HomogeneityOfVariance: Checking homogeneity of variance

In yhhc2/ancovall: Package that automates the process of doing ANCOVA on a large number of dependent variables

Description

Returns information that can be used to determine if the homogeneity of variance, an assumption for ANCOVA, is met.

Usage

HomogeneityOfVariance(inputted.data, dependent.variable, independent.variable)

Arguments

inputted.data	A dataframe
dependent.variable	A string that specifies the column name of the column to use as the dependent variable. Column must be numeric.
independent.variable	A string that specifies the column name of the column to use as the independent variable. Column can be numeric or factor. If it's a factor, then it can only have two levels.

Details

To test homogeneity of regression slopes, the Levene test is used. If the p-value is significant, then this means the assumption of homogeneity of variance is not met.

Value

A matrix with two rows. The first row specify what the values are in the second row. The second row: The first element is the formula used to evaluate Levene test. The next element is the p-value from the Levene test.

Examples

dependent.col <- c(10.1, 11.3, 12.1, 13.7, 14.2, 1.6, 2.3, 3.2, 4.1, 5.3)
independent.col <- as.factor(c(1, 1, 1, 1, 1, 0, 0, 0, 0, 0))
covariate.one.col <- c(1, 2, 3, 4, 5, 1, 2, 3, 4, 5)
covariate.two.col <- as.factor(c(1, 0, 1, 0, 1, 0, 1, 0, 1, 0))

inputted.data <- data.frame(dependent.col, independent.col, covariate.one.col,
                            covariate.two.col)

results <- HomogeneityOfVariance(inputted.data, "dependent.col",
                                         "independent.col")

results

yhhc2/ancovall documentation built on Dec. 23, 2021, 7:19 p.m.