SCIPY STATS LINREGRESS LICENSE
See the License for information about copying. This does not mean this is the best model for the data, just that the model fits well.Ĭopyright (C) 2013 by John Kitchin. zero is not included in any of the parameter confidence intervals. All of the parameters appear to be significant, i.e. Plt.savefig( 'images/linregress-conf.png')Ī fourth order polynomial fits the data well, with a good R^2 value. Plt.plot(time, Ca, 'bo', label= 'raw data') # plot fit import matplotlib.pyplot as plt Print ']'.format(beta - ci, beta + ci, beta) ST = t.ppf(1.0 - alpha/2.0, n - k) # student T multiplier Se = np.sqrt(np.diag(C)) # standard errorĪlpha = 0.05 # 100*(1 - alpha) confidence level Sigma2 = np.sum((Ca - np.dot(T, p))**2) / (n - k) # RMSEĬ = sigma2 * np.linalg.inv(np.dot(T.T, T)) # covariance matrix # the parameters are now in p # compute the confidence intervals To all entries.Time = np.array()Ĭa = np.array()*1e-3 Regressed arrays are identical, but one has also been adjusted by adding a scalar I won't work around the larger issue, since it only alters the result when the Wrong, using multiples of eps slowly converged, but with jumpy steps. Ignore the different intercepts, regressor or are both
SCIPY STATS LINREGRESS CODE
Here's some code to demonstrate the issue: > import numpy > from scipy.stats import linregress > from regressor import linregress as linreg2 > a = numpy. The divergence only occurs with some inputĪrrays of random numbers, about 55% of runs in my tests, depending on the is also affectedīy this, but to a lesser degree. The divergence only occurs with some input arrays of random numbers, about 55 of runs in my tests, depending on the input array size. is also affected by this, but to a lesser degree. It returns five values: the slope, intercept, correlation, p-value against the null hypothesis that the slope is zero. However, the p-value can divergeĭue to imprecision from float addition. However, the p-value can diverge due to imprecision from float addition. You can extract the slope and intercept as follows: m,b,r,p, (x,y) In this exercise we are only interested in Question: The function (x,y) performs a linear regression of y on s. Should be shifted up by the incremented value. array + 1.2e-7), we expect the same slope and p-value, but the intercept When regressing again with itself incremented slightly when you regress a small array with itself, the p-value I could only find one scenario where the behavior of this package differs from Reliability across a wide range of P-values. Sampled values with varying correlation between the X and Y arrays to assess The graphs below demonstrate this consistency by comparing betas, r-values and The regression results from this package match to withinĤ decimal places (for the slope, intercept, r-value, p-value and standard error). In general, this package performs simple linear regression in about 1/20th of This package and, starting from arrays with 10 elements The graph below compares the times required for simple linear regressions between float32 ) > linregress ( x, y ) LinregressResult ( slope =- 1.0, intercept = 6.0, rvalue =- 1.0, pvalue = 1.2e-30, stderr = 0.0 ) Performance Usage > import numpy as np > from regressor import linregress > x = np. On x86-64 hardware with AVX extensions (most desktops and servers as of 2020). The input numpy arrays need to be 1D with 32-bit floats.Īs a result, this is ~20X faster than, but only runs This uses vectorized operations to speed up calculating dot This is a small library that tries to run simple linear regression quickly on