Package {confoundvis}

Type:	Package
Title:	Visualization Tools for Sensitivity Analysis of Unmeasured Confounding
Version:	0.2.0
Description:	Visualization and reporting tools for sensitivity analysis to unmeasured confounding in observational studies. A common 'confoundsens' object stores a sensitivity path (the treatment effect as a function of hypothetical confounder strength) regardless of the framework that produced it, so the same robustness curves, contour plots, covariate benchmark ("sensitivity Love") plots, and plain-language reports can be drawn for impact threshold analysis (Frank, 2000, <doi:10.1177/0049124100029002001>), partial R-squared omitted-variable bias analysis (Cinelli and Hazlett, 2020, <doi:10.1111/rssb.12348>), and E-values (VanderWeele and Ding, 2017, <doi:10.7326/M16-2607>). Paths can be computed directly from fitted linear models or converted from results produced by the 'sensemakr', 'konfound', and 'EValue' packages.
License:	GPL-3
Encoding:	UTF-8
Language:	en-US
Depends:	R (≥ 4.1.0)
Imports:	ggplot2 (≥ 3.4.0), rlang, graphics, grid, stats
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, gridExtra, sensemakr, konfound, EValue
Config/testthat/edition:	3
VignetteBuilder:	knitr
URL:	https://github.com/subirhait/confoundvis
BugReports:	https://github.com/subirhait/confoundvis/issues
NeedsCompilation:	no
Config/roxygen2/version:	8.0.0
Packaged:	2026-09-29 19:39:44 UTC; subir
Author:	Subir Hait [aut, cre]
Maintainer:	Subir Hait <haitsubi@msu.edu>
Repository:	CRAN
Date/Publication:	2026-09-30 21:21:02 UTC

confoundvis: Visualization and Reporting Tools for Sensitivity Analysis

Description

Draws and reports sensitivity analyses for unmeasured confounding. A common 'confoundsens' object stores a sensitivity path (the treatment estimate as a function of hypothetical confounder strength) regardless of the framework that produced it, so the same displays and reports work for impact threshold (ITCV) analysis, partial R-squared omitted-variable bias analysis, and E-values.

Details

Main entry points:

• Paths from fitted models: [sens_path_lm()], [itcv_lm()].

• Paths from other packages: [from_sensemakr()], [from_konfound()], [from_evalue()], or [as_confoundsens()] for data frames of precomputed (including stratified or multilevel) paths.

• Covariate benchmarks: [covariate_impacts()].

• Plots: [plot_robustness_curve()], [plot_sensitivity_love()], [plot_sensitivity_contour()], [plot_local_taylor()].

• Reporting: [robustness_points()], [sens_report()].

• Conceptual illustrations built on stylized models: [plot_reversal_cone()], [plot_taylor_panels()].

A sensitivity display shows how strong confounding would have to be to change a conclusion. It cannot show whether such a confounder exists, and it inherits the assumptions of the framework that produced the path.

Author(s)

Maintainer: Subir Hait haitsubi@msu.edu (ORCID)

Authors:

• Subir Hait haitsubi@msu.edu (ORCID)

See Also

Useful links:

• https://github.com/subirhait/confoundvis

• Report bugs at https://github.com/subirhait/confoundvis/issues

Coerce a confoundsens object to a data frame

Description

Coerce a confoundsens object to a data frame

Usage

## S3 method for class 'confoundsens'
as.data.frame(x, ...)

Arguments

Value

A data.frame with columns 'lambda' and 'theta', plus optional columns 'level', 'se', and 't' when present in 'x'.

Examples

x <- new_confoundsens(
  lambda = seq(0, 0.2, length.out = 5),
  theta  = seq(1, 0.8, length.out = 5),
  se     = rep(0.05, 5)
)
as.data.frame(x)

Convert a data frame to a confoundsens object

Description

Converts a data frame containing sensitivity analysis results into a 'confoundsens' object suitable for use with 'confoundvis' plotting functions.

Usage

as_confoundsens(data, ...)

## Default S3 method:
as_confoundsens(data, ...)

## S3 method for class 'lm'
as_confoundsens(data, ...)

## S3 method for class 'sensemakr'
as_confoundsens(data, ...)

## S3 method for class 'data.frame'
as_confoundsens(
  data,
  lambda = "lambda",
  theta = "theta",
  level = NULL,
  se = NULL,
  t = NULL,
  ...
)

Arguments

Details

'as_confoundsens()' is generic. The data-frame method (the 0.1.0 behaviour) maps columns to fields. Methods for fitted 'lm' models and 'sensemakr' objects compute the partial R^2 path via [sens_path_lm()] and [from_sensemakr()]; see also [from_konfound()] and [from_evalue()].

