Package 'samplr'

Description

As described in (Zhu et al. 2020). Vectors can be provided for each parameter, allowing multiple estimates at once.

Usage

Bayesian_Sampler(
  a_and_b,
  b_and_not_a,
  a_and_not_b,
  not_a_and_not_b,
  beta,
  N,
  N2 = NULL
)

Arguments

Value

Named list with predicted probabilities for every possible combination of A and B.

Examples

Bayesian_Sampler(
    a_and_b = c(.4, .25),
    b_and_not_a = c(.4,  .25),
    a_and_not_b = c(.1, .25),
    not_a_and_not_b = c(.1, .25),
    beta = 1,
    N <- c(10, 12),
    N2 <- c(10, 10)
)

Description

Calculates all diagnostic functions in the samplr package for a given chain. Optionally, plots them.

Usage

calc_all(chain, plot = TRUE, acf.alpha = 0.05, acf.lag.max = 100)

Arguments

Value

A list with all diagnostic calculations (a list of lists); and optionally a grid of plots.

Examples

set.seed(1)
chain1 <- sampler_mh(1, "norm", c(0,1), diag(1))
diagnostics <- calc_all(chain1[[1]])
names(diagnostics)

Description

Calculates the autocorrelation of a given sequence, or of the size of the steps (returns).

Usage

calc_autocorr(chain, change = TRUE, alpha = 0.05, lag.max = 100, plot = FALSE)

Arguments

Details

Markets display no significant autocorrelations in the returns of a given asset.

Value

A vector with the standard deviations at each lag

Examples

set.seed(1)
chain1 <- sampler_mh(1, "norm", c(0,1), diag(1))
calc_autocorr(chain1[[1]], plot=TRUE)

Description

This function analyses if the length of the jumps the sampler is making ($l$) belongs to a Levy probability density distribution, $P(l) \approx l^{-\mu}$.

Usage

calc_levy(chain, plot = FALSE)

Arguments

Details

Values of $\mu \approx 2$ have been used to describe foraging in animals, and produce the most effective foraging (Viswanathan et al. 1999). See Zhu et al. (2018) for a comparison of Levy Flight and PSD measures for different samplers in multimodal representations.

Value

If plot is true, it returns a simple plot with the log absolute difference in estimates and their frequency, as well as an estimate for the $\mu$ parameter. If false it returns a list with what's required to make the plot.

References

Viswanathan GM, Buldyrev SV, Havlin S, Da Luz MGE, Raposo EP, Stanley HE (1999). “Optimizing the Success of Random Searches.” Nature, 401(6756), 911–914. doi:10.1038/44831.

Zhu J, Sanborn AN, Chater N (2018). “Mental Sampling in Multimodal Representations.” Advances in Neural Information Processing Systems, 31, 5748–5759.

Examples

set.seed(1)
chain1 <- sampler_mh(1, "norm", c(0,1), diag(1))
calc_levy(chain1[[1]], plot=TRUE)

Description

This function estimates the log power spectral density against the log frequency, and calculates a slope $\alpha$.

Usage

calc_PSD(chain, plot = FALSE)

Arguments

Details

A number of studies have reported that cognitive activities contain a long-range slowly decaying autocorrelation. In the frequency domain, this is expressed as $S(f)$ ~ $1/f^{-\alpha}$, with $f$ being frequency, $S(f)$ being spectral power, and $\alpha$ $\epsilon$ $[0.5,1.5]$ is considered $1/f$ scaling. See See Zhu et al. (2018) for a comparison of Levy Flight and PSD measures for different samplers in multimodal representations.

Value

Returns a list with log frequencies, log PSDs, and slope and intercept estimates.

References

Zhu J, Sanborn AN, Chater N (2018). “Mental Sampling in Multimodal Representations.” Advances in Neural Information Processing Systems, 31, 5748–5759.

