def calculate_regressionParameters_v1(self, calibrator_samples, component_name_I, fit_I, weighting_I, use_area_I):
'''calculate regression parameters for a given component
from a specified quantitation method id
NOTE: intended to be used in a loop
input:
component_name
calibrators_samples (class member)
fit
weighting
use_area
ouput:
slope
intercept
correlation
lloq
uloq
points'''
# query calibrators for specific component_name from specific experiment_id
if use_area_I:
calibrators = self.session.query(data_stage01_quantification_MQResultsTable.actual_concentration,
data_stage01_quantification_MQResultsTable.area_ratio,
data_stage01_quantification_MQResultsTable.dilution_factor).filter(
data_stage01_quantification_MQResultsTable.sample_name.like(calibrator_samples.c.sample_name),
data_stage01_quantification_MQResultsTable.used_.is_(True),
data_stage01_quantification_MQResultsTable.is_.isnot(True),
data_stage01_quantification_MQResultsTable.component_name.like(component_name_I)).all();
else:
calibrators = self.session.query(data_stage01_quantification_MQResultsTable.actual_concentration,
data_stage01_quantification_MQResultsTable.height_ratio,
data_stage01_quantification_MQResultsTable.dilution_factor).filter(
data_stage01_quantification_MQResultsTable.sample_name.like(calibrator_samples.c.sample_name),
data_stage01_quantification_MQResultsTable.used_.is_(True),
data_stage01_quantification_MQResultsTable.is_.isnot(True),
data_stage01_quantification_MQResultsTable.component_name.like(component_name_I)).all();
# variable to check quantitation
calc_regress = True;
# extraction concentrations
concentration = [];
for c in calibrators:
if c.actual_concentration: concentration.append(c.actual_concentration);
else: calc_regress = False;
if len(concentration)==0:
calc_regress = False;
# extraction ratio
ratio = [];
if use_area_I:
for c in calibrators:
if c.area_ratio: ratio.append(c.area_ratio);
else: ratio.append(0.0);
else:
for c in calibrators:
if c.height_ratio: ratio.append(c.height_ratio);
else: ratio.append(0.0);
if len(ratio)==0:
calc_regress = False;
# extraction diluton factor
dilution_factor = [];
for c in calibrators:
if c.dilution_factor: dilution_factor.append(c.dilution_factor);
else: dilution_factor.append(1.0);
# correct the concentration for the dilution factor
for n in range(len(concentration)):
concentration[n] = concentration[n]/dilution_factor[n];
if (not(calc_regress)):
return 0,0,0,0,0,0;
# lloq, uloq, points
lloq_O = min(concentration);
uloq_O = max(concentration);
points_O = len(concentration);
# Call to R
try:
stats = importr('stats');
# generate weights:
'''From MultiQuant Manual:
Weighting type Weight (w)
None Always 1.0.
1 / x If |x| < 10-5 then w = 10e5; otherwise w = 1 / |x|.
1 / x2 If |x| < 10-5 then w = 10e10; otherwise w = 1 / x2.
1 / y If |y| < 10-8 then w = 10e8; otherwise w = 1 / |y|.
1 / y2 If |y| < 10-8 then w = 10e16; otherwise w = 1 / y2.
ln x If x < 0 an error is generated; otherwise if x < 10-5 then w = ln 105,
otherwise w = |ln x|.'''
wts = [];
if weighting_I == 'ln (x)':
for c in concentration:
if c<10e-5:
wts.append(log(10e5));
else:
wts.append(abs(log(c)));
elif weighting_I == 'None':
for c in concentration:
wts.append(1.0);
elif weighting_I == '1 / x':
for c in concentration:
if c<10e-5:
wts.append(1/10e5);
else:
wts.append(1/abs(c));
elif weighting_I == '1 / y':
for c in ratio:
if c<10e-8:
wts.append(1/10e8);
else:
wts.append(1/abs(c));
else:
print(("weighting " + weighting_I + " not yet supported"));
print("linear weighting used instead");
for c in concentration:
wts.append(1.0);
# convert lists to R objects
x = robjects.FloatVector(concentration);
y = robjects.FloatVector(ratio);
w = robjects.FloatVector(wts);
if fit_I == 'Linear':
fmla = robjects.Formula('y ~ x'); # generate the R formula for lm
elif fit_I == 'Linear Through Zero':
fmla = robjects.Formula('y ~ -1 + x'); # generate the R formula for lm
elif fit_I == 'Quadratic':
fmla = robjects.Formula('y ~ x + I(x^2)'); # generate the R formula for lm
elif fit_I == 'Power':
fmla = robjects.Formula('log(y) ~ log(x)'); # generate the R formula for lm
else:
print(("fit " + fit_I + " not yet supported"));
print("linear model used instead");
fmla = robjects.Formula('y ~ x');
