Title:	Effect Sizes for Meta-Analysis of Interactions from Factorial Experiments
Version:	0.1.0
Description:	Compute effect sizes and their sampling variances from factorial experimental designs. The package supports calculation of simple effects, overall effects, and interaction effects for use in factorial meta-analyses. See Gurevitch et al. (2000) <doi:10.1086/303337>, Morris et al. (2007) <doi:10.1890/06-0442>, Lajeunesse (2011) <doi:10.1890/11-0423.1> and Macartney et al. (2022) <doi:10.1016/j.neubiorev.2022.104554>.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
URL:	https://fdecunta.github.io/minter/
BugReports:	https://github.com/fdecunta/minter/issues
Imports:	checkmate
Depends:	R (≥ 3.5)
LazyData:	true
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition:	3
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-09-09 14:35:41 UTC; fdecunta
Author:	Facundo Decunta [aut, cre], Shinichi Nakagawa [ctb], Daniel Noble [ctb]
Maintainer:	Facundo Decunta <fdecunta@agro.uba.ar>
Repository:	CRAN
Date/Publication:	2025-10-06 06:00:02 UTC

Compute And Format

Description

Compute effect size and do some pre-return tasks

Usage

.compute_and_format(effsize_func, effsize_args, data, col_names, append)

Arguments

Interaction effect: Standardized mean difference

Description

Computes the interaction effect between factors A and B in factorial data.

Usage

.interaction_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

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 - fdecunta@agro.uba.ar

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

data <- data.frame(
  study_id = 1:2,
  control_mean = c(24.8, 27.2), control_sd = c(4.1, 4.6), control_n = c(18, 16),
  salinity_mean = c(19.3, 21.7), salinity_sd = c(3.8, 4.2), salinity_n = c(17, 18),
  temperature_mean = c(28.9, 31.4), temperature_sd = c(4.7, 5.1), temperature_n = c(19, 15),
  salt_temp_mean = c(15.2, 17.8), salt_temp_sd = c(3.1, 3.5), salt_temp_n = c(16, 17)
)

result <- SMD_inter(
  data = data,
  Ctrl_mean = "control_mean", Ctrl_sd = "control_sd", Ctrl_n = "control_n",
  A_mean = "salinity_mean", A_sd = "salinity_sd", A_n = "salinity_n",
  B_mean = "temperature_mean", B_sd = "temperature_sd", B_n = "temperature_n",
  AB_mean = "salt_temp_mean", AB_sd = "salt_temp_sd", AB_n = "salt_temp_n"
)

Correction for small-sample bias

Description

Small-sample bias correction for Hedges' g

Usage

.j_correction(m)

Arguments

Required columns for computing lnVR

Description

'inter' use the same arguments than 'main',

Usage

.lnVR_args

Format

An object of class list of length 2.

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

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 - fdecunta@agro.uba.ar

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"
)

Pooled Standard Deviation for SMD in factorial experiments

Description

Compute the pooled standard deviation for SMD. It computes the pooled SD for 2 or 4 groups depending on the arguments passed. Simple SMD has only 2 groups, while main and interactions had 4 groups.

Usage

.pooled_sd(
  Ctrl_sd,
  Ctrl_n,
  A_sd,
  A_n,
  B_sd = NULL,
  B_n = NULL,
  AB_n = NULL,
  AB_sd = NULL
)

Arguments

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

Simple effect: Standardized Mean Difference

Description

Computes the individual or simple effect of Factor A over the Control.

Usage

.simple_SMD(
  Ctrl_mean,
  Ctrl_sd,
  Ctrl_n,
  A_mean,
  A_sd,
  A_n,
  hedges_correction = TRUE
)

Arguments

Details

It is the classic Standardized Mean Difference (SMD), which can also be computed with metafor's escalc() function using measure = "SMD".

