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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

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
#  -----------------------------------------------------------------------------------------------
#

jmv

The 'jamovi' Analyses

v1.2.23

GPL (>= 2)

Authors

Ravi Selker, Jonathon Love, Damian Dropmann

Initial release

2020-06-26