Package {minter}

Title:	Effect Sizes for Meta-Analysis of Interactions from Factorial Experiments
Version:	0.2.0
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
URL:	https://fdecunta.github.io/minter/
BugReports:	https://github.com/fdecunta/minter/issues
Depends:	R (≥ 3.5)
LazyData:	true
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition:	3
Config/roxygen2/version:	8.1.0
Config/Needs/website:	rmarkdown
NeedsCompilation:	no
Packaged:	2026-10-01 17:58:10 UTC; fdecunta
Author:	Facundo Decunta [aut, cre], Shinichi Nakagawa [ctb], Daniel Noble [ctb]
Maintainer:	Facundo Decunta <fdecunta@agro.uba.ar>
Repository:	CRAN
Date/Publication:	2026-10-01 18:20:02 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

The interaction is computed as:

d_{inter} = \frac{ (\bar{X}_{AB} - \bar{X}_B) - (\bar{X}_A - \bar{X}_{Ctrl}) }{S_{pooled}} \cdot J(m)

With the pooled standard deviation:

S_{pooled} = \sqrt{ \frac{ (n_A-1)sd_A^2 + (n_B-1)sd_B^2 + (n_{AB}-1)sd_{AB}^2 + (n_{Ctrl}-1)sd_{Ctrl}^2 }{ n_A + n_B + n_{AB} + n_{Ctrl} - 4 } }

And the Hedges correction as:

J(m) = 1 - \frac{3}{4m-1}

with:

m = n_A + n_B + n_{AB} + n_{Ctrl} - 4

The sampling variance is computed as:

var(d_{inter}) = \frac{1}{n_A} + \frac{1}{n_B} + \frac{1}{n_{AB}} + \frac{1}{n_{Ctrl}} + \frac{d_{inter}^2}{2(n_A + n_B + n_{AB} + n_{Ctrl})}

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

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

The main SMD of Factor A is computed as:

d_{main} = \frac{ (\bar{X}_A + \bar{X}_{AB}) - (\bar{X}_{B} + \bar{X}_{Ctrl}) }{2S_{pooled}} \cdot J(m)

With the pooled standard deviation:

S_{pooled} = \sqrt{ \frac{ (n_A-1)sd_A^2 + (n_B-1)sd_B^2 + (n_{AB}-1)sd_{AB}^2 + (n_{Ctrl}-1)sd_{Ctrl}^2 }{ n_A + n_B + n_{AB} + n_{Ctrl} - 4 } }

And the Hedges correction as:

J(m) = 1 - \frac{3}{4m-1}

with:

m = n_A + n_B + n_{AB} + n_{Ctrl} - 4

The sampling variance is computed as:

var(d_{main}) = \frac{1}{4} \left[ \frac{1}{n_A} + \frac{1}{n_B} + \frac{1}{n_{AB}} + \frac{1}{n_{Ctrl}} + \frac{d_{main}^2}{2(n_A + n_B + n_{AB} + n_{Ctrl})} \right]

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

Individual effect: Standardized Mean Difference

Description

Computes the 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".

The SMD of Factor A over the Control is computed as:

d_{ind} = \frac{\bar{X}_A - \bar{X}_{Ctrl}}{S_{pooled}} \cdot J(m)

where the pooled standard deviation is:

S_{pooled} = \sqrt{\frac{(n_A-1)sd_A^2 + (n_{Ctrl}-1)sd_{Ctrl}^2}{n_A + n_{Ctrl} - 2}}

And the Hedges correction:

J(m) = 1 - \frac{3}{4m-1}

with :

m = n_A + n_{Ctrl} - 2

The sampling variance is:

var(d_{ind}) = \frac{1}{n_A} + \frac{1}{n_{Ctrl}} + \frac{d^2}{2(n_A + n_{Ctrl})}

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

Individual effect: Standardized Mean Difference

Description

Computes the 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".

