This function applies a non-parametric method to estimate the failure probabilities of complete data taking (multiple) right-censored observations into account.

estimate_cdf(x, ...) # S3 method for wt_reliability_data estimate_cdf( x, methods = c("mr", "johnson", "kaplan", "nelson"), options = list(), ... )

x | A tibble with class |
---|---|

... | Further arguments passed to or from other methods. Currently not used. |

methods | One or multiple methods of |

options | A list of named options. See 'Options'. |

A tibble with class `wt_cdf_estimation`

containing the following columns:

`id`

: Identification for every unit.`x`

: Lifetime characteristic.`status`

: Binary data (0 or 1) indicating whether a unit is a right censored observation (= 0) or a failure (= 1).`rank`

: The (computed) ranks. Determined for methods`"mr"`

and`"johnson"`

, filled with`NA`

for other methods or if`status = 0`

.`prob`

: Estimated failure probabilities,`NA`

if`status = 0`

.`cdf_estimation_method`

: Specified method for the estimation of failure probabilities.

One or multiple techniques can be used for the `methods`

argument:

`"mr"`

: Method*Median Ranks*is used to estimate the failure probabilities of failed units without considering censored items. Tied observations can be handled in three ways (See 'Options'):`"max"`

: Highest observed rank is assigned to tied observations.`"min"`

: Lowest observed rank is assigned to tied observations.`"average"`

: Mean rank is assigned to tied observations.

Two formulas can be used to determine cumulative failure probabilities

*F(t)*(See 'Options'):`"benard"`

: Benard's approximation for Median Ranks.`"invbeta"`

: Exact Median Ranks using the inverse beta distribution.

`"johnson"`

: The*Johnson*method is used to estimate the failure probabilities of failed units, taking censored units into account. Compared to complete data, correction of probabilities is done by the computation of adjusted ranks. Two formulas can be used to determine cumulative failure probabilities*F(t)*(See 'Options'):`"benard"`

: Benard's approximation for Median Ranks.`"invbeta"`

: Exact Median Ranks using the inverse beta distribution.

`"kaplan"`

: The method of*Kaplan*and*Meier*is used to estimate the survival function*S(t)*with respect to (multiple) right censored data. The complement of*S(t)*, i.e.*F(t)*, is returned. In contrast to the original*Kaplan-Meier*estimator, one modification is made (see 'References').`"nelson"`

: The*Nelson-Aalen*estimator models the cumulative hazard rate function in case of (multiple) right censored data. Equating the formal definition of the hazard rate with that according to*Nelson-Aalen*results in a formula for the calculation of failure probabilities.

Argument `options`

is a named list of options:

Method | Name | Value |

`mr` | `mr_method` | `"benard"` (default) or `"invbeta"` |

`mr` | `mr_ties.method` | `"max"` (default), `"min"` or `"average"` |

`johnson` | `johnson_method` | `"benard"` (default) or `"invbeta"` |

*NIST/SEMATECH e-Handbook of Statistical Methods*,
*8.2.1.5. Empirical model fitting - distribution free (Kaplan-Meier) approach*,
NIST SEMATECH,
December 3, 2020

# Reliability data: data <- reliability_data( alloy, x = cycles, status = status ) # Example 1 - Johnson method: prob_tbl <- estimate_cdf( x = data, methods = "johnson" ) # Example 2 - Multiple methods: prob_tbl_2 <- estimate_cdf( x = data, methods = c("johnson", "kaplan", "nelson") ) # Example 3 - Method 'mr' with options: prob_tbl_3 <- estimate_cdf( x = data, methods = "mr", options = list( mr_method = "invbeta", mr_ties.method = "average" ) )#># Example 4 - Multiple methods and options: prob_tbl_4 <- estimate_cdf( x = data, methods = c("mr", "johnson"), options = list( mr_ties.method = "max", johnson_method = "invbeta" ) )#>