Examples

set.seed(1)
chain1 <- sampler_mh(1, "norm", c(0,1), diag(1))
calc_PSD(chain1[[1]], plot= TRUE)

Description

Estimates values for a QQ plot of Empirical values against Theoretical values from a normal distribution, for either the chain points or the distances between successive points. Optionally, returns a plot as well as the values.

Usage

calc_qqplot(chain, change = TRUE, plot = FALSE)

Arguments

Value

A list with the theoretical and empirical quantiles, and the intercept and slope of the line connecting the points

Examples

set.seed(1)
chain1 <- sampler_mh(1, "norm", c(0,1), diag(1))
calc_qqplot(chain1[[1]], plot = TRUE)

Description

Calculates the sigma scaling of the chain, and optionally plots the result.

Usage

calc_sigma_scaling(chain, plot = FALSE)

Arguments

Details

Sigma scaling is defined as the slope of the regression connecting log time lags and the standard deviation of value changes across time lags. Markets show values of 0.5.

Value

A list containing the vector of possible lags, the sd of the distances at each lag, their log10 counterparts, and the calculated intercept and slope.

Examples

set.seed(1)
chain1 <- sampler_mh(1, "norm", c(0,1), diag(1))
calc_sigma_scaling(chain1[[1]], plot = TRUE)

Description

Estimates number of samples and prior parameters of the Bayesian Sampler using the Mean/Variance relationship as shown by (Sundh et al. 2023). For consistency with the Bayesian Sampler function we call beta the prior parameter, and b0 and b1 slope and intercept respectively.

Usage

Mean_Variance(rawData, idCol)

Arguments

Value

A dataframe with values for the intercept (b0) and slope (b1) of the estimated regression, as well as estimates for N, d, and beta (termed b in the paper) for each participant.

Examples

library(dplyr)
library(tidyr)
library(magrittr)
library(samplrData)
data <- sundh2023.meanvariance.e3 %>%
  group_by(ID, querydetail) %>% 
  mutate(iteration = LETTERS[1:n()]) %>% 
  pivot_wider(id_cols = c(ID, querydetail), 
      values_from = estimate, names_from = iteration) %>% 
  mutate(across(where(is.numeric), \(x){x/100})) %>% 
  ungroup %>% 
  select(-querydetail)
head(data)
head(Mean_Variance(data, "ID"))

Description

Plots a 2D map of the density of a distribution. If plot = FALSE, returns a dataframe with the density for each cell in the grid

Usage

plot_2d_density(
  start,
  size,
  cellsPerRow = 50,
  names = NULL,
  params = NULL,
  weights = NULL,
  customDensity = NULL,
  plot = TRUE
)

Arguments

Value

Density Plot or dataframe

Examples

# plot supported distribution
plot_2d_density(
c(-5, -5), 10, cellsPerRow = 100, names = c("mvnorm", "mvnorm"),
params = list(list(c(-2,1), diag(2)), list(c(2,1), diag(2)))
)

# plot custom distribution
customDensity_r <- function(x){
    if (x[1] > 0 && x[1] < 3 && x[2] < 0 && x[2] > -3){
        return (1)
    } else {
        return (0)
    }
}
plot_2d_density(start = c(0,-4), size = 5, customDensity = customDensity_r)

Description

Plots the value of a one-dimensional series against the iteration where it occurred. Useful to see the general pattern of the chain (white noise, random walk, volatility clustering)

Usage

plot_series(chain, change = FALSE)

Arguments

Value

A series plot

Examples

set.seed(1)
chain1 <- sampler_mh(1, "norm", c(0,1), diag(1))
plot_series(chain1[[1]])

Description

Hamiltonian Monte-Carlo, also called Hybrid Monte Carlo, is a sampling algorithm that uses Hamiltonian Dynamics to approximate a posterior distribution. Unlike MH and MC3, HMC uses not only the current position, but also a sense of momentum, to draw future samples. An introduction to HMC can be read in Betancourt (2018).

