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R: Finds extreme points in the empirical density estimated from...

findPeaksAndValleys

R Documentation

Finds extreme points in the empirical density estimated from data

Description

Finds extreme points in the empirical density estimated from data.

Usage

## S3 method for class 'density'
findPeaksAndValleys(x, tol=0, ...)
## S3 method for class 'numeric'
findPeaksAndValleys(x, ..., tol=0, na.rm=TRUE)

Arguments

`x`	A `numeric` `vector` containing data points or a `density` object.
`...`	Arguments passed to `density`. Ignored if `x` is a `density` object.
`tol`	A non-negative `numeric` threshold specifying the minimum density at the extreme point in order to accept it.
`na.rm`	If `TRUE`, missing values are dropped, otherwise not.

Value

Returns a data.frame (of class 'PeaksAndValleys') containing of "peaks" and "valleys" filtered by tol.

Author(s)

Henrik Bengtsson

Examples

layout(matrix(1:3, ncol=1))
par(mar=c(2,4,4,1)+0.1)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# A unimodal distribution
# - - - - - - - - - - - - - - - - - - - - - - - - - - - -
x1 <- rnorm(n=10000, mean=0, sd=1)
x <- x1
fit <- findPeaksAndValleys(x)
print(fit)
plot(density(x), lwd=2, main="x1")
abline(v=fit$x)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# A trimodal distribution
# - - - - - - - - - - - - - - - - - - - - - - - - - - - -
x2 <- rnorm(n=10000, mean=4, sd=1)
x3 <- rnorm(n=10000, mean=8, sd=1)
x <- c(x1,x2,x3)
fit <- findPeaksAndValleys(x)
print(fit)
plot(density(x), lwd=2, main="c(x1,x2,x3)")
abline(v=fit$x)


# - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# A trimodal distribution with clear separation
# - - - - - - - - - - - - - - - - - - - - - - - - - - - -
x1b <- rnorm(n=10000, mean=0, sd=0.1)
x2b <- rnorm(n=10000, mean=4, sd=0.1)
x3b <- rnorm(n=10000, mean=8, sd=0.1)
x <- c(x1b,x2b,x3b)

# Illustrating explicit usage of density()
d <- density(x)
fit <- findPeaksAndValleys(d, tol=0)
print(fit)
plot(d, lwd=2, main="c(x1b,x2b,x3b)")
abline(v=fit$x)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(aroma.light)
aroma.light v3.2.0 (2016-01-06) successfully loaded. See ?aroma.light for help.
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/aroma.light/findPeaksAndValleys.Rd_%03d_medium.png", width=480, height=480)
> ### Name: findPeaksAndValleys
> ### Title: Finds extreme points in the empirical density estimated from
> ###   data
> ### Aliases: findPeaksAndValleys findPeaksAndValleys.density
> ###   findPeaksAndValleys.numeric
> ### Keywords: methods internal
> 
> ### ** Examples
> 
> layout(matrix(1:3, ncol=1))
> par(mar=c(2,4,4,1)+0.1)
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # A unimodal distribution
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> x1 <- rnorm(n=10000, mean=0, sd=1)
> x <- x1
> fit <- findPeaksAndValleys(x)
> print(fit)
  type          x  density
1 peak -0.1334831 0.392287
> plot(density(x), lwd=2, main="x1")
> abline(v=fit$x)
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # A trimodal distribution
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> x2 <- rnorm(n=10000, mean=4, sd=1)
> x3 <- rnorm(n=10000, mean=8, sd=1)
> x <- c(x1,x2,x3)
> fit <- findPeaksAndValleys(x)
> print(fit)
    type           x    density
1   peak -0.08501183 0.12317918
2 valley  1.99788931 0.04586722
3   peak  3.97835269 0.12463149
4 valley  5.95881607 0.04457639
5   peak  7.93927946 0.12506953
> plot(density(x), lwd=2, main="c(x1,x2,x3)")
> abline(v=fit$x)
> 
> 
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # A trimodal distribution with clear separation
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> x1b <- rnorm(n=10000, mean=0, sd=0.1)
> x2b <- rnorm(n=10000, mean=4, sd=0.1)
> x3b <- rnorm(n=10000, mean=8, sd=0.1)
> x <- c(x1b,x2b,x3b)
> 
> # Illustrating explicit usage of density()
> d <- density(x)
> fit <- findPeaksAndValleys(d, tol=0)
> print(fit)
    type           x      density
1   peak -0.01396756 3.427949e-01
2 valley  1.97896545 1.227938e-06
3   peak  3.97189847 3.422092e-01
4 valley  5.98626087 1.178856e-06
5   peak  7.97919389 3.426187e-01
> plot(d, lwd=2, main="c(x1b,x2b,x3b)")
> abline(v=fit$x)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>