Passing the true value of an estimand (

`true`

argument) is no longer required; if`true`

is not passed to`simsum`

or`multisimsum`

, bias, coverage, and mean squared error are not computed;Passing estimated standard errors per replication (

`se`

argument) is no longer required; if so, average and median variances, model-based standard errors, relative error, coverage probability, bias-eliminated coverage probability, power are not computed.

- Fixed bug introduced in
`rsimsum`

0.6.1 (average and median variances were not printed).

Fixed labelling bug in zipper plots (thanks to @syriop-elisa for reporting it);

Clarified that

`simsum`

and`multisimsum`

report average (or median) estimated variances, not standard errors (thanks to Ian R. White for reporting this).

Implemented fully automated nested loop plots for simulation studies with several data-generating mechanisms:

`autoplot(object, type = "nlp")`

;Added

`data("nlp", package = "rsimsum")`

, a dataset from a simulation study with 150 data-generating. This is particularly useful to illustrate nested loop plots;Added a new vignette on nested loop plots;

Improved ordering of vignettes.

Updated unquoting for compatibility with

`rlang`

0.4.0;Fixed missing details and options in the documentation of

`autoplot.multisimsum`

and`autoplot.summary.multisimsum`

.

- Fixed labelling when facetting for some plot types, now all defaults to
`ggplot2::label_both`

for ‘by’ factors (when included).

- Fixed calculations for “Relative % increase in precision” (thanks to Ian R. White for reporting this).

- Implemented
`autoplot`

method for`multisimsum`

and`summary.multisimsum`

objects; - Implemented heat plot types for both
`simsum`

and`multisimsum`

objects; - All
`autoplot`

methods pick the value of`true`

passed to`simsum`

,`multisimsum`

when inferring the target value if`stats = (thetamean, thetamedian)`

and`target = NULL`

. In plain English, the true value of the estimand is picked as target value when plotting the mean (or median) of the estimated value; - Updated vignettes and references;
- Updated
`pkgdown`

website, published at https://ellessenne.github.io/rsimsum/; - Improved code coverage.

- Fixed a bug in
`autoplot`

caused by premature slicing of`by`

arguments, where no`by`

arguments were included.

Implemented `autoplot`

method for `simsum`

and `summary.simsum`

objects; when calling `autoplot`

on `summary.simsum`

objects, confidence intervals based on Monte Carlo standard errors will be included as well (if sensible).

Supported plot types are:

- forest plot of estimated summary statistics;
- lolly plot of summary statistics;
- zip plot for coverage probability;
- scatter plot of methods-wise comparison (e.g. X vs Y) of point estimates and standard errors, per replication;
- same as the above, but implemented as a Bland-Altman type plot;
- ridgeline plot of estimates, standard errors to compare the distribution of estimates, standard errors by method.

Several options to customise the behaviour of `autoplot`

, see `?autoplot.simsum`

and `?autoplot.summary.simsum`

for further details.

Fixed a bug in `dropbig`

and related internal function that was returning standardised values instead of actual observed values.

`rsimsum`

0.4.0 is a large refactoring of `rsimsum`

. There are several improvements and breaking changes, outlined below.

`rsimsum`

is more robust to using factor variables (e.g. as`methodvar`

or`by`

factor), with ordering that will be preserved if defined in the dataset passed to`simsum`

(or`multisimsum`

);- Confidence intervals based on Monte Carlo standard errors can be now computed using quantiles from a t distribution; see
`help(summary.simsum)`

for more details; - Added comparison with results from Stata’s
`simsum`

for testing purposes - differences are negligible, and there are some calculations in`simsum`

that are wrong (already reported). Most differences can be attributed to calculations (and conversions, for comparison) on different scales.

