Package 'fastmatrix' reference manual

Title:	Fast Computation of some Matrices Useful in Statistics
Description:	Small set of functions to fast computation of some matrices and operations useful in statistics and econometrics. Currently, there are functions for efficient computation of duplication, commutation and symmetrizer matrices with minimal storage requirements. Some commonly used matrix decompositions (LU and LDL), basic matrix operations (for instance, Hadamard, Kronecker products and the Sherman-Morrison formula) and iterative solvers for linear systems are also available. In addition, the package includes a number of common statistical procedures such as the sweep operator, weighted mean and covariance matrix using an online algorithm, linear regression (using Cholesky, QR, SVD, sweep operator and conjugate gradients methods), ridge regression (with optimal selection of the ridge parameter considering several procedures), omnibus tests for univariate normality, functions to compute the multivariate skewness, kurtosis, the Mahalanobis distance (checking the positive defineteness), and the Wilson-Hilferty transformation of gamma variables. Furthermore, the package provides interfaces to C code callable by another C code from other R packages.
Authors:	Felipe Osorio [aut, cre] , Alonso Ogueda [aut]
Maintainer:	Felipe Osorio <[email protected]>
License:	GPL-3
Version:	0.5-772
Built:	2024-11-17 03:08:26 UTC
Source:	https://github.com/faosorios/fastmatrix

Array multiplication

Description

Multiplication of 3-dimensional arrays was first introduced by Bates and Watts (1980). More extensions and technical details can be found in Wei (1998).

Usage

array.mult(a, b, x)
array.mult(a, b, x)

Arguments

`a`	a numeric matrix.
`b`	a numeric matrix.
`x`	a three-dimensional array.

Details

Let $\bold{X} = (x_{tij})$ be a 3-dimensional $n\times p\times q$ where indices $t, i$ and $j$ indicate face, row and column, respectively. The product $\bold{Y} = \bold{AXB}$ is an $n\times r\times s$ array, with $\bold{A}$ and $\bold{B}$ are $r\times p$ and $q\times s$ matrices respectively. The elements of $\bold{Y}$ are defined as:

$y_{tkl} = \sum\limits_{i=1}^p\sum\limits_{j=1}^q a_{ki}x_{tij}b_{jl}$

Value

array.mult returns a 3-dimensional array of dimension $n\times r\times s$ .

References

Bates, D.M., Watts, D.G. (1980). Relative curvature measures of nonlinearity. Journal of the Royal Statistical Society, Series B 42, 1-25.

Wei, B.C. (1998). Exponential Family Nonlinear Models. Springer, New York.

Examples

x <- array(0, dim = c(2,3,3)) # 2 x 3 x 3 array
x[,,1] <- c(1,2,2,4,3,6)
x[,,2] <- c(2,4,4,8,6,12)
x[,,3] <- c(3,6,6,12,9,18)

a <- matrix(1, nrow = 2, ncol = 3)
b <- matrix(1, nrow = 3, ncol = 2)

y <- array.mult(a, b, x) # a 2 x 2 x 2 array
y
x <- array(0, dim = c(2,3,3)) # 2 x 3 x 3 array
x[,,1] <- c(1,2,2,4,3,6)
x[,,2] <- c(2,4,4,8,6,12)
x[,,3] <- c(3,6,6,12,9,18)

a <- matrix(1, nrow = 2, ncol = 3)
b <- matrix(1, nrow = 3, ncol = 2)

y <- array.mult(a, b, x) # a 2 x 2 x 2 array
y

Force a matrix to be symmetric

Description

Force a square matrix x to be symmetric

Usage

asSymmetric(x, lower = TRUE)
asSymmetric(x, lower = TRUE)

Arguments

`x`	a square matrix to be forced to be symmetric.
`lower`	logical, should the upper (lower) triangle be replaced with the lower (upper) triangle?

Value

a square symmetric matrix.

Examples

a <- matrix(1:16, ncol = 4)
isSymmetric(a) # FALSE
a <- asSymmetric(a) # copy lower triangle into upper triangle
a <- matrix(1:16, ncol = 4)
isSymmetric(a) # FALSE
a <- asSymmetric(a) # copy lower triangle into upper triangle

Computation of Bezier curve

Description

Computes the Bezier curve based on $n+1$ control points using the De Casteljau's method.

Usage

bezier(x, y, ngrid = 200)
bezier(x, y, ngrid = 200)

Arguments

`x`, `y`	vector giving the coordinates of the control points. Missing values are deleted.
`ngrid`	number of elements in the grid used to compute the smoother.

Details

Given $\bold{p}_0,\bold{p}_1,\dots,\bold{p}_n$ control points the Bezier curve is given by $B(t)$ defined as

$B(t) = \left({\begin{array}{c} x(t) \\ y(t) \end{array}}\right) = \sum\limits_{k=0}^n {n \choose k} t^k (1 - t)^k\bold{p}_k$

where $t\in[0,1]$ . To evaluate the Bezier curve the De Casteljau's method is used.

Value

A list containing xgrid and ygrid elements used to plot the Bezier curve.

Examples

# a tiny example
x <- c(1.0, 0.25, 1.25, 2.5, 4.00, 5.0)
y <- c(0.5, 2.00, 3.75, 4.0, 3.25, 1.0)
plot(x, y, type = "o")
z <- bezier(x, y, ngrid = 50)
lines(z$xgrid, z$ygrid, lwd = 2, lty = 2, col = "red")

# other simple example
x <- c(4,6,4,5,6,7)
y <- 1:6
plot(x, y, type = "o")
z <- bezier(x, y, ngrid = 50)
lines(z$xgrid, z$ygrid, lwd = 2, lty = 2, col = "red")
# a tiny example
x <- c(1.0, 0.25, 1.25, 2.5, 4.00, 5.0)
y <- c(0.5, 2.00, 3.75, 4.0, 3.25, 1.0)
plot(x, y, type = "o")
z <- bezier(x, y, ngrid = 50)
lines(z$xgrid, z$ygrid, lwd = 2, lty = 2, col = "red")

# other simple example
x <- c(4,6,4,5,6,7)
y <- 1:6
plot(x, y, type = "o")
z <- bezier(x, y, ngrid = 50)
lines(z$xgrid, z$ygrid, lwd = 2, lty = 2, col = "red")

Bracket product

Description

Bracket product of a matrix and a 3-dimensional array.

Usage

bracket.prod(a, x)
bracket.prod(a, x)

Arguments

`a`	a numeric matrix.
`x`	a three-dimensional array.

Details

Let $\bold{X} = (x_{tij})$ be a 3-dimensional $n\times p\times q$ array and $\bold{A}$ an $m\times n$ matrix, then $\bold{Y} = [\bold{A}][\bold{X}]$ is called the bracket product of $\bold{A}$ and $\bold{X}$ , that is an $m \times p\times q$ with elements

$y_{tij} = \sum\limits_{k=1}^n a_{tk}x_{kij}$

Value

bracket.prod returns a 3-dimensional array of dimension $m\times p\times q$ .

References

Wei, B.C. (1998). Exponential Family Nonlinear Models. Springer, New York.

Examples

x <- array(0, dim = c(2,3,3)) # 2 x 3 x 3 array
x[,,1] <- c(1,2,2,4,3,6)
x[,,2] <- c(2,4,4,8,6,12)
x[,,3] <- c(3,6,6,12,9,18)

a <- matrix(1, nrow = 3, ncol = 2)

y <- bracket.prod(a, x) # a 3 x 3 x 3 array
y
x <- array(0, dim = c(2,3,3)) # 2 x 3 x 3 array
x[,,1] <- c(1,2,2,4,3,6)
x[,,2] <- c(2,4,4,8,6,12)
x[,,3] <- c(3,6,6,12,9,18)

a <- matrix(1, nrow = 3, ncol = 2)

y <- bracket.prod(a, x) # a 3 x 3 x 3 array
y

Solve linear systems using the conjugate gradients method

Description

Conjugate gradients (CG) method is an iterative algorithm for solving linear systems with positive definite coefficient matrices.

Usage

cg(a, b, maxiter = 200, tol = 1e-7)
cg(a, b, maxiter = 200, tol = 1e-7)

Arguments

`a`	a symmetric positive definite matrix containing the coefficients of the linear system.
`b`	a vector of right-hand sides of the linear system.
`maxiter`	the maximum number of iterations. Defaults to `200`
`tol`	tolerance level for stopping iterations.

Value

a vector with the approximate solution, the iterations performed are returned as the attribute 'iterations'.

Warning

The underlying C code does not check for symmetry nor positive definitiveness.

References

Golub, G.H., Van Loan, C.F. (1996). Matrix Computations, 3rd Edition. John Hopkins University Press.

Hestenes, M.R., Stiefel, E. (1952). Methods of conjugate gradients for solving linear equations. Journal of Research of the National Bureau of Standards 49, 409-436.

Examples

a <- matrix(c(4,3,0,3,4,-1,0,-1,4), ncol = 3)
b <- c(24,30,-24)
z <- cg(a, b)
z # converged in 3 iterations
a <- matrix(c(4,3,0,3,4,-1,0,-1,4), ncol = 3)
b <- c(24,30,-24)
z <- cg(a, b)
z # converged in 3 iterations

Rank 1 update to Cholesky factorization

Description

function cholupdate, where R = chol(A) is the original Cholesky factorization of $\bold{A}$ , returns the upper triangular Cholesky factor of $\bold{A} + \bold{xx}^T$ , with $\bold{x}$ a column vector of appropriate dimension.

Usage

cholupdate(r, x)
cholupdate(r, x)

Arguments

`r`	a upper triangular matrix, the Cholesky factor of matrix a.
`x`	vector defining the rank one update.

References

Golub, G.H., Van Loan, C.F. (2013). Matrix Computations, 4th Edition. John Hopkins University Press.

Examples

a <- matrix(c(1,1,1,1,2,3,1,3,6), ncol = 3)
r <- chol(a)
x <- c(0,0,1)
b <- a + outer(x,x)
r1 <- cholupdate(r, x)
r1
all(r1 == chol(b)) # TRUE
a <- matrix(c(1,1,1,1,2,3,1,3,6), ncol = 3)
r <- chol(a)
x <- c(0,0,1)
b <- a + outer(x,x)
r1 <- cholupdate(r, x)
r1
all(r1 == chol(b)) # TRUE

Form a symmetric circulant matrix

Description

Forms a symmetric circulant matrix using a backwards shift of its first column

Usage

circulant(x)
circulant(x)

Arguments

`x`	the first column to form the circulant matrix.

Value

A symmetric circulant matrix.

Examples

x <- c(2,3,5,7,11,13)
circulant(x)
x <- c(2,3,5,7,11,13)
circulant(x)

Compact information to construct the commutation matrix

Description

This function provides the minimum information required to create the commutation matrix.

The commutation matrix is a square matrix of order $mn$ that, for an $m\times n$ matrix $\bold{A}$ , transform vec $(\bold{A}$ ) to vec $(\bold{A}^T)$ .

Usage

comm.info(m = 1, n = m, condensed = TRUE)
comm.info(m = 1, n = m, condensed = TRUE)

Arguments

`m`	a positive integer row dimension.
`n`	a positive integer column dimension.
`condensed`	logical. Information should be returned in compact form?

Details

This function returns a list containing two vectors that represent an element of the commutation matrix and is accesed by the indexes in vectors row and col. This information is used by function comm.prod to do some operations involving the commutation matrix without forming it. This information also can be obtained using function commutation.

Value

A list containing the following elements:

`row`	vector of indexes, each entry represents the row index of the commutation matrix.
`col`	vector of indexes, each entry represents the column index of the commutation matrix. Only present if `condensed = FALSE`.
`m`	positive integer, row dimension.
`n`	positive integer, column dimension.

