Simulate from the expert's distribution

Description

Simulate random variables from an SEL object.

Usage

rvSEL(n, object, nPoints = 1000)

Arguments

Details

The inverse of the distribution function is formed by evaluating the distribution function at nPoints points and interchanging the role of dependent and independent variable when building an function interpolating the data (using splinefun with "monoH.FC" option). Note that there are also direct ways of inverting a B-spline function, which however turned out to be less efficient for our purposes.

Value

A numeric vector containing pseudo-random variates from the expert's density.

Author(s)

Bjoern Bornkamp

References

Bornkamp, B. and Ickstadt, K. (2009). A Note on B-Splines for Semiparametric Elicitation. The American Statistician, 63, 373–377

See Also

SEL, splinefun

Examples

## bimodal example 
x2 <- c(0.1, 0.2, 0.5, 0.8, 0.9)
y2 <- c(0.2, 0.4, 0.45, 0.85, 0.99)

fit1 <- SEL(x2, y2, Delta=0.05, d = 4, inknts = x2)
fit2 <- SEL(x2, y2, Delta=0.05, d = 15, N = 0)
comparePlot(fit1, fit2, superpose = TRUE)

## sample from SEL object
xxx <- rvSEL(50000, fit1)
hist(xxx, breaks=100, freq=FALSE)
curve(predict(fit1, newdata=x), add=TRUE)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.

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'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(SEL)
Loading required package: splines
Loading required package: quadprog
Loading required package: lattice
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/SEL/rvSEL.Rd_%03d_medium.png", width=480, height=480)
> ### Name: rvSEL
> ### Title: Simulate from the expert's distribution
> ### Aliases: rvSEL
> ### Keywords: misc
> 
> ### ** Examples
> 
> ## bimodal example 
> x2 <- c(0.1, 0.2, 0.5, 0.8, 0.9)
> y2 <- c(0.2, 0.4, 0.45, 0.85, 0.99)
> 
> fit1 <- SEL(x2, y2, Delta=0.05, d = 4, inknts = x2)
> fit2 <- SEL(x2, y2, Delta=0.05, d = 15, N = 0)
> comparePlot(fit1, fit2, superpose = TRUE)
> 
> ## sample from SEL object
> xxx <- rvSEL(50000, fit1)
> hist(xxx, breaks=100, freq=FALSE)
> curve(predict(fit1, newdata=x), add=TRUE)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>