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Simple effect: Standardized Mean Difference

Source: R/SMD_wrappers.R
SMD_ind.Rd

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

Usage

SMD_ind(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  hedges_correction = TRUE,
  Ctrl_mean,
  Ctrl_sd,
  Ctrl_n,
  A_mean,
  A_sd,
  A_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

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 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

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.

Details

It is the classic Standardized Mean Difference (SMD), which can also be computed with metafor's escalc() function using measure = "SMD".

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

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

Author

Facundo Decunta - fdecunta@agro.uba.ar

Examples

data <- data.frame(
  study_id = 1:3,
  control_mean = c(45.2, 52.8, 38.9),
  control_sd = c(8.1, 11.2, 7.3),
  control_n = c(18, 23, 16),
  pollinator_exclusion_mean = c(28.7, 35.4, 22.1),
  pollinator_exclusion_sd = c(6.8, 9.1, 5.9),
  pollinator_exclusion_n = c(20, 22, 18)
)

# With Hedges' correction (default)
result <- SMD_ind(
  data = data,
  Ctrl_mean = "control_mean",
  Ctrl_sd = "control_sd",
  Ctrl_n = "control_n",
  A_mean = "pollinator_exclusion_mean",
  A_sd = "pollinator_exclusion_sd",
  A_n = "pollinator_exclusion_n",
  hedges_correction = TRUE
)

# Without Hedges' correction
result_no_hedges <- SMD_ind(
  data = data,
  Ctrl_mean = "control_mean",
  Ctrl_sd = "control_sd",
  Ctrl_n = "control_n",
  A_mean = "pollinator_exclusion_mean",
  A_sd = "pollinator_exclusion_sd",
  A_n = "pollinator_exclusion_n",
  hedges_correction = FALSE
)