xrpd_pca {powdR}

PCA of XRPD data

Description

xrpd_pca is used to apply principal component analysis to X-ray powder diffraction data.

Usage

xrpd_pca(x, mean_center, bin_size, root_transform, components)

Arguments

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 = TRUE).

bin_size

An integer between 1 and 10 defining whether to bin the XRPD data to a lower resolution. This bin_size defines the number of data points used in each bin.

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).

Details

Applies data pre-treatment and principal components analysis to XRPD data based based on the protocols detailed in Butler et al. (2020).

Value

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

References

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

Examples

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)