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
- 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- 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
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"
)