lnRR_main: Main effect: Log Response Ratio

In minter: Effect Sizes for Meta-Analysis of Interactions from Factorial Experiments

Main effect: Log Response Ratio

Description

Computes the main effect of Factor A across levels of Factor B, analogous to the main effect in a factorial ANOVA.

Usage

lnRR_main(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  method = "nakagawa",
  Ctrl_mean,
  Ctrl_sd,
  Ctrl_n,
  A_mean,
  A_sd,
  A_n,
  B_mean,
  B_sd,
  B_n,
  AB_mean,
  AB_sd,
  AB_n
)

Arguments

data	Data frame containing the variables used.
col_names	Vector of two strings to name the output columns for the effect size and its sampling variance. Default is 'yi' and 'vi'.
append	Logical. Append the results to data. Default is TRUE
method	Method to compute lnRR. Can be either "nakagawa" or "morris". Default is "nakagawa".
Ctrl_mean	Mean outcome from the Control treatment
Ctrl_sd	Standard deviation from the control treatment
Ctrl_n	Sample size from the control treatment
A_mean	Mean outcome from the A treatment
A_sd	Standard deviation from the A treatment
A_n	Sample size from the A treatment
B_mean	Mean outcome from the B treatment
B_sd	Standard deviation from the B treatment
B_n	Sample size from the B treatment
AB_mean	Mean outcome from the interaction AxB treatment
AB_sd	Standard deviation from the interaction AxB treatment
AB_n	Sample size from the interaction AxB treatment

Details

See the package vignette for a detailed description of the formula.

Value

A data frame containing the effect sizes and their sampling variance. By default, the columns are named yi (effect size) and vi (sampling variance). If append = TRUE, the results are appended to the input data; otherwise, only the computed effect size columns are returned.

Author(s)

Facundo Decunta - [e-mail]

References

Morris, W. F., Hufbauer, R. A., Agrawal, A. A., Bever, J. D., Borowicz, V. A., Gilbert, G. S., ... & Vázquez, D. P. (2007). Direct and interactive effects of enemies and mutualists on plant performance: a meta‐analysis. Ecology, 88(4), 1021-1029. https://doi.org/10.1890/06-0442

Lajeunesse, M. J. (2011). On the meta‐analysis of response ratios for studies with correlated and multi‐group designs. Ecology, 92(11), 2049-2055. https://doi.org/10.1890/11-0423.1

Macartney, E. L., Lagisz, M., & Nakagawa, S. (2022). The relative benefits of environmental enrichment on learning and memory are greater when stressed: A meta-analysis of interactions in rodents. Neuroscience & Biobehavioral Reviews, 135, 104554. https://doi.org/10.1016/j.neubiorev.2022.104554

Examples

# Example data for 2x2 factorial design (Fertilization x Warming)
data <- data.frame(
  study_id = 1:2,
  control_mean = c(10, 12), control_sd = c(2.0, 2.5), control_n = c(20, 18),
  fertilization_mean = c(15, 16), fertilization_sd = c(2.2, 2.8), fertilization_n = c(20, 19),
  warming_mean = c(11, 13), warming_sd = c(2.1, 2.6), warming_n = c(21, 17),
  fert_warm_mean = c(17, 19), fert_warm_sd = c(2.4, 3.0), fert_warm_n = c(19, 20)
)

# Compute main effect of fertilization
result <- lnRR_main(
  data = data,
  Ctrl_mean = "control_mean", Ctrl_sd = "control_sd", Ctrl_n = "control_n",
  A_mean = "fertilization_mean", A_sd = "fertilization_sd", A_n = "fertilization_n",
  B_mean = "warming_mean", B_sd = "warming_sd", B_n = "warming_n",
  AB_mean = "fert_warm_mean", AB_sd = "fert_warm_sd", AB_n = "fert_warm_n"
)

minter documentation built on May 3, 2026, 5:06 p.m.