caterpillars: caterpillars

In daniel1noble/orchaRd: Orchard Plots and Prediction Intervals for Meta-analysis and Meta-regression

caterpillars

Description

Using a metafor model object of class rma or rma.mv or a results table of class orchard, this function produces a caterpillar plot from mean effect size estimates for all levels of a given categorical moderator, their corresponding confidence intervals, and prediction intervals.

Usage

caterpillars(
  object,
  mod = "1",
  group,
  xlab,
  overall = TRUE,
  transfm = c("none", "tanh"),
  colerrorbar = "#00CD00",
  colpoint = "#FFD700",
  colpoly = "red",
  k = TRUE,
  g = TRUE,
  at = NULL,
  by = NULL,
  weights = "prop"
)

Arguments

object	Model object of class rma.mv, rma or orchard table of model results
mod	The name of a moderator variable. Otherwise, "1" for an intercept-only model.
group	The grouping variable that one wishes to plot beside total effect sizes, k. This could be study, species, or whatever other grouping variable one wishes to present sample sizes for.
xlab	The effect size measure label.
overall	Logical, indicating whether to re-label "Intrcpt" (the default label from rma or rma.mv intercept only models or meta-analyses) to "Overall". Defaults to TRUE.
transfm	If set to "tanh", a tanh transformation will be applied to effect sizes, converting Zr to a correlation or pulling in extreme values for other effect sizes (lnRR, lnCVR, SMD). Defaults to "none".
colerrorbar	Colour of the error bar in the caterpillars plot. Defaults to hex code - "#00CD00".
colpoint	Point estimate colour in the caterpillars plot. Defaults to hex code - "#FFD700".
colpoly	Polygon colour in the caterpillars plot. Defaults to "red".
k	If TRUE, it displays k (number of effect sizes) on the plot.
g	If TRUE, it displays g (number of grouping levels for each level of the moderator) on the plot.
at	Used when one wants marginalised means. List of levels one wishes to predict at for the corresponding variables in by, but is not presented in output. Provide a list as follows: list(mod = c("level1", "level2")).
by	Used when one wants marginalised means. Character vector indicating the name that predictions should be conditioned on for the levels of the moderator.
weights	Used when one wants marginalised means. How to marginalize categorical variables. The default is weights = "prop", which weights means for moderator levels based on their proportional representation in the data. For example, if "sex" is a moderator, and males have a larger sample size than females, then this will produce a weighted average, where males are weighted more towards the mean than females. This may not always be ideal. In the case of sex, for example, males and females are roughly equally prevalent in a population. As such, you can give the moderator levels equal weight using weights = "equal".

Value

Caterpillars plot

Author(s)

Shinichi Nakagawa - [e-mail]

Daniel Noble - [e-mail]

Examples

## Not run: 
data(eklof)
eklof<-metafor::escalc(measure="ROM", n1i=N_control, sd1i=SD_control,
m1i=mean_control, n2i=N_treatment, sd2i=SD_treatment, m2i=mean_treatment,
data=eklof)
# Add the unit level predictor
eklof$Datapoint<-as.factor(seq(1, dim(eklof)[1], 1))
# fit a MLMR - accouting for some non-independence
eklof_MR<-metafor::rma.mv(yi=yi, V=vi, mods=~ Grazer.type-1, random=list(~1|ExptID,
~1|Datapoint), data=eklof)
results <- mod_results(eklof_MR, mod = "Grazer.type",  group = "First.author")
caterpillars(results, mod = "Grazer.type",
group = "First.author", xlab = "log(Response ratio) (lnRR)", g = FALSE)

# Example 2
data(lim)
lim$vi<- 1/(lim$N - 3)
lim_MR<-metafor::rma.mv(yi=yi, V=vi, mods=~Phylum-1, random=list(~1|Article,
~1|Datapoint), data=lim)
results_lim <- mod_results(lim_MR, mod = "Phylum", group = "Article")
caterpillars(results_lim, mod = "Phylum",
group = "Article", xlab = "Correlation coefficient", transfm = "tanh")

## End(Not run)

daniel1noble/orchaRd documentation built on May 12, 2024, 7:46 a.m.