degrees 2theta, that is allowed during the shifting of selected phases. Default = 0. |
p |
a numeric parameter between 0 and 1 specifying the p-value limit for coefficients. Any reference patterns with a p-value greater than this value will be omitted from the linear regression and results recomputed. Must be greater than 0.000001 but no greater than 1. |
... |
Other arguments |
Requires a powdRlib library of reference patterns. Mineral concentrations
are not quantified and therefore reference intensity ratios are not required.
a powdRlm object 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 |
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 |
data(rockjock) data(rockjock_mixtures) #Compute the PCA and loadings x1 <- xrpd_pca(rockjock_mixtures, mean_center = TRUE, bin_size = 1, root_transform = 1) ## Not run: fps_lm_out <- fps_lm(rockjock, smpl = data.frame("x" = x1$loadings$tth, "y" = x1$loadings$Dim.1), refs = rockjock$phases$phase_id, std = "QUARTZ", align = 0.3, p = 0.01) plot(fps_lm_out, wavelength = "Cu", interactive = TRUE, group = TRUE) ## End(Not run)data(rockjock) data(rockjock_mixtures) #Compute the PCA and loadings x1 <- xrpd_pca(rockjock_mixtures, mean_center = TRUE, bin_size = 1, root_transform = 1) ## Not run: fps_lm_out <- fps_lm(rockjock, smpl = data.frame("x" = x1$loadings$tth, "y" = x1$loadings$Dim.1), refs = rockjock$phases$phase_id, std = "QUARTZ", align = 0.3, p = 0.01) plot(fps_lm_out, wavelength = "Cu", interactive = TRUE, group = TRUE) ## End(Not run)
fps_lm.powdRlib returns a simple fit of a given pattern using linear regression,
where coefficients may be either positive or negative. Does not return quantitative
data. For quantitative results use fps or afps.
## S3 method for class 'powdRlib' fps_lm( lib, smpl, harmonise, refs, std, tth_align, align, manual_align, tth_fps, shift, p, ... )## S3 method for class 'powdRlib' fps_lm( lib, smpl, harmonise, refs, std, tth_align, align, manual_align, tth_fps, shift, p, ... )
lib |
A |
smpl |
A data frame. First column is 2theta, second column is counts |
harmonise |
logical parameter defining whether to harmonise the |
refs |
A character string of reference pattern IDs or names from the specified library.
The IDs or names supplied must be present within the |
std |
The phase ID (e.g. "QUA.1") to be used as internal
standard. Must match an ID provided in the |
tth_align |
A vector defining the minimum and maximum 2theta values to be used during
alignment (e.g. |
align |
The maximum shift that is allowed during initial 2theta alignment (degrees). Default = 0.1. |
manual_align |
A logical operator denoting whether to optimise the alignment within the
negative/position 2theta range defined in the |
tth_fps |
A vector defining the minimum and maximum 2theta values to be used during
full pattern summation (e.g. |
shift |
A single numeric value denoting the maximum (positive or negative) shift, in degrees 2theta, that is allowed during the shifting of selected phases. Default = 0. |
p |
a numeric parameter between 0 and 1 specifying the p-value limit for coefficients. Any reference patterns with a p-value greater than this value will be omitted from the linear regression and results recomputed. Must be greater than 0.000001 but no greater than 1. |
... |
Other arguments |
Requires a powdRlib library of reference patterns. Mineral concentrations
are not quantified and therefore reference intensity ratios are not required.
a powdRlm object with components:
tth |
a vector of the 2theta scale of the fitted data |
fitted |
a vector of the count intensities of the fitted XRPD pattern |
measured |
a vector of the original count intensities of the XRPD measurement (aligned) |
residuals |
a vector of the residuals (fitted vs measured) |
phases |
a dataframe of the phases used to produce the fitted pattern and their concentrations |
phases_grouped |
the phases dataframe grouped by phase_name and concentrations summed |
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 |
data(rockjock) data(rockjock_mixtures) #Compute the PCA and loadings x1 <- xrpd_pca(rockjock_mixtures, mean_center = TRUE, bin_size = 1, root_transform = 1) ## Not run: fps_lm_out <- fps_lm(rockjock, smpl = data.frame("x" = x1$loadings$tth, "y" = x1$loadings$Dim.1), refs = rockjock$phases$phase_id, std = "QUARTZ", align = 0.3, p = 0.01) plot(fps_lm_out, wavelength = "Cu", interactive = TRUE, group = TRUE) ## End(Not run)data(rockjock) data(rockjock_mixtures) #Compute the PCA and loadings x1 <- xrpd_pca(rockjock_mixtures, mean_center = TRUE, bin_size = 1, root_transform = 1) ## Not run: fps_lm_out <- fps_lm(rockjock, smpl = data.frame("x" = x1$loadings$tth, "y" = x1$loadings$Dim.1), refs = rockjock$phases$phase_id, std = "QUARTZ", align = 0.3, p = 0.01) plot(fps_lm_out, wavelength = "Cu", interactive = TRUE, group = TRUE) ## End(Not run)
fps.powdRlib returns estimates of phase concentrations using full pattern
summation of X-ray powder diffraction data.
## S3 method for class 'powdRlib' fps( lib, smpl, harmonise, solver, obj, refs, std, force, std_conc, omit_std, normalise, closed, tth_align, align, manual_align, tth_fps, shift, remove_trace, weighting, ... )## S3 method for class 'powdRlib' fps( lib, smpl, harmonise, solver, obj, refs, std, force, std_conc, omit_std, normalise, closed, tth_align, align, manual_align, tth_fps, shift, remove_trace, weighting, ... )
lib |
A |
smpl |
A data frame. First column is 2theta, second column is counts |
harmonise |
logical parameter defining whether to harmonise the |
solver |
The optimisation routine to be used. One of |
obj |
The objective function to minimise when "BFGS", "Nelder-Mead",
or "CG" are used as the |
refs |
A character string of reference pattern IDs or names from the specified library.
The IDs or names supplied must be present within the |
std |
The phase ID (e.g. "QUA.1") to be used as an internal
standard. Must match an ID provided in the |
force |
An optional string of phase IDs or names specifying which phases should be forced to
remain throughout the automated full pattern summation. The IDs or names supplied must be present
within the |
std_conc |
The concentration of the internal standard (if known) in weight percent. If
unknown then omit the argument from the function call or use |
omit_std |
A logical parameter to be used when the |
normalise |
deprecated. Please use the |
closed |
A logical parameter to be used when the |
tth_align |
A vector defining the minimum and maximum 2theta values to be used during
alignment (e.g. |
align |
The maximum shift that is allowed during initial 2theta alignment (degrees). Default = 0.1. |
manual_align |
A logical operator denoting whether to optimise the alignment within the
negative/position 2theta range defined in the |
tth_fps |
A vector defining the minimum and maximum 2theta values to be used during
full pattern summation (e.g. |
shift |
A single numeric value denoting the maximum (positive or negative) shift, in degrees 2theta, that is allowed during the shifting of reference patterns. Default = 0. |
remove_trace |
A single numeric value representing the limit for the concentration
of trace phases to be retained, i.e. any mineral with an estimated concentration below
|
weighting |
an optional 2 column data frame specifying the 2theta values in the first
column and a numeric weighting vector in the second column that specifies areas of the pattern
to either emphasise (values > 1) or omit (values = 0) when minimising the objective function
defined in the |
... |
other arguments |
Applies full pattern summation (Chipera & Bish, 2002, 2013; Eberl, 2003) to an XRPD
sample to quantify phase concentrations. Requires a powdRlib library of reference
patterns with reference intensity ratios in order to derive mineral
concentrations. Details provided in Butler and Hillier (2021)
a powdRfps object 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 and harmonised) |
residuals |
a vector of the residuals (measured minus fitted) |
phases |
a dataframe of the phases used to produce the fitted pattern and their concentrations |
phases_grouped |
the phases dataframe grouped by phase_name and concentrations summed |
obj |
named vector of the objective parameters summarising the quality of the fit |
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 |
Butler, B. M., Hillier, S., 2021.powdR: An R package for quantitative mineralogy using full pattern summation of X-ray powder diffraction data. Comp. Geo. 147, 104662. doi:10.1016/j.cageo.2020.104662
Bish, D.L., Post, J.E., 1989. Modern powder diffraction. Mineralogical Society of America.
