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, 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.
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. 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.
|
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 any column in data
that is passed to simsum()
: stat
, est
, mcse
, lower
, upper
, :methodvar
, :true
.
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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.8086")}
Gasparini, A. 2018. rsimsum: Summarise results from Monte Carlo simulation studies. Journal of Open Source Software 3(26):739, \Sexpr[results=rd]{tools:::Rd_expr_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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