Type: Package
Title: Design Menu for Radiant: Business Analytics using R and Shiny
Version: 1.6.6
Date: 2024-5-14
Description: The Radiant Design menu includes interfaces for design of experiments, sampling, and sample size calculation. The application extends the functionality in 'radiant.data'.
Depends: R (≥ 4.3.0), radiant.data (≥ 1.6.6),
Imports: dplyr (≥ 1.0.7), magrittr (≥ 1.5), shiny (≥ 1.8.1), AlgDesign (≥ 1.1.7.3), import (≥ 1.1.0), pwr (≥ 1.1.2), randomizr (≥ 0.20.0), mvtnorm (≥ 1.2.0), polycor
Suggests: testthat (≥ 2.0.0), pkgdown (≥ 1.1.0)
URL: https://github.com/radiant-rstats/radiant.design/, https://radiant-rstats.github.io/radiant.design/, https://radiant-rstats.github.io/docs/
BugReports: https://github.com/radiant-rstats/radiant.design/issues/
License: AGPL-3 | file LICENSE
LazyData: true
Encoding: UTF-8
RoxygenNote: 7.3.1
NeedsCompilation: no
Packaged: 2024-05-15 02:25:33 UTC; vnijs
Author: Vincent Nijs [aut, cre]
Maintainer: Vincent Nijs <radiant@rady.ucsd.edu>
Repository: CRAN
Date/Publication: 2024-05-15 04:30:02 UTC

Create (partial) factorial design

Description

Create (partial) factorial design

Usage

doe(factors, int = "", trials = NA, seed = NA)

Arguments

factors

Categorical variables used as input for design

int

Vector of interaction terms to consider when generating design

trials

Number of trials to create. If NA then all feasible designs will be considered until a design with perfect D-efficiency is found

seed

Random seed to use as the starting point

Details

See https://radiant-rstats.github.io/docs/design/doe.html for an example in Radiant

Value

A list with all variables defined in the function as an object of class doe

See Also

summary.doe to summarize results

Examples

doe(c("price; $10; $13; $16", "food; popcorn; gourmet; no food"))
doe(
  c("price; $10; $13; $16", "food; popcorn; gourmet; no food"),
  int = "price:food", trials = 9, seed = 1234
)

Determine coefficients that can be estimated based on a partial factorial design

Description

A function to determine which coefficients can be estimated based on a partial factorial design. Adapted from a function written by Blakeley McShane at https://github.com/fzettelmeyer/mktg482/blob/master/R/expdesign.R

Usage

estimable(design)

Arguments

design

An experimental design generated by the doe function that includes a partial and full factorial design

Examples

design <- doe(c("price; $10; $13; $16", "food; popcorn; gourmet; no food"), trials = 6)
estimable(design)

Plot method for the sample_size_comp function

Description

Plot method for the sample_size_comp function

Usage

## S3 method for class 'sample_size_comp'
plot(x, ...)

Arguments

x

Return value from sample_size_comp

...

further arguments passed to or from other methods

Details

See https://radiant-rstats.github.io/docs/design/sample_size_comp.html for an example in Radiant

See Also

sample_size_comp to generate the results

Examples

sample_size_comp(
  type = "proportion", p1 = 0.1, p2 = 0.15,
  conf_lev = 0.95, power = 0.8
) %>% plot()

radiant.design

Description

Launch radiant.design in the default web browser

Usage

radiant.design(state, ...)

Arguments

state

Path to state file to load

...

additional arguments to pass to shiny::runApp (e.g, port = 8080)

Details

See https://radiant-rstats.github.io/docs/ for documentation and tutorials

Examples

## Not run: 
radiant.design()

## End(Not run)

Launch radiant.design in the Rstudio viewer

Description

Launch radiant.design in the Rstudio viewer

Usage

radiant.design_viewer(state, ...)

