simsum | R Documentation |
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, df = NULL, dropbig = FALSE, x = FALSE, control = list() )
data |
A |
estvarname |
The name of the variable containing the point estimates. |
se |
The name of the variable containing the standard errors of the point estimates. |
true |
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.
|
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 |
ref |
Specifies the reference method against which relative precision will be calculated.
Only useful if |
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 |
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 |
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 |
dropbig |
Specifies that point estimates or standard errors beyond the maximum acceptable values should be dropped. Defaults to |
x |
Set to |
control |
A list of parameters that control the behaviour of
|
The following names are not allowed for estvarname
, se
, methodvar
, by
: stat
, est
, mcse
, lower
, upper
, :methodvar
.
An object of class simsum
.
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
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")
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