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 - fdecunta@agro.uba.ar

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

data <- data.frame(
  study_id = 1:3,
  control_mean = c(45.2, 52.8, 38.9),
  control_sd = c(8.1, 11.2, 7.3),
  control_n = c(18, 23, 16),
  pollinator_exclusion_mean = c(28.7, 35.4, 22.1),
  pollinator_exclusion_sd = c(6.8, 9.1, 5.9),
  pollinator_exclusion_n = c(20, 22, 18)
)

# With Hedges' correction (default)
result <- SMD_ind(
  data = data,
  Ctrl_mean = "control_mean",
  Ctrl_sd = "control_sd",
  Ctrl_n = "control_n",
  A_mean = "pollinator_exclusion_mean",
  A_sd = "pollinator_exclusion_sd",
  A_n = "pollinator_exclusion_n",
  hedges_correction = TRUE
)

# Without Hedges' correction
result_no_hedges <- SMD_ind(
  data = data,
  Ctrl_mean = "control_mean",
  Ctrl_sd = "control_sd",
  Ctrl_n = "control_n",
  A_mean = "pollinator_exclusion_mean",
  A_sd = "pollinator_exclusion_sd",
  A_n = "pollinator_exclusion_n",
  hedges_correction = FALSE
)

Standardized Mean Difference for the interaction between Experimental Treatment and Time

Description

Standardized Mean Difference for the interaction between Experimental Treatment and Time

Usage

.time_interaction_SMD(
  t0_Ctrl_mean,
  t0_Ctrl_sd,
  t1_Ctrl_mean,
  t1_Ctrl_sd,
  Ctrl_n,
  Ctrl_cor,
  t0_Exp_mean,
  t0_Exp_sd,
  t1_Exp_mean,
  t1_Exp_sd,
  Exp_n,
  Exp_cor,
  hedges_correction = TRUE
)

Arguments

References

Shinichi Nakagawa and Daniel Noble, personal communication.

Log Coefficient of Variation Ratio: Interaction Between Experimental Treatment and Time

Description

Log Coefficient of Variation Ratio: Interaction Between Experimental Treatment and Time

Usage

.time_interaction_lnCVR(
  t0_Ctrl_mean,
  t0_Ctrl_sd,
  t1_Ctrl_mean,
  t1_Ctrl_sd,
  Ctrl_n,
  Ctrl_cor,
  t0_Exp_mean,
  t0_Exp_sd,
  t1_Exp_mean,
  t1_Exp_sd,
  Exp_n,
  Exp_cor
)

Arguments

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.

Log Response Ratio: Interaction Between Experimental Treatment and Time

Description

Log Response Ratio: Interaction Between Experimental Treatment and Time

Usage

.time_interaction_lnRR(
  t0_Ctrl_mean,
  t0_Ctrl_sd,
  t1_Ctrl_mean,
  t1_Ctrl_sd,
  Ctrl_n,
  Ctrl_cor,
  t0_Exp_mean,
  t0_Exp_sd,
  t1_Exp_mean,
  t1_Exp_sd,
  Exp_n,
  Exp_cor
)

Arguments

Log of Variability Ratio: Interaction Between Experimental Treatment and Time

Description

Log of Variability Ratio: Interaction Between Experimental Treatment and Time

Usage

.time_interaction_lnVR(
  t0_Ctrl_sd,
  t1_Ctrl_sd,
  Ctrl_n,
  Ctrl_cor,
  t0_Exp_sd,
  t1_Exp_sd,
  Exp_n,
  Exp_cor
)

Arguments

Pooled Standard Deviation for SMD in non-independent factorial

Description

Pooled Standard Deviation for SMD in non-independent factorial

Usage

.time_pooled_sd(t0_Ctrl_sd, t1_Ctrl_sd, Ctrl_n, t0_Exp_sd, t1_Exp_sd, Exp_n)

Arguments

Simple effect: Standardized Mean Difference

Description

Computes the individual or simple effect of Factor A over the Control.

Usage

SMD_ind(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  hedges_correction = TRUE,
  Ctrl_mean,
  Ctrl_sd,
  Ctrl_n,
  A_mean,
  A_sd,
  A_n
)

Arguments

Details

It is the classic Standardized Mean Difference (SMD), which can also be computed with metafor's escalc() function using measure = "SMD".

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 - fdecunta@agro.uba.ar

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

data <- data.frame(
  study_id = 1:3,
  control_mean = c(45.2, 52.8, 38.9),
  control_sd = c(8.1, 11.2, 7.3),
  control_n = c(18, 23, 16),
  pollinator_exclusion_mean = c(28.7, 35.4, 22.1),
  pollinator_exclusion_sd = c(6.8, 9.1, 5.9),
  pollinator_exclusion_n = c(20, 22, 18)
)

# With Hedges' correction (default)
result <- SMD_ind(
  data = data,
  Ctrl_mean = "control_mean",
  Ctrl_sd = "control_sd",
  Ctrl_n = "control_n",
  A_mean = "pollinator_exclusion_mean",
  A_sd = "pollinator_exclusion_sd",
  A_n = "pollinator_exclusion_n",
  hedges_correction = TRUE
)

# Without Hedges' correction
result_no_hedges <- SMD_ind(
  data = data,
  Ctrl_mean = "control_mean",
  Ctrl_sd = "control_sd",
  Ctrl_n = "control_n",
  A_mean = "pollinator_exclusion_mean",
  A_sd = "pollinator_exclusion_sd",
  A_n = "pollinator_exclusion_n",
  hedges_correction = FALSE
)

Interaction effect: Standardized mean difference

Description

Computes the interaction effect between factors A and B in factorial data.

