resresplot: Produces residual-residual (added-variable) plot
In ri: ri: R package for performing randomization-based inference for experiments

Description Usage Arguments Value Author(s) References See Also Examples

Residualizes the outcome variable and the treatment variable with covariates (via inverse probability weighted least squares regression) and plots the relationship. When weights are applied, the graph shows the relative weighting of each observation

1	resresplot(Y, Z, X, prob = NULL, scale = 1)

`Y`	numeric vector of length N, outcome variable
`Z`	binary vector (0 or 1) of length N, treatment indicator
`X`	N-by-k numeric matrix of covariates for regression adjustment
`prob`	numeric vector within the (0,1) interval of length N, probability of treatment assignment, as outputted by `genprob()` or `genprobexact()`. When `prob=NULL` (the default), assumes uniform probability of assignment to treatment equal to the mean of Z
`scale`	a scalar parameter controlling the size of the plotted points

produces a plot of residualized and weighted values

Peter M. Aronow <peter.aronow@yale.edu>; Cyrus Samii <cds2083@nyu.edu>

Gerber, Alan S. and Donald P. Green. 2012. Field Experiments: Design, Analysis, and Interpretation. New York: W.W. Norton.

estate

y <- c(8,6,2,0,3,1,1,1,2,2,0,1,0,2,2,4,1,1) 
Z <- c(1,1,0,0,1,1,0,0,1,1,1,1,0,0,1,1,0,0)
X <- c(1:18)
cluster <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9)
block <- c(rep(1,4),rep(2,6),rep(3,8))

probs <- genprobexact(Z,block,cluster) # generate probability of assignment

resresplot(y,Z,X,prob=probs,scale=3) # produce res-res plot