Package 'beezdiscounting' reference manual

Title:	Behavioral Economic Easy Discounting
Description:	Facilitates some of the analyses performed in studies of behavioral economic discounting. The package supports scoring of the 27-Item Monetary Choice Questionnaire (see Kaplan et al., 2016; <doi:10.1007/s40614-016-0070-9>), calculating k values (Mazur's simple hyperbolic and exponential) using nonlinear regression, calculating various Area Under the Curve (AUC) measures, plotting regression curves for both fit-to-group and two-stage approaches, checking for unsystematic discounting (Johnson & Bickel, 2008; <doi:10.1037/1064-1297.16.3.264>) and scoring of the minute discounting task (see Koffarnus & Bickel, 2014; <doi:10.1037/a0035973>) using the Qualtrics 5-trial discounting template (see the Qualtrics Minute Discounting User Guide; <doi:10.13140/RG.2.2.26495.79527>), which is also available as a .qsf file in this package.
Authors:	Brent A. Kaplan [aut, cre, cph]
Maintainer:	Brent A. Kaplan <[email protected]>
License:	GPL (>= 2)
Version:	0.3.2
Built:	2025-03-10 04:34:26 UTC
Source:	https://github.com/brentkaplan/beezdiscounting

Converts answers from 5.5 trial delay discounting from Qualtrics template

Description

Converts answers from 5.5 trial delay discounting from Qualtrics template

Usage

ans_dd(df)
ans_dd(df)

Arguments

`df`	A dataframe containing all the columns

Value

A dataframe with the ResponseId, index, and response (ss or ll).

Examples

ans_dd(five.fivetrial_dd)
ans_dd(five.fivetrial_dd)

Converts answers from 5.5 trial probability discounting from Qualtrics template

Description

Converts answers from 5.5 trial probability discounting from Qualtrics template

Usage

ans_pd(df)
ans_pd(df)

Arguments

`df`	A dataframe containing all the columns

Value

A dataframe with the ResponseId, index, and response (sc or lu).

Examples

ans_pd(five.fivetrial_pd)
ans_pd(five.fivetrial_pd)

Calculate Area-Under-the-Curve (AUC) Metrics for Delay Discounting Data

Description

This function calculates three types of Area-Under-the-Curve (AUC) metrics for delay discounting data: regular AUC (using raw delays), log10 AUC (using logarithmically scaled delays), and ordinal AUC (using ordinally scaled delays). These metrics provide different perspectives on the rate of delay discounting.

Usage

calc_aucs(dat)
calc_aucs(dat)

Arguments

dat

A data frame containing delay discounting data. It must include the following columns:

id: Participant or group identifier.
x: Delay values (e.g., in days).
y: Indifference point values (e.g., subjective value of the delayed reward).

Value

A tibble with the following columns:

id: The participant or group identifier.
auc_regular: The regular AUC, calculated using the raw delay values.
auc_log10: The log10 AUC, calculated using logarithmically transformed delay values.
auc_rank: The rank AUC, calculated using ordinally scaled delay values.

Examples

# Example data
data <- data.frame(
  id = rep("P1", 6),
  x = c(1, 7, 30, 90, 180, 365),
  y = c(0.8, 0.5, 0.3, 0.2, 0.1, 0.05)
)

# Calculate AUC metrics for a single participant
calc_aucs(data)
# Example data
data <- data.frame(
  id = rep("P1", 6),
  x = c(1, 7, 30, 90, 180, 365),
  y = c(0.8, 0.5, 0.3, 0.2, 0.1, 0.05)
)

# Calculate AUC metrics for a single participant
calc_aucs(data)

Calculate Confidence Intervals for a Parameter

Description

This function computes the lower and upper bounds of the confidence interval for a parameter estimate, given its standard error, a specified significance level, and the degrees of freedom from the model.

