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minter

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Factorial designs help us understand synergies and antagonisms between ecological factors. However, meta-analyses of factorial experiments are rare in ecology, likely because extracting effect sizes from factorial data is not straightforward.

minter is an R package that simplifies this process by providing functions to extract different effect sizes from factorial data, enabling researchers to conduct meta-analyses of interactions between factors.

Installation

devtools::install_github("fdecunta/minter")

Example: Fertilization × Drought Interactions

library(minter)

# Dummy data from 8 studies examining fertilization × drought on plant biomass
studies <- data.frame(
  study_id = 1:8,
  # Control: No fertilization, well-watered
  Ctrl_mean = c(12.3, 15.7, 10.8, 14.2, 11.9, 13.5, 16.1, 12.7),
  Ctrl_sd   = c(2.1, 3.2, 1.8, 2.7, 2.3, 2.9, 3.5, 2.4),
  Ctrl_n    = c(20, 24, 18, 22, 19, 25, 23, 21),
  # Fertilization only
  Fert_mean = c(18.5, 21.3, 16.2, 19.8, 17.1, 20.4, 22.7, 18.9),
  Fert_sd   = c(3.1, 4.1, 2.7, 3.6, 3.2, 3.8, 4.2, 3.4),
  Fert_n    = c(22, 25, 20, 24, 21, 26, 25, 23),
  # Drought only  
  Drought_mean = c(8.7, 11.2, 7.9, 10.1, 8.3, 9.8, 11.7, 9.4),
  Drought_sd   = c(1.8, 2.4, 1.6, 2.1, 1.9, 2.3, 2.6, 2.0),
  Drought_n    = c(19, 23, 17, 21, 18, 24, 22, 20),
  # Both treatments
  Both_mean = c(14.2, 17.8, 12.9, 16.3, 13.7, 16.1, 18.4, 15.2),
  Both_sd   = c(2.9, 3.7, 2.5, 3.3, 2.8, 3.4, 3.9, 3.1),
  Both_n    = c(21, 26, 19, 23, 20, 27, 24, 22)
)

# Calculate interaction effect: Does fertilization work differently under drought?
interaction_results <- lnRR_inter(
  data = studies,
  Ctrl_mean = "Ctrl_mean", Ctrl_sd = "Ctrl_sd", Ctrl_n = "Ctrl_n",
  A_mean = "Fert_mean", A_sd = "Fert_sd", A_n = "Fert_n", 
  B_mean = "Drought_mean", B_sd = "Drought_sd", B_n = "Drought_n",
  AB_mean = "Both_mean", AB_sd = "Both_sd", AB_n = "Both_n"
)

head(interaction_results)
#>   study_id Ctrl_mean Ctrl_sd ... yi        vi
#> 1        1      12.3     2.1     0.081  0.0069
#> 2        2      15.7     3.2     0.158  0.0068
#> 3        3      10.8     1.8     0.084  0.0073

# Meta-analysis
library(metafor)
res <- rma(yi, vi, ..., data = interaction_results)

Effect Size Measures

Effect Types

Acknowledgments

We thank Shinichi Nakagawa and Daniel Noble for generously sharing their unpublished formulas for meta-analysis of interactions, which form the theoretical foundation of this package.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.