Commit f3be1a6a authored by Hanna  JULIENNE's avatar Hanna JULIENNE

correct typos in doc

parent 92f3718b
Pipeline #8062 passed with stages
in 3 minutes and 22 seconds
......@@ -18,7 +18,7 @@ class ImputationLauncher(object):
lamb= 0.01, pinv_rcond = 0.01):
"""
Initialise the imputation object. Fix the windows size, the buffer size
and the king of imputation employed
and the kind of imputation employed
Args:
window_size (int): size of the imputation window in bp
......
......@@ -47,7 +47,7 @@ def prepare_zscore_for_imputation(ref_panel, zscore):
- filtering snps that are not present in the ref panel
- Adding columns that will contain information on imputation:
* Var : theoritical variance estimate of z
* Nsnp_to_impute : Number of known snp
* Nsnp_to_impute : Number of known snp used to perform imputation
* ld_score : the sum of the square correlation of the snp with all other
known snp (give an idea if the we have enough information to compute a
precise z estimate)
......@@ -119,10 +119,12 @@ def empty_imputed_dataframe():
"correct_inversion", "Nsnp_to_impute"]
zscore_results = pd.DataFrame(columns = column_order)
return zscore_results
def impg_like_imputation(ld_file, ref_panel, zscore, window_size, buffer, lamb,
rcond, unknowns=pd.Series([])):
"""
Each missing Snp is imputed by known snp found in a window centered on the SNP to impute
Each missing Snp is imputed by known snps found in a window
Argument.
Args:
ld_file (str): Linkage desiquilibrium matrix files
......
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