20_discrete_kernel_smoothing: Discrete Kernel Smoothing Models In probhat: Multivariate Generalized Kernel Smoothing and Related Statistical Methods

Description

Fit probability distributions, via discrete kernel smoothing over integer-indexed frequency data.

NOTE THAT THESE OBJECTS ARE LIKELY TO BE CONVERTED TO S4 OBJECTS, IN THE NEAR FUTURE.
ALSO, NOTE THAT THEIR INTERNAL STRUCTURE (THAT IS, THEIR ATTRIBUTES/SLOTS), IS SUBJECT TO CHANGE.

IN PRINCIPLE, YOU SHOULD NOT ACCESS ATTRIBUTES/SLOTS, DIRECTLY.

Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```pmfuv.dks (x = 1:length (h), h=1, ..., bw, smoothness=1, kernel=BIWEIGHT.CKERNEL, bw.method="ph.default", Xlim = c (a, b), a = min1 (x), b=Inf) cdfuv.dks (x = 1:length (h), h=1, ..., bw, smoothness=1, kernel=BIWEIGHT.CKERNEL, bw.method="ph.default", tail="lower", Xlim = c (a, b), a = min1 (x), b=Inf) qfuv.dks (x = 1:length (h), h=1, ..., bw, smoothness=1, kernel=BIWEIGHT.CKERNEL, bw.method="ph.default", Xlim = c (a, b), a = min1 (x), b=Inf) ```

Arguments

 `x` Integer vector of integer-indexed discrete observations, or bins of such observations. (If duplicates, they, along with their frequencies, are aggregated). Also, can be a single-column integer matrix, preferably with a column (variable) name, and optionally with row (bin) names. Defaults to a sequence from one to the length of h. `h` Positive numeric vector of frequencies (or weights), which can be fractional. (If scalar, it's recycled to match the length of pre-aggregate x). Defaults to one, such that each x value represents a single discrete value. `bw` Odd positive integer value, giving the bandwidth parameter. If missing, an initial bandwidth is computed via the the bandwidth method (see bw.method below), which is subject to the smoothness parameter. `smoothness` Positive numeric value, giving the relative bandwidth. Ignored, if bw is provided. `kernel` A (continuous) kernel object. `bw.method` String, the bandwidth selection method. Refer to Bandwidth Selection. `tail` String, either "lower" or "upper". If lower (the default), lower tail probabilities, P (X <= x), are used. If upper, upper tail probabilities, P (X >= x), are used. `Xlim` In principle, a length-two integer vector, giving the limits of X. But a numeric vector is allowed, to support -Inf/Inf. The corresponding random variable is regarded as bounded, if either limit is finite. In which case, a truncated smoothing algorithm is applied. `a, b` In principle, integer values. This is an alternative way of specifying Xlim, above. The min1 function will return one, if one is the minimum x value, otherwise, it will return zero. `...` Additional arguments not allowed.

Details

PLEASE SET NOTES IN DESCRIPTION FIELD.

Note that if x has non-unique values, then duplicated x (and their h) values are aggregated.
And currently, any row names will be ignored.

Also note that the truncation method may change in future updates.

Value

Self-referencing function objects.

Refer to Runtime Function Objects

References

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

Kernels

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

is.dks, ph.printf.phmodel, ph.plotf.dksuv

Bandwidth Selection

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```prep.ph.data () dfh <- pmfuv.dks (traffic.bins, traffic.freq) dFh <- cdfuv.dks (traffic.bins, traffic.freq) dFht <- qfuv.dks (traffic.bins, traffic.freq) plot (dfh) plot (dfh, TRUE) plot (dfh, freq=TRUE) plot (dFh, freq=TRUE) dFht (0.5) ```

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