Conditional Covariance

Usage

conditional_covariance(x, sigma, mu = 0)

Arguments

x: named numeric vector of predictor scores
sigma: named covariance matrix of predictor and outcome variables
mu: a single numeric mean for all variables or a named vector of means of predictor and outcome variables

Value

list of conditional means and a covariance matrix

mu_conditional - The means of the outcome variables conditioned on the values of the predictors in vector x.
mu_sigma - The covariance matrix of the outcome variables conditioned on the values of the predictors in vector x.
descriptives_conditional - A data frame of means and standard deviations of the outcome variables conditioned on the values of the predictors in vector x.
x - The predictor scores from the x parameter
sigma - The unconditional covariance matrix from the sigma parameter
mu - Anamed vector of unconditional means

Examples

# Named vector of predictor scores
x <- c(A = 1)

# Named vector of unconditional means
mu <- c(A = 0, B = 0, C = 0)

# Unconditional covariance matrix with row and column names
sigma <- matrix(c(1, .5, .5,
                  .5, 1, .5,
                  .5, .5, 1),
                nrow = 3,
                ncol = 3,
                dimnames = list(names(mu),
                                names(mu)))

# Conditoinal means and covariance matrix
conditional_covariance(x = x, sigma = sigma, mu = mu)
#> $mu_conditional
#>   B   C 
#> 0.5 0.5 
#> 
#> $sigma_conditional
#>      B    C
#> B 0.75 0.25
#> C 0.25 0.75
#> 
#> $descriptives_conditional
#>   construct mu_conditional sigma_conditional
#> B         B            0.5         0.8660254
#> C         C            0.5         0.8660254
#> 
#> $x
#> A 
#> 1 
#> 
#> $sigma
#>     A   B   C
#> A 1.0 0.5 0.5
#> B 0.5 1.0 0.5
#> C 0.5 0.5 1.0
#> 
#> $mu
#> A B C 
#> 0 0 0 
#>