Usage

calc_conf_int(estimate, std_error, model, alpha = 0.05)
calc_conf_int(estimate, std_error, model, alpha = 0.05)

Arguments

`estimate`	A numeric value representing the parameter estimate.
`std_error`	A numeric value representing the standard error of the parameter estimate.
`model`	A fitted model object that provides the residual degrees of freedom via `df.residual()`.
`alpha`	A numeric value representing the significance level. Default is 0.05 (95% confidence interval).

Value

A numeric vector of length two:

First element: Lower bound of the confidence interval.
Second element: Upper bound of the confidence interval.

Examples

# Example using a linear model
data <- data.frame(x = 1:10, y = c(2.3, 2.1, 3.7, 4.5, 5.1, 6.8, 7.3, 7.9, 9.2, 10.1))
lm_model <- lm(y ~ x, data = data)
calc_conf_int(estimate = 0.5, std_error = 0.1, model = lm_model, alpha = 0.05)
# Example using a linear model
data <- data.frame(x = 1:10, y = c(2.3, 2.1, 3.7, 4.5, 5.1, 6.8, 7.3, 7.9, 9.2, 10.1))
lm_model <- lm(y ~ x, data = data)
calc_conf_int(estimate = 0.5, std_error = 0.1, model = lm_model, alpha = 0.05)

Calculate scores, answers, and timing for 5.5 trial delay discounting from Qualtrics template

Description

Calculate scores, answers, and timing for 5.5 trial delay discounting from Qualtrics template

Usage

calc_dd(df)
calc_dd(df)

Arguments

`df`	A dataframe containing all the columns from the template.

Value

A dataframe with k/ed50 values, answers, timing

Examples

calc_dd(five.fivetrial_dd)
calc_dd(five.fivetrial_dd)

Calculate scores, answers, and timing for 5.5 trial probability discounting from Qualtrics template

Description

Calculate scores, answers, and timing for 5.5 trial probability discounting from Qualtrics template

Usage

calc_pd(df)
calc_pd(df)

Arguments

`df`	A dataframe containing all the columns from the template.

Value

A dataframe with h/ep50 values, answers, timing

Examples

calc_pd(five.fivetrial_pd)
calc_pd(five.fivetrial_pd)

Calculate R-Squared for a Model

Description

This function calculates the coefficient of determination ( $R^2$ ) for a given model by comparing the sum of squared errors (SSE) to the total sum of squares (SST).

Usage

calc_r2(model)
calc_r2(model)

Arguments

model

A fitted model object. The model must have resid() and fitted() methods to extract residuals and fitted values.

Value

A numeric value representing the $R^2$ value of the model. Returns NA if the model is NULL.

Examples

# Example using a simple linear model
data <- data.frame(x = 1:10, y = c(1, 2, 3, 4, 5, 6, 7, 9, 10, 11))
lm_model <- lm(y ~ x, data = data)
calc_r2(lm_model)
# Example using a simple linear model
data <- data.frame(x = 1:10, y = c(1, 2, 3, 4, 5, 6, 7, 9, 10, 11))
lm_model <- lm(y ~ x, data = data)
calc_r2(lm_model)

Check for Unsystematic Data Violations

Description

This function checks a dataset for violations of two criteria commonly used to identify unsystematic delay-discounting data:

Criterion 1: Any subsequent value of y exceeds the previous value by more than a specified proportion of the larger later reward (ll).
Criterion 2: The last value of y is not at least a specified proportion less than the first value of y.

Usage

check_unsystematic(dat, ll = 1, c1 = 0.2, c2 = 0.1)
check_unsystematic(dat, ll = 1, c1 = 0.2, c2 = 0.1)

Arguments

`dat`	A data frame containing the delay-discounting data. It must have at least two columns: `id`: A unique identifier for the data set. `y`: The indifference points to be analyzed.
`ll`	A numeric value representing the larger later reward. Default is 1.
`c1`	A numeric value for the threshold proportion for Criterion 1. Default is 0.2.
`c2`	A numeric value for the threshold proportion for Criterion 2. Default is 0.1.