Usage

sampler_hmc(
  start,
  distr_name = NULL,
  distr_params = NULL,
  epsilon = 0.5,
  L = 10,
  iterations = 1024,
  weights = NULL,
  custom_density = NULL
)

Arguments

Details

This implementations assumes that the momentum is drawn from a normal distribution with mean 0 and identity covariance matrix (p ~ N (0, I)). Hamiltonian Monte Carlo does not support discrete distributions.

This algorithm has been used to model human data in Aitchison and Lengyel (2016), Castillo et al. (2024) and Zhu et al. (2022) among others.

Value

A named list containing

1. Samples: the history of visited places (an n x d matrix, n = iterations; d = dimensions)

2. Momentums: the history of momentum values (an n x d matrix, n = iterations; d = dimensions). Nothing is proposed in the first iteration (the first iteration is the start value) and so the first row is NA

3. Acceptance Ratio: The proportion of proposals that were accepted.

References

Aitchison L, Lengyel M (2016). “The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics.” PLOS Computational Biology, 12(12), e1005186. doi:10.1371/journal.pcbi.1005186.

Betancourt M (2018). “A Conceptual Introduction to Hamiltonian Monte Carlo.” http://arxiv.org/abs/1701.02434.

Castillo L, León-Villagrá P, Chater N, Sanborn A (2024). “Explaining the Flaws in Human Random Generation as Local Sampling with Momentum.” PLOS Computational Biology, 20(1), 1–24. doi:10.1371/journal.pcbi.1011739.

Zhu J, León-Villagrá P, Chater N, Sanborn AN (2022). “Understanding the Structure of Cognitive Noise.” PLoS Computational Biology, 18(8), e1010312. doi:10.1371/journal.pcbi.1010312.

Examples

result <- sampler_hmc(
    distr_name = "norm", distr_params = c(0,1), 
    start = 1, epsilon = .01, L = 100
    )
cold_chain <- result$Samples

Description

This sampler is a variant of MH in which multiple parallel chains are run at different temperatures. The chains stochastically swap positions which allows the coldest chain to visit regions far from its starting point (unlike in MH). Because of this, an MC3 sampler can explore far-off regions, whereas an MH sampler may become stuck in a particular point of high density.

Usage

sampler_mc3(
  start,
  distr_name = NULL,
  distr_params = NULL,
  sigma_prop = NULL,
  nChains = 6,
  delta_T = 4,
  swap_all = TRUE,
  iterations = 1024L,
  weights = NULL,
  custom_density = NULL,
  alpha = 0
)

Arguments

Details

This algorithm has been used to model human data in Castillo et al. (2024), Zhu et al. (2022) and Zhu et al. (2018) among others.

Value

A named list containing

1. Samples: the history of visited places (an n x d x c array, n = iterations; d = dimensions; c = chain index, with c==1 being the 'cold chain')

2. Proposals: the history of proposed places (an n x d x c array, n = iterations; d = dimensions; c = chain index, with c==1 being the 'cold chain'). Nothing is proposed in the first iteration (the first iteration is the start value) and so the first row is NA

3. Acceptance Ratio: The proportion of proposals that were accepted (for each chain).

4. Beta Values: The set of temperatures used in each chain

5. Swap History: the history of chain swaps

6. Swap Acceptance Ratio: The ratio of swap acceptances

References

Castillo L, León-Villagrá P, Chater N, Sanborn A (2024). “Explaining the Flaws in Human Random Generation as Local Sampling with Momentum.” PLOS Computational Biology, 20(1), 1–24. doi:10.1371/journal.pcbi.1011739.

Zhu J, León-Villagrá P, Chater N, Sanborn AN (2022). “Understanding the Structure of Cognitive Noise.” PLoS Computational Biology, 18(8), e1010312. doi:10.1371/journal.pcbi.1010312.

Zhu J, Sanborn AN, Chater N (2018). “Mental Sampling in Multimodal Representations.” Advances in Neural Information Processing Systems, 31, 5748–5759.

Examples

# Sample from a normal distribution
result <- sampler_mc3(
    distr_name = "norm", distr_params = c(0,1), 
    start = 1, sigma_prop = diag(1)
)
cold_chain <- result$Samples[,,1]

Description

Metropolis-Coupled version of HMC, i.e. running multiple chains at different temperatures which stochastically swap positions.

