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Code to generate an interactive SVG graph of NZ general election polling.
###
##
## --- New Zealand General Election Polling ---
##
## Plots polling data scraped from Wikipedia.
##
## - GAM -
##
## The smoothed curves are calculated using a generalised additive model (GAM). The
## smoothing parameter is estimated using cross-validation. This means that the curves
## are estimated based on subsets of the data, and the parameter that best predicts
## the hold-out data is chosen. Note that the error bounds are based on the assumption
## that the smoothing parameter is correct, i.e. uncertainty in the smoothing parameter
## estimate are not accounted for.
##
## A separate smoothed curve is estimated for each combination of polling firm and party
## (the election is treated as a poll and given more weight than the opinion polls). The
## shape of the curves for each party is constrained to be the same for every polling firm,
## although they can be vertically offset from each other. The displayed curve is aligned
## so that it passes through the election result, but in the interactive version you can
## see the offset curves for each polling outfit.
##
## - SVG -
##
## The svg graphics format allows interactivity via Javascript. Note that the
## SVGAnnotation library comes from http://www.bioconductor.org/ rather that
## the CRAN repositories. This requires Cairo to be available (use
## capabilities("cairo") to check this).
##
###
library(stringr)
library(plyr)
library(XML)
library(xts)
library(ggplot2)
library(lubridate)
library(mgcv)
library(reshape)
library(Hmisc)
library(rje)
###
##
## Settings
##
###
# Options
opt_begin <- as.Date("2008-01-01") #as.Date("1996-01-01") #as.Date("2002-06-01")
opt_end <- today()
opt_filename <- "test"
opt_svg <- TRUE # set to FALSE if you can't get SVGAnnotation to work
opt_png <- !opt_svg
opt_filename <- str_c(opt_filename, if(opt_svg) ".svg" else ".png")
opt_bc <- TRUE # bias corrections
opt_bcplot <- FALSE # plot bias-corrected points
opt_bcreport <- TRUE # print bias estimates
opt_bccurves <- opt_svg # curves for each polling firm
opt_events <- FALSE # event markers
opt_thresh <- TRUE # 5% threshold marker
opt_ewt <- 1000 # weighting for election data points
opt_adapt <- FALSE # Adaptive smoother
opt_svgwidth <- 12 # width and height if the output file is svg
opt_svgheight <- 7 # these will be multiplied by 72 for png
opt_xwidth <- 3/2 * opt_svgwidth # 1 / opt_xwidth of the plot is reserved for final results
opt_xheight <- 3/2 * opt_svgheight # 1 / opt_xheight for event markers
opt_dashes <- "[\u002D\u2013\u2014\u2212]"
opt_spaces <- "[\u0020\u00A0]"
opt_plotcurves <- TRUE
opt_curvepts <- 500 # number of line segements to make "curves"
opt_curvett <- TRUE # tooltips on curves
opt_moe <- 0
opt_legend <- TRUE
# Wikipedia urls
urls <- str_c("http://en.wikipedia.org/wiki/Opinion_polling_for_the_New_Zealand_general_election,_", c(2005, 2008, 2011))
urls <- c(urls, "http://en.wikipedia.org/wiki/Opinion_polling_for_the_next_New_Zealand_general_election")
# Party details
party_ctl <- read.table(
header = TRUE,
row.names = "Party",
stringsAsFactors = FALSE,
comment = "",
text = "
Party Colour Contrast Include
ACT #FFE401 #FFFF80 -
Conservative #00AEEF #33CCFF -
Destiny #FF0000 #000000 -
Green #098137 #B3FFB3 Y
Labour #FF0000 #FFBAA8 Y
Mana #770808 #FF6E6E -
Maori #EF4A42 #FFCC80 -
National #00529F #CCDDFF Y
'NZ First' #000000 #CCCCCC Y
Progressive #9E9E9E #DDCCDD -
Internet #662C92 #EE77FF -
'Internet/Mana' #662C92 #FF6E6E -
'United Future' #501557 #DD99DD -
'Labour/Green' #FF0000 #B3FFB3 -
")
party_ctl[, "Include"] <- (party_ctl[, "Include"] == "Y")
