dat.moura2021 {metadat} | R Documentation |
Studies on Assortative Mating
Description
Results from 457 studies on assortative mating in various species.
Usage
dat.moura2021
Format
The object is a list containing a data frame called dat
that contains the following columns and a phylogenetic tree called tree
:
study.id | character | study id |
effect.size.id | numeric | effect size id |
species | character | species |
species.id | character | species id (as in the Open Tree of Life reference taxonomy) |
subphylum | character | the subphyla of the species |
phylum | character | the phyla of the species |
assortment.trait | character | the measure of body size |
trait.dimensions | character | dimensionality of the measure |
field.collection | character | whether data were collected in the field |
publication.year | numeric | publication year of the study |
pooled.data | character | whether data were pooled either spatially and/or temporally |
spatially.pooled | character | whether data were pooled spatially |
temporally.pooled | character | whether data were pooled temporally |
ri | numeric | correlation coefficient |
ni | numeric | sample size |
Details
The 457 studies included in this dataset provide 1828 correlation coefficients describing the similarity in some measure of body size in mating couples in 341 different species.
Concepts
ecology, evolution, correlation coefficients, multivariate models, phylogeny, meta-regression
Author(s)
Wolfgang Viechtbauer, wvb@metafor-project.org, https://www.metafor-project.org
Source
Rios Moura, R., Oliveira Gonzaga, M., Silva Pinto, N., Vasconcellos-Neto, J., & Requena, G. S. (2021). Assortative mating in space and time: Patterns and biases. Ecology Letters, 24(5), 1089–1102. https://doi.org/10.1111/ele.13690
References
Cinar, O., Nakagawa, S., & Viechtbauer, W. (in press). Phylogenetic multilevel meta-analysis: A simulation study on the importance of modelling the phylogeny. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.13760
Hadfield, J. D., & Nakagawa, S. (2010). General quantitative genetic methods for comparative biology: Phylogenies, taxonomies and multi-trait models for continuous and categorical characters. Journal of Evolutionary Biology, 23(3), 494–508. https://doi.org/10.1111/j.1420-9101.2009.01915.x
Nakagawa, S., & Santos, E. S. A. (2012). Methodological issues and advances in biological meta-analysis. Evolutionary Ecology, 26(5), 1253–1274. https://doi.org/10.1007/s10682-012-9555-5
Examples
### copy data into 'dat' and examine data
dat <- dat.moura2021$dat
head(dat)
## Not run:
### load metafor package
library(metafor)
### load ape package
library(ape, warn.conflicts=FALSE)
### calculate r-to-z transformed correlations and corresponding sampling variances
dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat)
### copy tree to 'tree'
tree <- dat.moura2021$tree
### turn tree into an ultrametric one
tree <- compute.brlen(tree)
### compute phylogenetic correlation matrix
A <- vcv(tree, corr=TRUE)
### make copy of the species.id variable
dat$species.id.phy <- dat$species.id
### fit multilevel phylogenetic meta-analytic model
res <- rma.mv(yi, vi,
random = list(~ 1 | study.id, ~ 1 | effect.size.id, ~ 1 | species.id, ~ 1 | species.id.phy),
R=list(species.id.phy=A), data=dat)
res
### examine if spatial and/or temporal pooling of data tends to yield larger correlations
res <- rma.mv(yi, vi,
mods = ~ spatially.pooled * temporally.pooled,
random = list(~ 1 | study.id, ~ 1 | effect.size.id, ~ 1 | species.id, ~ 1 | species.id.phy),
R=list(species.id.phy=A), data=dat)
res
### estimated average correlation without pooling, when pooling spatially,
### when pooling temporally, and when pooling spatially and temporally
predict(res, newmods = rbind(c(0,0,0),c(1,0,0),c(0,1,0),c(1,1,1)), transf=transf.ztor, digits=2)
## End(Not run)