SIMR: an R package for power analysis of generalized linear mixed models by simulation
Summary The r package simr allows users to calculate power for generalized linear mixed models from the lme4 package. The power calculations are based on Monte Carlo simulations. It includes tools for (i) running a power analysis for a given model and design; and (ii) calculating power curves to ass...
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| Published in: | Methods in ecology and evolution Vol. 7; no. 4; pp. 493 - 498 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
John Wiley & Sons, Inc
01-04-2016
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| Subjects: | |
| Online Access: | Get full text |
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| Abstract | Summary
The r package simr allows users to calculate power for generalized linear mixed models from the lme4 package. The power calculations are based on Monte Carlo simulations.
It includes tools for (i) running a power analysis for a given model and design; and (ii) calculating power curves to assess trade‐offs between power and sample size.
This paper presents a tutorial using a simple example of count data with mixed effects (with structure representative of environmental monitoring data) to guide the user along a gentle learning curve, adding only a few commands or options at a time. |
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| AbstractList | Summary
The
r
package
simr
allows users to calculate power for generalized linear mixed models from the
lme
4 package. The power calculations are based on Monte Carlo simulations.
It includes tools for (i) running a power analysis for a given model and design; and (ii) calculating power curves to assess trade‐offs between power and sample size.
This paper presents a tutorial using a simple example of count data with mixed effects (with structure representative of environmental monitoring data) to guide the user along a gentle learning curve, adding only a few commands or options at a time. Summary The r package simr allows users to calculate power for generalized linear mixed models from the lme4 package. The power calculations are based on Monte Carlo simulations. It includes tools for (i) running a power analysis for a given model and design; and (ii) calculating power curves to assess trade-offs between power and sample size. This paper presents a tutorial using a simple example of count data with mixed effects (with structure representative of environmental monitoring data) to guide the user along a gentle learning curve, adding only a few commands or options at a time. The r package simr allows users to calculate power for generalized linear mixed models from the lme4 package. The power calculations are based on Monte Carlo simulations.It includes tools for (i) running a power analysis for a given model and design; and (ii) calculating power curves to assess trade‐offs between power and sample size.This paper presents a tutorial using a simple example of count data with mixed effects (with structure representative of environmental monitoring data) to guide the user along a gentle learning curve, adding only a few commands or options at a time. Summary The r package simr allows users to calculate power for generalized linear mixed models from the lme4 package. The power calculations are based on Monte Carlo simulations. It includes tools for (i) running a power analysis for a given model and design; and (ii) calculating power curves to assess trade‐offs between power and sample size. This paper presents a tutorial using a simple example of count data with mixed effects (with structure representative of environmental monitoring data) to guide the user along a gentle learning curve, adding only a few commands or options at a time. 1. The r package simr allows users to calculate power for generalized linear mixed models from the lme4 package. The power calculations are based on Monte Carlo simulations. 2. It includes tools for (i) running a power analysis for a given model and design; and (ii) calculating power curves to assess trade-offs between power and sample size. 3. This paper presents a tutorial using a simple example of count data with mixed effects (with structure representative of environmental monitoring data) to guide the user along a gentle learning curve, adding only a few commands or options at a time. |
| Author | MacLeod, Catriona J. Nakagawa, Shinichi Green, Peter |
| Author_xml | – sequence: 1 givenname: Peter orcidid: 0000-0002-0238-9852 surname: Green fullname: Green, Peter organization: Landcare Research – sequence: 2 givenname: Catriona J. surname: MacLeod fullname: MacLeod, Catriona J. organization: Landcare Research – sequence: 3 givenname: Shinichi surname: Nakagawa fullname: Nakagawa, Shinichi |
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| ContentType | Journal Article |
| Copyright | 2015 The Authors. Methods in Ecology and Evolution © 2015 British Ecological Society Methods in Ecology and Evolution © 2016 British Ecological Society |
