CVs: Coeficients of Variation

In multiColl: Collinearity Detection in a Multiple Linear Regression Model

Coeficients of Variation

Description

The function returns the Coeficient of Variation (CV) of a matrix with quantitative columns.

Usage

CVs(X, dummy = FALSE, pos = NULL)

Arguments

X	A numeric design matrix that should contain more than one regressor (intercept included).
dummy	A logical value that indicates if there are dummy variables in the design matrix X. By default dummy=FALSE.
pos	A numeric vector that indicates the position of the dummy variables, if these exist, in the design matrix X. By default pos=NULL.

Details

Due to the calculation of the CV only makes sense for quantitative data, other kind of data should be ignored in the calculation. For this reason, it is necessary to indicate if there are non-quantitative variables and also its position in the matrix.

Value

The CV of each column of X.

Author(s)

R. Salmerón ([e-mail]) and C. García ([e-mail]).

See Also

CV.

Examples

# random

cte = array(1, 50)
x1 = sample(1:50, 25)
x2 = sample(1:50, 25)
Z = cbind(cte, x1, x2)
head(Z)
CVs(Z)

x3 = sample(c(array(1,25), array(0,25)), 25)
W = cbind(Z, x3)
head(W)
CVs(W, dummy=TRUE, pos = 4)

x0 = sample(c(array(1,25), array(0,25)), 25)
Y = cbind(cte, x0, x1, x2, x3)
head(Y)
CVs(Y, dummy=TRUE, pos=c(2,5))

multiColl documentation built on July 21, 2022, 9:06 a.m.