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.
#Load the minerals library data(minerals) # Load the soils data data(soils) #Since the reference library is relatively small, #the whole library can be used at once to get an #estimate of the phases within each sample. ## Not run: fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) fps_lime <- fps(lib = minerals, smpl = soils$limestone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) fps_granite <- fps(lib = minerals, smpl = soils$granite, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) #Alternatively run all 3 at once using lapply fps_soils <- lapply(soils, fps, lib = minerals, std = "QUA.2", refs = minerals$phases$phase_id, align = 0.2) #Using the rockjock library: data(rockjock) data(rockjock_mixtures) rockjock_1 <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", align = 0.3) #Alternatively you can specify the internal standard #concentration if known: rockjock_1s <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", std_conc = 20, align = 0.3) ## End(Not run)#Load the minerals library data(minerals) # Load the soils data data(soils) #Since the reference library is relatively small, #the whole library can be used at once to get an #estimate of the phases within each sample. ## Not run: fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) fps_lime <- fps(lib = minerals, smpl = soils$limestone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) fps_granite <- fps(lib = minerals, smpl = soils$granite, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) #Alternatively run all 3 at once using lapply fps_soils <- lapply(soils, fps, lib = minerals, std = "QUA.2", refs = minerals$phases$phase_id, align = 0.2) #Using the rockjock library: data(rockjock) data(rockjock_mixtures) rockjock_1 <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", align = 0.3) #Alternatively you can specify the internal standard #concentration if known: rockjock_1s <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", std_conc = 20, align = 0.3) ## End(Not run)
interpolate takes an XY, multiXY or powdRlib object and
interpolates the data onto a new 2theta scale using a natural spline.
See additional help via ?interpolate.XY,
?interpolate.multiXY or ?interpolate.powdRlib.
interpolate(x, new_tth, ...)interpolate(x, new_tth, ...)
x |
an |
new_tth |
a numeric vector of the new 2theta scale. |
... |
other arguments |
an XY or multiXY object.
#Define a new 2theta scale: data(rockjock_mixtures) tth <- seq(10, 60, 0.04) #interpolate multiXY object of data onto new scale i1 <- interpolate(rockjock_mixtures, new_tth = tth) #interpolate XY object onto new scale i2 <- interpolate(rockjock_mixtures$Mix1, new_tth = tth) #interpolate powdRlib object onto new scale i3 <- interpolate(minerals, new_tth = tth)#Define a new 2theta scale: data(rockjock_mixtures) tth <- seq(10, 60, 0.04) #interpolate multiXY object of data onto new scale i1 <- interpolate(rockjock_mixtures, new_tth = tth) #interpolate XY object onto new scale i2 <- interpolate(rockjock_mixtures$Mix1, new_tth = tth) #interpolate powdRlib object onto new scale i3 <- interpolate(minerals, new_tth = tth)
interpolate takes a multiXY object, which may contain
XY data frames with varying 2theta scales, and interpolates all
data frames onto the same scale using cubic splines.
## S3 method for class 'multiXY' interpolate(x, new_tth, ...)## S3 method for class 'multiXY' interpolate(x, new_tth, ...)
x |
a |
new_tth |
a numeric vector of the new 2theta scale. |
... |
other arguments |
a multiXY object.
data(rockjock_mixtures) #Define a new 2theta scale: tth <- seq(10, 60, 0.04) #interpolate data onto new scale i1 <- interpolate(rockjock_mixtures, new_tth = tth)data(rockjock_mixtures) #Define a new 2theta scale: tth <- seq(10, 60, 0.04) #interpolate data onto new scale i1 <- interpolate(rockjock_mixtures, new_tth = tth)
powdRlib object onto a given 2theta scale.interpolate takes a powdRlib object and interpolates the
data onto a new 2theta scale using a cubic spline.
## S3 method for class 'powdRlib' interpolate(x, new_tth, ...)## S3 method for class 'powdRlib' interpolate(x, new_tth, ...)
x |
a |
new_tth |
a numeric vector of the new 2theta scale. |
... |
other arguments |
a powdRlib object.
data(minerals) #Define a new 2theta scale: tth <- seq(10, 60, 0.04) #interpolate data onto new scale i1 <- interpolate(minerals, new_tth = tth)data(minerals) #Define a new 2theta scale: tth <- seq(10, 60, 0.04) #interpolate data onto new scale i1 <- interpolate(minerals, new_tth = tth)
XY object onto a given 2theta scale.interpolate takes an XY object and interpolates the
data onto a new 2theta scale using a cubic spline.
## S3 method for class 'XY' interpolate(x, new_tth, ...)## S3 method for class 'XY' interpolate(x, new_tth, ...)
x |
an |
new_tth |
a numeric vector of the new 2theta scale. |
... |
other arguments |
an XY object.
data(rockjock_mixtures) #Define a new 2theta scale: tth <- seq(10, 60, 0.04) #interpolate data onto new scale i1 <- interpolate(rockjock_mixtures$Mix1, new_tth = tth)data(rockjock_mixtures) #Define a new 2theta scale: tth <- seq(10, 60, 0.04) #interpolate data onto new scale i1 <- interpolate(rockjock_mixtures$Mix1, new_tth = tth)
merge.powdRlib allows two powdRlib objects (which must have) the
same 2theta scale) to be merged into a single powdRlib object.
## S3 method for class 'powdRlib' merge(x, y, ...)## S3 method for class 'powdRlib' merge(x, y, ...)
x |
a |
y |
a |
... |
other arguments |
a powdRlib object.
#Load the minerals library data(minerals) #Load the rockjock library data(rockjock) #interpolate minerals library onto same 2theta as rockjock minerals_i <- interpolate(minerals, new_tth = rockjock$tth) #merge the libraries merged_lib <- merge(rockjock, minerals_i)#Load the minerals library data(minerals) #Load the rockjock library data(rockjock) #interpolate minerals library onto same 2theta as rockjock minerals_i <- interpolate(minerals, new_tth = rockjock$tth) #merge the libraries merged_lib <- merge(rockjock, minerals_i)
This powdRlib object, built using the powdRlib constructor function,
contains a range of measured XRPD data (collected using Cu K-alpha radiation) along with
their reference intensity ratios. The library is designed for simple examples only and
can be used with the soils data for relatively fast tests of fps and
afps.
mineralsminerals
A powdRlib object of 3 components
A dataframe of all the count intensities of all reference patterns. Column names denote the unique phase ID of each reference pattern
A vector of the 2theta scale for all reference patterns in the library
A dataframe the phase IDs, names and reference intensity ratios (RIR)
A data frame of associated phase information for the minerals_xrd data.
Together these two data frames can be combined with the powdRlib constructor
function to create an example reference library (see ?powdRlib). Use the same
layout to create custom reference libraries.
minerals_phasesminerals_phases
A 3 column data frame consisting of:
A string defining the unique phase IDs that
should match those defined as column names of the minerals table
(e.g. minerals_xrd).
A string defining the mineral group that each reference pattern belongs to.
