contour_plot: Bias Contour Plots
In senseweight: Sensitivity Analysis for Weighted Estimators

contour_plot

R Documentation

Bias Contour Plots

Description

Generates bias contour plots to aid with sensitivity analysis

Usage

contour_plot(
  varW,
  sigma2,
  killer_confounder,
  df_benchmark,
  benchmark = TRUE,
  shade = FALSE,
  shade_var = NULL,
  shade_fill = "#35a4bf",
  shade_alpha = 0.25,
  contour_width = 1,
  binwidth = NULL,
  label_size = 0.25,
  point_size = 1,
  nudge = 0.05,
  axis_text_size = 12,
  axis_title_size = 14,
  axis_line_width = 1,
  print = FALSE
)

Arguments

`varW`	Variance of the estimated weights
`sigma2`	Estimated variance of the outcome (i.e., stats::var(Y) for obervational setting; stats::var(tau) for generalization setting)#'
`killer_confounder`	Threshold for bias considered large enough to be a killer confounder. For example, if researchers are concerned about the bias large enough to reduce an estimated treatment effect to zero or change directional sign, set `killer_confounder` equal to the point estimate.
`df_benchmark`	Data frame containing formal benchmarking results. The data.frame must contain the columns `variable` (for the covariate name), `R2_benchmark`, and `rho_benchmark`.
`benchmark`	Flag for whether or not to display benchmarking results (`benchmark = TRUE` if we want to add benchmarking results to plot, `benchamrk=FALSE` otherwise). If set to `TRUE`, `df_benchmark` must contain valid benchmarking results.
`shade`	Flag for whether or not a specific benchmarking covariate (or set of benchmarked covariates) should be shaded a different color (`shade = TRUE` indicates that we want to highlight specific variables)
`shade_var`	If `shade = TRUE`, this contains either a vector containing the variables we want to highlight
`shade_fill`	Color to fill the highlighted variables. Default is set to `"#35a4bf"`.
`shade_alpha`	Alpha value for the fill color. Default is set to `0.25`.
`contour_width`	Width of the contour lines. Default is set to `1`.
`binwidth`	If set to a numeric value, the function will generate a contour plot with the specified binwidth. Default is set to `NULL`.
`label_size`	Size of the labels. Default is set to `0.25`.
`point_size`	Size of the points. Default is set to `1`.
`nudge`	Nudge value for the labels. Default is set to `0.05`.
`axis_text_size`	Size of the axis text. Default is set to `12`.
`axis_title_size`	Size of the axis title. Default is set to `14`.
`axis_line_width`	Width of the axis lines. Default is set to `1`.
`print`	If set to `TRUE`, the function will return a list with two elements: `plot` which contains the generated bias contour plot, and `data`, which provides the data.frame for generating the contour plot. If set to `FALSE`, the function will simply generate the bias contour plot. Default is set to `FALSE`.

Value

A ggplot2 object, containing the bias contour plot

Examples

# For the external validity setting: 
data(jtpa_women)
site_name <- "NE"
df_site <- jtpa_women[which(jtpa_women$site == site_name), ]
df_else <- jtpa_women[which(jtpa_women$site != site_name), ]

# Estimate unweighted estimator:
model_dim <- estimatr::lm_robust(Y ~ T, data = df_site)
PATE <- coef(lm(Y ~ T, data = df_else))[2]
DiM <- coef(model_dim)[2]
# Generate weights using observed covariates:
df_all <- jtpa_women
df_all$S <- ifelse(jtpa_women$site == "NE", 1, 0)
model_ps <- WeightIt::weightit(
  (1 - S) ~ . - site - T - Y, 
  data = df_all, method = "ebal", estimand = "ATT"
)
weights <- model_ps$weights[df_all$S == 1]
# Estimate IPW model:
model_ipw <- estimatr::lm_robust(Y ~ T, data = df_site, weights = weights)
ipw <- coef(model_ipw)[2]
# Estimate bound for var(tau):
vartau <- var(df_site$Y[df_site$T == 1]) - var(df_site$Y[df_site$T == 0])
RV <- robustness_value(estimate = ipw, b_star = 0, sigma2 = vartau, weights = weights)

# Select weighting variables:
weighting_vars <- names(df_all)[which(!names(df_all) %in% c("site", "S", "Y", "T"))]

# Run benchmarking:
df_benchmark <- run_benchmarking(
  weighting_vars = weighting_vars,
  data = df_all[, -1],
  treatment = "T", outcome = "Y", selection = "S",
  estimate = ipw,
  RV = RV, sigma2 = vartau,
  estimand = "PATE"
)
# Generate bias contour plot:
contour_plot(
var(weights), vartau, ipw, df_benchmark,
benchmark = TRUE, shade = TRUE,
shade_var = c("age", "prevearn"),
label_size = 4
)

senseweight documentation built on Aug. 23, 2025, 1:11 a.m.