minter (version 0.1.0)

lnRR_inter: 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
)

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.

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

Author

Facundo Decunta - [e-mail]

Details

See the package vignette for a detailed description of the formula.

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"
)