rsimsum 0.6.0 Unreleased

Improvements:

• 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.

Bug fixes:

• Updated unquoting for compatibility with rlang 0.4.0;

• Fixed missing details and options in the documentation of autoplot.multisimsum and autoplot.summary.multisimsum.

rsimsum 0.5.2 2019-04-25

Bug fixes:

• Fixed labelling when facetting for some plot types, now all defaults to ggplot2::label_both for ‘by’ factors (when included).

rsimsum 0.5.1 2019-03-15

Bug fixes:

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

rsimsum 0.5.0 2019-02-21

Improvements:

• 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.

Bug fixes:

• Fixed a bug in autoplot caused by premature slicing of by arguments, where no by arguments were included.

rsimsum 0.4.2 Unreleased

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.

rsimsum 0.4.1 Unreleased

Fixed a bug in dropbig and related internal function that was returning standardised values instead of actual observed values.

rsimsum 0.4.0 Unreleased

rsimsum 0.4.0 is a large refactoring of rsimsum. There are several improvements and breaking changes, outlined below.

Improvements

• 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.

Breaking changes

• 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.

rsimsum 0.3.5 Unreleased

Breaking changes

• The zip method has been renamed to zipper() to avoid name collision with utils::zip().

rsimsum 0.3.4 Unreleased

• 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 2018-06-20

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 Unreleased

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.

rsimsum 0.3.1 2018-04-04

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).

rsimsum 0.3.0 2018-02-22

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.

rsimsum 0.2.0 2018-02-15

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.

rsimsum 0.1.0 2018-02-05

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.