Usage

sampler_mchmc(
  start,
  distr_name = NULL,
  distr_params = NULL,
  epsilon = 0.5,
  L = 10,
  nChains = 6,
  delta_T = 4,
  swap_all = TRUE,
  iterations = 1024L,
  weights = NULL,
  custom_density = NULL
)

Arguments

Details

Metropolis-Coupled HMC does not support discrete distributions.

This algorithm has been used to model human data in Castillo et al. (2024).

Value

A named list containing

1. Samples: the history of visited places (an n x d x c array, n = iterations; d = dimensions; c = chain index, with c==1 being the 'cold chain')

2. Momentums: the history of momentum values (an n x d x c array, n = iterations; d = dimensions; c = chain index, with c==1 being the 'cold chain'). Nothing is proposed in the first iteration (the first iteration is the start value) and so the first row is NA

3. Acceptance Ratio: The proportion of proposals that were accepted (for each chain).

4. Beta Values: The set of temperatures used in each chain

5. Swap History: the history of chain swaps

6. Swap Acceptance Ratio: The ratio of swap acceptances

References

Castillo L, León-Villagrá P, Chater N, Sanborn A (2024). “Explaining the Flaws in Human Random Generation as Local Sampling with Momentum.” PLOS Computational Biology, 20(1), 1–24. doi:10.1371/journal.pcbi.1011739.

Examples

result <- sampler_mchmc(
    distr_name = "norm", distr_params = c(0,1), 
    start = 1, epsilon = .01, L = 100
)
cold_chain <- result$Samples[,,1]

Description

Metropolis-Coupled version of Recycled-Momentum HMC, i.e. running multiple chains at different temperatures which stochastically swap positions.

Usage

sampler_mcrec(
  start,
  distr_name = NULL,
  distr_params = NULL,
  epsilon = 0.5,
  L = 10,
  alpha = 0.1,
  nChains = 6,
  delta_T = 4,
  swap_all = TRUE,
  iterations = 1024L,
  weights = NULL,
  custom_density = NULL
)

Arguments

Details

Metropolis-Coupled Recycled-Momentum HMC does not support discrete distributions.

This algorithm has been used to model human data in Castillo et al. (2024).

Value

A named list containing

1. Samples: the history of visited places (an n x d x c array, n = iterations; d = dimensions; c = chain index, with c==1 being the 'cold chain')

2. Momentums: the history of momentum values (an n x d x c array, n = iterations; d = dimensions; c = chain index, with c==1 being the 'cold chain'). Nothing is proposed in the first iteration (the first iteration is the start value) and so the first row is NA

3. Acceptance Ratio: The proportion of proposals that were accepted (for each chain).

4. Beta Values: The set of temperatures used in each chain

5. Swap History: the history of chain swaps

6. Swap Acceptance Ratio: The ratio of swap acceptances

References

Castillo L, León-Villagrá P, Chater N, Sanborn A (2024). “Explaining the Flaws in Human Random Generation as Local Sampling with Momentum.” PLOS Computational Biology, 20(1), 1–24. doi:10.1371/journal.pcbi.1011739.

Examples

result <- sampler_mcrec(
    distr_name = "norm", distr_params = c(0,1), 
    start = 1, epsilon = .01, L = 100
)
cold_chain <- result$Samples[,,1]

Description

This sampler navigates the proposal distribution following a random walk. At each step, it generates a new proposal from a proposal distribution (in this case a Gaussian centered at the current position) and chooses to accept it or reject it following the Metropolis-Hastings rule: it accepts it if the density of the posterior distribution at the proposed point is higher than at the current point. If the current position is denser, it still may accept the proposal with probability proposal_density / current_density.