A vector defining the reference intensity ratios of each reference pattern.
minerals dataExample regrouping structure for the minerals data
minerals_regroupminerals_regroup
A 2 column data frame.
First column contains the unique phase IDs of all phases
in the minerals data. Second column contains the grouping structure for the data
(Non-clay, Clay or Amorphous).
A table of 14 reference patterns and their corresponding two theta scale that can
be combined with the minerals_phases table to create a powdRlib object using
the powdRlib constructor function. Use the same layout to create custom
reference libraries.
minerals_xrdminerals_xrd
A dataframe
The first column defines the two theta scale, and remaining columns are individual reference patterns of pure minerals or amorphous phases. Each column title should be a unique mineral ID
multi_xy_to_df converts multiXY objects to a column-wise data frame.
multi_xy_to_df(x, tth, ...)multi_xy_to_df(x, tth, ...)
x |
a |
tth |
a logical value denoting whether the 2theta scale is appended as the
first column. Default |
... |
other arguments |
A data.frame.
#Load the minerals library data(soils) soils_df1 <- multi_xy_to_df(soils, tth = TRUE) soils_df2 <- multi_xy_to_df(soils, tth = FALSE)#Load the minerals library data(soils) soils_df1 <- multi_xy_to_df(soils, tth = TRUE) soils_df2 <- multi_xy_to_df(soils, tth = FALSE)
multi_xy_to_df.multiXY converts multiXY objects to a column-wise data frame.
## S3 method for class 'multiXY' multi_xy_to_df(x, tth, ...)## S3 method for class 'multiXY' multi_xy_to_df(x, tth, ...)
x |
a |
tth |
a logical value denoting whether the 2theta scale is appended as the
first column. Default |
... |
other arguments |
A data.frame.
#Load the minerals library data(soils) soils_df1 <- multi_xy_to_df(soils, tth = TRUE) soils_df2 <- multi_xy_to_df(soils, tth = FALSE)#Load the minerals library data(soils) soils_df1 <- multi_xy_to_df(soils, tth = TRUE) soils_df2 <- multi_xy_to_df(soils, tth = FALSE)
powdRfps or
powdRafps objectomit_std adjusts phase concentrations in a powdRfps or powdRafps object
(derived from fps() and afps(), respectively) by removing the concentrations of
the internal standard. Relevant information for the calculation is automatically
extracted from x$inputs$std and x$inputs$std_conc. For more information see
?omit_std.powdRfps and omit_std.powdRafps.
omit_std(x, ...)omit_std(x, ...)
x |
A |
... |
other arguments |
a powdRfps or powdRafps object with components:
tth |
a vector of the 2theta scale of the fitted data |
fitted |
a vector of the fitted XRPD pattern |
measured |
a vector of the original XRPD measurement (aligned and harmonised) |
residuals |
a vector of the residuals (measured minus fitted) |
phases |
a dataframe of the phases used to produce the fitted pattern and their concentrations |
phases_grouped |
the phases dataframe grouped by phase_name and concentrations summed |
obj |
named vector of the objective parameters summarising the quality of the fit |
weighted_pure_patterns |
a dataframe of reference patterns used to produce the fitted pattern. All patterns have been weighted according to the coefficients used in the fit |
coefficients |
a named vector of coefficients used to produce the fitted pattern |
inputs |
a list of input arguments used in the function call |
## Not run: data(rockjock) data(rockjock_mixtures) rockjock_1 <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", align = 0.3, std_conc = 20) rockjock_1o <- omit_std(rockjock_1) rockjock_a1 <- afps(lib = rockjock, smpl = rockjock_mixtures$Mix1, std = "CORUNDUM", align = 0.3, lod = 1, std_conc = 20) rockjock_a1o <- omit_std(rockjock_a1) ## End(Not run)## Not run: data(rockjock) data(rockjock_mixtures) rockjock_1 <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", align = 0.3, std_conc = 20) rockjock_1o <- omit_std(rockjock_1) rockjock_a1 <- afps(lib = rockjock, smpl = rockjock_mixtures$Mix1, std = "CORUNDUM", align = 0.3, lod = 1, std_conc = 20) rockjock_a1o <- omit_std(rockjock_a1) ## End(Not run)
powdRafps objectomit_std.powdRafps adjusts phase concentrations in a powdRafps object by removing
the concentrations of the internal standard. Relevant information for the calculation is
automatically extracted from x$inputs$std and x$inputs$std_conc.
## S3 method for class 'powdRafps' omit_std(x, ...)## S3 method for class 'powdRafps' omit_std(x, ...)
x |
A |
... |
other arguments |
a powdRafps object with components:
tth |
a vector of the 2theta scale of the fitted data |
fitted |
a vector of the fitted XRPD pattern |
measured |
a vector of the original XRPD measurement (aligned and harmonised) |
residuals |
a vector of the residuals (measured minus fitted) |
phases |
a dataframe of the phases used to produce the fitted pattern and their concentrations |
phases_grouped |
the phases dataframe grouped by phase_name and concentrations summed |
obj |
named vector of the objective parameters summarising the quality of the fit |
weighted_pure_patterns |
a dataframe of reference patterns used to produce the fitted pattern. All patterns have been weighted according to the coefficients used in the fit |
coefficients |
a named vector of coefficients used to produce the fitted pattern |
inputs |
a list of input arguments used in the function call |
## Not run: data(rockjock) data(rockjock_mixtures) rockjock_a1 <- afps(lib = rockjock, smpl = rockjock_mixtures$Mix1, std = "CORUNDUM", align = 0.3, lod = 1, std_conc = 20) rockjock_a1o <- omit_std(rockjock_a1) ## End(Not run)## Not run: data(rockjock) data(rockjock_mixtures) rockjock_a1 <- afps(lib = rockjock, smpl = rockjock_mixtures$Mix1, std = "CORUNDUM", align = 0.3, lod = 1, std_conc = 20) rockjock_a1o <- omit_std(rockjock_a1) ## End(Not run)
powdRfps objectomit_std.powdRfps adjusts phase concentrations in a powdRfps object by removing
the concentrations of the internal standard. Relevant information for the calculation is
automatically extracted from x$inputs$std and x$inputs$std_conc.
## S3 method for class 'powdRfps' omit_std(x, ...)## S3 method for class 'powdRfps' omit_std(x, ...)
x |
A |
... |
other arguments |
a powdRfps object with components:
tth |
a vector of the 2theta scale of the fitted data |
fitted |
a vector of the fitted XRPD pattern |
measured |
a vector of the original XRPD measurement (aligned and harmonised) |
residuals |
a vector of the residuals (measured minus fitted) |
phases |
a dataframe of the phases used to produce the fitted pattern and their concentrations |
phases_grouped |
the phases dataframe grouped by phase_name and concentrations summed |
obj |
named vector of the objective parameters summarising the quality of the fit |
weighted_pure_patterns |
a dataframe of reference patterns used to produce the fitted pattern. All patterns have been weighted according to the coefficients used in the fit |
coefficients |
a named vector of coefficients used to produce the fitted pattern |
inputs |
a list of input arguments used in the function call |
## Not run: data(rockjock) data(rockjock_mixtures) rockjock_1 <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", align = 0.3, std_conc = 20) rockjock_1o <- omit_std(rockjock_1) ## End(Not run)## Not run: data(rockjock) data(rockjock_mixtures) rockjock_1 <- fps(lib = rockjock, smpl = rockjock_mixtures$Mix1, refs = c("ORDERED_MICROCLINE", "LABRADORITE", "KAOLINITE_DRY_BRANCH", "MONTMORILLONITE_WYO", "ILLITE_1M_RM30", "CORUNDUM"), std = "CORUNDUM", align = 0.3, std_conc = 20) rockjock_1o <- omit_std(rockjock_1) ## End(Not run)
plot.multiXY is designed to provide easy, adaptable plots
of multiple XRPD patterns.
