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
- 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- hedges_correction
Logical. Apply or not Hedges' correction for small-sample bias. Default is TRUE
- 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 treatment
- A_sd
Standard deviation from the treatment
- A_n
Sample size from the 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
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.
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"
)