mod_results: mod_results

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

mod_results

Description

Using a metafor model object of class rma or rma.mv, this function creates a table of model results containing the mean effect size estimates for all levels of a given categorical moderator, and their corresponding confidence and prediction intervals. The function is capable of calculating marginal means from meta-regression models, including those with multiple moderator variables of mixed types (i.e. continuous and categorical variables).

Usage

mod_results(
  model,
  mod = "1",
  group,
  N = NULL,
  weights = "prop",
  by = NULL,
  at = NULL,
  subset = FALSE,
  upper = TRUE,
  ...
)

Arguments

model	rma.mv model object
mod	Moderator variable of interest that one wants marginal means for. Defaults to the intercept, i.e. "1".
group	The grouping variable that one wishes to plot beside total effect sizes, k. This could be study, species, or any grouping variable one wishes to present sample sizes for.
N	The name of the column in the data specifying the sample size so that each effect size estimate is scaled to the sample size, N. Defaults to NULL, so that precision is used for scaling each raw effect size estimate instead of sample size.
weights	How to marginalize categorical variables. The default is weights = "prop", which weights moderator level means 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".
by	Character vector indicating the name that predictions should be conditioned on for the levels of the moderator.
at	List of levels one wishes to predict at for the corresponding variables in by. Used when one wants marginalised means. This argument can also be used to suppress levels of the moderator when argument subset = TRUE. Provide a list as follows: list(mod = c("level1", "level2")).
subset	Used when one wishes to only plot a subset of levels within the main moderator of interest defined by mod. Default is FALSE, but use TRUE if you wish to subset levels of a moderator plotted (defined by mod) for plotting. Levels one wishes to plot are specified as a list, with the level names as a character string in the at argument. For subsetting to work, the at argument also needs to be specified so that the mod_results function knows what levels one wishes to plot.
upper	Logical, defaults to TRUE, indicating that the first letter of the character string for the moderator variable should be capitalized.
...	Additional arguments passed to emmeans::emmeans().

Value

A data frame containing all the model results including mean effect size estimate, confidence and prediction intervals

Author(s)

Shinichi Nakagawa - [e-mail]

Daniel Noble - [e-mail]

Examples

## Not run: 
# Simple eklof data
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, random=list(~1|ExptID,
~1|Datapoint), data = eklof)
results <- mod_results(eklof_MR, mod = "Grazer.type", group = "ExptID")

# Fish example demonstrating marginalised means
data(fish)
warm_dat <- fish
model <- metafor::rma.mv(yi = lnrr, V = lnrr_vi,
random = list(~1 | group_ID, ~1 | es_ID),
mods = ~ experimental_design + trait.type + deg_dif + treat_end_days,
method = "REML", test = "t",
control=list(optimizer="optim", optmethod="Nelder-Mead"), data = warm_dat)
  overall <- mod_results(model, group = "group_ID")
across_trait <- mod_results(model, group = "group_ID", mod = "trait.type")
across_trait_by_degree_diff <- mod_results(model, group = "group_ID",
mod = "trait.type", at = list(deg_dif = c(5, 10, 15)), by = "deg_dif")
across_trait_by_degree_diff_at_treat_end_days10 <- mod_results(model, group = "group_ID",
mod = "trait.type", at = list(deg_dif = c(5, 10, 15), treat_end_days = 10),
by = "deg_dif",data = warm_dat)
across_trait_by_degree_diff_at_treat_end_days10And50 <- mod_results(model, group = "group_ID",
mod = "trait.type", at = list(deg_dif = c(5, 10, 15),
 treat_end_days = c(10, 50)), by = "deg_dif")
across_trait_by_treat_end_days10And50 <- mod_results(model, group = "group_ID",
mod = "trait.type", at = list(deg_dif = c(5, 10, 15), treat_end_days = c(10, 50)),
by = "treat_end_days")
across_trait_by_treat_end_days10And50_ordinaryMM <- mod_results(model, group = "group_ID",
mod = "trait.type", at = list(deg_dif = c(5, 10, 15), treat_end_days = c(10, 50)),
by = "treat_end_days", weights = "prop")

# Fish data example with a heteroscedastic error
model_het <- metafor::rma.mv(yi = lnrr, V = lnrr_vi, random = list(~1 | group_ID, ~1 + trait.type| es_ID), mods = ~ trait.type + deg_dif, method = "REML", test = "t", rho = 0, struc = "HCS", control=list(optimizer="optim", optmethod="Nelder-Mead"), data = warm_dat)
HetModel <- mod_results(model_het, group = "group_ID", mod = "trait.type", at = list(deg_dif = c(5, 10, 15)), by = "deg_dif", weights = "prop")
orchard_plot(HetModel, xlab = "lnRR")

## End(Not run)

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