dot-main_SMD: Main effect: Standardized Mean Difference

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

Main effect: Standardized Mean Difference

Description

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

Usage

.main_SMD(
  Ctrl_mean,
  Ctrl_sd,
  Ctrl_n,
  A_mean,
  A_sd,
  A_n,
  B_mean,
  B_sd,
  B_n,
  AB_mean,
  AB_sd,
  AB_n,
  hedges_correction = TRUE
)

Arguments

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
hedges_correction	Boolean. If TRUE correct for small-sample bias. Default is TRUE.

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

Gurevitch, J., Morrison, J. A., & Hedges, L. V. (2000). The interaction between competition and predation: a meta-analysis of field experiments. The American Naturalist, 155(4), 435-453.

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

Examples

# Main effect of Mycorrhiza in 2x2 factorial design (AMF x Phosphorus)
data <- data.frame(
  study_id = 1:2,
  control_mean = c(12.4, 15.1), control_sd = c(2.8, 3.2), control_n = c(16, 14),
  mycorrhizae_mean = c(18.7, 21.3), mycorrhizae_sd = c(3.4, 3.9), mycorrhizae_n = c(15, 16),
  phosphorus_mean = c(14.9, 17.8), phosphorus_sd = c(3.1, 3.6), phosphorus_n = c(17, 13),
  myco_phos_mean = c(22.1, 25.4), myco_phos_sd = c(4.2, 4.8), myco_phos_n = c(14, 15)
)

result <- SMD_main(
  data = data,
  Ctrl_mean = "control_mean", Ctrl_sd = "control_sd", Ctrl_n = "control_n",
  A_mean = "mycorrhizae_mean", A_sd = "mycorrhizae_sd", A_n = "mycorrhizae_n",
  B_mean = "phosphorus_mean", B_sd = "phosphorus_sd", B_n = "phosphorus_n",
  AB_mean = "myco_phos_mean", AB_sd = "myco_phos_sd", AB_n = "myco_phos_n"
)

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