NumCosmoMath.MSetCatalog.largest_error

@accepts(NumCosmoMath.MSetCatalog)
@returns(float)
def largest_error(self):
    # Python wrapper for ncm_mset_catalog_largest_error()
  

Calculates the largest proportional error of the parameters included, i.e., $\text{lre} = \sigma_{\hat{p}}/(|\hat{p}|\sqrt{n})$ where $n$ represents the number of samples in the catalog, $\hat{p}$ is the estimated mean of the parameter $p$ and $\sigma_{\hat{p}}$ its standard deviation.

It tries to guess when $p = 0$. In this case $\sigma_{\hat{p}} \approx |\hat{p}|\sqrt{n}$, therefore for $n > 10$ it tests if $\text{lre} \approx 1$ and if it is the case it returns $\text{lre} = \sigma_{\hat{p}}/\sqrt{n}$ instead.