Description Usage Arguments Details Value Note References See Also Examples

Fit conditional categorical or continuous distributions with mixed categorical-continuous input.

(These resemble conditional CAT and CKS models).

And the categorical distributions can be used for statistical classification purposes.

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.

1 2 3 4 5 6 7 8 9 | ```
#conditional categorical
ph4.pmfc.gmix (g, x, ..., conditions, warning=TRUE, w)
ph4.cdfc.gmix (g, x, ..., conditions, warning=TRUE, w)
ph4.qfc.gmix (g, x, ..., conditions, warning=TRUE, w)
#conditional continuous
ph4.pdfc.xmix (g, x, ..., conditions, warning=TRUE, w)
ph4.cdfc.xmix (g, x, ..., conditions, warning=TRUE, w)
ph4.qfc.xmix (g, x, ..., conditions, warning=TRUE, w)
``` |

`g` |
Integer/factor/character vector of groups. |

`x` |
A numeric vector or a numeric matrix, preferably with column names. |

`conditions` |
Refer to the conditions arg in categorical and continuous conditional models. |

`w` |
Optional numeric vector of weights. |

`warning` |
Logical, if true, generate warning if there's no observations within the conditional window. |

`...` |
In categorical distributions, further arguments for pdfmv.cks, which is called on the continuous conditioning variables. |

PLEASE SET NOTES IN DESCRIPTION FIELD.

Refer to the vignette for more information.

Default variable names are generated, if the g/x list/matrix are unnamed.

(And a warning is generated).

Note that categorical and continuous variables need different names.

Self-referencing function objects.

Refer to Runtime Function Objects

Except:

The constructors for conditional distributions, return NULL, if there's no observations within the conditional window.

(And by default, generate a warning).

Note you can check names (and their order), with names method.

This may be useful for conditional distributions.

(By default, only the conditional variables are returned).

WARNING:

If a conditional categorical distribution is constructed with integer g values, the category indices won't necessarily equal the category names. e.g. If unique g values were 10, 11 and 12, in contrast to 1, 2 and 3.

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

Succinct Constructors

Discrete Kernel Smoothing, Continuous Kernel Smoothing

Categorical Distributions, Empirical-Like Distributions

is.cat, ph.printf.phmodel, ph.plotf.catuv

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
prep.ph.data ()
fh.gmix <- ph4.pmfc.gmix (species, cbind (sepal.length, sepal.width),
conditions = c (sepal.length=6, sepal.width=3) )
Fht.gmix <- ph4.qfc.gmix (species, cbind (sepal.length, sepal.width),
conditions = c (sepal.length=6, sepal.width=3) )
plot (fh.gmix)
ph.mode (fh.gmix)
ph.mode (fh.gmix, level.names=TRUE)
Fht.gmix (0.5)
Fht.gmix (0.5, category=TRUE)
``` |

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