jmv (version 1.2.5)

mancova: MANCOVA

Description

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.

Usage

mancova(data, deps, factors = NULL, covs = NULL,
  multivar = list("pillai", "wilks", "hotel", "roy"), boxM = FALSE,
  shapiro = FALSE, qqPlot = FALSE)

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

Value

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					an image

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$multivar$asDF

as.data.frame(results$multivar)

Examples

# NOT RUN {
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
#  -----------------------------------------------------------------------------------------------
#

# }