jmv

MANCOVA

Multivariate Analysis of Covariance

Example usage

data('iris')

mancova(data = iris,
    deps = c('Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width'),
    factors = 'Species')

#
#  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)