References

Magnus, J.R., Neudecker, H. (1979). The commutation matrix: some properties and applications. The Annals of Statistics 7, 381-394.

Examples

z <- comm.info(m = 3, n = 2, condensed = FALSE)
z # where are the ones in commutation matrix of order '3,2'?

K32 <- commutation(m = 3, n = 2, matrix = TRUE)
K32 # only recommended if m and n are very small
z <- comm.info(m = 3, n = 2, condensed = FALSE)
z # where are the ones in commutation matrix of order '3,2'?

K32 <- commutation(m = 3, n = 2, matrix = TRUE)
K32 # only recommended if m and n are very small

Matrix multiplication envolving the commutation matrix

Description

Given the row and column dimensions of a commutation matrix $\bold{K}$ of order $mn$ and a conformable matrix $\bold{x}$ , performs one of the matrix-matrix operations:

$\bold{Y} = \bold{KX}$ , if side = "left" and transposed = FALSE, or
$\bold{Y} = \bold{K}^T\bold{X}$ , if side = "left" and transposed = TRUE, or
$\bold{Y} = \bold{XK}$ , if side = "right" and transposed = FALSE, or
$\bold{Y} = \bold{XK}^T$ , if side = "right" and transposed = TRUE.

The main aim of comm.prod is to do this matrix multiplication without forming the commutation matrix.

Usage

comm.prod(m = 1, n = m, x = NULL, transposed = FALSE, side = "left")
comm.prod(m = 1, n = m, x = NULL, transposed = FALSE, side = "left")

Arguments

`m`	a positive integer row dimension.
`n`	a positive integer column dimension.
`x`	numeric matrix (or vector).
`transposed`	logical. Commutation matrix should be transposed?
`side`	a string selecting if commutation matrix is pre-multiplying `x`, that is `side = "left"` or post-multiplying `x`, by using `side = "right"`.

Details

Underlying Fortran code only uses information provided by comm.info to performs the matrix multiplication. The commutation matrix is never created.

Examples

K42 <- commutation(m = 4, n = 2, matrix = TRUE)
x <- matrix(1:24, ncol = 3)
y <- K42 %*% x

z <- comm.prod(m = 4, n = 2, x) # K42 is not stored
all(z == y) # matrices y and z are equal!
K42 <- commutation(m = 4, n = 2, matrix = TRUE)
x <- matrix(1:24, ncol = 3)
y <- K42 %*% x

z <- comm.prod(m = 4, n = 2, x) # K42 is not stored
all(z == y) # matrices y and z are equal!

Commutation matrix

Description

This function returns the commutation matrix of order $mn$ which transforms, for an $m\times n$ matrix $\bold{A}$ , vec $(\bold{A})$ to vec $(\bold{A}^T)$ .

Usage

commutation(m = 1, n = m, matrix = FALSE, condensed = FALSE)
commutation(m = 1, n = m, matrix = FALSE, condensed = FALSE)

Arguments

`m`	a positive integer row dimension.
`n`	a positive integer column dimension.
`matrix`	a logical indicating whether the commutation matrix will be returned.
`condensed`	logical. Information should be returned in compact form?

Details

This function is a wrapper function for the function comm.info. This function provides the minimum information required to create the commutation matrix. If option matrix = FALSE the commutation matrix is stored in two vectors containing the coordinate list of indexes for rows and columns. Option condensed = TRUE only returns vector of indexes for the rows of commutation matrix.

Warning: matrix = TRUE is not recommended, unless the order m and n be small. This matrix can require a huge amount of storage.

Value

Returns an $mn$ by $mn$ matrix (if requested).

References

Magnus, J.R., Neudecker, H. (1979). The commutation matrix: some properties and applications. The Annals of Statistics 7, 381-394.

Magnus, J.R., Neudecker, H. (2007). Matrix Differential Calculus with Applications in Statistics and Econometrics, 3rd Edition. Wiley, New York.

Examples

z <- commutation(m = 100, condensed = TRUE)
object.size(z) # 40.6 Kb of storage

z <- commutation(m = 100, condensed = FALSE)
object.size(z) # 80.7 Kb of storage

K100 <- commutation(m = 100, matrix = TRUE) # time: < 2 secs
object.size(K100) # 400 Mb of storage, do not request this matrix!

# a small example
K32 <- commutation(m = 3, n = 2, matrix = TRUE)
a <- matrix(1:6, ncol = 2)
v <- K32 %*% vec(a)
all(vec(t(a)) == as.vector(v)) # vectors are equal!
z <- commutation(m = 100, condensed = TRUE)
object.size(z) # 40.6 Kb of storage

z <- commutation(m = 100, condensed = FALSE)
object.size(z) # 80.7 Kb of storage

K100 <- commutation(m = 100, matrix = TRUE) # time: < 2 secs
object.size(K100) # 400 Mb of storage, do not request this matrix!

# a small example
K32 <- commutation(m = 3, n = 2, matrix = TRUE)
a <- matrix(1:6, ncol = 2)
v <- K32 %*% vec(a)
all(vec(t(a)) == as.vector(v)) # vectors are equal!

AR(1) correlation structure

Description

This function is a constructor for the corAR1 correlation matrix representing an autocorrelation structure of order 1.

Usage

corAR1(rho, p = 2)
corAR1(rho, p = 2)

Arguments

`rho`	the value of the lag 1 autocorrelation, which must be between -1 and 1.
`p`	dimension of the requested correlation matrix.

Value

Returns a $p$ by $p$ matrix.

Examples

R <- corAR1(rho = 0.8, p = 5)
R <- corAR1(rho = 0.8, p = 5)

Compound symmetry correlation structure

Description

This function is a constructor for the corCS correlation matrix representing a compound symmetry structure corresponding to uniform correlation.

Usage

corCS(rho, p = 2)
corCS(rho, p = 2)

Arguments

`rho`	the value of the correlation between any two correlated observations, which must be between -1 and 1.
`p`	dimension of the requested correlation matrix.

Value

Returns a $p$ by $p$ matrix.

Examples

R <- corCS(rho = 0.8, p = 5)
R <- corCS(rho = 0.8, p = 5)

Mean Square Successive Difference (MSSD) estimator of the covariance matrix

Description

Returns a list containing the mean and covariance matrix of the data.

Usage

cov.MSSD(x)
cov.MSSD(x)

Arguments

`x`	a matrix or data frame. As usual, rows are observations and columns are variables.

Details

This procedure uses the Holmes-Mergen method using the difference between each successive pairs of observations also known as Mean Square Successive Method (MSSD) to estimate the covariance matrix.

Value

A list containing the following named components:

`mean`	an estimate for the center (mean) of the data.
`cov`	the estimated covariance matrix.

References

Holmes, D.S., Mergen, A.E. (1993). Improving the performance of the $T^2$ control chart. Quality Engineering 5, 619-625.

Examples

x <- cbind(1:10, c(1:3, 8:5, 8:10))
z0 <- cov(x)
z0
z1 <- cov.MSSD(x)
z1
x <- cbind(1:10, c(1:3, 8:5, 8:10))
z0 <- cov(x)
z0
z1 <- cov.MSSD(x)
z1

Weighted covariance matrices

Description

Returns a list containing estimates of the weighted mean and covariance matrix of the data.

Usage

cov.weighted(x, weights = rep(1, nrow(x)))
cov.weighted(x, weights = rep(1, nrow(x)))

Arguments

`x`	a matrix or data frame. As usual, rows are observations and columns are variables.
`weights`	a non-negative and non-zero vector of weights for each observation. Its length must equal the number of rows of `x`.

Details

The covariance matrix is divided by the number of observations, which arise for instance, when we use the class of elliptical contoured distributions. This differs from the behaviour of function cov.wt.

Value

A list containing the following named components:

`mean`	an estimate for the center (mean) of the data.
`cov`	the estimated (weighted) covariance matrix.

References

Clarke, M.R.B. (1971). Algorithm AS 41: Updating the sample mean and dispersion matrix. Applied Statistics 20, 206-209.

Examples

x <- cbind(1:10, c(1:3, 8:5, 8:10))
z0 <- cov.weighted(x) # all weights are 1
D2 <- Mahalanobis(x, center = z0$mean, cov = z0$cov)
p <- ncol(x)
wts <- (p + 1) / (1 + D2) # nice weights!
z1 <- cov.weighted(x, weights = wts)
z1
x <- cbind(1:10, c(1:3, 8:5, 8:10))
z0 <- cov.weighted(x) # all weights are 1
D2 <- Mahalanobis(x, center = z0$mean, cov = z0$cov)
p <- ncol(x)
wts <- (p + 1) / (1 + D2) # nice weights!
z1 <- cov.weighted(x, weights = wts)
z1

Matrix crossproduct envolving the duplication matrix

Description

Given the order of two duplication matrices and a conformable matrix $\bold{X}$ , this function performs the operation: $\bold{Y} = \bold{D}_n^T\bold{X}\bold{D}_k$ , where $\bold{D}_n$ and $\bold{D}_k$ are duplication matrices of order $n$ and $k$ , respectively.

Usage

dupl.cross(n = 1, k = n, x = NULL)
dupl.cross(n = 1, k = n, x = NULL)

Arguments

`n`	order of the duplication matrix used pre-multiplying `x`.
`k`	order of the duplication matrix used post-multiplying `x`. By default `k = n` is used.
`x`	numeric matrix, this argument is required.

Details

This function calls dupl.prod to performs the matrix multiplications required but without forming any duplication matrices.

Examples

D2 <- duplication(n = 2, matrix = TRUE)
D3 <- duplication(n = 3, matrix = TRUE)
x <- matrix(1, nrow = 9, ncol = 4)
y <- t(D3) %*% x %*% D2

z <- dupl.cross(n = 3, k = 2, x) # D2 and D3 are not stored
all(z == y) # matrices y and z are equal!

x <- matrix(1, nrow = 9, ncol = 9)
z <- dupl.cross(n = 3, x = x) # same matrix is used to pre- and post-multiplying x
z # print result
D2 <- duplication(n = 2, matrix = TRUE)
D3 <- duplication(n = 3, matrix = TRUE)
x <- matrix(1, nrow = 9, ncol = 4)
y <- t(D3) %*% x %*% D2

z <- dupl.cross(n = 3, k = 2, x) # D2 and D3 are not stored
all(z == y) # matrices y and z are equal!

x <- matrix(1, nrow = 9, ncol = 9)
z <- dupl.cross(n = 3, x = x) # same matrix is used to pre- and post-multiplying x
z # print result

Compact information to construct the duplication matrix

Description

This function provides the minimum information required to create the duplication matrix.

Usage

dupl.info(n = 1, condensed = TRUE)
dupl.info(n = 1, condensed = TRUE)

Arguments

`n`	order of the duplication matrix.
`condensed`	logical. Information should be returned in compact form?

Details

This function returns a list containing two vectors that represent an element of the duplication matrix and is accesed by the indexes in vectors row and col. This information is used by function dupl.prod to do some operations involving the duplication matrix without forming it. This information also can be obtained using function duplication

Value

A list containing the following elements:

`row`	vector of indexes, each entry represents the row index of the duplication matrix. Only present if `condensed = FALSE`.
`col`	vector of indexes, each entry represents the column index of the duplication matrix.
`order`	order of the duplication matrix.

Examples

z <- dupl.info(n = 3, condensed = FALSE)
z # where are the ones in duplication of order 3?

D3 <- duplication(n = 3, matrix = TRUE)
D3 # only recommended if n is very small
z <- dupl.info(n = 3, condensed = FALSE)
z # where are the ones in duplication of order 3?

