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)

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.