Usage

SMD_inter(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  hedges_correction = TRUE,
  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

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 - fdecunta@agro.uba.ar

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

data <- data.frame(
  study_id = 1:2,
  control_mean = c(24.8, 27.2), control_sd = c(4.1, 4.6), control_n = c(18, 16),
  salinity_mean = c(19.3, 21.7), salinity_sd = c(3.8, 4.2), salinity_n = c(17, 18),
  temperature_mean = c(28.9, 31.4), temperature_sd = c(4.7, 5.1), temperature_n = c(19, 15),
  salt_temp_mean = c(15.2, 17.8), salt_temp_sd = c(3.1, 3.5), salt_temp_n = c(16, 17)
)

result <- SMD_inter(
  data = data,
  Ctrl_mean = "control_mean", Ctrl_sd = "control_sd", Ctrl_n = "control_n",
  A_mean = "salinity_mean", A_sd = "salinity_sd", A_n = "salinity_n",
  B_mean = "temperature_mean", B_sd = "temperature_sd", B_n = "temperature_n",
  AB_mean = "salt_temp_mean", AB_sd = "salt_temp_sd", AB_n = "salt_temp_n"
)

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

SMD_main(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  hedges_correction = TRUE,
  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

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 - fdecunta@agro.uba.ar

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"
)

Individual Effect: Log Coefficient Of Variation Ratio

Description

Computes the Log of the Coefficient of Variation Ratio between Factor A and the Control treatment.

Usage

lnCVR_ind(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  Ctrl_mean,
  Ctrl_sd,
  Ctrl_n,
  A_mean,
  A_sd,
  A_n
)

Arguments

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 - fdecunta@agro.uba.ar

References

Nakagawa, S., Poulin, R., Mengersen, K., Reinhold, K., Engqvist, L., Lagisz, M., & Senior, A. M. (2015). Meta‐analysis of variation: ecological and evolutionary applications and beyond. Methods in Ecology and Evolution, 6(2), 143-152.

Examples

data <- data.frame(
  study_id = 1:3,
  control_mean = c(8.5, 12.3, 6.8),
  control_sd = c(1.8, 2.9, 1.4),
  control_n = c(18, 24, 16),
  nutrient_mean = c(11.2, 16.7, 9.3),
  nutrient_sd = c(3.1, 4.8, 2.7),
  nutrient_n = c(19, 22, 17)
)

result <- lnCVR_ind(
  data = data,
  Ctrl_mean = "control_mean", Ctrl_sd = "control_sd", Ctrl_n = "control_n",
  A_mean = "nutrient_mean", A_sd = "nutrient_sd", A_n = "nutrient_n"
)

Interaction Effect: Log Coefficient of Variation Ratio

Description

Computes the interaction effect between Factors A and B in factorial experiments on the coefficient of variation ratio.

Usage

lnCVR_inter(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  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

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 - fdecunta@agro.uba.ar

Examples

# Interaction effect logCVR (Light x Nutrients)
data <- data.frame(
  study_id = 1:2,
  control_mean = c(7.3, 8.9),
  control_sd = c(1.4, 1.7),
  control_n = c(20, 18),
  light_mean = c(12.8, 14.2),
  light_sd = c(3.1, 3.5),
  light_n = c(19, 20),
  nutrients_mean = c(9.6, 11.1),
  nutrients_sd = c(1.9, 2.2),
  nutrients_n = c(21, 17),
  light_nutrients_mean = c(18.4, 20.7),
  light_nutrients_sd = c(4.8, 5.3),
  light_nutrients_n = c(18, 19)
)

result <- lnCVR_inter(
  data = data,
  Ctrl_mean = "control_mean",
  Ctrl_sd = "control_sd",
  Ctrl_n = "control_n",
  A_mean = "light_mean",
  A_sd = "light_sd",
  A_n = "light_n",
  B_mean = "nutrients_mean",
  B_sd = "nutrients_sd",
  B_n = "nutrients_n",
  AB_mean = "light_nutrients_mean",
  AB_sd = "light_nutrients_sd",
  AB_n = "light_nutrients_n"
)

Main Effect: Log Coefficient Of Variation Ration

Description

Computes the main effect of Factor A across levels of Factor B in factorial experiments on the coefficient of variation.

