Description Usage Arguments Details Value References Examples
View source: R/rank_regression.R
This function fits an x on y regression to a linearized two- or three-parameter 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).
1 2 3 4 5 6 7 8 9 10 | rank_regression(x, ...)
## S3 method for class 'wt_cdf_estimation'
rank_regression(
x,
distribution = c("weibull", "lognormal", "loglogistic", "normal", "logistic", "sev",
"weibull3", "lognormal3", "loglogistic3"),
conf_level = 0.95,
...
)
|
x |
Object of class |
... |
Further arguments passed to or from other methods. Currently not used. |
distribution |
Supposed distribution of the random variable. |
conf_level |
Confidence level of the interval. If |
If distribution
is "weibull"
or "weibull3"
, the approximated
confidence intervals for the parameters can only be estimated on the following
confidence levels (see 'References' (Mock, 1995)):
conf_level
= 0.90,
conf_level
= 0.95,
conf_level
= 0.99.
If the distribution is not the Weibull, the confidence intervals of the parameters are computed on the basis of a heteroscedasticity-consistent covariance matrix. Here it should be said that there is no statistical foundation to determine the standard errors of the parameters using Least Squares in context of Rank Regression. For an accepted statistical method use maximum likelihood.
Returns a list with classes wt_model
, wt_rank_regression
and
wt_model_estimation
containing the following elements:
coefficients
: A named vector of estimated coefficients
(parameters of the assumed distribution). Note: The parameters
are given in location-scale-parameterization.
confint
: Confidence intervals for parameters. If
distribution
is "lognormal3"
or "loglogistic3"
no
confidence interval for the threshold parameter is computed.
varcov
: Provided, if distribution
is not
"weibull"
or "weibull3"
. Estimated heteroscedasticity-consistent
variance-covariance matrix for the (log-)location-scale parameters.
shape_scale_coefficients
: Only included if
distribution
is "weibull"
or "weibull3"
(parameterization used in stats::Weibull
).
shape_scale_confint
: Only included if distribution
is
"weibull"
or "weibull3"
. Approximated confidence intervals
for scale η and shape β (and threshold γ)
if distribution
is "weibull3"
.
r_squared
: Coefficient of determination.
data
: A tibble with class wt_cdf_estimation
returned from estimate_cdf
.
distribution
: Specified distribution.
If more than one method was specified in estimate_cdf
,
the resulting output is a list with class wt_model_estimation_list
.
In this case each list element has classes wt_rank_regression
and
wt_model_estimation
and the items listed above, are included.
Mock, R., Methoden zur Datenhandhabung in Zuverlässigkeitsanalysen, vdf Hochschulverlag AG an der ETH Zürich, 1995
Meeker, William Q; Escobar, Luis A., Statistical methods for reliability data, New York: Wiley series in probability and statistics, 1998
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | # 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
)
# Probability estimation:
prob_tbl_2p <- estimate_cdf(
data_2p,
methods = "johnson"
)
prob_tbl_3p <- estimate_cdf(
data_3p,
methods = "johnson"
)
prob_tbl_mult <- estimate_cdf(
data_3p,
methods = c("johnson", "kaplan")
)
# Example 1 - Fitting a two-parametric weibull distribution:
rr_2p <- rank_regression(
x = prob_tbl_2p,
distribution = "weibull"
)
# Example 2 - Fitting a three-parametric lognormal distribution:
rr_3p <- rank_regression(
x = prob_tbl_3p,
distribution = "lognormal3",
conf_level = 0.99
)
# Example 3 - Fitting a three-parametric loglogistic distribution if multiple
# methods in estimate_cdf were specified:
rr_lists <- rank_regression(
x = prob_tbl_mult,
distribution = "loglogistic3",
conf_level = 0.90
)
|
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