balanceLovePlot: Create Love plot of standardized covariate mean differences

In hyunseungkang/ivmodel: Statistical Inference and Sensitivity Analysis for Instrumental Variables Model

Create Love plot of standardized covariate mean differences

Description

balanceLovePlot creates a Love plot of the standardized covariate mean differences across the treatment and the instrument. Can also display the permutation quantiles for these quantities. This function is used to create Figure 3a in Branson and Keele (2020).

Usage

balanceLovePlot(X, D, Z, permQuantiles = FALSE, alpha = 0.05, perms = 1000)

Arguments

X	Covariate matrix (with units as rows and covariates as columns).
D	Indicator vector for a binary treatment (must contain 1 or 0 for each unit).
Z	Indicator vector for a binary instrument (must contain 1 or 0 for each unit).
permQuantiles	If TRUE, displays the permutation quantiles for the standardized covariate mean differences.
alpha	The significance level used for the permutation quantiles. For example, if alpha = 0.05, then the 2.5% and 97.5% permutation quantiles are displayed.
perms	Number of permutations used to approximate the permutation quantiles.

Value

Plot of the standardized covariate mean differences across the treatment and the instrument.

Author(s)

Zach Branson and Luke Keele

References

Branson, Z. and Keele, L. (2020). Evaluating a Key Instrumental Variable Assumption Using Randomization Tests. American Journal of Epidemiology. To appear.

Examples

	#load the data
	data(icu.data)
	#the covariate matrix is
	X = as.matrix(subset(icu.data, select = -c(open_bin, icu_bed)))
	#the treatment
	D = icu.data$icu_bed
	#the instrument
	Z = icu.data$open_bin
	#make the Love plot with permutation quantiles
	## Not run: balanceLovePlot(X = X, D = D, Z = Z, permQuantiles = TRUE, perms = 500)

hyunseungkang/ivmodel documentation built on April 20, 2023, 9:20 p.m.