def results():
x, y, e = get_data()
reg = PolynomialRegressor(degree=1, xs=x, ys=y)
mi, ma = min(x), max(x)
pad = (ma - mi) * 0.1
fxs = linspace(mi - pad, ma + pad)
plt.errorbar(x, y, yerr=e,)
reg.calculate()
l, u = reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'b')
plt.plot(fxs, u, 'b')
# plt.plot(fxs, reg.predict(fxs), 'b-')
print('Age={}, SD={} SEM={} CI={}'.format(reg.predict(328), reg.predict_error(328), reg.predict_error(328, 'SEM'),
reg.predict_error(328, 'CI')))
reg = WeightedPolynomialRegressor(degree=1, xs=x, ys=y, yserr=e)
reg.calculate()
plt.plot(fxs, reg.predict(fxs), 'g')
l,u=reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'r')
plt.plot(fxs, u, 'r')
print('Weighted fit Age={}, SD={} SEM={} CI={}'.format(reg.predict(328),
reg.predict_error(328), reg.predict_error(328,'SEM'),
reg.predict_error(328, 'CI')))
plt.show()
def results():
x, y, e = get_data()
reg = PolynomialRegressor(degree=1, xs=x, ys=y)
mi, ma = min(x), max(x)
pad = (ma - mi) * 0.1
fxs = linspace(mi - pad, ma + pad)
plt.errorbar(
x,
y,
yerr=e,
)
reg.calculate()
l, u = reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'b')
plt.plot(fxs, u, 'b')
# plt.plot(fxs, reg.predict(fxs), 'b-')
print('Age={}, SD={} SEM={} CI={}'.format(reg.predict(328),
reg.predict_error(328),
reg.predict_error(328, 'SEM'),
reg.predict_error(328, 'CI')))
reg = WeightedPolynomialRegressor(degree=1, xs=x, ys=y, yserr=e)
reg.calculate()
plt.plot(fxs, reg.predict(fxs), 'g')
l, u = reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'r')
plt.plot(fxs, u, 'r')
print('Weighted fit Age={}, SD={} SEM={} CI={}'.format(
reg.predict(328), reg.predict_error(328),
reg.predict_error(328, 'SEM'), reg.predict_error(328, 'CI')))
plt.show()
