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  • 1.  PCA for animal stomach food composition?

    Posted Tue November 30, 2021 12:05 AM
    Hi, I'm doing PCA on the stomach food composition of 13 animals species and their 6 types of food composition measured in percentage in a scale of 0-100%.

    Question 1:
    Since all 6 variables are measured on the same scale (0-100%), let said despite of missing data (it is natural that some food are consumed by some but no others), and some low (~0-20%) vs high (~90-100%) values, is data standardization required here?

    Question 2:
    My understanding is that missing data in PCA need to be deal with. Consider the nature of the missing data in my case, what suggestion would you give? Should I fill in the missing values with calculated standardized data, mean imputation technique, or replace them with constant?

    Thank you.

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    CP T
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  • 2.  RE: PCA for animal stomach food composition?

    Posted Tue November 30, 2021 01:48 AM
    Edited by Rick Marcantonio Tue November 30, 2021 01:50 AM
    Hi, CP. As for your first question, please see this.

    As for the second question, it depends. It is true that you want to understand missing data, but not always true that you have to actually *do* something. Sometimes listwise deletion is fine.

    I do not advise you to add constants to the data, but to begin with the Missing Values Analysis feature in SPSS. Select the variables of interest and run Little's test of MCAR (missing completely at random). If that comes back non-significant, then ignoring missing data (MISSING=LISTWISE) may be OK; PAIRWISE can also be used if you want to make better use of the available data.

    If, however, Little's test comes back significant, then most likely the data are at least MAR (missing at random; see here for a better understanding of MCAR vs. MAR). In that event, you do not want PAIRWISE or MEANSUB (which I don't recommend anyway, since substituting the mean does nothing more than underestimate the covariance and give the analysis degrees of freedom it doesn't necessarily warrant having - see the first few chapters of Little and Rubin's 1987 book on analysis with missing data to learn more about all of that).

    Sorry; this is kind of a difficult subject but, as you already know, missingness in data is important to understand.

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    Rick Marcantonio
    Quality Assurance
    IBM
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  • 3.  RE: PCA for animal stomach food composition?

    Posted Tue November 30, 2021 03:40 AM
    Thank you Rick, that is very informative. 

    I tried to perform Little's test under Missing Value Analysis. The result came back non-significant but also followed by an error message said: "EM estimation does not converge at 25 iteration." 
    I did tried raise the iteration values to 100 but it is still the same. 

    From here, it said that I need to increase the convergence criterion, I'm not sure how to do that? 

    Thank you.

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    CP T
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  • 4.  RE: PCA for animal stomach food composition?

    Posted Tue November 30, 2021 07:54 AM
    The menu system does not have all the options available to you; for this one, you will need the syntax. Use the dialog to put in your variables and the EM option, and then paste the syntax. Go to that syntax window and in your syntax be sure you have these:

     /LISTWISE
     /EM(TOLERANCE=0.001 CONVERGENCE=0.0001 ITERATIONS=25).

    Even if the data are MCAR, the LISTWISE number of cases may be small relative to the total N; that can be problematic. Look at the patterns of missing data and see if there is a subset of variables with a high amount missing relative to the other variables. If so, there must be a reason; it would need investigation. If the listwise N is small, try changing the /LISTWISE option to /PAIRWISE.

    As for CONVERGENCE, its value must be between 0 and 1. Closer to zero is better, so don't change it too drastically.

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    Rick Marcantonio
    Quality Assurance
    IBM
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  • 5.  RE: PCA for animal stomach food composition?

    Posted Tue November 30, 2021 09:00 AM
    Dear Rick, 
    Thank you for the great input. It helps!

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    CP T
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