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Analyses of simulation studies including Monte Carlo error

Source: R/simsum.R
simsum.Rd

simsum() computes performance measures for simulation studies in which each simulated data set yields point estimates by one or more analysis methods. Bias, relative 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.

Usage

simsum(
  data,
  estvarname,
  se = NULL,
  true = NULL,
  methodvar = NULL,
  ref = NULL,
  by = NULL,
  ci.limits = NULL,
  df = NULL,
  dropbig = FALSE,
  x = FALSE,
  control = list()
)

Arguments

data

A data.frame in which variable names are interpreted. It has to be in tidy format, e.g. each variable forms a column and each observation forms a row.

estvarname

The name of the variable containing the point estimates. Note that some column names are forbidden: these are listed below in the Details section.

se

The name of the variable containing the standard errors of the point estimates. Note that some column names are forbidden: these are listed below in the Details section.

true

The true value of the parameter; this is used in calculations of bias, relative bias, coverage, and mean squared error and is required whenever these performance measures are requested. true can be a numeric value or a string that identifies a column in data. In the former setting, simsum will assume the same value for all replications; conversely, each replication will use a distinct value for true as identified by each row of data. See vignette("E-custom-inputs", package = "rsimsum") for more details. Note that some column names are forbidden: these are listed below in the Details section.

methodvar

The name of the variable containing the methods to compare. For instance, methods could be the models compared within a simulation study. Can be NULL. If a vector of column names is passed to simsum(), those columns will be combined into a single column named :methodvar using the base::interaction() function before computing all performance measures. Note that some column names are forbidden: these are listed below in the Details section.

ref

Specifies the reference method against which relative precision will be calculated. Only useful if methodvar is specified.

by

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 NULL. Note that some column names are forbidden: these are listed below in the Details section.

ci.limits

Can be used to specify the limits (lower and upper) of confidence intervals used to calculate coverage and bias-eliminated coverage. Useful for non-Wald type estimators (e.g. bootstrap). Defaults to NULL, where Wald-type confidence intervals based on the provided SEs are calculated for coverage; otherwise, it can be a numeric vector (for fixed confidence intervals) or a vector of strings that identify columns in data with replication-specific lower and upper limits. See vignette("E-custom-inputs", package = "rsimsum") for more details. Note that some column names are forbidden: these are listed below in the Details section.

df

Can be used to specify that a column containing the replication-specific number of degrees of freedom that will be used to calculate confidence intervals for coverage (and bias-eliminated coverage) assuming t-distributed critical values (rather than normal theory intervals). See vignette("E-custom-inputs", package = "rsimsum") for more details. Note that some column names are forbidden: these are listed below in the Details section.

dropbig

Specifies that point estimates or standard errors beyond the maximum acceptable values should be dropped. Defaults to FALSE.

x

Set to TRUE to include the data argument used to calculate summary statistics (i.e. after pre-processing the input dataset e.g. removing values deemed too large via the dropbig argument) as a slot. Calling simsum with x = TRUE is required to produce zipper plots. The downside is that the size of the returned object increases considerably, therefore it is set to FALSE by default.

control

A list of parameters that control the behaviour of simsum. Possible values are:

  • mcse, whether to calculate Monte Carlo standard errors. Defaults to TRUE;

  • level, the significance level used for coverage, bias-eliminated coverage, and power. Defaults to 0.95;

  • power_df, whether to use robust critical values from a t distribution with power_df degrees of freedom when calculating power. Defaults to NULL, in which case a Gaussian distribution is used;

  • na.rm, whether to remove point estimates or standard errors where either (or both) is missing. Defaults to TRUE;

  • char.sep, a character utilised when splitting the input dataset data. Generally, this should not be changed;

  • dropbig.max, specifies the maximum acceptable absolute value of the point estimates, after standardisation. Defaults to 10;

  • dropbig.semax, specifies the maximum acceptable absolute value of the standard error, after standardisation. Defaults to 100

  • dropbig.robust, specifies whether to use robust standardisation (using median and inter-quartile range) rather than normal standardisation (using mean and standard deviation). Defaults to TRUE, in which case robust standardisation will be used for dropbig.

Value

An object of class simsum.

Details

The following names are not allowed for any column in data that is passed to simsum(): stat, est, mcse, lower, upper, :methodvar, :true.

References

White, I.R. 2010. simsum: Analyses of simulation studies including Monte Carlo error. The Stata Journal 10(3): 369-385. https://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

Examples

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")
#> 'ref' method was not specified, CC set as the reference