Value

A tibble with the following columns:

id: The unique identifier for the data set.
c1_pass: Logical value indicating whether Criterion 1 was passed.
c2_pass: Logical value indicating whether Criterion 2 was passed.

Examples

data <- tibble::tibble(
  id = c(rep("P1", 6)),
  x = c(1, 7, 30, 90, 180, 365), # delays
  y = c(0.9, 0.5, 0.3, 0.2, 0.1, 0.05) # indifference points
)
check_unsystematic(data, ll = 1, c1 = 0.2, c2 = 0.1)
data <- tibble::tibble(
  id = c(rep("P1", 6)),
  x = c(1, 7, 30, 90, 180, 365), # delays
  y = c(0.9, 0.5, 0.3, 0.2, 0.1, 0.05) # indifference points
)
check_unsystematic(data, ll = 1, c1 = 0.2, c2 = 0.1)

Delay Discounting Data

Description

A dataset containing a set of fake delay discounting responses

Usage

dd_ip
dd_ip

Format

A data frame with delay discounting responses

Fit Delay-Discounting Model

Description

This function fits a delay-discounting model to the given dataset using the specified equation and method.

Usage

fit_dd(dat, equation, method)
fit_dd(dat, equation, method)

Arguments

dat

A data frame containing delay (x) and indifference point (y) data. For "two stage" methods, the data must include an id column to identify participants.

equation

A character string specifying the delay-discounting equation to use. Options include:

"mazur" or "hyperbolic": Hyperbolic delay-discounting model ( $y = 1 / (1 + k \cdot x)$ ).
"exponential": Exponential delay-discounting model ( $y = \exp(-k \cdot x)$ ).

method

A character string specifying the method for fitting the model. Options include:

"pooled" or "agg": Fits the model using pooled data.
"mean": Fits the model using the mean of indifference points at each delay.
"ts" or "two stage": Fits the model separately for each participant (requires an id column in dat).

Value

A list object of class "fit_dd", containing:

The fitted model(s).
The original dataset (dat).
The specified method (method).

Examples

data <- data.frame(
  id = rep(1:2, each = 6),
  x = rep(c(1, 7, 30, 90, 180, 365), 2),
  y = c(0.9, 0.5, 0.3, 0.2, 0.1, 0.05, 0.85, 0.55, 0.35, 0.15, 0.1, 0.05)
)
fit_dd(data, equation = "mazur", method = "two stage")
data <- data.frame(
  id = rep(1:2, each = 6),
  x = rep(c(1, 7, 30, 90, 180, 365), 2),
  y = c(0.9, 0.5, 0.3, 0.2, 0.1, 0.05, 0.85, 0.55, 0.35, 0.15, 0.1, 0.05)
)
fit_dd(data, equation = "mazur", method = "two stage")

Example Qualtrics output from the 5.5 trial delay discounting template.

Description

An example dataset containing four participants' data (two typical discounting patterns and two patterns suggesting potential misattention to the task).

Usage

five.fivetrial_dd
five.fivetrial_dd

Format

Example Qualtrics output

Example Qualtrics output from the 5.5 trial probability discounting template.

Description

An example dataset containing four participants' data.

Usage

five.fivetrial_pd
five.fivetrial_pd

Format

Example Qualtrics output

Generate fake MCQ data

Description

Generate fake MCQ data

Usage

generate_data_mcq(n_ids = 100, n_items = 27, seed = 1234, prop_na = 0)
generate_data_mcq(n_ids = 100, n_items = 27, seed = 1234, prop_na = 0)

Arguments

`n_ids`	Number of subjectids
`n_items`	Number of trials
`seed`	Random seed
`prop_na`	Proportion of NAs in the entire data set

