Help for package radiant.model

Type:

Package

Title:

Model Menu for Radiant: Business Analytics using R and Shiny

Version:

1.6.7

Date:

2024-10-7

Description:

The Radiant Model menu includes interfaces for linear and logistic regression, naive Bayes, neural networks, classification and regression trees, model evaluation, collaborative filtering, decision analysis, and simulation. The application extends the functionality in 'radiant.data'.

Depends:

R (≥ 4.3.0), radiant.data (≥ 1.6.6)

Imports:

radiant.basics (≥ 1.6.6), shiny (≥ 1.8.1), nnet (≥ 7.3.12), NeuralNetTools (≥ 1.5.1), sandwich (≥ 2.3.4), car (≥ 2.1.3), ggplot2 (≥ 3.4.2), scales (≥ 1.2.1), data.tree (≥ 0.7.4), stringr (≥ 1.1.0), lubridate (≥ 1.7.2), tidyr (≥ 0.8.2), dplyr (≥ 1.1.2), tidyselect (≥ 1.2.0), rlang (≥ 0.4.10), magrittr (≥ 1.5), DiagrammeR (≥ 1.0.9), import (≥ 1.1.0), psych (≥ 1.8.4), e1071 (≥ 1.6.8), rpart (≥ 4.1.11), ggrepel (≥ 0.8), broom (≥ 0.7.0), patchwork (≥ 1.0.0), ranger (≥ 0.11.2), xgboost (≥ 1.6.0.1), pdp (≥ 0.8.1), vip (≥ 0.3.2), stringi, yaml

Suggests:

testthat (≥ 2.0.0), pkgdown (≥ 1.1.0)

URL:

https://github.com/radiant-rstats/radiant.model/, https://radiant-rstats.github.io/radiant.model/, https://radiant-rstats.github.io/docs/

BugReports:

https://github.com/radiant-rstats/radiant.model/issues/

License:

AGPL-3 | file LICENSE

LazyData:

true

Encoding:

UTF-8

Language:

en-US

RoxygenNote:

7.3.2

NeedsCompilation:

Packaged:

2024-10-11 05:07:50 UTC; vnijs

Author:

Vincent Nijs [aut, cre]

Maintainer:

Vincent Nijs <radiant@rady.ucsd.edu>

Repository:

CRAN

Date/Publication:

2024-10-11 05:50:02 UTC

Convenience function used in "simulater"

Description

Convenience function used in "simulater"

Usage

.as_int(x, dataset = list())

Arguments

x

Character vector to be converted to integer

dataset

Data list

Value

An integer vector

Convenience function used in "simulater"

Description

Convenience function used in "simulater"

Usage

.as_num(x, dataset = list())

Arguments

x

Character vector to be converted to an numeric value

dataset

Data list

Value

An numeric vector

Mean Absolute Error

Description

Mean Absolute Error

Usage

MAE(pred, rvar)

Arguments

pred

Prediction (vector)

rvar

Response (vector)

Value

Mean Absolute Error

Root Mean Squared Error

Description

Root Mean Squared Error

Usage

RMSE(pred, rvar)

Arguments

pred

Prediction (vector)

rvar

Response (vector)

Value

Root Mean Squared Error

R-squared

Description

R-squared

Usage

Rsq(pred, rvar)

Arguments

pred

Prediction (vector)

rvar

Response (vector)

Value

R-squared

Area Under the RO Curve (AUC)

Description

Area Under the RO Curve (AUC)

Usage

auc(pred, rvar, lev)

Arguments

pred

Prediction or predictor

rvar

Response variable

lev

The level in the response variable defined as success

Details

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

Value

AUC statistic

Examples

auc(runif(20000), dvd$buy, "yes")
auc(ifelse(dvd$buy == "yes", 1, 0), dvd$buy, "yes")

Catalog sales for men's and women's apparel

Description

Catalog sales for men's and women's apparel

Usage

data(catalog)

Format

A data frame with 200 rows and 5 variables

Details

Description provided in attr(catalog, "description")

Confidence interval for robust estimators

Description

Confidence interval for robust estimators

Usage

confint_robust(object, level = 0.95, dist = "norm", vcov = NULL, ...)

Arguments

object

A fitted model object

level

The confidence level required

dist

Distribution to use ("norm" or "t")

vcov

Covariance matrix generated by, e.g., sandwich::vcovHC

...

Additional argument(s) for methods

Details

Wrapper for confint with robust standard errors. See https://stackoverflow.com/questions/3817182/vcovhc-and-confidence-interval/3820125#3820125

Confusion matrix

Description

Confusion matrix

Usage

confusion(
  dataset,
  pred,
  rvar,
  lev = "",
  cost = 1,
  margin = 2,
  scale = 1,
  train = "All",
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame(),
  ...
)

Arguments

dataset

Dataset

pred

Predictions or predictors

rvar

Response variable

lev

The level in the response variable defined as success

cost

Cost for each connection (e.g., email or mailing)

margin

Margin on each customer purchase

scale

Scaling factor to apply to calculations

train

Use data from training ("Training"), test ("Test"), both ("Both"), or all data ("All") to evaluate model evalbin

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

envir

Environment to extract data from

...

further arguments passed to or from other methods

Details

Confusion matrix and additional metrics to evaluate binary classification models. See https://radiant-rstats.github.io/docs/model/evalbin.html for an example in Radiant

Value

A list of results

Examples

data.frame(buy = dvd$buy, pred1 = runif(20000), pred2 = ifelse(dvd$buy == "yes", 1, 0)) %>%
  confusion(c("pred1", "pred2"), "buy") %>%
  str()

Collaborative Filtering

Description

Collaborative Filtering

Usage

crs(
  dataset,
  id,
  prod,
  pred,
  rate,
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset

Dataset

id

String with name of the variable containing user ids

prod

String with name of the variable with product ids

pred

Products to predict for

rate

String with name of the variable with product ratings

data_filter

Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "training == 1")

arr

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

rows

Rows to select from the specified dataset

envir

Environment to extract data from

Details

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

Value

A data.frame with the original data and a new column with predicted ratings

Examples

crs(ratings,
  id = "Users", prod = "Movies", pred = c("M6", "M7", "M8", "M9", "M10"),
  rate = "Ratings", data_filter = "training == 1"
) %>% str()

Classification and regression trees based on the rpart package

Description

Classification and regression trees based on the rpart package

Usage

crtree(
  dataset,
  rvar,
  evar,
  type = "",
  lev = "",
  wts = "None",
  minsplit = 2,
  minbucket = round(minsplit/3),
  cp = 0.001,
  pcp = NA,
  nodes = NA,
  K = 10,
  seed = 1234,
  split = "gini",
  prior = NA,
  adjprob = TRUE,
  cost = NA,
  margin = NA,
  check = "",
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset

Dataset

rvar

The response variable in the model

evar

Explanatory variables in the model

type

Model type (i.e., "classification" or "regression")

lev

The level in the response variable defined as _success_

wts

Weights to use in estimation

minsplit

The minimum number of observations that must exist in a node in order for a split to be attempted.

minbucket

the minimum number of observations in any terminal <leaf> node. If only one of minbucket or minsplit is specified, the code either sets minsplit to minbucket*3 or minbucket to minsplit/3, as appropriate.

cp

Minimum proportion of root node deviance required for split (default = 0.001)

pcp

Complexity parameter to use for pruning

nodes

Maximum size of tree in number of nodes to return

K

Number of folds use in cross-validation

seed

Random seed used for cross-validation

split

Splitting criterion to use (i.e., "gini" or "information")

prior

Adjust the initial probability for the selected level (e.g., set to .5 in unbalanced samples)

adjprob

Setting a prior will rescale the predicted probabilities. Set adjprob to TRUE to adjust the probabilities back to their original scale after estimation

cost

Cost for each treatment (e.g., mailing)

margin

Margin associated with a successful treatment (e.g., a purchase)

check

Optional estimation parameters (e.g., "standardize")

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

envir

Environment to extract data from

Details

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

Value

A list with all variables defined in crtree as an object of class tree

Examples

crtree(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
result <- crtree(titanic, "survived", c("pclass", "sex")) %>% summary()
result <- crtree(diamonds, "price", c("carat", "clarity"), type = "regression") %>% str()

Cross-validation for Classification and Regression Trees

Description

Cross-validation for Classification and Regression Trees

Usage

cv.crtree(
  object,
  K = 5,
  repeats = 1,
  cp,
  pcp = seq(0, 0.01, length.out = 11),
  seed = 1234,
  trace = TRUE,
  fun,
  ...
)

Arguments

object

Object of type "rpart" or "crtree" to use as a starting point for cross validation

K

Number of cross validation passes to use

repeats

Number of times to repeat the K cross-validation steps

cp

Complexity parameter used when building the (e.g., 0.0001)

pcp

Complexity parameter to use for pruning

seed

Random seed to use as the starting point

trace

Print progress

fun

Function to use for model evaluation (e.g., auc for classification or RMSE for regression)

...

Additional arguments to be passed to 'fun'

Details

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

Value

A data.frame sorted by the mean, sd, min, and max of the performance metric

Examples

## Not run: 
result <- crtree(dvd, "buy", c("coupon", "purch", "last"))
cv.crtree(result, cp = 0.0001, pcp = seq(0, 0.01, length.out = 11))
cv.crtree(result, cp = 0.0001, pcp = c(0, 0.001, 0.002), fun = profit, cost = 1, margin = 5)
result <- crtree(diamonds, "price", c("carat", "color", "clarity"), type = "regression", cp = 0.001)
cv.crtree(result, cp = 0.001, pcp = seq(0, 0.01, length.out = 11), fun = MAE)

## End(Not run)

Cross-validation for Gradient Boosted Trees

Description

Cross-validation for Gradient Boosted Trees

Usage

cv.gbt(
  object,
  K = 5,
  repeats = 1,
  params = list(),
  nrounds = 500,
  early_stopping_rounds = 10,
  nthread = 12,
  train = NULL,
  type = "classification",
  trace = TRUE,
  seed = 1234,
  maximize = NULL,
  fun,
  ...
)

Arguments

object

Object of type "gbt" or "ranger"

K

Number of cross validation passes to use (aka nfold)

repeats

Repeated cross validation

params

List of parameters (see XGBoost documentation)

nrounds

Number of trees to create

early_stopping_rounds

Early stopping rule

nthread

Number of parallel threads to use. Defaults to 12 if available

train

An optional xgb.DMatrix object containing the original training data. Not needed when using Radiant's gbt function

type

Model type ("classification" or "regression")

trace

Print progress

seed

Random seed to use as the starting point

maximize

When a custom function is used, xgb.cv requires the user indicate if the function output should be maximized (TRUE) or minimized (FALSE)

fun

Function to use for model evaluation (i.e., auc for classification and RMSE for regression)

...

