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
- 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_sd
Standard deviation from the control treatment
- Ctrl_n
Sample size from the control treatment
- A_sd
Standard deviation from the A treatment
- A_n
Sample size from the A treatment
- B_sd
Standard deviation from the B treatment
- B_n
Sample size from the B 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.
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"
)