2008
DOI: 10.1002/bimj.200810425
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Simultaneous Inference in General Parametric Models

Abstract: Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified throu… Show more

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Cited by 11,449 publications
(8,350 citation statements)
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References 23 publications
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“…Statistical analyses were performed using R (version 3.2.2; R Core Team 2015) with packages lme4 (Bates, Maechler, Bolker, & Walker, 2015), multcomp (Hothorn, Bretz, & Westfall, 2008), agricolae (de Mendiburu, 2015), and piecewiseSEM (Lefcheck, 2015), and results were plotted with ggplot2 (Wickham, 2009). Linear mixed‐effects analyses were used to test for differences among site types (site as fixed effect and colony as random effect) in proportion of pollen collected from site crop, percent protein, percent amino acids, number of residues detected, and PHQs.…”
Section: Methodsmentioning
confidence: 99%
“…Statistical analyses were performed using R (version 3.2.2; R Core Team 2015) with packages lme4 (Bates, Maechler, Bolker, & Walker, 2015), multcomp (Hothorn, Bretz, & Westfall, 2008), agricolae (de Mendiburu, 2015), and piecewiseSEM (Lefcheck, 2015), and results were plotted with ggplot2 (Wickham, 2009). Linear mixed‐effects analyses were used to test for differences among site types (site as fixed effect and colony as random effect) in proportion of pollen collected from site crop, percent protein, percent amino acids, number of residues detected, and PHQs.…”
Section: Methodsmentioning
confidence: 99%
“…Significance of all factors was evaluated with Type II tests using the Anova function in the car package (Fox & Weisberg, 2011), calculating Wald chi‐square statistics for models in lme and F‐ratio statistics for models in lm . Post hoc Tukey's tests of main effects were carried out using the glht function in the package multcomp (Hothorn, Bretz, & Westfall, 2008), with main effects averaged over covariates.…”
Section: Methodsmentioning
confidence: 99%
“…The Central European lowland wolf population has been increasing exponentially from a total of three German packs in 2007 to 31 recorded packs in Germany in 2014. Post hoc tests between age categories were performed using the R package multcomp v1.4–5 (Hothorn, Bretz, & Westfall, 2008). In a next step, we tested whether potential “wolf‐specialized” Sarcocystis are more likely to persist with increasing wolf age than “non‐wolf specialists” that should be cleared by immune response.…”
Section: Methodsmentioning
confidence: 99%