time_lnCVR: Log Coefficient of Variation Ratio: Interaction Between...

In minter: Effect Sizes for Meta-Analysis of Interactions from Factorial Experiments

Log Coefficient of Variation Ratio: Interaction Between Treatment and Time

Description

Log Coefficient of Variation Ratio: Interaction Between Treatment and Time

Usage

time_lnCVR(
  data,
  col_names = c("yi", "vi"),
  append = TRUE,
  t0_Ctrl_mean,
  t0_Ctrl_sd,
  t1_Ctrl_mean,
  t1_Ctrl_sd,
  Ctrl_n,
  Ctrl_cor,
  t0_Exp_mean,
  t0_Exp_sd,
  t1_Exp_mean,
  t1_Exp_sd,
  Exp_n,
  Exp_cor
)

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
t0_Ctrl_mean	Sample mean from the control group at time 0
t0_Ctrl_sd	Standard deviation from the control group at time 0
t1_Ctrl_mean	Sample mean from the control group at time 1
t1_Ctrl_sd	Standard deviation from the control group at time 1
Ctrl_n	Sample size of the control group
Ctrl_cor	Number or numeric vector. Correlation between the means of the control group at t0 and t1
t0_Exp_mean	Sample mean from the experimental group at time 0
t0_Exp_sd	Standard deviation from the experimental group at time 0
t1_Exp_mean	Sample mean from the experimental group at time 1
t1_Exp_sd	Standard deviation from the experimental group at time 1
Exp_n	Sample size of the experimental group
Exp_cor	Number or numeric vector. Correlation between the means of the experimental group at t0 and t1

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.

Author(s)

Facundo Decunta - [e-mail]

References

Shinichi Nakagawa and Daniel Noble, personal communication.

Examples

# Pre-post design for coefficient of variation changes over time (Disturbance experiment)
data <- data.frame(
  study_id = 1:2,
  pre_control_mean = c(12.8, 15.4), pre_control_sd = c(2.6, 3.1),
  post_control_mean = c(13.2, 15.9), post_control_sd = c(2.7, 3.2),
  control_n = c(20, 18),
  pre_disturbed_mean = c(12.9, 15.2), pre_disturbed_sd = c(2.5, 3.0),
  post_disturbed_mean = c(8.7, 10.1), post_disturbed_sd = c(3.8, 4.3),
  disturbed_n = c(19, 21)
)

result <- time_lnCVR(
  data = data,
  t0_Ctrl_mean = "pre_control_mean", t0_Ctrl_sd = "pre_control_sd",
  t1_Ctrl_mean = "post_control_mean", t1_Ctrl_sd = "post_control_sd",
  Ctrl_n = "control_n", Ctrl_cor = 0.8,
  t0_Exp_mean = "pre_disturbed_mean", t0_Exp_sd = "pre_disturbed_sd",
  t1_Exp_mean = "post_disturbed_mean", t1_Exp_sd = "post_disturbed_sd",
  Exp_n = "disturbed_n", Exp_cor = 0.5
)

minter documentation built on May 3, 2026, 5:06 p.m.