Value

A 'confoundsens' object.

Examples

df <- data.frame(
  lambda = seq(0, 0.2, length.out = 10),
  theta  = seq(1, 0.5, length.out = 10),
  se     = rep(0.1, 10),
  level  = rep(c("within", "between"), length.out = 10)
)
x <- as_confoundsens(df)
x

# Directly from a fitted model
fit <- lm(mpg ~ am + wt + hp, data = mtcars)
as_confoundsens(fit, treatment = "am")

Benchmark observed covariates against a sensitivity threshold

Description

For each covariate term in a fitted linear model (other than the treatment and the intercept), computes how strongly it is related to the treatment and to the outcome. These are the observed benchmarks that analysts compare with a sensitivity threshold ("would an omitted variable as strong as this covariate overturn the result?").

Usage

covariate_impacts(
  model,
  treatment,
  metric = c("itcv", "partial_r2"),
  alpha = 0.05
)

Arguments

Details

Two metrics are returned:

'impact' (ITCV metric)

r_{DZ|W} \times r_{YZ|W}, the product of partial correlations of the covariate with the treatment and with the outcome, each conditioning on the remaining covariates W (as in Frank, 2000). Compared with the ITCV. Only positive values (same sign as the estimate) push the estimate toward zero; the returned 'impact' is signed so that positive means "reduces the estimate". Defined only for single-column terms.

'bias_index' (partial R^2 metric)

\sqrt{R^2_{Y \sim Z|D,W} R^2_{D \sim Z|W} / (1 - R^2_{D \sim Z|W})}. An omitted confounder with this pair of partial R^2 values would move the estimate by 'bias_index' \times\, se \sqrt{df}, so it would bring the point estimate to zero when 'bias_index' exceeds |t|/\sqrt{df}. Defined for multi-column terms (factors) as well.

The returned data frame carries an attribute 'threshold' for the selected 'metric', so it can be passed straight to [plot_sensitivity_love()]. Its 'r_yu', 'r_du', and 'label' columns can be passed as 'benchmarks' to [plot_sensitivity_contour()].

Covariate benchmarks describe *observed* variables. Whether an unobserved confounder could be as strong as a given covariate is a substantive judgment that no benchmark can settle.

Value

A data frame with one row per covariate term and columns 'covariate', 'term_df', 'r_du', 'r_yu', 'itcv_impact', 'r2dz.x', 'r2yz.dx', 'bias_index', 'impact', and 'label', and attributes 'threshold', 'metric', 'itcv', and 'f_threshold'.

Examples

fit <- lm(mpg ~ am + wt + hp + qsec, data = mtcars)
imp <- covariate_impacts(fit, "am")
imp
plot_sensitivity_love(imp)

Fit a local quadratic approximation

Description

Fits a second-order Taylor (quadratic) approximation of an effect path \theta(\delta) near \delta = 0 using ordinary least squares:

\theta(\delta) \approx a + b\delta + c\delta^2.

Usage

fit_local_quadratic(
  data = NULL,
  delta = NULL,
  theta = NULL,
  local_max_delta = 0.2,
  include_intercept = TRUE,
  tol = 1e-12
)

Arguments

Details

You may supply either a data.frame containing columns 'delta' and 'theta', or supply numeric vectors 'delta' and 'theta' directly.

Value

A named list with elements: * 'coef' — named coefficient vector from [stats::lm()]. * 'intercept', 'slope', 'quad' — individual coefficients (NA when absent). * 'model' — the fitted 'lm' object. * 'local_data' — the data.frame used for fitting. * 'local_max_delta' — the window half-width used.

Examples

df <- data.frame(
  delta = seq(0, 0.3, length.out = 60),
  theta = 0.4 - 0.7 * seq(0, 0.3, length.out = 60) +
          0.4 * seq(0, 0.3, length.out = 60)^2
)
fit_local_quadratic(df, local_max_delta = 0.2)

Convert an E-value result to a confoundsens path

Description

Builds a bias-adjusted risk-ratio path from an 'evalue' object (as returned by 'EValue::evalues.RR()', 'evalues.OR()', 'evalues.HR()', 'evalues.OLS()', and related functions). Confounder strength \lambda is the common risk ratio relating the confounder to both treatment and outcome; the corresponding bias factor is B = \lambda^2/(2\lambda - 1) (VanderWeele and Ding, 2017). The path is on the log risk-ratio scale, so it crosses the null (0) exactly at the E-value.

