# Perform a test that indicates whether a given `Analyse()` function should be executed This function is designed to prevent specific analysis function executions when the design conditions are not met. Primarily useful when the `analyse` argument to `runSimulation` was input as a named list object, however some of the analysis functions are not interesting/compatible with the generated data and should therefore be skipped. ```r AnalyseIf(x, condition = NULL) ``` ## Arguments - `x`: logical statement to evaluate. If the statement evaluates to `TRUE` then the remainder of the defined function will be evaluated - `condition`: (optional) the current design condition. This does not need to be supplied if the expression in `x` evaluates to valid logical (e.g., use `Attach(condition)` prior to using `AnalyseIf`, or use `with(condition, AnalyseIf(someLogicalTest))`) ## Examples ```r ## Not run: Design <- createDesign(N=c(10,20,30), var.equal = c(TRUE, FALSE)) Generate <- function(condition, fixed_objects) { Attach(condition) dat <- data.frame(DV = rnorm(N*2), IV = gl(2, N, labels=c('G1', 'G2'))) dat } # always run this analysis for each row in Design Analyse1 <- function(condition, dat, fixed_objects) { mod <- t.test(DV ~ IV, data=dat) mod$p.value } # Only perform analysis when variances are equal and N = 20 or 30 Analyse2 <- function(condition, dat, fixed_objects) { AnalyseIf(var.equal && N %in% c(20, 30), condition) mod <- t.test(DV ~ IV, data=dat, var.equal=TRUE) mod$p.value } Summarise <- function(condition, results, fixed_objects) { ret <- EDR(results, alpha=.05) ret } #------------------------------------------------------------------- # append names 'Welch' and 'independent' to associated output res <- runSimulation(design=Design, replications=100, generate=Generate, analyse=list(Welch=Analyse1, independent=Analyse2), summarise=Summarise) res # leave results unnamed res <- runSimulation(design=Design, replications=100, generate=Generate, analyse=list(Analyse1, Analyse2), summarise=Summarise) ## End(Not run) ``` ## References Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package. `The Quantitative Methods for Psychology, 16`(4), 248-280. tools:::Rd_expr_doi("10.20982/tqmp.16.4.p248") Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. `Journal of Statistics Education, 24`(3), 136-156. tools:::Rd_expr_doi("10.1080/10691898.2016.1246953") ## See Also `Analyse`, `runSimulation` ## Author(s) Phil Chalmers rphilip.chalmers@gmail.com

AnalyseIf function