This is a minor release, with the following changes:
Updated hex sticker.
Updated maintainer e-mail.
Fixed conflicts with tidy() function from the
{broom} package when both {broom} and {rsimsum} are loaded at the same
time. This could lead to an error of this kind:
#> Error: No tidy method recognized for this list.Thanks to Theodosia Salika for reporting this.
The {pkgdown} website with the documentation has been updated to use Bootstrap 5 (and {pkgdown} ≥ 2.0.0). The new site can be found here: https://ellessenne.github.io/rsimsum/
Updated a DOI that was returning HTTP Error 503.
print.summary.simsum() now return (invisibly) a list
with each section of the output, e.g. by performance measure. This is
useful for printing small sections of the output, e.g. using
kable() (thanks @ge-li, see discussion in #22):
library(rsimsum)
s2 <- simsum(data = relhaz, estvarname = "theta", true = -0.50, se = "se", methodvar = "model", by = c("baseline", "n"))
out <- print(summary(s2, stats = "bias"))
library(knitr)
kable(out[[1]], caption = names(out)[1], align = "r")This is implemented for print.summary.multisimsum() as
well, with an additional level of nesting (by parameter).
.Rmd files), thanks to @remlapmot for reporting
this (#36).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.
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.
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.
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.
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.
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/;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:
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: * 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:
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.