clusterv (version 1.1.1)

rand.norm: Random generation of normal distributed data

Description

Random generation of a matrix of n columns with with diagonal covariance matrix (rand.norm.generate) or with full covariance matrix (rand.norm.generate.full). These functions are used by generate.sampleN functions \(0 \leq N \leq 5\) to generate the data.

Usage

rand.norm.generate(n = 5, mean = 0, sd = 1)
rand.norm.generate.full(n = 5, mean = c(0, 0), 
                        Sigma = matrix(c(0.1, 0, 0, 0.1), 2, 2))

Value

a matrix of n columns with length(mean) rows. With rand.norm.generate Row[i] has mean mean[i] and standard deviation sd[i]. With rand.norm.generate.full Row[i] has mean mean[i]

Arguments

n

number of samples to be generated

mean

vector of means

sd

vector of standard deviations

Sigma

Covariance matrix

Author

Giorgio Valentini

See Also

generate.sample0, generate.sample1, generate.sample2

generate.sample3, generate.sample4, generate.sample5

Examples

library(MASS)
rand.norm.generate(n = 10)
rand.norm.generate(n = 10, mean = c(0,1,2), sd = c(1,1,5))
rand.norm.generate.full()
rand.norm.generate.full(n = 10, mean = c(0, 0, 2), 
                        Sigma = matrix(seq(1,1.8, by=0.1), 3, 3))