| Copyright_xml | – notice: 2015 The Authors. Methods in Ecology and Evolution © 2015 British Ecological Society – notice: Methods in Ecology and Evolution © 2016 British Ecological Society |
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| DOI | 10.1111/2041-210X.12504 |
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| Editor | Nakagawa, Shinichi |
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| References_xml | – volume: 7 start-page: e35564 year: 2012 article-title: Empirical power and sample size calculations for cluster‐randomized and cluster‐randomized crossover studies publication-title: PLoS One – year: 2009 – volume: 55 start-page: 19 year: 2001 end-page: 24 article-title: The abuse of power: the pervasive fallacy of power calculations for data analysis publication-title: The American Statistician – volume: 34 start-page: 1040 year: 2011 end-page: 1048 article-title: Monitoring temporal trends in spatially structured populations: how should sampling effort be allocated between space and time? publication-title: Ecography – volume: 11 start-page: 94 year: 2011 article-title: Simulation methods to estimate design power: an overview for applied research publication-title: BMC Medical Research Methodology – volume: 67 start-page: 1 year: 2015 end-page: 48 article-title: Fitting linear mixed‐effects models using 4 publication-title: Journal of Statistical Software – volume: 6 start-page: 133 year: 2015 end-page: 142 article-title: Power analysis for generalized linear mixed models in ecology and evolution publication-title: Methods in Ecology and Evolution – volume: 2 start-page: 362 year: 2011 end-page: 374 article-title: Measuring individual differences in reaction norms in field and experimental studies: a power analysis of random regression models publication-title: Methods in Ecology and Evolution – volume: 52 start-page: 3283 year: 2008 end-page: 3299 article-title: Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models publication-title: Computational Statistics & Data Analysis – year: 2008 – volume: 24 start-page: 127 year: 2009 end-page: 135 article-title: Generalized linear mixed models: a practical guide for ecology and evolution publication-title: Trends in Ecology and Evolution – volume: 5 start-page: 199 year: 2000 end-page: 213 article-title: Statistical power and optimal design for multisite randomised trials publication-title: Psychological Methods – volume: 32 start-page: 485 year: 2007 end-page: 491 article-title: Making monitoring meaningful publication-title: Austral Ecology – volume: 59 start-page: 1 year: 2014 end-page: 30 article-title: A Kenward‐Roger approximation and parametric bootstrap methods for tests in linear mixed models – the package publication-title: Journal of Statistical Software – year: 2015 – year: 2013 – volume: 78 start-page: 194 year: 2006 end-page: 199 article-title: Why most conservation monitoring is, but need not be, a waste of time publication-title: Journal of Environmental Management – year: 2012 – ident: e_1_2_14_4_1 doi: 10.2307/j.ctvcm4g37 – ident: e_1_2_14_9_1 doi: 10.18637/jss.v059.i09 – ident: e_1_2_14_14_1 doi: 10.1111/j.2041-210X.2010.00084.x – ident: e_1_2_14_11_1 doi: 10.1111/2041-210X.12306 – ident: e_1_2_14_5_1 doi: 10.1016/j.tree.2008.10.008 – ident: e_1_2_14_6_1 – ident: e_1_2_14_10_1 doi: 10.1198/000313001300339897 – ident: e_1_2_14_8_1 doi: 10.1007/978-1-4614-3900-4 – volume-title: Linear Mixed Models for Longitudinal Data year: 2009 ident: e_1_2_14_20_1 contributor: fullname: Verbeke G. – volume-title: R: A Language and Environment for Statistical Computing year: 2015 ident: e_1_2_14_15_1 contributor: fullname: R Core Team – ident: e_1_2_14_17_1 doi: 10.1371/journal.pone.0035564 – ident: e_1_2_14_3_1 doi: 10.18637/jss.v067.i01 – ident: e_1_2_14_16_1 doi: 10.1037/1082-989X.5.2.199 – ident: e_1_2_14_7_1 doi: 10.1111/j.1442-9993.2007.01715.x – ident: e_1_2_14_13_1 – ident: e_1_2_14_12_1 doi: 10.1016/j.jenvman.2005.04.016 – ident: e_1_2_14_2_1 doi: 10.1186/1471-2288-11-94 – ident: e_1_2_14_19_1 doi: 10.1016/j.csda.2007.10.022 – ident: e_1_2_14_18_1 doi: 10.1111/j.1600-0587.2011.06370.x |
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The r package simr allows users to calculate power for generalized linear mixed models from the lme4 package. The power calculations are based on Monte... Summary The r package simr allows users to calculate power for generalized linear mixed models from the lme 4 package. The power calculations are based on... Summary The r package simr allows users to calculate power for generalized linear mixed models from the lme4 package. The power calculations are based on Monte... The r package simr allows users to calculate power for generalized linear mixed models from the lme4 package. The power calculations are based on Monte Carlo... 1. The r package simr allows users to calculate power for generalized linear mixed models from the lme4 package. The power calculations are based on Monte... |
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| Title | SIMR: an R package for power analysis of generalized linear mixed models by simulation |
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