Usage

lnCVR_main(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  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

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 - fdecunta@agro.uba.ar

Examples

data <- data.frame(
  study_id = 1:2,
  control_mean = c(14.2, 16.8), control_sd = c(2.8, 3.1), control_n = c(16, 14),
  irrigation_mean = c(19.5, 22.1), irrigation_sd = c(5.2, 5.8), irrigation_n = c(15, 16),
  co2_mean = c(16.8, 19.4), co2_sd = c(3.1, 3.6), co2_n = c(17, 13),
  irrigation_co2_mean = c(24.3, 27.9), irrigation_co2_sd = c(6.8, 7.4), irrigation_co2_n = c(14, 15)
)

result <- lnCVR_main(
  data = data,
  Ctrl_mean = "control_mean", Ctrl_sd = "control_sd", Ctrl_n = "control_n",
  A_mean = "irrigation_mean", A_sd = "irrigation_sd", A_n = "irrigation_n",
  B_mean = "co2_mean", B_sd = "co2_sd", B_n = "co2_n", 
  AB_mean = "irrigation_co2_mean", AB_sd = "irrigation_co2_sd", AB_n = "irrigation_co2_n"
)

Simple effect: Log Response Ratio

Description

Computes the individual or simple effect of Factor A over the Control.

Usage

lnRR_ind(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  Ctrl_mean,
  Ctrl_sd,
  Ctrl_n,
  A_mean,
  A_sd,
  A_n
)

Arguments

Details

It is the classic Log Response Ratio (lnRR), which can also be computed with metafor's escalc() function using measure = "ROM".

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 - fdecunta@agro.uba.ar

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

Examples

data <- data.frame(
  study_id = 1:3,
  control_mean = c(10, 15, 12),
  control_sd = c(2.1, 3.2, 2.8),
  control_n = c(20, 25, 18),
  drought_mean = c(12, 18, 14),
  drought_sd = c(2.3, 3.5, 3.1),
  drought_n = c(22, 24, 20)
)

# Compute individual effect of drought vs control
result <- lnRR_ind(
  data = data,
  Ctrl_mean = "control_mean",
  Ctrl_sd = "control_sd", 
  Ctrl_n = "control_n",
  A_mean = "drought_mean",
  A_sd = "drought_sd",
  A_n = "drought_n"
)

Interaction effect: Log Response Ratio

Description

Computes the interaction effect between factors A and B in factorial data.

Usage

lnRR_inter(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  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

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 - fdecunta@agro.uba.ar

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

Examples

data <- data.frame(
  study_id = 1:2,
  control_mean = c(25, 28), control_sd = c(3.2, 3.8), control_n = c(15, 17),
  predation_mean = c(18, 20), predation_sd = c(2.9, 3.1), predation_n = c(16, 18),
  competition_mean = c(22, 24), competition_sd = c(3.0, 3.5), competition_n = c(14, 16),
  pred_comp_mean = c(12, 15), pred_comp_sd = c(2.1, 2.6), pred_comp_n = c(15, 17)
)

# Compute interaction effect between predation and competition
result <- lnRR_inter(
  data = data,
  Ctrl_mean = "control_mean", Ctrl_sd = "control_sd", Ctrl_n = "control_n",
  A_mean = "predation_mean", A_sd = "predation_sd", A_n = "predation_n",
  B_mean = "competition_mean", B_sd = "competition_sd", B_n = "competition_n",
  AB_mean = "pred_comp_mean", AB_sd = "pred_comp_sd", AB_n = "pred_comp_n"
)

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

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 - fdecunta@agro.uba.ar

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"
)

Individual effect: Log of Variability Ratio

Description

Computes the Log of the Variability Ratio between a Factor A and the Control treatment in factorial experiments.

