fastei (version 0.0.0.12)

bootstrap: Runs a Bootstrap to Estimate the Standard Deviation of Predicted Probabilities

Description

This function computes the Expected-Maximization (EM) algorithm "nboot" times. It then computes the standard deviation from the nboot estimated probability matrices on each component.

Usage

bootstrap(
  object = NULL,
  X = NULL,
  W = NULL,
  json_path = NULL,
  nboot = 100,
  allow_mismatch = TRUE,
  seed = NULL,
  ...
)

Value

Returns an eim object with the sd field containing the estimated standard deviations of the probabilities and the avg_prob field with the average bootstrapped probability matrix. If an eim object is provided, its attributes (see run_em) are retained in the returned object.

Arguments

object

An object of class eim, which can be created using the eim function. This parameter should not be used if either (i) X and W matrices or (ii) json_path is supplied. See Note.

X

A (b x c) matrix representing candidate votes per ballot box.

W

A (b x g) matrix representing group votes per ballot box.

json_path

A path to a JSON file containing X and W fields, stored as nested arrays. It may contain additional fields with other attributes, which will be added to the returned object.

nboot

Integer specifying how many times to run the EM algorithm.

allow_mismatch

Boolean, if TRUE, allows a mismatch between the voters and votes for each ballot-box. If FALSE, throws an error if there is a mismatch. By default it is TRUE. See Notes for more details.

seed

An optional integer indicating the random seed for the randomized algorithms. This argument is only applicable if initial_prob = "random" or method is either "mcmc" or "mvn_cdf". Aditionally, it sets the random draws of the ballot boxes.

...

Additional arguments passed to the run_em function that will execute the EM algorithm.

See Also

The eim object and run_em implementation.

Examples

# \donttest{
# Example 1: Using an 'eim' object directly
simulations <- simulate_election(
    num_ballots = 200,
    num_candidates = 5,
    num_groups = 3,
)

model <- eim(X = simulations$X, W = simulations$W)

model <- bootstrap(
    object = model,
    nboot = 30,
    method = "mult",
    maxiter = 500,
    verbose = FALSE,
)

# Access standard deviation throughout 'model'
print(model$sd)


# Example 2: Providing 'X' and 'W' matrices directly
model <- bootstrap(
    X = simulations$X,
    W = simulations$W,
    nboot = 15,
    method = "mvn_pdf",
    maxiter = 100,
    maxtime = 5,
    param_threshold = 0.01,
    allow_mismatch = FALSE
)

print(model$sd)
# }

# Example 3: Using a JSON file as input

if (FALSE) {
model <- bootstrap(
    json_path = "path/to/election_data.json",
    nboot = 70,
    method = "mult",
)

print(model$sd)
}