D3 <- duplication(n = 3, matrix = TRUE)
D3 # only recommended if n is very small

Matrix multiplication envolving the duplication matrix

Description

Given the order of a duplication and a conformable matrix $\bold{X}$ , performs one of the matrix-matrix operations:

$\bold{Y} = \bold{DX}$ , if side = "left" and transposed = FALSE, or
$\bold{Y} = \bold{D}^T\bold{X}$ , if side = "left" and transposed = TRUE, or
$\bold{Y} = \bold{XD}$ , if side = "right" and transposed = FALSE, or
$\bold{Y} = \bold{XD}^T$ , if side = "right" and transposed = TRUE,

where $\bold{D}$ is the duplication matrix of order $n$ . The main aim of dupl.prod is to do this matrix multiplication without forming the duplication matrix.

Usage

dupl.prod(n = 1, x, transposed = FALSE, side = "left")
dupl.prod(n = 1, x, transposed = FALSE, side = "left")

Arguments

`n`	order of the duplication matrix.
`x`	numeric matrix (or vector).
`transposed`	logical. Duplication matrix should be transposed?
`side`	a string selecting if duplication matrix is pre-multiplying `x`, that is `side = "left"` or post-multiplying `x`, by using `side = "right"`.

Details

Underlying C code only uses information provided by dupl.info to performs the matrix multiplication. The duplication matrix is never created.

Examples

D4 <- duplication(n = 4, matrix = TRUE)
x <- matrix(1, nrow = 16, ncol = 2)
y <- crossprod(D4, x)

z <- dupl.prod(n = 4, x, transposed = TRUE) # D4 is not stored
all(z == y) # matrices y and z are equal!
D4 <- duplication(n = 4, matrix = TRUE)
x <- matrix(1, nrow = 16, ncol = 2)
y <- crossprod(D4, x)

z <- dupl.prod(n = 4, x, transposed = TRUE) # D4 is not stored
all(z == y) # matrices y and z are equal!

Duplication matrix

Description

This function returns the duplication matrix of order $n$ which transforms, for a symmetric matrix $\bold{A}$ , vech $(\bold{A})$ into vec $(\bold{A})$ .

Usage

duplication(n = 1, matrix = FALSE, condensed = FALSE)
duplication(n = 1, matrix = FALSE, condensed = FALSE)

Arguments

`n`	order of the duplication matrix.
`matrix`	a logical indicating whether the duplication matrix will be returned.
`condensed`	logical. Information should be returned in compact form?.

Details

This function is a wrapper function for the function dupl.info. This function provides the minimum information required to create the duplication matrix. If option matrix = FALSE the duplication matrix is stored in two vectors containing the coordinate list of indexes for rows and columns. Option condensed = TRUE only returns vector of indexes for the columns of duplication matrix.

Warning: matrix = TRUE is not recommended, unless the order n be small. This matrix can require a huge amount of storage.

Value

Returns an $n^2$ by $n(n + 1)/2$ matrix (if requested).

References

Magnus, J.R., Neudecker, H. (1980). The elimination matrix, some lemmas and applications. SIAM Journal on Algebraic Discrete Methods 1, 422-449.

Magnus, J.R., Neudecker, H. (2007). Matrix Differential Calculus with Applications in Statistics and Econometrics, 3rd Edition. Wiley, New York.

Examples

z <- duplication(n = 100, condensed = TRUE)
object.size(z) # 40.5 Kb of storage

z <- duplication(n = 100, condensed = FALSE)
object.size(z) # 80.6 Kb of storage

D100 <- duplication(n = 100, matrix = TRUE)
object.size(D100) # 202 Mb of storage, do not request this matrix!

# a small example
D3 <- duplication(n = 3, matrix = TRUE)
a <- matrix(c( 1, 2, 3,
               2, 3, 4,
               3, 4, 5), nrow = 3)
upper <- vech(a)
v <- D3 %*% upper
all(vec(a) == as.vector(v)) # vectors are equal!
z <- duplication(n = 100, condensed = TRUE)
object.size(z) # 40.5 Kb of storage

z <- duplication(n = 100, condensed = FALSE)
object.size(z) # 80.6 Kb of storage

D100 <- duplication(n = 100, matrix = TRUE)
object.size(D100) # 202 Mb of storage, do not request this matrix!

# a small example
D3 <- duplication(n = 3, matrix = TRUE)
a <- matrix(c( 1, 2, 3,
               2, 3, 4,
               3, 4, 5), nrow = 3)
upper <- vech(a)
v <- D3 %*% upper
all(vec(a) == as.vector(v)) # vectors are equal!

Equilibration of a rectangular or symmetric matrix

Description

Equilibrate a rectangular or symmetric matrix using 2-norm.

Usage

equilibrate(x, scale = TRUE)
equilibrate(x, scale = TRUE)

Arguments

`x`	a numeric matrix.
`scale`	a logical value, `x` must be scaled to norm unity?

Value

For scale = TRUE, the equilibrated matrix. The scalings and an approximation of the condition number, are returned as attributes "scales" and "condition". If x is a rectangular matrix, only the columns are equilibrated.

Examples

x <- matrix(c(1, 1, 1,
              1, 2, 1,
              1, 3, 1,
              1, 1,-1,
              1, 2,-1,
              1, 3,-1), ncol = 3, byrow = TRUE)
z <- equilibrate(x)
apply(z, 2, function(x) sum(x^2)) # all 1

xx <- crossprod(x)
equilibrate(xx)
x <- matrix(c(1, 1, 1,
              1, 2, 1,
              1, 3, 1,
              1, 1,-1,
              1, 2,-1,
              1, 3,-1), ncol = 3, byrow = TRUE)
z <- equilibrate(x)
apply(z, 2, function(x) sum(x^2)) # all 1

xx <- crossprod(x)
equilibrate(xx)

Frank matrix

Description

This function returns the Frank matrix of order $n$ .

Usage

frank(n = 1)
frank(n = 1)

Arguments

`n`	order of the Frank matrix.

Details

A Frank matrix of order $n$ is a square matrix $\bold{F}_n = (f_{ij})$ defined as

$f_{ij} = \left\{ {\begin{array}{ll} n - j + 1, & i \le j, \\ n - j, & i = j + 1, \\ 0, & i \ge j + 2 \end{array}} \right.$

Value

Returns an $n$ by $n$ matrix.

References

Frank, W.L. (1958). Computing eigenvalues of complex matrices by determinant evaluation and by methods of Danilewski and Wielandt. Journal of the Society for Industrial and Applied Mathematics 6, 378-392.

Examples

F5 <- frank(n = 5)
det(F5) # equals 1
F5 <- frank(n = 5)
det(F5) # equals 1

Geometric mean

Description

It calculates the geometric mean using a Fused-Multiply-and-Add (FMA) compensated scheme for accurate computation of floating-point product.

Usage

geomean(x)
geomean(x)

Arguments

`x`	a numeric vector containing the sample observations.

Details

If x contains any non-positive values, geomean returns NA and a warning message is displayed.

The geometric mean is a measure of central tendency, which is defined as

$G = \sqrt[n]{x_1 x_2 \ldots x_n} = \Big(\prod_{i=1}^n x_i\Big)^{1/n}.$

This procedure calculates the product required in the geometric mean safely using a compensated scheme as proposed by Graillat (2009).

Value

The geometric mean of the sample, a non-negative number.

References

Graillat, S. (2009). Accurate floating-point product and exponentiation. IEEE Transactions on Computers 58, 994-1000.

Oguita, T., Rump, S.M., Oishi, S. (2005). Accurate sum and dot product. SIAM Journal on Scientific Computing 26, 1955-1988.

Examples

set.seed(149)
x <- rlnorm(1000)
mean(x)    # 1.68169
median(x)  # 0.99663
geomean(x) # 1.01688
set.seed(149)
x <- rlnorm(1000)
mean(x)    # 1.68169
median(x)  # 0.99663
geomean(x) # 1.01688

Hadamard product of two matrices

Description

This function returns the Hadamard or element-wise product of two matrices x and y, that have the same dimensions.

Usage

hadamard(x, y = x)
hadamard(x, y = x)

Arguments

`x`	a numeric matrix or vector.
`y`	a numeric matrix or vector.

Value

A matrix with the same dimension of x (and y) which corresponds to the element-by-element product of the two matrices.

References

Styan, G.P.H. (1973). Hadamard products and multivariate statistical analysis, Linear Algebra and Its Applications 6, 217-240.

Examples

x <- matrix(rep(1:10, times = 5), ncol = 5)
y <- matrix(rep(1:5, each = 10), ncol = 5)
z <- hadamard(x, y)
z
x <- matrix(rep(1:10, times = 5), ncol = 5)
y <- matrix(rep(1:5, each = 10), ncol = 5)
z <- hadamard(x, y)
z

Test for variance homogeneity of correlated variables

Description

Performs large-sample methods for testing equality of $p \ge 2$ correlated variables.

Usage

harris.test(x, test = "Wald")
harris.test(x, test = "Wald")

Arguments

`x`	a matrix or data frame. As usual, rows are observations and columns are variables.
`test`	test statistic to be used. One of "Wald" (default), "log", "robust" or "log-robust".

Value

A list of class 'harris.test' with the following elements:

`statistic`	value of the statistic, i.e. the value of either Wald test, using the log-transformation, or distribution-robust versions of the test (robust and log-robust).
`parameter`	the degrees of freedom for the test statistic, which is chi-square distributed.
`p.value`	the p-value for the test.
`estimate`	the estimated covariance matrix.
`method`	a character string indicating what type of test was performed.

References

Harris, P. (1985). Testing the variance homogeneity of correlated variables. Biometrika 72, 103-107.

Examples

x <- iris[,1:4]
z <- harris.test(x, test = "robust")
z
x <- iris[,1:4]
z <- harris.test(x, test = "robust")
z

Helmert matrix

Description

This function returns the Helmert matrix of order $n$ .

Usage

helmert(n = 1)
helmert(n = 1)

Arguments

`n`	order of the Helmert matrix.

Details

A Helmert matrix of order $n$ is a square matrix defined as

$\bold{H}_n = \left[ {\begin{array}{ccccc} 1/\sqrt{n} & 1/\sqrt{n} & 1/\sqrt{n} & \dots & 1/\sqrt{n} \\ 1/\sqrt{2} & -1/\sqrt{2} & 0 & \dots & 0 \\ 1/\sqrt{6} & 1/\sqrt{6} & -2/\sqrt{6} & \dots & 0 \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ \frac{1}{\sqrt{n(n-1)}} & \frac{1}{\sqrt{n(n-1)}} & \frac{1}{\sqrt{n(n-1)}} & \dots & -\frac{(n-1)}{\sqrt{n(n-1)}} \end{array}} \right].$

Helmert matrix is orthogonal and is frequently used in the analysis of variance (ANOVA).

Value

Returns an $n$ by $n$ matrix.

References

Lancaster, H.O. (1965). The Helmert matrices. The American Mathematical Monthly 72, 4-12.

Gentle, J.E. (2007). Matrix Algebra: Theory, Computations, and Applications in Statistics. Springer, New York.

Examples

n <- 1000
set.seed(149)
x <- rnorm(n)

H <- helmert(n)
object.size(H) # 7.63 Mb of storage
K <- H[2:n,]
z <- c(K %*% x)
sum(z^2) # 933.1736

# same that
(n - 1) * var(x)
n <- 1000
set.seed(149)
x <- rnorm(n)

H <- helmert(n)
object.size(H) # 7.63 Mb of storage
K <- H[2:n,]
z <- c(K %*% x)
sum(z^2) # 933.1736

# same that
(n - 1) * var(x)

Check if a matrix is lower or upper triangular

Description

Returns TRUE if the given matrix is lower or upper triangular matrix.