## S3 method for class 'multiXY' plot(x, wavelength, xlim, normalise, interactive, ...)## S3 method for class 'multiXY' plot(x, wavelength, xlim, normalise, interactive, ...)
x |
a multiXY object |
wavelength |
One of "Cu", "Co" or a custom numeric value defining the wavelength (in Angstroms). Used to compute d-spacings.When "Cu" or "Co" are supplied, wavelengths of 1.54056 or 1.78897 are used, respectively. |
xlim |
A numeric vector providing limits of the x-axis (E.g. |
normalise |
Logical. If TRUE then count intensities will be normalised to a
minimum of zero and maximum of 1. Default |
interactive |
Logical. If TRUE then the output will be an interactive ggplotly object. If FALSE then the output will be a ggplot object. |
... |
other arguments |
Plots can be made interactive using the logical interactive argument.
# Load the minerals library data(rockjock_mixtures) ## Not run: plot(as_multi_xy(rockjock_mixtures), wavelength = "Cu") plot(as_multi_xy(rockjock_mixtures), wavelength = "Cu", interactive = TRUE) ## End(Not run)# Load the minerals library data(rockjock_mixtures) ## Not run: plot(as_multi_xy(rockjock_mixtures), wavelength = "Cu") plot(as_multi_xy(rockjock_mixtures), wavelength = "Cu", interactive = TRUE) ## End(Not run)
plot.powdRafps is designed to provide easy, adaptable plots
of full pattern summation outputs produced from afps.
## S3 method for class 'powdRafps' plot(x, wavelength, mode, group, xlim, show_excluded, interactive, ...)## S3 method for class 'powdRafps' plot(x, wavelength, mode, group, xlim, show_excluded, interactive, ...)
x |
a powdRafps object |
wavelength |
One of "Cu", "Co" or a custom numeric value defining the wavelength (in Angstroms). Used to compute d-spacings.When "Cu" or "Co" are supplied, wavelengths of 1.54056 or 1.78897 are used, respectively. |
mode |
One of "fit", "residuals" or "both" defining whether to plot the fitted patterns, the residuals of the fit, or both, respectively. Default = "fit". |
group |
A logical parameter used to specify whether the plotted data are grouped according to the phase name. Default = FALSE. |
xlim |
A numeric vector providing limits of the x-axis (E.g. |
show_excluded |
A logical value specifying whether the areas excluded from the
fitting are identified in the plot as grey rectangles. Default |
interactive |
logical. If TRUE then the output will be an interactive ggplotly object. If FALSE then the output will be a ggplot object. |
... |
other arguments |
When seeking to inspect the results from full pattern summation, interactive
plots are particularly useful and can be specified with the interactive
argument.
#Load the minerals library data(minerals) # Load the soils data data(soils) ## Not run: afps_sand <- afps(lib = minerals, smpl = soils$sandstone, std = "QUA.1", amorphous = "ORG", align = 0.2, lod = 0.1) plot(afps_sand, wavelength = "Cu") plot(afps_sand, wavelength = "Cu", interactive = TRUE) ## End(Not run)#Load the minerals library data(minerals) # Load the soils data data(soils) ## Not run: afps_sand <- afps(lib = minerals, smpl = soils$sandstone, std = "QUA.1", amorphous = "ORG", align = 0.2, lod = 0.1) plot(afps_sand, wavelength = "Cu") plot(afps_sand, wavelength = "Cu", interactive = TRUE) ## End(Not run)
plot.powdRbkg is designed to provide quick plots to inspect the
fitted backgrounds obtained from bkg.
## S3 method for class 'powdRbkg' plot(x, interactive, ...)## S3 method for class 'powdRbkg' plot(x, interactive, ...)
x |
a powdRbkg object |
interactive |
Logical. If TRUE then the output will be an interactive ggplotly object. If FALSE then the output will be a ggplot object. |
... |
other arguments |
The only mandatory argument is x, which must be a powdRbkg object. Plots can
be made interactive using the logical interactive argument.
# Load the minerals library data(minerals) ## Not run: plot(minerals, interactive = TRUE) ## End(Not run)# Load the minerals library data(minerals) ## Not run: plot(minerals, interactive = TRUE) ## End(Not run)
plot.powdRfps is designed to provide easy, adaptable plots
of full pattern summation outputs produced from fps.
## S3 method for class 'powdRfps' plot(x, wavelength, mode, group, xlim, show_excluded, interactive, ...)## S3 method for class 'powdRfps' plot(x, wavelength, mode, group, xlim, show_excluded, interactive, ...)
x |
a powdRfps object |
wavelength |
One of "Cu", "Co" or a custom numeric value defining the wavelength (in Angstroms). Used to compute d-spacings.When "Cu" or "Co" are supplied, wavelengths of 1.54056 or 1.78897 are used, respectively. |
mode |
One of "fit", "residuals" or "both" defining whether to plot the fitted patterns, the residuals of the fit, or both, respectively. Default = "fit". |
group |
A logical parameter used to specify whether the plotted data are grouped according to the phase name. Default = FALSE. |
xlim |
A numeric vector providing limits of the x-axis (E.g. |
show_excluded |
A logical value specifying whether the areas excluded from the
fitting are identified in the plot as grey rectangles. Default |
interactive |
logical. If TRUE then the output will be an interactive ggplotly object. If FALSE then the output will be a ggplot object. |
... |
other arguments |
When seeking to inspect the results from full pattern summation, interactive
plots are particularly useful and can be specified with the interactive
argument.
#Load the minerals library data(minerals) # Load the soils data data(soils) ## Not run: fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) plot(fps_sand, wavelength = "Cu") plot(fps_sand, wavelength = "Cu", interactive = TRUE) ## End(Not run)#Load the minerals library data(minerals) # Load the soils data data(soils) ## Not run: fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) plot(fps_sand, wavelength = "Cu") plot(fps_sand, wavelength = "Cu", interactive = TRUE) ## End(Not run)
plot.powdRlib is designed to provide easy, adaptable plots
of an XRPD reference library built using the powdRlib constructor
function.
## S3 method for class 'powdRlib' plot(x, wavelength, refs, interactive, ...)## S3 method for class 'powdRlib' plot(x, wavelength, refs, interactive, ...)
x |
a powdRlib object |
wavelength |
One of "Cu", "Co" or a custom numeric value defining the wavelength (in Angstroms). Used to compute d-spacings.When "Cu" or "Co" are supplied, wavelengths of 1.54056 or 1.78897 are used, respectively. |
refs |
a character string of reference pattern id's to be plotted |
interactive |
Logical. If TRUE then the output will be an interactive ggplotly object. If FALSE then the output will be a ggplot object. |
... |
other arguments |
Plots can be made interactive using the logical interactive argument.
# Load the minerals library data(minerals) ## Not run: plot(minerals, wavelength = "Cu", refs = "ALB") plot(minerals, wavelength = "Cu", refs = "ALB", interactive = TRUE) ## End(Not run)# Load the minerals library data(minerals) ## Not run: plot(minerals, wavelength = "Cu", refs = "ALB") plot(minerals, wavelength = "Cu", refs = "ALB", interactive = TRUE) ## End(Not run)
plot.powdRlm is designed to provide easy, adaptable plots
of full pattern summation outputs produced from fps_lm.
