R/kmqte.R
In pedrohcgs/kmte: An R Package for Treatment Effects with Censored Outcomes
Defines functions
kmqte
Documented in
kmqte
#' Kaplan-Meier Quantile Treatment Effect
#'
#' \emph{kmqte} computes the Quantile Treatment Effect for possibly right-censored
#' outcomes. The estimator relies on the unconfoundedness assumption, and on
#' estimating the propensity score. For details of the estimation procedure, see
#' Sant'Anna (2016a), 'Program Evaluation with Right-Censored Data'.
#'
#'
#'@param out vector containing the outcome of interest
#'@param delta vector containing the censoring indicator (1 if observed, 0 if censored)
#'@param treat vector containing the treatment indicator (1 if treated, 0 if control)
#'@param xpscore matrix (or data frame) containing the covariates (and their
#' transformations) to be included in the propensity score estimation.
#' Propensity score estimation is based on Logit.
#'@param probs scalar or vector of probabilities with values in (0,1) for which
#' the quantile treatment effect is computed. Default is 0.5, returning
#' the median.
#'@param b The number of bootstrap replicates to be performed. Default is 1,000.
#'@param ci A scalar or vector with values in (0,1) containing the confidence level(s)
#' of the required interval(s). Default is a vector with
#' 0,90, 0.95 and 0.99
#'@param standardize Default is TRUE, which normalizes propensity score weights to
#' sum to 1 within each treatment group.
#' Set to FALSE to return Horvitz-Thompson weights.
#'@param cores number of processesors to be used during the bootstrap (default is 1).
#' If cores>1, the bootstrap is conducted using snow
#'
#'@return a list containing the quantile treatment effect estimate, qte,
#' and the bootstrapped \emph{ci} confidence
#' confidence interval, qte.lb (lower bound), and qte.ub (upper bound).
#'@export
#'@importFrom stats glm approxfun
#'@importFrom parallel makeCluster stopCluster clusterExport
#'@importFrom boot boot.ci boot
#'@importFrom Rearrangement rearrangement
#-----------------------------------------------------------------------------
kmqte <- function(out, delta, treat, probs = 0.5,
xpscore, b = 1000, ci = c(0.90,0.95,0.99),
standardize = TRUE, cores = 1) {
#-----------------------------------------------------------------------------
# first, we merge all the data into a single datafile
fulldata <- data.frame(cbind(out, delta, treat, xpscore))
#-----------------------------------------------------------------------------
# Next, we set up the bootstrap function
boot1.kmqte <- function(fulldata, i, probs1 = probs,
standardize1 = standardize){
#----------------------------------------------------------------------------
# Select the data for the bootstrap (like the original data)
df.b=fulldata[i,]
#----------------------------------------------------------------------------
# Compute Kaplan-Meier Weigths - data is now sorted!
df.b <- kmweight(1, 2, df.b)
# Dimension of data matrix df.b
dim.b <- dim(df.b)[2]
# Next, we rename the variable in xpscore to avoid problems
xpscore1.b <- df.b[, (4:(dim.b - 1))]
datascore.b <- data.frame(y = df.b[, 3], xpscore1.b)
#-----------------------------------------------------------------------------
# estimate the propensity score
pscore.b <- stats::glm(y ~ ., data = datascore.b,
family = binomial("logit"))
df.b$pscore <- pscore.b$fit
#-----------------------------------------------------------------------------
# Create id to help on ordering
df.b$id <- 1:length(df.b[, 1])
# Update Dimension of data matrix fulldata
dim.all <- dim(df.b)[2]