rownames(party_ctl)[match("Maori", rownames(party_ctl))] <- "M\u0101ori"
if(opt_svg) library(SVGAnnotation, quietly=TRUE)
###
##
## Helper function definitions
##
###
get_data <- function(data_url, quiet=FALSE, i=1) {
if(!quiet) message(str_c("Reading data from ", data_url))
h <- htmlParse(data_url, encoding="UTF-8")
x <- readHTMLTable(h, encoding="UTF-8", stringsAsFactors=FALSE)
x $ toc <- NULL
x <- x[[i]] # the poll data usually the first table, not counting "table" of contents
names(x) <- trim(names(x))
x[,1] <- trim(x[,1])
# If the second column is NA, then we have some sort of non-poll event
r_events <- is.na(x[2])
events <- unlist(str_split(x[r_events, 1], "\n"))
events <- str_split(events, opt_dashes, n=2)
Date <- sapply(events, `[`, 1)
Date <- as.Date(Date, "%d %b %Y")
Event <- sapply(events, `[`, 2)
Event <- trim(Event)
events <- data.frame(Date=Date, Event=Event, stringsAsFactors=FALSE)
# Polls are everything else, minus "heading" rows
r_polls <- !r_events & !(x$Date == "Date")
c_polls <- (names(x) != "")
polls <- x[r_polls, c_polls]
# Conversions
polls $ Poll [str_detect(polls $ Poll, "election")] <- "Election"
polls $ Poll <- as.factor(polls $ Poll)
polls $ Poll <- reorder(polls $ Poll, polls $ Poll, function(x) if(x[1] == "Election") 1 else 2)
polls $ Date <- as.character(polls $ Date)
for(col in names(polls)[-c(1,2)])
polls[col] <- suppressWarnings(as.numeric(polls[[col]])) # suppress conversion to NA warnings
# Return both data frames
attr(polls, "events") <- events
null0 <- function(x) if(is.null(x)) 0 else ifelse(is.na(x), 0, x)
# Combinations
polls[['Labour/Green']] <- rep(0, nrow(polls)) +
null0(polls$Labour) +
null0(polls$Green)
IM <- rep(0, nrow(polls)) +
null0(polls$Internet) +
null0(polls$Mana) +
null0(polls$`Internet Mana`)
IM <- ifelse(IM==0, NA, IM)
polls[['Internet/Mana']] <- IM
return(polls)
}
trim <- function(x) {
x <- str_replace_all(x, "\\[(.*?)\\]", "") # remove any Wikipedia footnotes, e.g. [1]
x <- str_trim(x) # trim leading/trailing spaces
return(x)
}
alpha <- function(x, alpha=1) rgb(t(col2rgb(x)), alpha=255*alpha, max=255)
date <- function(x) {
temp <- lapply(str_split(x, opt_dashes), rev) # Four different kinds of dashes used!
# Date that poll coverage ends
a <- sapply(temp, `[`, 1)
a <- str_replace(a, "c\\.", "")
a <- str_replace(a, "Released", "")
a <- str_trim(a)
date_end <- as.Date(a, "%d %b %Y")
# Date that poll coverage starts
b <- sapply(temp, `[`, 2)
b <- str_trim(b)
b <- str_split(b, opt_spaces) # Second space is a non-breaking space.
year_start <- as.numeric(sapply(b, `[`, 3))
month_start <- match(sapply(b, `[`, 2), month.name)
day_start <- as.numeric(sapply(b, `[`, 1))
date_start <- date_end
s <- !is.na(year_start); if(any(s)) date_start[s] <- update(date_start[s], years=year_start[s])
s <- !is.na(month_start); if(any(s)) date_start[s] <- update(date_start[s], months=month_start[s])
s <- !is.na(day_start); if(any(s)) date_start[s] <- update(date_start[s], days=day_start[s])
# Polls with only month and year
s <- is.na(date_start)
if(any(s)) {
date_start[s] <- as.Date(as.yearmon(a[s]))
date_end[s] <- date_start[s] + new_period(month=1) - new_period(day=1)
}
# Combine and return
date_mean <- as.Date(apply(cbind(date_start, date_end), 1, mean))
data.frame(orig=x, date_start, date_end, date_mean)
}
loop <- function(x, y=x) c(x, rev(y))
format_num <- function(x) formatC(x, digits=1, format="f")
format_date <- function(x) format(as.Date(x), "%d %B %Y")
format_date_range <- function(x, y) {
x_day <- day(x)
x_month <- month.name[month(x)]
x_year <- year(x)
y_day <- day(y)
y_month <- month.name[month(y)]
y_year <- year(y)
en <- "\u2013"
ifelse(
x_year != y_year,
str_c(x_day, x_month, x_year, en, y_day, y_month, y_year, sep=" "),
