library(fdth)
#======================
# Vectors: univariated
#======================
set.seed(1); x <- rnorm(n=1e3, mean=5, sd=1)
# x
d <- fdt(x); d
# x, alternative breaks
d <- fdt(x, breaks='Scott'); d
# x, k
d <- fdt(x, k=20); d
# x, star, end
range(x)
d <- fdt(x, start=1.5, end=9); d
# x, start, end, h
d <- fdt(x, start=1, end=9, h=1); d
# Effect of right
x <- rep(1:3, 3); sort(x)
d <- fdt(x, start=1, end=4, h=1); d
d <- fdt(x, start=0, end=3, h=1, right=TRUE); d
#=============================================
# Data.frames: multivariated with categorical
#=============================================
mdf <- data.frame(X1 = rep(LETTERS[1:4], 25),
X2 = as.factor(rep(1:10, 10)),
Y1 = c(NA, NA, rnorm(96, 10, 1), NA, NA),
Y2 = rnorm(100, 60, 4),
Y3 = rnorm(100, 50, 4),
Y4 = rnorm(100, 40, 4))
d <- fdt(mdf); d
levels(mdf$X1)
d <- fdt(mdf, k=5, by='X1'); d
d <- fdt(mdf, breaks='FD', by='X1')
str(d)
d
levels(mdf$X2)
d <- fdt(mdf, breaks='FD', by='X2'); d
d <- fdt(mdf, k=5, by='X2'); d
d <- fdt(iris, k=5); d
d <- fdt(iris, k=10); d
levels(iris$Species)
d <- fdt(iris, k=5, by='Species'); d
#=========================
# Matrices: multivariated
#=========================
d <-fdt(state.x77); d
d <-fdt(volcano); d
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