mise: Mean Integrated Squared Error

Description Usage Arguments Examples

View source: R/utils.R

Description

Computes the mean integrated squared error (MISE) for two given Bounded density objects.

Usage

1
mise(model1,model2,discreteApproximation = TRUE)

Arguments

model1

a bounded density object. See getSubclasses("BoundedDensity") to see all the allowed class objects

model2

a bounded density object. See getSubclasses("BoundedDensity") to see all the allowed class objects

discreteApproximation

If TRUE, the mise is calculated using the data stored in the cache. Otherwise the integral is computed.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
# a general approximation to a Beta(1,10) distribution using BoundedDensity objects
cache <- seq(0,1,0.01)
dens  <- dbeta(cache,1,10)
bd    <- boundedDensity(x=cache,densities=dens)

# a BrVitale approximation to the Beta(1,10) distribution using a random data sample to 
# learn the model
dataSample <- rbeta(100,1,10)
kernel     <- hirukawaTSKernel(dataPoints=dataSample, b=0.1, c=0.3, 
                                dataPointsCache=cache, modified=FALSE)

# compute the mise
mise(bd,kernel,discreteApproximation=TRUE)
mise(bd,kernel,discreteApproximation=FALSE)

bde documentation built on May 19, 2017, 10:08 p.m.

Search within the bde package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs in the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.