Package 'robustcov'

Sample contaminated normal

Description

This function samples normal distribution with normal contamination

Usage

conta_normal(
  n,
  Omega,
  byrow = FALSE,
  cont_rate = 0.05,
  mu = 10,
  sd = sqrt(0.2)
)

Arguments

n	samplesize
Omega	precision matrix of the normal
byrow	whether the contamination happened by row? FALSE stand for cellwise contamination
cont_rate	how many cells/rows are contaminated?
mu	mean of the contamination
sd	standard deviation of the contamination

Value

a matrix of contaminated (multivariate) normal distributed data, row as sample

---

Kendall's tau

Description

This routine calculates the Kendall's tau

Usage

corKendall(data)

Arguments

data	the n by p raw data matrix

Value

a matrix with dimension p by p, Kendall's tau

Examples

corKendall(matrix(rnorm(500),100,5))

---

Quadrant correlation coefficients

Description

This routine calculates Quadrant correlation coefficients

Usage

corQuadrant(data)

Arguments

data	the n by p raw data matrix

Value

a matrix with dimension p by p, Quadrant correlation coefficients

Examples

corQuadrant(matrix(rnorm(500),100,5))

---

Spearman correlation

Description

This routine calculates the Spearman correlation

Usage

corSpearman(data)

Arguments

data	the n by p raw data matrix

Value

a matrix with dimension p by p of spearman correlations

Examples

corSpearman(matrix(rnorm(500),100,5))

---

Gnanadesikan-Kettenring estimator for *covariance*

Description

This routine calculates the Gnanadesikan-Kettenring estimator, diagonal will be MAD

Usage

covGK(data)

Arguments

data	the n by p raw data matrix

Value

a matrix with dimension p by p, GK estimator, note that it's not necessarily positive

Examples

covGK(matrix(rnorm(500),100,5))

---

NPD estimator for *covariance* based on Qn

Description

This routine calculates the NPD estimator for *covariance* based on Qn

Usage

covNPD(data, eigenTol = 1e-06, convTol = 1e-07, psdTol = 1e-08, maxit = 1000L)

Arguments

data	the n by p raw data matrix
eigenTol	tolerance in eigen system, used in finding nearest positive matrix
convTol	tolerance in cov, used in finding nearest positive matrix
psdTol	tolerance in psd, used in finding nearest positive matrix
maxit	max iterations in finding nearest positive matrix

Value

a matrix with dimension p by p, NPD estimator

Examples

covNPD(matrix(rnorm(500),100,5))

---

Orthogonalized Gnanadesikan-Kettenring (OGK) estimator for *covariance*

Description

This routine calculates the Orthogonalized Gnanadesikan-Kettenring (OGK) estimator for *covariance*, using scale estimation of Gn, as in Maronna and Zamar

Usage

covOGK(data)

Arguments

data	the n by p raw data matrix

Value

a matrix with dimension p by p, OGK estimator

Examples

covOGK(matrix(rnorm(500),100,5))

---

SpearmanU estimator for *covariance*

Description

This routine calculates the SpearmanU, the pairwise covariance matrix estimator proposed in Oellererand Croux

Usage

covSpearmanU(data)

Arguments

data	the n by p raw data matrix

Value

a matrix with dimension p by p of spearmanU correlation

Examples

covSpearmanU(matrix(rnorm(500),100,5))

---

Cross validation to chose tuning parameter of glasso

Description

This routine use k fold cross validation to chose tuning parameter

Usage

cvglasso(
  data,
  k = 10,
  covest = cov,
  rhos = seq(0.1, 1, 0.1),
  evaluation = negLLrobOmega,
  ...
)

Arguments

data	The full dataset, should be a matrix or a data.frame, row as sample
k	number of folds
covest	a *function* or name of a function (string) that takes a matrix to estimate covariance
rhos	a vector of tuning parameter to be tested
evaluation	a *function* or name of a function (string) that takes only two arguments, the estimated covariance and the test covariance, when NULL, we use negative log likelihood on test sets
...	extra arguments send to glasso

Value

a matrix with k rows, each row is the evaluation loss of that fold

Examples

cvglasso(matrix(rnorm(100),20,5))

---

nearest positive semi-definite projection of a matrix

Description

This routine calculate the nearest positive semi0definite projection

Usage

nearPPSD(X, eigenTol = 1e-06, convTol = 1e-07, psdTol = 1e-08, maxit = 1000L)

Arguments

X	the matrix
eigenTol	tolerance in eigen system, used in finding nearest positive matrix
convTol	tolerance in cov, used in finding nearest positive matrix
psdTol	tolerance in psd, used in finding nearest positive matrix
maxit	max iterations in finding nearest positive matrix

Value

a matrix which is the nearest positive semi-definite matrix of input X

---

-log Likelihood on test set

Description

The default evaluation function in corss validation, -log liekihood on test set

Usage

negLLrobOmega(Sigma_hat, Sigma)

Arguments

Sigma_hat	the estimated *covariance* matrix of training set
Sigma	the *covariance* matrix of test sets

Value

-log likelihood

---

Alternative multivariate t distribution

Description

This routine samples alternative multivarate t distribution

Usage

raltert(n, Omega, nu)

Arguments

n	sample size
Omega	**precision** matrix of dimension p by p
nu	degree of freedom

Value

a matrix with dimension n by p, each row is a sample

---

Multivariate normal distribution with 0 mean

Description

This routine samples multivarate normal distribution of mean 0 from precision matrix

Usage

rmvnorm(n, Omega)

Arguments

n	sample size
Omega	**precision** matrix of dimension p by p

Value

a matrix with dimension n by p, each row is a sample

---

Multivariate t distribution

Description

This routine samples multivarate t distribution

Usage

rmvt(n, Omega, nu)

Arguments

n	sample size
Omega	**precision** matrix of dimension p by p
nu	degree of freedom

Value

a matrix with dimension n by p, each row is a sample

---

glasso with robust covariance estimations

Description

This routine fits glasso using a robust covariance matrix

Usage

robglasso(
  data,
  covest = cov,
  rho = 0.1,
  CV = FALSE,
  k = 10,
  grids = 15,
  evaluation = negLLrobOmega,
  ...
)

Arguments

data	raw data, should be a matrix or a data.frame, row as sample
covest	a *function* or name of a function (string) that takes a matrix to estimate covariance
rho	a scalar or vector of tuning parameters to be chosen, if CV=FALSE, should be a scalar, if CV=TRUE scalar input will be override and tuning parameter will be chosen based on CV
CV	bool, whether doing cross validation for tuning parameter, if set to TRUE and rho is a scalar, the candidate will be chosen automatically by log spacing between 0.01 max covariance and max covariance with number of grids
k	fold for cross validation if applicable
grids	number of candidate tuning parameters in cross validation
evaluation	a *function* or name of a function (string) that takes only two arguments, the estimated *covariance* and the test *covariace*, when NULL, we use negative log likelihood on test sets
...	extra argument sent to glasso::glasso

Value

a glasso return (see ?glasso::glasso), most important one is $X the estimated sparse precision,with an extra entry of tuning parameter lambda

Examples

robglasso(matrix(rnorm(100),20,5))

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