Additional arguments to be passed to 'fun'

Details

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

Value

A data.frame sorted by the mean of the performance metric

Examples

## Not run: 
result <- gbt(dvd, "buy", c("coupon", "purch", "last"))
cv.gbt(result, params = list(max_depth = 1:6))
cv.gbt(result, params = list(max_depth = 1:6), fun = "logloss")
cv.gbt(
  result,
  params = list(learning_rate = seq(0.1, 1.0, 0.1)),
  maximize = TRUE, fun = profit, cost = 1, margin = 5
)
result <- gbt(diamonds, "price", c("carat", "color", "clarity"), type = "regression")
cv.gbt(result, params = list(max_depth = 1:2, min_child_weight = 1:2))
cv.gbt(result, params = list(learning_rate = seq(0.1, 0.5, 0.1)), fun = Rsq, maximize = TRUE)
cv.gbt(result, params = list(learning_rate = seq(0.1, 0.5, 0.1)), fun = MAE, maximize = FALSE)

## End(Not run)

Cross-validation for a Neural Network

Description

Cross-validation for a Neural Network

Usage

cv.nn(
  object,
  K = 5,
  repeats = 1,
  decay = seq(0, 1, 0.2),
  size = 1:5,
  seed = 1234,
  trace = TRUE,
  fun,
  ...
)

Arguments

object

Object of type "nn" or "nnet"

K

Number of cross validation passes to use

repeats

Repeated cross validation

decay

Parameter decay

size

Number of units (nodes) in the hidden layer

seed

Random seed to use as the starting point

trace

Print progress

fun

Function to use for model evaluation (i.e., auc for classification and RMSE for regression)

...

Additional arguments to be passed to 'fun'

Details

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

Value

A data.frame sorted by the mean of the performance metric

Examples

## Not run: 
result <- nn(dvd, "buy", c("coupon", "purch", "last"))
cv.nn(result, decay = seq(0, 1, .5), size = 1:2)
cv.nn(result, decay = seq(0, 1, .5), size = 1:2, fun = profit, cost = 1, margin = 5)
result <- nn(diamonds, "price", c("carat", "color", "clarity"), type = "regression")
cv.nn(result, decay = seq(0, 1, .5), size = 1:2)
cv.nn(result, decay = seq(0, 1, .5), size = 1:2, fun = Rsq)

## End(Not run)

Cross-validation for a Random Forest

Description

Cross-validation for a Random Forest

Usage

cv.rforest(
  object,
  K = 5,
  repeats = 1,
  mtry = 1:5,
  num.trees = NULL,
  min.node.size = 1,
  sample.fraction = NA,
  trace = TRUE,
  seed = 1234,
  fun,
  ...
)

Arguments

object

Object of type "rforest" or "ranger"

K

Number of cross validation passes to use

repeats

Repeated cross validation

mtry

Number of variables to possibly split at in each node. Default is the (rounded down) square root of the number variables

num.trees

Number of trees to create

min.node.size

Minimal node size

sample.fraction

Fraction of observations to sample. Default is 1 for sampling with replacement and 0.632 for sampling without replacement

trace

Print progress

seed

Random seed to use as the starting point

fun

Function to use for model evaluation (i.e., auc for classification and RMSE for regression)

...

Additional arguments to be passed to 'fun'

Details

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

Value

A data.frame sorted by the mean of the performance metric

Examples

## Not run: 
result <- rforest(dvd, "buy", c("coupon", "purch", "last"))
cv.rforest(
  result,
  mtry = 1:3, min.node.size = seq(1, 10, 5),
  num.trees = c(100, 200), sample.fraction = 0.632
)
result <- rforest(titanic, "survived", c("pclass", "sex"), max.depth = 1)
cv.rforest(result, mtry = 1:3, min.node.size = seq(1, 10, 5))
cv.rforest(result, mtry = 1:3, num.trees = c(100, 200), fun = profit, cost = 1, margin = 5)
result <- rforest(diamonds, "price", c("carat", "color", "clarity"), type = "regression")
cv.rforest(result, mtry = 1:3, min.node.size = 1)
cv.rforest(result, mtry = 1:3, min.node.size = 1, fun = Rsq)

## End(Not run)

Direct marketing data

Description

Direct marketing data

Usage

data(direct_marketing)

Format

A data frame with 1,000 rows and 12 variables

Details

Description provided in attr(direct_marketing, "description")

Create a decision tree

Description

Create a decision tree

Usage

dtree(yl, opt = "max", base = character(0), envir = parent.frame())

Arguments

yl

A yaml string or a list (e.g., from yaml::yaml.load_file())

opt

Find the maximum ("max") or minimum ("min") value for each decision node

base

List of variable definitions from a base tree used when calling a sub-tree

envir

Environment to extract data from

Details

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

Value

A list with the initial tree, the calculated tree, and a data.frame with results (i.e., payoffs, probabilities, etc.)

Examples

yaml::as.yaml(movie_contract) %>% cat()
dtree(movie_contract, opt = "max") %>% summary(output = TRUE)
dtree(movie_contract)$payoff
dtree(movie_contract)$prob
dtree(movie_contract)$solution_df

Parse yaml input for dtree to provide (more) useful error messages

Description

Parse yaml input for dtree to provide (more) useful error messages

Usage

dtree_parser(yl)

Arguments

yl

A yaml string

Details

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

Value

An updated yaml string or a vector messages to return to the users

Data on DVD sales

Description

Data on DVD sales

Usage

data(dvd)

Format

A data frame with 20,000 rows and 4 variables

Details

Binary purchase response to coupon value. Description provided in attr(dvd,"description")

Evaluate the performance of different (binary) classification models

Description

Evaluate the performance of different (binary) classification models

Usage

evalbin(
  dataset,
  pred,
  rvar,
  lev = "",
  qnt = 10,
  cost = 1,
  margin = 2,
  scale = 1,
  train = "All",
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset

Dataset

pred

Predictions or predictors

rvar

Response variable

lev

The level in the response variable defined as success

qnt

Number of bins to create

cost

Cost for each connection (e.g., email or mailing)

margin

Margin on each customer purchase

scale

Scaling factor to apply to calculations

train

Use data from training ("Training"), test ("Test"), both ("Both"), or all data ("All") to evaluate model evalbin

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

envir

Environment to extract data from

Details

Evaluate different (binary) classification models based on predictions. See https://radiant-rstats.github.io/docs/model/evalbin.html for an example in Radiant

Value

A list of results

Examples

data.frame(buy = dvd$buy, pred1 = runif(20000), pred2 = ifelse(dvd$buy == "yes", 1, 0)) %>%
  evalbin(c("pred1", "pred2"), "buy") %>%
  str()

Evaluate the performance of different regression models

Description

Evaluate the performance of different regression models

Usage

evalreg(
  dataset,
  pred,
  rvar,
  train = "All",
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset

Dataset

pred

Predictions or predictors

rvar

Response variable

train

Use data from training ("Training"), test ("Test"), both ("Both"), or all data ("All") to evaluate model evalreg

data_filter

Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "training == 1")

arr

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

rows

Rows to select from the specified dataset

envir

Environment to extract data from

Details

Evaluate different regression models based on predictions. See https://radiant-rstats.github.io/docs/model/evalreg.html for an example in Radiant

Value

A list of results

Examples

data.frame(price = diamonds$price, pred1 = rnorm(3000), pred2 = diamonds$price) %>%
  evalreg(pred = c("pred1", "pred2"), "price") %>%
  str()

Find maximum value of a vector

Description

Find maximum value of a vector

Usage

find_max(x, y)

Arguments

x

Variable to find the maximum for

y

Variable to find the value for at the maximum of var

Details

Find the value of y at the maximum value of x

Value

Value of val at the maximum of var

Examples

find_max(1:10, 21:30)

Find minimum value of a vector

Description

Find minimum value of a vector

Usage

find_min(x, y)

Arguments

x

Variable to find the minimum for

y

Variable to find the value for at the maximum of var

Details

Find the value of y at the minimum value of x

Value

Value of val at the minimum of var

Examples

find_min(1:10, 21:30)

Gradient Boosted Trees using XGBoost

Description

Gradient Boosted Trees using XGBoost

Usage

gbt(
  dataset,
  rvar,
  evar,
  type = "classification",
  lev = "",
  max_depth = 6,
  learning_rate = 0.3,
  min_split_loss = 0,
  min_child_weight = 1,
  subsample = 1,
  nrounds = 100,
  early_stopping_rounds = 10,
  nthread = 12,
  wts = "None",
  seed = NA,
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame(),
  ...
)

Arguments

dataset

Dataset

rvar

The response variable in the model

evar

Explanatory variables in the model

type

Model type (i.e., "classification" or "regression")

lev

Level to use as the first column in prediction output

max_depth

Maximum 'depth' of tree

learning_rate

Learning rate (eta)

min_split_loss

Minimal improvement (gamma)

min_child_weight

Minimum number of instances allowed in each node

subsample

Subsample ratio of the training instances (0-1)

nrounds

Number of trees to create

early_stopping_rounds

Early stopping rule

nthread

Number of parallel threads to use. Defaults to 12 if available

wts

Weights to use in estimation

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

envir

Environment to extract data from

...

Further arguments to pass to xgboost

Details

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

Value

A list with all variables defined in gbt as an object of class gbt

Examples

## Not run: 
gbt(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
gbt(titanic, "survived", c("pclass", "sex")) %>% str()

## End(Not run)
gbt(
  titanic, "survived", c("pclass", "sex"), lev = "Yes",
  early_stopping_rounds = 0, nthread = 1
) %>% summary()
gbt(
  titanic, "survived", c("pclass", "sex"),
  early_stopping_rounds = 0, nthread = 1
) %>% str()
gbt(
  titanic, "survived", c("pclass", "sex"),
  eval_metric = paste0("error@", 0.5 / 6), nthread = 1
) %>% str()
gbt(
  diamonds, "price", c("carat", "clarity"), type = "regression", nthread = 1
) %>% summary()

Houseprices

Description

Houseprices

Usage

data(houseprices)

Format

A data frame with 128 home sales and 6 variables

Details

Description provided in attr(houseprices, "description")

Ideal data for linear regression

Description

Ideal data for linear regression

Usage

data(ideal)

Format

A data frame with 1,000 rows and 4 variables

Details

Description provided in attr(ideal, "description")

Kaggle uplift

Description

Kaggle uplift

Usage

data(kaggle_uplift)

Format

A data frame with 1,000 rows and 22 variables

Details

Use uplift modeling to quantify the effectiveness of an experimental treatment

Data on ketchup choices

Description

Data on ketchup choices

Usage

data(ketchup)

Format

A data frame with 2,798 rows and 14 variables

Details

Choice behavior for a sample of 300 individuals in a panel of households in Springfield, Missouri (USA). Description provided in attr(ketchup,"description")

Logistic regression

Description

Logistic regression

Usage

logistic(
  dataset,
  rvar,
  evar,
  lev = "",
  int = "",
  wts = "None",
  check = "",
  form,
  ci_type,
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset

Dataset

rvar

The response variable in the model

evar

Explanatory variables in the model

lev

The level in the response variable defined as _success_

int

Interaction term to include in the model

wts

Weights to use in estimation

check

Use "standardize" to see standardized coefficient estimates. Use "stepwise-backward" (or "stepwise-forward", or "stepwise-both") to apply step-wise selection of variables in estimation. Add "robust" for robust estimation of standard errors (HC1)

form

Optional formula to use instead of rvar, evar, and int

ci_type

To use the profile-likelihood (rather than Wald) for confidence intervals use "profile". For datasets with more than 5,000 rows the Wald method will be used, unless "profile" is explicitly set

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

envir

Environment to extract data from

Details

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

Value

A list with all variables defined in logistic as an object of class logistic

Examples

logistic(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
logistic(titanic, "survived", c("pclass", "sex")) %>% str()

Calculate min and max before standardization

Description

Calculate min and max before standardization

Usage

minmax(dataset)

Arguments

dataset

Data frame

Value

Data frame min and max attributes

Multinomial logistic regression

Description

Multinomial logistic regression

Usage

mnl(
  dataset,
  rvar,
  evar,
  lev = "",
  int = "",
  wts = "None",
  check = "",
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset

Dataset

rvar

The response variable in the model

evar

Explanatory variables in the model

lev

The level in the response variable to use as the baseline

int

Interaction term to include in the model

wts

Weights to use in estimation

check

Use "standardize" to see standardized coefficient estimates. Use "stepwise-backward" (or "stepwise-forward", or "stepwise-both") to apply step-wise selection of variables in estimation.

