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# QQ plot to evaluate balance
qqprep <- function (x, discrete.cutoff, which.subclass = NULL, numdraws = 5000,
interactive = T, which.xs = NULL, ...) {
X <- x$X
varnames <- colnames(X)
for (var in varnames) {
if (is.factor(X[, var])) {
tempX <- X[, !colnames(X) %in% c(var)]
form <- formula(substitute(~dummy - 1, list(dummy = as.name(var))))
X <- cbind(tempX, model.matrix(form, X))
}
}
covariates <- X
if (!is.null(which.xs)) {
if (sum(which.xs %in% dimnames(covariates)[[2]]) != length(which.xs)) {
stop("which.xs is incorrectly specified")
}
covariates <- covariates[, which.xs, drop = F]
}
treat <- x$treat
matched <- x$weights != 0
ratio <- x$call$ratio
if (is.null(ratio)) {
ratio <- 1
}
if (identical(x$call$method, "full") | (ratio != 1)) {
t.plot <- sample(names(treat)[treat == 1], numdraws/2,
replace = TRUE, prob = x$weights[treat == 1])
c.plot <- sample(names(treat)[treat == 0], numdraws/2,
replace = TRUE, prob = x$weights[treat == 0])
m.covariates <- x$X[c(t.plot, c.plot), ]
m.treat <- x$treat[c(t.plot, c.plot)]
}
else {
m.covariates <- covariates[matched, , drop = F]
m.treat <- treat[matched]
}
if (!is.null(which.subclass)) {
subclass <- x$subclass
sub.index <- subclass == which.subclass & !is.na(subclass)
sub.covariates <- covariates[sub.index, , drop = F]
sub.treat <- treat[sub.index]
sub.matched <- matched[sub.index]
m.covariates <- sub.covariates[sub.matched, , drop = F]
m.treat <- sub.treat[sub.matched]
}
nn <- dimnames(covariates)[[2]]
nc <- length(nn)
covariates <- data.matrix(covariates)
toplot <- NULL
for (i in 1:nc) {
xi <- covariates[, i]
m.xi <- m.covariates[, i]
rr <- range(xi)
eqqplot <- function(x,y) {
sx <- sort(x)
sy <- sort(y)
lenx <- length(sx)
leny <- length(sy)
if (leny < lenx)
sx <- approx(1:lenx, sx, n = leny, method = "constant")$y
if (leny > lenx)
sy <- approx(1:leny, sy, n = lenx, method = "constant")$y
return(list(x = sx,y = sy))
}
toplot <- bind_rows(toplot,
bind_rows(data.frame(eqqplot(x = xi[treat == 0],y = xi[treat == 1]),type = "Raw",cov = nn[i],rrlb = rr[1],rrub = rr[2]),
data.frame(eqqplot(x = m.xi[m.treat == 0],y = m.xi[m.treat == 1]),type = "Matched",cov = nn[i],rrlb = rr[1],rrub = rr[2])))
}
return(list(toplot = toplot))
}
# Ineration plot
interaction_plot_continuous <- function(model, effect = "", moderator = "", varcov="default", minimum="min", maximum="max",colr = "grey",
incr="default", num_points = 10, conf=.95, mean=FALSE, median=FALSE, alph=80, rugplot=T,
histogram=F, title="Marginal effects plot", xlabel="Value of moderator",
ylabel="Estimated marginal coefficient",pointsplot = F,plot = F,show_est = F) {
# Extract Variance Covariance matrix
if (varcov == "default"){
covMat = vcov(model)
}else{
covMat = varcov
}
# Extract the data frame of the model
mod_frame = model.frame(model)
# Get coefficients of variables
if(effect == "") {
int.string <- rownames(summary(model)$coefficients)[grepl(":",rownames(summary(model)$coefficients))]
effect <- substr(int.string,1,regexpr(":",int.string)[1]-1)
}
if(moderator == "") {
int.string <- rownames(summary(model)$coefficients)[grepl(":",rownames(summary(model)$coefficients))]
moderator <- substr(int.string,regexpr(":",int.string)[1]+1,nchar(int.string))
}
interaction <- paste(effect,":",moderator,sep="")
beta_1 = summary(model)$coefficients[effect,1]
beta_3 = summary(model)$coefficients[interaction,1]
# Set range of the moderator variable
# Minimum
if (minimum == "min"){
min_val = min(mod_frame[[moderator]])
}else{
min_val = minimum
}
# Maximum
if (maximum == "max"){
max_val = max(mod_frame[[moderator]])
}else{
max_val = maximum
}
# Check if minimum smaller than maximum
if (min_val > max_val){
stop("Error: Minimum moderator value greater than maximum value.")