## S3 method for class 'powdRlm' plot(x, wavelength, mode, xlim, group, show_excluded, interactive, ...)## S3 method for class 'powdRlm' plot(x, wavelength, mode, xlim, group, show_excluded, interactive, ...)
x |
a powdRlm object |
wavelength |
One of "Cu", "Co" or a custom numeric value defining the wavelength (in Angstroms). Used to compute d-spacings.When "Cu" or "Co" are supplied, wavelengths of 1.54056 or 1.78897 are used, respectively. |
mode |
One of "fit", "residuals" or "both" defining whether to plot the fitted patterns, the residuals of the fit, or both, respectively. Default = "fit". |
xlim |
A numeric vector providing limits of the x-axis (E.g. |
group |
A logical parameter used to specify whether the plotted data are grouped according to the phase name. Default = FALSE. |
show_excluded |
A logical value specifying whether the areas excluded from the
fitting are identified in the plot as grey rectangles. Default |
interactive |
logical. If TRUE then the output will be an interactive ggplotly object. If FALSE then the output will be a ggplot object. |
... |
other arguments |
When seeking to inspect the results from full pattern summation, interactive
plots are particularly useful and can be specified with the interactive
argument.
data(rockjock) data(rockjock_mixtures) #Compute the PCA and loadings x1 <- xrpd_pca(rockjock_mixtures, mean_center = TRUE, bin_size = 1, root_transform = 1) ## Not run: fps_lm_out <- fps_lm(rockjock, smpl = data.frame("x" = x1$loadings$tth, "y" = x1$loadings$Dim.1), refs = rockjock$phases$phase_id, std = "QUARTZ", align = 0.3, p = 0.01) plot(fps_lm_out, wavelength = "Cu", interactive = TRUE, group = TRUE) ## End(Not run)data(rockjock) data(rockjock_mixtures) #Compute the PCA and loadings x1 <- xrpd_pca(rockjock_mixtures, mean_center = TRUE, bin_size = 1, root_transform = 1) ## Not run: fps_lm_out <- fps_lm(rockjock, smpl = data.frame("x" = x1$loadings$tth, "y" = x1$loadings$Dim.1), refs = rockjock$phases$phase_id, std = "QUARTZ", align = 0.3, p = 0.01) plot(fps_lm_out, wavelength = "Cu", interactive = TRUE, group = TRUE) ## End(Not run)
plot.XY is designed to provide easy, adaptable plots
of an XRPD pattern.
## S3 method for class 'XY' plot(x, wavelength, xlim, normalise, interactive, ...)## S3 method for class 'XY' plot(x, wavelength, xlim, normalise, interactive, ...)
x |
an XY object |
wavelength |
One of "Cu", "Co" or a custom numeric value defining the wavelength (in Angstroms). Used to compute d-spacings.When "Cu" or "Co" are supplied, wavelengths of 1.54056 or 1.78897 are used, respectively. |
xlim |
A numeric vector providing limits of the x-axis (E.g. |
normalise |
Logical. If TRUE then count intensities will be normalised to a
minimum of zero and maximum of 1. Default |
interactive |
Logical. If TRUE then the output will be an interactive ggplotly object. If FALSE then the output will be a ggplot object. |
... |
other arguments |
Plots can be made interactive using the logical interactive argument.
# Load the minerals library data(rockjock_mixtures) ## Not run: plot(rockjock_mixtures$Mix1, wavelength = "Cu") plot(rockjock_mixtures$Mix1, wavelength = "Cu", interactive = TRUE) ## End(Not run)# Load the minerals library data(rockjock_mixtures) ## Not run: plot(rockjock_mixtures$Mix1, wavelength = "Cu") plot(rockjock_mixtures$Mix1, wavelength = "Cu", interactive = TRUE) ## End(Not run)
An implementation of the full pattern summation approach to quantitative mineralogy from X-ray powder diffraction data (Chipera & Bish, 2002, 2013; Eberl, 2003; Butler & Hillier 2021).
Benjamin Butler, The James Hutton Institute, Aberdeen, UK
Butler, B. M., Hillier, S., 2021.powdR: An R package for quantitative mineralogy using full pattern summation of X-ray powder diffraction data. Comp. Geo. 147, 104662. doi:10.1016/j.cageo.2020.104662
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.
A constructor function for creating a powdRlib object from two tables of
data. The resulting powdRlib object is required when
using fps or afps.
powdRlib(xrd_table, phases_table, check_names)powdRlib(xrd_table, phases_table, check_names)
xrd_table |
A data frame of the count intensities of the XRPD reference patterns, all scaled to same maximum intensity, with their 2theta axis as the first column. |
phases_table |
A data frame of the required data (phase ID, phase name, and reference intensity ratio) for each reference pattern. |
check_names |
A logical argument defining whether the column names in the data
supplied in |
a powdRlib object with components:
xrd |
a data frame of the count intensities of the reference patterns |
tth |
a vector of the 2theta axis |
phases |
a 3 column data frame of the IDs, names and reference intensity ratios of the reference pattern |
#load an example xrd_table data(minerals_xrd) #load an example phases_table data(minerals_phases) #Create a reference library object xrd_lib <- powdRlib(xrd_table = minerals_xrd, phases_table = minerals_phases)#load an example xrd_table data(minerals_xrd) #load an example phases_table data(minerals_phases) #Create a reference library object xrd_lib <- powdRlib(xrd_table = minerals_xrd, phases_table = minerals_phases)
r computes the difference between a measured and fitted pattern. See
equation for R in section 2.1 of Butler and Hillier (2021).
r(measured, fitted, weighting)r(measured, fitted, weighting)
measured |
a vector of count intensities for a measured pattern |
fitted |
a vector of count intensities for a fitted pattern |
weighting |
an optional weighting vector of the same length as those specified
in |
a single numeric value
Butler, B.M., Hillier, S., 2021. powdR: An R package for quantitative mineralogy using full pattern summation of X-ray powder diffraction data. Computers and Geosciences. 147, 104662. doi:10.1016/j.cageo.2020.104662
# Load soils xrd data data(soils) #Load minerals library data(minerals) ## Not run: #Produce a fit fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) r(measured = fps_sand$measured, fitted = fps_sand$fitted) ## End(Not run)# Load soils xrd data data(soils) #Load minerals library data(minerals) ## Not run: #Produce a fit fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) r(measured = fps_sand$measured, fitted = fps_sand$fitted) ## End(Not run)
read_xy is a wrapper for read.csv that is designed for space separated XRPD
data.
read_xy(files, header, sep)read_xy(files, header, sep)
files |
path of the file(s) to be imported. |
header |
a logical value indicating whether the file contains the names
of the variables as its first line. Default = |
sep |
the field separator character. Values on each line of the file
are separated by this character. Default = |
If only one path is supplied then an XY data frame with 2 columns is returned, the first being the 2theta axis and the second being the count intensities. If more than one path is supplied then a multiXY list is returned, with each item in the list being an XY data frame as already described.