Usage

sampler_mh(
  start,
  distr_name = NULL,
  distr_params = NULL,
  sigma_prop = NULL,
  iterations = 1024L,
  weights = NULL,
  custom_density = NULL,
  alpha = 0
)

Arguments

Details

As mentioned, the proposal distribution is a Normal distribution. Its mean is the current position, and its variance is equal to the sigma_prop parameter, which defaults to the identity matrix if not specified.

This algorithm has been used to model human data in many places (e.g. Castillo et al. 2024; Dasgupta et al. 2017; Lieder et al. 2018; Zhu et al. 2022).

Value

A named list containing

1. Samples: the history of visited places (an n x d matrix, n = iterations; d = dimensions)

2. Proposals: the history of proposed places (an n x d matrix, n = iterations; d = dimensions). Nothing is proposed in the first iteration (the first iteration is the start value) and so the first row is NA

3. Acceptance Ratio: The proportion of proposals that were accepted.

References

Castillo L, León-Villagrá P, Chater N, Sanborn A (2024). “Explaining the Flaws in Human Random Generation as Local Sampling with Momentum.” PLOS Computational Biology, 20(1), 1–24. doi:10.1371/journal.pcbi.1011739.

Dasgupta I, Schulz E, Gershman SJ (2017). “Where Do Hypotheses Come From?” Cognitive Psychology, 96, 1–25. doi:10.1016/j.cogpsych.2017.05.001.

Lieder F, Griffiths TL, M. Huys QJ, Goodman ND (2018). “The Anchoring Bias Reflects Rational Use of Cognitive Resources.” Psychonomic Bulletin & Review, 25(1), 322–349. doi:10.3758/s13423-017-1286-8.

Zhu J, León-Villagrá P, Chater N, Sanborn AN (2022). “Understanding the Structure of Cognitive Noise.” PLoS Computational Biology, 18(8), e1010312. doi:10.1371/journal.pcbi.1010312.

Examples

# Sample from a normal distribution
result <- sampler_mh(
         distr_name = "norm", distr_params = c(0,1),
         start = 1, sigma_prop = diag(1)
         )
cold_chain <- result$Samples

Description

Recycled-Momentum HMC is a sampling algorithm that uses Hamiltonian Dynamics to approximate a posterior distribution. Unlike in standard HMC, proposals are autocorrelated, as the momentum of the current trajectory is not independent of the last trajectory, but is instead updated by a parameter alpha (see Details).

Usage

sampler_rec(
  start,
  distr_name = NULL,
  distr_params = NULL,
  epsilon = 0.5,
  L = 10,
  alpha = 0.1,
  iterations = 1024L,
  weights = NULL,
  custom_density = NULL
)

Arguments

Details

While in HMC the momentum in each iteration is an independent draw,, here the momentum of the last utterance $p^{n-1}$ is also involved. In each iteration, the momentum $p$ is obtained as follows

$p \gets \alpha \times p^{n-1} + (1 - \alpha^2)^{\frac{1}{2}} \times v$

; where $v \sim N(0, I)$.

Recycled-Momentum HMC does not support discrete distributions.

This algorithm has been used to model human data in Castillo et al. (2024)

Value

A named list containing

1. Samples: the history of visited places (an n x d x c array, n = iterations; d = dimensions; c = chain index, with c==1 being the 'cold chain')

2. Momentums: the history of momentum values (an n x d matrix, n = iterations; d = dimensions). Nothing is proposed in the first iteration (the first iteration is the start value) and so the first row is NA

3. Acceptance Ratio: The proportion of proposals that were accepted (for each chain).

References

Castillo L, León-Villagrá P, Chater N, Sanborn A (2024). “Explaining the Flaws in Human Random Generation as Local Sampling with Momentum.” PLOS Computational Biology, 20(1), 1–24. doi:10.1371/journal.pcbi.1011739.

Examples

result <- sampler_rec(
    distr_name = "norm", distr_params = c(0,1), 
    start = 1, epsilon = .01, L = 100
)
cold_chain <- result$Samples

Description

Calculates identities Z1 to Z18 as defined in (Costello and Watts 2016; Zhu et al. 2020). Probability theory predicts that these will all equal 0.

