ml_estimation: ML Estimation for Parametric Lifetime Distributions

In weibulltools: Statistical Methods for Life Data Analysis

ML Estimation for Parametric Lifetime Distributions

Description

This function estimates the parameters of a parametric lifetime distribution for complete and (multiple) right-censored data. The parameters are determined in the frequently used (log-)location-scale parameterization.

For the Weibull, estimates are additionally transformed such that they are in line with the parameterization provided by the stats package (see Weibull).

Usage

ml_estimation(x, ...)

## S3 method for class 'wt_reliability_data'
ml_estimation(
  x,
  distribution = c("weibull", "lognormal", "loglogistic", "sev", "normal", "logistic",
    "weibull3", "lognormal3", "loglogistic3", "exponential", "exponential2"),
  wts = rep(1, nrow(x)),
  conf_level = 0.95,
  start_dist_params = NULL,
  control = list(),
  ...
)

Arguments

x	A tibble with class wt_reliability_data returned by reliability_data.
...	Further arguments passed to or from other methods. Currently not used.
distribution	Supposed distribution of the random variable.
wts	Optional vector of case weights. The length of wts must be equal to the number of observations in x.
conf_level	Confidence level of the interval.
start_dist_params	Optional vector with initial values of the (log-)location-scale parameters.
control	A list of control parameters (see 'Details' and optim).

Details

Within ml_estimation, optim is called with method = "BFGS" and control$fnscale = -1 to estimate the parameters that maximize the log-likelihood (see loglik_function). For threshold models, the profile log-likelihood is maximized in advance (see loglik_profiling). Once the threshold parameter is determined, the threshold model is treated like a distribution without threshold (lifetime is reduced by threshold estimate) and the general optimization routine is applied.

Normal approximation confidence intervals for the parameters are computed as well.

Value

A list with classes wt_model, wt_ml_estimation and wt_model_estimation which contains:

• coefficients : A named vector of estimated coefficients (parameters of the assumed distribution). Note: The parameters are given in the (log-)location-scale-parameterization.

• confint : Confidence intervals for the (log-)location-scale parameters.

• shape_scale_coefficients : Only included if distribution is "weibull" or "weibull3" (parameterization used in Weibull).

• shape_scale_confint : Only included if distribution is "weibull" or "weibull3". Confidence intervals for scale \eta and shape \beta (and threshold \gamma if distribution = "weibull3").

• varcov : Estimated variance-covariance matrix of (log-)location-scale parameters.

• logL : The log-likelihood value.

• aic : Akaike Information Criterion.

• bic : Bayesian Information Criterion.

• data : A tibble with class wt_reliability_data returned by

• distribution : Specified distribution.

References

Meeker, William Q; Escobar, Luis A., Statistical methods for reliability data, New York: Wiley series in probability and statistics, 1998

Examples

# Reliability data preparation:
## Data for two-parametric model:
data_2p <- reliability_data(
  shock,
  x = distance,
  status = status
)

## Data for three-parametric model:
data_3p <- reliability_data(
  alloy,
  x = cycles,
  status = status
)

# Example 1 - Fitting a two-parametric weibull distribution:
ml_2p <- ml_estimation(
  data_2p,
  distribution = "weibull"
)

# Example 2 - Fitting a three-parametric lognormal distribution:
ml_3p <- ml_estimation(
  data_3p,
  distribution = "lognormal3",
  conf_level = 0.99
)

weibulltools documentation built on April 5, 2023, 5:10 p.m.