Value

Dataframe of subjectid, questionid, and response

Examples

generate_data_mcq(n_ids = 2, n_items = 27, prop_na = .01)
generate_data_mcq(n_ids = 2, n_items = 27, prop_na = .01)

Get internal lookup table for the 27-item MCQ

Description

Get internal lookup table for the 27-item MCQ

Usage

get_lookup_table()
get_lookup_table()

Value

Dataframe with questionid, magnitude, and kindiff

Examples

get_lookup_table()
get_lookup_table()

Calculates item nearest neighbor imputation approach discussed by Yeh et al. (2023)

Description

Calculates item nearest neighbor imputation approach discussed by Yeh et al. (2023)

Usage

inn(dat, random, verbose)
inn(dat, random, verbose)

Arguments

`dat`	A single subject's 27-item MCQ data in long form
`random`	Boolean whether to insert a random draw (0 or 1) for NAs
`verbose`	Boolean whether to print subject and question ids pertaining to missing data

Value

An imputed data set to be scored

Reshape MCQ data long to wide

Description

Reshape MCQ data long to wide

Usage

long_to_wide_mcq(dat, q_col = "questionid", ans_col = "response")
long_to_wide_mcq(dat, q_col = "questionid", ans_col = "response")

Arguments

`dat`	Long format MCQ
`q_col`	Name of the question column (default is "questionid")
`ans_col`	Name of the answer column (defualt is "response")

Value

Wide format data frame

Reshape MCQ data from long to wide (as used in the 21- and 27-Item Monetary Choice Questionnaire Automated Scorer)

Description

Reshape MCQ data from long to wide (as used in the 21- and 27-Item Monetary Choice Questionnaire Automated Scorer)

Usage

long_to_wide_mcq_excel(dat, subj_col = "subjectid", ans_col = "response")
long_to_wide_mcq_excel(dat, subj_col = "subjectid", ans_col = "response")

Arguments

`dat`	Long format MCQ data
`subj_col`	Character column name of subject ids
`ans_col`	Character column name of responses

Value

Wide format MCQ data that can be used in the Excel Automated Scorers

Examples

long_to_wide_mcq_excel(generate_data_mcq(2))
long_to_wide_mcq_excel(generate_data_mcq(2))

Example 27-item MCQ data

Description

A dataset containing two participants' data (same data as in the paper by Kaplan et al., 2016)

Usage

mcq27
mcq27

Format

Long-form data.frame with columns: subjectid, questionid, response.

Plot Delay-Discounting Model

Description

This function generates a plot of the delay-discounting data and the fitted model.

Usage

plot_dd(
  fit_dd_object,
  xlabel = "Delay",
  ylabel = "Indifference Point",
  title = "",
  logx = TRUE
)
plot_dd(
  fit_dd_object,
  xlabel = "Delay",
  ylabel = "Indifference Point",
  title = "",
  logx = TRUE
)

Arguments

`fit_dd_object`	A fitted delay-discounting model object of class `"fit_dd"`, created by the `fit_dd()` function.
`xlabel`	A character string specifying the label for the x-axis. Default is `"Delay"`.
`ylabel`	A character string specifying the label for the y-axis. Default is `"Indifference Point"`.
`title`	A character string specifying the plot title. Default is `""`.
`logx`	Logical. If `TRUE`, the x-axis is log-transformed. Default is `TRUE`.

Value

A ggplot object representing the fitted model and data.

Examples

data <- data.frame(
  id = rep(1:2, each = 6),
  x = rep(c(1, 7, 30, 90, 180, 365), 2),
  y = c(0.9, 0.5, 0.3, 0.2, 0.1, 0.05, 0.85, 0.55, 0.35, 0.15, 0.1, 0.05)
)
fit <- fit_dd(data, equation = "mazur", method = "mean")
plot_dd(fit)
data <- data.frame(
  id = rep(1:2, each = 6),
  x = rep(c(1, 7, 30, 90, 180, 365), 2),
  y = c(0.9, 0.5, 0.3, 0.2, 0.1, 0.05, 0.85, 0.55, 0.35, 0.15, 0.1, 0.05)
)
fit <- fit_dd(data, equation = "mazur", method = "mean")
plot_dd(fit)

Plot Proportion of SIR/SS Choices by k Value

Description

This function creates a plot of the proportion of SIR/SS choices by k value using the output of the prop_ss function.

