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Hints and pseudo code for Exercise 4.10.1 (data from Jiang et al., 2013)

  1. Write a function that takes as input the desired Taxon, and returns the mean value of r

    Hints:

    • first of all, we need to read in all the r values for each Taxon. For example, you might store them in a list (or a dictionary where each key is a taxon, and the value is a list of r values).
    import csv
    
    taxa = []
    r_values = []
    
    open the file and set up dictionary reader
    for each row:
       append to taxa
       append to r_values
    • now you need to write a function that takes as input the two lists, as well as a target taxon and computes the average r:
    def compute_avg_r(taxa, r_values, target_taxon = "Fish"):
        avg_taxon = 0.0
        num_occurrences = 0
        cycle through the values of taxa
           every time you find the right taxon, add its r value to avg_taxon
           and increment num_occurrences
        at the end, divide avg_taxon by num_occurrences and return the average
  2. You should see that fish have a positive value of r, but that this is also true for other taxa. Is the mean value of r especially high for fish? To test this, compute a p-value by repeatedly sampling 37 values of r at random (37 experiments on fish are reported in the database), and calculating the probability of observing a higher mean value of r. To get an accurate estimate of the p-value, use 50,000 randomizations.

    Hints: the idea is the following:

    • for the target taxon, compute the mean r value, and store it
    • now sample at random values (as many as the number of occurrences), and average them
    • keep a tally of how many times do you find a higher value than the one observed
    • the proportion of times this happens is the approximate p-value

    To sample at random, you need to use a function from scipy (for example, to shuffle the names of the taxa, or the values associated with the taxa)

    Pseudocode:

    def compute_pvalue(taxa, r_values, target_taxon = "Fish", num_rep = 1000):
        observed_r = compute the mean for the observed average r value
        pvalue = 0.0
        for i in range(num_rep):
           shuffle the r values
           random_r = compute the mean using the shuffled values
           if random_r >= observed_r:
    	   increment pvalue
        now divide pvalue by num_rep and return
  3. Repeat the procedure for all taxa.

    Hints: this is why having written a function is really good. You need to extract all taxa names from the list, and launch the function for each taxon.

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