python/pymethylprocess/MethylationDataTypes.py
def remove_na_samples(self, outcome_cols):
        """Remove samples of MethylationArray who have missing values in phenotype column.

        Parameters
        ----------
        outcome_cols
            Phenotype columns, if any rows contain missing values, samples are removed.
        """
        vals=self.pheno[outcome_cols].values
        if len(vals.shape) < 2:
            vals = vals[:,np.newaxis]
        remove_bool = ~np.isnan(vals).any(axis=1).flatten()
        self.pheno=self.pheno[remove_bool]
        self.beta=self.beta[remove_bool]
Remove samples of MethylationArray who have missing values in phenotype column.

        Parameters
        ----------
        outcome_cols
            Phenotype columns, if any rows contain missing values, samples are removed.
        
Remove samples of MethylationArray who have missing values in phenotype column.
Parameters

outcome_cols
Phenotype columns, if any rows contain missing values, samples are removed.
Remove samples of MethylationArray who have missing values in phenotype column . Parameters outcome_cols Phenotype columns if any rows contain missing values samples are removed .
def remove_na_samples(self, outcome_cols):
        vals=self.pheno[outcome_cols].values
        if len(vals.shape) < 2:
            vals = vals[:,np.newaxis]
        remove_bool = ~np.isnan(vals).any(axis=1).flatten()
        self.pheno=self.pheno[remove_bool]
        self.beta=self.beta[remove_bool]
def remove_na_samples ( self , outcome_cols ) : vals = self . pheno [ outcome_cols ] . values if len ( vals . shape ) < 2 : vals = vals [ : , np . newaxis ] remove_bool = ~ np . isnan ( vals ) . any ( axis = 1 ) . flatten ( ) self . pheno = self . pheno [ remove_bool ] self . beta = self . beta [ remove_bool ]
Remove samples of MethylationArray who have missing values in phenotype column.
Remove samples of MethylationArray who have missing values in phenotype column .
"""Remove samples of MethylationArray who have missing values in phenotype column.

        Parameters
        ----------
        outcome_cols
            Phenotype columns, if any rows contain missing values, samples are removed.
        """
self
outcome_cols