Description Usage Arguments Details Value
View source: R/model_anything_byMonteCarloSimulation.R
By relying on Law of Large Numbers estimate the statistical distribution by Monte Carlo simulation.
1 | model_anything_byMonteCarloSimulation(mod, spins, verbose = T)
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mod |
A list whose elements are vectors describing the normal or skew normal probability distributions which will be used in Monte Carlo simulation. For normal distribution the length of vector must be 2 with mean and st.dev being 1st and 2nd element respectively. For skew-normal distribution the length of vector must be 3 with mean (mu), st.dev (omega), and skewness (alpha) being 1st, 2nd and 3rd element respectively. |
spins |
A numeric of count of spins to be performed for the Monte Carlo simulation. |
verbose |
A boolean indicating whether to execute in verbose mode. |
The assumption is that the object to be modeled can be so by linear function with single or multiple terms (tasks) where the terms are summed-up. In the case of computing the time required to complete a project this means each term (task) is dependent on the completion of other sub-task. An underlying statistical distribution for the individual terms has to be assumed. In this case it is either normal or skew-normal distribution. For example the model can be TIME_TOTAL = TIME_TASK_1 + TIME_TASK_2 + TIME_TASK_3 which would have its P(TIME_TOTAL) = P(TIME_TASK_1) + P(TIME_TASK_2) + P(TIME_TASK_3) described by parameters of normal os skewed-normal distribution. An inverse of cumulative density function (iCDF) is used to compute the simulated values corresponding to randomly generated cumulative probability (within the range of 0 and 1).
A numeric vector of simulated values.
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