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

envir

Environment to extract data from

Details

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

Value

A list with all variables defined in mnl as an object of class mnl

Examples

result <- mnl(
  ketchup,
  rvar = "choice",
  evar = c("price.heinz28", "price.heinz32", "price.heinz41", "price.hunts32"),
  lev = "heinz28"
)
str(result)

Movie contract decision tree

Description

Movie contract decision tree

Usage

data(movie_contract)

Format

A nested list for decision and chance nodes, probabilities and payoffs

Details

Use decision analysis to create a decision tree for an actor facing a contract decision

Naive Bayes using e1071::naiveBayes

Description

Naive Bayes using e1071::naiveBayes

Usage

nb(
  dataset,
  rvar,
  evar,
  laplace = 0,
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset

Dataset

rvar

The response variable in the logit (probit) model

evar

Explanatory variables in the model

laplace

Positive double controlling Laplace smoothing. The default (0) disables Laplace smoothing.

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

envir

Environment to extract data from

Details

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

Value

A list with all variables defined in nb as an object of class nb

Examples

nb(titanic, "survived", c("pclass", "sex", "age")) %>% summary()
nb(titanic, "survived", c("pclass", "sex", "age")) %>% str()

Neural Networks using nnet

Description

Neural Networks using nnet

Usage

nn(
  dataset,
  rvar,
  evar,
  type = "classification",
  lev = "",
  size = 1,
  decay = 0.5,
  wts = "None",
  seed = NA,
  check = "standardize",
  form,
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset

Dataset

rvar

The response variable in the model

evar

Explanatory variables in the model

type

Model type (i.e., "classification" or "regression")

lev

The level in the response variable defined as _success_

size

Number of units (nodes) in the hidden layer

decay

Parameter decay

wts

Weights to use in estimation

seed

Random seed to use as the starting point

check

Optional estimation parameters ("standardize" is the default)

form

Optional formula to use instead of rvar and evar

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

envir

Environment to extract data from

Details

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

Value

A list with all variables defined in nn as an object of class nn

Examples

nn(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
nn(titanic, "survived", c("pclass", "sex")) %>% str()
nn(diamonds, "price", c("carat", "clarity"), type = "regression") %>% summary()

One hot encoding of data.frames

Description

One hot encoding of data.frames

Usage

onehot(dataset, all = FALSE, df = FALSE)

Arguments

dataset

Dataset to endcode

all

Extract all factor levels (e.g., for tree-based models)

df

Return a data.frame (tibble)

Examples

head(onehot(diamonds, df = TRUE))
head(onehot(diamonds, all = TRUE, df = TRUE))

Create Partial Dependence Plots

Description

Create Partial Dependence Plots

Usage

pdp_plot(
  x,
  plot_list = list(),
  incl,
  incl_int,
  fix = TRUE,
  hline = TRUE,
  nr = 20,
  minq = 0.025,
  maxq = 0.975
)

Arguments

x

Return value from a model

plot_list

List used to store plots

incl

Which variables to include in PDP plots

incl_int

Which interactions to investigate in PDP plots

fix

Set the desired limited on yhat or have it calculated automatically. Set to FALSE to have y-axis limits set by ggplot2 for each plot

hline

Add a horizontal line at the average of the target variable. When set to FALSE no line is added. When set to a specific number, the horizontal line will be added at that value

nr

Number of values to use to generate predictions for a numeric explanatory variable

minq

Quantile to use for the minimum value for simulation of numeric variables

maxq

Quantile to use for the maximum value for simulation of numeric variables

Plot method for the confusion matrix

Description

Plot method for the confusion matrix

Usage

## S3 method for class 'confusion'
plot(
  x,
  vars = c("kappa", "index", "ROME", "AUC"),
  scale_y = TRUE,
  size = 13,
  ...
)

Arguments

x

Return value from confusion

vars

Measures to plot, i.e., one or more of "TP", "FP", "TN", "FN", "total", "TPR", "TNR", "precision", "accuracy", "kappa", "profit", "index", "ROME", "contact", "AUC"

scale_y

Free scale in faceted plot of the confusion matrix (TRUE or FALSE)

size

Font size used

...

further arguments passed to or from other methods

Details

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

Examples

data.frame(buy = dvd$buy, pred1 = runif(20000), pred2 = ifelse(dvd$buy == "yes", 1, 0)) %>%
  confusion(c("pred1", "pred2"), "buy") %>%
  plot()

Plot method for the crs function

Description

Plot method for the crs function

Usage

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

Arguments

x

Return value from crs

...

further arguments passed to or from other methods

Details

Plot that compares actual to predicted ratings. See https://radiant-rstats.github.io/docs/model/crs.html for an example in Radiant

Plot method for the crtree function

Description

Plot method for the crtree function

Usage

## S3 method for class 'crtree'
plot(
  x,
  plots = "tree",
  orient = "LR",
  width = "900px",
  labs = TRUE,
  nrobs = Inf,
  dec = 2,
  incl = NULL,
  incl_int = NULL,
  shiny = FALSE,
  custom = FALSE,
  ...
)

Arguments

x

Return value from crtree

plots

Plots to produce for the specified rpart tree. "tree" shows a tree diagram. "prune" shows a line graph to evaluate appropriate tree pruning. "imp" shows a variable importance plot

orient

Plot orientation for tree: LR for vertical and TD for horizontal

width

Plot width in pixels for tree (default is "900px")

labs

Use factor labels in plot (TRUE) or revert to default letters used by tree (FALSE)

nrobs

Number of data points to show in dashboard scatter plots (-1 for all)

dec

Decimal places to round results to

incl

Which variables to include in a coefficient plot or PDP plot

incl_int

Which interactions to investigate in PDP plots

shiny

Did the function call originate inside a shiny app

custom

Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and https://ggplot2.tidyverse.org for options.

...

further arguments passed to or from other methods

Details

Plot a decision tree using mermaid, permutation plots , prediction plots, or partial dependence plots. For regression trees, a residual dashboard can be plotted. See https://radiant-rstats.github.io/docs/model/crtree.html for an example in Radiant.

Examples

result <- crtree(titanic, "survived", c("pclass", "sex"), lev = "Yes")
plot(result)
result <- crtree(diamonds, "price", c("carat", "clarity", "cut"))
plot(result, plots = "prune")
result <- crtree(dvd, "buy", c("coupon", "purch", "last"), cp = .01)
plot(result, plots = "imp")

Plot method for the dtree function

Description

Plot method for the dtree function

Usage

## S3 method for class 'dtree'
plot(
  x,
  symbol = "$",
  dec = 2,
  final = FALSE,
  orient = "LR",
  width = "900px",
  ...
)

Arguments

x

Return value from dtree

symbol

Monetary symbol to use ($ is the default)

dec

Decimal places to round results to

final

If TRUE plot the decision tree solution, else the initial decision tree

orient

Plot orientation: LR for vertical and TD for horizontal

width

Plot width in pixels (default is "900px")

...

further arguments passed to or from other methods

Details

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

Examples

dtree(movie_contract, opt = "max") %>% plot()
dtree(movie_contract, opt = "max") %>% plot(final = TRUE, orient = "TD")

Plot method for the evalbin function

Description

Plot method for the evalbin function

Usage

## S3 method for class 'evalbin'
plot(
  x,
  plots = c("lift", "gains"),
  size = 13,
  shiny = FALSE,
  custom = FALSE,
  ...
)

Arguments

x

Return value from evalbin

plots

Plots to return

size

Font size used

shiny

Did the function call originate inside a shiny app

custom

...

further arguments passed to or from other methods

Details

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

Examples

data.frame(buy = dvd$buy, pred1 = runif(20000), pred2 = ifelse(dvd$buy == "yes", 1, 0)) %>%
  evalbin(c("pred1", "pred2"), "buy") %>%
  plot()

Plot method for the evalreg function

Description

Plot method for the evalreg function

Usage

## S3 method for class 'evalreg'
plot(x, vars = c("Rsq", "RMSE", "MAE"), ...)

Arguments

x

Return value from evalreg

vars

Measures to plot, i.e., one or more of "Rsq", "RMSE", "MAE"

...

further arguments passed to or from other methods

Details

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

Examples

data.frame(price = diamonds$price, pred1 = rnorm(3000), pred2 = diamonds$price) %>%
  evalreg(pred = c("pred1", "pred2"), "price") %>%
  plot()

Plot method for the gbt function

Description

Plot method for the gbt function

Usage

## S3 method for class 'gbt'
plot(
  x,
  plots = "",
  nrobs = Inf,
  incl = NULL,
  incl_int = NULL,
  shiny = FALSE,
  custom = FALSE,
  ...
)

Arguments

x

Return value from gbt

plots

Plots to produce for the specified Gradient Boosted Tree model. Use "" to avoid showing any plots (default). Options are ...

nrobs

Number of data points to show in scatter plots (-1 for all)

incl

Which variables to include in a coefficient plot or PDP plot

incl_int

Which interactions to investigate in PDP plots

shiny

Did the function call originate inside a shiny app

custom

...

further arguments passed to or from other methods

Details

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

Examples

result <- gbt(
  titanic, "survived", c("pclass", "sex"),
  early_stopping_rounds = 0, nthread = 1
)
plot(result)

Plot method for the logistic function

Description

Plot method for the logistic function

Usage

## S3 method for class 'logistic'
plot(
  x,
  plots = "coef",
  conf_lev = 0.95,
  intercept = FALSE,
  incl = NULL,
  excl = NULL,
  incl_int = NULL,
  nrobs = -1,
  shiny = FALSE,
  custom = FALSE,
  ...
)