}
# Determine intervals between values of the moderator
if (incr == "default"){
increment = (max_val - min_val)/(num_points - 1)
}else{
increment = incr
}
# Create list of moderator values at which marginal effect is evaluated
x_2 <- seq(from=min_val, to=max_val, by=increment)
# Compute marginal effects
delta_1 = beta_1 + beta_3*x_2
# Compute variances
var_1 = covMat[effect,effect] + (x_2^2)*covMat[interaction, interaction] + 2*x_2*covMat[effect, interaction]
# Standard errors
se_1 = sqrt(var_1)
# Upper and lower confidence bounds
z_score = qnorm(1 - ((1 - conf)/2))
upper_bound = sapply(1:length(z_score), function(x) delta_1 + z_score[x]*se_1)
lower_bound = sapply(1:length(z_score), function(x) delta_1 - z_score[x]*se_1)
# Determine the bounds of the graphing area
max_y = max(upper_bound)
min_y = min(lower_bound)
# Make the histogram color
hist_col = colr
stars <- ifelse(abs(summary(model)$coefficients[interaction,3]) >2.6,"***",
ifelse(abs(summary(model)$coefficients[interaction,3]) > 1.96,"**",
ifelse(abs(summary(model)$coefficients[interaction,3]) > 1.65,"*","")))
est <- paste("Interaction: ",round(summary(model)$coefficients[interaction,1],3),stars," (",
round(summary(model)$coefficients[interaction,2],3),")",sep="")
# Initialize plotting window
if(plot) {
plot(x=c(), y=c(), ylim=c(min_y, max_y), xlim=c(min_val, max_val),
xlab=xlabel, ylab=ylabel, main=title)
# Plot estimated effects
if(!pointsplot) {
lines(y=delta_1, x=x_2,col = colr)
for(i in ncol(upper_bound):1) {
polygon(c(x_2,rev(x_2)),c(upper_bound[,i],rev(lower_bound[,i])),border = NA,col = colr)
}
}else{
points(y = delta_1,x = x_2,col = colr,pch = 19)
for(i in ncol(upper_bound):1) {
segments(x_2,upper_bound[,i],x_2,lower_bound[,i],col = colr,lwd = i)
}
}
# Add a dashed horizontal line for zero
abline(h=0, lty=3)
# Add a vertical line at the mean
if (mean){
abline(v = mean(mod_frame[[moderator]]), lty=2, col="red")
}
# Add a vertical line at the median
if (median){
abline(v = median(mod_frame[[moderator]]), lty=3, col="blue")
}
# Add Rug plot
if (rugplot){
rug(mod_frame[[moderator]])
}
if (show_est) {
text(par('usr')[ 2 ], par('usr')[ 4 ],adj=c(1.05,1.2),
labels = est)
}
#Add Histogram (Histogram only plots when minimum and maximum are the min/max of the moderator)
if (histogram & minimum=="min" & maximum=="max"){
par(new=T)
hist(mod_frame[[moderator]], axes=F, xlab="", ylab="",main="", border=hist_col, col=hist_col)
}
}
return(list(delta_1 = delta_1,x_2 = x_2,ub = upper_bound,lb = lower_bound,inc = increment,est = est))
}
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