PROB_BASEVAR-class: Object to store the distribution model for basic variables...
In TesiproV: Reliability Analysis Methods for Structural Engineering

PROB_BASEVAR-class

R Documentation

Object to store the distribution model for basic variables (PROB_BASEVAR)

Description

Stores the distribution model for a basic random variable that can be used in any limit-state function. The class knows how to transform between *mean / standard deviation* and the native distribution parameters.

Details

The method 'prepare()' calculates missing parameters and checks for consistency. 'getlDistr()' returns a list containing *four* functions ('d', 'p', 'q', 'r') together with meta-information; the result is cached to avoid repeated 'loadNamespace()' calls.

Fields

Id: Integer identifier (position inside the LSF vector).
Name: Human-readable name (e.g. '"f_ck"').
Description: Short textual description.
DistributionType: Distribution identifier ('"norm"', '"lnorm"', '"slnorm"', '"gumbel"', '"gamma"', '"exp"', '"beta"', '"st"', '"binom"','"emp"', '"unif"', '"weibull"', '"lt"').
Mean: Mean value of the variable.
Sd: Standard deviation.
Cov: Coefficient of variation ('Sd / Mean').
x0: Shifting parameter (used for shifted log-normal distribution '"slnorm"').
Package: Name of the R package that provides the density functions ('"stats"', '"evd"', '"EnvStats"' or '"TesiproV"').
DistributionParameters: Numeric vector with the *native* distribution parameters (e.g. 'c(mu, sigma)' for a normal).
.cache: Private environment that stores cached density functions (used internally, **do not** access from user code).

Methods

fromJCSS(BaseVar, NominalValue, ComponentType = "ready-mixed", ...): Sets the distribution according to the JCSS Probabilistic Model Code and modifies this object. BaseVar = 'fc': concrete compressive strength, NominalValue is the strength class (e.g. 'C30'), ComponentType is 'ready-mixed' or 'pre-cast'. The result is a log-Student-t distribution ('lt'). BaseVar = 'fy': yield strength, NominalValue is the steel grade (e.g. 'B500'), ComponentType is 'reinforcing steel'. The result is a normal distribution. Not all combinations are tabulated.
genHyperParam(n.gen.num = 15, n.gen.samp = 10000, ...): Returns a new PROB_BASEVAR with a Student-t ('st') or log-Student-t ('lt') distribution for this normal or lognormal variable. The hyperparameters c(m', s', n', v') are estimated by maximum likelihood (Rackwitz 1983) from n.gen.samp simulated samples of size n.gen.num. The result depends on the random numbers, use set.seed() for reproducible values.
plot( x_range = NULL, n = 500, add = FALSE, compare = FALSE, prior = FALSE, unit = NULL, ... ): Plots the probability density function of this basic variable. If prior = TRUE and this object was produced by updateBasevar(), the density of the prior is drawn into the same plot (prior vs. posterior). unit is an optional character string with the unit of the variable (e.g. "mm"). It is added to the x axis label as [unit]. If compare = TRUE and this object was produced by updateBasevar(), the fitted parametric curve (if a fit was attempted) and the raw empirical resample density are overlaid for visual comparison.
prepare(): Performs transformations between mean/sd and distribution parameters. Ensures that Mean, Sd and Cov are consistent and valid.
updateBasevar(PROB_QUALCON, debug.level = 0, min_r2 = 0.99): Bayesian update with the OC curve of a PROB_QUALCON object. This variable must have DistributionType 'st' or 'lt' with the hyperparameters c(m', s', n', v'). Returns a new PROB_BASEVAR with the distribution after acceptance of the lot, this object is not changed. The outgoing distribution is fitted by the same distribution type. If the fit gives R^2 < min_r2, an empirical distribution ('emp') is returned instead. debug.level = 1 prints progress messages.

Author(s)

Examples

## Normal random variable --------------------------------------------------
var1 <- PROB_BASEVAR(
  Name = "f_ck", DistributionType = "norm",
  Mean = 30, Sd = 1
)
var1$prepare()

## Gumbel random variable (package `evd`) ----------------------------------
var2 <- PROB_BASEVAR(
  Name = "M", DistributionType = "gumbel",
  Package = "evd", Mean = 2000, Sd = 200
)
var2$prepare()

TesiproV documentation built on Oct. 28, 2026, 5:13 p.m.