\title{trials on non-steroidal anti-inflammatory drugs in acute pain}
\description{results from 37 trials evaluating non-steroidal anti-inflammatory drugs (nsaids) in patients with acute pain.}
\bold{year}       \tab \code{integer}   \tab publication year \cr
\bold{ee}         \tab \code{integer}   \tab number of treatment successes (nsaids group) \cr
\bold{ne}         \tab \code{integer}   \tab number of patients (nsaids group) \cr
\bold{ec}         \tab \code{integer}   \tab number of treatment successes (control group) \cr
\bold{nc}         \tab \code{integer}   \tab number of patients (control group) \cr
\bold{nonenglish} \tab \code{integer}   \tab non-english publication \cr
\bold{medline}    \tab \code{integer}   \tab listed in medline \cr
\bold{grey}       \tab \code{integer}   \tab grey literature \cr
\bold{samecont}   \tab \code{integer}   \tab same control group \cr
\bold{journal}    \tab \code{character} \tab journal
moore et al. (1998) conducted a systematic review of 37 randomized placebo-controlled trials on the effectiveness and safety of topical non-steroidal anti-inflammatory drugs (nsaids) in acute pain. the main outcome was treatment success, defined as a reduction in pain of at least 50\%.
moore, r. a., tramèr, m. r., carroll, d., wiffen, p. j., & mcquay, h. j. (1998). quantitive systematic review of topically applied non-steroidal anti-inflammatory drugs. \emph{british medical journal}, \bold{316}(7128), 333--338. \verb{https://doi.org/10.1136/bmj.316.7128.333}
m <- metabin(ee, ne, ec, nc, data = dat.moore1998,
label.e = "nsaids", label.c = "placebo")
legend = fun$text.contour, fill = fun$col.contour)

\description{results from 457 studies on assortative mating in various species.}
the object is a list containing a data frame called \code{dat} that contains the following columns and a phylogenetic tree called \code{tree}:
\bold{study.id}          \tab \code{character} \tab study id \cr
\bold{effect.size.id}    \tab \code{numeric}   \tab effect size id \cr
\bold{species}           \tab \code{character} \tab species \cr
\bold{species.id}        \tab \code{character} \tab species id (as in the open tree of life reference taxonomy) \cr
\bold{subphylum}         \tab \code{character} \tab the subphyla of the species \cr
\bold{phylum}            \tab \code{character} \tab the phyla of the species \cr
\bold{assortment.trait}  \tab \code{character} \tab the measure of body size \cr
\bold{trait.dimensions}  \tab \code{character} \tab dimensionality of the measure \cr
\bold{field.collection}  \tab \code{character} \tab whether data were collected in the field \cr
\bold{publication.year}  \tab \code{numeric}   \tab publication year of the study \cr
\bold{pooled.data}       \tab \code{character} \tab whether data were pooled either spatially and/or temporally \cr
\bold{spatially.pooled}  \tab \code{character} \tab whether data were pooled spatially \cr
\bold{temporally.pooled} \tab \code{character} \tab whether data were pooled temporally \cr
\bold{ri}                \tab \code{numeric}   \tab correlation coefficient \cr
\bold{ni}                \tab \code{numeric}   \tab sample size
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.
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. \emph{ecology letters}, \bold{24}(5), 1089--1102. \verb{https://doi.org/10.1111/ele.13690}
### make copy of the species.id variable
random = list(~ 1 | study.id, ~ 1 | effect.size.id, ~ 1 | species.id, ~ 1 | species.id.phy),
### examine if spatial and/or temporal pooling of data tends to yield larger correlations
mods = ~ spatially.pooled * temporally.pooled,
### 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)
ecology, evolution, correlation coefficients, multivariate models, phylogeny, meta-regression