Usage

is.lower.tri(x, diag = FALSE)
is.upper.tri(x, diag = FALSE)
is.lower.tri(x, diag = FALSE)
is.upper.tri(x, diag = FALSE)

Arguments

`x`	a matrix of other R object with `length(dim(x)) = 2`.
`diag`	logical. Should the diagonal be included?

Value

Check if a matrix is lower or upper triangular. You can also include diagonal to the check.

Examples

  x <- matrix(rnorm(10 * 3), ncol = 3)
  R <- chol(crossprod(x))

  is.lower.tri(R)
  is.upper.tri(R)
x <- matrix(rnorm(10 * 3), ncol = 3)
  R <- chol(crossprod(x))

  is.lower.tri(R)
  is.upper.tri(R)

Solve linear systems using the Jacobi method

Description

Jacobi method is an iterative algorithm for solving a system of linear equations.

Usage

jacobi(a, b, start, maxiter = 200, tol = 1e-7)
jacobi(a, b, start, maxiter = 200, tol = 1e-7)

Arguments

`a`	a square numeric matrix containing the coefficients of the linear system.
`b`	a vector of right-hand sides of the linear system.
`start`	a vector for initial starting point.
`maxiter`	the maximum number of iterations. Defaults to `200`
`tol`	tolerance level for stopping iterations.

Details

Let $\bold{D}$ , $\bold{L}$ , and $\bold{U}$ denote the diagonal, lower triangular and upper triangular parts of a matrix $\bold{A}$ . Jacobi's method solve the equation $\bold{Ax} = \bold{b}$ , iteratively by rewriting $\bold{Dx} + (\bold{L} + \bold{U})\bold{x} = \bold{b}$ . Assuming that $\bold{D}$ is nonsingular leads to the iteration formula

$\bold{x}^{(k+1)} = -\bold{D}^{-1}(\bold{L} + \bold{U})\bold{x}^{(k)} + \bold{D}^{-1}\bold{b}$

Value

a vector with the approximate solution, the iterations performed are returned as the attribute 'iterations'.

References

Golub, G.H., Van Loan, C.F. (1996). Matrix Computations, 3rd Edition. John Hopkins University Press.

Examples

a <- matrix(c(5,-3,2,-2,9,-1,3,1,-7), ncol = 3)
b <- c(-1,2,3)
start <- c(1,1,1)
z <- jacobi(a, b, start)
z # converged in 15 iterations
a <- matrix(c(5,-3,2,-2,9,-1,3,1,-7), ncol = 3)
b <- c(-1,2,3)
start <- c(1,1,1)
z <- jacobi(a, b, start)
z # converged in 15 iterations

Jarque-Bera test for univariate normality

Description

Performs an omnibus test for univariate normality.

Usage

JarqueBera.test(x, test = "DH")
JarqueBera.test(x, test = "DH")

Arguments

`x`	a numeric vector containing the sample observations.
`test`	test statistic to be used. One of "DH" (Doornik-Hansen, the default), "JB" (Jarque-Bera), "robust" (robust modification by Gel and Gastwirth), "ALM" (Adjusted Lagrange multiplier).

Value

A list of class 'JarqueBera.test' with the following elements:

`statistic`	value of the statistic, i.e. the value of either Doornik-Hansen, Jarque-Bera, or Adjusted Lagrange multiplier test.
`parameter`	the degrees of freedom for the test statistic, which is chi-square distributed.
`p.value`	the p-value for the test.
`skewness`	the estimated skewness coefficient.
`kurtosis`	the estimated kurtosis coefficient.
`method`	a character string indicating what type of test was performed.

References

Doornik, J.A., Hansen, H. (2008). An omnibus test for univariate and multivariate normality. Oxford Bulletin of Economics and Statistics 70, 927-939.

Gel, Y.R., Gastwirth, J.L. (2008). A robust modification of the Jarque-Bera test of normality. Economics Letters 99, 30-32.

Jarque, C.M., Bera, A.K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters 6, 255-259.

Urzua, C.M. (1996). On the correct use of omnibus tests for normality. Economics Letters 53, 247-251.

Examples

set.seed(149)
x <- rnorm(100)
z <- JarqueBera.test(x, test = "DH")
z

set.seed(173)
x <- runif(100)
z <- JarqueBera.test(x, test = "DH")
z
set.seed(149)
x <- rnorm(100)
z <- JarqueBera.test(x, test = "DH")
z

set.seed(173)
x <- runif(100)
z <- JarqueBera.test(x, test = "DH")
z

Kronecker product on matrices

Description

Computes the kronecker product of two matrices, x and y.

Usage

kronecker.prod(x, y = x)
kronecker.prod(x, y = x)

Arguments

`x`	a numeric matrix or vector.
`y`	a numeric matrix or vector.

Details

Let $\bold{X}$ be an $m\times n$ and $\bold{Y}$ a $p\times q$ matrix. The $mp\times nq$ matrix defined by

$\left[{\begin{array}{ccc} x_{11}\bold{Y} & \dots & x_{1n}\bold{Y} \\ \vdots & & \vdots \\ x_{m1}\bold{Y} & \dots & x_{mn}\bold{Y} \end{array}}\right],$

is called the Kronecker product of $\bold{X}$ and $\bold{Y}$ .

Value

An array with dimensions dim(x) * dim(y).

References

Magnus, J.R., Neudecker, H. (2007). Matrix Differential Calculus with Applications in Statistics and Econometrics, 3rd Edition. Wiley, New York.

Examples

# block diagonal matrix:
a <- diag(1:3)
b <- matrix(1:4, ncol = 2)
kronecker.prod(a, b)

# examples with vectors
ones <- rep(1, 4)
y <- 1:3
kronecker.prod(ones, y) # 12-dimensional vector
kronecker.prod(ones, t(y)) # 3 x 3 matrix
# block diagonal matrix:
a <- diag(1:3)
b <- matrix(1:4, ncol = 2)
kronecker.prod(a, b)

# examples with vectors
ones <- rep(1, 4)
y <- 1:3
kronecker.prod(ones, y) # 12-dimensional vector
kronecker.prod(ones, t(y)) # 3 x 3 matrix

Computes a Krylov matrix

Description

Given $\bold{A}$ an $n$ by $n$ real matrix and an $n$ -vector $\bold{b}$ , this function constructs the Krylov matrix $\bold{K}$ , where

$\bold{K} = [\bold{b},\bold{Ab},\dots,\bold{A}^{m-1}\bold{b}].$

Usage

krylov(a, b, m = ncol(a))
krylov(a, b, m = ncol(a))

Arguments

`a`	a numeric square matrix of order $n$ by $n$ for which the Krylov matrix is to be computed.
`b`	a numeric vector of length $n$ .
`m`	length of the Krylov sequence.

Value

Returns an $n$ by $m$ matrix.

Examples

a <- matrix(c(1, 3, 2, -5, 1, 7, 1, 5, -4), ncol = 3, byrow = TRUE)
b <- c(1, 1, 1)
k <- krylov(a, b, m = 4)
k
a <- matrix(c(1, 3, 2, -5, 1, 7, 1, 5, -4), ncol = 3, byrow = TRUE)
b <- c(1, 1, 1)
k <- krylov(a, b, m = 4)
k

Mardia's multivariate skewness and kurtosis coefficients

Description

Functions to compute measures of multivariate skewness $(b_{1p})$ and kurtosis $(b_{2p})$ proposed by Mardia (1970),

$b_{1p} = \frac{1}{n^2}\sum\limits_{i=1}^n\sum\limits_{j=1}^n ((\bold{x}_i - \overline{\bold{x}})^T\bold{S}^{-1}(\bold{x}_j - \overline{\bold{x}}))^3,$

and

$b_{2p} = \frac{1}{n}\sum\limits_{i=1}^n ((\bold{x}_i - \overline{\bold{x}})^T \bold{S}^{-1}(\bold{x}_j - \overline{\bold{x}}))^2.$

Usage

kurtosis(x)

skewness(x)
kurtosis(x)

skewness(x)

Arguments

`x`	matrix of data with, say, $p$ columns.

References

Mardia, K.V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika 57, 519-530.

Mardia, K.V., Zemroch, P.J. (1975). Algorithm AS 84: Measures of multivariate skewness and kurtosis. Applied Statistics 24, 262-265.

Examples

setosa <- iris[1:50,1:4]
kurtosis(setosa)
skewness(setosa)
setosa <- iris[1:50,1:4]
kurtosis(setosa)
skewness(setosa)

The LDL decomposition

Description

Compute the LDL decomposition of a real symmetric matrix.

Usage

ldl(x)
ldl(x)

Arguments

`x`	a symmetric numeric matrix whose LDL decomposition is to be computed.

Value

The factorization has the form $\bold{X} = \bold{LDL}^T$ , where $\bold{D}$ is a diagonal matrix and $\bold{L}$ is unitary lower triangular.

The LDL decomposition of $\bold{x}$ is returned as a list with components:

`lower`	the unitary lower triangular factor $\bold{L}$ .
`d`	a vector containing the diagonal elements of $\bold{D}$ .

References

Golub, G.H., Van Loan, C.F. (1996). Matrix Computations, 3rd Edition. John Hopkins University Press.

Examples

a <- matrix(c(2,-1,0,-1,2,-1,0,-1,1), ncol = 3)
z <- ldl(a)
z # information of LDL factorization

# computing det(a)
prod(z$d) # product of diagonal elements of D

# a non-positive-definite matrix
m <- matrix(c(5,-5,-5,3), ncol = 2)
try(chol(m)) # fails
ldl(m)
a <- matrix(c(2,-1,0,-1,2,-1,0,-1,1), ncol = 3)
z <- ldl(a)
z # information of LDL factorization

# computing det(a)
prod(z$d) # product of diagonal elements of D

# a non-positive-definite matrix
m <- matrix(c(5,-5,-5,3), ncol = 2)
try(chol(m)) # fails
ldl(m)

The LU factorization of a square matrix

Description

lu computes the LU factorization of a matrix.

Usage

lu(x)
## Default S3 method:
lu(x)

## S3 method for class 'lu'
solve(a, b, ...)

is.lu(x)
lu(x)
## Default S3 method:
lu(x)

## S3 method for class 'lu'
solve(a, b, ...)

is.lu(x)

Arguments

`x`	a square numeric matrix whose LU factorization is to be computed.
`a`	an LU factorization of a square matrix.
`b`	a vector or matrix of right-hand sides of equations.
`...`	further arguments passed to or from other methods

Details

The LU factorization plays an important role in many numerical procedures. In particular it is the basic method to solve the equation $\bold{Ax} = \bold{b}$ for given matrix $\bold{A}$ , and vector $\bold{b}$ .

solve.lu is the method for solve for lu objects.

is.lu returns TRUE if x is a list and inherits from "lu".

Unsuccessful results from the underlying LAPACK code will result in an error giving a positive error code: these can only be interpreted by detailed study of the Fortran code.

Value

The LU factorization of the matrix as computed by LAPACK. The components in the returned value correspond directly to the values returned by DGETRF.

`lu`	a matrix with the same dimensions as `x`. The upper triangle contains the $\bold{U}$ of the decomposition and the strict lower triangle contains information on the $\bold{L}$ of the factorization.
`pivot`	information on the pivoting strategy used during the factorization.

Note

To compute the determinant of a matrix (do you really need it?), the LU factorization is much more efficient than using eigenvalues (eigen). See det.

LAPACK uses column pivoting and does not attempt to detect rank-deficient matrices.

References

Anderson. E., Bai, Z., Bischof, C., Blackford, S., Demmel, J., Dongarra, J., Du Croz, J., Greenbaum, A., Hammarling, S., McKenney, A. Sorensen, D. (1999). LAPACK Users' Guide, 3rd Edition. SIAM.

Golub, G.H., Van Loan, C.F. (1996). Matrix Computations, 3rd Edition. John Hopkins University Press.

