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staRdom (version 1.0.3)

PARAFAC Analysis of EEMs from DOM

Description

This is a user-friendly way to run a parallel factor (PARAFAC) analysis (Harshman, 1971) on excitation emission matrix (EEM) data from dissolved organic matter (DOM) samples (Murphy et al., 2013) . The analysis includes profound methods for model validation. Some additional functions allow the calculation of absorbance slope parameters and create beautiful plots.

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Version

Install

install.packages('staRdom')

Monthly Downloads

482

Version

1.0.3

License

AGPL

Maintainer

Matthias Pucher

Last Published

April 16th, 2018

Functions in staRdom (1.0.3)

3D plots of PARAFAC components

Plot correlations of components in samples

eempf_load_plot

Plot amount of each component in each sample as bar plot

eem_raman_normalisation2

Wrapper function to eem_raman_normalisation (eemR).

Cut EEM data matching a given wavelength range

Full join of a list of data frames.

Extract EEM matrix for single components determined in the PARAFAC analysis

eempf_comp_load_plot

Plot components from a PARAFAC model

eempf_leverage_plot

Plot leverage of emission wavelengths, excitation wavelengths and samples.

eempf_leverage_ident

Plot leverage of emission wavelengths, excitation wavelengths and samples.

eempf_openfluor

Write out PARAFAC components to submit to openfluor.org.

Compensate for normalisation in C-modes

Plot results from a splithalf analysis

Fluorescence data of 52 corrected samples.

Calculating correlations between the component loadings in all samples (C-Modes).

Normalise 3-dimensional array in first and second dimension

eempf_plot_comps

Plot all components of PARAFAC models

Reorders components of different PARAFAC models according to best fit (TCC)

eempf_residuals

Calculate residuals of EEM data according to a certain model

eem_overview_plot

Plot fluorescence data from several samples split into several plots.

eem_red2smallest

Reduce wavelength range of EEM spectra to widest available in the whole sample set.

Remove Raman and Rayleigh scattering in fluorescence data

Calculate the leverage of each emission and excitation wavelength and each sample

Determine the range of fluorescence values in a set of samples

splithalf_splits

Extracting a list of sample names in each subsample from a splithalf analysis

Extract data from emission and excitation wavelengths of the components of a PARAFAC model

Extracting TCC values from a splithalf analysis

Fits vs. components of PARAFAC models are plotted

eempf_fit_sample

Fitting amounts of components of any samples to a certain model

Caluclate Tucker's Congruence Coefficient of PARAFAC components

Smooth fluorescence data by calculating rolling mean along excitation wavelengths.

EEM spectra plotted with ggplot2

eempf_leverage_data

Combine leverages into one data frame and add optional labels.

Absorption data of 7 samples

result from PARAFAC split-half analysis

eempf_residuals_plot

Plot samples by means of whole sample, each single component and residuum

Running a Split-Half analysis on a PARAFAC model

as.data.frame.eem

Converting EEM data from class eem to data.frame.

Calculating slopes and slope ratios of a data frame of absorbance data.

absorbance_read

Reading absorbance data from txt and csv files.

eem_name_replace

Replace matched patterns in sample names

Runs a PARAFAC analysis on EEM data

Calculate the amount of each component for samples not involved in model building

Opens an R markdown template for an easy and userfriendly analysis of EEM data.

Data from an eemlist is transformed into an array

Exclude complete wavelengths or samples form data set

Fit absorbance data to exponential curve. drm is used for the fitting process.

Modifying fluorescence data according to dilution.

Load all eemlist obects saved in different Rdata files in folder

Calculate raman area of EEM samples

eem_ife_correction

Wrapper function to allow eem_inner_filter_effect (eemR) handling different cuvette lengths.

raw data from absorbance measurements, 5cm pathlength

Plot a set of PARAFAC models to compare the single components

Determines the the biggest range of EEM spectrum where data is available from each sample.

Missing values are interpolated within EEM data