ifelse(
x_month != y_month,
str_c(x_day, x_month, en, y_day, y_month, y_year, sep=" "),
ifelse(
x_day != y_day,
str_c(x_day, en, y_day, y_month, y_year, sep=" "),
str_c(y_day, y_month, y_year, sep=" ")
)
)
)
}
embiggen <- function(node) {
for(i in seq_along(node)) {
xmlAttrs(node[[i]]) <- c(
`onmouseover` = "embiggen(this)",
`onmouseout` = "unembiggen(this)",
`onclick` = "click(this)"
)
}
}
link <- function(pt_node, sm_node, party, poll) {
id <- str_c(party, poll)
for(i in seq_along(pt_node)) {
xmlAttrs(pt_node[[i]]) <- c(
`id` = str_c(id, i),
`onmouseover` = sprintf("link('%s', %i)", id, length(pt_node)),
`onmouseout` = sprintf("unlink('%s', %i)", id, length(pt_node)),
`onclick` = sprintf("clink('%s', %i)", id, length(pt_node))
)
}
if(!is.null(sm_node)) {
xmlAttrs(sm_node) <- c(`id` = id)
modifyStyle(sm_node, `stroke-opacity`="0")
}
}
get_party_data <- function(p) {
s <- !is.na(poll_matrix[, p])
n <<- sum(s)
x <<- as.numeric(x_date[s])
x0 <<- poll_data $ date_start[s]
x1 <<- poll_data $ date_end[s]
y <<- poll_matrix[s, p]
z <<- poll_data $ Poll[s]
this_colour <<- party_ctl[p, "Colour"]
this_contrast <<- party_ctl[p, "Contrast"]
xs <<- seq(min(x), max(x), length=opt_curvepts)
new_X <<- data.frame(x=xs, z="Election")
s_elect <<- (z == "Election")
invisible()
}
###
##
## Collect the data.
##
###
data_list <- lapply(urls, get_data)
#load('datalist.rda')
#data_list[[5]] <- data.frame(Poll="3 News Reid Research", Date="25 May 2014", Labour=29.5, National=50.3, `NZ First`=5.6, Green=10.2, check.names=FALSE, stringsAsFactors=FALSE)
#attr(data_list[[5]], "events") <- data.frame(Date=as.Date(Sys.Date()), Event="Today")
poll_data <- Reduce(function(x, y) merge(x, y, all=TRUE), data_list)
event_data_list <- lapply(data_list, attr, "events")
event_data <- Reduce(function(x, y) merge(x, y, all=TRUE), event_data_list)
event_data <- event_data[!is.na(event_data $ Date), ]
poll_data <- cbind(poll_data, date(poll_data $ Date)) # Parse dates
poll_data <- poll_data[!is.na(poll_data $ date_mean), ] # Remove any polls where we can't parse a date
poll_data <- sort_df(poll_data, 'date_mean') # Sort from earliest to latest poll
# Restrict to selected dates
s_date <- (poll_data $ date_start >= opt_begin & poll_data $ date_end <= opt_end)
poll_data <- poll_data[s_date, ]
poll_data <- droplevels(poll_data) # Remove unused polling firms
incl_parties <- rownames(party_ctl)[party_ctl $ Include]
poll_matrix <- as.matrix(poll_data[, incl_parties]) # Restrict to parties of interest
x_date <- poll_data $ date_mean
###
##
## Analysis and plotting.
##
###
if(opt_svg) {
svg(opt_filename, width=opt_svgwidth, height=opt_svgheight)
}
if(opt_png && !opt_svg) {
png(opt_filename, width=opt_svgwidth*72, height=opt_svgheight*72)
#pdf(opt_filename, width=opt_svgwidth, height=opt_svgheight, bg='white')
}
svg_counter <- 1
svg_index <- list()
xw <- as.numeric(opt_end - opt_begin) / (opt_xwidth - 1)
xh <- if(opt_events) max(poll_matrix, na.rm=TRUE) / (opt_xheight - 1) else 0
# This sets up the plot region
matplot(
x_date, poll_matrix,
pch = NA,
main = "New Zealand General Election Polling",
xlim = c(min(x_date), max(x_date) + xw), xlab = "", xaxt = "n",
ylim = c(-xh, max(6, 1.1 * max(poll_matrix, na.rm=TRUE))), ylab = "Party Percentage (%)"
)
axis.Date(1, x_date)
abline(h=0); inc(svg_counter) <- 1
is_election <- (poll_data $ Poll == "Election")
abline(v=x_date[is_election]); inc(svg_counter) <- sum(is_election)
if(opt_thresh) {
abline(h=5, lty=2)
inc(svg_counter) <- 1
}
# Fit the Generalised Additive Model
a_list <- list()
b_list <- list()
cf_list <- list()
final <- character()
for(p in incl_parties) {
get_party_data(p)
##logit - add logit links
#y <- y / 100
if(opt_bc) {