Examples

a <- matrix(c(3,2,6,17,4,18,10,-2,-12), ncol = 3)
z <- lu(a)
z # information of LU factorization

# computing det(a)
prod(diag(z$lu)) # product of diagonal elements of U

# solve linear equations
b <- matrix(1:6, ncol = 2)
solve(z, b)
a <- matrix(c(3,2,6,17,4,18,10,-2,-12), ncol = 3)
z <- lu(a)
z # information of LU factorization

# computing det(a)
prod(diag(z$lu)) # product of diagonal elements of U

# solve linear equations
b <- matrix(1:6, ncol = 2)
solve(z, b)

Reconstruct the L, U, or X matrices from an LU object

Description

Returns the original matrix from which the object was constructed or the components of the factorization.

Usage

constructX(x)
extractL(x)
extractU(x)
constructX(x)
extractL(x)
extractU(x)

Arguments

`x`	object representing an LU factorization. This will typically have come from a previous call to `lu`.

Value

constructX returns $\bold{X}$ , the original matrix from which the lu object was constructed (because of the pivoting the $\bold{X}$ matrix is not exactly the product between $\bold{L}$ and $\bold{U}$ ).

extractL returns $\bold{L}$ . This may be pivoted.

extractU returns $\bold{U}$ .

Examples

a <- matrix(c(10,-3,5,-7,2,-1,0,6,5), ncol = 3)
z <- lu(a)
L <- extractL(z)
L
U <- extractU(z)
U
X <- constructX(z)
all(a == X)
a <- matrix(c(10,-3,5,-7,2,-1,0,6,5), ncol = 3)
z <- lu(a)
L <- extractL(z)
L
U <- extractU(z)
U
X <- constructX(z)
all(a == X)

Inverse from LU factorization

Description

Invert a square matrix from its LU factorization.

Usage

lu2inv(x)
lu2inv(x)

Arguments

`x`	object representing an LU factorization. This will typically have come from a previous call to `lu`.

Value

The inverse of the matrix whose LU factorization was given.

Unsuccessful results from the underlying LAPACK code will result in an error giving a positive error code: these can only be interpreted by detailed study of the Fortran code.

Source

This is an interface to the LAPACK routine DGETRI. LAPACK is from https://netlib.org/lapack/ and its guide is listed in the references.

References

Anderson. E., Bai, Z., Bischof, C., Blackford, S., Demmel, J., Dongarra, J., Du Croz, J., Greenbaum, A., Hammarling, S., McKenney, A. Sorensen, D. (1999). LAPACK Users' Guide, 3rd Edition. SIAM.

Golub, G.H., Van Loan, C.F. (1996). Matrix Computations, 3rd Edition. John Hopkins University Press.

Examples

a <- matrix(c(3,2,6,17,4,18,10,-2,-12), ncol = 3)
z <- lu(a)
a %*% lu2inv(z)
a <- matrix(c(3,2,6,17,4,18,10,-2,-12), ncol = 3)
z <- lu(a)
a %*% lu2inv(z)

Mahalanobis distance

Description

Returns the squared Mahalanobis distance of all rows in $\bold{x}$ and the vector $\bold{\mu}$ = center with respect to $\bold{\Sigma}$ = cov. This is (for vector $\bold{x}$ ) defined as

$D^2 = (\bold{x} - \bold{\mu})^T \bold{\Sigma}^{-1} (\bold{x} - \bold{\mu})$

Usage

Mahalanobis(x, center, cov, inverted = FALSE)
Mahalanobis(x, center, cov, inverted = FALSE)

Arguments

`x`	vector or matrix of data. As usual, rows are observations and columns are variables.
`center`	mean vector of the distribution.
`cov`	covariance matrix ( $p \times p$ ) of the distribution, must be positive definite.
`inverted`	logical. If `TRUE`, `cov` is supposed to contain the inverse of the covariance matrix.

Details

Unlike function mahalanobis, the covariance matrix is factorized using the Cholesky decomposition, which allows to assess if cov is positive definite. Unsuccessful results from the underlying LAPACK code will result in an error message.

Examples

x <- cbind(1:6, 1:3)
xbar <- colMeans(x)
S <- matrix(c(1,4,4,1), ncol = 2) # is negative definite
D2 <- mahalanobis(x, center = xbar, S)
all(D2 >= 0) # several distances are negative

## next command produces the following error:
## Covariance matrix is possibly not positive-definite
## Not run: D2 <- Mahalanobis(x, center = xbar, S)
x <- cbind(1:6, 1:3)
xbar <- colMeans(x)
S <- matrix(c(1,4,4,1), ncol = 2) # is negative definite
D2 <- mahalanobis(x, center = xbar, S)
all(D2 >= 0) # several distances are negative

## next command produces the following error:
## Covariance matrix is possibly not positive-definite
## Not run: D2 <- Mahalanobis(x, center = xbar, S)

Compute the inner product between two rectangular matrices

Description

Computes the inner product between two rectangular matrices calling BLAS.

Usage

matrix.inner(x, y = x)
matrix.inner(x, y = x)

Arguments

`x`	a numeric matrix.
`y`	a numeric matrix.

Value

a real value, indicating the inner product between two matrices.

Examples

x <- matrix(c(1, 1, 1,
              1, 2, 1,
              1, 3, 1,
              1, 1,-1,
              1, 2,-1,
              1, 3,-1), ncol = 3, byrow = TRUE)
y <- matrix(1, nrow = 6, ncol = 3)
matrix.inner(x, y)

# must be equal
matrix.norm(x, type = "Frobenius")^2
matrix.inner(x)
x <- matrix(c(1, 1, 1,
              1, 2, 1,
              1, 3, 1,
              1, 1,-1,
              1, 2,-1,
              1, 3,-1), ncol = 3, byrow = TRUE)
y <- matrix(1, nrow = 6, ncol = 3)
matrix.inner(x, y)

# must be equal
matrix.norm(x, type = "Frobenius")^2
matrix.inner(x)

Compute the norm of a rectangular matrix

Description

Computes a matrix norm of x using LAPACK. The norm can be the one ("1") norm, the infinity ("inf") norm, the Frobenius norm, the maximum modulus ("maximum") among elements of a matrix, as determined by the value of type.

Usage

matrix.norm(x, type = "Frobenius")
matrix.norm(x, type = "Frobenius")

Arguments

x

a numeric matrix.

type

character string, specifying the type of matrix norm to be computed. A character indicating the type of norm desired.

"1": specifies the one norm, (maximum absolute column sum);
"Inf": specifies the infinity norm (maximum absolute row sum);
"Frobenius": specifies the Frobenius norm (the Euclidean norm of x treated as if it were a vector);
"maximum": specifies the maximum modulus of all the elements in x.

Details

As function norm in package base, method of matrix.norm calls the LAPACK function DLANGE.

Note that the 1-, Inf- and maximum norm is faster to calculate than the Frobenius one.

Value

The matrix norm, a non-negative number.

Examples

# a tiny example
x <- matrix(c(1, 1, 1,
              1, 2, 1,
              1, 3, 1,
              1, 1,-1,
              1, 2,-1,
              1, 3,-1), ncol = 3, byrow = TRUE)
matrix.norm(x, type = "Frobenius")
matrix.norm(x, type = "1")
matrix.norm(x, type = "Inf")

# an example not that small
n <- 1000
x <- .5 * diag(n) + 0.5 * matrix(1, nrow = n, ncol = n)
matrix.norm(x, type = "Frobenius")
matrix.norm(x, type = "1")
matrix.norm(x, type = "Inf")
matrix.norm(x, type = "maximum") # equal to 1
# a tiny example
x <- matrix(c(1, 1, 1,
              1, 2, 1,
              1, 3, 1,
              1, 1,-1,
              1, 2,-1,
              1, 3,-1), ncol = 3, byrow = TRUE)
matrix.norm(x, type = "Frobenius")
matrix.norm(x, type = "1")
matrix.norm(x, type = "Inf")

# an example not that small
n <- 1000
x <- .5 * diag(n) + 0.5 * matrix(1, nrow = n, ncol = n)
matrix.norm(x, type = "Frobenius")
matrix.norm(x, type = "1")
matrix.norm(x, type = "Inf")
matrix.norm(x, type = "maximum") # equal to 1

The modified Cholesky factorization

Description

Compute the Cholesky factorization of a real symmetric but not necessarily positive definite matrix.

Usage

mchol(x)
mchol(x)

Arguments

`x`	a symmetric but not necessarily positive definite matrix to be factored.

Value

The lower triangular factor of modified Cholesky decomposition, i.e., the matrix $\bold{L}$ such that $\bold{X} + \bold{E} = \bold{LL}^T$ , where $\bold{E}$ is a nonnegative diagonal matrix that is zero if $\bold{X}$ es sufficiently positive definite.

References

Gill, P.E., Murray, W., Wright, M.H. (1981). Practical Optimization. Academic Press, London.

Nocedal, J., Wright, S.J. (1999). Numerical Optimization. Springer, New York.

Examples

# a non-positive-definite matrix
a <- matrix(c(4,2,1,2,6,3,1,3,-.004), ncol = 3)
try(chol(a)) # fails
z <- mchol(a)
z # triangular factor

# modified 'a' matrix
tcrossprod(z)
# a non-positive-definite matrix
a <- matrix(c(4,2,1,2,6,3,1,3,-.004), ncol = 3)
try(chol(a)) # fails
z <- mchol(a)
z # triangular factor

# modified 'a' matrix
tcrossprod(z)

Mediancenter

Description

It calculates the mediancenter (or geometric median) of multivariate data.

Usage

mediancenter(x)
mediancenter(x)

Arguments

`x`	a matrix or data frame. As usual, rows are observations and columns are variables.

Details

The mediancenter for a sample of multivariate observations is computed using a steepest descend method combined with bisection. The mediancenter invariant to rotations of axes and is useful as a multivariate generalization of the median of univariate sample.

Value

A list containing the following named components:

`median`	an estimate for the mediancenter of the data.
`iter`	the number of iterations performed, it is negative if a degenerate solution is found.

References

Gower, J.C. (1974). Algorithm AS 78: The mediancentre. Applied Statistics 23, 466-470.

Examples

x <- cbind(1:10, c(1:3, 8:5, 8:10))
z <- mediancenter(x)$median # degenerate solution
xbar <- colMeans(x)
plot(x, xlab = "", ylab = "")
points(x = xbar[1], y = xbar[2], pch = 16, col = "red")
points(x = z[1], y = z[2], pch = 3, col = "blue", lwd = 2)
x <- cbind(1:10, c(1:3, 8:5, 8:10))
z <- mediancenter(x)$median # degenerate solution
xbar <- colMeans(x)
plot(x, xlab = "", ylab = "")
points(x = xbar[1], y = xbar[2], pch = 16, col = "red")
points(x = z[1], y = z[2], pch = 3, col = "blue", lwd = 2)

Computes the p-norm of a vector

Description

Computes a $p$ -norm of vector $\bold{x}$ . The norm can be the one ( $p = 1$ ) norm, Euclidean ( $p = 2$ ) norm, the infinity ( $p$ = Inf) norm. The underlying C or Fortran code is inspired on ideas of BLAS Level 1.

Usage

minkowski(x, p = 2)
minkowski(x, p = 2)

Arguments

`x`	a numeric vector.
`p`	a number, specifying the type of norm desired. Possible values include real number greater or equal to 1, or Inf, Default value is $p = 2$ .

Details

Method of minkowski for $p$ = Inf calls idamax BLAS function. For other values, C or Fortran subroutines using unrolled cycles are called.

Value

The vector $p$ -norm, a non-negative number.

