Tools for working with parameter values
Setters and validators for parameter values. Additionally, tools for creating sequences of parameter values and for transforming parameter values are provided.
value_validate(object, values) value_seq(object, n, original = TRUE) value_sample(object, n, original = TRUE) value_transform(object, values) value_inverse(object, values) value_set(object, values)
object |
An object with class |
values |
A numeric vector or list (including |
n |
An integer for the (maximum) number of values to return. In some
cases where a sequence is requested, the result might have less than |
original |
A single logical. Should the range values be in the natural
units ( |
For sequences of integers, the code uses
unique(floor(seq(min, max, length.out = n)))
and this may generate an
uneven set of values shorter than n
. This also means that if n
is larger
than the range of the integers, a smaller set will be generated. For
qualitative parameters, the first n
values are returned.
If a single value sequence is requested, the default value is returned (if any). If no default is specified, the regular algorithm is used.
For quantitative parameters, any values
contained in the object
are sampled with replacement. Otherwise, a sequence of values
between the range
values is returned. It is possible that less
than n
values are returned.
For qualitative parameters, sampling of the values
is conducted
with replacement. For qualitative values, a random uniform distribution
is used.
value_validate()
throws an error or silently returns values
if they are
contained in the values of the object
.
value_transform()
and value_inverse()
return a vector of
numeric values.
value_seq()
and value_sample()
return a vector of values consistent
with the type
field of object
.
library(dplyr) penalty() %>% value_set(-4:-1) # Is a specific value valid? penalty() penalty() %>% range_get() value_validate(penalty(), 17) # get a sequence of values cost_complexity() cost_complexity() %>% value_seq(4) cost_complexity() %>% value_seq(4, original = FALSE) on_log_scale <- cost_complexity() %>% value_seq(4, original = FALSE) nat_units <- value_inverse(cost_complexity(), on_log_scale) nat_units value_transform(cost_complexity(), nat_units) # random values in the range set.seed(3666) cost_complexity() %>% value_sample(2)
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