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
- data
Data frame containing the variables used.
- col_names
Vector of two strings to name the output columns for the effect size and it's sampling variance. Default is 'yi' and 'vi'.
- append
Logical. Append the results to
data. Default is TRUE- hedges_correction
Boolean. If TRUE correct 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 streatment
- A_mean
Mean outcome from the A treatment
- A_sd
Standard deviation from the A treatment
- A_n
Sample size from the A 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
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