CCA - Science topic

You can add a site for a type IIs enzyme downstream of your intended cut sire position at the correct distance to cut right after the CCA sequence.

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CANONICAL CORRESPONDENCE ANALYSIS (CCA AND PARTIAL CCA)

Canonical correspondence analysis investigates the links between a contingency table and a set of variables. Run CCA in Excel using the XLSTAT software.

Put this in google and read this there.

Yes, it is possible to perform canonical correlation analysis (CCA) with two sets of binary variables. According to a post on Stack Exchange, it is okay to use standard CCA with data that are partly or all binary variables. Since a binary variable has only two levels, it behaves identically whether it is seen as numeric or categorical.

You can read this article. It is very useful to select the appropriate multivariable method, and the "mathematical treatment" to employ your data.

You can use PAST software.

Just google the topic and find articles which deal with the general topic :)

@all Your question is a common challenge in ecology and can be difficult to address definitively. However, there are several statistical approaches that you can use to explore the possible drivers of changes in fish communities in your study site.

One potential approach is to use multivariate analyses, such as RDA or CCA, to explore the relationship between fish communities and environmental factors, including water quality parameters and the temporal component (before and after the fishing ban). This approach can help identify which environmental factors are most strongly associated with changes in the fish community over time. To implement this, you would need to split your data into two groups based on the time period (before and after the fishing ban) and then perform the RDA or CCA analysis using those two groups as the explanatory variables. Additionally, you could perform a permutation test to evaluate the significance of the temporal component in explaining the changes in fish communities.

Another potential approach is to use structural equation modeling (SEM), which allows you to test hypotheses about causal relationships among multiple variables. In your case, you could use SEM to test the relative contributions of the fishing ban and water quality to changes in the fish community over time, while also considering potential interactions between these factors. SEM can help you to evaluate the strength and direction of causal relationships among variables, but it requires more data and a well-formulated hypothesis.

Finally, it may also be helpful to use additional univariate analyses to explore the effects of individual environmental factors on the fish community, such as regression or correlation analyses. These analyses can help you to identify which environmental factors are most strongly associated with changes in the fish community and provide additional evidence to support or refute potential hypotheses.

In summary, RDA/CCA, SEM, and univariate analyses can all be useful approaches for exploring the potential drivers of changes in fish communities in your study site. It is important to choose the appropriate statistical method based on your study design, data structure, and research question. I hope this helps!

Dear Priya,

CCA (Canonical Correspondence Analysis) is a powerful multivariate technique that can be used to investigate the relationship between species (in this case, different orders of insects) and environmental variables (in this case, soil physical-chemical parameters). PAST (Paleontological Statistics) is a software package that can perform CCA and other multivariate analyses.

To use CCA in PAST, you must organize your data in a specific format. It would be best to have a matrix or data frame with the insect order (species) as rows and the soil physical-chemical parameters as columns. The values in the matrix should be the abundance or occurrence of each insect order in each soil sample.

You should also ensure that the data is adequately transformed to meet the assumptions of CCA. For example, you should use a log transformation if the data is skewed. Also, you should ensure the data has been standardized (mean = 0, standard deviation = 1) before performing the analysis.

Once your data is in the appropriate format, you can use PAST to perform the CCA analysis.

To draw a biplot graph in PAST, use the "Ordination" option, then select "CCA." Once the analysis is completed, you can use the "Graph" option to create the biplot graph. The biplot graph will show the relationship between the insect orders and the soil physical-chemical parameters on the ordination space.

It's important to note that CCA assumes that the environmental variables (soil physical-chemical parameters) are independent, so if there is a correlation between the variables, it's better to use PCA (Principal component analysis) or RDA (Redundancy analysis) before running the CCA.

It's also essential to make sure that the data meet the assumptions of the CCA, such as linearity and normality, and the data should be transformed if the premise is unmet.

It's also essential to consult with experts in the field or consult the literature to have more information about the recent developments in the area and also the interpretation of the results.

I hope I was able to answer your question.

Yours sincerely,

Edgar M Cambaza

Dear Abdallah Aouadi , I agree with you that the choice is dictated by the type of the data. However, your conclusion at the end does not make sense to me.

If you expect a lot of variability/heterogenity in your data, this means that your data would be better with the unimodal type of the analysis, therefore CCA. Lower values of the length of the first axis suggest linear response of the depedent variables (eg., species) = RDA. Not other way around as you suggested.