Usage

lnVR_ind(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  Ctrl_sd,
  Ctrl_n,
  A_sd,
  A_n
)

Arguments

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 - fdecunta@agro.uba.ar

References

Nakagawa, S., Poulin, R., Mengersen, K., Reinhold, K., Engqvist, L., Lagisz, M., & Senior, A. M. (2015). Meta‐analysis of variation: ecological and evolutionary applications and beyond. Methods in Ecology and Evolution, 6(2), 143-152.

Examples

# Example focusing on variability differences (Herbivory effect)
data <- data.frame(
  study_id = 1:3,
  control_sd = c(2.1, 1.8, 2.5),
  control_n = c(20, 22, 18),
  herbivory_sd = c(3.2, 2.9, 3.8),
  herbivory_n = c(21, 20, 19)
)

result <- lnVR_ind(
  data = data,
  Ctrl_sd = "control_sd",
  Ctrl_n = "control_n",
  A_sd = "herbivory_sd", 
  A_n = "herbivory_n"
)

Interaction effect: Log Variability Ratio

Description

Computes the interaction of Factors A and B measured as the log of the variability ratio.

Usage

lnVR_inter(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  Ctrl_sd,
  Ctrl_n,
  A_sd,
  A_n,
  B_sd,
  B_n,
  AB_sd,
  AB_n
)

Arguments

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 - fdecunta@agro.uba.ar

Examples

# Example for interaction effect in 2x2 factorial focusing on variability (Drought x Temperature)
data <- data.frame(
  study_id = 1:2,
  control_sd = c(1.8, 2.1), control_n = c(22, 19),
  drought_sd = c(2.6, 2.9), drought_n = c(20, 21),
  temperature_sd = c(2.0, 2.3), temperature_n = c(21, 18),
  drought_temp_sd = c(3.2, 3.6), drought_temp_n = c(19, 20)
)

result <- lnVR_inter(
  data = data,
  Ctrl_sd = "control_sd", Ctrl_n = "control_n",
  A_sd = "drought_sd", A_n = "drought_n", 
  B_sd = "temperature_sd", B_n = "temperature_n",
  AB_sd = "drought_temp_sd", AB_n = "drought_temp_n"
)

Main Effect: Log of the Variability Ratio

Description

Computes the overral log of the variability ratio for Factor A across levels of Factor B.

Usage

lnVR_main(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  Ctrl_sd,
  Ctrl_n,
  A_sd,
  A_n,
  B_sd,
  B_n,
  AB_sd,
  AB_n
)

Arguments

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 - fdecunta@agro.uba.ar

Examples

# Example for main effect in 2x2 factorial focusing on variability (Fire x Grazing)
data <- data.frame(
  study_id = 1:2,
  control_sd = c(2.0, 2.3), control_n = c(20, 18),
  fire_sd = c(2.8, 3.1), fire_n = c(19, 20),
  grazing_sd = c(2.2, 2.5), grazing_n = c(21, 17),
  fire_grazing_sd = c(3.5, 3.8), fire_grazing_n = c(18, 19)
)

result <- lnVR_main(
  data = data,
  Ctrl_sd = "control_sd", Ctrl_n = "control_n",
  A_sd = "fire_sd", A_n = "fire_n",
  B_sd = "grazing_sd", B_n = "grazing_n",
  AB_sd = "fire_grazing_sd", AB_n = "fire_grazing_n"
)

Fake factorial dataset with pre-computed effect sizes

Description

This dataset contains fake data from 2×2 factorial experiments with four treatment groups: Control (C), Factor A, Factor B, and the combination AB (A and B together). It includes means, standard deviations, and sample sizes for each group, as well as pre-computed effect sizes: log response ratio (lnRR), standardized mean difference (SMD), and their variances, for the main effects of A, B, and the interaction (AB).

Usage

testing_data

Format

A data frame with 10 rows and 24 columns:

C_n, C_mean, C_sd

Sample size, mean, and SD of the Control group

A_n, A_mean, A_sd

Sample size, mean, and SD of the A treatment

B_n, B_mean, B_sd

Sample size, mean, and SD of the B treatment

AB_n, AB_mean, AB_sd

Sample size, mean, and SD of the AB (A and B combined) treatment

A_main_lnRR, A_main_lnRRv

Log response ratio for main effect A and its variance

B_main_lnRR, B_main_lnRRv

Log response ratio for main effect B and its variance

AB_main_lnRR, AB_main_lnRRv

Log response ratio for the AB interaction and its variance

A_main_SMD, A_main_SMDv

Standardized mean difference (Hedges' g) for main effect A and its variance

B_main_SMD, B_main_SMDv

Standardized mean difference for main effect B and its variance

AB_main_SMD, AB_main_SMDv

Standardized mean difference for the AB interaction and its variance

Details

This dataset is intended for validating functions that compute or test effect sizes in meta-analysis workflows involving factorial designs.

