nawtilus (version 0.1.4)

plot.nawt: Plot a scattered plot comparing the navigated weighting and naive estimation

Description

Plots a scattered plot comparing the resulting inverse probability weights estimated by the navigated weighting and the standard logistic regression.

Usage

# S3 method for nawt
plot(x, ...)

Arguments

x

an object of class <U+201C>nawt<U+201D>, usually, a result of a call to nawt.

...

additional arguments to be passed to plot.

Value

No retrun value, called for side effects.

Details

The x-axis shows the inverse probability weights estimated by estimating propensity scores with the standard logistic regression whereas the y-axis shows those with the navigated weighting. Excessively heavy weights on only a few observations in the navigated weighting may indicate the failure of the estimation.

Position of the legend is determined internally.

See Also

nawt, plot

Examples

# NOT RUN {
# Simulation from Kang and Shafer (2007) and Imai and Ratkovic (2014)
tau <- 10
set.seed(12345)
n <- 1000
X <- matrix(rnorm(n * 4, mean = 0, sd = 1), nrow = n, ncol = 4)
prop <- 1 / (1 + exp(X[, 1] - 0.5 * X[, 2] + 0.25 * X[, 3] + 0.1 * X[, 4]))
treat <- rbinom(n, 1, prop)
y <- 210 + 27.4 * X[, 1] + 13.7 * X[, 2] + 13.7 * X[, 3] + 13.7 * X[, 4] + 
     tau * treat + rnorm(n)

# Data frame and formulas for propensity score estimation
df <- data.frame(X, treat, y)
colnames(df) <- c("x1", "x2", "x3", "x4", "treat", "y")
formula_c <- as.formula(treat ~ x1 + x2 + x3 + x4)

# Power weighting function with alpha = 2
# ATT estimation
fitatt <- nawt(formula = formula_c, outcome = "y", estimand = "ATT", 
               method = "score", data = df, alpha = 2)
plot(fitatt)

# ATE estimation
fitate <- nawt(formula = formula_c, outcome = "y", estimand = "ATE", 
               method = "score", data = df, alpha = 2)
plot(fitate)
# }