repeatedm {stats4teaching}

Repeated Measures (ANOVA & Multiple Regression)

Description

Repeated Measures (ANOVA & Multiple Regression)

Usage

repeatedm(k, n, mean = 0, sigma = 1, coefvar = NULL,
          sigmaSup = NULL, conf.level = 0.95,
          random = FALSE, dec = 2)

Arguments

k

number of variables.

n

number of observations.

mean

vector of means.

sigma

vector of standard deviations/covariance-correlation matrix.

coefvar

vector (optional) of coefficients of variation.

sigmaSup

vector (optional) of standard deviations if sigma is a correlation matrix.

conf.level

confidence level for interval in T-Test.

random

a logical indicating whether you want a random covariance/variance matrix.

dec

number of decimals for observations.

Details

Number of variables must be greater than 3, in order to ensure an ANOVA of repeated measures or a multiple Linear Regression.

sigma can represent a vector or a covariance/correlation matrix. In case sigma is a vector, independent samples are created. By other hand, if it's a correlation matrix parameter sigmaSup is required. For covariance matrices, the function does not require any other parameter or special treatment.

If random = TRUE, a random covariance matrix is generated by using genpositiveDefMat().

Value

A data frame.

See Also

[clusterGeneration::genpositiveDefMat()]

Examples

randm <- clusterGeneration::genPositiveDefMat(8, covMethod = "unifcorrmat")
mcov <- randm$Sigma
Sigma <- cov2cor(mcov)
is.corrmatrix(Sigma)
repeatedm(k = 8, n = 8, mean = c(20,5, 30, 15),sigma = Sigma, sigmaSup = 2,  dec = 2)

repeatedm(k = 5, n = 5, mean = c(8,10,5,14,22.5), random = TRUE)
repeatedm(k = 3, n = 8, mean = c(10,5,22.5), sigma = c(3.3,1.5,5), dec = 2)