Arguments

x

Return value from logistic

plots

Plots to produce for the specified GLM model. Use "" to avoid showing any plots (default). "dist" shows histograms (or frequency bar plots) of all variables in the model. "scatter" shows scatter plots (or box plots for factors) for the response variable with each explanatory variable. "coef" provides a coefficient plot and "influence" shows (potentially) influential observations

conf_lev

Confidence level to use for coefficient and odds confidence intervals (.95 is the default)

intercept

Include the intercept in the coefficient plot (TRUE or FALSE). FALSE is the default

incl

Which variables to include in a coefficient plot

excl

Which variables to exclude in a coefficient plot

incl_int

Which interactions to investigate in PDP plots

nrobs

Number of data points to show in scatter plots (-1 for all)

shiny

Did the function call originate inside a shiny app

custom

...

further arguments passed to or from other methods

Details

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

Examples

result <- logistic(titanic, "survived", c("pclass", "sex"), lev = "Yes")
plot(result, plots = "coef")

Plot method for the mnl function

Description

Plot method for the mnl function

Usage

## S3 method for class 'mnl'
plot(
  x,
  plots = "coef",
  conf_lev = 0.95,
  intercept = FALSE,
  nrobs = -1,
  shiny = FALSE,
  custom = FALSE,
  ...
)

Arguments

x

Return value from mnl

plots

Plots to produce for the specified MNL model. Use "" to avoid showing any plots (default). "dist" shows histograms (or frequency bar plots) of all variables in the model. "scatter" shows scatter plots (or box plots for factors) for the response variable with each explanatory variable. "coef" provides a coefficient plot

conf_lev

Confidence level to use for coefficient and relative risk ratios (RRRs) intervals (.95 is the default)

intercept

Include the intercept in the coefficient plot (TRUE or FALSE). FALSE is the default

nrobs

Number of data points to show in scatter plots (-1 for all)

shiny

Did the function call originate inside a shiny app

custom

...

further arguments passed to or from other methods

Details

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

Examples

result <- mnl(
  ketchup,
  rvar = "choice",
  evar = c("price.heinz28", "price.heinz32", "price.heinz41", "price.hunts32"),
  lev = "heinz28"
)
plot(result, plots = "coef")

Plot method for mnl.predict function

Description

Plot method for mnl.predict function

Usage

## S3 method for class 'mnl.predict'
plot(x, xvar = "", facet_row = ".", facet_col = ".", color = ".class", ...)

Arguments

x

Return value from predict function predict.mnl

xvar

Variable to display along the X-axis of the plot

facet_row

Create vertically arranged subplots for each level of the selected factor variable

facet_col

Create horizontally arranged subplots for each level of the selected factor variable

color

Adds color to a scatter plot to generate a heat map. For a line plot one line is created for each group and each is assigned a different color

...

further arguments passed to or from other methods

Examples

result <- mnl(
  ketchup,
  rvar = "choice",
  evar = c("price.heinz28", "price.heinz32", "price.heinz41", "price.hunts32"),
  lev = "heinz28"
)
pred <- predict(result, pred_cmd = "price.heinz28 = seq(3, 5, 0.1)")
plot(pred, xvar = "price.heinz28")

Plot method for model.predict functions

Description

Plot method for model.predict functions

Usage

## S3 method for class 'model.predict'
plot(
  x,
  xvar = "",
  facet_row = ".",
  facet_col = ".",
  color = "none",
  conf_lev = 0.95,
  ...
)

Arguments

x

Return value from predict functions (e.g., predict.regress)

xvar

Variable to display along the X-axis of the plot

facet_row

Create vertically arranged subplots for each level of the selected factor variable

facet_col

Create horizontally arranged subplots for each level of the selected factor variable

color

Adds color to a scatter plot to generate a heat map. For a line plot one line is created for each group and each is assigned a different color

conf_lev

Confidence level to use for prediction intervals (.95 is the default)

...

further arguments passed to or from other methods

Examples

regress(diamonds, "price", c("carat", "clarity")) %>%
  predict(pred_cmd = "carat = 1:10") %>%
  plot(xvar = "carat")
logistic(titanic, "survived", c("pclass", "sex", "age"), lev = "Yes") %>%
  predict(pred_cmd = c("pclass = levels(pclass)", "sex = levels(sex)", "age = 0:100")) %>%
  plot(xvar = "age", color = "sex", facet_col = "pclass")

Plot method for the nb function

Description

Plot method for the nb function

Usage

## S3 method for class 'nb'
plot(x, plots = "correlations", lev = "All levels", nrobs = 1000, ...)

Arguments

x

Return value from nb

plots

Plots to produce for the specified model. Use "" to avoid showing any plots. Use "vimp" for variable importance or "correlations" to examine conditional independence

lev

The level(s) in the response variable used as the basis for plots (defaults to "All levels")

nrobs

Number of data points to show in scatter plots (-1 for all)

...

further arguments passed to or from other methods

Details

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

Examples

result <- nb(titanic, "survived", c("pclass", "sex"))
plot(result)
result <- nb(titanic, "pclass", c("sex", "age"))
plot(result)

Plot method for nb.predict function

Description

Plot method for nb.predict function

Usage

## S3 method for class 'nb.predict'
plot(x, xvar = "", facet_row = ".", facet_col = ".", color = ".class", ...)

Arguments

x

Return value from predict function predict.nb

xvar

Variable to display along the X-axis of the plot

facet_row

Create vertically arranged subplots for each level of the selected factor variable

facet_col

Create horizontally arranged subplots for each level of the selected factor variable

color

Adds color to a scatter plot to generate a heat map. For a line plot one line is created for each group and each is assigned a different color

...

further arguments passed to or from other methods

Examples

result <- nb(titanic, "survived", c("pclass", "sex", "age"))
pred <- predict(
  result,
  pred_cmd = c("pclass = levels(pclass)", "sex = levels(sex)", "age = seq(0, 100, 20)")
)
plot(pred, xvar = "age", facet_col = "sex", facet_row = "pclass")
pred <- predict(result, pred_data = titanic)
plot(pred, xvar = "age", facet_col = "sex")

Plot method for the nn function

Description

Plot method for the nn function

Usage

## S3 method for class 'nn'
plot(
  x,
  plots = "vip",
  size = 12,
  pad_x = 0.9,
  nrobs = -1,
  incl = NULL,
  incl_int = NULL,
  shiny = FALSE,
  custom = FALSE,
  ...
)

Arguments

x

Return value from nn

plots

Plots to produce for the specified Neural Network model. Use "" to avoid showing any plots (default). Options are "olden" or "garson" for importance plots, or "net" to depict the network structure

size

Font size used

pad_x

Padding for explanatory variable labels in the network plot. Default value is 0.9, smaller numbers (e.g., 0.5) increase the amount of padding

nrobs

Number of data points to show in dashboard scatter plots (-1 for all)

incl

Which variables to include in a coefficient plot or PDP plot

incl_int

Which interactions to investigate in PDP plots

shiny

Did the function call originate inside a shiny app

custom

...

further arguments passed to or from other methods

Details

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

Examples

result <- nn(titanic, "survived", c("pclass", "sex"), lev = "Yes")
plot(result, plots = "net")
plot(result, plots = "olden")

Plot method for the regress function

Description

Plot method for the regress function

Usage

## S3 method for class 'regress'
plot(
  x,
  plots = "",
  lines = "",
  conf_lev = 0.95,
  intercept = FALSE,
  incl = NULL,
  excl = NULL,
  incl_int = NULL,
  nrobs = -1,
  shiny = FALSE,
  custom = FALSE,
  ...
)

Arguments

x

Return value from regress

plots

Regression plots to produce for the specified regression model. Enter "" to avoid showing any plots (default). "dist" to shows histograms (or frequency bar plots) of all variables in the model. "correlations" for a visual representation of the correlation matrix selected variables. "scatter" to show scatter plots (or box plots for factors) for the response variable with each explanatory variable. "dashboard" for a series of six plots that can be used to evaluate model fit visually. "resid_pred" to plot the explanatory variables against the model residuals. "coef" for a coefficient plot with adjustable confidence intervals and "influence" to show (potentially) influential observations

lines

Optional lines to include in the select plot. "line" to include a line through a scatter plot. "loess" to include a polynomial regression fit line. To include both use c("line", "loess")

conf_lev

Confidence level used to estimate confidence intervals (.95 is the default)

intercept

Include the intercept in the coefficient plot (TRUE, FALSE). FALSE is the default

incl

Which variables to include in a coefficient plot or PDP plot

excl

Which variables to exclude in a coefficient plot

incl_int

Which interactions to investigate in PDP plots

nrobs

Number of data points to show in scatter plots (-1 for all)

shiny

Did the function call originate inside a shiny app

custom

...

further arguments passed to or from other methods

Details

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

Examples

result <- regress(diamonds, "price", c("carat", "clarity"))
plot(result, plots = "coef", conf_lev = .99, intercept = TRUE)
## Not run: 
plot(result, plots = "dist")
plot(result, plots = "scatter", lines = c("line", "loess"))
plot(result, plots = "resid_pred", lines = "line")
plot(result, plots = "dashboard", lines = c("line", "loess"))

## End(Not run)

Plot repeated simulation

Description

Plot repeated simulation

Usage

## S3 method for class 'repeater'
plot(x, bins = 20, shiny = FALSE, custom = FALSE, ...)

Arguments

x

Return value from repeater

bins

Number of bins used for histograms (1 - 50)

shiny

Did the function call originate inside a shiny app

custom

...

further arguments passed to or from other methods

Plot method for the rforest function

Description

Plot method for the rforest function

Usage

## S3 method for class 'rforest'
plot(
  x,
  plots = "",
  nrobs = Inf,
  incl = NULL,
  incl_int = NULL,
  shiny = FALSE,
  custom = FALSE,
  ...
)

Arguments

x

Return value from rforest

plots

Plots to produce for the specified Random Forest model. Use "" to avoid showing any plots (default). Options are ...

nrobs

Number of data points to show in dashboard scatter plots (-1 for all)

incl

Which variables to include in PDP or Prediction plots

incl_int

Which interactions to investigate in PDP or Prediction plots

shiny

Did the function call originate inside a shiny app

custom

...

further arguments passed to or from other methods

Details

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

Examples

result <- rforest(titanic, "survived", c("pclass", "sex"), lev = "Yes")

Plot method for rforest.predict function

Description

Plot method for rforest.predict function

Usage

## S3 method for class 'rforest.predict'
plot(x, xvar = "", facet_row = ".", facet_col = ".", color = "none", ...)

Arguments

x

Return value from predict function predict.rforest

xvar

Variable to display along the X-axis of the plot

facet_row

Create vertically arranged subplots for each level of the selected factor variable

facet_col

Create horizontally arranged subplots for each level of the selected factor variable

color

Adds color to a scatter plot to generate a heat map. For a line plot one line is created for each group and each is assigned a different color

...

further arguments passed to or from other methods

Examples

result <- mnl(
  ketchup,
  rvar = "choice",
  evar = c("price.heinz28", "price.heinz32", "price.heinz41", "price.hunts32"),
  lev = "heinz28"
)
pred <- predict(result, pred_cmd = "price.heinz28 = seq(3, 5, 0.1)")
plot(pred, xvar = "price.heinz28")