Source

Fake.

Standardized Mean Difference: Interaction Between Treatment and Time

Description

Standardized Mean Difference: Interaction Between Treatment and Time

Usage

time_SMD(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  hedges_correction = TRUE,
  t0_Ctrl_mean,
  t0_Ctrl_sd,
  t1_Ctrl_mean,
  t1_Ctrl_sd,
  Ctrl_n,
  Ctrl_cor,
  t0_Exp_mean,
  t0_Exp_sd,
  t1_Exp_mean,
  t1_Exp_sd,
  Exp_n,
  Exp_cor
)

Arguments

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 - fdecunta@agro.uba.ar

References

Shinichi Nakagawa and Daniel Noble, personal communication.

Examples

# Pre-post design for standardized mean difference with time interaction (Conservation experiment)
data <- data.frame(
  study_id = 1:2,
  pre_control_mean = c(18.3, 21.7), pre_control_sd = c(4.1, 4.8),
  post_control_mean = c(18.8, 22.1), post_control_sd = c(4.2, 4.9),
  control_n = c(16, 14),
  pre_conservation_mean = c(18.1, 21.4), pre_conservation_sd = c(4.0, 4.7),
  post_conservation_mean = c(26.7, 31.2), post_conservation_sd = c(5.8, 6.4),
  conservation_n = c(15, 16)
)

result <- time_SMD(
  data = data,
  t0_Ctrl_mean = "pre_control_mean", t0_Ctrl_sd = "pre_control_sd",
  t1_Ctrl_mean = "post_control_mean", t1_Ctrl_sd = "post_control_sd",
  Ctrl_n = "control_n", Ctrl_cor = 0.9,
  t0_Exp_mean = "pre_conservation_mean", t0_Exp_sd = "pre_conservation_sd",
  t1_Exp_mean = "post_conservation_mean", t1_Exp_sd = "post_conservation_sd",
  Exp_n = "conservation_n", Exp_cor = 0.7,
  hedges_correction = TRUE
)

# Without Hedges' correction
result_no_hedges <- time_SMD(
  data = data,
  t0_Ctrl_mean = "pre_control_mean", t0_Ctrl_sd = "pre_control_sd",
  t1_Ctrl_mean = "post_control_mean", t1_Ctrl_sd = "post_control_sd",
  Ctrl_n = "control_n", Ctrl_cor = 0.9,
  t0_Exp_mean = "pre_conservation_mean", t0_Exp_sd = "pre_conservation_sd",
  t1_Exp_mean = "post_conservation_mean", t1_Exp_sd = "post_conservation_sd",
  Exp_n = "conservation_n", Exp_cor = 0.7,
  hedges_correction = FALSE
)

Log Coefficient of Variation Ratio: Interaction Between Treatment and Time

Description

Log Coefficient of Variation Ratio: Interaction Between Treatment and Time

Usage

time_lnCVR(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  t0_Ctrl_mean,
  t0_Ctrl_sd,
  t1_Ctrl_mean,
  t1_Ctrl_sd,
  Ctrl_n,
  Ctrl_cor,
  t0_Exp_mean,
  t0_Exp_sd,
  t1_Exp_mean,
  t1_Exp_sd,
  Exp_n,
  Exp_cor
)

Arguments

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 - fdecunta@agro.uba.ar

References

Shinichi Nakagawa and Daniel Noble, personal communication.

Examples

# Pre-post design for coefficient of variation changes over time (Disturbance experiment)
data <- data.frame(
  study_id = 1:2,
  pre_control_mean = c(12.8, 15.4), pre_control_sd = c(2.6, 3.1),
  post_control_mean = c(13.2, 15.9), post_control_sd = c(2.7, 3.2),
  control_n = c(20, 18),
  pre_disturbed_mean = c(12.9, 15.2), pre_disturbed_sd = c(2.5, 3.0),
  post_disturbed_mean = c(8.7, 10.1), post_disturbed_sd = c(3.8, 4.3),
  disturbed_n = c(19, 21)
)

result <- time_lnCVR(
  data = data,
  t0_Ctrl_mean = "pre_control_mean", t0_Ctrl_sd = "pre_control_sd",
  t1_Ctrl_mean = "post_control_mean", t1_Ctrl_sd = "post_control_sd",
  Ctrl_n = "control_n", Ctrl_cor = 0.8,
  t0_Exp_mean = "pre_disturbed_mean", t0_Exp_sd = "pre_disturbed_sd",
  t1_Exp_mean = "post_disturbed_mean", t1_Exp_sd = "post_disturbed_sd",
  Exp_n = "disturbed_n", Exp_cor = 0.5
)

