factor_estimate: factor_estimate
In daviddoret/GRCRToolkit: GRC Toolkit

Description Usage Format Value Fields Inherits Methods Examples

A risk model "factor estimate". A "factor estimate" is an abstraction that separates the factor estimation method from the model factors. This approach makes it possible to work separately on the risk model and on the best possible estimation methods to estimate the model factors. factor_estimate is an "abstract" class that shouldn't be used directly. Instead, use the specialized classes that inherits from it.

1	factor_estimate

An object of class R6ClassGenerator of length 24.

An instance of the factor_estimate R6Class.

estimation_method_name: A descriptive name identifying the estimation method, e.g.: "PERT-like 3 points estimate".
distribution_name: The name of the probability distribution, e.g. "Poisson", "Composite", etc.
distribution_type: Either "Continuous" or "Discrete".
limit_min_value: A strict lower bound applied to the factor simulation values. If NULL or NA, no lower bound will be applied.
limit_max_value: A strict upper bound applied to the factor simulation values. If NULL or NA, no upper bound will be applied.
limit_min_behavior: One of the following options determining how values will be maintained within limit_min_value: "Limit" (default), "Replace", "Discard". "Limit": When an out of bound value is drawn, apply min/max functions to force it within bounds. "Replace": When an out of bound value is drawn, we replace it until it is within bound. "Discard": When an out of bound value is drawn, remove it from the sample.
limit_max_behavior: One of the following options determining how values will be maintained within limit_max_value: "Limit" (default), "Replace", "Discard". "Limit": When an out of bound value is drawn, apply min/max functions to force it within bounds. "Replace": When an out of bound value is drawn, we replace it until it is within bound. "Discard": When an out of bound value is drawn, remove it from the sample.

This is a root class.

get_random(n = 1, output_class = "vector"): Returns a random sample of size n. Returns a vector by default. If output_class = "data.frame", returns a data.frame with a column "factor_value". This second parameterization may be enriched by R6 subclasses to provide additional columns with complementary information.