PISA 2012 - multi dimensional Gaussian merging In factorMerger: The Merging Path Plot

knitr::opts_chunk\$set(collapse = TRUE, comment = "#>", warning = FALSE, message = FALSE,
fig.height = 5, fig.width = 10)

Libraries

library(factorMerger)
library(ggplot2)
library(dplyr)
library(reshape2)

data("pisa2012")

Explore

pisa2012 %>% ggplot(aes(x = country)) + geom_bar() +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
meltedPisa <- pisa2012 %>% melt(na.rm = TRUE)
pisaResultsBySubject <-  meltedPisa %>%
ggplot(aes(x = reorder(country, value, FUN = median), y = value)) + geom_boxplot() +
facet_wrap(~variable) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Country")
pisaResultsBySubject +
geom_hline(data = meltedPisa %>% group_by(variable) %>% summarise(mean = mean(value)),
aes(yintercept = mean, group = variable), col = "red")

TODO: Find countries significantly better, worse and not significantly different from global averages. Cluster countries into three groups.

Run MANOVA

manova(cbind(math, reading, science) ~ country, pisa2012) %>% summary()

It seems that there exist some differences among countries included in PISA. Let's find them!

Factor Merger

Let's now have a try using factorMerger for exploration.

It's faster to use "fast-adaptive" or "fast-fixed" methods on a big dataset. They enable comparisons between neighbours only (neighbours are pairs of groups with close means).

pisaFMHClustEurope <- mergeFactors(response = pisa2012[,1:3],
factor = factor(pisa2012\$country),
method = "fast-fixed")

plot(pisaFMHClustEurope)

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factorMerger documentation built on July 4, 2019, 1:02 a.m.