lcc is a package under development based on estimation procedures for
longitudinal concordance correlation (lcc), longitudinal Pearson
correlation (lpc), and longitudinal accuracy (la) through fixed effects
and variance components of polynomial mixed-effect regression model. The
main features of the package are its ability to perform inference about
the extent of agreement and use a numerical and graphical to summary the
fitted values, sampled values, and confidence intervals. Morever, our
approach accommodate balanced or unbalanced experimental design, allows
to model heteroscedasticity among within-group errors using or not the
time as covariate, and also allows for inclusion of covariates in the
linear predictor to control systematic variations in the response
variable. It was developed by Thiago de Paula Oliveira [cre, aut],
Rafael de Andrade Moral [aut], John Hinde [aut], Silvio Sandoval
Zocchi [ctb], Clarice Garcia Borges Demétrio [ctb].
It has been available on CRAN since 2018 (https://CRAN.R-project.org/package=lcc). Its last version was updated on 2021-02-26. CRAN has lcc's stable version, which is recommended for most users.
This github page has its version under development. New functions will be added as experimental work and, once it is done and running correctly, we will synchronize the repositories and add it to the CRAN.
We worked hard to release a new stable version allowing users to analyze data sets, where the objective is studied the extent of the agreement profile among methods considering time as covariable.
lcc comprises a set of functions that allows users build and summaries
the fitted model, estimates and bootstrap confidence intervals for lcc,
lpc and la statistics, and build graphical summaries for them. Some
functions are used internally by the package, and should not be used
directly.
install.packages("lcc")install.packages("devtools")
devtools::install_github("Prof-ThiagoOliveira/lcc")If you use Windows, first install Rtools. If you are facing problems with Rtools installation, try to do it by selecting Run as Admnistrator option with right mouse button. On a Mac, you will need Xcode (available on the App Store).
lcc can also be installed by downloading the appropriate files
directly at the CRAN web site and following the instructions given in
the section 6.3 Installing Packages of the R Installation and
Administration
manual.
We hope you learn more about the LCC using the LCC App. We develop this application to facilitate understanding of how each parameter can affects the LCC estimate over time. Have fun!
You can read lcc tutorials going to our work published at PeerJ (https://doi.org/10.7717/peerj.9850), or by clicking in the link below: