metadat (version 1.4-0)

dat.moura2021: 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

Author

Wolfgang Viechtbauer, , https://www.metafor-project.org

Concepts

ecology, evolution, correlation coefficients, multivariate models, phylogeny, meta-regression

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.

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)

if (FALSE) {
### 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)
}