#-----------------------------------------------------------------------------
# sample size
n.total.b <- as.numeric(length(df.b[, 1]))
# subset of treated individuals
data.treat.b <- subset(df.b, df.b[, 3] == 1)
# subset of not-treated individuals
data.control.b <- subset(df.b, df.b[, 3] == 0)
#-----------------------------------------------------------------------------
# Compute Kaplan-Meier weigth for treated
data.treat.b <- kmweight(1, 2, data.treat.b)
n.treat.b <- as.numeric(length(data.treat.b[, 1]))
data.treat.b$w <- data.treat.b$w * (n.treat.b/n.total.b)
#-----------------------------------------------------------------------------
# Compute Kaplan-Meier weigth for control
data.control.b <- kmweight(1, 2, data.control.b)
n.control.b <- as.numeric(length(data.control.b[, 1]))
data.control.b$w <- data.control.b$w * (n.control.b/n.total.b)
#-----------------------------------------------------------------------------
# Let's put everything in a single data
# correct KM weigths
# First, the datasets
df.b <- data.frame(rbind(data.treat.b, data.control.b))
# Sort wrt id
df.b <- df.b[order(as.numeric(df.b[, dim.all])), ]
#-----------------------------------------------------------------------------
# Compute weigths for treatment and control groups
w1km.b <- ((df.b$treat * df.b$w) / df.b$pscore)
w0km.b <- ((1 - df.b$treat) * df.b$w / (1 - df.b$pscore))
if (standardize1 == TRUE) {
w1km.b <- w1km.b / mean(df.b$treat / df.b$pscore)
w0km.b <- w0km.b / mean((1 - df.b$treat) / (1 - df.b$pscore))
}
#-----------------------------------------------------------------------------
# Compute Counterfactual quantiles, and the QTE
# First, we KM estimates of the potential outcomes distribution
kmcdf.y1 <- w.ecdf(df.b$out, w1km.b)
kmcdf.y0 <- w.ecdf(df.b$out, w0km.b)
qy1 <- quantile.2skm(kmcdf.y1, probs = probs1)
qy0 <- quantile.2skm(kmcdf.y0, probs = probs1)
qte <- qy1 - qy0
#-----------------------------------------------------------------------------
return(cbind(qy1, qy0, qte))
}
#-----------------------------------------------------------------------------
# Number of bootstrap draws
nboot <- b
#COmput the bootstrap
if (cores == 1){
boot.kmqte <- boot::boot(fulldata, boot1.kmqte, R = nboot,
stype = "i", sim = "ordinary")
}
if (cores > 1){
cl <- parallel::makeCluster(cores)
#clusterExport(cl, "kmweight")
parallel::clusterSetRNGStream(cl)
boot.kmqte <- boot::boot(fulldata, boot1.kmqte, R = nboot, parallel = "snow",
ncpus = cores, stype = "i", sim = "ordinary")
parallel::stopCluster(cl)
}
#----------------------------------------------------------------------------
# Compute Counterfactual quantiles and the QTE
qy1 <- matrix(boot.kmqte$t0[,1],1,length(probs))
rownames(qy1) <- "Quantile Y(1)"
colnames(qy1) <- names(quantile(1, probs = probs))
qy0 <- matrix(boot.kmqte$t0[,2],1,length(probs))
rownames(qy0) <- "Quantile Y(0)"
colnames(qy0) <- names(quantile(1, probs = probs))
qte <- matrix(boot.kmqte$t0[,3],1,length(probs))
rownames(qte) <- "QTE"
colnames(qte) <- names(quantile(1, probs = probs))
#----------------------------------------------------------------------------
#Compute the confidence interval for qte
n.probs <- length(probs)
n.ci <- length(ci)
if (n.ci == 1 & n.probs == 1){
qte.lb <- matrix(NA, n.ci, n.probs)
qte.ub <- matrix(NA, n.ci, n.probs)
boot.kmqte.na=na.omit(boot.kmqte$t0)
n.probs.na <- length(boot.kmqte.na)/3
if(n.probs.na > 0){
qte.lb[,1] <- boot::boot.ci(boot.kmqte, type="perc", index = 3, conf = ci)$percent[4]
qte.ub [,1] <- boot::boot.ci(boot.kmqte, type="perc", index = 3, conf = ci)$percent[5]
}
}
if (n.ci >1 & n.probs == 1){
qte.lb <- matrix(NA, n.ci, n.probs)
