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ftnonpar (version 0.1-88)

pmden: Piecewise monotone density estimation with taut strings

Description

Applies the taut string method to one-dimensional data.

Usage

pmden(x, DISCR=FALSE,verbose = FALSE, bandwidth=-1, extrema.nr = -1, accuracy = mad(x)/1000, extrema.mean = TRUE,maxkuipnr=19,asympbounds=FALSE, tolerance = 1e-08,localsq=TRUE,locsq.factor=0.9)

Arguments

x
observed values
DISCR
logical, if T a discrete density is fitted
verbose
logical, if T progress (for each iteration) is illustrated grahically
bandwidth
if set to a positive value the specified bandwidth is used instead of the automatic criterion based on generalized Kuiper metrics.
extrema.nr
if set to a positive integer an approximation with the specified number of local extreme values is calculated
accuracy
Precision of the data. Handling of identical observations depends on this parameter.
extrema.mean
logical, if T the value at the local extrema is changed to the mean frequency of observations on that interval
maxkuipnr
The order of the highest generalized Kuiper metric used for the automatic choice of the bandwidth
asympbounds
If set to T asymptotic bounds derived from a Brwonian Bridge are used for the Kuiper criterion. Otherwise simulated bounds for various sample sizes are interpolated for the size of the data x
tolerance
Accuracy used for the determination of the bandwidth when extrema.nr is greater than 0.
localsq
If set to TRUE (default) performs, if necessary, additional local squeezing after the Kuiper metrics are small enough
locsq.factor
The amount of decrement applied to the bandwidthes if local squeezing is carried out.

Value

y
values of the density approximation between the observations
widthes
bandwidth used for the taut string approximation
nmax
number of local extreme values
ind
indices of knots points of the taut string
trans
taut string at the observations, should look like uniform noise

References

Davies, P. L. and Kovac, A. (2003) Densities, Spectral Densities and Modality

See Also

pmreg,l1pmreg,pmspec

Examples

Run this code
aaa <- rclaw(500)
pmden(aaa,verb=TRUE)$n

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