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: