simsum computes performance measures for simulation studies in which each simulated data set yields point estimates by one or more analysis methods.
Bias, empirical standard error and precision relative to a reference method can be computed for each method.
If, in addition, model-based standard errors are available then
simsum can compute the average model-based standard error, the relative error in the model-based standard error, the coverage of nominal confidence intervals, the coverage under the assumption that there is no bias (bias-eliminated coverage), and the power to reject a null hypothesis.
Monte Carlo errors are available for all estimated quantities.
simsum( data, estvarname, se = NULL, true = NULL, methodvar = NULL, ref = NULL, by = NULL, ci.limits = NULL, dropbig = FALSE, x = FALSE, control = list() )
The name of the variable containing the point estimates.
The name of the variable containing the standard errors of the point estimates.
The true value of the parameter; this is used in calculations of bias, coverage, and mean squared error and is required whenever these performance measures are requested.
The name of the variable containing the methods to compare.
For instance, methods could be the models compared within a simulation study.
Specifies the reference method against which relative precision will be calculated.
Only useful if
A vector of variable names to compute performance measures by a list of factors. Factors listed here are the (potentially several) data-generating mechanisms used to simulate data under different scenarios (e.g. sample size, true distribution of a variable, etc.). Can be
A numeric vector of length 2 specifying the limits (lower and upper) of confidence intervals used to calculate coverage. Useful for non-Wald type estimators (e.g. bootstrap). Defaults to
Specifies that point estimates or standard errors beyond the maximum acceptable values should be dropped. Defaults to
A list of parameters that control the behaviour of
An object of class
The following names are not allowed for
White, I.R. 2010. simsum: Analyses of simulation studies including Monte Carlo error. The Stata Journal 10(3): 369-385. http://www.stata-journal.com/article.html?article=st0200
Morris, T.P., White, I.R. and Crowther, M.J. 2019. Using simulation studies to evaluate statistical methods. Statistics in Medicine, doi: 10.1002/sim.8086
Gasparini, A. 2018. rsimsum: Summarise results from Monte Carlo simulation studies. Journal of Open Source Software 3(26):739, doi: 10.21105/joss.00739
data("MIsim", package = "rsimsum") s <- simsum(data = MIsim, estvarname = "b", true = 0.5, se = "se", methodvar = "method", ref = "CC") # If 'ref' is not specified, the reference method is inferred s <- simsum(data = MIsim, estvarname = "b", true = 0.5, se = "se", methodvar = "method")#>