#load example XY file file <- system.file("extdata/D5000/xy/D5000_1.xy", package = "powdR") xy <- read_xy(file) #Load multiple XY files files <- dir(system.file("extdata/D5000/xy", package = "powdR"), full.names = TRUE) xy_list <- read_xy(files) ## Not run: plot(xy_list, wavelength = "Cu") plot(xy_list, wavelength = "Cu", interactive = TRUE) ## End(Not run)#load example XY file file <- system.file("extdata/D5000/xy/D5000_1.xy", package = "powdR") xy <- read_xy(file) #Load multiple XY files files <- dir(system.file("extdata/D5000/xy", package = "powdR"), full.names = TRUE) xy_list <- read_xy(files) ## Not run: plot(xy_list, wavelength = "Cu") plot(xy_list, wavelength = "Cu", interactive = TRUE) ## End(Not run)
regroup allows an alternative grouping structure to be applied to powdRfps
and powdRafps objects. For more details see ?regroup.powdRfps or
?regroup.powdRafps.
regroup(x, ...)regroup(x, ...)
x |
A |
... |
Other parameters passed to methods e.g. |
powdRfps and powdRafps objects contain a data frame called phases_grouped
that summarises phase concentrations based on defined mineral groups from the powdRlib
reference library. regroup allows you to change this grouping structure by supplying
new group identities.
a powdRfps or powdRafps object with components:
tth |
a vector of the 2theta scale of the fitted data |
fitted |
a vector of the count intensities of fitted XRPD pattern |
measured |
a vector of the count intensities of original XRPD measurement (aligned) |
residuals |
a vector of the residuals (measured minus fitted) |
phases |
a dataframe of the phases used to produce the fitted pattern |
phases_grouped |
the phases dataframe grouped and summed by phase_name |
obj |
named vector of the objective parameters summarising the quality of the fit |
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 |
#Load the minerals library data(minerals) #Load the soils data data(soils) #Load the regrouping structure data(minerals_regroup) ## Not run: fps_sandstone <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) fps_sandstone_regrouped <- regroup(fps_sandstone, minerals_regroup) fps_sandstone_regrouped$phases_grouped ## End(Not run)#Load the minerals library data(minerals) #Load the soils data data(soils) #Load the regrouping structure data(minerals_regroup) ## Not run: fps_sandstone <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) fps_sandstone_regrouped <- regroup(fps_sandstone, minerals_regroup) fps_sandstone_regrouped$phases_grouped ## End(Not run)
regroup.powdRafps allows an alternative grouping structure to be applied to
powdRafps objects.
## S3 method for class 'powdRafps' regroup(x, y, ...)## S3 method for class 'powdRafps' regroup(x, y, ...)
x |
A |
y |
A data frame. First column contains the phase IDs covering all those present in
|
... |
other arguments |
powdRafps objects contain a data frame called phases_grouped
that summarises phase concentrations based on defined mineral groups from the powdRlib
reference library. regroup allows you to change this grouping structure by supplying
new group identities.
a powdRafps object with components:
tth |
a vector of the 2theta scale of the fitted data |
fitted |
a vector of the count intensities of fitted XRPD pattern |
measured |
a vector of the count intensities of original XRPD measurement (aligned) |
residuals |
a vector of the residuals (measured minus fitted) |
phases |
a dataframe of the phases used to produce the fitted pattern |
phases_grouped |
the phases dataframe grouped and summed by phase_name |
obj |
named vector of the objective parameters summarising the quality of the fit |
weighted_pure_patterns |
a dataframe of reference patterns used to produce the fitted pattern. All patterns have been weighted according to the coefficients used in the fit |
coefficients |
a named vector of coefficients used to produce the fitted pattern |
inputs |
a list of input arguments used in the function call |
#Load the minerals library data(minerals) # Load the soils data data(soils) #Load the regrouping structure data(minerals_regroup) ## Not run: afps_sandstone <- afps(lib = minerals, smpl = soils$sandstone, std = "QUA.2", align = 0.2, lod = 0.2, amorphous = "ORG", amorphous_lod = 1) afps_sandstone_regrouped <- regroup(afps_sandstone, minerals_regroup) afps_sandstone_regrouped$phases_grouped ## End(Not run)#Load the minerals library data(minerals) # Load the soils data data(soils) #Load the regrouping structure data(minerals_regroup) ## Not run: afps_sandstone <- afps(lib = minerals, smpl = soils$sandstone, std = "QUA.2", align = 0.2, lod = 0.2, amorphous = "ORG", amorphous_lod = 1) afps_sandstone_regrouped <- regroup(afps_sandstone, minerals_regroup) afps_sandstone_regrouped$phases_grouped ## End(Not run)
regroup.powdRfps allows an alternative grouping structure to be applied to powdRfps
objects.
## S3 method for class 'powdRfps' regroup(x, y, ...)## S3 method for class 'powdRfps' regroup(x, y, ...)
x |
A |
y |
A data frame. First column contains the phase IDs covering all those present in
|
... |
other arguments |
powdRfps objects contain a data frame called phases_grouped
that summarises phase concentrations based on defined mineral groups from the powdRlib
reference library. regroup allows you to change this grouping structure by supplying
new group identities.
a powdRfps object with components:
tth |
a vector of the 2theta scale of the fitted data |
fitted |
a vector of the count intensities of fitted XRPD pattern |
measured |
a vector of the count intensities of original XRPD measurement (aligned) |
residuals |
a vector of the residuals (measured minus fitted) |
phases |
a dataframe of the phases used to produce the fitted pattern |
phases_grouped |
the phases dataframe grouped and summed by phase_name |
obj |
named vector of the objective parameters summarising the quality of the fit |
weighted_pure_patterns |
a dataframe of reference patterns used to produce the fitted pattern. All patterns have been weighted according to the coefficients used in the fit |
coefficients |
a named vector of coefficients used to produce the fitted pattern |
inputs |
a list of input arguments used in the function call |
#Load the minerals library data(minerals) #Load the soils data data(soils) #Load the regrouping structure data(minerals_regroup) ## Not run: fps_sandstone <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) fps_sandstone_regrouped <- regroup(fps_sandstone, minerals_regroup) fps_sandstone_regrouped$phases_grouped ## End(Not run)#Load the minerals library data(minerals) #Load the soils data data(soils) #Load the regrouping structure data(minerals_regroup) ## Not run: fps_sandstone <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) fps_sandstone_regrouped <- regroup(fps_sandstone, minerals_regroup) fps_sandstone_regrouped$phases_grouped ## End(Not run)
A powdRlib object of 168 pure reference patterns from the RockJock
library (Cu K-alpha radiation) along with reference intensity ratios. Note that compared
to same library supplied with RockJock the powdR patterns have been normalised to 10,000
counts and reference intensity ratios transformed so that all are relative
to that of corundum, which has been set to a value of 1.0.
Can be used with the fps() and afps() functions for quantitative
analysis. Example mixtures for testing the rockjock library with known
concentrations are available in the rockjock_mixtures data. See
?rockjock_mixtures.
rockjockrockjock
A powdRlib object of 3 components
A dataframe of all the count intensities of all reference patterns. Column names denote the unique phase ID of each reference pattern
A vector of the 2theta scale for all reference patterns in the library
A dataframe the phase IDs, names and reference intensity ratios (RIR)
Dennis Eberl
Eberl, D.D., 2003. User's guide to RockJock - A program for determining quantitative mineralogy from powder X-ray diffraction data. Boulder, CA.
A multiXY list containing 8 XRPD measurements (Cu K-alpha radiation) of synthetic mixtures
that can be used to assess accuracy of quantitative analysis from the fps()
and afps() functions. The mixtures contain various amounts of quartz (QUARTZ
standard of the rockjock library), K-feldspar (ORDERED_MICROCLINE),
plagioclase (LABRADORITE), kaolinite (KAOLINITE_DRY_BRANCH), dioctahedral smectite
(MONTMORILLIONITE_WYO), illite (ILLITE_1M_RM30) and corundum (CORUNDUM).
rockjock_mixturesrockjock_mixtures
A multiXY list of 8 components, each comprised of two columns. Column tth specifies
the 2theta axis and counts specifies the count intensities. The mixtures have to following
compositions that are also tabulated in the rockjock_weights data.
Contains: 4 % K-feldspar, 20 % plagioclase, 12 % kaolinite, 36 % dioctahedral smectite, 8 % illite and 20 % corundum.
Contains: 4 % quartz, 8 % K-feldspar, 36 % plagioclase, 20 % kaolinite, 12 % illite and 20 % corundum.
