VManova {MVET}

Various Multivariate Anova(VManova)

Description

Perform various types of multivariate analysis of variance (MANOVA) that satisfy tests of multivariate normality and homogeneity of covariance matrices.

Usage

VManova(data,
        grp1.name,
        grp2.name,
        way = "one",
        method = "all",
        plot.scale = FALSE)

Arguments

data

A numeric matrix or data frame. If data frames, group(class) column can be a factor or a string.

grp1.name

The name of the first group (or class) column in the input data, specified as a string.

grp2.name

The name of the second group (or class) column in the input data, specified as a string. Used to represent the second group(class) in a two-way MANOVA.

way

The type of MANOVA to perform ("one" for one-way or "two" for two-way). (default = "one")

method

The method for MANOVA analysis. "Wilks" represents Wilks' lambda, "LH" represents Lawley-Hotelling trace, "Pillai" represents Pillai-Bartlett trace, "Roy" represents Roy's largest root, and "all" represents all methods. (default is "all")

plot.scale

If TRUE, the data will be scaled before calculating mean values and used in the plot. It has no direct effect on the MANOVA analysis itself. (default plot.scale = FALSE)

Value

Mean.val.plot

Plot the mean value parallel coordinates, representing the two samples using the mean values for each variable.

One.all

Outputs the results of a one-way MANOVA test. It displays the degrees of freedom (Df1, Df2) of the F-distribution, statistics for Wilks, Lawley-Hotelling, Pillai, and Roy, the F-distribution test statistic, and the significance level in that order.

Two.all

Outputs the results of a two-way MANOVA test. It displays the degrees of freedom (Df1, Df2) of the F-distribution, statistics for Wilks, Lawley-Hotelling, Pillai, and Roy, the F-distribution test statistic, and the significance level in that order.

References

Rencher, A. C., & Christensen, W. F. (2002). Methods of Multivariate Analysis. John Wiley & Sons, Inc., New York.

See Also

mardiatest for multivariate normality (Includes outlier remove)

PPCCtest for multivariate normality

SPCCtest for multivariate normality

boxMtest for homogeneity of covariance matrices

Examples

data(wine)

## one way
VManova(wine, grp1.name = "class", way = "one", method = "all", plot.scale = TRUE)

## two way
newwine <- wine
# (1: low, 2: medium, 3: high)
newwine$v4 <- ifelse(wine$v4 <= 17, 1,
                     ifelse(wine$v4 <= 22, 2, 3))
VManova(newwine, grp1.name = "class", grp2.name = "v4",
        way = "two", method = "all", plot.scale = TRUE)