Plot method for the simulater function

Description

Plot method for the simulater function

Usage

## S3 method for class 'simulater'
plot(x, bins = 20, shiny = FALSE, custom = FALSE, ...)

Arguments

x

Return value from simulater

bins

Number of bins used for histograms (1 - 50)

shiny

Did the function call originate inside a shiny app

custom

...

further arguments passed to or from other methods

Details

See https://radiant-rstats.github.io/docs/model/simulater for an example in Radiant

Examples

simdat <- simulater(
  const = "cost 3",
  norm = "demand 2000 1000",
  discrete = "price 5 8 .3 .7",
  form = "profit = demand * (price - cost)",
  seed = 1234
)
plot(simdat, bins = 25)

Plot method for the uplift function

Description

Plot method for the uplift function

Usage

## S3 method for class 'uplift'
plot(
  x,
  plots = c("inc_uplift", "uplift"),
  size = 13,
  shiny = FALSE,
  custom = FALSE,
  ...
)

Arguments

x

Return value from evalbin

plots

Plots to return

size

Font size used

shiny

Did the function call originate inside a shiny app

custom

...

further arguments passed to or from other methods

Details

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

Examples

data.frame(buy = dvd$buy, pred1 = runif(20000), pred2 = ifelse(dvd$buy == "yes", 1, 0)) %>%
  evalbin(c("pred1", "pred2"), "buy") %>%
  plot()

Prediction Plots

Description

Prediction Plots

Usage

pred_plot(
  x,
  plot_list = list(),
  incl,
  incl_int,
  fix = TRUE,
  hline = TRUE,
  nr = 20,
  minq = 0.025,
  maxq = 0.975
)

Arguments

x

Return value from a model

plot_list

List used to store plots

incl

Which variables to include in prediction plots

incl_int

Which interactions to investigate in prediction plots

fix

Set the desired limited on yhat or have it calculated automatically. Set to FALSE to have y-axis limits set by ggplot2 for each plot

hline

Add a horizontal line at the average of the target variable. When set to FALSE no line is added. When set to a specific number, the horizontal line will be added at that value

nr

Number of values to use to generate predictions for a numeric explanatory variable

minq

Quantile to use for the minimum value for simulation of numeric variables

maxq

Quantile to use for the maximum value for simulation of numeric variables

Details

Faster, but less robust, alternative for PDP plots. Variable values not included in the prediction are set to either the mean or the most common value (level)

Predict method for the crtree function

Description

Predict method for the crtree function

Usage

## S3 method for class 'crtree'
predict(
  object,
  pred_data = NULL,
  pred_cmd = "",
  conf_lev = 0.95,
  se = FALSE,
  dec = 3,
  envir = parent.frame(),
  ...
)

Arguments

object

Return value from crtree

pred_data

Provide the dataframe to generate predictions (e.g., titanic). The dataset must contain all columns used in the estimation

pred_cmd

Generate predictions using a command. For example, ‘pclass = levels(pclass)' would produce predictions for the different levels of factor 'pclass'. To add another variable, create a vector of prediction strings, (e.g., c(’pclass = levels(pclass)', 'age = seq(0,100,20)')

conf_lev

Confidence level used to estimate confidence intervals (.95 is the default)

se

Logical that indicates if prediction standard errors should be calculated (default = FALSE)

dec

Number of decimals to show

envir

Environment to extract data from

...

further arguments passed to or from other methods

Details

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

Examples

result <- crtree(titanic, "survived", c("pclass", "sex"), lev = "Yes")
predict(result, pred_cmd = "pclass = levels(pclass)")
result <- crtree(titanic, "survived", "pclass", lev = "Yes")
predict(result, pred_data = titanic) %>% head()

Predict method for the gbt function

Description

Predict method for the gbt function

Usage

## S3 method for class 'gbt'
predict(
  object,
  pred_data = NULL,
  pred_cmd = "",
  dec = 3,
  envir = parent.frame(),
  ...
)

Arguments

object

Return value from gbt

pred_data

Provide the dataframe to generate predictions (e.g., diamonds). The dataset must contain all columns used in the estimation

pred_cmd

dec

Number of decimals to show

envir

Environment to extract data from

...

further arguments passed to or from other methods

Details

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

Examples

result <- gbt(
  titanic, "survived", c("pclass", "sex"),
  early_stopping_rounds = 2, nthread = 1
)
predict(result, pred_cmd = "pclass = levels(pclass)")
result <- gbt(diamonds, "price", "carat:color", type = "regression", nthread = 1)
predict(result, pred_cmd = "carat = 1:3")
predict(result, pred_data = diamonds) %>% head()

Predict method for the logistic function

Description

Predict method for the logistic function

Usage

## S3 method for class 'logistic'
predict(
  object,
  pred_data = NULL,
  pred_cmd = "",
  conf_lev = 0.95,
  se = TRUE,
  interval = "confidence",
  dec = 3,
  envir = parent.frame(),
  ...
)

Arguments

object

Return value from logistic

pred_data

Provide the dataframe to generate predictions (e.g., titanic). The dataset must contain all columns used in the estimation

pred_cmd

conf_lev

Confidence level used to estimate confidence intervals (.95 is the default)

se

Logical that indicates if prediction standard errors should be calculated (default = FALSE)

interval

Type of interval calculation ("confidence" or "none"). Set to "none" if se is FALSE

dec

Number of decimals to show

envir

Environment to extract data from

...

further arguments passed to or from other methods

Details

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

Examples

result <- logistic(titanic, "survived", c("pclass", "sex"), lev = "Yes")
predict(result, pred_cmd = "pclass = levels(pclass)")
logistic(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>%
  predict(pred_cmd = "sex = c('male','female')")
logistic(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>%
  predict(pred_data = titanic)

Predict method for the mnl function

Description

Predict method for the mnl function

Usage

## S3 method for class 'mnl'
predict(
  object,
  pred_data = NULL,
  pred_cmd = "",
  pred_names = "",
  dec = 3,
  envir = parent.frame(),
  ...
)

Arguments

object

Return value from mnl

pred_data

Provide the dataframe to generate predictions (e.g., ketchup). The dataset must contain all columns used in the estimation

pred_cmd

pred_names

Names for the predictions to be stored. If one name is provided, only the first column of predictions is stored. If empty, the levels in the response variable of the mnl model will be used

dec

Number of decimals to show

envir

Environment to extract data from

...

further arguments passed to or from other methods

Details

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

Examples

result <- mnl(
  ketchup,
  rvar = "choice",
  evar = c("price.heinz28", "price.heinz32", "price.heinz41", "price.hunts32"),
  lev = "heinz28"
)
predict(result, pred_cmd = "price.heinz28 = seq(3, 5, 0.1)")
predict(result, pred_data = slice(ketchup, 1:20))

Predict method for the nb function

Description

Predict method for the nb function

Usage

## S3 method for class 'nb'
predict(
  object,
  pred_data = NULL,
  pred_cmd = "",
  pred_names = "",
  dec = 3,
  envir = parent.frame(),
  ...
)

Arguments

object

Return value from nb

pred_data

Provide the dataframe to generate predictions (e.g., titanic). The dataset must contain all columns used in the estimation

pred_cmd

pred_names

Names for the predictions to be stored. If one name is provided, only the first column of predictions is stored. If empty, the level in the response variable of the nb model will be used

dec

Number of decimals to show

envir

Environment to extract data from

...

further arguments passed to or from other methods

Details

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

Examples

result <- nb(titanic, "survived", c("pclass", "sex", "age"))
predict(result, pred_data = titanic)
predict(result, pred_data = titanic, pred_names = c("Yes", "No"))
predict(result, pred_cmd = "pclass = levels(pclass)")
result <- nb(titanic, "pclass", c("survived", "sex", "age"))
predict(result, pred_data = titanic)
predict(result, pred_data = titanic, pred_names = c("1st", "2nd", "3rd"))
predict(result, pred_data = titanic, pred_names = "")

Predict method for the nn function

Description

Predict method for the nn function

Usage

## S3 method for class 'nn'
predict(
  object,
  pred_data = NULL,
  pred_cmd = "",
  dec = 3,
  envir = parent.frame(),
  ...
)

Arguments

object

Return value from nn

pred_data

Provide the dataframe to generate predictions (e.g., diamonds). The dataset must contain all columns used in the estimation

pred_cmd

dec

Number of decimals to show

envir

Environment to extract data from

...

further arguments passed to or from other methods

Details

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

Examples

result <- nn(titanic, "survived", c("pclass", "sex"), lev = "Yes")
predict(result, pred_cmd = "pclass = levels(pclass)")
result <- nn(diamonds, "price", "carat:color", type = "regression")
predict(result, pred_cmd = "carat = 1:3")
predict(result, pred_data = diamonds) %>% head()

Predict method for the regress function

Description

Predict method for the regress function

Usage

## S3 method for class 'regress'
predict(
  object,
  pred_data = NULL,
  pred_cmd = "",
  conf_lev = 0.95,
  se = TRUE,
  interval = "confidence",
  dec = 3,
  envir = parent.frame(),
  ...
)

Arguments

object

Return value from regress

pred_data

Provide the dataframe to generate predictions (e.g., diamonds). The dataset must contain all columns used in the estimation

pred_cmd

Command used to generate data for prediction

conf_lev

Confidence level used to estimate confidence intervals (.95 is the default)

se

Logical that indicates if prediction standard errors should be calculated (default = FALSE)

interval

Type of interval calculation ("confidence" or "prediction"). Set to "none" if se is FALSE

dec

Number of decimals to show

envir

Environment to extract data from

...

further arguments passed to or from other methods

Details

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

Examples

result <- regress(diamonds, "price", c("carat", "clarity"))
predict(result, pred_cmd = "carat = 1:10")
predict(result, pred_cmd = "clarity = levels(clarity)")
result <- regress(diamonds, "price", c("carat", "clarity"), int = "carat:clarity")
predict(result, pred_data = diamonds) %>% head()

Predict method for the rforest function

Description

Predict method for the rforest function

Usage

## S3 method for class 'rforest'
predict(
  object,
  pred_data = NULL,
  pred_cmd = "",
  pred_names = "",
  OOB = NULL,
  dec = 3,
  envir = parent.frame(),
  ...
)

Arguments

object

Return value from rforest

pred_data

Provide the dataframe to generate predictions (e.g., diamonds). The dataset must contain all columns used in the estimation

pred_cmd

Generate predictions using a command. For example, 'pclass = levels(pclass)' would produce predictions for the different levels of factor ‘pclass'. To add another variable, create a vector of prediction strings, (e.g., c(’pclass = levels(pclass)', 'age = seq(0,100,20)')

pred_names

Names for the predictions to be stored. If one name is provided, only the first column of predictions is stored. If empty, the levels in the response variable of the rforest model will be used

OOB

Use Out-Of-Bag predictions (TRUE or FALSE). Relevant when evaluating predictions for the training sample. If set to NULL, datasets will be compared to determine if OOB predictions should be used

dec

Number of decimals to show

envir

Environment to extract data from

...

