MANCOVA
Multivariate Analysis of (Co)Variance (MANCOVA) is used to explore the relationship between multiple dependent variables, and one or more categorical and/or continuous explanatory variables.
Example usage
data('iris')
mancova(data = iris,
deps = vars(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width),
factors = Species)
#
# MANCOVA
#
# Multivariate Tests
# ───────────────────────────────────────────────────────────────────────────
# value F df1 df2 p
# ───────────────────────────────────────────────────────────────────────────
# Species Pillai's Trace 1.19 53.5 8 290 < .001
# Wilks' Lambda 0.0234 199 8 288 < .001
# Hotelling's Trace 32.5 581 8 286 < .001
# Roy's Largest Root 32.2 1167 4 145 < .001
# ───────────────────────────────────────────────────────────────────────────
#
#
# Univariate Tests
# ───────────────────────────────────────────────────────────────────────────────────────────────
# Dependent Variable Sum of Squares df Mean Square F p
# ───────────────────────────────────────────────────────────────────────────────────────────────
# Species Sepal.Length 63.21 2 31.6061 119.3 < .001
# Sepal.Width 11.34 2 5.6725 49.2 < .001
# Petal.Length 437.10 2 218.5514 1180.2 < .001
# Petal.Width 80.41 2 40.2067 960.0 < .001
# Residuals Sepal.Length 38.96 147 0.2650
# Sepal.Width 16.96 147 0.1154
# Petal.Length 27.22 147 0.1852
# Petal.Width 6.16 147 0.0419
# ───────────────────────────────────────────────────────────────────────────────────────────────
#
Arguments
| data | the data as a data frame |
| deps | a string naming the dependent variable from data, variable must be numeric |
| factors | a vector of strings naming the factors from data |
| covs | a vector of strings naming the covariates from data |
| multivar | one or more of 'pillai', 'wilks', 'hotel', or 'roy'; use Pillai's Trace, Wilks' Lambda, Hotelling's Trace, and Roy's Largest Root multivariate statistics, respectively |
| boxM | TRUE or FALSE (default), provide Box's M test |
| shapiro | TRUE or FALSE (default), provide Shapiro-Wilk test |
| qqPlot | TRUE or FALSE (default), provide a Q-Q plot of multivariate normality |
Returns
A results object containing:
| results$multivar | a table |
| results$univar | a table |
| results$assump$boxM | a table |
| results$assump$shapiro | a table |
| results$assump$qqPlot |
Tables can be converted to data frames with asDF or as.data.frame(). For example:
results$multivar$asDF
as.data.frame(results$multivar)