Description Objects from the Class Slots Methods Author(s) Examples

This class deals with Kernel estimators for bounded densities using boundary kernels where the same kernel function is used for all regions: left boundary, interior and right boundary. The kernel estimator is computed using the provided data samples. Using this kernel estimator, the methods implemented in the class can be used to compute densities, values of the distribution function, quantiles, sample the distribution and obtain graphical representations. Note that this kernel estimator is not normalized and therefore it is not a probability distribution (the cumulative density function may return values greater than 1).

Objects can be created by using the generator function `noBoundaryKernel`

.

`dataPointsCache`

:a numeric vector containing points within the

`[lower.limit,upper.limit]`

interval`densityCache`

:a numeric vector containing the density for each point in

`dataPointsCache`

`distributionCache`

:a numeric vector used to cache the values of the distribution function. This slot is included to improve the performance of the methods when multiple calculations of the distribution function are used

`dataPoints`

:a numeric vector containing data samples within the

`[lower.limit,upper.limit]`

interval. These data samples are used to obtain the kernel estimator`b`

:the bandwidth of the kernel estimator

`mu`

:a integer value indicating the degree of smoothness for the boundary kernel.

`mu`

can take the following values: 0 (uniform kernel), 1 (Epanechnikov kernel), 2 (biweight kernel) or 3 (triweight kernel)`lower.limit`

:a numeric value for the lower limit of the bounded interval for the data

`upper.limit`

:a numeric value for the upper limit of the bounded interval for the data

- density
See

`"density"`

for details- distribution
See

`"distribution"`

for details- quantile
See

`"quantile"`

for details- rsample
See

`"rsample"`

for details- plot
See

`"plot"`

for details- getdataPointsCache
See

`"getdataPointsCache"`

for details- getdensityCache
See

`"getdensityCache"`

for details- getdistributionCache
See

`"getdistributionCache"`

for details- getdataPoints
See

`"getdataPoints"`

for details- getb
See

`"getb"`

for details- getmu
See

`"getmu"`

for details

Guzman Santafe, Borja Calvo and Aritz Perez

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
# create the model
kernel <- noBoundaryKernel(dataPoints = tuna.r, b = 0.01, mu = 2)
# examples of usual functions
density(kernel,0.5)
distribution(kernel,0.5,discreteApproximation=FALSE)
# graphical representation
hist(tuna.r,freq=FALSE,main="Tuna Data")
lines(kernel, col="red",lwd=2)
# graphical representation using ggplot2
graph <- gplot(kernel, show=TRUE, includePoints=TRUE)
``` |

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