Contains: 8 % quartz, 12 % K-feldspar, 36 % kaolinite, 4 % dioctahedral smectite, 20 % illite and 20 % corundum.
Contains: 12 % quartz, 20 % K-feldspar, 4 % plagioclase, 8 % dioctahedral smectite, 36 % illite and 20 % corundum.
Contains: 20 % quartz, 36 % K-feldspar, 8 % plagioclase, 4 % kaolinite, 12 % dioctahedral smectite and 20 % corundum.
Contains: 36 % quartz, 12 % plagioclase, 8 % kaolinite, 20 % dioctahedral smectite, 4 % illite and 20 % corundum.
Contains: 8 % K-feldspar, 40 % plagioclase, 4 % kaolinite, 12 % dioctahedral smectite, 16 % illite and 20 % corundum.
Contains: 8 % quartz, 4 % K-feldspar, 4 % plagioclase, 24 % dioctahedral smectite, 40 % illite and 20 % corundum.
Dennis Eberl
Eberl, D.D., 2003. User's guide to RockJock - A program for determining quantitative mineralogy from powder X-ray diffraction data. Boulder, CA.
A data frame containing an example re-grouping structure for the rockjock
reference library, which results in a slightly coarser description of clay
minerals and Fe/Ti-(hydr)oxides in powdRfps or powdRafps objects
when used with regroup().
rockjock_regrouprockjock_regroup
A data frame with three columns:
the phase IDs present in afsis$phases$phase_id
.
The phase names that constitute the first regrouping structure.
The phase names that constitute the second regrouping structure
rockjock_mixtures dataA dataframe summarising the weighed mineral concentrations of the rockjock_mixtures data,
all in units of weight percent.
rockjock_weightsrockjock_weights
An 8 column dataframe, with each row detailing the composition of a sample.
Dennis Eberl
Eberl, D.D., 2003. User's guide to RockJock - A program for determining quantitative mineralogy from powder X-ray diffraction data. Boulder, CA.
A wrapper for shiny::runApp to start the powdR
background fitting Shiny app.
run_bkg(...)run_bkg(...)
... |
further arguments to pass to |
## Not run: run_powdR() ## End(Not run)## Not run: run_powdR() ## End(Not run)
A wrapper for shiny::runApp to start the Shiny app for powdR.
run_powdR(...)run_powdR(...)
... |
further arguments to pass to |
## Not run: run_powdR() ## End(Not run)## Not run: run_powdR() ## End(Not run)
rwp computes the difference between a measured and fitted pattern. See
equation for Rwp in section 2.1 of Butler and Hillier (2021).
rwp(measured, fitted, weighting)rwp(measured, fitted, weighting)
measured |
a vector of count intensities for a measured pattern |
fitted |
a vector of count intensities for a fitted pattern |
weighting |
an optional weighting vector of the same length as those specified
in |
a single numeric value
Butler, B.M., Hillier, S., 2021. powdR: An R package for quantitative mineralogy using full pattern summation of X-ray powder diffraction data. Computers and Geosciences. 147, 104662. doi:10.1016/j.cageo.2020.104662
# Load soils xrd data data(soils) #Load minerals library data(minerals) ## Not run: #Produce a fit fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) rwp(measured = fps_sand$measured, fitted = fps_sand$fitted) ## End(Not run)# Load soils xrd data data(soils) #Load minerals library data(minerals) ## Not run: #Produce a fit fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) rwp(measured = fps_sand$measured, fitted = fps_sand$fitted) ## End(Not run)
3 soil samples from different parent materials measured by XRPD (Cu K-alpha radiation)
soilssoils
A multiXY list of 3 XY dataframes (named according to parent material type), with each XY dataframe containing two columns of:
The 2theta measurement intervals
The count intensities
subset.powdRlib is designed to provide an easy way of
subsetting a powdRlib object by defining the phase ID's
that the user wishes to either keep or remove.
## S3 method for class 'powdRlib' subset(x, refs, mode, ...)## S3 method for class 'powdRlib' subset(x, refs, mode, ...)
x |
a |
refs |
a string of the phase IDs or names of reference patterns to be subset. The ID's
or names supplied must be present within the |
mode |
denotes whether the phase IDs or names defined in the |
... |
other arguments |
A powdRlib object.
#Load the minerals library data(minerals) minerals_keep <- subset(minerals, refs = c("QUA.1", "QUA.2"), mode = "keep") minerals_remove <- subset(minerals, refs = c("QUA.1", "QUA.2"), mode = "remove")#Load the minerals library data(minerals) minerals_keep <- subset(minerals, refs = c("QUA.1", "QUA.2"), mode = "keep") minerals_remove <- subset(minerals, refs = c("QUA.1", "QUA.2"), mode = "remove")
summarise_mineralogy creates a summary table of quantified mineral
concentrations across a given dataset using a list of multiple powdRfps
or powdRafps derived from fps() and afps(), respectively.
summarise_mineralogy(x, type, order, rwp, r, delta)summarise_mineralogy(x, type, order, rwp, r, delta)
x |
a list of |
type |
a string specifying whether the table uses all phase ID's, or
summarises them according to the phase name. One of |
order |
a logical operator denoting whether the columns of the resulting summary table are ordered in descending order according to the summed abundance of each phase across the dataset. |
rwp |
a logical operator denoting whether to include the Rwp value as the final column in the output. This provides an objective measure of the difference between the fitted and measured patterns. |
r |
a logical operator denoting whether to include the R value as the final column in the output. This provides an objective measure of the difference between the fitted and measured patterns. |
delta |
a logical operator denoting whether to include the Delta value as the final column in the output. This provides an objective measure of the difference between the fitted and measured patterns. |
A data frame
data(minerals) data(soils) ## Not run: multiple_afps <- lapply(soils, afps, lib = minerals, std = "QUA.1", align = 0.2, lod = 0.1, amorphous = "ORG", amorphous_lod = 1) sm1 <- summarise_mineralogy(multiple_afps, type = "all", order = TRUE) sm2 <- summarise_mineralogy(multiple_afps, type = "grouped", order = TRUE) sm3 <- summarise_mineralogy(multiple_afps, type = "grouped", order = TRUE, rwp = TRUE) ## End(Not run)data(minerals) data(soils) ## Not run: multiple_afps <- lapply(soils, afps, lib = minerals, std = "QUA.1", align = 0.2, lod = 0.1, amorphous = "ORG", amorphous_lod = 1) sm1 <- summarise_mineralogy(multiple_afps, type = "all", order = TRUE) sm2 <- summarise_mineralogy(multiple_afps, type = "grouped", order = TRUE) sm3 <- summarise_mineralogy(multiple_afps, type = "grouped", order = TRUE, rwp = TRUE) ## End(Not run)
tabulate_lod takes a powdRfps or powdRafps object and
estimates the limit of detection of quantified phases using
a hypothetical limit of detection of an internal standard and
its reference intensity ratio.
tabulate_lod(x, type, std_rir, std_lod, ...)tabulate_lod(x, type, std_rir, std_lod, ...)
x |
a |
type |
one of |
std_rir |
a numeric value specifying the reference intensity ratio of a hypothetical internal standard. |
std_lod |
a numeric value specifying an estimated limit of detection (weight percent) for the hypothetical internal standard. |
... |
other arguments |
a data frame.
## Not run: #Load the minerals library data(minerals) Load the soils data data(soils) fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) #Tabulate lod estimates based on the assumption that #0.1 % quartz can be detected, with a RIR of 5. tabulate_lod(fps_sand, std_rir = 5, std_lod = 0.1) ## End(Not run)## Not run: #Load the minerals library data(minerals) Load the soils data data(soils) fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) #Tabulate lod estimates based on the assumption that #0.1 % quartz can be detected, with a RIR of 5. tabulate_lod(fps_sand, std_rir = 5, std_lod = 0.1) ## End(Not run)
tabulate_lod takes a powdRfps or powdRafps object and
estimates the limit of detection of quantified phases using
a hypothetical limit of detection of an internal standard and
its reference intensity ratio.
