Help for package countts

Title:

Thomson Sampling for Zero-Inflated Count Outcomes

Version:

0.1.0

Description:

A specialized tool is designed for assessing contextual bandit algorithms, particularly those aimed at handling overdispersed and zero-inflated count data. It offers a simulated testing environment that includes various models like Poisson, Overdispersed Poisson, Zero-inflated Poisson, and Zero-inflated Overdispersed Poisson. The package is capable of executing five specific algorithms: Linear Thompson sampling with log transformation on the outcome, Thompson sampling Poisson, Thompson sampling Negative Binomial, Thompson sampling Zero-inflated Poisson, and Thompson sampling Zero-inflated Negative Binomial. Additionally, it can generate regret plots to evaluate the performance of contextual bandit algorithms. This package is based on the algorithms by Liu et al. (2023) <doi:10.48550/arXiv.2311.14359>.

Maintainer:

Tanujit Chakraborty <tanujitisi@gmail.com>

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

Encoding:

UTF-8

RoxygenNote:

7.2.3

Imports:

MASS, parallel, fastDummies, matrixStats, ggplot2, stats

NeedsCompilation:

Packaged:

2023-11-29 04:15:35 UTC; mad-s

Author:

Xueqing Liu [aut], Nina Deliu [aut], Tanujit Chakraborty

[aut, cre, cph], Lauren Bell [aut], Bibhas Chakraborty [aut]

Repository:

CRAN

Date/Publication:

2023-11-29 14:00:10 UTC

Apply the algorithms to make decisions for Thompson sampling Zero-inflated Negative Binomial (TS-ZINB) algorithm

Description

Apply the algorithms to make decisions for Thompson sampling Zero-inflated Negative Binomial (TS-ZINB) algorithm

Usage

apply_ZINB(context, beta_ZINB, gamma_ZINB)

Arguments

context

context at the current decision time

beta_ZINB

the randomly sampled Bayesian estimate for the Poisson component

gamma_ZINB

the randomly sampled Bayesian estimate for the zero component

Value

Intervention option

Examples

apply_ZINB(matrix(1:10, nrow = 2),matrix(11:20, nrow = 5),matrix(21:30, nrow = 5))

Apply the algorithms to make decisions for Thompson sampling Zero-inflated Poisson (TS-ZIP) algorithm

Description

Apply the algorithms to make decisions for Thompson sampling Zero-inflated Poisson (TS-ZIP) algorithm

Usage

apply_ZIP(context, beta_ZIP, gamma_ZIP)

Arguments

context

context at the current decision time

beta_ZIP

the randomly sampled Bayesian estimate for the Poisson component

gamma_ZIP

the randomly sampled Bayesian estimate for the zero component

Value

Intervention option

Examples

apply_ZIP(matrix(1:10, nrow = 2),matrix(11:20, nrow = 5),matrix(21:30, nrow = 5))

Apply the algorithms to make decisions for Thompson sampling Poisson (TS-Poisson) algorithms

Description

Apply the algorithms to make decisions for Thompson sampling Poisson (TS-Poisson) algorithms

Usage

apply_laplacePoisson(context, beta_laplacePoisson)

Arguments

context

context at the current decision time

beta_laplacePoisson

the randomly sampled Bayesian estimate

Value

Intervention option

Examples

apply_laplacePoisson(matrix(1:10, nrow = 2),matrix(11:20, nrow = 5))

Apply the algorithms to make decisions for Linear Thompson sampling (TS) algorithms

Description

Apply the algorithms to make decisions for Linear Thompson sampling (TS) algorithms

Usage

apply_linearTS(context, beta_linearTS)

Arguments

context

context at the current decision time

beta_linearTS

the randomly sampled Bayesian estimate

Value

Intervention option

Examples

apply_linearTS(matrix(1:10, nrow = 2),matrix(11:20, nrow = 5))

Apply the algorithms to make decisions for Thompson sampling Negative Binomial (TS-NB) algorithms

Description

Apply the algorithms to make decisions for Thompson sampling Negative Binomial (TS-NB) algorithms

Usage

apply_normalNB(context, beta_normalNB)

Arguments

context

context at the current decision time

beta_normalNB

the randomly sampled Bayesian estimate

Value

Intervention option

Examples

apply_normalNB(matrix(1:10, nrow = 2),matrix(11:20, nrow = 5))

Summarize the simulation results and generate the regret plot

Description

Summarize the simulation results and generate the regret plot

Usage

output_summary(
  S = 30,
  num_cov = 4,
  T.init = 20,
  T0 = 1000,
  alpha = 1,
  gam = 25,
  K = 20,
  dist_env = c("Negative Binomial", "Poisson", "Linear TS", "ZIP", "ZINB"),
  show_figure = TRUE
)

Arguments

S

number of replicates of the experiment (greater than 1). Default is 30.

num_cov

dimension for beta and gamma; we assume that they have the same dimensions for now. Default is 4.

T.init

length of the initial exploration stage. Default is 20.

T0

number of decision times. Default is 1000.

alpha

tuning parameter that controls the exploration-exploitation tradeoff. Default is 1.

gam

over dispersion level of the environment model; this is only useful when the environment model is negative binomial or zero-inflated negative binomial. Default is 25.

K

number of actions/intervention options. Default is 20.

dist_env

tuning parameter that controls which environment model to use, with the options "Negative Binomial", "Poisson", "Linear TS", "ZIP", "ZINB"

show_figure

A logical flag specifying that the regret plot of the model should be returned if true (default), otherwise, false.

Value

The summary of the simulation results with cumulative regret, regret, and parameters is generated along with the optional output of the regret plot (show_figure = TRUE).

References

Liu, X., Deliu, N., Chakraborty, T., Bell, L., & Chakraborty, B. (2023). Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study. arXiv preprint arXiv:2311.14359. https://arxiv.org/abs/2311.14359

Examples

output_summary(S = 2, num_cov = 2, T.init = 3, T0 = 5, dist_env = "Negative Binomial")

Updating parameters in algorithm

Description

Updating parameters in algorithm

Usage

update_algorithm(
  dist = c("Negative Binomial", "Poisson", "Linear TS", "ZIP", "ZINB"),
  Y_dist = 2,
  X_dist = 3,
  alpha_dist = 4,
  Bt = NULL,
  bt = NULL
)

Arguments

dist

tuning parameter that controls which algorithm should be updated, with the options "Negative Binomial", "Poisson", "Linear TS", "ZIP", "ZINB"

Y_dist

History of the observed stochastic outcome at the current decision time

X_dist

History of the observed context at the current decision time

alpha_dist

tuning parameter that controls the exploration-exploitation tradeoff. Default is 1.

Bt

Outer product of contexts, only for dist = "Linear TS", default is NULL

bt

Sum of contexts weighted by the outcome, only for dist = "Linear TS", default is NULL.

Value

The updated parameter estimates.

Examples

update_algorithm(dist = "Negative Binomial")