scalar_parameters: Scalar parameters

In oppr: Optimal Project Prioritization

Scalar parameters

Description

These functions are used to create parameters that consist of a single number. Parameters have a name, a value, a defined range of acceptable values, a default value, a class, and a shiny::shiny() widget for modifying them. If values are supplied to a parameter that are unacceptable then an error is thrown.

Usage

proportion_parameter(name, value)

binary_parameter(name, value)

integer_parameter(
  name,
  value,
  lower_limit = as.integer(-.Machine$integer.max),
  upper_limit = as.integer(.Machine$integer.max)
)

numeric_parameter(
  name,
  value,
  lower_limit = .Machine$double.xmin,
  upper_limit = .Machine$double.xmax
)

Arguments

name	character name of parameter.
value	integer or double value depending on the parameter.
lower_limit	integer or double value representing the smallest acceptable value for value. Defaults to the smallest possible number on the system.
upper_limit	integer or double value representing the largest acceptable value for value. Defaults to the largest possible number on the system.

Details

Below is a list of parameter generating functions and a brief description of each.

proportion_parameter

A parameter that is a double and bounded between zero and one.

integer_parameter

A parameter that is a integer.

numeric_parameter

A parameter that is a double.

binary_parameter

A parameter that is restricted to integer values of zero or one.

Value

ScalarParameter object.

Examples

# proportion parameter
p1 <- proportion_parameter('prop', 0.5) # create new object
print(p1) # print it
p1$get() # get value
p1$id # get id
p1$validate(5) # check if 5 is a validate input
p1$validate(0.1) # check if 0.1 is a validate input
p1$set(0.1) # change value to 0.1
print(p1)

# binary parameter
p2 <- binary_parameter('bin', 0) # create new object
print(p2) # print it
p2$get() # get value
p2$id # get id
p2$validate(5) # check if 5 is a validate input
p2$validate(1L) # check if 1L is a validate input
p2$set(1L) # change value to 1L
print(p1) # print it again

# integer parameter
p3 <- integer_parameter('int', 5L) # create new object
print(p3) # print it
p3$get() # get value
p3$id # get id
p3$validate(5.6) # check if 5.6 is a validate input
p3$validate(2L) # check if 2L is a validate input
p3$set(2L) # change value to 2L
print(p3) # print it again

# numeric parameter
p4 <- numeric_parameter('dbl', -7.6) # create new object
print(p4) # print it
p4$get() # get value
p4$id # get id
p4$validate(NA) # check if NA is a validate input
p4$validate(8.9) # check if 8.9 is a validate input
p4$set(8.9) # change value to 8.9
print(p4) # print it again

# numeric parameter with lower bounds
p5 <- numeric_parameter('bdbl', 6, lower_limit=0) # create new object
print(p5) # print it
p5$get() # get value
p5$id # get id
p5$validate(-10) # check if -10 is a validate input
p5$validate(90) # check if 90 is a validate input
p5$set(90) # change value to 8.9
print(p5) # print it again

oppr documentation built on Sept. 8, 2022, 5:07 p.m.