Nothing

```
hist.pscore <- function(x, xlab="Propensity Score", freq = FALSE, ...){
.pardefault <- par(no.readonly = TRUE)
on.exit(par(.pardefault))
treat <- x$treat
pscore <- x$distance[!is.na(x$distance)]
s.weights <- if (is.null(x$s.weights)) rep(1, length(treat)) else x$s.weights
weights <- x$weights * s.weights
matched <- weights != 0
q.cut <- x$q.cut
minp <- min(pscore)
maxp <- max(pscore)
ratio <- x$call$ratio
if (is.null(ratio)) ratio <- 1
for (i in unique(treat, nmax = 2)) {
if (freq) s.weights[treat == i] <- s.weights[treat == i]/mean(s.weights[treat == i])
else s.weights[treat == i] <- s.weights[treat == i]/sum(s.weights[treat == i])
if (freq) weights[treat == i] <- weights[treat == i]/mean(weights[treat == i])
else weights[treat == i] <- weights[treat == i]/sum(weights[treat == i])
}
ylab <- if (freq) "Count" else "Proportion"
par(mfrow = c(2,2))
# breaks <- pretty(na.omit(pscore), 10)
breaks <- seq(minp, maxp, length = 11)
xlim <- range(breaks)
for (n in c("Raw Treated", "Matched Treated", "Raw Control", "Matched Control")) {
if (startsWith(n, "Raw")) w <- s.weights
else w <- weights
if (endsWith(n, "Treated")) t <- 1
else t <- 0
#Create histogram using weights
#Manually assign density, which is used as height of the bars. The scaling
#of the weights above determine whether they are "counts" or "proportions".
#Regardless, set freq = FALSE in plot() to ensure density is used for bar
#height rather than count.
pm <- hist(pscore[treat==t], plot = FALSE, breaks = breaks)
pm[["density"]] <- vapply(seq_len(length(pm$breaks) - 1), function(i) {
sum(w[treat == t & pscore >= pm$breaks[i] & pscore < pm$breaks[i+1]])
}, numeric(1L))
plot(pm, xlim = xlim, xlab = xlab, main = n, ylab = ylab,
freq = FALSE, col = "lightgray", ...)
if (!startsWith(n, "Raw") && !is.null(q.cut)) abline(v = q.cut, lty=2)
}
}
```

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