Usage

## S3 method for class 'prop_ss_output'
plot(
  x,
  ...,
  pt_shape = 21,
  pt_fill = "white",
  pt_size = 3,
  title = "Proportion of SIR/SS choices by k value",
  xlab = "k value rank",
  ylab = "Proportion of SS choices"
)
## S3 method for class 'prop_ss_output'
plot(
  x,
  ...,
  pt_shape = 21,
  pt_fill = "white",
  pt_size = 3,
  title = "Proportion of SIR/SS choices by k value",
  xlab = "k value rank",
  ylab = "Proportion of SS choices"
)

Arguments

`x`	Output from the `prop_ss` function
`...`	Additional arguments passed to `ggplot2::geom_point()`
`pt_shape`	Shape of the points in the plot. Default is 21.
`pt_fill`	Fill color of the points in the plot. Default is "white".
`pt_size`	Size of the points in the plot. Default is 3.
`title`	Title of the plot. Default is "Proportion of SIR/SS choices by k value".
`xlab`	Label for the x-axis. Default is "k value rank".
`ylab`	Label for the y-axis. Default is "Proportion of SS choices".

Value

A ggplot object.

Examples

plot(prop_ss(mcq27))
plot(prop_ss(mcq27))

Plot MCQ-27 Scores

Description

This function creates a plot of the MCQ-27 scores for different metrics (small_k, medium_k, large_k, geomean_k, overall_k). The function handles different logarithmic transformations of the k-values and adjusts the y-axis label accordingly.

Usage

## S3 method for class 'score_mcq27_output'
plot(x, ..., xlab = "Metric", alpha = 0.3)
## S3 method for class 'score_mcq27_output'
plot(x, ..., xlab = "Metric", alpha = 0.3)

Arguments

`x`	A data frame returned by the `score_mcq27` function.
`...`	Additional arguments passed to methods.
`xlab`	Label for the x-axis. Default is "Metric".
`alpha`	Transparency of the points in the plot. Default is 0.3.

Value

A ggplot object showing the boxplot of MCQ-27 scores.

Examples

plot(score_mcq27(mcq27))
plot(score_mcq27(mcq27))

Calculate proportion of SIR/SS responses at each k value

Description

Calculate proportion of SIR/SS responses at each k value

Usage

prop_ss(dat)
prop_ss(dat)

Arguments

dat

Dataframe (longform) with subjectid, questionid, and response (0 for SIR/SS and 1 for LDR/LL)

Value

Dataframe with proportion of SIR/SS responses at each k rank

Examples

prop_ss(mcq27)
prop_ss(mcq27)

Extract Results from Delay-Discounting Model

Description

This function extracts model parameter estimates, fit statistics, and confidence intervals from a fitted delay-discounting model.

Usage

results_dd(fit_dd_object)
results_dd(fit_dd_object)

Arguments

fit_dd_object

A fitted delay-discounting model object of class "fit_dd", created by the fit_dd() function.

Value

A tibble containing the following columns:

id: The participant or group ID (if applicable).
term: The model parameter (e.g., k).
estimate: The estimated value of the parameter.
std.error: The standard error of the parameter estimate.
statistic: The t-statistic for the parameter estimate.
p.value: The p-value for the parameter estimate.
conf_low: The lower bound of the 95% confidence interval.
conf_high: The upper bound of the 95% confidence interval.
R2: The coefficient of determination ( $R^2$ ).

