extraSuperpower (version 1.5.0)

gencovariancemat: Function that generates a covariance matrix taking as input a correlation matrix and a standard deviation matrix or value.

Description

May be run independently or internally as part of 'calculate_mean_matrix'.

Usage

gencovariancemat(
  correlation_matrix,
  sd_matrix,
  withinf,
  label_list = NULL,
  nlfA,
  nlfB
)

Value

Covariance matrix

Arguments

correlation_matrix

Matrix - Expected correlation between combinations of factor levels

sd_matrix

Numeric or matrix - Standard deviation value or matrix of standard deviation values for combinations of factor levels.

withinf

Character- Factor for which measurements are repeated, options are NULL, "fA", "fB" and "both". If NULL (default) independent measurements will be considered.

label_list

List length 2 - Names of factor levels

nlfA

Integer - number of levels of factor A

nlfB

Integer - number of levels of factor B

Examples

meanvals <- c(seq(3,9,2),seq(2,8,2),seq(1,7,2))
mean_matrix <- matrix(meanvals, 3, 4, byrow = TRUE,
                   dimnames = list(A=LETTERS[1:3], B=letters[1:4]))

mean_matrix
sd_matrix <- mean_matrix*0.2

cor_matrix <- gencorrelationmat(mean_matrix = mean_matrix,
                                rho = 0.7, withinf = "fB", nlfA = 3, nlfB = 4)

gencovariancemat(cor_matrix, sd_matrix, withinf = "fB", nlfA = 3, nlfB = 4)

##correlation gradient over levels of factor B
cor_matrix <- gencorrelationmat(mean_matrix = mean_matrix,
                                rho = c(0.7, 0.4), withinf = "fB", nlfA = 3, nlfB = 4)

gencovariancemat(cor_matrix, sd_matrix, withinf = "fB", nlfA = 3, nlfB = 4)