Fit_Weibull_CR¶

class
reliability.Fitters.
Fit_Weibull_CR
(failures=None, right_censored=None, show_probability_plot=True, print_results=True, CI=0.95, optimizer=None)¶ Fits a Weibull Competing Risks Model consisting of 2 x Weibull_2P distributions (this does not fit the gamma parameter). This is different to the Weibull Mixture model as the overall Survival Function is the product of the individual Survival Functions rather than being the sum as is the case in the Weibull Mixture Model. Competing Risks ==> SF_model = SF_1 x SF_2 Mixture ==> SF_model = (proportion_1 x SF_1) + ((1proportion_1) x SF_2)
Similar to the mixture model, you can use this model when you think there are multiple failure modes acting to create the failure data.
Whilst some failure modes may not be fitted as well by a Weibull distribution as they may be by another distribution, it is unlikely that data from a competing risks model will be fitted noticeably better by other types of competing risks models than would be achieved by a Weibull Competing Risks model. For this reason, other types of competing risks models are not implemented.
Inputs: failures  an array or list of the failure data. There must be at least 4 failures, but it is highly recommended to use another model if you have
less than 20 failures.right_censored  an array or list of right censored data print_results  True/False. This will print results to console. Default is True. CI  confidence interval for estimating confidence limits on parameters. Must be between 0 and 1. Default is 0.95 for 95% CI. 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 show_probability_plot  True/False. This will show the probability plot with the fitted Weibull_CR CDF. Default is True.
Outputs: alpha_1  the fitted Weibull_2P alpha parameter for the first distribution beta_1  the fitted Weibull_2P beta parameter for the first distribution alpha_2  the fitted Weibull_2P alpha parameter for the second distribution beta_2  the fitted Weibull_2P beta parameter for the second distribution alpha_1_SE  the standard error on the parameter beta_1_SE  the standard error on the parameter alpha_2_SE  the standard error on the parameter beta_2_SE  the standard error on the parameter alpha_1_upper  the upper confidence interval estimate of the parameter alpha_1_lower  the lower confidence interval estimate of the parameter beta_1_upper  the upper confidence interval estimate of the parameter beta_1_lower  the lower confidence interval estimate of the parameter alpha_2_upper  the upper confidence interval estimate of the parameter alpha_2_lower  the lower confidence interval estimate of the parameter beta_2_upper  the upper confidence interval estimate of the parameter beta_2_lower  the lower confidence interval estimate of the 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) results  a dataframe of the results (point estimate, standard error, Lower CI and Upper CI for each parameter) goodness_of_fit  a dataframe of the goodness of fit values (Loglikelihood, AICc, BIC, AD). probability_plot  the axes handle for the probability plot (only returned if show_probability_plot = True)

static
LL
(params, T_f, T_rc)¶

static
logR
(t, a1, b1, a2, b2)¶

static
logf
(t, a1, b1, a2, b2)¶

static