Description Usage Arguments Details Value References Examples
View source: R/confidence_intervals.R
This function computes normal-approximation confidence intervals for quantiles and failure probabilities.
1 2 3 4 5 6 7 8 9 10 11 | confint_fisher(x, ...)
## S3 method for class 'wt_model'
confint_fisher(
x,
b_lives = c(0.01, 0.1, 0.5),
bounds = c("two_sided", "lower", "upper"),
conf_level = 0.95,
direction = c("y", "x"),
...
)
|
x |
Object with classes |
... |
Further arguments passed to or from other methods. Currently not used. |
b_lives |
A numeric vector indicating the probabilities p of the B_p-lives (quantiles) to be considered. |
bounds |
A character string specifying of which bounds have to be computed.
One of |
conf_level |
Confidence level of the interval. |
direction |
A character string specifying the direction of the
confidence interval. One of |
The basis for the calculation of these confidence bounds are the standard errors determined by the delta method and hence the required (log-)location-scale parameters as well as the variance-covariance matrix of these have to be estimated with maximum likelihood.
The bounds on the probability are determined by the z-procedure. See 'References' for more information on this approach.
A tibble with class wt_confint
containing the following
columns:
x
: An ordered sequence of the lifetime characteristic regarding
the failed units, starting at min(x)
and ending up at max(x)
.
With b_lives = c(0.01, 0.1, 0.5)
the 1%, 10% and 50% quantiles
are additionally included in x
, but only if the specified
probabilities are in the range of the estimated probabilities.
prob
: An ordered sequence of probabilities with specified
b_lives
included.
std_err
: Estimated standard errors with respect to
direction
.
lower_bound
: Provided, if bounds
is one of
"two_sided"
or "lower"
. Lower limit of the confidence region
with respect to direction
, i.e. quantiles or probabilities.
upper_bound
: Provided, if bounds
is one of
"two_sided"
or "upper"
. Upper limit of the confidence region
with respect to direction
, i.e. quantiles or probabilities.
distribution
: Specified distribution (determined when calling
ml_estimation
).
bounds
: Specified bound(s).
direction
: Specified direction.
cdf_estimation_method
: A character that is always
NA_character
. For the generic visualization functions this
column has to be provided.
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 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | # 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
)
# Model estimation with ml_estimation():
ml_2p <- ml_estimation(
data_2p,
distribution = "weibull"
)
ml_3p <- ml_estimation(
data_3p,
distribution = "lognormal3",
conf_level = 0.90
)
# Example 1 - Two-sided 95% confidence interval for probabilities ('y'):
conf_fisher_1 <- confint_fisher(
x = ml_2p,
bounds = "two_sided",
conf_level = 0.95,
direction = "y"
)
# Example 2 - One-sided lower/upper 90% confidence interval for quantiles ('x'):
conf_fisher_2_1 <- confint_fisher(
x = ml_2p,
bounds = "lower",
conf_level = 0.90,
direction = "x"
)
conf_fisher_2_2 <- confint_fisher(
x = ml_2p,
bounds = "upper",
conf_level = 0.90,
direction = "x"
)
# Example 3 - Two-sided 90% confidence intervals for both directions using
# a three-parametric model:
conf_fisher_3_1 <- confint_fisher(
x = ml_3p,
bounds = "two_sided",
conf_level = 0.90,
direction = "y"
)
conf_fisher_3_2 <- confint_fisher(
x = ml_3p,
bounds = "two_sided",
conf_level = 0.90,
direction = "x"
)
|
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