I am using 2 as my threshold, instead of yours 5), therefore if the length of the first axis in my data is less then 2 = RDA, more then 2 = CCA. Not a foolproof, but quite robust as using unimodal method with linear data is not a big problem, but using linear method for unimodal data could be. :)

There is also a possibility to transform the data, so the RDA could be used. For a brief explanation of why, see response of Gavin Simpson here: https://stat.ethz.ch/pipermail/r-sig-ecology/2011-August/002280.html

I have the same feelings with you that coherence can be a better criterion for SSVEP decoding. Do you have any progress on the coherence bbased SSVEP decoding? Thanks.

OK is good, as for the interpretation of the results you must look to the principal component scores (SOC and ENAB) most loaded on canonical variates. The original variables loadings on their components will allowyou to interpret the meaning of the principal components and then of the canonical solution, see for interpretation of components by their loadings (correlation coefficient between variables and their components) see the attached file. (Chapter: Catching the sense of experimental results: a case study on animal behavior).

Dear Khan,

I presume this graph has been generated via PAST?

I was facing the similar issues, as past doesn't generate a corresponding dataset for us to interpret (besides the axis scores).

Some links that helped me to interpret this was

From what I can infer from my graph, the lines (green) depicte the environmental variables, while the dotted depict your biotic components and the traingle, I presume is site?

To understand the relationship between the dots and the lines, you can look up the above link.

You basically have to make an angle from the dots to the lines to understand whichever is nearer. Kindly look at the section "Figure 2: Illustrative example of CCA triplot" under the given link.

Best regards

If you are using the non-parametric approaches (Bray-Curtis, anosim, etc.) it would make sense to use BIOENV to link your environmental variables to the biotic pattern.

A CCA should be applied for analysis of species composition and its relation to environmental variables from place to place along environmental gradients. If you want to analyse species distribution and its relation to environmental variables on one site with a set of subplots you should use an RDA (redundancy analysis). The CCA gives a unimodal model, the RDA a linear model, both are direct gradient analysis. There is no minimum of data samples, but a good CCA and RDA (without distortive arch-effect) requires much less environmental variables than samples or inversely much more samples than env. variables. The selection of the variables to use can by done by a PCA. Keep in mind that the samples have to be independent viz. from different places and best within one comparable season. If you want to analyse a time series of true replicates as for an experiment you should use time series analysis, e.g., a DFA (dynamic factor analysis) - or modelling (GLM, GAM), which could also be a good alternative for your issue.

Hi Omar,

Please see Table 1 and relevant studies/stages in the following recently published JR article. The article also provides an extensive web appendix, which should help you with PLS-SEM based analysis and reporting. Please note that this is an open/free access article. Good luck!

Syed Mahmudur Rahman, Jamie Carlson, Siegfried P. Gudergan, Martin Wetzels, Dhruv Grewal. (2022). Perceived Omnichannel Customer Experience (OCX): Concept, measurement, and impact. Journal of Retailing, https://doi.org/10.1016/j.jretai.2022.03.003

Hello Adhi,

the practical use of eigenvalues is to rank the corresponding scores in decreasing order of their role in building the factorial space. It is often expressed as the percentage of the total variance.

An important (mathematically) phenomenom will have a large eigenvalue.

But it often appears that these mathematically important phenomena are not the more important for your research ! It is up to you to find the more interesting subspace for your work.

Nevertheless, a very small eigenvalue (say a few percent of the total variance) may be an artefact.

Kind regards,

Dominique

I thinking, when there is no significant difference found Through, its improper to use it as control variable. Better is allow PLS to create its own groups if any were present in the data.

try temperature gradient and use DMSO at no more than 5% of the final volume.

as what is done for difficult PCR with low informations on genome sequence of the species you're working on.

all the best

fred

ADANCO 2.3 is the first software with graphical user interface that has implemented CCA.

@Imran Anwar..... Thank you, downloaded.

Hi, I don't understand why are you setting permutations = 999 is there any specific reason for it?

I suggest you remove it first and then run your command.

secondly, yes you might get a higher Pr > F value if your p-value is greater than 0.05. If the variables you are including are important (I assume they are) then you might want to go for principle component analysis (PCA). You can find many YouTube videos on it.

@Felix I didn't find any way to test only reflective model (CFA) using Smart-PLS. I suggest you to use either AMOS, JASP, or JAMOVI to run CFA.

Firstly, I would suggest you to run a normality test (i.e. shapiro-wilk test). If the data are normally distributed then you can use General Linear Model (GLM) as it takes in more than 2 factors with different levels. If your data is non-normal, then you can use the Generalized Linear Model (GLzM). I hope these suggestions help out.