Usage

from_evalue(x, lambda = seq(1, 5, by = 0.02))

Arguments

Value

A 'confoundsens' object with 'theta' = log of the bias-adjusted point risk ratio and, when the confidence interval is available, 'se' on the log scale.

References

VanderWeele, T. J., & Ding, P. (2017). Sensitivity analysis in observational research: Introducing the E-value. *Annals of Internal Medicine*, 167(4), 268–274. doi:10.7326/M16-2607

Examples

e <- EValue::evalues.RR(est = 1.8, lo = 1.3, hi = 2.5)
path <- from_evalue(e)
robustness_points(path)

Convert konfound output to a confoundsens path

Description

Builds the ITCV sensitivity path from the observed and critical correlations reported by 'konfound::konfound()' or 'konfound::pkonfound()' when called with 'to_return = "raw_output"'.

Usage

from_konfound(
  x,
  dof = NULL,
  lambda = seq(0, 0.5, by = 0.01),
  treatment = NA_character_
)

Arguments

Value

A 'confoundsens' object on the correlation scale; 'meta$itcv' holds the conditional ITCV implied by ‘obs_r' and 'critical_r' (konfound’s 'itcvGz').

See Also

[itcv_lm()]

Examples

k <- konfound::pkonfound(est_eff = 2, std_err = 0.4, n_obs = 100,
                         n_covariates = 3, index = "IT",
                         to_return = "raw_output")
path <- from_konfound(k)
path$meta$itcv

Convert a sensemakr result to a confoundsens path

Description

Builds the partial R^2 sensitivity path from the summary statistics stored in a 'sensemakr' object (estimate, standard error, and residual degrees of freedom), using the same bias formulas as [sens_path_lm()]. Benchmark bounds stored in the object are attached as 'meta$bounds'.

Usage

from_sensemakr(x, lambda = seq(0, 0.5, by = 0.01), ratio = 1)

Arguments

Value

A 'confoundsens' object.

See Also

[sens_path_lm()]

Examples

data("darfur", package = "sensemakr")
fit <- lm(peacefactor ~ directlyharmed + age + farmer_dar + herder_dar +
            pastvoted + hhsize_darfur + female + village, data = darfur)
s <- sensemakr::sensemakr(fit, treatment = "directlyharmed",
                          benchmark_covariates = "female", kd = 1:3)
path <- from_sensemakr(s)
sens_report(path)

Impact threshold for a confounding variable from a fitted linear model

Description

Computes Frank's (2000) impact threshold for a confounding variable (ITCV) for one coefficient of a linear model, together with the ITCV sensitivity path: the partial correlation between treatment and outcome after conditioning on a confounder whose impact k = r_{DU} r_{YU} is split equally between the two correlations (r_{DU} = r_{YU} = \sqrt{k}).

Usage

itcv_lm(model, treatment, alpha = 0.05, lambda = seq(0, 0.5, by = 0.01))

Arguments

Details

Along this path the adjusted correlation is (r - k)/(1 - k), which equals the critical correlation r^\# exactly at k = \mathrm{ITCV} = (|r| - |r^\#|)/(1 - |r^\#|).

The conditional ITCV equals 'itcvGz' and the unconditional ITCV equals 'itcv' in the raw output of 'konfound::konfound()' (version 1.0).

Value

A list with elements 'r' (observed partial correlation), 'r_crit' (critical partial correlation), ‘itcv' (the ITCV for a confounder’s correlations *conditional on* the model covariates), 'dof', 'path' (a 'confoundsens' object on the correlation scale), 'r2xz' and 'r2yz' (the R^2 of the covariates with treatment and outcome), and 'itcv_unconditional' (the ITCV expressed in unconditional correlations, 'itcv * sqrt((1 - r2xz) * (1 - r2yz))').

