I have a distribution map produced with only presence data. And there is a certain number of presence data that is in no way included in the model. How can I evaluate the compatibility of the presence data not included in the model I have with the predictive values corresponding to these points in the potential distribution map? So we can also think like this: I have two columns. The first column has only 1 values, the second column has the predictive values. Which method would be the best approach to examine the relationship between these two columns?
I'm not sure I fully understand your question but when you have a column with a constant value (e.g., 1), this constant by definition cannot covary with another column/variable. A constant does not have any variance and therefore, the covariance/correlation with another variable will also be zero by definition.
As Christian Geiser, I am not sure I understand what you mean. You do not need to give away your specific research question, but maybe you could explain to us broadly what you want to predict by what? If you only have one value for a variable (e.g. you want to study age and you only have 20-year old people), you cannot use this to predict another variable (e.g., health)
But maybe you are asking for something else? If you have missing data only for some cells of the column, there are methods to get around this issue (e.g., multiple imputation), which may or may not be suitable for your research question.
You can use a Chi square test or compare beta diversity values, which examine presence-absence data :)
Prediction is based on a probabilistic analysis. It is impossible to predict only one number. Some environmental variables need to be added. For example, if a child is 6 years old and 1 meter tall in Japan, how tall is he assuming he is 20 years old? These environmental variables are another regression model, such as the height of different ages in Japan in the 1960s, to establish a regression model.
no statistical examination is attached to single value, you can't examine it
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