Implemented fully automated nested loop plots for simulation studies with several data-generating mechanisms:
autoplot(object, type = "nlp");
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_bothfor ‘by’ factors (when included).
autoplotmethods pick the value of
multisimsumwhen 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;
pkgdownwebsite, published at https://ellessenne.github.io/rsimsum/;
autoplot method for
summary.simsum objects; when calling
summary.simsum objects, confidence intervals based on Monte Carlo standard errors will be included as well (if sensible).
Supported plot types are:
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.
rsimsumis more robust to using factor variables (e.g. as
byfactor), with ordering that will be preserved if defined in the dataset passed to
help(summary.simsum)for more details;
simsumfor testing purposes - differences are negligible, and there are some calculations in
simsumthat are wrong (already reported). Most differences can be attributed to calculations (and conversions, for comparison) on different scales.
multisimsumhas been slightly changed, with some arguments being removed and others being moved to a
controllist with several tuning parameters. Please check the updated examples for more details;
dropbigis no longer an S3 method for
dropbigis an exported function that can be used to identify rows of the input
data.framethat would be dropped by
dropbig = TRUE)are no longer included in the returned object; therefore, the S3 method
misshas been removed;
get_datais no longer an S3 method, but still requires an object of class
summary.multisimsumto be passed as input;
zipmethod has been renamed to
zipper()to avoid name collision with
ci.limitsargument (#6, @MvanSmeden). This functionality is to be considered experimental, hence feedback would be much appreciated;
relhazdataset bundled with
rsimsum 0.3.3 focuses on improving the documentation of the package.
descriptionargument to each
get_datamethod, to append a column with a description of each summary statistics exported; defaults to
rsimsum 0.3.2 is a small maintenance release:
heat()now appropriately pick a discrete X (or Y) axis scale for methods (if defined) when the method variable is numeric;
methodvarvariable to string format (if specified and not already string);
Updated code of conduct (
CONDUCT.md) and contributing guidelines (
Removed dependency on the
tidyverse package (thanks Mara Averick).
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:
lolly.multisimsumis now not required; if not provided, plots will be faceted by estimand (as well as any other
Added S3 methods for
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.
x argument to
multisimsum to include original dataset as a slot of the returned object.
miss function for obtaining basic information on missingness in simulation results.
miss has methods
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.