# Bias Contour Plots Generates bias contour plots to aid with sensitivity analysis ```r 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`. ## Returns A ggplot2 object, containing the bias contour plot ## Examples ```r # 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 ) ```

contour_plot function