Plot the selection weights implied by an estimated selection model.

For a fitted model of class "selmodel", create a plot of the selection weights implied by the model parameter estimates. If the model includes bootstrapped confidence intervals, then the plot will also display the selection weights implied by each bootstrap replicate of the parameter estimates.

Usage

selection_plot(
  mod,
  limits = c(0, 1),
  pts = 200L,
  ref_pval = NULL,
  transform = "identity",
  expand = ggplot2::expansion(0, 0.01),
  ...
)

# S3 method for class 'selmodel'
selection_plot(
  mod,
  limits = c(0, 1),
  pts = 200L,
  ref_pval = NULL,
  transform = "identity",
  expand = ggplot2::expansion(0, 0.01),
  fill = "blue",
  alpha = 0.5,
  step_linetype = "dashed",
  ...
)

# S3 method for class 'boot.selmodel'
selection_plot(
  mod,
  limits = c(0, 1),
  pts = 200L,
  ref_pval = NULL,
  transform = "identity",
  expand = ggplot2::expansion(0, 0.01),
  color = "black",
  linewidth = 1.2,
  step_linetype = "dashed",
  draw_boots = TRUE,
  fill = "blue",
  alpha = 0.5,
  boot_color = "blue",
  boot_alpha = 0.1,
  ...
)

Arguments

mod: Fitted model of class "selmodel".
limits: numeric vector of length 2 specifying the minimum and maximum p-values to plot.
pts: Number of points for which to calculate selection weights, with a default of 200 points, evenly spaced between the specified limits.
ref_pval: Numeric value of a p-value at which to standardize the weights. If not NULL, then a p-value of ref_pval will have selection weight of 1 and selection weights for all other p-values will be calculated relative to ref_pval.
transform: Character string specifying the name of a transformation function or the transformation function itself, as defined in the scales package. The transform is passed to ggplot2::scale_x_continuous. The default transform is "identity". Other useful transforms for p-values are "sqrt" for square-root or "asn" for the arc-sin square root.
expand: Passed to the expand argument of ggplot2::scale_x_continuous.
...: further arguments passed to ggplot2::scale_x_continuous.
fill: character string specifying the fill-color to use when mod does not include bootstrap replications, with a default of "blue". Passed to ggplot2::geom_area().
alpha: numeric value specifying the opacity of the filled area plot, with a default of 0.5. Passed to ggplot2::geom_area(). Only used when mod does not include bootstrap replications.
step_linetype: character string specifying the type of line to draw to indicate p-value thresholds assumed in mod.
color: character string specifying the line color to use for drawing the estimated selection weights, with a default of "black". Passed to ggplot2::geom_line(). Only used when mod includes bootstrap replications.
linewidth: numeric value specifying the line width to use for drawing the estimated selection weights, with a default of 1.2. Passed to ggplot2::geom_line(). Only used when mod includes bootstrap replications.
draw_boots: logical value indicating whether to draw the selection weights for each bootstrap replication, with a default of TRUE.
boot_color: character string specifying the line color to use for drawing the selection weights of each bootstrap replication, with a default of "blue". Passed to ggplot2::geom_line(). Only used when mod includes bootstrap replications.
boot_alpha: numeric value specifying the opacity of the lines for drawing the selection weights of each bootstrap replication, with a default of "blue". Passed to ggplot2::geom_line(). Only used when mod includes bootstrap replications.

Value

A ggplot2 object.

Examples

mod <- selection_model(
  data = self_control,
  yi = g,
  sei = se_g,
  cluster = studyid,
  steps = c(0.025, .5),
  estimator = "CML",
  bootstrap = "none"
)

selection_plot(mod, fill = "purple")


# rescale the horizontal axis using arc-sin square root
selection_plot(mod, fill = "purple", transform = "asn") 



mod_boot <- selection_model(
  data = self_control,
  yi = g,
  sei = se_g,
  cluster = studyid,
  steps = c(0.025, .5),
  estimator = "ARGL",
  bootstrap = "multinomial",
  CI_type = "percentile",
  R = 9
)

 selection_plot(mod_boot, transform = "sqrt")

 selection_plot(mod_boot, transform = "sqrt", draw_boots = FALSE) # turn off bootstrap lines

 selection_plot(mod_boot, transform = "sqrt", color = "red", boot_color = "orange") # change colors