2023
DOI: 10.1111/2041-210x.14152
|View full text |Cite
|
Sign up to set email alerts
|

orchaRd 2.0: An R package for visualising meta‐analyses with orchard plots

Abstract: Although meta‐analysis has become an essential tool in ecology and evolution, reporting of meta‐analytic results can still be much improved. To aid this, we have introduced the orchard plot, which presents not only overall estimates and their confidence intervals, but also shows corresponding heterogeneity (as prediction intervals) and individual effect sizes. Here, we have added significant enhancements by integrating many new functionalities into orchaRd 2.0. This updated version allows the visualisation of … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
14
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
8
1

Relationship

1
8

Authors

Journals

citations
Cited by 37 publications
(14 citation statements)
references
References 35 publications
0
14
0
Order By: Relevance
“…However, because there were few estimates for treatments involving olfactory cues either on their own or in combination with other cue types (see Results), we also constructed models that were restricted to estimates from studies based on A, V and AV treatment levels. Although we did not have a priori predictions regarding how multimodal cue integration would affect variance in response to manipulations of perceived predation risk, visualisations using orchard plots revealed a clear difference in variability among different treatment levels 171 , 172 . Therefore, we considered both homoscedastic and heteroscedastic models, as recommended by ref.…”
Section: Methodsmentioning
confidence: 84%
See 1 more Smart Citation
“…However, because there were few estimates for treatments involving olfactory cues either on their own or in combination with other cue types (see Results), we also constructed models that were restricted to estimates from studies based on A, V and AV treatment levels. Although we did not have a priori predictions regarding how multimodal cue integration would affect variance in response to manipulations of perceived predation risk, visualisations using orchard plots revealed a clear difference in variability among different treatment levels 171 , 172 . Therefore, we considered both homoscedastic and heteroscedastic models, as recommended by ref.…”
Section: Methodsmentioning
confidence: 84%
“…For all models, we assessed the importance of moderators by calculating marginal R 2 sensu 174 . We visualised meta-analytic results as well as other relevant results mainly using the R packages ggplot2 175 , orchaRd 171 , 172 , ggalluvial 176 and ggtree 177 . Data and reproducible analyses are provided in Supplementary Information S5 ( https://itchyshin.github.io/multimodality/ ).…”
Section: Methodsmentioning
confidence: 99%
“…The metafor package version 4.2 (Viechtbauer, 2010) was used for effect size calculation, fitting meta regression models and investigating publication bias. The orchaRd 2.0 package (Nakagawa et al, 2023) was used for creating the orchard plot and calculating I 2 .…”
Section: Discussionmentioning
confidence: 99%
“…We used the metafor 64 package to build all meta-analytical models. We visualised the results from the models using scatterplots (for numeric moderators) and orchard plots (for categorical moderators) from the orchaRd 65 package, using 95% confidence intervals to indicate the most likely location of the cross-study average effect. We visualised the geographic distribution of studies using the rnaturalearth 66 package.…”
Section: Methodsmentioning
confidence: 99%