plot.nawt {nawtilus}

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 class 'nawt'
plot(x, ...)

Arguments

x

an object of class “nawt”, usually, a result of a call to nawt.

...

additional arguments to be passed to plot.

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.

Value

No retrun value, called for side effects.

Author(s)

Hiroto Katsumata

See Also

nawt, plot

Examples

# 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)