return_mmc: S3 class 'mmc'.
In julienneves/maxMC: Maximized Monte Carlo

Description Usage Arguments Examples

S3 class mmc.

1 2	return_mmc(S0, y, statistic, dgp, est, lower, upper, N, type, method, alpha, control, call, seed, lmc, opt_result, opt_trace)

`S0`	An atomic vector. Value of the test statistic applied to the data.
`y`	A vector or data frame.
`statistic`	A function or a character string that specifies how the statistic is computed. The function needs to input the `y` and output a scalar.
`dgp`	A function. The function inputs the first argument `y` and a vector of nuisance parameters `v` and outputs a simulated `y`. It should represent the data generating process under the null. Default value is the function `sample(y, replace = TRUE)`, i.e. the bootstrap resampling of `y`.
`est`	A vector with length of v. It is the starting point of the algorithm. If `est` is a consistent estimate of `v` then `mmc` will return both the MMC and Local Monte Carlo (LMC). Default is NULL, in which case, default values will be generated automatically.
`lower`	A vector with length of v. Lower bounds for nuisance parameters under the null. See Details.
`upper`	A vector with length of v. Upper bounds for nuisance parameters under the null. See Details.
`N`	An atomic vector. Number of replications of the test statistic.
`type`	A character string. It specifies the type of test the p-value function produces. The possible values are `geq`, `leq`, `absolute` and `two-tailed`. Default is `geq`.
`method`	A character string. Type of algorithm to be used for global optimization. The four available methods are simulated annealing (`GenSA`), particle swarm (`pso`), genetic algorithm (`GA`), and grid search (`gridSearch`) Default is `GenSA`,
`alpha`	An atomic vector. If `mmc` finds a p-value over `alpha`, then the algorithm will stop. This is particularly useful if we are only looking at testing a hypothesis at a particular level. Default is NULL.
`control`	A list. Arguments to be used to control the behavior of the algorithm chosen in `method`. See controls section for more details.
`call`	An object of class `call`. Original call to `mmc`
`seed`	An integer vector. Value of `.Random.seed` at the start of `mmc` call.
`lmc`	An object of class `mc`. If `par` is specified, it returns an object of class `mc` corresponding the Local Monte Carlo test.
`opt_result`	An object returning the optimization results.
`opt_trace`	An object returning the optimization results.

# Generate typical data found inside the function mmc()
S0 <-  0
y <-  rnorm(100)
statistic <-  function(y, v) mean(y) * v
dgp <-  function(y, v) sample(y, replace = TRUE)
est <- 1
lower <- -1
upper <- 1
N <-  99
type <-  "geq"
method <- "GenSA"
alpha <- 0.05
control <- MaxMC:::get_control(method)
call <- call("mc",y= y, statistic = statistic, dgp = dgp, N = 99, type = type)
seed <-  NULL
lmc <- list(pval = 0.10)
pval <-  0.65
opt_result <- list(value = 0.10)
opt_trace  <- as.data.frame(matrix(data = NA, control$maxit, 3,
                                   dimnames = list(NULL,c("ite","pval","max"))))

# Combine data and return it as an object of class 'mmc'
MaxMC:::return_mmc(S0 = S0, y = y, statistic = statistic, dgp = dgp, est = est, lower = lower,
                   upper = upper, N = N, type = type, method = method, alpha = alpha,
                   control = control, call = call, seed = seed, lmc = lmc,
                   opt_result = opt_result, opt_trace = opt_trace)