a <- if(opt_adapt) {
gam(y ~ s(x, bs="ad") + z, weights=ifelse(z=="Election", opt_ewt, 1))
#gam(y ~ s(x, bs="ad") + z, weights=ifelse(z=="Election", opt_ewt, 1), family=gaussian(link="logit"))
} else {
gam(y ~ s(x) + z, weights=ifelse(z=="Election", opt_ewt, 1))
###gamm(y ~ s(x) + z)
#gam(y ~ s(x) + z, weights=ifelse(z=="Election", opt_ewt, 1), family=gaussian(link="logit"))
}
a_list[[p]] <- a
###a_list[[p]] <- a$gam
cf <- coef(a)
###cf <- coef(a $ gam)
names(cf)[1] <- "zElection"; cf[1] <- 0
cf_list[[p]] <- cf[str_c("z", levels(poll_data $ Poll)[-1])] # Keep track of bias estimates
names(cf_list[[p]]) <- levels(poll_data $ Poll)[-1]
}
else {
a <- if(opt_adapt) {
gam(y ~ s(x, bs="ad"), weights=ifelse(z=="Election", opt_ewt, 1))
#gam(y ~ s(x, bs="ad"), weights=ifelse(z=="Election", opt_ewt, 1), family=gaussian(link="logit"))
} else {
gam(y ~ s(x), weights=ifelse(z=="Election", opt_ewt, 1))
#gam(y ~ s(x), weights=ifelse(z=="Election", opt_ewt, 1), family=gaussian(link="logit"))
}
a_list[[p]] <- a
}
}
bias <- do.call(cbind, cf_list)
colnames(bias)[grep("Maori", colnames(bias))] <- "M\u0101ori"
# Plot spline curves
if(opt_plotcurves) for(p in incl_parties) {
get_party_data(p)
a <- a_list[[p]]
b <- predict(a, newdata=new_X, se=TRUE)
#b$fit <- 100 * expit(b$fit) ##logit
b_list[[p]] <- b
polygon(
loop(xs),
loop(b$fit) + 1.96 * loop(b$se.fit, -b$se.fit),
col = alpha(this_contrast, 1/2),
border = NA
)
lines(xs, b$fit, lwd=3, col=this_colour)
svg_index[[str_c("sm_err:", p)]] <- svg_counter; inc(svg_counter) <- 1
svg_index[[str_c("sm:", p)]] <- svg_counter; inc(svg_counter) <- 1
if(opt_curvett) {
points(xs, b$fit, col=alpha(this_colour, 0), bg=alpha(this_contrast, 0.1), pch=21)
svg_index[[str_c("smpts:", p)]] <- seq(svg_counter, length=opt_curvepts)
inc(svg_counter) <- opt_curvepts
}
}
# Plot data points
for(p in incl_parties) {
get_party_data(p)
if(opt_bc && opt_bcplot) {
cf <- cf_list[[p]]
y[!s_elect] <- y[!s_elect] - cf[as.character(z[!s_elect])] # Bias correction
}
n <- sum(!is.na(y))
if(opt_moe > 0) {
x_me <- ggplot2:::interleave(x, x, NA)
y_me <- ggplot2:::interleave(y-opt_moe, y+opt_moe, NA)
lines(x_me, y_me, col=this_colour)
inc(svg_counter) <- n
}
points(x, y, pch=21, cex=1, col=this_colour, bg=this_contrast)
svg_index[[str_c("pts:", p)]] <- seq(svg_counter, length=n); inc(svg_counter) <- n
}
# Uncorrected smooths
for(p in incl_parties) {
get_party_data(p)
b <- b_list[[p]]
if(opt_bc && opt_bccurves && !opt_bcplot) {
for(i in levels(droplevels(z))[-1]) {
lines(xs, b$fit + bias[i, p], lwd=2, col=alpha(this_colour, 1/2))
svg_index[[sprintf("psm:%s:%s", p, i)]] <- svg_counter; inc(svg_counter) <- 1
}
}
}
# Election results
for(p in incl_parties) {
get_party_data(p)
points(
x[s_elect], y[s_elect],
pch = 21, cex = 2,
col = party_ctl[p, "Colour"],
bg = party_ctl[p, "Contrast"]
)
n_elect <- sum(s_elect)
svg_index[[str_c("election:", p)]] <- seq(svg_counter, length=n_elect); inc(svg_counter) <- n_elect
}
# Plot final estimates
for(p in incl_parties) {
get_party_data(p)
b <- b_list[[p]]
final_x <- tail(x_date, 1)
final_fit <- tail(b $ fit, 1)
final_err <- 1.96 * tail(b $ se.fit, 1)
final[p] <- sprintf("%s \u00B1 %s%%", format_num(final_fit), format_num(final_err))
final_date <- tail(x_date, 1)
right_x <- par("usr")[2]
avail_w <- right_x - as.numeric(final_date)
test_str <- sprintf("%s \u00B1 %s%%_", format_num(800/9), format_num(80/9)) # "88.8 ± 8.8%_"
test_w <- strwidth(test_str, font=4)
text(
final_x, final_fit, final[p],
pos = 4, font = 4, xpd = TRUE,
col = this_colour,
cex = avail_w / test_w,
)
}
# Events
if(opt_events) {
s_date <- (event_data $ Date >= opt_begin & event_data $ Date <= opt_end)
event_data <- event_data[s_date, ]
