Package 'countts'

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))

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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))

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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))

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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))

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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))

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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")

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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")

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