env = fmla.environment; # set the local environmental variables for lm
env['x'] = x;
env['y'] = y;
#fit = r('lm(%s)' %fmla.r_repr()); # direct call to R
fit = stats.lm(fmla, weights = w); # return the lm fitted model from R
sum = stats.summary_lm(fit) # return the summary of the fit
intercept_O = sum.rx2('coefficients')[0]; #intercept
slope_O = sum.rx2('coefficients')[1]; #slope
correlation_O = sum.rx2('r.squared')[0]; #r-squared
return slope_O, intercept_O, correlation_O, lloq_O, uloq_O, points_O;
except:
print('error in R')
def calculate_regressionParameters(self, fit_I, weighting_I,
actual_concentration_I,ratio_I,dilution_factor_I):
'''calculate regression parameters for a given component
from a specified quantitation method id
NOTE: intended to be used in a loop
input:
sample_names_I = string array, samples that were used to generate the calibration curve
component_name_I = string, component_name
fit_I = string, type of fit
weighting_I = string, type of weighting
actual_concentration_I = float array, actual concentration of each sample
area_ratio_I = float array, area ratio of the analyte to IS
height_ratio_I = float array, peak height ratio of the analyte to IS
dilution_factor_I = float array, dilution factor of the sample (e.g., 1, 10, etc.);
ouput:
slope
intercept
correlation
lloq
uloq
points'''
calc_regress, concentration, ratio = self._parse_calibrators(
actual_concentration_I,ratio_I,dilution_factor_I);
self.qmethod['fit'] = fit_I;
self.qmethod_row['weighting'] = weighting_I;
self.qmethod_row['intercept'] = 0.0;
self.qmethod_row['slope'] = 0.0;
self.qmethod_row['correlation'] = 0.0;
self.qmethod_row['use_area'] = use_area_I;
self.qmethod_row['lloq'] = 0.0;
self.qmethod_row['uloq'] = 0.0;
self.qmethod_row['points'] = 0;
if (not(calc_regress)):
print('bad regression data: regression not performed');
return;
#return 0,0,0,0,0,0;
# lloq, uloq, points
lloq_O = min(concentration);
uloq_O = max(concentration);
points_O = len(concentration);
# Call to R
try:
stats = importr('stats');
# generate weights:
'''From MultiQuant Manual:
Weighting type Weight (w)
None Always 1.0.
1 / x If |x| < 10-5 then w = 10e5; otherwise w = 1 / |x|.
1 / x2 If |x| < 10-5 then w = 10e10; otherwise w = 1 / x2.
1 / y If |y| < 10-8 then w = 10e8; otherwise w = 1 / |y|.
1 / y2 If |y| < 10-8 then w = 10e16; otherwise w = 1 / y2.
ln x If x < 0 an error is generated; otherwise if x < 10-5 then w = ln 105,
otherwise w = |ln x|.'''
wts = [];
if weighting_I == 'ln (x)':
for c in concentration:
if c<10e-5:
wts.append(log(10e5));
else:
wts.append(abs(log(c)));
elif weighting_I == 'None':
for c in concentration:
wts.append(1.0);
elif weighting_I == '1 / x':
for c in concentration:
if c<10e-5:
wts.append(1/10e5);
else:
wts.append(1/abs(c));
elif weighting_I == '1 / y':
for c in ratio:
if c<10e-8:
wts.append(1/10e8);
else:
wts.append(1/abs(c));
else:
print(("weighting " + weighting_I + " not yet supported"));
print("linear weighting used instead");
for c in concentration:
wts.append(1.0);
# convert lists to R objects
x = robjects.FloatVector(concentration);
y = robjects.FloatVector(ratio);
w = robjects.FloatVector(wts);
if fit_I == 'Linear':
fmla = robjects.Formula('y ~ x'); # generate the R formula for lm
elif fit_I == 'Linear Through Zero':
fmla = robjects.Formula('y ~ -1 + x'); # generate the R formula for lm
elif fit_I == 'Quadratic':
fmla = robjects.Formula('y ~ x + I(x^2)'); # generate the R formula for lm
elif fit_I == 'Power':
fmla = robjects.Formula('log(y) ~ log(x)'); # generate the R formula for lm
else:
print(("fit " + fit_I + " not yet supported"));
print("linear model used instead");
fmla = robjects.Formula('y ~ x');
env = fmla.environment; # set the local environmental variables for lm
env['x'] = x;
env['y'] = y;
#fit = r('lm(%s)' %fmla.r_repr()); # direct call to R
fit = stats.lm(fmla, weights = w); # return the lm fitted model from R
sum = stats.summary_lm(fit) # return the summary of the fit
intercept_O = sum.rx2('coefficients')[0]; #intercept
slope_O = sum.rx2('coefficients')[1]; #slope
correlation_O = sum.rx2('r.squared')[0]; #r-squared
self.qmethod_row['intercept'] = intercept_O;
self.qmethod_row['slope'] = slope_O;
self.qmethod_row['correlation'] = correlation_O;
self.qmethod_row['lloq'] = lloq_O;