further arguments passed to or from other methods

Details

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

Examples

result <- rforest(titanic, "survived", c("pclass", "sex"), lev = "Yes")
predict(result, pred_cmd = "pclass = levels(pclass)")
result <- rforest(diamonds, "price", "carat:color", type = "regression")
predict(result, pred_cmd = "carat = 1:3")
predict(result, pred_data = diamonds) %>% head()

Predict method for model functions

Description

Predict method for model functions

Usage

predict_model(
  object,
  pfun,
  mclass,
  pred_data = NULL,
  pred_cmd = "",
  conf_lev = 0.95,
  se = FALSE,
  dec = 3,
  envir = parent.frame(),
  ...
)

Arguments

object

Return value from regress

pfun

Function to use for prediction

mclass

Model class to attach

pred_data

Dataset to use for prediction

pred_cmd

Command used to generate data for prediction (e.g., 'carat = 1:10')

conf_lev

Confidence level used to estimate confidence intervals (.95 is the default)

se

Logical that indicates if prediction standard errors should be calculated (default = FALSE)

dec

Number of decimals to show

envir

Environment to extract data from

...

further arguments passed to or from other methods

Details

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

Print method for predict.crtree

Description

Print method for predict.crtree

Usage

## S3 method for class 'crtree.predict'
print(x, ..., n = 10)

Arguments

x

Return value from prediction method

...

further arguments passed to or from other methods

n

Number of lines of prediction results to print. Use -1 to print all lines

Print method for predict.gbt

Description

Print method for predict.gbt

Usage

## S3 method for class 'gbt.predict'
print(x, ..., n = 10)

Arguments

x

Return value from prediction method

...

further arguments passed to or from other methods

n

Number of lines of prediction results to print. Use -1 to print all lines

Print method for logistic.predict

Description

Print method for logistic.predict

Usage

## S3 method for class 'logistic.predict'
print(x, ..., n = 10)

Arguments

x

Return value from prediction method

...

further arguments passed to or from other methods

n

Number of lines of prediction results to print. Use -1 to print all lines

Print method for mnl.predict

Description

Print method for mnl.predict

Usage

## S3 method for class 'mnl.predict'
print(x, ..., n = 10)

Arguments

x

Return value from prediction method

...

further arguments passed to or from other methods

n

Number of lines of prediction results to print. Use -1 to print all lines

Print method for predict.nb

Description

Print method for predict.nb

Usage

## S3 method for class 'nb.predict'
print(x, ..., n = 10)

Arguments

x

Return value from prediction method

...

further arguments passed to or from other methods

n

Number of lines of prediction results to print. Use -1 to print all lines

Print method for predict.nn

Description

Print method for predict.nn

Usage

## S3 method for class 'nn.predict'
print(x, ..., n = 10)

Arguments

x

Return value from prediction method

...

further arguments passed to or from other methods

n

Number of lines of prediction results to print. Use -1 to print all lines

Print method for predict.regress

Description

Print method for predict.regress

Usage

## S3 method for class 'regress.predict'
print(x, ..., n = 10)

Arguments

x

Return value from prediction method

...

further arguments passed to or from other methods

n

Number of lines of prediction results to print. Use -1 to print all lines

Print method for predict.rforest

Description

Print method for predict.rforest

Usage

## S3 method for class 'rforest.predict'
print(x, ..., n = 10)

Arguments

x

Return value from prediction method

...

further arguments passed to or from other methods

n

Number of lines of prediction results to print. Use -1 to print all lines

Print method for the model prediction

Description

Print method for the model prediction

Usage

print_predict_model(x, ..., n = 10, header = "")

Arguments

x

Return value from prediction method

...

further arguments passed to or from other methods

n

Number of lines of prediction results to print. Use -1 to print all lines

header

Header line

Calculate Profit based on cost:margin ratio

Description

Calculate Profit based on cost:margin ratio

Usage

profit(pred, rvar, lev, cost = 1, margin = 2)

Arguments

pred

Prediction or predictor

rvar

Response variable

lev

The level in the response variable defined as success

cost

Cost per treatment (e.g., mailing costs)

margin

Margin, or benefit, per 'success' (e.g., customer purchase). A cost:margin ratio of 1:2 implies the cost of False Positive are equivalent to the benefits of a True Positive

Value

profit

Examples

profit(runif(20000), dvd$buy, "yes", cost = 1, margin = 2)
profit(ifelse(dvd$buy == "yes", 1, 0), dvd$buy, "yes", cost = 1, margin = 20)
profit(ifelse(dvd$buy == "yes", 1, 0), dvd$buy)

radiant.model

Description

Launch radiant.model in the default web browser

Usage

radiant.model(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.model()

## End(Not run)

Deprecated function(s) in the radiant.model package

Description

These functions are provided for compatibility with previous versions of radiant. They will eventually be removed.

Usage

ann(...)

Arguments

...

Parameters to be passed to the updated functions

Details

ann is now a synonym for nn

scaledf is now a synonym for scale_df

Launch radiant.model in the Rstudio viewer

Description

Launch radiant.model in the Rstudio viewer

Usage

radiant.model_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.model_viewer()

## End(Not run)

Launch radiant.model in an Rstudio window

Description

Launch radiant.model in an Rstudio window

Usage

radiant.model_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.model_window()

## End(Not run)

Movie ratings

Description

Movie ratings

Usage

data(ratings)

Format

A data frame with 110 rows and 4 variables

Details

Use collaborative filtering to create recommendations based on ratings from existing users. Description provided in attr(ratings, "description")

Linear regression using OLS

Description

Linear regression using OLS

Usage

regress(
  dataset,
  rvar,
  evar,
  int = "",
  check = "",
  form,
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset

Dataset

rvar

The response variable in the regression

evar

Explanatory variables in the regression

int

Interaction terms to include in the model

check

form

Optional formula to use instead of rvar, evar, and int

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

envir

Environment to extract data from

Details

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

Value

A list of all variables used in the regress function as an object of class regress

Examples

regress(diamonds, "price", c("carat", "clarity"), check = "standardize") %>% summary()
regress(diamonds, "price", c("carat", "clarity")) %>% str()

Remove comments from formula before it is evaluated

Description

Remove comments from formula before it is evaluated

Usage

remove_comments(x)

Arguments

x

Input string

Value

Cleaned string

Method to render DiagrammeR plots

Description

Method to render DiagrammeR plots

Usage

## S3 method for class 'DiagrammeR'
render(object, shiny = shiny::getDefaultReactiveDomain(), ...)

Arguments

object

DiagrammeR plot

shiny

Check if function is called from a shiny application

...

Additional arguments

Repeated simulation

Description

Repeated simulation

Usage

repeater(
  dataset,
  nr = 12,
  vars = "",
  grid = "",
  sum_vars = "",
  byvar = ".sim",
  fun = "sum",
  form = "",
  seed = NULL,
  name = "",
  envir = parent.frame()
)

Arguments

dataset

Return value from the simulater function

nr

Number times to repeat the simulation

vars

Variables to use in repeated simulation

grid

Character vector of expressions to use in grid search for constants

sum_vars

(Numeric) variables to summaries

byvar

Variable(s) to group data by before summarizing

fun

Functions to use for summarizing

form

A character vector with the formula to apply to the summarized data

seed

Seed for the repeated simulation

name

Deprecated argument

envir

Environment to extract data from

Examples

simdat <- simulater(
  const = c("var_cost 5", "fixed_cost 1000"),
  norm = "E 0 100;",
  discrete = "price 6 8 .3 .7;",
  form = c(
    "demand = 1000 - 50*price + E",
    "profit = demand*(price-var_cost) - fixed_cost",
    "profit_small = profit < 100"
  ),
  seed = 1234
)

repdat <- repeater(
  simdat,
  nr = 12,
  vars = c("E", "price"),
  sum_vars = "profit",
  byvar = ".sim",
  form = "profit_365 = profit_sum < 36500",
  seed = 1234,
)

head(repdat)
summary(repdat)
plot(repdat)

Random Forest using Ranger

Description

Random Forest using Ranger

Usage

rforest(
  dataset,
  rvar,
  evar,
  type = "classification",
  lev = "",
  mtry = NULL,
  num.trees = 100,
  min.node.size = 1,
  sample.fraction = 1,
  replace = NULL,
  num.threads = 12,
  wts = "None",
  seed = NA,
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame(),
  ...
)

Arguments

dataset

Dataset

rvar

The response variable in the model

evar

Explanatory variables in the model

type

Model type (i.e., "classification" or "regression")

lev

Level to use as the first column in prediction output

mtry

Number of variables to possibly split at in each node. Default is the (rounded down) square root of the number variables

num.trees

Number of trees to create

min.node.size

Minimal node size

sample.fraction

Fraction of observations to sample. Default is 1 for sampling with replacement and 0.632 for sampling without replacement

replace

Sample with (TRUE) or without (FALSE) replacement. If replace is NULL it will be reset to TRUE if the sample.fraction is equal to 1 and will be set to FALSE otherwise

num.threads

Number of parallel threads to use. Defaults to 12 if available

wts

Case weights to use in estimation

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

envir

Environment to extract data from

...

Further arguments to pass to ranger

Details

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

Value

A list with all variables defined in rforest as an object of class rforest

Examples

rforest(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
rforest(titanic, "survived", c("pclass", "sex")) %>% str()
rforest(titanic, "survived", c("pclass", "sex"), max.depth = 1)
rforest(diamonds, "price", c("carat", "clarity"), type = "regression") %>% summary()

Relative Information Gain (RIG)

Description

Relative Information Gain (RIG)

Usage

rig(pred, rvar, lev, crv = 1e-07, na.rm = TRUE)

Arguments

pred

Prediction or predictor

rvar

Response variable

lev

The level in the response variable defined as success

crv

Correction value to avoid log(0)

na.rm

Logical that indicates if missing values should be removed (TRUE) or not (FALSE)

Details

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

Value

RIG statistic

Examples

rig(runif(20000), dvd$buy, "yes")
rig(ifelse(dvd$buy == "yes", 1, 0), dvd$buy, "yes")

Center or standardize variables in a data frame

Description

Center or standardize variables in a data frame

Usage

scale_df(dataset, center = TRUE, scale = TRUE, sf = 2, wts = NULL, calc = TRUE)

Arguments

dataset

Data frame

center

Center data (TRUE or FALSE)

scale

Scale data (TRUE or FALSE)

sf

Scaling factor (default is 2)

wts

Weights to use (default is NULL for no weights)

calc

Calculate mean and sd or use attributes attached to dat

Value

Scaled data frame

Standard deviation of weighted sum of variables

Description

Standard deviation of weighted sum of variables

Usage

sdw(...)

Arguments

...

A matched number of weights and stocks

Value

A vector of standard deviation estimates

Method to evaluate sensitivity of an analysis

Description

Method to evaluate sensitivity of an analysis

Usage

sensitivity(object, ...)

Arguments

object

Object of relevant class for which to evaluate sensitivity

...

