Summarising simsum objects

The summary() method for objects of class simsum returns confidence intervals for performance measures based on Monte Carlo standard errors.

Usage

# S3 method for simsum
summary(object, ci_level = 0.95, df = NULL, stats = NULL, ...)

Arguments

object

An object of class simsum.

ci_level

Significance level for confidence intervals based on Monte Carlo standard errors. Ignored if a simsum object with control parameter mcse = FALSE is passed.

df

Degrees of freedom of a t distribution that will be used to calculate confidence intervals based on Monte Carlo standard errors. If NULL (the default), quantiles of a Normal distribution will be used instead. However, using Z-based or t-based confidence intervals is valid only for summary statistics such a bias and coverage. Confidence intervals for other quantities may not be appropriate, therefore their usage is not recommended.

stats

Summary statistics to include; can be a scalar value or a vector (for multiple summary statistics at once). Possible choices are:

nsim, the number of replications with non-missing point estimates and standard error.
thetamean, average point estimate.
thetamedian, median point estimate.
se2mean, average variance.
se2median, median variance.
bias, bias in point estimate.
rbias, relative (to the true value) bias in point estimate.
empse, empirical standard error.
mse, mean squared error.
relprec, percentage gain in precision relative to the reference method.
modelse, model-based standard error.
relerror, relative percentage error in standard error.
cover, coverage of a nominal level\
becover, bias corrected coverage of a nominal level\
power, power of a (1 - level)\

Defaults to NULL, in which case all possible summary statistics are included.

...

Ignored.

Value

An object of class summary.simsum.

Examples

data("MIsim")
object <- simsum(
  data = MIsim, estvarname = "b", true = 0.5, se = "se",
  methodvar = "method"
)
#> 'ref' method was not specified, CC set as the reference
xs <- summary(object)
xs
#> Values are:
#> 	Point Estimate (Monte Carlo Standard Error)
#> 
#> Non-missing point estimates/standard errors:
#>    CC MI_LOGT MI_T
#>  1000    1000 1000
#> 
#> Average point estimate:
#>      CC MI_LOGT   MI_T
#>  0.5168  0.5009 0.4988
#> 
#> Median point estimate:
#>      CC MI_LOGT   MI_T
#>  0.5070  0.4969 0.4939
#> 
#> Average variance:
#>      CC MI_LOGT   MI_T
#>  0.0216  0.0182 0.0179
#> 
#> Median variance:
#>      CC MI_LOGT   MI_T
#>  0.0211  0.0172 0.0169
#> 
#> Bias in point estimate:
#>               CC         MI_LOGT             MI_T
#>  0.0168 (0.0048) 0.0009 (0.0042) -0.0012 (0.0043)
#> 
#> Relative bias in point estimate:
#>               CC         MI_LOGT             MI_T
#>  0.0335 (0.0096) 0.0018 (0.0083) -0.0024 (0.0085)
#> 
#> Empirical standard error:
#>               CC         MI_LOGT            MI_T
#>  0.1511 (0.0034) 0.1320 (0.0030) 0.1344 (0.0030)
#> 
#> % gain in precision relative to method CC:
#>               CC          MI_LOGT             MI_T
#>  0.0000 (0.0000) 31.0463 (3.9375) 26.3682 (3.8424)
#> 
#> Mean squared error:
#>               CC         MI_LOGT            MI_T
#>  0.0231 (0.0011) 0.0174 (0.0009) 0.0181 (0.0009)
#> 
#> Model-based standard error:
#>               CC         MI_LOGT            MI_T
#>  0.1471 (0.0005) 0.1349 (0.0006) 0.1338 (0.0006)
#> 
#> Relative % error in standard error:
#>                CC         MI_LOGT             MI_T
#>  -2.6594 (2.2055) 2.2233 (2.3323) -0.4412 (2.2695)
#> 
#> Coverage of nominal 95% confidence interval:
#>               CC         MI_LOGT            MI_T
#>  0.9430 (0.0073) 0.9490 (0.0070) 0.9430 (0.0073)
#> 
#> Bias-eliminated coverage of nominal 95% confidence interval:
#>               CC         MI_LOGT            MI_T
#>  0.9400 (0.0075) 0.9490 (0.0070) 0.9430 (0.0073)
#> 
#> Power of 5% level test:
#>               CC         MI_LOGT            MI_T
#>  0.9460 (0.0071) 0.9690 (0.0055) 0.9630 (0.0060)

Usage

Arguments

Value

See also

Examples