points(event_data $ Date, rep(-c(0, 2, 4, 1, 3), length=nrow(event_data)) * (xh / 4), pch=21, cex=1.5, bg="#FC7928")
svg_index[["events"]] <- seq(svg_counter, length=nrow(event_data)); inc(svg_counter) <- nrow(event_data)
}
# Legend (SVG is broken for this, do it last)
if(opt_legend) {
party_col <- sapply(incl_parties, function(p) {get_party_data(p); this_colour})
party_bg <- sapply(incl_parties, function(p) {get_party_data(p); this_contrast})
legend("topleft", legend=incl_parties, pch=21, col=party_col, pt.bg=party_bg)
for(p in incl_parties) {
svg_index[[str_c("legend:", p)]] <- svg_counter
inc(svg_counter) <- 1
}
}
if(opt_svg || opt_png) dev.off()
# Bias estimates
if(opt_bc && opt_bcreport) print(round(bias, 1))
###
##
## Fancy SVG stuff.
##
###
if(opt_svg) {
doc <- xmlParse(opt_filename)
svg_points <- unlist(getPlotPoints(doc))
addChildren(xmlChildren(doc) $ svg, kids=list(newXMLNode(name="title", "New Zealand General Election Polling")), at=0)
addECMAScripts(doc, I('
function embiggen(path) {
d = path.getAttributeNS(null, "d").match(/-?\\d+(\\.\\d+)?/ig)
x = ( parseFloat(d[0]) + parseFloat(d[4]) ) / 4;
y = parseFloat(d[1]) / 2;
path.setAttributeNS(null, "transform", "scale(2) translate(-" + x + ", -" + y + ")");
}
function unembiggen(path) {
clicked = path.getAttributeNS(null, "desc")
if(clicked != "clicked") {
path.setAttributeNS(null, "transform", "scale(1)");
}
}
function link(desc, n) {
for(i = 1; i <= n; i++) {
path = document.getElementById(desc + i);
embiggen(path);
}
path = document.getElementById(desc);
bigline(path);
}
function unlink(desc, n) {
for(i = 1; i <= n; i++) {
path = document.getElementById(desc + i);
unembiggen(path);
}
path = document.getElementById(desc);
smallline(path);
}
function clink(desc, n) {
for(i =1; i <= n; i++) {
path = document.getElementById(desc + i);
click(path)
}
path = document.getElementById(desc);
click(path)
}
function click(path) {
desc = path.getAttributeNS(null, "desc")
if(desc == "clicked") {
path.setAttributeNS(null, "desc", "");
}
else {
path.setAttributeNS(null, "desc", "clicked");
}
}
function bigline(path) {
path.style.setProperty("stroke-opacity", "0.5");
}
function smallline(path) {
clicked = path.getAttributeNS(null, "desc")
if(clicked != "clicked") {
path.style.setProperty("stroke-opacity", "0");
}
}
'), at=1)
# Poll results
for(p in incl_parties) {
get_party_data(p)
poll_index <- svg_index[[str_c("pts:", p)]]
pt1 <- format_date_range(x0, x1)
pt2 <- z
pt3 <- sprintf("%s: %s%%", p, y)
poll_text <- str_c(pt1, pt2, pt3, sep=", \n")
addToolTips(doc, text=poll_text, path=svg_points[poll_index])
# for(i in levels(poll_data $ Poll)[-1]) {
for(i in levels(droplevels(z))[-1]) {
if(opt_bc && opt_bccurves && !opt_bcplot) {
sm_index <- svg_index[[sprintf("psm:%s:%s", p, i)]]
link(svg_points[poll_index[z==i]], svg_points[[sm_index]], p, i)
}
else {
link(svg_points[poll_index[z==i]], NULL, p, i)
}
}
elect_index <- svg_index[[str_c("election:", p)]]
et1 <- format_date(x[s_elect])
et2 <- "Election"
et3 <- sprintf("%s: %s%%", p, y[s_elect])
elect_text <- str_c(et1, et2, et3, sep="\n")
addToolTips(doc, text=elect_text, path=svg_points[elect_index])
embiggen(svg_points[elect_index])
if(opt_curvett) {
b <- b_list[[p]]
smpts_date <- format_date(xs)
smpts_pc <- str_c(round(b$fit, 1), '%')
smpts_text <- str_c(smpts_date, smpts_pc, sep='\n')
smpts_index <- svg_index[[str_c("smpts:", p)]]
addToolTips(doc, text=smpts_text, path=svg_points[smpts_index])
}
}
# Events
if(opt_events) {
event_index <- svg_index[["events"]]
event_text <- with(event_data, str_c(format_date(Date), "\n", Event))
addToolTips(doc, text=event_text, path=svg_points[event_index])
embiggen(svg_points[event_index])
}
saveXML(doc, opt_filename)
}
Owner