Additional arguments

Evaluate sensitivity of the decision tree

Description

Evaluate sensitivity of the decision tree

Usage

## S3 method for class 'dtree'
sensitivity(
  object,
  vars = NULL,
  decs = NULL,
  envir = parent.frame(),
  shiny = FALSE,
  custom = FALSE,
  ...
)

Arguments

object

Return value from dtree

vars

Variables to include in the sensitivity analysis

decs

Decisions to include in the sensitivity analysis

envir

Environment to extract data from

shiny

Did the function call originate inside a shiny app

custom

...

Additional arguments

Details

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

Examples

dtree(movie_contract, opt = "max") %>%
  sensitivity(
    vars = "legal fees 0 100000 10000",
    decs = c("Sign with Movie Company", "Sign with TV Network"),
    custom = FALSE
  )

Clean input command string

Description

Clean input command string

Usage

sim_cleaner(x)

Arguments

x

Input string

Value

Cleaned string

Simulate correlated normally distributed data

Description

Simulate correlated normally distributed data

Usage

sim_cor(n, rho, means, sds, exact = FALSE)

Arguments

n

The number of values to simulate (i.e., the number of rows in the simulated data)

rho

A vector of correlations to apply to the columns of the simulated data. The number of values should be equal to one or to the number of combinations of variables to be simulated

means

A vector of means. The number of values should be equal to the number of variables to simulate

sds

A vector of standard deviations. The number of values should be equal to the number of variables to simulate

exact

A logical that indicates if the inputs should be interpreted as population of sample characteristics

Value

A data.frame with the simulated data

Examples

sim <- sim_cor(100, .74, c(0, 10), c(1, 5), exact = TRUE)
cor(sim)
sim_summary(sim)

Split input command string

Description

Split input command string

Usage

sim_splitter(x, symbol = " ")

Arguments

x

Input string

symbol

Symbol used to split the command string

Value

Split input command string

Print simulation summary

Description

Print simulation summary

Usage

sim_summary(dataset, dc = get_class(dataset), fun = "", dec = 4)

Arguments

dataset

Simulated data

dc

Variable classes

fun

Summary function to apply

dec

Number of decimals to show

Examples

simulater(
  const = "cost 3",
  norm = "demand 2000 1000",
  discrete = "price 5 8 .3 .7",
  form = c("profit = demand * (price - cost)", "profit5K = profit > 5000"),
  seed = 1234
) %>% sim_summary()

Simulate data for decision analysis

Description

Simulate data for decision analysis

Usage

simulater(
  const = "",
  lnorm = "",
  norm = "",
  unif = "",
  discrete = "",
  binom = "",
  pois = "",
  sequ = "",
  grid = "",
  data = NULL,
  form = "",
  funcs = "",
  seed = NULL,
  nexact = FALSE,
  ncorr = NULL,
  name = "",
  nr = 1000,
  dataset = NULL,
  envir = parent.frame()
)

Arguments

const

A character vector listing the constants to include in the analysis (e.g., c("cost = 3", "size = 4"))

lnorm

A character vector listing the log-normally distributed random variables to include in the analysis (e.g., "demand 2000 1000" where the first number is the log-mean and the second is the log-standard deviation)

norm

A character vector listing the normally distributed random variables to include in the analysis (e.g., "demand 2000 1000" where the first number is the mean and the second is the standard deviation)

unif

A character vector listing the uniformly distributed random variables to include in the analysis (e.g., "demand 0 1" where the first number is the minimum value and the second is the maximum value)

discrete

A character vector listing the random variables with a discrete distribution to include in the analysis (e.g., "price 5 8 .3 .7" where the first set of numbers are the values and the second set the probabilities

binom

A character vector listing the random variables with a binomial distribution to include in the analysis (e.g., "crash 100 .01") where the first number is the number of trials and the second is the probability of success)

pois

A character vector listing the random variables with a poisson distribution to include in the analysis (e.g., "demand 10") where the number is the lambda value (i.e., the average number of events or the event rate)

sequ

A character vector listing the start and end for a sequence to include in the analysis (e.g., "trend 1 100 1"). The number of 'steps' is determined by the number of simulations

grid

A character vector listing the start, end, and step for a set of sequences to include in the analysis (e.g., "trend 1 100 1"). The number of rows in the expanded will over ride the number of simulations

data

Dataset to be used in the calculations

form

A character vector with the formula to evaluate (e.g., "profit = demand * (price - cost)")

funcs

A named list of user defined functions to apply to variables generated as part of the simulation

seed

Optional seed used in simulation

nexact

Logical to indicate if normally distributed random variables should be simulated to the exact specified values

ncorr

A string of correlations used for normally distributed random variables. The number of values should be equal to one or to the number of combinations of variables simulated

name

Deprecated argument

nr

Number of simulations

dataset

Data list from previous simulation. Used by repeater function

envir

Environment to extract data from

Details

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

Value

A data.frame with the simulated data

Examples

simulater(
  const = "cost 3",
  norm = "demand 2000 1000",
  discrete = "price 5 8 .3 .7",
  form = "profit = demand * (price - cost)",
  seed = 1234
) %>% str()

Deprecated: Store method for the crs function

Description

Deprecated: Store method for the crs function

Usage

## S3 method for class 'crs'
store(dataset, object, name, ...)

Arguments

dataset

Dataset

object

Return value from crs

name

Name to assign to the dataset

...

further arguments passed to or from other methods

Details

Return recommendations See https://radiant-rstats.github.io/docs/model/crs.html for an example in Radiant

Store predicted values generated in the mnl function

Description

Store predicted values generated in the mnl function

Usage

## S3 method for class 'mnl.predict'
store(dataset, object, name = NULL, ...)

Arguments

dataset

Dataset to add predictions to

object

Return value from model function

name

Variable name(s) assigned to predicted values. If empty, the levels of the response variable will be used

...

Additional arguments

Details

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

Examples

result <- mnl(
  ketchup,
  rvar = "choice",
  evar = c("price.heinz28", "price.heinz32", "price.heinz41", "price.hunts32"),
  lev = "heinz28"
)
pred <- predict(result, pred_data = ketchup)
ketchup <- store(ketchup, pred, name = c("heinz28", "heinz32", "heinz41", "hunts32"))

Store residuals from a model

Description

Store residuals from a model

Usage

## S3 method for class 'model'
store(dataset, object, name = "residuals", ...)

Arguments

dataset

Dataset to append residuals to

object

Return value from a model function

name

Variable name(s) assigned to model residuals

...

Additional arguments

Details

The store method for objects of class "model". Adds model residuals to the dataset while handling missing values and filters. See https://radiant-rstats.github.io/docs/model/regress.html for an example in Radiant

Examples

regress(diamonds, rvar = "price", evar = c("carat", "cut"), data_filter = "price > 1000") %>%
  store(diamonds, ., name = "resid") %>%
  head()

Store predicted values generated in model functions

Description

Store predicted values generated in model functions

Usage

## S3 method for class 'model.predict'
store(dataset, object, name = "prediction", ...)

Arguments

dataset

Dataset to add predictions to

object

Return value from model function

name

Variable name(s) assigned to predicted values

...

Additional arguments

Details

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

Examples

regress(diamonds, rvar = "price", evar = c("carat", "cut")) %>%
  predict(pred_data = diamonds) %>%
  store(diamonds, ., name = c("pred", "pred_low", "pred_high")) %>%
  head()

Store predicted values generated in the nb function

Description

Store predicted values generated in the nb function

Usage

## S3 method for class 'nb.predict'
store(dataset, object, name = NULL, ...)

Arguments

dataset

Dataset to add predictions to

object

Return value from model function

name

Variable name(s) assigned to predicted values. If empty, the levels of the response variable will be used

...

Additional arguments

Details

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

Examples

result <- nb(titanic, rvar = "survived", evar = c("pclass", "sex", "age"))
pred <- predict(result, pred_data = titanic)
titanic <- store(titanic, pred, name = c("Yes", "No"))

Store predicted values generated in the rforest function

Description

Store predicted values generated in the rforest function

Usage

## S3 method for class 'rforest.predict'
store(dataset, object, name = NULL, ...)

Arguments

dataset

Dataset to add predictions to

object

Return value from model function

name

Variable name(s) assigned to predicted values. If empty, the levels of the response variable will be used

...

Additional arguments

Details

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

Examples

result <- rforest(
  ketchup,
  rvar = "choice",
  evar = c("price.heinz28", "price.heinz32", "price.heinz41", "price.hunts32"),
  lev = "heinz28"
)
pred <- predict(result, pred_data = ketchup)
ketchup <- store(ketchup, pred, name = c("heinz28", "heinz32", "heinz41", "hunts32"))

Summary method for the confusion matrix

Description

Summary method for the confusion matrix

Usage

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

Arguments

object

Return value from confusion

dec

Number of decimals to show

...

further arguments passed to or from other methods

Details

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

Examples

data.frame(buy = dvd$buy, pred1 = runif(20000), pred2 = ifelse(dvd$buy == "yes", 1, 0)) %>%
  confusion(c("pred1", "pred2"), "buy") %>%
  summary()

Summary method for Collaborative Filter

Description

Summary method for Collaborative Filter

Usage

## S3 method for class 'crs'
summary(object, n = 36, dec = 2, ...)

Arguments

object

Return value from crs

n

Number of lines of recommendations to print. Use -1 to print all lines

dec

Number of decimals to show

...

further arguments passed to or from other methods

Details

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

Examples

crs(ratings,
  id = "Users", prod = "Movies", pred = c("M6", "M7", "M8", "M9", "M10"),
  rate = "Ratings", data_filter = "training == 1"
) %>% summary()

Summary method for the crtree function

Description

Summary method for the crtree function

Usage

## S3 method for class 'crtree'
summary(object, prn = TRUE, splits = FALSE, cptab = FALSE, modsum = FALSE, ...)

Arguments

object

Return value from crtree

prn

Print tree in text form

splits

Print the tree splitting metrics used

cptab

Print the cp table

modsum

Print the model summary

...

further arguments passed to or from other methods

Details

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

Examples

result <- crtree(titanic, "survived", c("pclass", "sex"), lev = "Yes")
summary(result)
result <- crtree(diamonds, "price", c("carat", "color"), type = "regression")
summary(result)

Summary method for the dtree function

Description

Summary method for the dtree function

Usage

## S3 method for class 'dtree'
summary(object, input = TRUE, output = FALSE, dec = 2, ...)

Arguments

object

Return value from simulater

input

Print decision tree input

output

Print decision tree output

dec

Number of decimals to show

...

further arguments passed to or from other methods

Details

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

Examples

dtree(movie_contract, opt = "max") %>% summary(input = TRUE)
dtree(movie_contract, opt = "max") %>% summary(input = FALSE, output = TRUE)

Summary method for the evalbin function

Description

Summary method for the evalbin function

Usage

## S3 method for class 'evalbin'
summary(object, prn = TRUE, dec = 3, ...)

Arguments

object

Return value from evalbin

prn

Print full table of measures per model and bin

dec

Number of decimals to show

...

further arguments passed to or from other methods

Details

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

Examples

data.frame(buy = dvd$buy, pred1 = runif(20000), pred2 = ifelse(dvd$buy == "yes", 1, 0)) %>%
  evalbin(c("pred1", "pred2"), "buy") %>%
  summary()