Arguments

state

Path to state file to load

...

additional arguments to pass to shiny::runApp (e.g, port = 8080)

Details

See https://radiant-rstats.github.io/docs/ for documentation and tutorials

Examples

## Not run: 
radiant.design_viewer()

## End(Not run)

Launch radiant.design in an Rstudio window

Description

Launch radiant.design in an Rstudio window

Usage

radiant.design_window(state, ...)

Arguments

state

Path to state file to load

...

additional arguments to pass to shiny::runApp (e.g, port = 8080)

Details

See https://radiant-rstats.github.io/docs/ for documentation and tutorials

Examples

## Not run: 
radiant.design_window()

## End(Not run)

Randomize cases into experimental conditions

Description

Randomize cases into experimental conditions

Usage

randomizer(
  dataset,
  vars,
  conditions = c("A", "B"),
  blocks = NULL,
  probs = NULL,
  label = ".conditions",
  seed = 1234,
  data_filter = "",
  arr = "",
  rows = NULL,
  na.rm = FALSE,
  envir = parent.frame()
)

Arguments

dataset

Dataset to sample from

vars

The variables to sample

conditions

Conditions to assign to

blocks

A vector to use for blocking or a data.frame from which to construct a blocking vector

probs

A vector of assignment probabilities for each treatment conditions. By default each condition is assigned with equal probability

label

Name to use for the generated condition variable

seed

Random seed to use as the starting point

data_filter

Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000")

arr

Expression to arrange (sort) the data on (e.g., "color, desc(price)")

rows

Rows to select from the specified dataset

na.rm

Remove rows with missing values (FALSE or TRUE)

envir

Environment to extract data from

Details

Wrapper for the complete_ra and block_ra from the randomizr package. See https://radiant-rstats.github.io/docs/design/randomizer.html for an example in Radiant

Value

A list of variables defined in randomizer as an object of class randomizer

See Also

summary.sampling to summarize results

Examples

randomizer(rndnames, "Names", conditions = c("test", "control")) %>% str()

100 random names

Description

100 random names

Usage

data(rndnames)

Format

A data frame with 100 rows and 2 variables

Details

A list of 100 random names. Description provided in attr(rndnames,"description")

Sample size calculation

Description

Sample size calculation

Usage

sample_size(
  type,
  err_mean = 2,
  sd_mean = 10,
  err_prop = 0.1,
  p_prop = 0.5,
  conf_lev = 0.95,
  incidence = 1,
  response = 1,
  pop_correction = "no",
  pop_size = 1e+06
)

Arguments

type

Choose "mean" or "proportion"

err_mean

Acceptable Error for Mean

sd_mean

Standard deviation for Mean

err_prop

Acceptable Error for Proportion

p_prop

Initial proportion estimate for Proportion

conf_lev

Confidence level

incidence

Incidence rate (i.e., fraction of valid respondents)

response

Response rate

pop_correction

Apply correction for population size ("yes","no")

pop_size

Population size

Details

See https://radiant-rstats.github.io/docs/design/sample_size.html for an example in Radiant

Value

A list of variables defined in sample_size as an object of class sample_size

See Also

summary.sample_size to summarize results

Examples

sample_size(type = "mean", err_mean = 2, sd_mean = 10)

Sample size calculation for comparisons

Description

Sample size calculation for comparisons

Usage

sample_size_comp(
  type,
  n1 = NULL,
  n2 = NULL,
  p1 = NULL,
  p2 = NULL,
  delta = NULL,
  sd = NULL,
  conf_lev = NULL,
  power = NULL,
  ratio = 1,
  alternative = "two.sided"
)

Arguments

type

Choose "mean" or "proportion"

n1

Sample size for group 1

n2

Sample size for group 2

p1

Proportion 1 (only used when "proportion" is selected)

p2

Proportion 2 (only used when "proportion" is selected)

delta

Difference in means between two groups (only used when "mean" is selected)

sd

Standard deviation (only used when "mean" is selected)

conf_lev

Confidence level

power

Power

ratio

Sampling ratio (n1 / n2)

alternative

Two or one sided test

Details

See https://radiant-rstats.github.io/docs/design/sample_size_comp.html for an example in Radiant

