dat.lim2014.RdResults from studies examining the association between maternal size, offspring size, and number of offsprings.
dat.lim2014The object is a list containing data frames m_o_size, m_o_fecundity, o_o_unadj, and o_o_adj that contain the following columns and the corresponding phylogenetic trees called m_o_size_tree, m_o_fecundity_tree, o_o_unadj_tree, and o_o_adj_tree:
| article | numeric | article id |
| author | character | study author(s) |
| year | numeric | publication year |
| species | character | species |
| amniotes | character | whether the species was amniotic |
| environment | character | whether the species were wild or captive |
| reprounit | character | whether the data were based on lifetime reproductive output or a single reproductive event (only in m_o_size and m_o_fecundity) |
| ri | numeric | correlation coefficient |
| ni | numeric | sample size |
The object dat.lim2014 includes 4 datasets:
m_o_size | on the correlation between maternal size and offspring size |
m_o_fecundity | on the correlation between maternal size and number of offsprings |
o_o_unadj | on the correlation between offspring size and number of offsprings |
o_o_adj | on the correlation between offspring size and number of offsprings adjusted for maternal size |
Objects m_o_size_tree, m_o_fecundity_tree, o_o_unadj_tree, and o_o_adj_tree are the corresponding phylogenetic trees for the species included in each of these datasets.
Lim, J. N., Senior, A. M., & Nakagawa, S. (2014). Heterogeneity in individual quality and reproductive trade-offs within species. Evolution, 68(8), 2306–2318. https://doi.org/10.1111/evo.12446
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
ecology, evolution, correlation coefficients, multilevel models, phylogeny
### copy data into 'dat' and examine data
dat <- dat.lim2014$o_o_unadj
dat[1:14, -c(2:3)]
#> article species amniotes environment ri ni
#> 1 1 Sceloporus_virgatus yes wild 0.100 21
#> 2 1 Sceloporus_virgatus yes wild -0.170 14
#> 3 1 Sceloporus_virgatus yes wild -0.070 21
#> 4 1 Sceloporus_virgatus yes wild -0.140 14
#> 5 2 Marmota_marmota yes wild -0.540 74
#> 6 3 Vipera_ursinii yes wild 0.487 105
#> 7 4 Pantherophis_obsoletus yes wild -0.290 104
#> 8 5 Anas_platyrhynchos yes captive 0.395 49
#> 9 6 Tropidonophis_mairii yes wild -0.130 318
#> 10 10 Urocitellus_richardsonii yes wild -0.670 51
#> 11 10 Urocitellus_richardsonii yes wild -0.400 38
#> 12 11 Urocitellus_richardsonii yes wild -0.530 134
#> 13 11 Urocitellus_richardsonii yes wild -0.500 43
#> 14 15 Daphnia_magna no captive 0.030 215
### load metafor package
library(metafor)
### load ape package
library(ape, warn.conflicts=FALSE)
### calculate r-to-z transformed correlations and corresponding sampling variances
dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat)
### copy tree to 'tree'
tree <- dat.lim2014$o_o_unadj_tree
### compute branch lengths
tree <- compute.brlen(tree)
### compute phylogenetic correlation matrix
A <- vcv(tree, corr=TRUE)
### make copy of the species variable
dat$species.phy <- dat$species
### create effect size id variable
dat$esid <- 1:nrow(dat)
### fit multilevel phylogenetic meta-analytic model
res <- rma.mv(yi, vi,
random = list(~ 1 | article, ~ 1 | esid, ~ 1 | species, ~ 1 | species.phy),
R=list(species.phy=A), data=dat)
res
#>
#> Multivariate Meta-Analysis Model (k = 170; method: REML)
#>
#> Variance Components:
#>
#> estim sqrt nlvls fixed factor R
#> sigma^2.1 0.1387 0.3725 125 no article no
#> sigma^2.2 0.0093 0.0962 170 no esid no
#> sigma^2.3 0.0000 0.0000 120 no species no
#> sigma^2.4 0.0572 0.2392 120 no species.phy yes
#>
#> Test for Heterogeneity:
#> Q(df = 169) = 1823.4239, p-val < .0001
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> -0.1564 0.1277 -1.2242 0.2209 -0.4067 0.0940
#>
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>