Fit_Exponential_1P¶

class
reliability.Fitters.
Fit_Exponential_1P
(failures=None, right_censored=None, show_probability_plot=True, print_results=True, CI=0.95, percentiles=None, method='MLE', optimizer=None, **kwargs)¶ Fits a 1parameter Exponential distribution (Lambda) to the data provided.
Inputs: failures  an array or list of failure data right_censored  an array or list of right censored data show_probability_plot  True/False. Defaults to True. print_results  True/False. Defaults to True. Prints a dataframe of the point estimate, standard error, Lower CI and Upper CI for each parameter. CI  confidence interval for estimating confidence limits on parameters. Must be between 0 and 1. Default is 0.95 for 95% CI. percentiles  percentiles to produce a table of percentiles failed with lower, point, and upper estimates. Default is None which results in no output. True or ‘auto’ will use default array [1, 5, 10,…, 95, 99]. If an array or list is specified then it will be used instead of the default array. method  ‘MLE’ (maximum likelihood estimation), ‘LS’ (least squares estimation), ‘RRX’ (Rank regression on X), ‘RRY’ (Rank regression on Y). LS will perform both RRX and RRY and return the better one. Default is ‘MLE’. optimizer  ‘LBFGSB’, ‘TNC’, or ‘powell’. These are all bound constrained methods. If the bounded method fails, neldermead will be used. If neldermead fails then the initial guess will be returned with a warning. For more information on optimizers see https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize kwargs are accepted for the probability plot (eg. linestyle, label, color)
Outputs: Lambda  the fitted Exponential_1P lambda parameter loglik  Log Likelihood (as used in Minitab and Reliasoft) loglik2  LogLikelihood*2 (as used in JMP Pro) AICc  Akaike Information Criterion BIC  Bayesian Information Criterion AD  the Anderson Darling (corrected) statistic (as reported by Minitab) distribution  an Exponential_Distribution object with the parameters of the fitted distribution Lambda_SE  the standard error (sqrt(variance)) of the parameter Lambda_upper  the upper CI estimate of the parameter Lambda_lower  the lower CI estimate of the parameter results  a dataframe of the results (point estimate, standard error, Lower CI and Upper CI for the parameter) goodness_of_fit  a dataframe of the goodness of fit values (Loglikelihood, AICc, BIC, AD). percentiles  a dataframe of the percentiles with bounds on time. This is only produced if percentiles is ‘auto’ or a list or array. Since percentiles defaults to None, this output is not normally produced. probability_plot  the axes handle for the probability plot (only returned if show_probability_plot = True)
*Note that this is a 1 parameter distribution but Lambda_inv is also provided as some programs (such as Minitab and scipy.stats) use this instead of Lambda

static
LL
(params, T_f, T_rc)¶

static
logR
(t, L)¶

static
logf
(t, L)¶

static