Anytime n<p potential problems arise. Be very careful here. I assume regularized means your estimator is like ridge or lasso, etc. I f not think about using something like them. Perhaps you could start with this link: Computer Age Statistical Inference: Algorithms, Evidence, and Data Science | Bradley Efron, Trevor Hastie | download (b-ok.cc) These guys invented a lot of this stuff. best< D. Booth

You could employ redundancy analysis.Please see attached paper.

Hello Giacomo,

I agree with your premise that it's always important to understand fully the meaning of a given model (and its implications for aims that motivated the building of a model in the first place).

Can reduced models be identified with a "tolerable" reduction in explanatory power? In many instances, absolutely.

Does it make sense therefore to seek reduced models? No, not necessarily. If there are cost or efficiency reasons to abandon specific variables, then that's a different consideration altogether. Also, as David Eugene Booth indicates, there are many technical reasons to avoid using any step-based method (forward, backward, stepwise) as a mechanism to find a "best" sub-model for a given set of IVs. Generalizability (or the lack thereof) is one good reason; overfitting is a threat with such methods.

Good luck with your workl.

You need to pay attention to the amount of gradient length. If it was less than 3 RDA if it was over 4.5 CCA. Between 3-4.5 usually CCA

Hello

Look at this link, maybe it will help you in this area

Thank you very much. I've modified the graph and now they look much better.

Dear all,

please also the following paper, which distinguish between CCA as originally developed and the evaluation step known from PLS-SEM that were recently dubbed CCA:

Best regards,

Florian

You can use ANOVA one-way model to analysis your case study if you find significant differences between methods in this time use multiple comparison to determine which method are good.

With Best Regards

I encourage you to discuss your topic on the light of

Bandura's social cognitive theory (self-efficacy). I believe it is the best fit for your study.

Good luck!

Similar question:

This may be of help

The Advantages of Canonical Correspondence Analysis

https://www.ohio.edu › staff › mccarthy › multivariate › Palmer1993

by MW PALMER

You can try browsing through Brainstorm software website https://neuroimage.usc.edu/brainstorm/Introduction . Another resource you can make use is Mike X Cohen lectures http://mikexcohen.com/lectures.html .

I am follow answer

If you use PCA, you can run any analysis you want on the newly created PCA variables, including correlation with other variables.

If you use CCA, you need an environmental matrix.

Your answer lies in your data structure and what you hope to accomplish.

From a more philosophical perspective I prefer to keep the analyses separate rather than to conduct one (e.g. CCA) that attempts to combine very different types of variable into one ordination. I am also wary of the idea that many of these relationships are linear. You could do some analyses based on the underlying resemblance matrices (Bray-Curtis for fish, normalised euclidean distance for environmental variables) such as BIO-ENV, which could show which habitat variables most closely match patterns in fish community structure, and then visualise those relationships using something like bubble-plots in MDS. In Primer you can also produce NMDS plots on which you can overlay vectors showing the direction of increase in the environmental variables.

The following files can be helpful.

Andrew Paul McKenzie Pegman Ok, thanks.

I get what you mean with the "what if" guy, but I don't see how it hurts to wonder.

Hi Guillaume Adeux,

I don't think your model structure is accounting for the type of permutation you want. I recommend you read Winkler et al (2015) to make sure which effects you really want to test using your experimental design. Once you decided, you can build a small randomization script to build the null models for your hypothesis.

An alternative is to explore the function nested.anova.dbrda() in the BiodiversityR package.

I hope this helps.

Cheers

Winkler AM, Webster MA, Vidaurre D, Nichols TE, Smith SM (2015) Multi-level block permutation. Neuroimage 123:253–268. the Condition() terms your are using to test your model is working properly

Also I'm looking for it. Following up.

Lucas, Hellinger transformation is meant to solve the issue of double absences when computing Euclidean-based ordination techniques (PCA, RDA). Thus, it is not appropriate for CCA. Now, in the context of indicator species, you have to ask yourself if transforming your data (Hellinger or otherwise) is meaningful, especially if same sampling effort is put into collecting species from each of the categories you want to find indicators for. Remember that the indicator value doesn't depend only on the relative abundance of the species among categories but also its relative frequency, and the fact that a species has much higher abundances than others in the system must have some ecological implications. Now, the indicator value of one species is independent from the value of the other species in the assemblage, thus transforming for the purpose of accounting for large differences in abundance has little use. If you apply Hellinger (the square root of the relative abundance among species within a sample) and then compute the indicator value (highest value among sites of the product of the relative abundance and relative frequency of a species within a site) you are no longer values independent among species. Hope this helps.

