minter (version 0.1.0)

lnRR_ind: Simple effect: Log Response Ratio

Description

Computes the individual or simple effect of Factor A over the Control.

Usage

lnRR_ind(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  Ctrl_mean,
  Ctrl_sd,
  Ctrl_n,
  A_mean,
  A_sd,
  A_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 experimental treatment

A_sd

Standard deviation from the experimental treatment

A_n

Sample size from the experimental treatment

Author

Facundo Decunta - [e-mail]

Details

It is the classic Log Response Ratio (lnRR), which can also be computed with metafor's escalc() function using measure = "ROM".

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

Lajeunesse, M. J. (2011). On the meta‐analysis of response ratios for studies with correlated and multi‐group designs. Ecology, 92(11), 2049-2055. https://doi.org/10.1890/11-0423.1

Examples

data <- data.frame(
  study_id = 1:3,
  control_mean = c(10, 15, 12),
  control_sd = c(2.1, 3.2, 2.8),
  control_n = c(20, 25, 18),
  drought_mean = c(12, 18, 14),
  drought_sd = c(2.3, 3.5, 3.1),
  drought_n = c(22, 24, 20)
)

# Compute individual effect of drought vs control
result <- lnRR_ind(
  data = data,
  Ctrl_mean = "control_mean",
  Ctrl_sd = "control_sd", 
  Ctrl_n = "control_n",
  A_mean = "drought_mean",
  A_sd = "drought_sd",
  A_n = "drought_n"
)