Nothing
weightit2ps <- function(covs, treat, s.weights, subset, estimand, focal,
stabilize, subclass, missing, ps, .data, verbose, ...) {
A <- list(...)
fit.obj <- NULL
n <- length(treat)
p.score <- NULL
treat <- factor(treat)
treat_sub <- factor(treat[subset])
t.lev <- get_treated_level(treat)
c.lev <- setdiff(levels(treat_sub), t.lev)
if (is.matrix(ps) || is.data.frame(ps)) {
if (nrow(ps) == n) {
if (ncol(ps) == 1) {
ps <- data.frame(ps[subset,1], 1-ps[subset,1])
names(ps) <- c(t.lev, c.lev)
p.score <- ps[[t.lev]]
}
else if (ncol(ps) == 2) {
if (all(colnames(ps) %in% levels(treat_sub))) {
ps <- as.data.frame(ps[subset, , drop = FALSE])
}
else {
ps <- as.data.frame(ps[subset, , drop = FALSE])
names(ps) <- levels(treat_sub)
}
p.score <- ps[[t.lev]]
}
}
}
else if (is.numeric(ps)) {
if (length(ps) == n) {
ps <- data.frame(ps[subset], 1-ps[subset])
names(ps) <- c(t.lev, c.lev)
p.score <- ps[[t.lev]]
}
}
if (is_null(p.score)) .err("`ps` must be a numeric vector with a propensity score for each unit")
#ps should be matrix of probs for each treat
#Computing weights
w <- .get_w_from_ps_internal_bin(ps = p.score, treat = as.numeric(treat_sub == t.lev), estimand,
stabilize = stabilize, subclass = subclass)
list(w = w, ps = p.score, fit.obj = fit.obj)
}
weightit2ps.multi <- function(covs, treat, s.weights, subset, estimand, focal,
stabilize, subclass, missing, ps, .data, verbose, ...) {
n <- length(treat)
treat <- factor(treat)
treat_sub <- factor(treat[subset])
bad.ps <- FALSE
if (is.matrix(ps) || is.data.frame(ps)) {
if (all(dim(ps) == c(n, nunique(treat)))) {
ps <- setNames(as.data.frame(ps), levels(treat))[subset, , drop = FALSE]
}
else if (all(dim(ps) == c(n, 1))) {
ps <- setNames(list2DF(lapply(levels(treat), function(x) {
p_ <- rep(1, length(treat))
p_[treat == x] <- ps[treat == x, 1]
p_
})), levels(treat))[subset, , drop = FALSE]
}
else {
bad.ps <- TRUE
}
}
else if (is.numeric(ps)) {
if (length(ps) == n) {
ps <- setNames(list2DF(lapply(levels(treat), function(x) {
p_ <- rep(1, length(treat))
p_[treat == x] <- ps[treat == x]
p_
})), levels(treat))[subset, , drop = FALSE]
}
else {
bad.ps <- TRUE
}
}
else bad.ps <- TRUE
if (bad.ps) .err("`ps` must be a numeric vector with a propensity score for each unit or a matrix \n\twith the probability of being in each treatment for each unit")
#ps should be matrix of probs for each treat
#Computing weights
w <- .get_w_from_ps_internal_multi(ps = ps, treat = treat_sub, estimand, focal = focal,
stabilize = stabilize, subclass = subclass)
list(w = w)
}
weightit2ps.cont <- function(covs, treat, s.weights, subset, stabilize, missing, ps, verbose, ...) {
A <- list(...)
treat <- treat[subset]
s.weights <- s.weights[subset]
#Process density params
densfun <- .get_dens_fun(use.kernel = isTRUE(A[["use.kernel"]]), bw = A[["bw"]],
adjust = A[["adjust"]], kernel = A[["kernel"]],
n = A[["n"]], treat = treat, density = A[["density"]],
weights = s.weights)
#Stabilization - get dens.num
dens.num <- densfun(scale_w(treat, s.weights))
#Get weights
r <- treat - ps
dens.denom <- densfun(r / sqrt(col.w.v(r, s.weights)))
w <- dens.num / dens.denom
if (isTRUE(A[["plot"]])) {
d.n <- attr(dens.num, "density")
d.d <- attr(dens.denom, "density")
plot_density(d.n, d.d)
}
list(w = w)
}
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