lazyCov function

Create covariance matrix from correlation and standard deviation information

This is a flexible function that allows lazy R programmers to create covariance matrix. The user may be lazy because the correlation and standard deviation infomation may be supplied in a variety of formats.

lazyCov(Rho, Sd, d)

Arguments

• Rho: Required. May be a single value (correlation common among all variables), a vector of the lower triangular values (vech) of a correlation matrix, or a symmetric matrix of correlation coefficients.
• Sd: Required. May be a single value (standard deviation common among all variables) or a vector of standard deviations, one for each variable.
• d: Optional. Number of rows or columns. lazyCov may be able to deduce the required dimension of the final matrix from the input. However, when the user supplies only a single value for both Rho and Sd, d is necessary.

Returns

covariance matrix.

Examples

##correlation 0.8 for all pairs, standard deviation 1.0 of each
lazyCov(Rho = 0.8, Sd = 1.0, d = 3)
## supply a vech (lower triangular values in a column)
lazyCov(Rho = c(0.1, 0.2, 0.3), Sd = 1.0)
## supply vech with different standard deviations
lazyCov(Rho = c(0.1, 0.2, 0.3), Sd = c(1.0, 2.2, 3.3))
newRho <- lazyCor(c(0.5, 0.6, 0.7, -0.1, 0.1, 0.2))
lazyCov(Rho = newRho, Sd = 1.0)
lazyCov(Rho = newRho, Sd = c(3, 4, 5, 6))

Author(s)

pauljohn@ku.edu