self.qmethod_row['uloq'] = uloq_O;
self.qmethod_row['points'] = points_O;
#return slope_O, intercept_O, correlation_O, lloq_O, uloq_O, points_O;
except:
print('error in R')
def calculate_regressionParameters(
self, fit_I, weighting_I, use_area_I, actual_concentration_I, area_ratio_I, height_ratio_I, dilution_factor_I
):
"""calculate regression parameters for a given component
from a specified quantitation method id
NOTE: intended to be used in a loop
input:
sample_names_I = string array, samples that were used to generate the calibration curve
component_name_I = string, component_name
fit_I = string, type of fit
weighting_I = string, type of weighting
use_area_I = boolean, use the area for quantification if true, use the peak height for quantification if false
actual_concentration_I = float array, actual concentration of each sample
area_ratio_I = float array, area ratio of the analyte to IS
height_ratio_I = float array, peak height ratio of the analyte to IS
dilution_factor_I = float array, dilution factor of the sample (e.g., 1, 10, etc.);
ouput:
slope
intercept
correlation
lloq
uloq
points"""
calc_regress, concentration, ratio = self._parse_calibrators(
use_area_I, actual_concentration_I, area_ratio_I, height_ratio_I, dilution_factor_I
)
self.qmethod["fit"] = fit_I
self.qmethod_row["weighting"] = weighting_I
self.qmethod_row["intercept"] = 0.0
self.qmethod_row["slope"] = 0.0
self.qmethod_row["correlation"] = 0.0
self.qmethod_row["use_area"] = use_area_I
self.qmethod_row["lloq"] = 0.0
self.qmethod_row["uloq"] = 0.0
self.qmethod_row["points"] = 0
if not (calc_regress):
print("bad regression data: regression not performed")
return
# return 0,0,0,0,0,0;
# lloq, uloq, points
lloq_O = min(concentration)
uloq_O = max(concentration)
points_O = len(concentration)
# Call to R
try:
stats = importr("stats")
# generate weights:
"""From MultiQuant Manual:
Weighting type Weight (w)
None Always 1.0.
1 / x If |x| < 10-5 then w = 10e5; otherwise w = 1 / |x|.
1 / x2 If |x| < 10-5 then w = 10e10; otherwise w = 1 / x2.
1 / y If |y| < 10-8 then w = 10e8; otherwise w = 1 / |y|.
1 / y2 If |y| < 10-8 then w = 10e16; otherwise w = 1 / y2.
ln x If x < 0 an error is generated; otherwise if x < 10-5 then w = ln 105,
otherwise w = |ln x|."""
wts = []
if weighting_I == "ln (x)":
for c in concentration:
if c < 10e-5:
wts.append(log(10e5))
else:
wts.append(abs(log(c)))
elif weighting_I == "None":
for c in concentration:
wts.append(1.0)
elif weighting_I == "1 / x":
for c in concentration:
if c < 10e-5:
wts.append(1 / 10e5)
else:
wts.append(1 / abs(c))
elif weighting_I == "1 / y":
for c in ratio:
if c < 10e-8:
wts.append(1 / 10e8)
else:
wts.append(1 / abs(c))
else:
print(("weighting " + weighting_I + " not yet supported"))
print("linear weighting used instead")
for c in concentration:
wts.append(1.0)
# convert lists to R objects
x = robjects.FloatVector(concentration)
y = robjects.FloatVector(ratio)
w = robjects.FloatVector(wts)
if fit_I == "Linear":
fmla = robjects.Formula("y ~ x")
# generate the R formula for lm
elif fit_I == "Linear Through Zero":
fmla = robjects.Formula("y ~ -1 + x")
# generate the R formula for lm
elif fit_I == "Quadratic":
fmla = robjects.Formula("y ~ x + I(x^2)")
# generate the R formula for lm
elif fit_I == "Power":
fmla = robjects.Formula("log(y) ~ log(x)")
# generate the R formula for lm
else:
print(("fit " + fit_I + " not yet supported"))
print("linear model used instead")
fmla = robjects.Formula("y ~ x")
env = fmla.environment
# set the local environmental variables for lm
env["x"] = x
env["y"] = y
# fit = r('lm(%s)' %fmla.r_repr()); # direct call to R
fit = stats.lm(fmla, weights=w)
# return the lm fitted model from R
sum = stats.summary_lm(fit) # return the summary of the fit
intercept_O = sum.rx2("coefficients")[0]
# intercept
slope_O = sum.rx2("coefficients")[1]
# slope
correlation_O = sum.rx2("r.squared")[0]
# r-squared
self.qmethod_row["intercept"] = intercept_O
self.qmethod_row["slope"] = slope_O
self.qmethod_row["correlation"] = correlation_O
self.qmethod_row["lloq"] = lloq_O
self.qmethod_row["uloq"] = uloq_O
self.qmethod_row["points"] = points_O
# return slope_O, intercept_O, correlation_O, lloq_O, uloq_O, points_O;
except:
print("error in R")
