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Least-Squares Means: The R Package lsmeans
Abstract
Least-squares means are predictions from a linear model, or averages thereof. They are useful in the analysis of experimental data for summarizing the effects of factors, and for testing linear contrasts among predictions. The lsmeans package (Lenth 2016) provides a simple way of obtaining least-squares means and contrasts thereof. It supports many models fitted by R (R Core Team 2015) core packages (as well as a few key contributed ones) that fit linear or mixed models, and provides a simple way of extending it to cover more model classes.
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Post-hoc pairwise contrasts between walking conditions were conducted with a Holm-Bonferroni p-value correction.
“For models where significant differences in walking conditions were detected, we used the ‘lsmeans’ package (Lenth, 2016) to conduct post-hoc pairwise contrasts between walking conditions, with a Holm–Bonferroni p-value correction.”
From Forest terrains influence walking kinematics among indigenous Tsimane of the Bolivian Amazon (2022) · cites this paper as Lenth, 2016
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Metadata
- Authors
- Lenth, R. V.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2016
- Volume
- 69
- Issue
- 1
- ISSN
- 1548-7660
- Cited by
- 5,075
- References
- 0
- Language
- en
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