Hey Ohad, not aware of any universal method but you may have given the answer in your question " %cover to biomass conversion tables would be an awesome way to go"... In the case you did not find it elsewhere, you may collect samples to build up your own relationship (at a given quadrat size obviously)... Cheers, JC

It is very important to know the different way of plant adaptation:

For exemples, in fam. of Orchidaceae

- the first is lytophyte, growing on the rock;

and after - terrestrial orchids, growing in the ground;

- epiphytic growing on the trees

Thank you @ Malcolm Baptie !

That helps a lot :)

From what I remember there is bootstrap option while doing multivariate analyses in PAST, so each time you call the function with same data your results my be slighlty different, since it uses some randomization techniques to calculate result on different subsets, which in effect lead to different outcomes each time you run a function.

I agree with Raam. NCA is currently more widely used in automobile industry and is well investigated. High Ni NMC is considered to be next generation, although the stability issue is not completely solved yet and is being extensively investigated.

I think the short answer is that CCA will be overkill for the data you have. With only two sites, doing any kind of statistical test might not be feasible. You would be better off doing a descriptive comparison of what the species compositions are at the two sites (along with a description of the abiotic/environmental factors for which you have information) and not waste time finding a statistical test.

But if your two sites are actually made up of multiple sub-samples, each with their own environmental data, then analysis options can be explored. Some more information about your study design would help make a decision (e.g. are site A and B made up of sub samples, what abiotic factors do you have data for, etc.)

No worries, thank you David!

"The gradient length measures the beta diversity in community composition (the extent of species turnover) along the individual independent gradients (ordination axes). Now you locate the largest value (the longest gradient) and if that value is larger than 4.0, you should use unimodal methods (DCA, CA, or CCA). Use of a linear method would not be appropriate, since the data are too heterogeneous and too many species deviate from the assumed model of linear response (see also Section 3.2). On the other hand, if the longest gradient is shorter than 3.0, the linear method is probably a better choice (not necessarily, see Section 3.4 of Ter Braak & Smilauer ˇ 2002). In the range between 3 and 4, both types of ordination methods work reasonably well"

Multivariate Analysis of Ecological Data using CANOCO,

Jan Leps and Petr Smilauer

But you can still use RDA if you want to, after a transformation...

"Alternatively, if your data are heterogeneous, but you still want to use linear ordination methods (PCA, RDA), apply them on Hellinger transformed species composition data to calculate ordination based on Hellinger distances (as recommended e.g. by Legendre & Gallagher (2001)). "

link: http://www.davidzeleny.net/anadat-r/doku.php/en:ordination

Depends on your objective (if you want to observe distribution along environmental gradients, etc.) you can use percentage (mainly if you have one / two dominant species) or transformed abundance / biomass values (if you have high diversity). It is important that you first verify through a preliminary detrended of correspondence analysis (DCA) that the gradient length in units of standard deviation obtained is less than 4 (RDA) or greater than 4 (CCA) (Lepš and Šmilauer, 2003). Also important to check: 1) the overall significance of the ordination and of the first two axes by Monte Carlo permutation test (P <0.05); 2) the% of the total variance explained considering the relations of the species-environment variables, and 3) that inflation factor of the variance for each physicochemical variable is less than 20 (to avoid collinearity). I used the CANOCO Version 4.5 program.

Hi Francisco,

I've had a simillar question and I found this:

" The length of first DCA axis > 4 S.D. indicates heterogeneous dataset on which unimodal methods should be used, while the length < 3 S.D. indicates homogeneous dataset for which linear methods are suitable (see Fig. 5. In the gray zone between 3 and 4 S.D., both linear and unimodal methods are OK. Note that while linear methods should not be used for heterogeneous data, unimodal methods can be used for homogeneous data, but linear methods, in this case, are more powerful and should be preferred. Alternatively, if your data are heterogeneous, but you still want to use linear ordination methods (PCA, RDA), apply them on Hellinger transformed species composition data to calculate ordination based on Hellinger distances (as recommended e.g. by Legendre & Gallagher (2001)). "

link: http://www.davidzeleny.net/anadat-r/doku.php/en:ordination

Legendre & Gallagher (2001): http://adn.biol.umontreal.ca/~numericalecology/Reprints/Legendre_&_Gallagher.pdf

All the best,

Jordi

R would work in your case. Go for R.

Instead of mutating the PAM you could also introduce several silent mutations within the seed region of the sgRNA adjacent to the PAM. Two to three silent mutations may do the trick and inhibit efficient sgRNA binding. Also, if you are doing a knock-in, depending on your repair template, size and location of the insert, you may have already split the sgRNA target sequence in half or something by inserting your knock-in. So modification of PAM is not always needed.

