Response-Surface Methods in R, Using rsm

By Russell V. Lenth. 2009

683 citations recorded by the source corpus.

This introduction to the R package rsm is a modied version of Lenth (2009), published in the Journal of Statistical Software. The package rsm was designed to provide R support for standard response-surface methods. Functions are provided to generate central-composite and Box-Behnken designs. For analysis of the resulting data, the package provides for estimating the response surface, testing its lack of t, displaying an ensemble of contour plots of the tted surface, and doing follow-up analyses such as steepest ascent, canonical analysis, and ridge analysis. It also implements a coded-data structure to aid in this essential aspect of the methodology. The functions are designed in hopes of providing an intuitive and eective user interface. Potential exists for expanding the package in a variety of ways.

Subfields: Management Science and Operations Research, Nature and Landscape Conservation, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7338d72b496a252f2c34ed4ed64cea274a9632ed

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Citations in the corpus

1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana (2016, methodology): “Allele density spectrums were smoothed and plotted with the sm package in GNU R (Lenth, 2009).”
Protein Hydrolysates from Agricultural Crops—Bioactivity and Potential for Functional Food Development (2013, methodology): “conditions through experimental methods [38]) is effective in the optimisation of a number of parameters simultaneously to produce a hydrolysate with maximum ACE inhibitory activity.”
Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R' (2014, methodology): “The approach of using metamodels to predict the model output of non-simulated input factor combinations is strongly related to the response surface method (RSM, see R package rsm, Lenth 2009) (Ascough Il et al. 2005).”
Interactive Effects of Water Temperature and Salinity on Growth and Mortality of Eastern Oysters, Crassostrea virginica: A Meta-Analysis Using 40 Years of Monitoring Data (2017, neutral): “Packages used include ‘‘pspline’’ (Ripley 2015) for the interpolated temperature and salinity splines, ‘‘rsm’’ (Lenth 2009) for the response surface regressions, and ‘‘mgcv’’ (Wood 2011) for the GAM.”