There are a lot of options available to do PCA.

If you are familiar with SPSS, it will be a good choice to do it.

Otherwise, it can also be done using R programming or Python.

For doing this operation in SPSS, we need to have the data in Excel sheet (Spreadsheet) format and for the rest two programming based options, any type of data can be easily handled.

you can look here https://towardsdatascience.com/a-one-stop-shop-for-principal-component-analysis-5582fb7e0a9c

We are interested with R. How can i do it? Give me some suggeation please.

Thank you very much @Dr Atul Saini

Thank you very much and wishes you very best of luck @Dr Abdullah Al-Oshoush

Dear Dr. Ahasan Ullah Khan,

Hi, you can try multiDimBio package of r-cran project, if you interested in R.

Also, you can use either PAST or NTSYS-pc as the user friendly and easy to use software.

Regards,

Saeed

Install multiDimBio in the link below:

https://cran.r-project.org/web/packages/multiDimBio/index.html

Initially download multiDimBio manual

https://cran.r-project.org/web/packages/multiDimBio/multiDimBio.pdf

You could to use the commonplace statistical software (spss, minitab, past and so on) to do the PCA analysis or using by the specified packages in R statistical software such as ADE4 and Vegan.

you should to observe the preconceptions of parametric tests, initially. Then, you should to form the data matrix in Excel and it transfers to the statistical software according to the the relevant settings. After that, based on the relevant instruments run the PCA. Finally, you should to analyze its results (output of PCA axis).

There were many software which could help you.

For me I use the SPSS, it is useful and the result meaningful.

Thank you very much for your responsed. We found a professor who is learn me about PCA analysis method and he not interest in authorship that's why select him.

I am also interested to learn from you. Please give me your valuable suggestions please about PCA method by R and SPSS.

Dear Ahasan Ullah Khan

CCA (anonical correspondence analysis) or RDA (redundancy analysis) would be a good alternative methods for identifing the correlations between response variables (such as diversity indices of tea) and independent variables (diversity indices of insects).

CCA and RDA in R is easy to perform, function cca( ) and rda( ) in package vegan would work. or check the source codes in github to make your plots looking good:

https://github.com/tanchaozhong/Master-Degree-Project/blob/master/Lake%20Part%20V%20-%20CCA%20analysis.R

But you may need some experiences to run and debug this code.

Once, getting into statistical analysis, you may need statistical beckground knowledge, here are some papers you could follow:

- Braak. 1986 - Canonical correspondence analysis: A new eigenvector technique for multivariate direct gradient analysis. Ecology. Vol. 76, No. 5, pp. 1167-1179.

- Legendare et al. 2011 - Testing the significance of canonical axes in redundancy analysis. Methods in Ecology and Evolution. Vol. 2, pp. 269-277.

- Tan et al. 2020 - Water level fluctuations affect the alpha and beta diversity of benthic macroinvertebrates in Poyang Lake, China. Fundamental and Applied Limnology. in press.

Good luck,

Tan

Thank you very much and wishes you very best of luck @Dr Tan chaozhong

Very helpful link on how to input data and analyze result of PCA

https://statistics.laerd.com/spss-tutorials/principal-components-analysis-pca-using-spss-statistics.php

Goodluck!

PCA is straightfoward in MATLAB, you just need to get the eigenvectors of the covariance matrix and projected your data Into a lower dimensional domain using these, you may need to scale your data to have unit variance. Interpretation of your results will be driven by the research question

PCA can be done with various statistical packages. SPSS is a very good package. Some free packages can also perform this analysis.

PCA has multiple uses and can be performed in several statistical packages including excel, SPSS, XLSTAT, R, SAS, STATA. One of the PCA uses is indexing which we tried in the attached paper and shared for your kind perusal.

Chapter Principal Component Analysis (PCA) based Indexing

Application for Vulnerability Indexing

Article Extent of Vulnerability in Wheat Producing Agro-Ecologies of...

Minitab software is great for PCA. it was useful for me.

I think the SPSS would be useful

Thank you very much and wishes you very besy of luck @Dr Jay P. Picardal

Thank you very much & wishes you best of luck @Dr Rhodri Smith

Thank you very much & wishes you best of luck @Dr Jayanta Dutta

Thank you very much & wishes you best of luck @Dr Sendhil Ramadas

thank you very much & wishes you best of luck @Dr Seyed Mohammad Nasir Mousavi

Thank you very much for your suggestions & wishes you best of luck @Dr Lamia Al-Naama

Dear Dr. Ahasan Ullah Khan

PCA is very common in relationschip between variables and parameters.

For exemple, you can applied this analysis using XLSTAT. See this tutorial: https://help.xlstat.com/s/article/principal-component-analysis-pca-in-excel?language=en_US.

But, in the case of qualitative and quantitative variables, i suggest CCA analysis for your data. This can give you more informations.

Regarding PCA analysis, one can be used SPSS, STATA, Mini tab, PAST, R packages.

PCA (Principal Component Analysis) consists of three steps: (1) Standardization, (2) Covariance matrix computation, and (3) Computing the Eigenvectors and Eigenvalues of the covariance matrix to identify the principal components. For detail analysis see the link: https://builtin.com > data-science > step-step

A step by step explanation of PCA by Zakaria Jaadi.

PCA essentially sorts the data according to variance in features. By considering only high variance features and ignoring others, it is possible to reduce the dimension of the data which is useful for visualizing the pattern of distribution.

Thank you very much for your nice suggestions @Dr Bara Mouslim

Thank you very much for your contributions @Dr Md sarower E Mahfuj

Thank you very much for your comments @Dr Munira Nasiruddin

Thank you very much for your suggestions @Dr Kumar Anjneya

Thank you very much for your comment and wishes you very best of luck @Dr Ranjan Parekh

Thank you for sharing this question

Dear Ahasan Ullah Khan

Try to use a program ْXlstat that is very easy

Watch YouTube

Thank you very much and wishes you best of luck Dr Benamor Naceur

PCA is mainly a dimensionality reduction method and helps to interpret a large number of variables in your dataset. This article can help you to understand and how to apply PCA to your dataset https://www.reneshbedre.com/blog/reverse-complementary.html

Thank you so much and keep me in your prayers Dr Renesh Bedre