## S3 method for class 'powdRafps' tabulate_lod(x, type, std_rir, std_lod, ...)## S3 method for class 'powdRafps' tabulate_lod(x, type, std_rir, std_lod, ...)
x |
a |
type |
one of |
std_rir |
a numeric value specifying the reference intensity ratio of a hypothetical internal standard. |
std_lod |
a numeric value specifying an estimated limit of detection (weight percent) for the hypothetical internal standard. |
... |
other arguments |
a data frame.
## Not run: #Load the minerals library data(minerals) Load the soils data data(soils) fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) #Tabulate lod estimates based on the assumption that #0.1 % quartz can be detected, with a RIR of 5. tabulate_lod(fps_sand, std_rir = 5, std_lod = 0.1) ## End(Not run)## Not run: #Load the minerals library data(minerals) Load the soils data data(soils) fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) #Tabulate lod estimates based on the assumption that #0.1 % quartz can be detected, with a RIR of 5. tabulate_lod(fps_sand, std_rir = 5, std_lod = 0.1) ## End(Not run)
tabulate_lod takes a powdRfps or powdRafps object and
estimates the limit of detection of quantified phases using
a hypothetical limit of detection of an internal standard and
its reference intensity ratio.
## S3 method for class 'powdRfps' tabulate_lod(x, type, std_rir, std_lod, ...)## S3 method for class 'powdRfps' tabulate_lod(x, type, std_rir, std_lod, ...)
x |
a |
type |
one of |
std_rir |
a numeric value specifying the reference intensity ratio of a hypothetical internal standard. |
std_lod |
a numeric value specifying an estimated limit of detection (weight percent) for the hypothetical internal standard. |
... |
other arguments |
a data frame.
## Not run: #Load the minerals library data(minerals) Load the soils data data(soils) fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) #Tabulate lod estimates based on the assumption that #0.1 % quartz can be detected, with a RIR of 5. tabulate_lod(fps_sand, std_rir = 5, std_lod = 0.1) ## End(Not run)## Not run: #Load the minerals library data(minerals) Load the soils data data(soils) fps_sand <- fps(lib = minerals, smpl = soils$sandstone, refs = minerals$phases$phase_id, std = "QUA.1", align = 0.2) #Tabulate lod estimates based on the assumption that #0.1 % quartz can be detected, with a RIR of 5. tabulate_lod(fps_sand, std_rir = 5, std_lod = 0.1) ## End(Not run)
tth_transform converts the two theta axis from one wavelength to another via Bragg's law.
Use this function with caution if intending the apply fps() or afps() to wavelength
transformed samples or libraries because background signals can vary with wavelength which may
therefore affect the quality of the fit.
tth_transform(tth, from, to)tth_transform(tth, from, to)
tth |
the 2theta vector to be transformed |
from |
numeric value defining the wavelength (Angstroms) to transform from |
to |
numeric value defining the wavelength (Angstroms) to transform to |
a transformed 2theta vector
data(soils) sandstone2 <- soils$sandstone #Convert from Cu (1.54056 Angstroms) to Co (1.78897 Angstroms) sandstone2$tth <- tth_transform(sandstone2$tth, from = 1.54056, to = 1.78897) sandstone_list <- as_multi_xy(list("sandstone" = soils$sandstone, "sandstone2" = sandstone2)) #plot the change plot(sandstone_list, wavelength = "Cu") #Alternatively convert the 2theta axis of a library data(minerals) minerals2 <- minerals minerals2$tth <- tth_transform(minerals2$tth, from = 1.54056, to = 1.78897) #Plot the difference plot(x = minerals$tth, y = minerals$xrd$QUA.1, type = "l", xlim = c(0, 85)) lines(x = minerals2$tth, y = minerals2$xrd$QUA.1, col = "red")data(soils) sandstone2 <- soils$sandstone #Convert from Cu (1.54056 Angstroms) to Co (1.78897 Angstroms) sandstone2$tth <- tth_transform(sandstone2$tth, from = 1.54056, to = 1.78897) sandstone_list <- as_multi_xy(list("sandstone" = soils$sandstone, "sandstone2" = sandstone2)) #plot the change plot(sandstone_list, wavelength = "Cu") #Alternatively convert the 2theta axis of a library data(minerals) minerals2 <- minerals minerals2$tth <- tth_transform(minerals2$tth, from = 1.54056, to = 1.78897) #Plot the difference plot(x = minerals$tth, y = minerals$xrd$QUA.1, type = "l", xlim = c(0, 85)) lines(x = minerals2$tth, y = minerals2$xrd$QUA.1, col = "red")
xrpd_pca is used to apply principal component analysis to X-ray powder
diffraction data.
xrpd_pca(x, mean_center, bin_size, root_transform, components)xrpd_pca(x, mean_center, bin_size, root_transform, components)
x |
A multiXY list containing the XRPD data, where each item in the list is a 2 column XY dataframe defining the x (2theta) and y (counts) axes of each measurement. Each item in the list must have a name corresponding to a unique sample ID. |
mean_center |
A logical argument defining whether mean centering
is applied to the XRPD data (default |
bin_size |
An integer between 1 and 10 defining whether to bin the
XRPD data to a lower resolution. This |
root_transform |
An integer between 1 and 8 defining the root transform to apply to the XRPD data |
components |
An integer defining the number of principal components to include in the output. Must be at least 1 less than the number of XRPD patterns in the dataset (the default). |
Applies data pre-treatment and principal components analysis to XRPD data based based on the protocols detailed in Butler et al. (2020).
a list with components:
coords |
a dataframe containing the sample ID's for each sample and the PCA coordinates for each dimension |
loadings |
a dataframe containing the 2theta axis and the loading of each dimension |
eig |
a dataframe summarising the variance explained by each dimension |
Butler, B.M., Sila, A.M., Shepherd, K.D., Nyambura, M., Gilmore, C.J., Kourkoumelis, N., Hillier, S., 2019. Pre-treatment of soil X-ray powder diffraction data for cluster analysis. Geoderma 337, 413-424. doi:10.4236/ampc.2013.31A007
data(rockjock_mixtures) x1 <- xrpd_pca(rockjock_mixtures, mean_center = TRUE, bin_size = 1, root_transform = 1) #Plot the loading of dimension 1 plot(x = x1$loadings$tth, y = x1$loadings$Dim.1, type = "l") ## Not run: #Fit loading 1 to the rockjock library f1 <- fps_lm(rockjock, smpl = data.frame("tth" = x1$loadings$tth, "counts" = x1$loadings$Dim.1), refs = rockjock$phases$phase_id, std = "QUARTZ", align = 0, p = 0.05) plot(f1, wavelength = "Cu", interactive = TRUE) ## End(Not run)data(rockjock_mixtures) x1 <- xrpd_pca(rockjock_mixtures, mean_center = TRUE, bin_size = 1, root_transform = 1) #Plot the loading of dimension 1 plot(x = x1$loadings$tth, y = x1$loadings$Dim.1, type = "l") ## Not run: #Fit loading 1 to the rockjock library f1 <- fps_lm(rockjock, smpl = data.frame("tth" = x1$loadings$tth, "counts" = x1$loadings$Dim.1), refs = rockjock$phases$phase_id, std = "QUARTZ", align = 0, p = 0.05) plot(f1, wavelength = "Cu", interactive = TRUE) ## End(Not run)