The SMD of Factor A over the Control is computed as:

d_{ind} = \frac{\bar{X}_A - \bar{X}_{Ctrl}}{S_{pooled}} \cdot J(m)

where the pooled standard deviation is:

S_{pooled} = \sqrt{\frac{(n_A-1)sd_A^2 + (n_{Ctrl}-1)sd_{Ctrl}^2}{n_A + n_{Ctrl} - 2}}

And the Hedges correction:

J(m) = 1 - \frac{3}{4m-1}

with :

m = n_A + n_{Ctrl} - 2

The sampling variance is:

var(d_{ind}) = \frac{1}{n_A} + \frac{1}{n_{Ctrl}} + \frac{d^2}{2(n_A + n_{Ctrl})}

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

The interaction is computed as:

d_{inter} = \frac{ (\bar{X}_{AB} - \bar{X}_B) - (\bar{X}_A - \bar{X}_{Ctrl}) }{S_{pooled}} \cdot J(m)

With the pooled standard deviation:

S_{pooled} = \sqrt{ \frac{ (n_A-1)sd_A^2 + (n_B-1)sd_B^2 + (n_{AB}-1)sd_{AB}^2 + (n_{Ctrl}-1)sd_{Ctrl}^2 }{ n_A + n_B + n_{AB} + n_{Ctrl} - 4 } }

And the Hedges correction as:

J(m) = 1 - \frac{3}{4m-1}

with:

m = n_A + n_B + n_{AB} + n_{Ctrl} - 4

The sampling variance is computed as:

var(d_{inter}) = \frac{1}{n_A} + \frac{1}{n_B} + \frac{1}{n_{AB}} + \frac{1}{n_{Ctrl}} + \frac{d_{inter}^2}{2(n_A + n_B + n_{AB} + n_{Ctrl})}

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

The main SMD of Factor A is computed as:

d_{main} = \frac{ (\bar{X}_A + \bar{X}_{AB}) - (\bar{X}_{B} + \bar{X}_{Ctrl}) }{2S_{pooled}} \cdot J(m)

With the pooled standard deviation:

S_{pooled} = \sqrt{ \frac{ (n_A-1)sd_A^2 + (n_B-1)sd_B^2 + (n_{AB}-1)sd_{AB}^2 + (n_{Ctrl}-1)sd_{Ctrl}^2 }{ n_A + n_B + n_{AB} + n_{Ctrl} - 4 } }

And the Hedges correction as:

J(m) = 1 - \frac{3}{4m-1}

with:

m = n_A + n_B + n_{AB} + n_{Ctrl} - 4

The sampling variance is computed as:

var(d_{main}) = \frac{1}{4} \left[ \frac{1}{n_A} + \frac{1}{n_B} + \frac{1}{n_{AB}} + \frac{1}{n_{Ctrl}} + \frac{d_{main}^2}{2(n_A + n_B + n_{AB} + n_{Ctrl})} \right]

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

lnCVR_{ind} = \ln\left( \frac{CV_{A}}{CV_{Ctrl}} \right) + \frac{1}{2(n_{A} - 1)} - \frac{1}{2(n_{Ctrl} - 1)}

var(lnCVR_{ind}) = \frac{sd_{Ctrl}^2}{n_{Ctrl}\bar{X}_{Ctrl}^2} + \frac{1}{2(n_{Ctrl} - 1)} + \frac{sd_A^2}{n_A\bar{X}_A^2} + \frac{1}{2(n_A - 1)}

This assumes no correlation between mean and variance (Nakagawa et al. 2015) and is computed as the sum of lnRR and lnVR variances.

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

lnCVR_{inter} = \ln\left( \frac{CV_{AB} / CV_{B}}{CV_{A} / CV_{Ctrl}} \right) + \frac{1}{2(n_{AB} - 1)} - \frac{1}{2(n_{A} - 1)} - \frac{1}{2(n_{B} - 1)} + \frac{1}{2(n_{Ctrl} - 1)}

var(lnCVR_{inter}) = var(\ln RR_{inter}) + var(\ln VR_{inter})

This follows the assumption of no correlation between mean and variance.

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

lnCVR_{main} = \frac{1}{2} \ln\left(\frac{CV_{AB}CV_{A}}{CV_{B}CV_{Ctrl}} \right) + \frac{1}{2} \left( \frac{1}{2(n_{AB} - 1)} + \frac{1}{2(n_{A} - 1)} - \frac{1}{2(n_{B} - 1)} - \frac{1}{2(n_{Ctrl} - 1)} \right)

var(\ln CVR_{main}) = var(\ln RR_{main}) + var(\ln VR_{main})

This follows the assumption of no correlation between mean and variance.

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"
)

Individual 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".

