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library(lattice) | |
key <- list( | |
rep=FALSE, | |
lines=list(col=c("#00526D", "blue"), type=c("p","l"), pch=1), | |
text=list(lab=c("Observation","Estimate")), | |
rectangles = list(col=adjustcolor("yellow", alpha.f=0.5), border="grey"), | |
text=list(lab="95% Prediction credible interval")) | |
xyplot(l.95..CI + u.95..CI + Estimate + Units_sold ~ Temperature | Model, | |
data=modelData, as.table=TRUE, main="Ice cream model comparision", | |
xlab="Temperatures (C)", ylab="Units sold", |
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modelData <- data.frame( | |
Model=factor(c(rep("Linear model", n), | |
rep("Log-transformed LM", n), | |
rep("Poisson (log)",n), | |
rep("Binomial (logit)",n)), | |
levels=c("Linear model", | |
"Log-transformed LM", | |
"Poisson (log)", | |
"Binomial (logit)"), | |
ordered = TRUE), |
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log.lin.mod | |
## Family: gaussian (identity) | |
## Formula: log_units ~ temp | |
## Data: NULL (Number of observations: 12) | |
## Samples: 2 chains, each with n.iter = 2000; n.warmup = 500; n.thin = 1; | |
## total post-warmup samples = 3000 | |
## WAIC: -9.76 | |
## | |
## Fixed Effects: | |
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat |
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temp <- c(11.9,14.2,15.2,16.4,17.2,18.1,18.5,19.4,22.1,22.6,23.4,25.1) | |
units <- c(185L,215L,332L,325L,408L,421L,406L,412L,522L,445L,544L,614L) | |
log_units <- log(units) | |
n <- length(units) | |
market.size <- rep(800, n) |
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# http://www.theguardian.com/news/reality-check/2014/jan/31/sex-guardian-readers-confess-all | |
# Source: https://docs.google.com/spreadsheet/ccc?key=0At6CC4x_yBnMdDduelJocVo5RDZzalltd0dSQzdXUmc&usp=sharing#gid=0 | |
dat <- data.frame(AgeGroup=factor( | |
1:9, | |
labels= c("Younger than 16", "16-24", | |
"25-34", "35-44", "45-54", | |
"55-64", "65-74", | |
"Older than 74", "(blank)"), | |
ordered=TRUE), | |
No=c(34, 2079, 2585, 1593, |
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with(dat, { | |
prop.test(Yes, Yes + No) | |
}) | |
# | |
# 9-sample test for equality of proportions without continuity correction | |
# | |
# data: Yes out of Yes + No | |
# X-squared = 198.4, df = 8, p-value < 2.2e-16 | |
# alternative hypothesis: two.sided | |
# sample estimates: |
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set.seed(12345) | |
dat <- data.frame(category=rep(c("Food", "Drinks"),20), | |
product=rep(c("Cheese", "Wine", "Bread", "Beer"), 10), | |
year=sort(rep(c(2004:2013),4)), | |
value=sort(rnorm(40))) | |
dat <- dat[with(dat, order(category, year)),] | |
library(lattice) | |
# Change some of the default lattice settings |
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set.seed(1234) | |
dat <- data.frame( | |
product=c(rep("Mobile",2), | |
rep(c("Smartphone", "Mobile"),3), | |
rep(c("Video rental shop"),3), | |
rep(c("Video rental shop", | |
"Online movie rental"),2)), | |
year=c(2008:2010, 2010, 2011, 2011, 2012, 2012, | |
2008:2010, 2011, 2011, 2012, 2012), | |
category=c(rep("Phone", 8), rep("Film", 7)), |
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library(data.table) | |
dt <- data.table(dat, key="product,year") | |
# Create a table that list all years for all products. | |
py <- CJ(product=levels(dt[, product]), | |
year=unique(dt[,year])) | |
# Create a mapping table from old to new products. | |
# The new products are named product, so that I can | |
# join them with the original data and get a new colulmn | |
# with the old product category | |
mapping <- data.table( |
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dat <- data.frame(Position=c(rep("President", 3), rep("Vice", 3)), | |
Name=c("Washington", "Adams", "Jefferson", | |
"Adams", "Jefferson", "Burr"), | |
start=as.Date(x=rep(c("1789-03-29", "1797-02-03", | |
"1801-02-03"),2)), | |
end=as.Date(x=rep(c("1797-02-03", "1801-02-03", | |
"1809-02-03"),2))) | |
plot( | |
gvisTimeline(data=dat, rowlabel="Name",barlabel="Position", | |
start="start", end="end", |
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