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

In HenrikBengtsson/aroma.light: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types

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

See Also

This function is used by callNaiveGenotypes().

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)

HenrikBengtsson/aroma.light documentation built on July 3, 2023, 1:57 a.m.