Examples

# a tiny example
x <- rnorm(1000)
minkowski(x, p = 1)
minkowski(x, p = 1.5)
minkowski(x, p = 2)
minkowski(x, p = Inf)

x <- x / minkowski(x)
minkowski(x, p = 2) # equal to 1
# a tiny example
x <- rnorm(1000)
minkowski(x, p = 1)
minkowski(x, p = 1.5)
minkowski(x, p = 2)
minkowski(x, p = Inf)

x <- x / minkowski(x)
minkowski(x, p = 2) # equal to 1

Central moments

Description

It calculates up to fourth central moments (or moments about the mean), and the skewness and kurtosis coefficients using an online algorithm.

Usage

moments(x)
moments(x)

Arguments

`x`	a numeric vector containing the sample observations.

Details

The $k$ -th central moment is defined as

$m_k = \frac{1}{n}\sum_{i=1}^n (x_i - \overline{x})^k.$

In particular, the second central moment is the variance of the sample. The sample skewness and kurtosis are defined, respectively, as

$b_1 = \frac{m_3}{m_2^{3/2}}, \qquad b_2 = \frac{m_4}{m_2^2}.$

Value

A list containing second, third and fourth central moments, and skewness and kurtosis coefficients.

References

Spicer, C.C. (1972). Algorithm AS 52: Calculation of power sums of deviations about the mean. Applied Statistics 21, 226-227.

Examples

set.seed(149)
x <- rnorm(1000)
z <- moments(x)
z
set.seed(149)
x <- rnorm(1000)
z <- moments(x)
z

Fit linear regression model

Description

Returns an object of class "ols" that represents a linear model fit.

Usage

ols(formula, data, subset, na.action, method = "qr", tol = 1e-7, maxiter = 100,
  x = FALSE, y = FALSE, contrasts = NULL, ...)
ols(formula, data, subset, na.action, method = "qr", tol = 1e-7, maxiter = 100,
  x = FALSE, y = FALSE, contrasts = NULL, ...)

Arguments

`formula`	an object of class `"formula"` (or one that can be coerced to that class): a symbolic description of the model to be fitted.
`data`	an optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in `data`, the variables are taken from `environment(formula)`, typically the environment from which `ols` is called.
`subset`	an optional vector specifying a subset of observations to be used in the fitting process.
`na.action`	a function which indicates what should happen when the data contain `NA`s. The default is set by the `na.action` setting of `options`, and is `na.fail` if that is unset.
`method`	the least squares fitting method to be used; the options are `"cg"` (conjugate gradients), `"chol"`, `"qr"` (the default), `"svd"` and `"sweep"`.
`tol`	tolerance for the conjugate gradients (`gc`) method. Default is `tol = 1e-7`.
`maxiter`	The maximum number of iterations for the conjugate gradients (`gc`) method. Defaults to 100.
`x`, `y`	logicals. If `TRUE` the corresponding components of the fit (the model matrix, the response) are returned.
`contrasts`	an optional list. See the `contrasts.arg` of `model.matrix.default`.
`...`	additional arguments (currently disregarded).

Value

ols returns an object of class "ols".

The function summary is used to obtain and print a summary of the results. The generic accessor functions coefficients, fitted.values and residuals extract various useful features of the value returned by ols.

An object of class "ols" is a list containing at least the following components:

`coefficients`	a named vector of coefficients
`residuals`	the residuals, that is response minus fitted values.
`fitted.values`	the fitted mean values.
`RSS`	the residual sum of squares.
`cov.unscaled`	a $p \times p$ matrix of (unscaled) covariances of the $\hat\beta_j$ , $j=1, \dots, p$ .
`call`	the matched call.
`terms`	the `terms` object used.
`contrasts`	(only where relevant) the contrasts used.
`y`	if requested, the response used.
`x`	if requested, the model matrix used.
`model`	if requested (the default), the model frame used.

Examples

# tiny example of regression
y <- c(1, 3, 3, 2, 2, 1)
x <- matrix(c(1, 1,
              2, 1,
              3, 1,
              1,-1,
              2,-1,
              3,-1), ncol = 2, byrow = TRUE)
f0 <- ols(y ~ x) # intercept is included by default
f0 # printing results (QR method was used)

f1 <- ols(y ~ x, method = "svd") # using SVD method instead
f1
# tiny example of regression
y <- c(1, 3, 3, 2, 2, 1)
x <- matrix(c(1, 1,
              2, 1,
              3, 1,
              1,-1,
              2,-1,
              3,-1), ncol = 2, byrow = TRUE)
f0 <- ols(y ~ x) # intercept is included by default
f0 # printing results (QR method was used)

f1 <- ols(y ~ x, method = "svd") # using SVD method instead
f1

Fitter functions for linear models

Description

This function is a switcher among various numerical fitting functions (ols.fit.cg, ols.fit.chol, ols.fit.qr, ols.fit.svd and ols.fit.sweep). The argument method does the switching: "qr" for ols.fit.qr, etc. This should usually not be used directly unless by experienced users.

Usage

ols.fit(x, y, method = "qr", tol = 1e-7, maxiter = 100)
ols.fit(x, y, method = "qr", tol = 1e-7, maxiter = 100)

Arguments

`x`	design matrix of dimension $n\times q$ .
`y`	vector of observations of length $n$ .
`method`	currently, methods `"cg"`, `"chol"`, `"qr"` (default), `"svd"` and `"sweep"` are supported.
`tol`	tolerance for the conjugate gradients (`gc`) method. Default is `tol = 1e-7`.
`maxiter`	The maximum number of iterations for the conjugate gradients (`gc`) method. Defaults to 100.

Value

a list with components:

`coefficients`	a named vector of coefficients
`residuals`	the residuals, that is response minus fitted values.
`fitted.values`	the fitted mean values.
`RSS`	the residual sum of squares.
`cov.unscaled`	a $p \times p$ matrix of (unscaled) covariances of the $\hat\beta_j$ , $j=1, \dots, p$ .

Examples

set.seed(151)
n <- 100
p <- 2
x <- matrix(rnorm(n * p), n, p) # no intercept!
y <- rnorm(n)
fm <- ols.fit(x = x, y = y, method = "chol")
fm
set.seed(151)
n <- 100
p <- 2
x <- matrix(rnorm(n * p), n, p) # no intercept!
y <- rnorm(n)
fm <- ols.fit(x = x, y = y, method = "chol")
fm

Fit a linear model

Description

Fits a linear model, returning the bare minimum computations.

Usage

ols.fit.cg(x, y, tol = 1e-7, maxiter = 100)
ols.fit.chol(x, y)
ols.fit.qr(x, y)
ols.fit.svd(x, y)
ols.fit.sweep(x, y)
ols.fit.cg(x, y, tol = 1e-7, maxiter = 100)
ols.fit.chol(x, y)
ols.fit.qr(x, y)
ols.fit.svd(x, y)
ols.fit.sweep(x, y)

Arguments

`x`, `y`	numeric vectors or matrices for the predictors and the response in a linear model. Typically, but not necessarily, `x` will be constructed by one of the fitting functions.
`tol`	tolerance for the conjugate gradients (`gc`) method. Default is `tol = 1e-7`.
`maxiter`	The maximum number of iterations for the conjugate gradients (`gc`) method. Defaults to 100.

Value

The bare bones of an ols object: the coefficients, residuals, fitted values, and some information used by summary.ols.

Examples

set.seed(151)
n <- 100
p <- 2
x <- matrix(rnorm(n * p), n, p) # no intercept!
y <- rnorm(n)
z <- ols.fit.chol(x, y)
z
set.seed(151)
n <- 100
p <- 2
x <- matrix(rnorm(n * p), n, p) # no intercept!
y <- rnorm(n)
z <- ols.fit.chol(x, y)
z

Power method to approximate dominant eigenvalue and eigenvector

Description

The power method seeks to determine the eigenvalue of maximum modulus, and a corresponding eigenvector.

Usage

power.method(x, only.value = FALSE, maxiter = 100, tol = 1e-8)
power.method(x, only.value = FALSE, maxiter = 100, tol = 1e-8)

Arguments

`x`	a symmetric matrix.
`only.value`	if `TRUE`, only the dominant eigenvalue is returned, otherwise both dominant eigenvalue and eigenvector are returned.
`maxiter`	the maximum number of iterations. Defaults to `100`
`tol`	a numeric tolerance.

Value

When only.value is not true, as by default, the result is a list with components "value" and "vector". Otherwise only the dominan eigenvalue is returned. The performed number of iterations to reach convergence is returned as attribute "iterations".

Examples

n <- 1000
x <- .5 * diag(n) + 0.5 * matrix(1, nrow = n, ncol = n)

# dominant eigenvalue must be (n + 1) / 2
z <- power.method(x, only.value = TRUE)
n <- 1000
x <- .5 * diag(n) + 0.5 * matrix(1, nrow = n, ncol = n)

# dominant eigenvalue must be (n + 1) / 2
z <- power.method(x, only.value = TRUE)

Generation of deviates uniformly distributed in a unitary ball

Description

Random vector generation uniformly in the unitary ball.

Usage

rball(n = 1, p = 2)
rball(n = 1, p = 2)

Arguments

`n`	the number of samples requested
`p`	dimension of the unitary sphere

Details

The function rball is an interface to C routines, which make calls to subroutines from BLAS.

Value

If n = 1 a p-dimensional vector, otherwise a matrix of n rows of random vectors.

References

Hormann, W., Leydold, J., Derflinger, G. (2004). Automatic Nonuniform Random Variate Generation. Springer, New York.

Examples

# generate the sample
z <- rball(n = 500)

# scatterplot of a random sample of 500 points uniformly distributed
# in the unitary ball
par(pty = "s")
plot(z, xlab = "x", ylab = "y")
title("500 points in the ball", font.main = 1)
# generate the sample
z <- rball(n = 500)

# scatterplot of a random sample of 500 points uniformly distributed
# in the unitary ball
par(pty = "s")
plot(z, xlab = "x", ylab = "y")
title("500 points in the ball", font.main = 1)

Ridge regression

Description

Fit a linear model by ridge regression, returning an object of class "ridge".

Usage

ridge(formula, data, subset, lambda = 1.0, method = "GCV", ngrid = 200, tol = 1e-07,
  maxiter = 50, na.action, x = FALSE, y = FALSE, contrasts = NULL, ...)
ridge(formula, data, subset, lambda = 1.0, method = "GCV", ngrid = 200, tol = 1e-07,
  maxiter = 50, na.action, x = FALSE, y = FALSE, contrasts = NULL, ...)

Arguments

`formula`	an object of class `"formula"` (or one that can be coerced to that class): a symbolic description of the model to be fitted.
`data`	an optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in `data`, the variables are taken from `environment(formula)`, typically the environment from which `ridge` is called.
`subset`	an optional vector specifying a subset of observations to be used in the fitting process.
`na.action`	a function which indicates what should happen when the data contain `NA`s. The default is set by the `na.action` setting of `options`, and is `na.fail` if that is unset.
`lambda`	a scalar or vector of ridge constants. A value of 0 corresponds to ordinary least squares.
`method`	the method for choosing the ridge parameter lambda. If `method = "none"`, then lambda is 'fixed'. If `method = "GCV"` (the default) then the ridge parameter is chosen automatically using the generalized cross validation (GCV) criterion. For `method = "grid"`, optimal value of lambda is selected computing the GCV criterion over a grid. If `method = "MSE"` the optimal ridge parameter is selected minimizing the mean squared estimation error criterion, this is the `ORPS1` subroutine by Lee (1987).
`ngrid`	number of elements in the grid used to compute the GCV criterion. Only required if `method = "grid"` and `lambda` is a scalar.
`tol`	tolerance for the optimization of the GCV criterion. Default is `1e-7`.
`maxiter`	maximum number of iterations. The default is 50.
`x`, `y`	logicals. If `TRUE` the corresponding components of the fit (the model matrix, the response) are returned.
`contrasts`	an optional list. See the `contrasts.arg` of `model.matrix.default`.
`...`	additional arguments to be passed to the low level regression fitting functions (not implemented).

