View source: R/plot_continuous.R
plot_weighting_continuous | R Documentation |
This provides a simple plot for the distribution of a single continuous covariate in the nominal sample and the implicit sample defined by the Aronow and Samii (2015) doi: 10.1111/ajps.12185 regression weights.
plot_weighting_continuous(mod, covariate, alpha = 0.05, num_eval = 250, ...)
mod |
Weighting model object |
covariate |
Covariate vector |
alpha |
Number between zero and one indicating the desired alpha level for confidence intervals. |
num_eval |
Number of points at which to evaluate the density. |
... |
unused arguments |
Kernel density estimates use the bias-corrected methods of Cattaneo et al (2020).
A ggplot2::ggplot
object.
Cattaneo, Jansson and Ma (2021): lpdensity: Local Polynomial Density Estimation and Inference. Journal of Statistical Software, forthcoming.
Cattaneo, Jansson and Ma (2020): Simple Local Polynomial Density Estimators. Journal of the American Statistical Association 115(531): 1449-1455.
lpdensity::lpdensity()
y <- rnorm(100) a <- rbinom(100, 1, 0.5) x <- rnorm(100) cov <- runif(100) mod <- stats::lm(y ~ a + x) rw_mod <- calculate_weights(mod, "a") plot_weighting_continuous(rw_mod, cov, num_eval = 25)
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