The log response ratio of Factor A over Control is computed as:

Formulas:

lnRR_{ind} = \ln\left(\frac{\bar{X}_A}{\bar{X}_{Ctrl}}\right)

var(lnRR_{ind}) = \frac{sd_A^2}{n_A\bar{X}_A^2} + \frac{sd_{Ctrl}^2}{n_{Ctrl}\bar{X}_{Ctrl}^2}

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

lnRR_{inter} = (\ln\bar{X}_{AB} - \ln\bar{X}_B) - (\ln\bar{X}_A -\ln\bar{X}_{Ctrl})

var(lnRR_{inter}) = \frac{sd_{AB}^2}{n_{AB}\bar{X}_{AB}^2} + \frac{sd_A^2}{n_A\bar{X}_A^2} + \frac{sd_B^2}{n_B\bar{X}_B^2} + \frac{sd_{Ctrl}^2}{n_{Ctrl}\bar{X}_{Ctrl}^2}

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

There are two ways of computing the lnRR. One is the original method used by Morris et al. 2007, and the other is the one proposed by Shinichi Nakagawa (in prep). Default method is 'nakagawa':

Nakagawa formula

lnRR_{main} = \frac{1}{2} \ln(\frac{\bar{X}_A\bar{X}_{AB}}{\bar{X}_B\bar{X}_{Ctrl}})

var(lnRR_{main}) = \frac{1}{4} ( \frac{sd_A^2}{n_A\bar{X}_A^2} + \frac{sd_B^2}{n_B\bar{X}_B^2} + \frac{sd_{AB}^2}{n_{AB}\bar{X}_{AB}^2} + \frac{sd_{Ctrl}^2}{n_{Ctrl}\bar{X}_{Ctrl}^2} )

Morris formula

lnRR_{main} = \ln(\frac{\bar{X}_A + \bar{X}_{AB}}{\bar{X}_B + \bar{X}_{Ctrl}})

var(lnRR_{main}) = (\frac{1}{\bar{X}_A + \bar{X}_{AB}})^2 (\frac{sd_A^2}{n_A} + \frac{sd_{AB}^2}{n_{AB}}) + (\frac{1}{\bar{X}_B + \bar{X}_{Ctrl}})^2 (\frac{sd_B^2}{n_B} + \frac{sd_{Ctrl}^2}{n_{Ctrl}})

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

lnVR_{ind} = \ln\left(\frac{sd_{A}}{sd_{Ctrl}}\right) + \frac{1}{2(n_{A} - 1)} - \frac{1}{2(n_{Ctrl} - 1)}

var(lnVR_{ind}) = \frac{1}{2(n_{A} - 1)} + \frac{1}{2(n_{Ctrl} - 1)}

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

lnVR_{inter} = \ln\left( \frac{sd_{AB} / sd_{B}}{sd_{A} / sd_{Ctrl}} \right) + \frac{1}{2(n_{AB} - 1)} - \frac{1}{2(n_{A} - 1)} - \frac{1}{2(n_{B} - 1)} + \frac{1}{2(n_{Ctrl} - 1)}

var(lnVR_{inter}) = \frac{1}{2(n_{AB} - 1)} + \frac{1}{2(n_{A} - 1)} + \frac{1}{2(n_{B} - 1)} + \frac{1}{2(n_{Ctrl} - 1)}

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

lnVR_{main} = \frac{1}{2} \ln\left( \frac{sd_{AB}sd_{A}}{sd_{B}sd_{Ctrl}} \right) + \frac{1}{2} \left( \frac{1}{2(n_{AB} - 1)} + \frac{1}{2(n_{A} - 1)} - \frac{1}{2(n_{B} - 1)} - \frac{1}{2(n_{Ctrl} - 1)} \right)

var(lnVR_{main}) = \frac{1}{4} \left( \frac{1}{2(n_{AB} - 1)} + \frac{1}{2(n_{A} - 1)} + \frac{1}{2(n_{B} - 1)} + \frac{1}{2(n_{Ctrl} - 1)} \right)

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

Details

Formulas

d_{f:t} = \frac{(\bar{X}_{t1,Exp} - \bar{X}_{t1,Ctrl}) - (\bar{X}_{t0,Exp} - \bar{X}_{t0,Ctrl})}{S_{pooled}} \cdot J

Pooled standard deviation:

S_{pooled} = \sqrt{\frac{(n_{Exp} - 1)(sd_{t0,Exp}^2 + sd_{t1,Exp}^2) + (n_{Ctrl} - 1)(sd_{t0,Ctrl}^2 + sd_{t1,Ctrl}^2)}{2(n_{Exp} + n_{Ctrl} - 2)}}

Sampling variance:

var(d_{f:t}) = \frac{2(1 - r_{Exp})}{n_{Exp}} + \frac{2(1 - r_{Ctrl})}{n_{Ctrl}} + \frac{d^2}{2(n_{Exp} + n_{Ctrl})}

where r_{Exp} and r_{Ctrl} are the correlations between time points within each group.

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
)