SQuID stands for Statistical
Quantification of Individual
Differences and is the product of the SQuID working
group. The package aims to help scholars who, like us, are interested in
understanding patterns of phenotypic variance. Individual differences
are the raw material for natural selection to act on and hence the basis
of evolutionary adaptation. Understanding the sources of phenotypic
variance is thus a most essential feature of biological investigation
and mixed effects models offer a great, albeit challenging tool.
Disseminating the properties, potentials and interpretational challenges
in the research community is thus a foremost goal of SQuID.
The squid package has two main objectives: First, it
provides an educational tool useful for students, teachers and
researchers who want to learn to use mixed-effects models. Users can
experience how the mixed-effects model framework can be used to
understand distinct biological phenomena by interactively exploring
simulated multilevel data. Second, squid offers research
opportunities to those who are already familiar with mixed-effects
models, as squid enables the generation of datasets that
users may download and use for a range of simulation-based statistical
analyses such as power and sensitivity analysis of multilevel and
multivariate data.
To install the latest released version from CRAN:
install.packages("squid")To install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("hallegue/squid")Get more information about the installation of the devtools package.
The phenotype of a trait in an individual results from a sum of genetic and environmental influences. Phenotypic variation is structured in a hierarchical way and the hierarchical modelling in mixed effect models is great tool to analyze and decompose such variation. Phenotypes vary across species, across populations of the same species, across individuals of the same population, and across repeated observations of the same individual. We focused on the individual level because it represents one of the most important biological levels to both ecological and evolutionary processes. Different sources of variation are at the origin of the phenotype of an individual. Individuals may differ in their phenotypes because they carry different gene variants (i.e. alleles). But individuals also experience different environments during their lifetime. Some environmental influences impose a lasting mark on the phenotype, while others are more ephemerous. The former tend to produce long-lasting, among-individual variation, while the latter causes within-individual variation. However, this depends on the time scale at which the measurements of the phenotypes are done relative that of the environmental influences. Furthermore, individuals differ not only in their average phenotypes but also in how they respond to changes in their environment (i.e. differences in individual phenotypic plasticity). This represents an interaction between the among- and the within-individual levels of variation. The patterns of variation can, thus, be very complex. Selection can act differently on these different components of variance in the phenotypes of a trait, and this is why it is important to quantify their magnitude.
Mixed models are very flexible statistical tools that provide a way
to estimate the variation at these different levels, and represent the
general statistical framework for evolutionary biology. Because of the
progress in computational capacities mixed models have become
increasingly popular among ecologists and evolutionary biologists over
the last decade. However, fitting mixed model is not a straightforward
exercise, and the way data are sampled among and within individuals can
have strong implications on the outcome of the model. This is why we
created the squid simulation tool that could help new users
interested in decomposing phenotypic variance to get more familiar with
the concept of hierarchical organization of traits, with mixed models
and to avoid pitfalls caused by inappropriate sampling.
squid is a simulation-based tool that can be used for
research and educational purposes. squid creates a world
inhabited by individuals whose phenotypes are generated by a
user-defined phenotypic equation, which allows easy translation of
biological hypotheses into mathematically quantifiable parameters. The
framework is suitable for performing simulation studies, determining
optimal sampling designs for user-specific biological problems, and
making simulation based inferences to aid in the interpretation of
empirical studies. squid is also a teaching tool for
biologists interested in learning, or teaching others, how to implement
and interpret mixed-effects models, when studying the processes causing
phenotypic variation. squid is based on a mathematical
model that creates a group of individuals (i.e. study population)
repeatedly expressing phenotypes, for one or two different traits, in
uniform time. Phenotypic values of traits are generated following the
general principle of the phenotypic equation (Dingemanse
& Dochtermann 2013, Journal of Animal Ecology): phenotypes are
assumed to be the summed effects of a series of components and the
phenotypic variance (Vp) is the sum of the respective variances in
theses causal components. The user has thus the flexibility to add
different variance components that will form the phenotype of the
individual at each time step, and to set up the relative importance of
each component through the definition of environmental effects.
squid then allows the user to collect a sub-sample of
phenotypes for each simulated individual (i.e. operational data set),
according to a specific sampling design. The major difference between
squid and other R packages that also allow performance
analysis through data simulation (e.g. pamm, odprism,
simr),
is that only squid allows separate steps for generating the
world first and then model a sampling process from it.
squid is subject to evolution and is designed to adapt to
more complex scenarios in the future.
squid has two main functions; squidApp()
and squidR():
squidApp(): runs the SQuID application
which is a browser-based interface created with the shiny package (we
recommend to update your default web browser to its latest version).
SQuID is built up as a series of modules that guide the user into
situations of increasing complexity to explore the phenotypic equation
model and the dynamics between the way phenotypes are sampled and the
estimation of parameters of specific interest; The last module is the
full model simulation that allows the user to generate data sets that
can then be used to run analyses in the statistical package of their
choice for specific research questions. For most of the modules, the
simulated data set is automatically fed into a statistical model in R
and the main results of the analysis shown in an output. For the full
model the user has the opportunity to download the operational data set
for further analyses. The SQuID application also has a tab (Full model
(Step by step)) describing in details the SQuID full model.# run SQuID application
library(squid)
squidApp()squidR(): is a traditional R function
that allows data generation and sampling without the browser-based
interface. This function can be used for more advanced and efficient
simulations once you understand how SQuID works. squidR()
can be easily included in R scripts.It all started in Hannover in November 2013 at the occasion of a workshop on personality organised by Susanne Foitzik, Franjo Weissing, and Niels Dingemanse and funded by the Volkswagen Foundation. During this workshop, a group of researchers discussed the potential issues related to sampling designs on the estimation of components of the phenotypic variance and covariance. It became obvious that there was an urgent need to develop a simulation package to help anyone interested in using a mixed model approach at getting familiar with this methods and avoiding the pitfalls related to the interpretation of the results. A first model and a working version of the package were created in January 2014, during a meeting at Université du Québec à Montréal. The current version was produced during a workshop in November 2014, at the Max Plank Institute for Ornithology in Seewiesen.
Allegue, H., Araya-Ajoy, Y.G., Dingemanse, N.J., Dochtermann N.A., Garamszegi, L.Z., Nakagawa, S., Réale, D., Schielzeth, H. and Westneat, D.F. (2016). SQuID - Statistical Quantification of Individual Differences: an educational and statistical tool for understanding multi-level phenotypic data in linear mixed models. Methods in Ecology and Evolution, 8:257-267. DOI: 10.1111/2041-210X.12659
Dingemanse, N.J. and Dochtermann N.A. (2013). Quantifying individual variation in behaviour: mixed-effect modelling approaches. Journal of Animal Ecology, 82:39-54. DOI: 10.1111/1365-2656.12013