SimDesign (version 0.7)

analyse: Compute estimates and statistics

Description

Computes all relevant test statistics, parameter estimates, detection rates, and so on. This is the computational heavy lifting portion of the Monte Carlo simulation.

Usage

analyse(condition, dat, fixed_objects = NULL, parameters = NULL)

Arguments

condition

a single row from the design input (as a data.frame), indicating the simulation conditions

dat

the dat object returned from the generate function (usually a data.frame, matrix, or vector).

fixed_objects

object passed down from runSimulation

parameters

the (optional) list object named 'parameters' returned from the generate function when a list is returned. Otherwise, this will be an just an empty list

Value

• returns a named numeric vector with the values of interest (e.g., p-values, effects sizes, etc), or a list containing values of interest (e.g., separate matrix and vector of parameter estimates corresponding to elements in parameters)

Details

In some cases, it may be easier to change the output to a named list containing different parameter configurations (e.g., when determining RMSE values for a large set of population parameters).

The use of try functions is generally not required because the function is internally wrapped in a try call. Therefore, if a function stops early then this will cause the function to halt iternally, the message which triggered the stop will be recorded, and generate will be called again to obtain a different dataset. That being said, it may be useful for users to throw their own stop commands if the data should be redrawn for other reasons (e.g., a model terminated correctly but the maximum number of iterations were reached).

See Also

stop

Examples

myanalyse <- function(condition, dat, fixed_objects = NULL, parameters = NULL){

    # require packages/define functions if needed, or better yet index with the :: operator
    require(stats)
    mygreatfunction <- function(x) print('Do some stuff')

    #wrap computational statistics in try() statements to control estimation problems
    welch <- t.test(DV ~ group, dat)
    ind <- stats::t.test(DV ~ group, dat, var.equal=TRUE)

    # In this function the p values for the t-tests are returned,
    #  and make sure to name each element, for future reference
    ret <- c(welch = welch$p.value,
             independent = ind$p.value)

    return(ret)
}