Frequently Asked Questions (FAQ) - Revision history

Laschp: /* How to interpret intensity values of 2D correlation functions? */

2025-04-10T07:26:11Z

How to interpret intensity values of 2D correlation functions?

← Older revision		Revision as of 09:26, 10 April 2025
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	For example, the symmetric 2D function can be interpreted as a statistical value, namely the covariance between two variables. Unlike the correlation, the covariance is not normalized by the standard variation and can thus take very small or very large values. This depends, among other things, on the absolute values of the intensity changes in the spectra examined. For this reason, some researchers do not attach much importance to the absolute intensity values of the 2D-COS functions. It is more important that the maxima and minima of the 2D correlation functions are clearly above the noise, which can be checked by analyzing the correlation slices.		For example, the symmetric 2D function can be interpreted as a statistical value, namely the covariance between two variables. Unlike the correlation, the covariance is not normalized by the standard variation and can thus take very small or very large values. This depends, among other things, on the absolute values of the intensity changes in the spectra examined. For this reason, some researchers do not attach much importance to the absolute intensity values of the 2D-COS functions. It is more important that the maxima and minima of the 2D correlation functions are clearly above the noise, which can be checked by analyzing the correlation slices.

	It is also ~~interesting to note~~ that the intensities of the 2D functions differ depending on the method used to calculate the 2D correlation functions (statistical or FFT based). This can be easily tested using the example data provided with the ''mat2dcorr'' toolbox. The different intensities obtained by the distinct 2D-COS functions are not due to programming errors and are not discussed in the scientific literature.		It is also noteworthy that the intensities of the 2D functions differ depending on the method used to calculate the 2D correlation functions (e.g. statistical or FFT based). This can be easily tested using the example data provided with the ''mat2dcorr'' toolbox. The different intensities obtained by the distinct 2D-COS functions are not due to programming errors and are not discussed in the scientific literature.

	== Links to other non-commercial 2D-COS software solutions ==		== Links to other non-commercial 2D-COS software solutions ==

Laschp: /* Can the mat2corr toolbox account for unevenly spaced sampling of spectra? */

2025-04-10T07:23:07Z

Can the mat2corr toolbox account for unevenly spaced sampling of spectra?

← Older revision		Revision as of 09:23, 10 April 2025
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	Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.		Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.

	In the literature, there are basically three different ways to deal with ~~a perturbation~~ that ~~does~~ not ~~fulfil~~ the equidistance condition:		In the literature, there are basically three different ways to deal with perturbations that do not fulfill the equidistance condition:
	:1. Ignore the requirement for equidistant perturbation ~~values~~ and use the data as they are. This is what the mat2dcorr toolbox v.1.05 does.		:1. Ignore the requirement for equidistant perturbation vectors and use the data as they are. This is what the current mat2dcorr toolbox version 1.05 does.
	:2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for uneven sampling of the perturbation variable.		:2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for uneven sampling of the perturbation variable.
	:3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.		:3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.

Laschp at 14:05, 9 April 2025

2025-04-09T14:05:59Z

← Older revision		Revision as of 16:05, 9 April 2025
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	~~[[File:GraphAbstr.gif\|400px\|thumb\|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]~~

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			[[File:GraphAbstr.gif\|400px\|thumb\|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]
	== Can the mat2corr toolbox account for unevenly spaced sampling of spectra? ==		== Can the mat2corr toolbox account for unevenly spaced sampling of spectra? ==

Laschp at 14:05, 9 April 2025

2025-04-09T14:05:14Z

← Older revision		Revision as of 16:05, 9 April 2025
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			[[File:GraphAbstr.gif\|400px\|thumb\|mat2dcorr: Illustration of heterospectral 2D-COS (FTIR vs. Raman)]]

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Laschp: /* Links to other non-commercial 2D-COS software solutions */

2025-04-09T13:22:04Z

Links to other non-commercial 2D-COS software solutions

← Older revision		Revision as of 15:22, 9 April 2025
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	* [https://arxiv.org/abs/1808.00685 corr2D (R)], - Implementation of two-dimensional correlation analysis in R, developed by Robert Geitner.		* [https://arxiv.org/abs/1808.00685 corr2D (R)], - Implementation of two-dimensional correlation analysis in R, developed by Robert Geitner.
	<br>		<br>
	~~(to be continued)~~

Laschp: /* How to interpret intensity values in 2D correlation functions? */

2025-04-09T13:21:33Z

How to interpret intensity values in 2D correlation functions?

← Older revision		Revision as of 15:21, 9 April 2025
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	For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as an ''x-'' and a second time as a ''y-data'' set. Only then will the buttons be available for analysis.		For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as an ''x-'' and a second time as a ''y-data'' set. Only then will the buttons be available for analysis.

	== How to interpret intensity values in 2D correlation functions? ==		== How to interpret intensity values of 2D correlation functions? ==

	For example, the symmetric 2D function can be interpreted as a statistical value, namely the covariance between two variables. Unlike the correlation, the covariance is not normalized by the standard variation and can thus take very small or very large values. This depends, among other things, on the absolute values of the intensity changes in the spectra examined. For this reason, some researchers do not attach much importance to the absolute intensity values of the 2D-COS functions. It is more important that the maxima and minima of the 2D correlation functions are clearly above the noise, which can be checked by analyzing the correlation slices.		For example, the symmetric 2D function can be interpreted as a statistical value, namely the covariance between two variables. Unlike the correlation, the covariance is not normalized by the standard variation and can thus take very small or very large values. This depends, among other things, on the absolute values of the intensity changes in the spectra examined. For this reason, some researchers do not attach much importance to the absolute intensity values of the 2D-COS functions. It is more important that the maxima and minima of the 2D correlation functions are clearly above the noise, which can be checked by analyzing the correlation slices.