References

Frank, K. A. (2000). Impact of a confounding variable on a regression coefficient. *Sociological Methods & Research*, 29(2), 147–194. doi:10.1177/0049124100029002001

Frank, K. A., Maroulis, S. J., Duong, M. Q., & Kelcey, B. M. (2013). What would it take to change an inference? Using Rubin's causal model to interpret the robustness of causal inferences. *Educational Evaluation and Policy Analysis*, 35(4), 437–460. doi:10.3102/0162373713493129

See Also

[from_konfound()], [sens_path_lm()]

Examples

fit <- lm(mpg ~ am + wt + hp, data = mtcars)
it <- itcv_lm(fit, "am")
it$itcv
plot_robustness_curve(it$path, points = FALSE)

Create a confoundsens object

Description

Constructs a 'confoundsens' object — a lightweight container for storing sensitivity paths (e.g., \theta(\lambda)) and optional uncertainty or stratification information used by 'confoundvis' plotting functions.

Usage

new_confoundsens(
  lambda,
  theta,
  level = NULL,
  se = NULL,
  t = NULL,
  meta = list()
)

Arguments

Value

A 'confoundsens' object (a list with class '"confoundsens"').

Examples

x <- new_confoundsens(
  lambda = seq(0, 0.2, length.out = 10),
  theta  = seq(1, 0.6, length.out = 10),
  se     = rep(0.1, 10)
)
x

Compare reversal cones across multilevel components

Description

Produces a two-panel plot comparing the reversal cone cross-sections for the within-cluster and between-cluster confounding components in multilevel settings. Each panel calls [plot_reversal_cone()] and they are displayed side by side using faceting.

Usage

plot_cone_comparison(
  theta0_within = 0.35,
  theta0_between = 0.5,
  delta_within = 0.2,
  delta_between = 0.3,
  ...
)

Arguments

Value

Invisibly, a list with ggplot objects 'within' and 'between'. The plots are drawn side-by-side to the current device.

Examples

plot_cone_comparison(
  theta0_within  = 0.35, theta0_between = 0.50,
  delta_within   = 0.20, delta_between  = 0.30
)

Local Taylor diagnostic plot

Description

Plots local Taylor series components (or any multi-series decomposition) as a function of 'delta' from a long-form data.frame. Lines are distinguished by colour and linetype, keyed by 'series'.

Usage

plot_local_taylor(df, facet = FALSE)

Arguments

Value

A [ggplot2::ggplot()] object.

Examples

df <- data.frame(
  delta  = rep(seq(0, 0.2, length.out = 25), 3),
  series = rep(c("path", "tangent", "quadratic"), each = 25),
  value  = c(
    0.4 - 0.7 * seq(0, 0.2, length.out = 25) - 0.4 * seq(0, 0.2, length.out = 25)^2,
    0.4 - 0.7 * seq(0, 0.2, length.out = 25),
    0.4 - 0.7 * seq(0, 0.2, length.out = 25) + 0.2 * seq(0, 0.2, length.out = 25)^2
  )
)
plot_local_taylor(df)
plot_local_taylor(df, facet = TRUE)

Reversal cone geometry plot

Description

Visualizes the two-dimensional cross-section of the reversal cone C_l at a fixed confounding effect magnitude |\delta|. The cone partitions the (q, p) confounding parameter space into an **attenuation zone** (where the effect shrinks but does not reverse sign) and a **reversal zone** (where the sign changes). The boundary between zones is the reversal curve.

Usage

plot_reversal_cone(
  theta0 = 0.4,
  delta = 0.2,
  q_range = c(0, 1),
  p_range = c(-1, 1),
  grid_n = 200L,
  show_boundary = TRUE,
  show_volume = TRUE
)

Arguments

Details

The display uses the stylized bilinear bias model \theta_{adj} = \theta_0 - |\delta|\, q\, p, so the reversal boundary is the hyperbola p = \theta_0 / (|\delta| q). This is a conceptual map of the parameter space, not an estimate: the annotated "reversal share" is the fraction of the *displayed* grid that falls in the reversal zone and therefore depends on 'q_range' and 'p_range'. It should not be reported as a probability or as a property of the data.

Value

A ggplot object.

Examples

plot_reversal_cone(theta0 = 0.40, delta = 0.20)
plot_reversal_cone(theta0 = 0.40, delta = 0.50)

Plot a robustness curve

Description

Plots the sensitivity path stored in a 'confoundsens' object as a function of 'lambda'. By default, plots the effect path 'theta(lambda)'; optionally plots the test-statistic path 't(lambda)'. Pointwise 95 are drawn when standard errors are available.

Usage

plot_robustness_curve(
  x,
  what = c("theta", "t"),
  bands = TRUE,
  points = TRUE,
  facet_level = TRUE,
  reference = TRUE
)

Arguments

Value

A [ggplot2::ggplot()] object.