Value

A list of variables defined in sample_size_comp as an object of class sample_size_comp

See Also

summary.sample_size_comp to summarize results

Examples

sample_size_comp(
  type = "proportion", p1 = 0.1, p2 = 0.15,
  conf_lev = 0.95, power = 0.8
)

Simple random sampling

Description

Simple random sampling

Usage

sampling(
  dataset,
  vars,
  sample_size,
  seed = 1234,
  data_filter = "",
  arr = "",
  rows = NULL,
  na.rm = FALSE,
  envir = parent.frame()
)

Arguments

dataset

Dataset to sample from

vars

The variables to sample

sample_size

Number of units to select

seed

Random seed to use as the starting point

data_filter

Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000")

arr

Expression to arrange (sort) the data on (e.g., "color, desc(price)")

rows

Rows to select from the specified dataset

na.rm

Remove rows with missing values (FALSE or TRUE)

envir

Environment to extract data from

Details

See https://radiant-rstats.github.io/docs/design/sampling.html for an example in Radiant

Value

A list of class 'sampling' with all variables defined in the sampling function

See Also

summary.sampling to summarize results

Examples

sampling(rndnames, "Names", 10)

Summary method for doe function

Description

Summary method for doe function

Usage

## S3 method for class 'doe'
summary(object, eff = TRUE, part = TRUE, full = TRUE, est = TRUE, dec = 3, ...)

Arguments

object

Return value from doe

eff

If TRUE print efficiency output

part

If TRUE print partial factorial

full

If TRUE print full factorial

est

If TRUE print number of effects that will be estimable using the partial factorial design

dec

Number of decimals to show

...

further arguments passed to or from other methods.

Details

See https://radiant-rstats.github.io/docs/design/doe.html for an example in Radiant

See Also

doe to calculate results

Examples

c("price; $10; $13; $16", "food; popcorn; gourmet; no food") %>%
  doe() %>%
  summary()

Summary method for the randomizer function

Description

Summary method for the randomizer function

Usage

## S3 method for class 'randomizer'
summary(object, dec = 3, ...)

Arguments

object

Return value from randomizer

dec

Number of decimals to show

...

further arguments passed to or from other methods

Details

See https://radiant-rstats.github.io/docs/design/randomizer.html for an example in Radiant

See Also

randomizer to generate the results

Examples

randomizer(rndnames, "Names", conditions = c("test", "control")) %>% summary()

Summary method for the sample_size function

Description

Summary method for the sample_size function

Usage

## S3 method for class 'sample_size'
summary(object, ...)

Arguments

object

Return value from sample_size

...

further arguments passed to or from other methods

Details

See https://radiant-rstats.github.io/docs/design/sample_size.html for an example in Radiant

See Also

sample_size to generate the results

Examples

sample_size(type = "mean", err_mean = 2, sd_mean = 10) %>%
  summary()

Summary method for the sample_size_comp function

Description

Summary method for the sample_size_comp function

Usage

## S3 method for class 'sample_size_comp'
summary(object, ...)

Arguments

object

Return value from sample_size_comp

...

further arguments passed to or from other methods

Details

See https://radiant-rstats.github.io/docs/design/sample_size_comp.html for an example in Radiant

See Also

sample_size_comp to generate the results

Examples

sample_size_comp(
  type = "proportion", p1 = 0.1, p2 = 0.15,
  conf_lev = 0.95, power = 0.8
) %>% summary()

Summary method for the sampling function

Description

Summary method for the sampling function

Usage

## S3 method for class 'sampling'
summary(object, dec = 3, ...)

Arguments

object

Return value from sampling

dec

Number of decimals to show

...

further arguments passed to or from other methods

Details

See https://radiant-rstats.github.io/docs/design/sampling.html for an example in Radiant

See Also

sampling to generate the results

Examples

sampling(rndnames, "Names", 10) %>% summary()