Laschp: /* I have loaded spectral data, but the buttons are still grayed out? */

2025-04-09T13:21:04Z

I have loaded spectral data, but the buttons are still grayed out?

← Older revision		Revision as of 15:21, 9 April 2025
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	This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox.		This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox.
	For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as x- and a second time as y-data set. Only then will the buttons be available for analysis.		For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as an ''x-'' and a second time as a ''y-data'' set. Only then will the buttons be available for analysis.

	== How to interpret intensity values in 2D correlation functions? ==		== How to interpret intensity values in 2D correlation functions? ==

Laschp at 13:19, 9 April 2025

2025-04-09T13:19:51Z

← Older revision		Revision as of 15:19, 9 April 2025
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	This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox.		This is not a bug! It is important to understand that the mat2dcos toolbox was originally developed for heterospectral 2D correlation analysis (2D-COS). This means that spectral series from two different modalities are analyzed. For example, if IR and Raman data are to be analyzed by heterospectral 2D-COS, two different spectral series must be loaded into the toolbox.
	For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as x- and a second time as y-data set. Only then will the buttons be available for analysis.		For the probably more common case of autocorrelation 2D-COS, this means that the data set to be analyzed has to be loaded twice, once as x- and a second time as y-data set. Only then will the buttons be available for analysis.

			== How to interpret intensity values in 2D correlation functions? ==

			For example, the symmetric 2D function can be interpreted as a statistical value, namely the covariance between two variables. Unlike the correlation, the covariance is not normalized by the standard variation and can thus take very small or very large values. This depends, among other things, on the absolute values of the intensity changes in the spectra examined. For this reason, some researchers do not attach much importance to the absolute intensity values of the 2D-COS functions. It is more important that the maxima and minima of the 2D correlation functions are clearly above the noise, which can be checked by analyzing the correlation slices.

			It is also interesting to note that the intensities of the 2D functions differ depending on the method used to calculate the 2D correlation functions (statistical or FFT based). This can be easily tested using the example data provided with the ''mat2dcorr'' toolbox. The different intensities obtained by the distinct 2D-COS functions are not due to programming errors and are not discussed in the scientific literature.

	== Links to other non-commercial 2D-COS software solutions ==		== Links to other non-commercial 2D-COS software solutions ==

Laschp at 13:02, 9 April 2025

2025-04-09T13:02:16Z

← Older revision		Revision as of 15:02, 9 April 2025
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	Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.		Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.

	~~The~~ literature ~~mentions~~ basically three different ~~possibilities~~ to deal with a ~~perturbing variable~~ that does not ~~fulfill~~ the condition ~~of equidistance~~:		In the literature, there are basically three different ways to deal with a perturbation that does not fulfil the equidistance condition:
	:1. Ignore the requirement for equidistant perturbation values and use the data as ~~it is~~. This is what the mat2dcorr toolbox v.1.05 does.		:1. Ignore the requirement for equidistant perturbation values and use the data as they are. This is what the mat2dcorr toolbox v.1.05 does.
	:2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for ~~the~~ uneven sampling of the perturbation variable.		:2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for uneven sampling of the perturbation variable.
	:3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.		:3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.

Laschp at 13:00, 9 April 2025

2025-04-09T13:00:36Z

← Older revision		Revision as of 15:00, 9 April 2025
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	== Can the mat2corr toolbox account for ~~the lack~~ of ~~equidistance of the perturbation variable~~? ==		== Can the mat2corr toolbox account for unevenly spaced sampling of spectra? ==

	Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.		Unfortunately, the current version of the ''mat2dcorr'' toolbox (v. 1.05) does not consider non-equidistant vectors of the perturbing variable. However, this would be a good idea for future versions of the toolbox as an addition to its functionality.

	The literature mentions basically three different possibilities to deal with a perturbing variable that does not fulfill the condition of equidistance:		The literature mentions basically three different possibilities to deal with a perturbing variable that does not fulfill the condition of equidistance:
	1. Ignore the requirement for equidistant perturbation values and use the data as it is. This is what the mat2dcorr toolbox does ~~today~~		:1. Ignore the requirement for equidistant perturbation values and use the data as it is. This is what the mat2dcorr toolbox v.1.05 does.
	2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for the uneven sampling of the perturbation variable.		:2. Use modified correlation equations as described in [https://journals.sagepub.com/doi/10.1366/000370203322259039 Noda (2003)] to account for the uneven sampling of the perturbation variable.
	3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.		:3. Interpolate the perturbation values and the associated spectral data to get an equidistant distribution of the perturbation values.

			Option 3 would be fairly easy to implement programmatically. It is already on the to-do list.

	== I have loaded spectral data, but the buttons are still grayed out? ==		== I have loaded spectral data, but the buttons are still grayed out? ==