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plot_check_ipws.R
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plot_check_ipws.R
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library(rstanarm)
library(tikzDevice)
library(RColorBrewer)
library(data.table)
library(Hmisc)
library(wCorr)
get_tables = function(ipw_data,weights) {
return(
lapply(c("parity","Age","edu"), function(v) {
cbind(prop.table(wtd.table(ipw_data[,get(v)], weights = ipw_data$N.ssb)[[2]]),
prop.table(wtd.table(ipw_data[,get(v)], weights = ipw_data$N.moba)[[2]]),
prop.table(wtd.table(ipw_data[,get(v)], weights = ipw_data$N.moba*weights)[[2]]))
})
)
}
wc = function(x,y,wght) {
weightedCorr(x,
y,
method = "Spearman",
weights = wght)
}
get_wcorr = function(ipw_data,weights) {
sapply(list(ipw_data$N.ssb,ipw_data$N.moba, ipw_data$N.moba*weights),
function(w){
c(wc(ipw_data$edu, ipw_data$Age, w),
wc(ipw_data$edu, ipw_data$parity, w),
wc(ipw_data$parity, ipw_data$Age, w))
} )
}
cols = brewer.pal(4,"RdYlBu")
cols = adjustcolor(c("blue","green","orange","red"),alpha = .2)
pps = c()
sipws = c()
t_age = matrix(0, nrow = 6, ncol = 3)
t_parity = matrix(0, nrow = 4, ncol = 3)
t_edu = matrix(0, nrow = 4, ncol = 3)
corrs = matrix(0, nrow = 3, ncol = 3)
for (k in 1:20) {
load(paste0("data/IPW_i",k,"_pADHD_2SB_v9.Rdata"))
pps = rbind(pps,colMeans(posterior_predict(mx2)))
sipw = sipw[,!apply(is.infinite(sipw),2,any)]
sipws = rbind(sipws,rowMeans(sipw))
tts = get_tables(ipw_data,sipw[,k])
t_age = t_age + (tts[[2]]-t_age)/k
t_parity = t_parity + (tts[[1]]-t_parity)/k
t_edu = t_edu + (tts[[3]]-t_edu)/k
corrs = corrs + (get_wcorr(ipw_data,sipw[,k])-corrs)/k
}
## plot balance of samples
tikz('LTX/figures/IPWbalance.tex', width=12/2.54, height=15/2.54, pointsize = 14, standAlone = F)
par(mar=c(3,3,2,1), mgp=c(2,.7,0), tck=-.01)
#layout(matrix(c(rep(1:3,3),rep(4,6)), nrow = 3))
layout(matrix(c(1,2,3,4,4),ncol = 1))
tts = list(Age = t_age, parity = t_parity, edu = t_edu)
ipw_data$parity = ordered(ipw_data$parity)
col = RColorBrewer::brewer.pal(3,"Dark2")
for (k in 1:3) {
barplot(t(tts[[k]]),
beside = T,
names.arg = levels(ipw_data[,get(names(tts)[k])]),
main = names(tts)[k],
col = col,
ylab = "proportion")
if (k == 2)
legend("topright",
legend = c("Population",
"Unweighted MoBa",
"Weighted MoBa"),
fill = col,
bty = "n", cex = 1)
}
lim = range(corrs)
plot(0, type = "n",
xlim = lim, ylim = lim,
xlab = "Population correlation",
ylab = "Sample correlation")
abline(0,1, lty = 2)
points(corrs[,1],corrs[,2], pch = 16, col = col[3], cex = 2)
points(corrs[,1],corrs[,3], pch = 17, col = col[2], cex = 2)
legend("topleft",
pch = c(17,16),
legend = c("Unweighted MoBa",
"Weighted MoBa"),
col = col[-1],
bty = "n", cex = 1.5)
dev.off()
## plot oberved vs predicted participation rates
obs = ipw_data$N.moba / ipw_data$N.ssb
pred = colMeans(pps) / ipw_data$N.ssb
sipw = colMeans(sipws)
for (L in levels(ipw_data$edu))
ipw_data[edu == L, col := cols[which(L == levels(ipw_data$edu))]]
tikz('LTX/figures/IPWpres.tex', width=20/2.54, height=10/2.54, pointsize = 14, standAlone = F)
par(mfrow = c(2,1))
par(mar=c(2,2,2,1), mgp=c(1,.7,0))
sobs = sqrt(obs)
spred = sqrt(pred)
lims = c(0,max(c(sobs,spred)))
plot(0, type = "n", pch = 20, col = "red", cex = .5, ylim = lims, xlim = lims,
xlab = "predicted participation (\\%)",ylab = "observed participation (\\%)",
xaxt = "n",
yaxt = "n",
bty = "n")
lines(c(0,lims[2]),c(0,lims[2]), "l")
points(sobs,spred, cex = sqrt(ipw_data$N.ssb)/20, pch = 19,
col = ipw_data$col)
#lbs = c(0, .01 , .025, .05, .1, .2, .3, .4)
lbs = c(0, .01 , .05, .1, .2, .4)
axis(1,at = sqrt(lbs), labels = lbs*100, pos = 0)
axis(2,at = sqrt(lbs), labels = lbs*100, pos = 0)
Ns = c(250,2500,10000, 40000)
ys = seq(sqrt(.001),sqrt(.075),length = 5)
points(rep(sqrt(.2),4),rep(ys[2],4), cex = sqrt(Ns)/20)
text(sqrt(.2),ys[3],
paste0("$N_{SSB}=$",paste(Ns, collapse = ", ")),
pos = 3)
legend(x = sqrt(.0005), y = sqrt(.4),
pch = 16, col = cols,
border = cols,
legend = levels(ipw_data$edu),bty = "n", pt.cex = 2)
breaks = seq(log(min(sipws*.99)),log(max(sipws*1.01)),length = 31)
hs = vector(length = 20, mode = "list")
for (k in 1:20) hs[[k]] = hist(log(sipws[k,]),breaks = breaks, plot = F)
ylim = c(0,max(sapply(hs,function(x) max(x$counts))))
h = hist(log(sipws[1,]),
xaxt = "n",
main = "",
xlab = "sIPW",
breaks = breaks,
ylim = ylim, yaxt = "n",
col = adjustcolor("black", alpha = .1), border = adjustcolor("black", alpha = .05))
for (k in 2:20) hist(log(sipws[k,]),breaks = breaks, add = T,
col = adjustcolor("black", alpha = .05),
border = adjustcolor("black", alpha = .05))
lbs = exp(h$mids)[seq(1,length(h$mids),by = 4)]
for (k in 1:length(lbs)) lbs[k] = ifelse(lbs[k] < 5, round(lbs[k],digits = 1), round(lbs[k]))
axis(1,at = log(lbs),labels = lbs, pos = 0)
axis(2,at = seq(2,max(ylim),by = 2), pos = log(lbs)[1])
dev.off()