Lattice: Multivariate Data Visualization with R

By Deepayan Sarkar. 2008

1,835 citations recorded by the source corpus.

R is rapidly growing in popularity as the environment of choice for data analysis and graphics both in academia and industry. Lattice brings the proven design of Trellis graphics (originally developed for S by William S. Cleveland and colleagues at Bell Labs) to R, considerably expanding its capabilities in the process. Lattice is a powerful and elegant high level data visualization system that is sufficient for most everyday graphics needs, yet flexible enough to be easily extended to handle demands of cutting edge research. Written by the author of the lattice system, this book describes it in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed. The book contains close to150 figures produced with lattice. Many of the examples emphasize principles of good graphical design; almost all use real data sets that are publicly available in various R packages. All code and figures in the book are also available online, along with supplementary material covering more advanced topics.

Subfields: Artificial Intelligence, Nature and Landscape Conservation, Plant Science

View Paper: https://www.semanticscholar.org/paper/9d55dab0a28c90c881a19f321e9d731024061d58

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Citations in the corpus

runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS (2016, background): “chains arguments, as well as a named list for each plot type which will be passed to the underlying lattice functions (Sarkar 2008).”
semPLS: Structural Equation Modeling Using Partial Least Squares (2012, methodology): “Figure 8 depicts the result of plot(ecsi) , using lattice (Sarkar 2008).”
Fitting Linear Mixed-Effects Models Using lme4 (2014, background): “General profile zeta and related plots lme4 provides several functions (built on lattice graphics, Sarkar 2008) for plotting the profile zeta functions (Section 5.1.2) and other related quantities.”
The fourth‐corner solution – using predictive models to understand how species traits interact with the environment (2014, methodology): “2c, using the level plot function from the lattice package on R (Sarkar 2008) to visualize the table of environment–trait interaction coefficients.”
ggfortify: Unified Interface to Visualize Statistical Results of Popular R Packages (2016, methodology): “Background R users have many plotting options to choose from, such as base graphics, grid graphics, and lattice graphics (Sarkar, 2008).”
A protocol for conducting and presenting results of regression‐type analyses (2016, methodology): “For models with multiple covariates, graphing requires some R coding skills (Sarkar 2008; Wickham 2009).”
Clinical translation of an ultrasmall inorganic optical-PET imaging nanoparticle probe (2014, methodology): “org), and multipanel time profiles of %ID/g were generated using the lattice package (41).”
Least-Squares Means: The R Package lsmeans (2016, background): “factors The function requires the lattice package (Sarkar 2008) to be installed.”
Improved multivariate portmanteau test (2012, methodology): “Lattice plot ([6], [3], [27], [31]) display of the empirical size and 95% confidence interval of a nominal 5% test based on N = 104 simulations using the asymptotic distribution for the D̂m test.”
Revisiting acoustic correlates of pharyngealization in Jordanian and Moroccan Arabic: Implications for formal representations (2017, methodology): “These were then plotted the following packages: lattice (Sarkar, 2008), latticeExtra (Sarkar & Andrews, 2016) and gridExtra (Auguie, 2016).”
Face Recognition Vendor Test (FRVT) Performance of Face Identification Algorithms NIST IR 8009 (2014, methodology): “Graphics: Many of the Figures in this report were produced using both Deepayan Sarkar’s Lattice package [22] and Hadley Wickham’s ggplot2 package running under R, the capabilities of which extend beyond those evident in this document.”
MICE: Multivariate Imputation by Chained Equations in R (2011, background): “9, an adapted version of the same function in the package lattice (Sarkar 2008).”
An Object-Oriented Framework for Robust Multivariate Analysis (2009, methodology): “We start with bringing the data set diabetes from package mclust into the workspace by typing R> data("diabetes", package = "mclust") Using the package lattice (Sarkar 2008) we produce a three dimensional cloud plot of the data (Figure 15).”
FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters (2008, methodology): “This plot method uses package lattice (Sarkar 2008) and the usual parameters can be speciﬁed to alter the plot, e.g. the argument layout determines the arrangement of the panels.”
Impact of the invasive rust Puccinia psidii (myrtle rust) on native Myrtaceae in natural ecosystems in Australia (2015, methodology): “All analyses were conducted using R (R Core Team 2014), nlme (Pinheiro et al. 2014) and plotting was done using ggplot2 (Wickham 2009) and lattice (Sarkar 2008).”
A linear mixed model analysis of masked repetition priming (2010, methodology): “This package and many others (e.g., we extensively used lattice, Sarkar, 2008, reshape, Wickham, 2007, and ggplot2, Wickham, 2009) are supplied in the R system for statistical computing (Ver.”