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Plotting the impact of the assumed sampling correlation on the overall average effect size estimate

Source: R/rho_impact.R
plot_rho_impact.map_rho.Rd

Creates a plot showing the impact on the assumed sampling correlation (\(\rho\)) on the overall average effect size and the variance estimation.

Usage

# S3 method for class 'map_rho'
plot_rho_impact(
  data,
  rho_used,
  prediction_interval = FALSE,
  ylab_beta = NULL,
  var_breaks = NULL
)

Arguments

data

Data/object for which the plot should be made. Much be of class map_rho.

rho_used

Numerical value indicating the (assumed) sampling correlation used to fit the main CHE-RVE model.

prediction_interval

Logical indicting whether a plot showing the impact of the sampling correlation on the prediction interval estimation.

ylab_beta

Optional character with the y-axis label for the overall mean effect plot.

var_breaks

Optional vector setting the y-axis breaks for the variance plot.

Value

A ggplot object

Details

Inspiration to plot found from Pustejovsky and Tipton (2021).

References

Pustejovsky, J. E., & Tipton, E. (2021). Meta-analysis with robust variance estimation: Expanding the range of working models. Prevention Science, 23(1), 425–438. doi:10.1007/s11121-021-01246-3

See also

map_rho_impact

Examples

Diet_dat <- Dietrichson2021_data |> dplyr::mutate(vg = SE_g^2)

map_rho_impact(
  data = head(Diet_dat, 100),
  yi = Effectsize_g,
  vi = vg,
  studyid = Study_ID
) |>
plot_rho_impact(rho_used = 0.7, var_breaks = seq(0, 0.35, 0.05))