- Even if
`power_df`

was passed to the`control`

argument, it was not used (regression introduced in {rsimsum} 0.9.0). Now fixed, thanks to @Kaladani (#33).

`get_data()`

is now deprecated in favour of`tidy()`

;`get_data()`

still works (and is fully tested), but now throws a warning and will be fully removed some time in the future.

`simsum()`

and`multisimsum()`

now accept multiple column inputs that identify unique methods (see e.g. #24, #30). Internally, this combines the unique values from each column factorially using the`interaction()`

function; then, methods are analysed and reported as such. See`vignette("E-custom-inputs", package = "rsimsum")`

for some examples.Two new datasets,

`MIsim2`

and`frailty2`

, are now bundled with`rsimsum`

to test the new functionality introduced above. They correspond to`MIsim`

and`frailty`

, respectively, with the only difference being that the (single) column identifying methods is now split into two distinct columns.

Improved printing for simulation studies with ‘non-standard’ way of passing true values (see e.g. #28 on GitHub);

Fixed a typo in introductory vignette;

Some internal housekeeping.

- The control argument
`df`

has been renamed to`power_df`

, and now affects power calculations only.

New

`df`

argument,`simsum`

and`multisimum`

now accept a column in`data`

containing a number of degrees of freedom that will be used to calculate confidence intervals for coverage (and bias-eliminated coverage) with t critical values (instead of normal-theory intervals, the default behaviour). Notably, zip plots behave accordingly when calculating and ranking confidence intervals;Calculations for zip plots are noticeably faster now;

Added a simple

`kable`

method for objects of class`simsum`

,`summary.simsum`

,`multisimsum`

,`summary.multisimsum`

to ease the creation of LaTeX/HTML/Markdown/reStructuredText tables.

- Fixed a bug that prevented zip plots with only
`by`

factors from being plotted.

`autoplot`

methods will now plot the number of non-missing point estimates/SEs by default (if the`stat`

argument is not set by the user). The previous default was to plot bias, which might not always be available anymore since`rsimsum 0.8.0`

.

Handling more plotting edge cases, for instance when standard errors or true values are not available;

Improved

`multisimsum`

example in vignette on custom inputs.

- Fixed typo in vignette with formulae (#25, thanks @samperochkin).

Added new argument

`zoom`

to`autoplot`

methods: it is now possible to*zoom*on the top x% of a zip plot to improve readability;Added a new example dataset from a toy simulation study assessing the robustness of the t-test. See

`?"tt"`

for more details;The

`true`

argument of`rsimsum`

and`multisimsum`

now accepts a string that identifies a column in`data`

. This is especially useful in settings where the true value varies across replications, e.g. when it depends on characteristics of the simulated data. See`vignette("E-custom-inputs", package = "rsimsum")`

for more details and examples;Analogously, the

`ci.limits`

argument now accepts a vector of strings that identifies lower and upper limits for custom-defined confidence intervals from columns in`data`

. Once again, more details are included in`vignette("E-custom-inputs", package = "rsimsum")`

;`rsimsum`

now correctly uses`inherits(obj, "someclass")`

instead of`class(obj) == "someclass"`

(#20);Fixed bugs and errors that appeared when auto-plotting results of simulation studies with no methods being compared (#23).

`autoplot`

supports two new visualisations: contour plots and hexbin plots, for either point estimates or standard errors. They can be obtained by selecting the argument`type = "est_density"`

,`type = "se_density"`

,`type = "est_hex"`

, or`type = "se_hex"`

.

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.