Examples

x <- new_confoundsens(
  lambda = seq(0, 0.2, length.out = 25),
  theta  = 1 - 2 * seq(0, 0.2, length.out = 25),
  se     = rep(0.05, 25),
  level  = rep(c("within", "between"), length.out = 25)
)
plot_robustness_curve(x)
plot_robustness_curve(x, bands = FALSE, facet_level = FALSE)

Sensitivity contour plot

Description

Draws an ITCV-style hyperbolic boundary in (r_{YU}, r_{DU}) space and optionally overlays observed covariate benchmarks as labelled points. The robust region is the interior of the hyperbola where |r_{YU} \cdot r_{DU}| < 'threshold'.

Usage

plot_sensitivity_contour(threshold, grid_n = 200, benchmarks = NULL)

Arguments

Value

A [ggplot2::ggplot()] object.

Examples

b <- data.frame(
  r_yu  = c(0.10, 0.15),
  r_du  = c(0.20, 0.12),
  label = c("SES", "BMI")
)
plot_sensitivity_contour(threshold = 0.02, benchmarks = b)

Sensitivity Love plot

Description

Benchmarks a sensitivity threshold against the empirical distribution of observed covariate impacts in a "Love plot"-style display. Each covariate appears as a point on a horizontal impact axis; the sensitivity threshold is shown as a vertical reference line. Covariates to the left of the line are weaker than the threshold; those to the right pose a credible threat.

Usage

plot_sensitivity_love(
  df,
  threshold = attr(df, "threshold"),
  sort = TRUE,
  top = NULL
)

Arguments

Value

A [ggplot2::ggplot()] object.

Examples

df <- data.frame(
  covariate = c("SES", "BMI", "Gender", "Race", "Age"),
  impact    = c(0.12, 0.05, 0.02, 0.08, 0.03)
)
plot_sensitivity_love(df, threshold = 0.10)
plot_sensitivity_love(df, threshold = 0.10, top = 3)

# From a fitted model: threshold is taken from covariate_impacts()
fit <- lm(mpg ~ am + wt + hp + qsec, data = mtcars)
plot_sensitivity_love(covariate_impacts(fit, "am"))

Three-panel Taylor approximation illustration

Description

Produces a three-panel figure (linear / concave-down / convex-up) showing a simulated confounding path together with its first-order (tangent) and second-order (local quadratic) approximations, for three curvature regimes: linear, concave-down, and convex-up.

Usage

plot_taylor_panels(
  delta_max = 1.5,
  step = 0.02,
  theta0 = 0.4,
  slope = -0.7,
  kappa = 0.4,
  local_max_delta = 0.2
)

Arguments

Details

This is a didactic illustration built from simulated paths ([simulate_taylor_demo()]); it does not analyse data. It was called 'plot_figure2_taylor_panels()' in version 0.1.0; that name still works but is deprecated.

Value

A named list with ggplot objects 'A' (linear), 'B' (concave), and 'C' (convex), returned invisibly. The three panels are also printed to the active graphics device via [gridExtra::grid.arrange()] if gridExtra is installed, or via [graphics::layout()] otherwise.

Examples

plots <- plot_taylor_panels()
# Access individual panels
plots$A

Print method for confoundsens objects

Description

Prints a concise summary of the key fields in a 'confoundsens' object.

Usage

## S3 method for class 'confoundsens'
print(x, ...)

Arguments

Value

'x', invisibly.

Examples

x <- new_confoundsens(
  lambda = seq(0, 0.2, length.out = 5),
  theta  = seq(1, 0.8, length.out = 5)
)
print(x)

Locate robustness thresholds along a sensitivity path

Description

Finds, for each level of a 'confoundsens' object, the confounder strength \lambda at which (i) the point estimate reaches the null value and (ii) statistical significance is lost. Crossings are located by linear interpolation between grid points, so their precision depends on the spacing of 'lambda'.

Usage

robustness_points(x, alpha = 0.05)

Arguments

Details

The significance criterion is chosen from what the object carries, in this order: a critical value on the 'theta' scale in 'meta$threshold' (ITCV paths); adjusted t statistics with residual degrees of freedom in 'meta$dof' (partial R^2 paths; critical value from a t distribution with 'dof - 1' degrees of freedom, as in Cinelli and Hazlett, 2020); standard errors (normal critical value); or t statistics without degrees of freedom (normal critical value).

Value

A data frame with columns 'level', 'theta_start', 'lambda_null', 'lambda_sig', and 'sig_rule'. 'NA' means the crossing is not reached within the supplied 'lambda' range.

