Langton's ant (from Rosetta Code)
Langton's ant is a cellular automaton that models an ant sitting on a plane of cells, all of which are white initially, the ant facing in one of four directions.
Each cell can either be black or white.
The ant moves according to the color of the cell it is currently sitting in, with the following rules:
| # Author: denis.engemann@gmail.com | |
| # License: simplified BSD (3 clause) | |
| # Note: code is based on scipy.stats.pearsonr | |
| def ss(a, axis): | |
| return np.sum(a * a, axis=axis) | |
| def compute_corr(x, y): | |
| x = np.asarray(x) | |
| y = np.asarray(y) |
This document describes a minor update to genomic SEM that provides the user with the option to control how the LD score intercept is used to apply genomic control to GenomicSEM GWAS and code to get quick initial genetic correlations and the standard errors of the genetic correlation from the ldsc() function.
Behind the scenes, and poorly documented (there were some comments in the code, that’s it), GenomicSEM was applying Genomic Control. The LD score regression intercept produces an expectation for the mean chi-square statistic under the null. As a chi2 distribution with 1 df has a mean of 1.0, an LDSC intercept greater than 1.0 can be used as an index of inflation of the test statistic attributable to uncontrolled confounding (Bulik Sullivan et al. 2015). Specifically, we estimate the univariate LD score intercept and inflate the SE of the estimated SNP-trait covarianc
