# summary.multisimsum: Summarising multisimsum objects In rsimsum: Analysis of Simulation Studies Including Monte Carlo Error

 summary.multisimsum R Documentation

## Summarising multisimsum objects

### Description

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

### Usage

``````## S3 method for class 'multisimsum'
summary(object, ci_level = 0.95, df = NULL, stats = NULL, ...)
``````

### Arguments

 `object` An object of class `multisimsum`. `ci_level` Significance level for confidence intervals based on Monte Carlo standard errors. Ignored if a `multisimsum` 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. `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 standard error. `se2median`, median standard error. `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.multisimsum`.

`multisimsum()`, `print.summary.multisimsum()`

### Examples

``````data(frailty)
ms <- multisimsum(
data = frailty, par = "par", true = c(
trt = -0.50,
fv = 0.75
), estvarname = "b", se = "se", methodvar = "model",
by = "fv_dist"
)
sms <- summary(ms)
sms
``````

rsimsum documentation built on May 29, 2024, 2:18 a.m.