TODO

library(decorate)
library(ggplot2)
data(iris)

# run sle.score on first 2 iris species
# this is not significant
df = data.frame(categ = droplevels(iris[1:100,5]), score = sle.score(iris[1:100,1:4], method="spearman"))

ggplot(df, aes(categ, score)) + geom_boxplot()

with(df[df$categ %in%c ("setosa", "versicolor"),], summary(lm(score~categ)))


# Evaluating all 3 species together changes the SLE metric 
# and the 3 species are not significantly different

df = data.frame(categ = droplevels(iris[,5]), score = sle.score(iris[,1:4], method="spearman"))

ggplot(df, aes(categ, score)) + geom_boxplot()

with(df[df$categ %in%c ("setosa", "versicolor"),], summary(lm(score~categ)))




library(decorate)
library(ggplot2)
data(iris)

# run sle.score on first 2 iris species
# this is not significant
df = data.frame(categ = droplevels(iris[1:100,5]), score = delaneau.score(iris[1:100,1:4], method="spearman"))

ggplot(df, aes(categ, score)) + geom_boxplot()

with(df[df$categ %in%c ("setosa", "versicolor"),], summary(lm(score~categ)))


# Evaluating all 3 species together changes the SLE metric 
# and the 3 species are not significantly different

df = data.frame(categ = droplevels(iris[,5]), score = delaneau.score(iris[,1:4], method="spearman"))

ggplot(df, aes(categ, score)) + geom_boxplot()

with(df[df$categ %in%c ("setosa", "versicolor"),], summary(lm(score~categ)))