qte.ub <- matrix(NA, n.ci, n.probs)
boot.kmqte.na=na.omit(boot.kmqte$t0)
n.probs.na <- length(boot.kmqte.na)/3
if(n.probs.na > 0){
qte.lb[,1] <- boot::boot.ci(boot.kmqte, type="perc", index = 3, conf = ci)$percent[,4]
qte.ub[,1] <- boot::boot.ci(boot.kmqte, type="perc", index = 3, conf = ci)$percent[,5]
}
}
if ((n.ci == 1) * (n.probs > 1) == 1){
qte.lb <- matrix(NA, n.ci, n.probs)
qte.ub <- matrix(NA, n.ci, n.probs)
boot.kmqte.na=na.omit(boot.kmqte$t0)
n.probs.na <- length(boot.kmqte.na)/3
if(n.probs.na > 0){
for (i in 1:n.probs.na){
qte.lb[,i] <- boot::boot.ci(boot.kmqte, type="perc",
index = (i+ 2* n.probs), conf = ci)$percent[4]
qte.ub[,i] <- boot::boot.ci(boot.kmqte, type="perc",
index = (i+ 2* n.probs), conf = ci)$percent[5]
}
}
}
if ((n.ci > 1) * (n.probs > 1) == 1){
qte.lb <- matrix(NA, n.ci, n.probs)
qte.ub <- matrix(NA, n.ci, n.probs)
boot.kmqte.na=na.omit(boot.kmqte$t0)
n.probs.na <- length(boot.kmqte.na)/3
if(n.probs.na > 0){
for (i in 1:n.probs.na){
qte.lb[,i] <- boot::boot.ci(boot.kmqte, type="perc",
index = (i+ 2* n.probs), conf = ci)$percent[,4]
qte.ub[,i] <- boot::boot.ci(boot.kmqte, type="perc",
index = (i+ 2* n.probs), conf = ci)$percent[,5]
}
}
}
#----------------------------------------------------------------------------
colnames(qte.lb) <- paste(names(quantile(1, probs = probs)), "quantile")
colnames(qte.ub) <- paste(names(quantile(1, probs = probs)), "quantile")
rownames(qte.ub) <- paste(names(quantile(1, probs = ci)), 'CI: UB')
rownames(qte.lb) <- paste(names(quantile(1, probs = ci)), 'CI: LB')
#----------------------------------------------------------------------------
# Return these
list(qte = qte,
qy1 = qy1,
qy0 = qy0,
#boot = boot.kmqte,
qte.lb = qte.lb,
qte.ub = qte.ub
)
}
pedrohcgs/kmte documentation built on May 24, 2019, 11:46 p.m.
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Defines functions kmqte
Documented in kmqte
#' Kaplan-Meier Quantile Treatment Effect
#'
#' \emph{kmqte} computes the Quantile Treatment Effect for possibly right-censored
#' outcomes. The estimator relies on the unconfoundedness assumption, and on
#' estimating the propensity score. For details of the estimation procedure, see
#' Sant'Anna (2016a), 'Program Evaluation with Right-Censored Data'.
#'
#'
#'@param out vector containing the outcome of interest
#'@param delta vector containing the censoring indicator (1 if observed, 0 if censored)
#'@param treat vector containing the treatment indicator (1 if treated, 0 if control)
#'@param xpscore matrix (or data frame) containing the covariates (and their
#' transformations) to be included in the propensity score estimation.
#' Propensity score estimation is based on Logit.
#'@param probs scalar or vector of probabilities with values in (0,1) for which
#' the quantile treatment effect is computed. Default is 0.5, returning
#' the median.
#'@param b The number of bootstrap replicates to be performed. Default is 1,000.
#'@param ci A scalar or vector with values in (0,1) containing the confidence level(s)
#' of the required interval(s). Default is a vector with
#' 0,90, 0.95 and 0.99
#'@param standardize Default is TRUE, which normalizes propensity score weights to
#' sum to 1 within each treatment group.
#' Set to FALSE to return Horvitz-Thompson weights.
#'@param cores number of processesors to be used during the bootstrap (default is 1).
#' If cores>1, the bootstrap is conducted using snow
#'
#'@return a list containing the quantile treatment effect estimate, qte,
#' and the bootstrapped \emph{ci} confidence
#' confidence interval, qte.lb (lower bound), and qte.ub (upper bound).