- The syntax of
`simsum`

and`multisimsum`

has been slightly changed, with some arguments being removed and others being moved to a`control`

list with several tuning parameters. Please check the updated examples for more details; `dropbig`

is no longer an S3 method for`simsum`

and`multisimsum`

objects. Now,`dropbig`

is an exported function that can be used to identify rows of the input`data.frame`

that would be dropped by`simsum`

(or`multisimsum`

);- Point estimates and standard errors dropped by
`simsum`

(or`multisimsum`

) when`dropbig = TRUE)`

are no longer included in the returned object; therefore, the S3 method`miss`

has been removed; `get_data`

is no longer an S3 method, but still requires an object of class`simsum`

,`summary.simsum`

,`multisimsum`

, or`summary.multisimsum`

to be passed as input;- All plotting methods have been removed in preparation of a complete overhaul planned for
`rsimsum`

0.5.0.

- The
`zip`

method has been renamed to`zipper()`

to avoid name collision with`utils::zip()`

.

- Added ability to define custom confidence interval limits for calculating coverage via the
`ci.limits`

argument (#6, @MvanSmeden). This functionality is to be considered experimental, hence feedback would be much appreciated; - Updated
*Simulating a simulation study*vignette and therefore the`relhaz`

dataset bundled with`rsimsum`

.

`rsimsum`

0.3.3 focuses on improving the documentation of the package.

Improvements: * Improved printing of confidence intervals for summary statistics based on Monte Carlo standard errors; * Added a `description`

argument to each `get_data`

method, to append a column with a description of each summary statistics exported; defaults to `FALSE`

; * Improved documentation and introductory vignette to clarify several points (#3, @lebebr01); * Improved plotting vignette to document how to customise plots (#4, @lebebr01).

New: * Added CITATION file with references to paper in JOSS.

`rsimsum`

0.3.2 is a small maintenance release: * Merged pull request #1 from @mllg adapting to new version of the `checkmate`

package; * Fixed a bug where automatic labels in `bar()`

and `forest()`

were not selected properly.

Bug fixes: * `bar()`

, `forest()`

, `lolly()`

, `heat()`

now appropriately pick a discrete X (or Y) axis scale for methods (if defined) when the method variable is numeric; * `simsum()`

and `multisimsum()`

coerce `methodvar`

variable to string format (if specified and not already string); * fixed typos for empirical standard errors in documentation here and there.

Updated code of conduct (`CONDUCT.md`

) and contributing guidelines (`CONTRIBUTING.md`

).

Removed dependency on the `tidyverse`

package (thanks Mara Averick).

Bug fixes: * `pattern()`

now appropriately pick a discrete colour scale for methods (if defined) when the method variable is numeric.

New plots are supported: * `forest()`

, for forest plots; * `bar()`

, for bar plots.

Changes to existing functionality: * the `par`

argument of `lolly.multisimsum`

is now not required; if not provided, plots will be faceted by estimand (as well as any other `by`

factor); * updated *Visualising results from rsimsum* vignette.

Added `CONTRIBUTING.md`

and `CONDUCT.md`

.

Internal housekeeping.

Added S3 methods for `simsum`

and `multisimsum`

objects to visualise results: * `lolly()`

, for lolly plots; * `zip()`

, for zip plots; * `heat()`

, for heat plots; * `pattern()`

, for scatter plots of estimates vs SEs.

Added a new vignette *Visualising results from rsimsum* to introduce the above-mentioned plots.

Added `x`

argument to `simsum`

and `multisimsum`

to include original dataset as a slot of the returned object.

Added a `miss`

function for obtaining basic information on missingness in simulation results. `miss`

has methods `print`

and `get_data`

.

First submission to CRAN. `rsimsum`

can handle:

- simulation studies with a single estimand
- simulation studies with multiple estimands
- simulation studies with multiple methods to compare
- simulation studies with multiple data-generating mechanisms (e.g. ‘by’ factors)

Summary statistics that can be computed are: bias, empirical standard error, mean squared error, percentage gain in precision relative to a reference method, model-based standard error, coverage, bias-corrected coverage, and power.

Monte Carlo standard errors for each summary statistic can be computed as well.