Usage

Z_identities(
  a = NULL,
  b = NULL,
  a_and_b = NULL,
  a_or_b = NULL,
  a_given_b = NULL,
  b_given_a = NULL,
  a_given_not_b = NULL,
  b_given_not_a = NULL,
  a_and_not_b = NULL,
  b_and_not_a = NULL,
  not_a = NULL,
  not_b = NULL
)

Arguments

Details

If some of the probability estimates are not given, calculation will proceed and equalities that cannot be calculated will be coded as NA.

Value

Dataframe with identities Z1 to Z18

Examples

Z_identities(
 a=.5, 
 b=.1, 
 a_and_b=.05, 
 a_or_b=.55, 
 a_given_b=.5,
 b_given_a=.1,
 a_given_not_b=.5,
 b_given_not_a=.1,
 a_and_not_b=.45,
 b_and_not_a=.05,
 )
#Get identities for a set of participants
library(magrittr)
library(dplyr)
library(tidyr)
data.frame(
 ID = LETTERS[1:20],
 a=runif(20),
 b=runif(20),
 a_and_b=runif(20),
 a_or_b=runif(20),
 a_given_b=runif(20),
 b_given_a=runif(20),
 a_given_not_b=runif(20),
 b_given_not_a=runif(20),
 a_and_not_b=runif(20),
 b_and_not_a=runif(20),
 not_a=runif(20),
 not_b=runif(20)
) %>% 
 group_by(ID) %>% 
 do(
   Z_identities(
     .$a,
     .$b,
     .$a_and_b,
     .$a_or_b,
     .$a_given_b,
     .$b_given_a,
     .$a_given_not_b,
     .$b_given_not_a,
     .$a_and_not_b,
     .$b_and_not_a,
     .$not_a,
     .$not_b
   )
 )

Description

This Auto-correlated Bayesian Sampler model (ABS, Zhu et al. 2024) is developed by Zhu.

Super class

samplr::CoreABS -> Zhu23ABS

Public fields

Methods

Public methods

• Zhu23ABS$new()

• Zhu23ABS$simulate()

• Zhu23ABS$confidence_interval()

• Zhu23ABS$reset_sim_results()

• Zhu23ABS$clone()

Method new()

Create a new 'Zhu23ABS' object.

Usage

Zhu23ABS$new(
  width,
  n_chains,
  nd_time,
  s_nd_time,
  lambda,
  distr_name = NULL,
  distr_params = NULL,
  custom_distr = NULL,
  custom_start = NULL
)

Arguments

Returns

A new 'Zhu23ABS' object.

Examples

zhuabs <- Zhu23ABS$new(
    width = 1, n_chains = 5, nd_time = 0.3, s_nd_time = 0.5, 
    lambda = 10, distr_name = 'norm', distr_params = 1
)

Method simulate()

Simulate the ABS model.

Usage

Zhu23ABS$simulate(stopping_rule, start_point = NA, ...)

Arguments

Details

The ABS model has two types of stopping rules: fixed and relative. The fixed stopping rule means that a fixed number of samples are drawn to complete the tasks such as estimations and confidence intervals. This rule applies to tasks such as estimation tasks. On the other hand, the relative stopping rule means that the model counts the difference in evidence between the two hypotheses, and terminates the sampling process whenever the accumulated difference exceeds a threshold. This rule applies to tasks such as two-alternative force choice tasks.

When the ⁠stopping rule⁠ is "fixed", the following arguments are required:

• n_sample an integer of the fixed number of samples for each trial.

• trial_stim a numeric vector of the stimulus of each trial.

When the ⁠stopping rule⁠ is "relative", the following arguments are required:

• delta an integer of the relative difference between the number of samples supporting each hypothesis.

• dec_bdry a numeric value of the decision boundary that separates the posterior hypothesis distribution.

• discrim a numeric value of the stimuli discriminability.

