MoTBFs (version 1.2)

MoTBF-Distribution: Random Generation for MoTBFs

Description

Random generation for mixtures of truncated basis functions defined in a specific domain. The inversion method is used. It is a technique to get random samples from a probability distribution.

Usage

rMoTBF(size, fx, domain = NULL)
inversionMethod(size, fx, domain = NULL, data = NULL)

Arguments

size

A "numeric" value indicating the number of records to generate.

fx

An object of class "motbf".

domain

A "numeric" vector with two values indicating the range where generating the data sample. By default it is NULL and the range is taken of the function, fx.

data

A "numeric" vector which contains the data which we want to compare with the generated function. By default it is NULL, if not the empirical cumulative distribution is plotted and the Kolmogorov Smirnov test is used to compare the generated sample and the data.

Value

rMoTBF() returns a "numeric" vector containing the simulated values. inversionMethod() returns a list with the simulated values and the results of the test, it also shows a plot with the cdf of the original data and the generated one by screen.

See Also

integralMoTBF

Examples

# NOT RUN {
## 1. EXAMPLE
## Data
X <- rnorm(1000, mean = 5, sd = 3)

## Learning
f <- univMoTBF(X, POTENTIAL_TYPE="MOP", nparam=10)
plot(f, xlim = f$Domain)

## Random sample
Y <- rMoTBF(size = 500, fx = f)
ks.test(X,Y)

## Plots
hist(Y, prob = TRUE, add = TRUE)

## 2. EXAMPLE 
## Data
X <- rweibull(5000, shape=2)

## Learning
f <- univMoTBF(X, POTENTIAL_TYPE="MOP", nparam=10)
plot(f, xlim = f$Domain)

## Random sample
inv <- inversionMethod(size = 500, fx = f, data = X)
attributes(inv)
inv$test
Y <- inv$sample 

## Plots
plot(f, xlim = f$Domain)
hist(Y, prob = TRUE, add = TRUE)

# }

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