library(fdth)
#======================
# Vectors: univariated
#======================
set.seed(1)
x <- rnorm(n=1e3,
mean=5,
sd=1)
# x
(d <- fdt(x))
# x, alternative breaks
(d <- fdt(x,
breaks='Scott'))
# x, k
(d <- fdt(x,
k=20))
# x, star, end
range(x)
(d <- fdt(x,
start=1.5,
end=9))
# x, start, end, h
(d <- fdt(x,
start=1,
end=9,
h=1))
# Effect of right
x <- rep(1:3, 3); sort(x)
(d <- fdt(x,
start=1,
end=4,
h=1))
(d <- fdt(x,
start=0,
end=3,
h=1,
right=TRUE))
#=============================================
# 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))
levels(mdf$X1)
(d <- fdt(mdf,
k=5,
by='X1'))
(d <- fdt(mdf,
breaks='FD',
by='X1'))
str(d)
d
levels(mdf$X2)
(d <- fdt(mdf,
breaks='FD',
by='X2'))
(d <- fdt(mdf,
k=5,
by='X2'))
(d <- fdt(iris,
k=5))
(d <- fdt(iris,
k=10))
levels(iris$Species)
(d <- fdt(iris,
k=5,
by='Species'))
#=========================
# Matrices: multivariated
#=========================
(d <-fdt(state.x77))
(d <-fdt(volcano))Run the code above in your browser using DataLab