#'@export
#'@importFrom stats glm approxfun
#'@importFrom parallel makeCluster stopCluster clusterExport
#'@importFrom boot boot.ci boot
#'@importFrom Rearrangement rearrangement
#-----------------------------------------------------------------------------
kmqte <- function(out, delta, treat, probs = 0.5,
xpscore, b = 1000, ci = c(0.90,0.95,0.99),
standardize = TRUE, cores = 1) {
#-----------------------------------------------------------------------------
# first, we merge all the data into a single datafile
fulldata <- data.frame(cbind(out, delta, treat, xpscore))
#-----------------------------------------------------------------------------
# Next, we set up the bootstrap function
boot1.kmqte <- function(fulldata, i, probs1 = probs,
standardize1 = standardize){
#----------------------------------------------------------------------------
# Select the data for the bootstrap (like the original data)
df.b=fulldata[i,]
#----------------------------------------------------------------------------
# Compute Kaplan-Meier Weigths - data is now sorted!
df.b <- kmweight(1, 2, df.b)
# Dimension of data matrix df.b
dim.b <- dim(df.b)[2]
# Next, we rename the variable in xpscore to avoid problems
xpscore1.b <- df.b[, (4:(dim.b - 1))]
datascore.b <- data.frame(y = df.b[, 3], xpscore1.b)
#-----------------------------------------------------------------------------
# estimate the propensity score
pscore.b <- stats::glm(y ~ ., data = datascore.b,
family = binomial("logit"))
df.b$pscore <- pscore.b$fit
#-----------------------------------------------------------------------------
# Create id to help on ordering
df.b$id <- 1:length(df.b[, 1])
# Update Dimension of data matrix fulldata
dim.all <- dim(df.b)[2]
#-----------------------------------------------------------------------------
# sample size
n.total.b <- as.numeric(length(df.b[, 1]))
# subset of treated individuals
data.treat.b <- subset(df.b, df.b[, 3] == 1)
# subset of not-treated individuals
data.control.b <- subset(df.b, df.b[, 3] == 0)
#-----------------------------------------------------------------------------
# Compute Kaplan-Meier weigth for treated
data.treat.b <- kmweight(1, 2, data.treat.b)
n.treat.b <- as.numeric(length(data.treat.b[, 1]))
data.treat.b$w <- data.treat.b$w * (n.treat.b/n.total.b)
#-----------------------------------------------------------------------------
# Compute Kaplan-Meier weigth for control
data.control.b <- kmweight(1, 2, data.control.b)
n.control.b <- as.numeric(length(data.control.b[, 1]))
data.control.b$w <- data.control.b$w * (n.control.b/n.total.b)
#-----------------------------------------------------------------------------
# Let's put everything in a single data
# correct KM weigths
# First, the datasets
df.b <- data.frame(rbind(data.treat.b, data.control.b))
# Sort wrt id
df.b <- df.b[order(as.numeric(df.b[, dim.all])), ]
#-----------------------------------------------------------------------------
# Compute weigths for treatment and control groups
w1km.b <- ((df.b$treat * df.b$w) / df.b$pscore)
w0km.b <- ((1 - df.b$treat) * df.b$w / (1 - df.b$pscore))
if (standardize1 == TRUE) {
w1km.b <- w1km.b / mean(df.b$treat / df.b$pscore)
w0km.b <- w0km.b / mean((1 - df.b$treat) / (1 - df.b$pscore))
}
#-----------------------------------------------------------------------------
# Compute Counterfactual quantiles, and the QTE
# First, we KM estimates of the potential outcomes distribution
kmcdf.y1 <- w.ecdf(df.b$out, w1km.b)
kmcdf.y0 <- w.ecdf(df.b$out, w0km.b)
qy1 <- quantile.2skm(kmcdf.y1, probs = probs1)