pitakakariki commented Dec 2, 2012

The smoothed curves are calculated using a generalised additive model (GAM). The smoothing parameter is estimated using cross-validation. This means that the curves are estimated based on subsets of the data, and the parameter that best predicts the hold-out data is chosen. Note that the error bounds are based on the assumption that the smoothing parameter is correct, i.e. uncertainty in the smoothing parameter estimate are not accounted for.

A separate smoothed curve is estimated for each combination of polling firm and party (the election is treated as a poll and given more weight than the opinion polls). The shape of the curves for each party is constrained to be the same for every polling firm, although they can be vertically offset from each other. The displayed curve is aligned so that it passes through the election result, but in the interactive version you can see the offset curves for each polling outfit.

Owner

pitakakariki commented Dec 2, 2012

The smoothed curves are calculated using a generalised additive model (GAM). The smoothing parameter is estimated using cross-validation. This means that the curves are estimated based on subsets of the data, and the parameter that best predicts the hold-out data is chosen. Note that the error bounds are based on the assumption that the smoothing parameter is correct, i.e. uncertainty in the smoothing parameter estimate are not accounted for.

A separate smoothed curve is estimated for each combination of polling firm and party (the election is treated as a poll and given more weight than the opinion polls). The shape of the curves for each party is constrained to be the same for every polling firm, although they can be vertically offset from each other. The displayed curve is aligned so that it passes through the election result, but in the interactive version you can see the offset curves for each polling outfit.

Owner

pitakakariki commented Dec 2, 2012

The smoothed curves are calculated using a generalised additive model (GAM). The smoothing parameter is estimated using cross-validation. This means that the curves are estimated based on subsets of the data, and the parameter that best predicts the hold-out data is chosen. Note that the error bounds are based on the assumption that the smoothing parameter is correct, i.e. uncertainty in the smoothing parameter estimate are not accounted for.

A separate smoothed curve is estimated for each combination of polling firm and party (the election is treated as a poll and given more weight than the opinion polls). The shape of the curves for each party is constrained to be the same for every polling firm, although they can be vertically offset from each other. The displayed curve is aligned so that it passes through the election result, but in the interactive version you can see the offset curves for each polling outfit.

Owner

pitakakariki commented Dec 2, 2012

Testing

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