zoom
to autoplot
methods: it is now possible to zoom on the top x% of a zip plot to improve readability.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 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.
ggplot2::label_both
for ‘by’ factors (when included).autoplot
method for multisimsum
and summary.multisimsum
objects;simsum
and multisimsum
objects;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;pkgdown
website, published at https://ellessenne.github.io/rsimsum/;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:
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
);help(summary.simsum)
for more details;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.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
);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;rsimsum
0.5.0.zip
method has been renamed to zipper()
to avoid name collision with utils::zip()
.ci.limits
argument (#6, @MvanSmeden). This functionality is to be considered experimental, hence feedback would be much appreciated;relhaz
dataset bundled with rsimsum
.rsimsum
0.3.3 focuses on improving the documentation of the package.
Improvements:
description
argument to each get_data
method, to append a column with a description of each summary statistics exported; defaults to FALSE
;New:
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);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:
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);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:
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.