Examples

data <- data.frame(
  id = rep(1:2, each = 6),
  x = rep(c(1, 7, 30, 90, 180, 365), 2),
  y = c(0.9, 0.5, 0.3, 0.2, 0.1, 0.05, 0.85, 0.55, 0.35, 0.15, 0.1, 0.05)
)
fit <- fit_dd(data, equation = "mazur", method = "two stage")
results_dd(fit)
data <- data.frame(
  id = rep(1:2, each = 6),
  x = rep(c(1, 7, 30, 90, 180, 365), 2),
  y = c(0.9, 0.5, 0.3, 0.2, 0.1, 0.05, 0.85, 0.55, 0.35, 0.15, 0.1, 0.05)
)
fit <- fit_dd(data, equation = "mazur", method = "two stage")
results_dd(fit)

Score 5.5 trial delay discounting from Qualtrics template

Description

Score 5.5 trial delay discounting from Qualtrics template

Usage

score_dd(df)
score_dd(df)

Arguments

`df`	A dataframe containing all the columns

Details

Currently assumes the attending questions are present and labeled "Attend-LL" and "Attend-SS"

Value

A dataframe with id, indexes, response, k value, and effective delay 50.

Examples

score_dd(five.fivetrial_dd)
score_dd(five.fivetrial_dd)

Score 27-item MCQ

Description

Score 27-item MCQ

Usage

score_mcq27(
  dat = dat,
  impute_method = "none",
  round = 6,
  random = FALSE,
  trans = "none",
  return_data = FALSE,
  verbose = FALSE
)
score_mcq27(
  dat = dat,
  impute_method = "none",
  round = 6,
  random = FALSE,
  trans = "none",
  return_data = FALSE,
  verbose = FALSE
)

Arguments

`dat`	Dataframe (longform) with subjectid, questionid, and response (0 for SIR/SS and 1 for LDR/LL)
`impute_method`	One of: "none", "ggm", "GGM", "inn", "INN"
`round`	Numeric specifying number of decimal places (passed to `base::round()`)
`random`	Boolean whether to insert a random draw (0 or 1) for NAs. Default is FALSE
`trans`	Transformation to apply to k values: "none", "log", or "ln". Default is "none"
`return_data`	Boolean whether to return the original data and new imputed responses. Default is FALSE.
`verbose`	Boolean whether to print subject and question ids pertaining to missing data. Default is FALSE.

Value

Summary dataframe

Examples

score_mcq27(mcq27)
score_mcq27(mcq27)

Score one subject's 27-item MCQ

Description

Score one subject's 27-item MCQ

Usage

score_one_mcq27(dat, impute_method = "none", round = 6)
score_one_mcq27(dat, impute_method = "none", round = 6)

Arguments

`dat`	One subject's 27 items from the MCQ
`impute_method`	One of: "none", "ggm", "GGM", "inn", "INN"
`round`	Numeric specifying number of decimal places (passed to `base::round()`)

Value

Vector with scored 27-item MCQ metrics

Examples

beezdiscounting:::score_one_mcq27(mcq27[mcq27$subjectid %in% 1, ])
beezdiscounting:::score_one_mcq27(mcq27[mcq27$subjectid %in% 1, ])

Score 5.5 trial probability discounting from Qualtrics template

Description

Score 5.5 trial probability discounting from Qualtrics template

Usage

score_pd(df)
score_pd(df)

Arguments

`df`	A dataframe containing all the columns

Details

Currently assumes the attending questions are present and labeled "Attend-LL" and "Attend-SS"

Value

A dataframe with id, indexes, response, h value, and effective probability 50.

Examples

score_pd(five.fivetrial_pd)
score_pd(five.fivetrial_pd)

Provide a summary of the results from the MCQ ouutput table.

Description

Provide a summary of the results from the MCQ ouutput table.