Matlab has build-in function for CCA, please check more details in https://www.mathworks.com/help/stats/canoncorr.html

The high frequency of zero means that the event is rare and this suggests a Poisson distribution. You are therefore right to use Extra Poisson Variation models. Compare the variance with the mean. If the variance is bigger than the mean it means that your data exhibit over-dispersion. In this case you should consider a negative binomial distribution.

Thank you sir

Are you interested in studying the fractal patterns and behavior present in this data for feature extraction? If so I recommend you to study WBFA method @ PRE

 link.aps.org/doi/10.1103/PhysRevE.72.046120

Let me know if you need any help from us.

Thank you everyone that really helped :)

What is exactly the CCA system? Can you please attach reference articles or any link pointing to the source. Thanks.

Dear Peter,

There is not, to my knowledge, studies confirming that treated timber lose its toxicity over time. The problem is that the risks of toxicity are both short and medium term (in tems of time). However, to minimize the risk of toxicity, there are recommendations. See the following links.

With my best regards

Prof. Bachir ACHOUR

How much exactly do you need? Unfortunately I do not know the place in US but perhaps it can be transported (by DHL or UPS) from Europe. Regarding the US contaminated soil. Perhaps you can contact Rufus Chaney from USDA. He has been working on remediation for many years now and he might help you. 

Thanks Elsts

Is there any scope to reduce contikiMAC timing constraints further like time spent on  CCA, t_i,t_c,t_d  etc...

Hi， Reza,

Actually, you can use a lot of methods that are much better than CCA.

Please find these methods in chapter 4 of this survey:

a comprehensive survey on pose-invariant face recognition, 2015

I believe that RDA are more appropriate than CCA in this instance.

I don't think that Permanova will work with your continuous variables (ex, depth and distance). 

This being said, have you considered the used of mixed models where you could control for both fixed and random effects of you sampling design?

Hope it helps!

MCCA is a extension of CCA through tensors, according to authors this is due to their inter-subjects variability and effects of ongling EEG and noises.

if you are using variance of EEG signals as the feature in classification , you can use the linear discriminant analysis for classificatiion , also you will need to test  the influence of outliers on the classification accuracy 

Given that beta is calculated based on price volatility compared to the market as a whole, a non-traded company will not have a large quantity of daily/hourly/minutely price data.  However the underlying principle of risk could still be evaluated qualitatively either by

Also bear in mind that beta has been subject to criticism as a calculation of equity risk premium, so you may also wish to take another approach.  Article attached in that regard. 

It looks that you have an over-fitted model. The number of constraints (environmental variables, vector arrows) is seven, and I only found eight points (that I assume to be your sampling locations). There is nothing "canonical" in such over-fitted model, but they are just ordinary non-canonical correspondence analysis plots with fitted arrows. It is just like an ordinary regression: if you have seven independent (explanatory) variables and eight observations, you can get a perfect fit but nothing real.

Further, you should remember that ordination is nothing but a fancy way of making graphs. Do not look at the numbers: they only confuse you. Only look at the graph.

I think it is best to get an instructor who tells you how to use these methods. The second best method is to get a book.

Good luck.

If you are interested into the rules of CCA plot interpretation, you could complete the Oksanen's slides by reading the following paper  :

Canonical correspondence analysis and related multivariate methods in aquatic ecology

by Cajo ter Braak and Piet Verdonschot

Aquatic Sciences 57/3, 1995, pp. 255-289.

DOI: 10.1007/BF00877430 

available at : http://link.springer.com/article/10.1007%2FBF00877430#page-1

Oldy but goldy... Cajo ter Braak is the father of CCA.

Nathalie,

I have already performed this kind of factorial analysis on an invertebrate dataset (biological traits). As suggested by Alain, the RLQ analysis is a first option to investigate the link between biological and environmental tables. But if you wish to assess only the relationship between a fuzzy-coded biological table (typically species traits) and an environmental table, you can also perform either a CCA or a CAIV.

Using the ade4 package in R as advised above, the two following sequences gave the same result in my case:

1) first perform a FCA on the fuzzy-coded array (needs the ‘prep.fuzzy.var’ and ‘dudi.fca’ functions in ade4) and then a CAIV between the FCA result and the environmental table (‘pcaiv’ function)

2) perform directly a CCA between the fuzzy-coded array and the environmental table (‘prep.fuzzy.var’ and ‘cca’ functions)

Hope that helps.

Mathieu

Great that you got some help! :)

We have used CCA in the attached paper. Have a look and see if it can help you.

Best,

G.Lemperiere