Log Response Ratio: Interaction Between Treatment and Time

Description

Log Response Ratio: Interaction Between Treatment and Time

Usage

time_lnRR(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  t0_Ctrl_mean,
  t0_Ctrl_sd,
  t1_Ctrl_mean,
  t1_Ctrl_sd,
  Ctrl_n,
  Ctrl_cor,
  t0_Exp_mean,
  t0_Exp_sd,
  t1_Exp_mean,
  t1_Exp_sd,
  Exp_n,
  Exp_cor
)

Arguments

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 - fdecunta@agro.uba.ar

References

Shinichi Nakagawa and Daniel Noble, personal communication.

Examples

data <- data.frame(
  study_id = 1:2,
  pre_control_mean = c(8.4, 10.2),     # Control before restoration
  pre_control_sd = c(1.8, 2.1),
  post_control_mean = c(8.9, 10.7),    # Control after restoration period
  post_control_sd = c(1.9, 2.2),
  control_n = c(22, 18),
  pre_restoration_mean = c(8.6, 10.1), # Restoration sites before
  pre_restoration_sd = c(1.9, 2.0),
  post_restoration_mean = c(15.3, 17.8), # Restoration sites after
  post_restoration_sd = c(3.2, 3.7),
  restoration_n = c(20, 19)
)

result <- time_lnRR(
  data = data,
  t0_Ctrl_mean = "pre_control_mean", t0_Ctrl_sd = "pre_control_sd",
  t1_Ctrl_mean = "post_control_mean", t1_Ctrl_sd = "post_control_sd",
  Ctrl_n = "control_n", Ctrl_cor = 0.7,  # Correlation within control sites
  t0_Exp_mean = "pre_restoration_mean", t0_Exp_sd = "pre_restoration_sd",
  t1_Exp_mean = "post_restoration_mean", t1_Exp_sd = "post_restoration_sd", 
  Exp_n = "restoration_n", Exp_cor = 0.6   # Correlation within restoration sites
)

# Using different correlations for each study
result2 <- time_lnRR(
  data = data,
  t0_Ctrl_mean = "pre_control_mean", t0_Ctrl_sd = "pre_control_sd",
  t1_Ctrl_mean = "post_control_mean", t1_Ctrl_sd = "post_control_sd",
  Ctrl_n = "control_n", Ctrl_cor = c(0.6, 0.8),
  t0_Exp_mean = "pre_restoration_mean", t0_Exp_sd = "pre_restoration_sd",
  t1_Exp_mean = "post_restoration_mean", t1_Exp_sd = "post_restoration_sd", 
  Exp_n = "restoration_n", Exp_cor = c(0.5, 0.7)
)

Log of Variability Ratio: Interaction Between Treatment and Time

Description

Log of Variability Ratio: Interaction Between Treatment and Time

Usage

time_lnVR(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  t0_Ctrl_sd,
  t1_Ctrl_sd,
  Ctrl_n,
  Ctrl_cor,
  t0_Exp_sd,
  t1_Exp_sd,
  Exp_n,
  Exp_cor
)

Arguments

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 - fdecunta@agro.uba.ar

References

Shinichi Nakagawa and Daniel Noble, personal communication.

Examples

data <- data.frame(
  study_id = 1:2,
  pre_control_sd = c(2.1, 2.4),
  post_control_sd = c(2.2, 2.5),
  control_n = c(24, 19),
  pre_invaded_sd = c(2.0, 2.3),
  post_invaded_sd = c(4.1, 4.6),
  invaded_n = c(21, 22)
)

result <- time_lnVR(
  data = data,
  t0_Ctrl_sd = "pre_control_sd", t1_Ctrl_sd = "post_control_sd",
  Ctrl_n = "control_n", Ctrl_cor = 0.6,
  t0_Exp_sd = "pre_invaded_sd", t1_Exp_sd = "post_invaded_sd",
  Exp_n = "invaded_n", Exp_cor = 0.4
)