Package 'pstratreg'

Print

Description

Print results from principal stratification regression.

Usage

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

Arguments

x	An object of class pstratreg, such as the output of a call to pstratreg()
...	Other arguments to print commands

Value

No return value, called for side effects.

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Estimate Principal Stratification Regression Bounds

Description

Uses principal stratification and parametric models to bound the average causal effect among those who would have a valid outcome under either treatment condition

Usage

pstratreg(
  formula_y,
  formula_s,
  formula_sq_resid = NULL,
  family_y = "gaussian",
  homoskedastic = TRUE,
  data,
  weights = NULL,
  treatment_name,
  monotonicity_positive = FALSE,
  monotonicity_negative = FALSE,
  mean_dominance_y1_positive = FALSE,
  mean_dominance_y1_negative = FALSE,
  mean_dominance_y0_positive = FALSE,
  mean_dominance_y0_negative = FALSE,
  aggregate = TRUE,
  group_vars = NULL
)

Arguments

formula_y	A model formula for the outcome
formula_s	A model formula for the binary indicator for whether the outcome exists
formula_sq_resid	A model formula for the log mean of squared residuals for a variance function regression. Should only use variables that are used in formula_y. Because the Gamma regression for squared residuals is more computationally demanding, one might want a simpler formula_sq_resid than formula_y, perhaps to allow only highly structured heteroskedasticity. Defaults to match formula_y. No outcome need be specified in this formula. Can be NULL if 'homoskedastic = TRUE' because then there is no model for the conditional variance, which is assumed to be constant.
family_y	Character family of the outcome, either "gaussian" or "binomial"
homoskedastic	A logical for whether homoskedasticity of the outcome should be assumed. If FALSE, estimates by variance function regression for log squared residuals with the same formula as in formula_y.
data	A data frame
weights	A numeric vector of weights, of length nrow(data)
treatment_name	A character string for the name of the treatment variable in data,
monotonicity_positive	A logical. Whether to assume M1 >= M0, so that treatment never causes the outcome to be undefined
monotonicity_negative	A logical. Whether to assume M1 <= M0, so that lack of treatment never causes the outcome to be undefined
mean_dominance_y1_positive	A logical. Whether to assume E(Y1 | S1 = 1, S0 = 1) >= E(Y1 | S1 = 1, S0 = 0), so that the always-survivor treated units have mean outcomes at least as great as the observed-survivor treated units
mean_dominance_y1_negative	A logical. Whether to assume E(Y1 | S1 = 1, S0 = 1) <= E(Y1 | S1 = 1, S0 = 0), so that the always-survivor treated units have mean outcomes no greater the observed-survivor treated units
mean_dominance_y0_positive	A logical. Whether to assume E(Y0 | S1 = 1, S0 = 1) >= E(Y0 | S1 = 0, S0 = 1), so that the always-survivor untreated units have mean outcomes at least as great as the observed-survivor untreated units
mean_dominance_y0_negative	A logical. Whether to assume E(Y0 | S1 = 1, S0 = 1) <= E(Y0 | S1 = 0, S0 = 1), so that the always-survivor untreated units have mean outcomes no greater the observed-survivor untreated units
aggregate	A logical. Whether to aggregate results or return one estimate per row of data
group_vars	Character vector of names of variables in data by which to group when aggregating results. Only relevant when aggregate = TRUE

Value

An object of class pstratreg, which is a list containing:

estimates_y	A data frame or tibble containing point bounds for the outcome values among those whose outcome exists regardless.
estimates_s	A data frame or tibble containing point estimates for outcome existence.
fit_s	The glm object for the model of outcome existence.
fit_y	The glm or lm object for the model of outcome values conditional on existence.
fit_sq_resid	The glm object for the model of squared outcome residuals under heteroskedasticity (if homoskedastic = FALSE).
call	A list of arguments passed to the function.

Examples

sim <- pstratreg_sim(n = 100)
result <- pstratreg(
     formula_y = formula(y ~ x + a),
     formula_s = formula(s ~ x + a),
     data = sim,
     treatment_name = "a"
)
result <- pstratreg(
     formula_y = formula(y ~ x + a),
     formula_s = formula(s ~ x + a),
     data = sim,
     treatment_name = "a",
     aggregate = TRUE,
     homoskedastic = FALSE,
     monotonicity_negative = TRUE
)

---

Standard errors for pstratreg

Description

Calculate standard errors by a nonparametric bootstrap, and 95

Usage

pstratreg_se(x, r = 100)

Arguments

x	An object of class pstratreg, such as the output of a call to pstratreg()
r	Number of bootstrap replicates

Value

Data frame with estimates, standard errors, and confidence intervals for many quantities of interest.

Examples

sim <- pstratreg_sim(n = 100)
result <- pstratreg(
     formula_y = formula(y ~ x + a),
     formula_s = formula(s ~ x + a),
     data = sim,
     treatment_name = "a"
)
se <- pstratreg_se(result, r = 10)

---

Simulate Data

Description

Simulate data to illustrate principal stratification regression bounding.

Usage

pstratreg_sim(n = 100)

Arguments

n	Integer number of cases

Value

Simulated data frame

Examples

pstratreg_sim(n = 100)

---

Summary

Description

Summarize results from principal stratification regression.

Usage

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

Arguments

object	An object of class pstratreg, such as the output of a call to pstratreg()
...	Other arguments to summary commands

Value

No return value, called for side effects.

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