Details

ridge function fits in linear ridge regression without scaling or centering the regressors and the response. In addition, If an intercept is present in the model, its coefficient is penalized.)

Value

A list with the following components:

`dims`	dimensions of model matrix.
`coefficients`	a named vector of coefficients.
`scale`	a named vector of coefficients.
`fitted.values`	the fitted mean values.
`residuals`	the residuals, that is response minus fitted values.
`RSS`	the residual sum of squares.
`edf`	the effective number of parameters.
`GCV`	vector (if `method = "grid"`) of GCV values.
`HKB`	HKB estimate of the ridge constant.
`LW`	LW estimate of the ridge constant.
`lambda`	vector (if `method = "grid"`) of lambda values; otherwise, for methods `method = "none"`, `"GCV"` or `"MSE"`, the value of ridge parameter used by the algorithm.
`optimal`	value of lambda with the minimum GCV (only relevant if `method = "grid"`).
`iterations`	number of iterations performed by the algorithm (only relevant if `method = "MSE"`).
`call`	the matched call.
`terms`	the `terms` object used.
`contrasts`	(only where relevant) the contrasts used.
`y`	if requested, the response used.
`x`	if requested, the model matrix used.
`model`	if requested, the model frame used.

References

Golub, G.H., Heath, M., Wahba, G. (1979). Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 21, 215-223.

Hoerl, A.E., Kennard, R.W., Baldwin, K.F. (1975). Ridge regression: Some simulations. Communication in Statistics 4, 105-123.

Hoerl, A.E., Kennard, R.W. (1970). Ridge regression: Biased estimation of nonorthogonal problems. Technometrics 12, 55-67.

Lawless, J.F., Wang, P. (1976). A simulation study of ridge and other regression estimators. Communications in Statistics 5, 307-323.

Lee, T.S (1987). Algorithm AS 223: Optimum ridge parameter selection. Applied Statistics 36, 112-118.

Examples

z <- ridge(GNP.deflator ~ ., data = longley, lambda = 4, method = "grid")
z # ridge regression on a grid over seq(0, 4, length = 200)

z <- ridge(GNP.deflator ~ ., data = longley)
z # ridge parameter selected using GCV (default)
z <- ridge(GNP.deflator ~ ., data = longley, lambda = 4, method = "grid")
z # ridge regression on a grid over seq(0, 4, length = 200)

z <- ridge(GNP.deflator ~ ., data = longley)
z # ridge parameter selected using GCV (default)

Multivariate normal random deviates

Description

Random number generation from the multivariate normal (Gaussian) distribution.

Usage

rmnorm(n = 1, mean = rep(0, nrow(Sigma)), Sigma = diag(length(mean)))
rmnorm(n = 1, mean = rep(0, nrow(Sigma)), Sigma = diag(length(mean)))

Arguments

`n`	the number of samples requested
`mean`	a vector giving the means of each variable
`Sigma`	a positive-definite covariance matrix

Details

The function rmnorm is an interface to C routines, which make calls to subroutines from LAPACK. The matrix decomposition is internally done using the Cholesky decomposition. If Sigma is not non-negative definite then there will be a warning message.

Value

If $n = 1$ a vector of the same length as mean, otherwise a matrix of $n$ rows of random vectors.

References

Devroye, L. (1986). Non-Uniform Random Variate Generation. Springer-Verlag, New York.

Examples

# covariance parameters
Sigma <- matrix(c(10,3,3,2), ncol = 2)
Sigma

# generate the sample
y <- rmnorm(n = 1000, Sigma = Sigma)
var(y)

# scatterplot of a random bivariate normal sample with mean
# vector zero and covariance matrix 'Sigma'
par(pty = "s")
plot(y, xlab = "", ylab = "")
title("bivariate normal sample", font.main = 1)

# QQ-plot of transformed distances
z <- WH.normal(y)
par(pty = "s")
qqnorm(z, main = "Transformed distances QQ-plot")
abline(c(0,1), col = "red", lwd = 2, lty = 2)
# covariance parameters
Sigma <- matrix(c(10,3,3,2), ncol = 2)
Sigma

# generate the sample
y <- rmnorm(n = 1000, Sigma = Sigma)
var(y)

# scatterplot of a random bivariate normal sample with mean
# vector zero and covariance matrix 'Sigma'
par(pty = "s")
plot(y, xlab = "", ylab = "")
title("bivariate normal sample", font.main = 1)

# QQ-plot of transformed distances
z <- WH.normal(y)
par(pty = "s")
qqnorm(z, main = "Transformed distances QQ-plot")
abline(c(0,1), col = "red", lwd = 2, lty = 2)

Generation of deviates uniformly located on a spherical surface

Description

Random vector generation uniformly on the sphere.

Usage

rsphere(n = 1, p = 2)
rsphere(n = 1, p = 2)

Arguments

`n`	the number of samples requested
`p`	dimension of the unitary sphere

Details

The function rsphere is an interface to C routines, which make calls to subroutines from BLAS.

Value

If n = 1 a p-dimensional vector, otherwise a matrix of n rows of random vectors.

References

Devroye, L. (1986). Non-Uniform Random Variate Generation. Springer-Verlag, New York.

Examples

# generate the sample
z <- rsphere(n = 200)

# scatterplot of a random sample of 200 points uniformly distributed
# on the unit circle
par(pty = "s")
plot(z, xlab = "x", ylab = "y")
title("200 points on the circle", font.main = 1)
# generate the sample
z <- rsphere(n = 200)

# scatterplot of a random sample of 200 points uniformly distributed
# on the unit circle
par(pty = "s")
plot(z, xlab = "x", ylab = "y")
title("200 points on the circle", font.main = 1)

Scaled condition number

Description

Compute the scaled condition number of a rectangular matrix.

Usage

scaled.condition(x, scales = FALSE)
scaled.condition(x, scales = FALSE)

Arguments

`x`	a numeric rectangular matrix.
`scales`	a logical value indicating whether the scaling factors that allow balancing the columns of `x` should be returned by the function.

Value

The columns of a rectangular matrix x are equilibrated (but not centered), then the scaled condition number is computed following the guidelines of Belsley (1990). If requested, the column scalings are returned as the attribute 'scales'.

References

Belsley, D.A. (1990). Conditioning Diagnostics: Collinearity and Weak Data in Regression. Wiley, New York.

Examples

x <- matrix(c(1, 1, 1,
              1, 2, 1,
              1, 3, 1,
              1, 1,-1,
              1, 2,-1,
              1, 3,-1), ncol = 3, byrow = TRUE)
scaled.condition(x)
x <- matrix(c(1, 1, 1,
              1, 2, 1,
              1, 3, 1,
              1, 1,-1,
              1, 2,-1,
              1, 3,-1), ncol = 3, byrow = TRUE)
scaled.condition(x)

Solve linear systems using the Gauss-Seidel method

Description

Gauss-Seidel method is an iterative algorithm for solving a system of linear equations.

Usage

seidel(a, b, start, maxiter = 200, tol = 1e-7)
seidel(a, b, start, maxiter = 200, tol = 1e-7)

Arguments

`a`	a square numeric matrix containing the coefficients of the linear system.
`b`	a vector of right-hand sides of the linear system.
`start`	a vector for initial starting point.
`maxiter`	the maximum number of iterations. Defaults to `200`
`tol`	tolerance level for stopping iterations.

Details

Let $\bold{D}$ , $\bold{L}$ , and $\bold{U}$ denote the diagonal, lower triangular and upper triangular parts of a matrix $\bold{A}$ . Gauss-Seidel method solve the equation $\bold{Ax} = \bold{b}$ , iteratively by rewriting $(\bold{L} + \bold{D})\bold{x} + \bold{Ux} = \bold{b}$ . Assuming that $\bold{L} + \bold{D}$ is nonsingular leads to the iteration formula

$\bold{x}^{(k+1)} = -(\bold{L} + \bold{D})^{-1}\bold{U}\bold{x}^{(k)} + (\bold{L} + \bold{D})^{-1}\bold{b}$

Value

a vector with the approximate solution, the iterations performed are returned as the attribute 'iterations'.

References

Golub, G.H., Van Loan, C.F. (1996). Matrix Computations, 3rd Edition. John Hopkins University Press.

Examples

a <- matrix(c(5,-3,2,-2,9,-1,3,1,-7), ncol = 3)
b <- c(-1,2,3)
start <- c(1,1,1)
z <- seidel(a, b, start)
z # converged in 10 iterations
a <- matrix(c(5,-3,2,-2,9,-1,3,1,-7), ncol = 3)
b <- c(-1,2,3)
start <- c(1,1,1)
z <- seidel(a, b, start)
z # converged in 10 iterations

Sherman-Morrison formula

Description

The Sherman-Morrison formula gives a convenient expression for the inverse of the rank 1 update $(\bold{A} + \bold{bd}^T)$ where $\bold{A}$ is a $n\times n$ matrix and $\bold{b}$ , $\bold{d}$ are $n$ -dimensional vectors. Thus

$(\bold{A} + \bold{bd}^T)^{-1} = \bold{A}^{-1} - \frac{\bold{A}^{-1}\bold{bd}^T \bold{A}^{-1}}{1 + \bold{d}^T\bold{A}^{-1}\bold{b}}.$

Usage

sherman.morrison(a, b, d = b, inverted = FALSE)
sherman.morrison(a, b, d = b, inverted = FALSE)

Arguments

`a`	a numeric matrix.
`b`	a numeric vector.
`d`	a numeric vector.
`inverted`	logical. If `TRUE`, `a` is supposed to contain its inverse.

Details

Method of sherman.morrison calls BLAS level 2 subroutines DGEMV and DGER for computational efficiency.

Value

a square matrix of the same order as a.

Examples

n <- 10
ones <- rep(1, n)
a <- 0.5 * diag(n)
z <- sherman.morrison(a, ones, 0.5 * ones)
z
n <- 10
ones <- rep(1, n)
a <- 0.5 * diag(n)
z <- sherman.morrison(a, ones, 0.5 * ones)
z

Gauss-Jordan sweep operator for symmetric matrices

Description

Perform the sweep operation (or reverse sweep) on the diagonal elements of a symmetric matrix.

Usage

sweep.operator(x, k = 1, reverse = FALSE)
sweep.operator(x, k = 1, reverse = FALSE)

Arguments

`x`	a symmetric matrix.
`k`	elements (if `k` is vector) of the diagonal which will be sweeped.
`reverse`	logical. If `reverse = TRUE` the reverse sweep is performed.

Details

The symmetric sweep operator is a powerful tool in computational statistics with uses in stepwise regression, conditional multivariate normal distributions, MANOVA, and more.

Value

a square matrix of the same order as x.

References

Goodnight, J.H. (1979). A tutorial on the SWEEP operator. The American Statistician 33, 149-158.

Examples

# tiny example of regression, last column contains 'y'
xy <- matrix(c(1, 1, 1, 1,
               1, 2, 1, 3,
               1, 3, 1, 3,
               1, 1,-1, 2,
               1, 2,-1, 2,
               1, 3,-1, 1), ncol = 4, byrow = TRUE)
z <- crossprod(xy)
z <- sweep.operator(z, k = 1:3)
cf <- z[1:3,4] # regression coefficients
RSS <- z[4,4]  # residual sum of squares

# an example not that small
x <- matrix(rnorm(1000 * 100), ncol = 100)
xx <- crossprod(x)
z <- sweep.operator(xx, k = 1)
# tiny example of regression, last column contains 'y'
xy <- matrix(c(1, 1, 1, 1,
               1, 2, 1, 3,
               1, 3, 1, 3,
               1, 1,-1, 2,
               1, 2,-1, 2,
               1, 3,-1, 1), ncol = 4, byrow = TRUE)
z <- crossprod(xy)
z <- sweep.operator(z, k = 1:3)
cf <- z[1:3,4] # regression coefficients
RSS <- z[4,4]  # residual sum of squares

# an example not that small
x <- matrix(rnorm(1000 * 100), ncol = 100)
xx <- crossprod(x)
z <- sweep.operator(xx, k = 1)

Compact information to construct the symmetrizer matrix

Description

This function provides the information required to create the symmetrizer matrix.