Examples

fit <- lm(mpg ~ am + wt + hp, data = mtcars)
robustness_points(sens_path_lm(fit, "am", lambda = seq(0, 0.9, by = 0.001)))

Sensitivity path from a fitted linear model (partial R-squared framework)

Description

Computes the bias-adjusted treatment coefficient as a function of the strength of a hypothetical omitted confounder, using the omitted-variable bias formulas of Cinelli and Hazlett (2020). Confounder strength is indexed by \lambda, the partial R^2 of the confounder with the treatment (R^2_{D \sim Z | X}); the partial R^2 with the outcome is R^2_{Y \sim Z | D, X} = \min(\code{ratio} \times \lambda, 1). The default 'ratio = 1' gives the equal-strength path along which the robustness value is defined.

Usage

sens_path_lm(model, treatment, lambda = seq(0, 0.5, by = 0.01), ratio = 1)

Arguments

Details

The confounder is assumed to act in the direction that shrinks the estimate toward zero (the conventional worst case for a reported effect).

Value

A 'confoundsens' object with 'theta' (adjusted estimate), 'se' (adjusted standard error), and 't' (adjusted t statistic), plus 'meta' recording the original estimate, degrees of freedom, and robustness value.

References

Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: Extending omitted variable bias. *Journal of the Royal Statistical Society: Series B*, 82(1), 39–67. doi:10.1111/rssb.12348

See Also

[from_sensemakr()], [itcv_lm()], [covariate_impacts()]

Examples

fit <- lm(mpg ~ am + wt + hp, data = mtcars)
path <- sens_path_lm(fit, treatment = "am")
path
plot_robustness_curve(path, points = FALSE)

Plain-language sensitivity report

Description

Summarises a 'confoundsens' path in a few sentences: the starting estimate, the framework-specific summary (robustness value, ITCV, or E-value) when available, and the confounder strengths at which the point estimate reaches the null and significance is lost. The report closes with a statement of what the analysis cannot establish.

Usage

sens_report(x, alpha = 0.05, digits = 3)

Arguments

Value

An object of class 'confoundvis_report': a list with 'text' (a character vector) and 'points' (the output of [robustness_points()]). It prints as text.

Examples

fit <- lm(mpg ~ am + wt + hp, data = mtcars)
sens_report(sens_path_lm(fit, "am", lambda = seq(0, 0.9, by = 0.001)))

Simulate demo confounding paths for Taylor panel figures

Description

Generates three synthetic sensitivity paths — linear, concave-down, and convex-up — sharing the same baseline effect \theta(0) and first-order slope, but differing in second-order curvature. These toy paths illustrate the difference between tangent-based (first-order) and local quadratic (second-order) sensitivity approximations.

Usage

simulate_taylor_demo(
  delta_max = 1.5,
  step = 0.02,
  theta0 = 0.4,
  slope = -0.7,
  kappa = 0.4
)

Arguments

Details

The three regimes are:

• Linear: \theta(\delta) = \theta_0 + s\delta

• Concave-down: \theta(\delta) = \theta_0 + s\delta - \kappa\delta^2

• Convex-up: \theta(\delta) = \theta_0 + s\delta + \kappa\delta^2

Value

A named list with elements 'linear', 'concave', and 'convex', each a [new_confoundsens()] object.

Examples

sims <- simulate_taylor_demo(
  delta_max = 1, step = 0.05, theta0 = 0.4, slope = -0.7, kappa = 0.4
)
sims$linear
plot_robustness_curve(sims$concave)

Summary method for confoundsens objects

Description

Produces a concise numerical summary of the sensitivity path, optionally broken down by level.

Usage

## S3 method for class 'confoundsens'
summary(object, ...)

Arguments

Value

An object of class 'summary.confoundsens' containing a 'table' data.frame with per-level summary statistics.

Examples

x <- new_confoundsens(
  lambda = seq(0, 0.2, length.out = 10),
  theta  = seq(1, 0.6, length.out = 10),
  se     = rep(0.08, 10),
  level  = rep(c("within", "between"), length.out = 10)
)
summary(x)

Validate a confoundsens object

Description

Internal validator used by constructors and methods. Checks that required fields are present and consistently sized, coerces optional fields, and issues a warning when 'lambda' is not nondecreasing.

Usage

validate_confoundsens(x)

Arguments

Value

The validated (and possibly coerced) object, invisibly. Errors if the object is structurally invalid.