Summary method for the evalreg function

Description

Summary method for the evalreg function

Usage

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

Arguments

object

Return value from evalreg

dec

Number of decimals to show

...

further arguments passed to or from other methods

Details

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

Examples

data.frame(price = diamonds$price, pred1 = rnorm(3000), pred2 = diamonds$price) %>%
  evalreg(pred = c("pred1", "pred2"), "price") %>%
  summary()

Summary method for the gbt function

Description

Summary method for the gbt function

Usage

## S3 method for class 'gbt'
summary(object, prn = TRUE, ...)

Arguments

object

Return value from gbt

prn

Print iteration history

...

further arguments passed to or from other methods

Details

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

Examples

result <- gbt(
  titanic, "survived", c("pclass", "sex"),
  early_stopping_rounds = 0, nthread = 1
)
summary(result)

Summary method for the logistic function

Description

Summary method for the logistic function

Usage

## S3 method for class 'logistic'
summary(object, sum_check = "", conf_lev = 0.95, test_var = "", dec = 3, ...)

Arguments

object

Return value from logistic

sum_check

Optional output. "vif" to show multicollinearity diagnostics. "confint" to show coefficient confidence interval estimates. "odds" to show odds ratios and confidence interval estimates.

conf_lev

Confidence level to use for coefficient and odds confidence intervals (.95 is the default)

test_var

Variables to evaluate in model comparison (i.e., a competing models Chi-squared test)

dec

Number of decimals to show

...

further arguments passed to or from other methods

Details

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

Examples


result <- logistic(titanic, "survived", "pclass", lev = "Yes")
result <- logistic(titanic, "survived", "pclass", lev = "Yes")
summary(result, test_var = "pclass")
res <- logistic(titanic, "survived", c("pclass", "sex"), int = "pclass:sex", lev = "Yes")
summary(res, sum_check = c("vif", "confint", "odds"))
titanic %>%
  logistic("survived", c("pclass", "sex", "age"), lev = "Yes") %>%
  summary("vif")

Summary method for the mnl function

Description

Summary method for the mnl function

Usage

## S3 method for class 'mnl'
summary(object, sum_check = "", conf_lev = 0.95, test_var = "", dec = 3, ...)

Arguments

object

Return value from mnl

sum_check

Optional output. "confint" to show coefficient confidence interval estimates. "rrr" to show relative risk ratios (RRRs) and confidence interval estimates.

conf_lev

Confidence level to use for coefficient and RRRs confidence intervals (.95 is the default)

test_var

Variables to evaluate in model comparison (i.e., a competing models Chi-squared test)

dec

Number of decimals to show

...

further arguments passed to or from other methods

Details

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

Examples

result <- mnl(
  ketchup,
  rvar = "choice",
  evar = c("price.heinz28", "price.heinz32", "price.heinz41", "price.hunts32"),
  lev = "heinz28"
)
summary(result)

Summary method for the nb function

Description

Summary method for the nb function

Usage

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

Arguments

object

Return value from nb

dec

Decimals

...

further arguments passed to or from other methods

Details

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

Examples

result <- nb(titanic, "survived", c("pclass", "sex", "age"))
summary(result)

Summary method for the nn function

Description

Summary method for the nn function

Usage

## S3 method for class 'nn'
summary(object, prn = TRUE, ...)

Arguments

object

Return value from nn

prn

Print list of weights

...

further arguments passed to or from other methods

Details

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

Examples

result <- nn(titanic, "survived", "pclass", lev = "Yes")
summary(result)

Summary method for the regress function

Description

Summary method for the regress function

Usage

## S3 method for class 'regress'
summary(object, sum_check = "", conf_lev = 0.95, test_var = "", dec = 3, ...)

Arguments

object

Return value from regress

sum_check

Optional output. "rsme" to show the root mean squared error and the standard deviation of the residuals. "sumsquares" to show the sum of squares table. "vif" to show multicollinearity diagnostics. "confint" to show coefficient confidence interval estimates.

conf_lev

Confidence level used to estimate confidence intervals (.95 is the default)

test_var

Variables to evaluate in model comparison (i.e., a competing models F-test)

dec

Number of decimals to show

...

further arguments passed to or from other methods

Details

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

Examples

result <- regress(diamonds, "price", c("carat", "clarity"))
summary(result, sum_check = c("rmse", "sumsquares", "vif", "confint"), test_var = "clarity")
result <- regress(ideal, "y", c("x1", "x2"))
summary(result, test_var = "x2")
ideal %>%
  regress("y", "x1:x3") %>%
  summary()

Summarize repeated simulation

Description

Summarize repeated simulation

Usage

## S3 method for class 'repeater'
summary(object, dec = 4, ...)

Arguments

object

Return value from repeater

dec

Number of decimals to show

...

further arguments passed to or from other methods

Summary method for the rforest function

Description

Summary method for the rforest function

Usage

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

Arguments

object

Return value from rforest

...

further arguments passed to or from other methods

Details

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

Examples

result <- rforest(titanic, "survived", "pclass", lev = "Yes")
summary(result)

Summary method for the simulater function

Description

Summary method for the simulater function

Usage

## S3 method for class 'simulater'
summary(object, dec = 4, ...)

Arguments

object

Return value from simulater

dec

Number of decimals to show

...

further arguments passed to or from other methods

Details

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

Examples

simdat <- simulater(norm = "demand 2000 1000", seed = 1234)
summary(simdat)

Summary method for the uplift function

Description

Summary method for the uplift function

Usage

## S3 method for class 'uplift'
summary(object, prn = TRUE, dec = 3, ...)

Arguments

object

Return value from evalbin

prn

Print full table of measures per model and bin

dec

Number of decimals to show

...

further arguments passed to or from other methods

Details

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

Examples

data.frame(buy = dvd$buy, pred1 = runif(20000), pred2 = ifelse(dvd$buy == "yes", 1, 0)) %>%
  evalbin(c("pred1", "pred2"), "buy") %>%
  summary()

Add interaction terms to list of test variables if needed

Description

Add interaction terms to list of test variables if needed

Usage

test_specs(tv, int)

Arguments

tv

List of variables to use for testing for regress or logistic

int

Interaction terms specified

Details

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

Value

A vector of variables names to test

Examples

test_specs("a", "a:b")
test_specs("a", c("a:b", "b:c"))
test_specs("a", c("a:b", "b:c", "I(c^2)"))
test_specs(c("a", "b", "c"), c("a:b", "b:c", "I(c^2)"))

Evaluate uplift for different (binary) classification models

Description

Evaluate uplift for different (binary) classification models

Usage

uplift(
  dataset,
  pred,
  rvar,
  lev = "",
  tvar,
  tlev = "",
  qnt = 10,
  cost = 1,
  margin = 2,
  scale = 1,
  train = "All",
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset

Dataset

pred

Predictions or predictors

rvar

Response variable

lev

The level in the response variable defined as success

tvar

Treatment variable

tlev

The level in the treatment variable defined as the treatment

qnt

Number of bins to create

cost

Cost for each connection (e.g., email or mailing)

margin

Margin on each customer purchase

scale

Scaling factor to apply to calculations

train

Use data from training ("Training"), test ("Test"), both ("Both"), or all data ("All") to evaluate model evalbin

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

envir

Environment to extract data from

Details

Evaluate uplift for different (binary) classification models based on predictions. See https://radiant-rstats.github.io/docs/model/evalbin.html for an example in Radiant

Value

A list of results

Examples

data.frame(buy = dvd$buy, pred1 = runif(20000), pred2 = ifelse(dvd$buy == "yes", 1, 0)) %>%
  evalbin(c("pred1", "pred2"), "buy") %>%
  str()

Check if main effects for all interaction effects are included in the model

Description

Check if main effects for all interaction effects are included in the model

Usage

var_check(ev, cn, intv = c())

Arguments

ev

List of explanatory variables provided to regress or logistic

cn

Column names for all explanatory variables in the dataset

intv

Interaction terms specified

Details

If ':' is used to select a range evar is updated. See https://radiant-rstats.github.io/docs/model/regress.html for an example in Radiant

Value

vars is a vector of right-hand side variables, possibly with interactions, iv is the list of explanatory variables, and intv are interaction terms

Examples

var_check("a:d", c("a", "b", "c", "d"))
var_check(c("a", "b"), c("a", "b"), "a:c")
var_check(c("a", "b"), c("a", "b"), "a:c")
var_check(c("a", "b"), c("a", "b"), c("a:c", "I(b^2)"))

Variable importance using the vip package and permutation importance

Description

Variable importance using the vip package and permutation importance

Usage

varimp(object, rvar, lev, data = NULL, seed = 1234)

Arguments

object

Model object created by Radiant

rvar

Label to identify the response or target variable

lev

Reference class for binary classifier (rvar)

data

Data to use for prediction. Will default to the data used to estimate the model

seed

Random seed for reproducibility

Plot permutation importance

Description

Plot permutation importance

Usage

varimp_plot(object, rvar, lev, data = NULL, seed = 1234)

Arguments

object

Model object created by Radiant

rvar

Label to identify the response or target variable

lev

Reference class for binary classifier (rvar)

data

Data to use for prediction. Will default to the data used to estimate the model

seed

Random seed for reproducibility

Write coefficient table for linear and logistic regression

Description

Write coefficient table for linear and logistic regression

Usage

write.coeff(object, file = "", sort = FALSE, intercept = TRUE)

Arguments

object

A fitted model object of class regress or logistic

file

A character string naming a file. "" indicates output to the console

sort

Sort table by variable importance

intercept

Include the intercept in the output (TRUE or FALSE). TRUE is the default

Details

Write coefficients and importance scores to csv or or return as a data.frame

Examples


regress(
  diamonds,
  rvar = "price", evar = c("carat", "clarity", "color", "x"),
  int = c("carat:clarity", "clarity:color", "I(x^2)"), check = "standardize"
) %>%
  write.coeff(sort = TRUE) %>%
  format_df(dec = 3)

logistic(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>%
  write.coeff(intercept = FALSE, sort = TRUE) %>%
  format_df(dec = 2)

Convenience function used in "simulater"

Description

Usage

Arguments

Value

Convenience function used in "simulater"

Description

Usage

Arguments

Value

Mean Absolute Error

Description

Usage

Arguments

Value

Root Mean Squared Error

Description

Usage

Arguments

Value

R-squared

Description

Usage

Arguments

Value

Area Under the RO Curve (AUC)

Description

Usage

Arguments

Details

Value

See Also

Examples

Catalog sales for men's and women's apparel

Description

Usage

Format

Details

Confidence interval for robust estimators

Description

Usage

Arguments

Details

Confusion matrix

Description

Usage

Arguments

Details

Value

See Also

Examples

Collaborative Filtering

Description

Usage

Arguments

Details

Value

See Also

Examples

Classification and regression trees based on the rpart package

Description

Usage

Arguments

Details

Value

See Also

Examples

Cross-validation for Classification and Regression Trees

Description

Usage

Arguments

Details

Value

See Also

Examples

Cross-validation for Gradient Boosted Trees

Description

Usage

Arguments

Details