Usage

summarize_mcq(res, na.rm = TRUE)
summarize_mcq(res, na.rm = TRUE)

Arguments

`res`	Dataframe with MCQ results (output from the `calc_mcq` function)
`na.rm`	Boolean whether to remove NAs from the calculation

Value

Dataframe with summary statistics

Examples

summarize_mcq(score_mcq27(mcq27))
summarize_mcq(score_mcq27(mcq27))

Extract timing metrics from 5.5 trial delay discounting from Qualtrics template

Description

Extract timing metrics from 5.5 trial delay discounting from Qualtrics template

Usage

timing_dd(df)
timing_dd(df)

Arguments

`df`	A dataframe containing all the columns

Details

Currently assumes the attending questions are present and labeled "Attend-LL" and "Attend-SS"

Value

A dataframe with ResponseId, indexes, values and timing

Examples

timing_dd(five.fivetrial_dd)
timing_dd(five.fivetrial_dd)

Extract timing metrics from 5.5 trial probability discounting from Qualtrics template

Description

Extract timing metrics from 5.5 trial probability discounting from Qualtrics template

Usage

timing_pd(df)
timing_pd(df)

Arguments

`df`	A dataframe containing all the columns

Details

Currently assumes the attending questions are present and labeled "Attend-LL" and "Attend-SS"

Value

A dataframe with ResponseId, indexes, values and timing

Examples

timing_pd(five.fivetrial_pd)
timing_pd(five.fivetrial_pd)

Reshape MCQ data wide to long

Description

Reshape MCQ data wide to long

Usage

wide_to_long_mcq(dat, items = 27)
wide_to_long_mcq(dat, items = 27)

Arguments

`dat`	Wide format MCQ assuming subject id is in column 1
`items`	Number of MCQ questions

Value

Long format data frame

Reshape MCQ data from wide (as used in the 21- and 27-Item Monetary Choice Questionnaire Automated Scorer) to long

Description

Reshape MCQ data from wide (as used in the 21- and 27-Item Monetary Choice Questionnaire Automated Scorer) to long

Usage

wide_to_long_mcq_excel(dat)
wide_to_long_mcq_excel(dat)

Arguments

dat

Wide format MCQ data as used in the Excel Automated Scorers

Value

Long format data frame

Examples

wide_to_long_mcq_excel(long_to_wide_mcq_excel(generate_data_mcq(2)))

wide_to_long_mcq_excel(long_to_wide_mcq_excel(generate_data_mcq(2)))

Package 'beezdiscounting'

Help Index

Converts answers from 5.5 trial delay discounting from Qualtrics template

Description

Usage

Arguments

Value

Examples

Converts answers from 5.5 trial probability discounting from Qualtrics template

Description

Usage

Arguments

Value

Examples

Calculate Area-Under-the-Curve (AUC) Metrics for Delay Discounting Data

Description

Usage

Arguments

Value

Examples

Calculate Confidence Intervals for a Parameter

Description

Usage

Arguments

Value

Examples

Calculate scores, answers, and timing for 5.5 trial delay discounting from Qualtrics template

Description

Usage

Arguments

Value

Examples

Calculate scores, answers, and timing for 5.5 trial probability discounting from Qualtrics template

Description

Usage

Arguments

Value

Examples

Calculate R-Squared for a Model

Description

Usage

Arguments

Value

Examples

Check for Unsystematic Data Violations

Description

Usage

Arguments

Value

Examples

Delay Discounting Data

Description

Usage

Format

Fit Delay-Discounting Model

Description

Usage

Arguments

Value

Examples

Example Qualtrics output from the 5.5 trial delay discounting template.

Description

Usage

Format

Example Qualtrics output from the 5.5 trial probability discounting template.

Description

Usage

Format

Generate fake MCQ data

Description

Usage

Arguments

Value

Examples

Get internal lookup table for the 27-item MCQ

Description

Usage

Value

Examples

Calculates item nearest neighbor imputation approach discussed by Yeh et al. (2023)