jeksterslabRdata

Ivan Jacob Agaloos Pesigan 2020-07-17

jeksterslabRdata is a collection of functions that I find useful in studying data generation and sampling.

Installation

You can install the released version of jeksterslabRdata from GitHub with:

library(devtools)
install_github("jeksterslabds/jeksterslabRdata")

Documentation

See GitHub Pages for package documentation.

Main functions

library(jeksterslabRdata)

univ()

Generates an n x 1 univariate data vector or a list of n x 1 univariate data vectors of length R. The default data generating function is the normal distribution.

Single Random Data Set

Run the function.

x <- univ(n = 100, rFUN = rnorm, mean = 100, sd = sqrt(225))

Explore the output.

str(x, list.len = 6)
#>  num [1:100] 98 102.7 77.3 94.1 74.9 ...
hist(x, main = expression(italic(N)(list(mu == 100, sigma^2 == 225))))

Multiple Random Data Sets

Run the function.

xstar <- univ(n = 100, rFUN = rnorm, mean = 100, sd = sqrt(225), R = 100)

Explore the output.

str(xstar, list.len = 6)
#> List of 100
#>  $ : num [1:100] 76.1 64.4 70.8 85.5 96.1 ...
#>  $ : num [1:100] 104 103 105 118 103 ...
#>  $ : num [1:100] 83.7 85.6 110.2 138.9 82.9 ...
#>  $ : num [1:100] 94.8 84.8 108 111.2 102.8 ...
#>  $ : num [1:100] 83.7 86.1 105.7 103.8 81.7 ...
#>  $ : num [1:100] 84.1 121.3 92.5 65.2 90.1 ...
#>   [list output truncated]

mvn()

Generates an n x k multivariate data matrix or a list of n x k multivariate data matrices of length R from the multivariate normal distribution. This function is a wrapper around MASS::mvrnorm().

Single Random Data Set

Set mu and Sigma.

mu <- c(100, 100, 100)
Sigma <- matrix(
  data = c(225, 112.50, 56.25, 112.5, 225, 112.5, 56.25, 112.50, 225),
  ncol = 3
)

Run the function.

X <- mvn(n = 100, mu = mu, Sigma = Sigma)

Explore the output.

str(X)
#>  num [1:100, 1:3] 120 106 140 102 106 ...
#>  - attr(*, "dimnames")=List of 2
#>   ..$ : NULL
#>   ..$ : NULL
pairs(X)

colMeans(X)
#> [1]  99.91708 100.82996 101.02465
cov(X)
#>           [,1]     [,2]      [,3]
#> [1,] 209.14395 117.7676  34.83066
#> [2,] 117.76760 261.4312  99.70080
#> [3,]  34.83066  99.7008 212.59476
cor(X)
#>           [,1]      [,2]      [,3]
#> [1,] 1.0000000 0.5036449 0.1651820
#> [2,] 0.5036449 1.0000000 0.4229058
#> [3,] 0.1651820 0.4229058 1.0000000

Multiple Random Data Sets

Run the function.

Xstar <- mvn(n = 100, mu = mu, Sigma = Sigma, R = 100)

Explore the output.

str(Xstar, list.len = 6)
#> List of 100
#>  $ : num [1:100, 1:3] 122.7 102.1 114.4 98 92.3 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
#>  $ : num [1:100, 1:3] 92.3 74.1 111.6 92.8 95.2 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
#>  $ : num [1:100, 1:3] 99.6 97.9 117.4 119.6 71.2 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
#>  $ : num [1:100, 1:3] 114.7 101.7 97.6 110.3 106.8 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
#>  $ : num [1:100, 1:3] 102.6 88.1 118.4 96.6 89.3 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
#>  $ : num [1:100, 1:3] 93.2 104.3 121.5 81.6 117.2 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
#>   [list output truncated]

mvnram()

Generates an n x k multivariate data matrix or a list of n x k multivariate data matrices of length R from the multivariate normal distribution. The model-implied matrices used to generate data is derived from the Reticular Action Model (RAM) Matrices.

Single Random Data Set

Set matrices.

mu <- c(100, 100, 100)
A <- matrix(
  data = c(0, sqrt(0.26), 0, 0, 0, sqrt(0.26), 0, 0, 0),
  ncol = 3
)
S <- diag(c(225, 166.5, 116.5))
F <- I <- diag(3)

Run the function.

X <- mvnram(n = 100, mu = mu, A = A, S = S, F = F, I = I)

Explore the output.

str(X)
#>  num [1:100, 1:3] 77.6 92.3 83.1 124.4 77.8 ...
#>  - attr(*, "dimnames")=List of 2
#>   ..$ : NULL
#>   ..$ : NULL
pairs(X)

colMeans(X)
#> [1] 100.9830 101.7738 101.8031
cov(X)
#>           [,1]     [,2]      [,3]
#> [1,] 264.35803 101.7747  54.38281
#> [2,] 101.77473 253.4147 155.28090
#> [3,]  54.38281 155.2809 184.37269
cor(X)
#>           [,1]      [,2]      [,3]
#> [1,] 1.0000000 0.3932130 0.2463299
#> [2,] 0.3932130 1.0000000 0.7183794
#> [3,] 0.2463299 0.7183794 1.0000000

Multiple Random Data Sets

Run the function.

Xstar <- mvnram(n = 100, mu = mu, A = A, S = S, F = F, I = I, R = 100)

Explore the output.

str(Xstar, list.len = 6)
#> List of 100
#>  $ : num [1:100, 1:3] 104.3 97.5 96.9 86.9 121.3 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
#>  $ : num [1:100, 1:3] 93.9 127.7 115.6 119.8 120.2 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
#>  $ : num [1:100, 1:3] 106.6 117.8 123.2 69.9 87.6 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
#>  $ : num [1:100, 1:3] 97.7 89.7 108 104.4 91.2 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
#>  $ : num [1:100, 1:3] 123.7 99.7 103.5 116.9 115.3 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
#>  $ : num [1:100, 1:3] 113.8 107.4 93.3 76.1 106.4 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : NULL
#>   [list output truncated]