base_pref_macros {rPref}

Useful Base Preference Macros

Description

In addition to the base preferences, rPref offers some macros to define preferences where a given interval or point is preferred.

Usage

around(expr, center, df = NULL)

between(expr, left, right, df = NULL)

pos(expr, pos_value, df = NULL)

layered(expr, ..., df = NULL)

Arguments

expr

A numerical expression (for around and between) or an arbitrary expression (for pos and layered). The objective are those tuples where expr evaluates to a value within the preferred interval, layer, etc. Regarding attributes, functions and variables, the same requirements as for base_pref apply.

center

Preferred numerical value for around.

df

(optional) Data frame for partial evaluation and association of preference and data set. See base_pref for details.

left

Lower limit (numerical) of the preferred interval for between.

right

Upper limit (numerical) of the preferred interval for between.

pos_value

A vector containing the preferred values for a pos preference. It has to be of the same type (numeric, logical, character, ...) as expr.

...

Layers (sets) for a layered preference. Each parameter corresponds to a layer and the first one characterizes the most preferred values.

Definition of the Preference Macros

between(expr, left, right)

Those tuples are preferred where expr evaluates to a value between left and right. For values not in this interval, the values nearest to the interval are preferred.

around(expr, center)

Same as between(expr, center, center).

pos(expr, pos_value)

Those tuples are preferred, where expr evaluates to a value which is contained in pos_value.

layered(expr, layer1, layer2, ..., layerN)

For the most preferred tuples expr must evaluate to a value in layer1. The second-best tuples are those where expr evaluates to a value in layer2 and so forth. Values occurring in none of the layers are considered worse than those in layerN. Technically, this is realized by a prioritization chain (lexicographical order) of true preferences.

Note that only the argument expr may contain columns from the data frame, all other variables must evaluate to explicit values. For example around(mpg, mean(mpg)) is not allowed. In this case, one can use around(mpg, mean(mtcars$mpg)) instead. Or alternatively, without using the base preference macros, low(abs(mpg - mean(mpg))) does the same. There, the actual mean value of mpg is calculated just when the preference selection via psel is called.

Examples

# Search for cars where mpg is near to 25.
psel(mtcars, around(mpg, 25))

# Consider cyl = 2 and cyl = 4 as equally good, while cyl = 6 is worse.
psel(mtcars, layered(cyl, c(2, 4), 6))