value_validate | R Documentation |
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, ..., call = caller_env())
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 |
... |
These dots are for future extensions and must be empty. |
call |
The call passed on to |
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.
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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