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Log Response Ratio: Interaction Between Treatment and Time

Source: R/time_lnRR.R
time_lnRR.Rd

Log Response Ratio: Interaction Between Treatment and Time

Usage

time_lnRR(
  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.

References

Shinichi Nakagawa and Daniel Noble, personal communication.

Author

Facundo Decunta - fdecunta@agro.uba.ar

Examples

data <- data.frame(
  study_id = 1:2,
  pre_control_mean = c(8.4, 10.2),     # Control before restoration
  pre_control_sd = c(1.8, 2.1),
  post_control_mean = c(8.9, 10.7),    # Control after restoration period
  post_control_sd = c(1.9, 2.2),
  control_n = c(22, 18),
  pre_restoration_mean = c(8.6, 10.1), # Restoration sites before
  pre_restoration_sd = c(1.9, 2.0),
  post_restoration_mean = c(15.3, 17.8), # Restoration sites after
  post_restoration_sd = c(3.2, 3.7),
  restoration_n = c(20, 19)
)

result <- time_lnRR(
  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 = 0.7,  # Correlation within control sites
  t0_Exp_mean = "pre_restoration_mean", t0_Exp_sd = "pre_restoration_sd",
  t1_Exp_mean = "post_restoration_mean", t1_Exp_sd = "post_restoration_sd", 
  Exp_n = "restoration_n", Exp_cor = 0.6   # Correlation within restoration sites
)

# Using different correlations for each study
result2 <- time_lnRR(
  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 = c(0.6, 0.8),
  t0_Exp_mean = "pre_restoration_mean", t0_Exp_sd = "pre_restoration_sd",
  t1_Exp_mean = "post_restoration_mean", t1_Exp_sd = "post_restoration_sd", 
  Exp_n = "restoration_n", Exp_cor = c(0.5, 0.7)
)