• trial_stim a factor that indicates the stimuli of each trial. It only consists of either one level or two levels. By definition, level 1 represents the stimulus below the decision boundary, while level 2 represents the stimulus above the decision boundary.

• prior_on_resp a numeric vector for the Beta prior on responses. Defaults to c(1,1) representing the distribution Beta(1,1).

• prior_depend a boolean variable that control whether the prior on responses changes regarding the last stimulus. Defaults to TRUE. Please refer to the vignette for more information.

• max_iterations an integer of the maximum length of the MC3 sampler. Defaults to 1000. The program will stop the sampling process after the length of the sampling sequence reaches to this limitation.

No values will be return after running this method, but the field sim_results will be updated instead. If the stopping rule is "fixed", simulation_results will be a data frame with five columns:

1. trial: The index of trials;

2. samples: The samples of ABS sampler for the trial;

3. stimulus: The stimuli of the experiment;

4. rt: The response time;

5. point_est: The response of point estimation;

On the other hand, if the stopping rule is "relative", sim_results will be a data frame with seven columns:

1. trial: The index of trials;

2. samples: The samples of ABS sampler for the trial;

3. response: The response predicted by ABS;

4. stimulus: The stimuli of the experiment;

5. accuracy: Whether the response is the same as the feedback. 0 represents error, and 1 represents correct;

6. rt: The response time, including both the non-decision and the decision time;

7. confidence: The confidence of the response.

Examples

trial_stim <- round(runif(5, 10, 50))
zhuabs$simulate(stopping_rule='fixed', n_sample = 5, trial_stim = trial_stim)
zhuabs$sim_results

zhuabs$reset_sim_results()
trial_stim <- factor(sample(c('left', 'right'), 5, TRUE))
zhuabs$simulate(stopping_rule='relative', 
   delta = 4, dec_bdry = 0, 
   discrim = 1, trial_stim = trial_stim
)
zhuabs$sim_results

Method confidence_interval()

This function calculates the confidence interval of the simulate method's results when the "fixed" stopping rule was used.

Usage

Zhu23ABS$confidence_interval(conf_level)

Arguments

Details

No values will be returned by this method. Instead, two new columns will be added to the sim_results:

1. conf_interval_l: The lower bound of the confidence interval with the given level;

2. conf_interval_u: The upper bound of the confidence interval with the given level;

Examples

zhuabs$confidence_interval(conf_level = 0.9)

Method reset_sim_results()

This function is for resetting the sim_results to run new simulations.

Usage

Zhu23ABS$reset_sim_results()

Method clone()

The objects of this class are cloneable with this method.

Usage

Zhu23ABS$clone(deep = FALSE)

Arguments

References

Zhu J, Sundh J, Spicer J, Chater N, Sanborn AN (2024). “The Autocorrelated Bayesian Sampler: A Rational Process for Probability Judgments, Estimates, Confidence Intervals, Choices, Confidence Judgments, and Response Times.” Psychological Review, 131(2), 456–493. doi:10.1037/rev0000427.

Examples

## ------------------------------------------------
## Method `Zhu23ABS$new`
## ------------------------------------------------

zhuabs <- Zhu23ABS$new(
    width = 1, n_chains = 5, nd_time = 0.3, s_nd_time = 0.5, 
    lambda = 10, distr_name = 'norm', distr_params = 1
)


## ------------------------------------------------
## Method `Zhu23ABS$simulate`
## ------------------------------------------------


trial_stim <- round(runif(5, 10, 50))
zhuabs$simulate(stopping_rule='fixed', n_sample = 5, trial_stim = trial_stim)
zhuabs$sim_results

zhuabs$reset_sim_results()
trial_stim <- factor(sample(c('left', 'right'), 5, TRUE))
zhuabs$simulate(stopping_rule='relative', 
   delta = 4, dec_bdry = 0, 
   discrim = 1, trial_stim = trial_stim
)
zhuabs$sim_results


## ------------------------------------------------
## Method `Zhu23ABS$confidence_interval`
## ------------------------------------------------

zhuabs$confidence_interval(conf_level = 0.9)