Usage

symm.info(n = 1)
symm.info(n = 1)

Arguments

`n`	order of the symmetrizer matrix.

Details

This function returns a list containing vectors that represent an element of the symmetrizer matrix and is accesed by the indexes in vectors row, col and values contained in val. This information is used by function symm.prod to do some operations involving the symmetrizer matrix without forming it. This information also can be obtained using function symmetrizer.

Value

A list containing the following elements:

`row`	vector of indexes, each entry represents the row index of the symmetrizer matrix.
`col`	vector of indexes, each entry represents the column index of the symmetrizer matrix.
`val`	vector of values, each entry represents the value of the symmetrizer matrix at element given by `row` and `col` indexes.
`order`	order of the symmetrizer matrix.

Examples

z <- symm.info(n = 3)
z # elements in symmetrizer matrix of order 3

N3 <- symmetrizer(n = 3, matrix = TRUE)
N3 # only recommended if n is very small
z <- symm.info(n = 3)
z # elements in symmetrizer matrix of order 3

N3 <- symmetrizer(n = 3, matrix = TRUE)
N3 # only recommended if n is very small

Matrix multiplication envolving the symmetrizer matrix

Description

Given the order of a symmetrizer matrix $\bold{N}$ of order $n$ and a conformable matrix $\bold{X}$ , performs one of the matrix-matrix operations:

$\bold{Y} = \bold{NX}$ , if side = "left", or
$\bold{Y} = \bold{XN}$ , if side = "right",

The main aim of symm.prod is to do this matrix multiplication without forming the symmetrizer matrix.

Usage

symm.prod(n = 1, x = NULL, side = "left")
symm.prod(n = 1, x = NULL, side = "left")

Arguments

`n`	order of the symmetrizer matrix.
`x`	numeric matrix (or vector).
`side`	a string selecting if symmetrizer matrix is pre-multiplying $\bold{X}$ , that is `side = "left"` or post-multiplying $\bold{X}$ , by using `side = "right"`.

Details

Underlying C code only uses information provided by symm.info to performs the matrix multiplication. The symmetrizer matrix is never created.

Examples

N4 <- symmetrizer(n = 4, matrix = TRUE)
x <- matrix(1:32, ncol = 2)
y <- N4 %*% x

z <- symm.prod(n = 4, x) # N4 is not stored
all(z == y) # matrices y and z are equal!
N4 <- symmetrizer(n = 4, matrix = TRUE)
x <- matrix(1:32, ncol = 2)
y <- N4 %*% x

z <- symm.prod(n = 4, x) # N4 is not stored
all(z == y) # matrices y and z are equal!

Symmetrizer matrix

Description

This function returns the symmetrizer matrix of order $n$ which transforms, for every $n\times n$ matrix $\bold{A}$ , vec $(\bold{A})$ into vec $((\bold{A} + \bold{A}^T)/2)$ .

Usage

symmetrizer(n = 1, matrix = FALSE)
symmetrizer(n = 1, matrix = FALSE)

Arguments

`n`	order of the symmetrizer matrix.
`matrix`	a logical indicating whether the symmetrizer matrix will be returned.

Details

This function is a wrapper function for the function symm.info. This function provides the information required to create the symmetrizer matrix. If option matrix = FALSE the symmetrizer matrix is stored in three vectors containing the coordinate list of indexes for rows, columns and the values.

Warning: matrix = TRUE is not recommended, unless the order n be small. This matrix can require a huge amount of storage.

Value

Returns an $n^2$ by $n^2$ matrix (if requested).

References

Abadir, K.M., Magnus, J.R. (2005). Matrix Algebra. Cambridge University Press.

Magnus, J.R., Neudecker, H. (2007). Matrix Differential Calculus with Applications in Statistics and Econometrics, 3rd Edition. Wiley, New York.

Examples

z <- symmetrizer(n = 100)
object.size(z) # 319 Kb of storage

N100 <- symmetrizer(n = 100, matrix = TRUE) # time: < 2 secs
object.size(N100) # 800 Mb of storage, do not request this matrix!

# a small example
N3 <- symmetrizer(n = 3, matrix = TRUE)
a <- matrix(rep(c(2,4,6), each = 3), ncol = 3)
a
b <- 0.5 * (a + t(a))
b
v <- N3 %*% vec(a)
all(vec(b) == as.vector(v)) # vectors are equal!
z <- symmetrizer(n = 100)
object.size(z) # 319 Kb of storage

N100 <- symmetrizer(n = 100, matrix = TRUE) # time: < 2 secs
object.size(N100) # 800 Mb of storage, do not request this matrix!

# a small example
N3 <- symmetrizer(n = 3, matrix = TRUE)
a <- matrix(rep(c(2,4,6), each = 3), ncol = 3)
a
b <- 0.5 * (a + t(a))
b
v <- N3 %*% vec(a)
all(vec(b) == as.vector(v)) # vectors are equal!

Vectorization of a matrix

Description

This function returns a vector obtained by stacking the columns of $\bold{X}$ .

Usage

  vec(x)
vec(x)

Arguments

`x`	a numeric matrix.

Value

Let $\bold{X}$ be a $n$ by $m$ matrix, then vec( $\bold{X}$ ) is a $nm$ -dimensional vector.

Examples

x <- matrix(rep(1:10, each = 10), ncol = 10)
x
y <- vec(x)
y
x <- matrix(rep(1:10, each = 10), ncol = 10)
x
y <- vec(x)
y

Vectorization the lower triangular part of a square matrix

Description

This function returns a vector obtained by stacking the lower triangular part of a square matrix.

Usage

vech(x)
vech(x)

Arguments

`x`	a square matrix.

Value

Let $\bold{X}$ be a $n$ by $n$ matrix, then vech( $\bold{X}$ ) is a $n(n+1)/2$ -dimensional vector.

Examples

x <- matrix(rep(1:10, each = 10), ncol = 10)
x
y <- vech(x)
y
x <- matrix(rep(1:10, each = 10), ncol = 10)
x
y <- vech(x)
y

Wilson-Hilferty transformation for chi-squared variates

Description

Returns the Wilson-Hilferty transformation of random variables with chi-squared distribution.

Usage

  WH.normal(x)
WH.normal(x)

Arguments

`x`	vector or matrix of data with, say, $p$ columns.

Details

Let $T = D^2/p$ be a random variable, where $D^2$ denotes the squared Mahalanobis distance defined as

$D^2 = (\bold{x} - \bold{\mu})^T \bold{\Sigma}^{-1} (\bold{x} - \bold{\mu})$

Thus the Wilson-Hilferty transformation is given by

$z = \frac{T^{1/3} - (1 - \frac{2}{9p})}{(\frac{2}{9p})^{1/2}}$

and $z$ is approximately distributed as a standard normal distribution. This is useful, for instance, in the construction of QQ-plots.

References

Wilson, E.B., and Hilferty, M.M. (1931). The distribution of chi-square. Proceedings of the National Academy of Sciences of the United States of America 17, 684-688.

Examples

x <- iris[,1:4]
z <- WH.normal(x)
par(pty = "s")
qqnorm(z, main = "Transformed distances QQ-plot")
abline(c(0,1), col = "red", lwd = 2, lty = 2)
x <- iris[,1:4]
z <- WH.normal(x)
par(pty = "s")
qqnorm(z, main = "Transformed distances QQ-plot")
abline(c(0,1), col = "red", lwd = 2, lty = 2)

Whitening transformation

Description

Applies the whitening transformation to a data matrix based on the Cholesky decomposition of the empirical covariance matrix.

Usage

whitening(x, Scatter = NULL)
whitening(x, Scatter = NULL)

Arguments

`x`	vector or matrix of data with, say, $p$ columns.
`Scatter`	covariance (or scatter) matrix ( $p \times p$ ) of the distribution, must be positive definite. If `NULL`, the covariance matrix is estimated from the data.

Value

Returns the whitened data matrix $\bold{Z} = \bold{X W}^T$ , where

$\bold{W}^T\bold{W} = \bold{S}^{-1},$

with $\bold{S}$ the empirical covariance matrix.

References

Kessy, A., Lewin, A., Strimmer, K. (2018). Optimal whitening and decorrelation. The American Statistician 72, 309-314.

Examples

x <- iris[,1:4]
species <- iris[,5]
pairs(x, col = species) # plot of Iris

# whitened data
z <- whitening(x)
pairs(z, col = species) # plot of
x <- iris[,1:4]
species <- iris[,5]
pairs(x, col = species) # plot of Iris

# whitened data
z <- whitening(x)
pairs(z, col = species) # plot of

Wilson-Hilferty transformation

Description

Returns the Wilson-Hilferty transformation of random variables with Gamma distribution.

Usage

  wilson.hilferty(x, shape, rate = 1)
wilson.hilferty(x, shape, rate = 1)

Arguments

`x`	a numeric vector containing Gamma distributed deviates.
`shape`, `rate`	shape and rate parameters. Must be positive.

Details

Let $X$ be a random variable following a Gamma distribution with parameters $a$ = shape and $b$ = rate with density

$f(x) = \frac{b^a}{\Gamma(a)} x^{a-1}\exp(-bx),$

where $x \ge 0$ , $a > 0$ , $b > 0$ and consider the random variable $T = X/(a/b)$ . Thus, the Wilson-Hilferty transformation

$z = \frac{T^{1/3} - (1 - \frac{1}{9a})}{(\frac{1}{9a})^{1/2}}$

is approximately distributed as a standard normal distribution. This is useful, for instance, in the construction of QQ-plots.

References

Terrell, G.R. (2003). The Wilson-Hilferty transformation is locally saddlepoint. Biometrika 90, 445-453.

Wilson, E.B., and Hilferty, M.M. (1931). The distribution of chi-square. Proceedings of the National Academy of Sciences of the United States of America 17, 684-688.

Examples

x <- rgamma(n = 300, shape = 2, rate = 1)
z <- wilson.hilferty(x, shape = 2, rate = 1)
par(pty = "s")
qqnorm(z, main = "Transformed Gamma QQ-plot")
abline(c(0,1), col = "red", lwd = 2, lty = 2)
x <- rgamma(n = 300, shape = 2, rate = 1)
z <- wilson.hilferty(x, shape = 2, rate = 1)
par(pty = "s")
qqnorm(z, main = "Transformed Gamma QQ-plot")
abline(c(0,1), col = "red", lwd = 2, lty = 2)

Package 'fastmatrix'

Help Index

Array multiplication

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Force a matrix to be symmetric

Description

Usage

Arguments

Value

Examples

Computation of Bezier curve

Description

Usage

Arguments

Details

Value

Examples

Bracket product

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Solve linear systems using the conjugate gradients method

Description

Usage

Arguments

Value

Warning

References

See Also

Examples

Rank 1 update to Cholesky factorization

Description

Usage

Arguments

References

See Also

Examples

Form a symmetric circulant matrix

Description

Usage

Arguments

Value

Examples

Compact information to construct the commutation matrix

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Matrix multiplication envolving the commutation matrix

Description

Usage

Arguments

Details

See Also

Examples

Commutation matrix

Description

Usage

Arguments

Details

Value

References

See Also

Examples