def compute_mad_neutral_state(self, df):
df_neutral_state = df[(df['state'] == 3) & (~df['copy'].isnull())]
if len(df_neutral_state) > 0:
mad_neutral_state = stand_mad(df_neutral_state['copy'], c=1)
else:
mad_neutral_state = float('NaN')
return mad_neutral_state
def compute_mad_hmmcopy(self, df):
df_hmmcopy = df[~df['copy'].isnull()]
if len(df_hmmcopy) > 0:
mad_hmmcopy = stand_mad(df_hmmcopy['copy'], c=1)
else:
mad_hmmcopy = float('NaN')
return mad_hmmcopy
def compute_mad_autosomes(self, df):
df_autosomes = df[(df['chr'] != 'X') & (df['chr'] != 'Y')]
if len(df_autosomes) > 0:
mad_autosomes = stand_mad(df_autosomes['copy'], c=1)
else:
mad_autosomes = float('NaN')
return mad_autosomes
def compute_chr_mad_hmmcopy(self, df, chrom):
df_hmmcopy = df[~df['copy'].isnull()]
df_chr = df_hmmcopy[df_hmmcopy['chr'] == str(chrom)]
if len(df_chr) > 0:
mad_chr = stand_mad(df_chr['copy'], c=1)
else:
mad_chr = float('NaN')
return mad_chr
def _estimate_scale(self, resid):
"""
Estimates the scale based on the option provided to the fit method.
"""
if isinstance(self.scale_est, str):
if self.scale_est.lower() == 'mad':
return scale.mad(resid)
if self.scale_est.lower() == 'stand_mad':
return scale.stand_mad(resid)
elif isinstance(self.scale_est, scale.HuberScale):
return scale.hubers_scale(self.df_resid, self.nobs, resid)
else:
return scale.scale_est(self, resid)**2
def _estimate_scale(self, resid):
"""
Estimates the scale based on the option provided to the fit method.
"""
if isinstance(self.scale_est, str):
if self.scale_est.lower() == 'mad':
return scale.mad(resid)
if self.scale_est.lower() == 'stand_mad':
return scale.stand_mad(resid)
elif isinstance(self.scale_est, scale.HuberScale):
return self.scale_est(self.df_resid, self.nobs, resid)
else:
return scale.scale_est(self, resid)**2
def test_axisneg1(self):
m = scale.stand_mad(self.X, axis=-1)
assert_equal(m.shape, (40,10))
def test_axis1(self):
m = scale.stand_mad(self.X, axis=1)
assert_equal(m.shape, (40,30))
def test_axis0(self):
m = scale.stand_mad(self.X, axis=0)
assert_equal(m.shape, (10,30))
def test_stand_mad(self):
m = scale.stand_mad(self.X)
assert_equal(m.shape, (10,))
def test_stand_mad(self):
assert_almost_equal(scale.stand_mad(self.chem), 0.52632, DECIMAL)