qy0 <- quantile.2skm(kmcdf.y0, probs = probs1)
qte <- qy1 - qy0
#-----------------------------------------------------------------------------
return(cbind(qy1, qy0, qte))
}
#-----------------------------------------------------------------------------
# Number of bootstrap draws
nboot <- b
#COmput the bootstrap
if (cores == 1){
boot.kmqte <- boot::boot(fulldata, boot1.kmqte, R = nboot,
stype = "i", sim = "ordinary")
}
if (cores > 1){
cl <- parallel::makeCluster(cores)
#clusterExport(cl, "kmweight")
parallel::clusterSetRNGStream(cl)
boot.kmqte <- boot::boot(fulldata, boot1.kmqte, R = nboot, parallel = "snow",
ncpus = cores, stype = "i", sim = "ordinary")
parallel::stopCluster(cl)
}
#----------------------------------------------------------------------------
# Compute Counterfactual quantiles and the QTE
qy1 <- matrix(boot.kmqte$t0[,1],1,length(probs))
rownames(qy1) <- "Quantile Y(1)"
colnames(qy1) <- names(quantile(1, probs = probs))
qy0 <- matrix(boot.kmqte$t0[,2],1,length(probs))
rownames(qy0) <- "Quantile Y(0)"
colnames(qy0) <- names(quantile(1, probs = probs))
qte <- matrix(boot.kmqte$t0[,3],1,length(probs))
rownames(qte) <- "QTE"
colnames(qte) <- names(quantile(1, probs = probs))
#----------------------------------------------------------------------------
#Compute the confidence interval for qte
n.probs <- length(probs)
n.ci <- length(ci)
if (n.ci == 1 & n.probs == 1){
qte.lb <- matrix(NA, n.ci, n.probs)
qte.ub <- matrix(NA, n.ci, n.probs)
boot.kmqte.na=na.omit(boot.kmqte$t0)
n.probs.na <- length(boot.kmqte.na)/3
if(n.probs.na > 0){
qte.lb[,1] <- boot::boot.ci(boot.kmqte, type="perc", index = 3, conf = ci)$percent[4]
qte.ub [,1] <- boot::boot.ci(boot.kmqte, type="perc", index = 3, conf = ci)$percent[5]
}
}
if (n.ci >1 & n.probs == 1){
qte.lb <- matrix(NA, n.ci, n.probs)
qte.ub <- matrix(NA, n.ci, n.probs)
boot.kmqte.na=na.omit(boot.kmqte$t0)
n.probs.na <- length(boot.kmqte.na)/3
if(n.probs.na > 0){
qte.lb[,1] <- boot::boot.ci(boot.kmqte, type="perc", index = 3, conf = ci)$percent[,4]
qte.ub[,1] <- boot::boot.ci(boot.kmqte, type="perc", index = 3, conf = ci)$percent[,5]
}
}
if ((n.ci == 1) * (n.probs > 1) == 1){
qte.lb <- matrix(NA, n.ci, n.probs)
qte.ub <- matrix(NA, n.ci, n.probs)
boot.kmqte.na=na.omit(boot.kmqte$t0)
n.probs.na <- length(boot.kmqte.na)/3
if(n.probs.na > 0){
for (i in 1:n.probs.na){
qte.lb[,i] <- boot::boot.ci(boot.kmqte, type="perc",
index = (i+ 2* n.probs), conf = ci)$percent[4]
qte.ub[,i] <- boot::boot.ci(boot.kmqte, type="perc",
index = (i+ 2* n.probs), conf = ci)$percent[5]
}
}
}
if ((n.ci > 1) * (n.probs > 1) == 1){
qte.lb <- matrix(NA, n.ci, n.probs)
qte.ub <- matrix(NA, n.ci, n.probs)
boot.kmqte.na=na.omit(boot.kmqte$t0)
n.probs.na <- length(boot.kmqte.na)/3
if(n.probs.na > 0){
for (i in 1:n.probs.na){
qte.lb[,i] <- boot::boot.ci(boot.kmqte, type="perc",
index = (i+ 2* n.probs), conf = ci)$percent[,4]
qte.ub[,i] <- boot::boot.ci(boot.kmqte, type="perc",
index = (i+ 2* n.probs), conf = ci)$percent[,5]
}
}
}
#----------------------------------------------------------------------------
colnames(qte.lb) <- paste(names(quantile(1, probs = probs)), "quantile")
colnames(qte.ub) <- paste(names(quantile(1, probs = probs)), "quantile")
rownames(qte.ub) <- paste(names(quantile(1, probs = ci)), 'CI: UB')
rownames(qte.lb) <- paste(names(quantile(1, probs = ci)), 'CI: LB')
#----------------------------------------------------------------------------
# Return these
list(qte = qte,
qy1 = qy1,
qy0 = qy0,
#boot = boot.kmqte,
qte.lb = qte.lb,
qte.ub = qte.ub
)
}
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