72_quantile-based_statistics: Robust Statistics

In probhat: Multivariate Generalized Kernel Smoothing and Related Statistical Methods

Description

Compute robust (quantile based) statistics from probability distributions.

Usage

ph.median (xf, ...)
ph.quantile (xf, p, ...)

iqr (xf, P=0.5, ...)

Arguments

xf	A numeric vector, suitable function object, or an object that can be coerced to a numeric vector. Here, a suitable function object is a quantile function. Refer to the references and see also sections.
P, p	Numeric vectors, the probabilities. P is the area (probability) between the lower and upper limits.
...	Other arguments. Refer to the details section.

Details

If xf is a numeric vector, a qfuv.el object is created using xf as the main argument.
Any arguments contained within ..., are passed to the qfuv.el constructor.

If xf is not a quantile function, these functions try to coerce it to a numeric vector, and apply the above.

Value

ph.median returns a single numeric value.

The other functions return a numeric vector.

References

Refer to the vignette for an overview, references and better examples.

See Also

Succinct Constructors
Discrete Kernel Smoothing, Continuous Kernel Smoothing, Empirical-Like Distributions

probmv, rng

ph.mean, moment
quartiles, ntiles
ph.mode, ph.modes

Examples

prep.ph.data ()

cFht <- qfuv.cks (height)
cFht (0.5)

ph.median (cFht)
#iqr (cFht)

probhat documentation built on May 12, 2021, 5:08 p.m.