この節の作者: Ravi Selker, Jonathon Love, Damian Dropmann
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 |
Output¶
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
# -----------------------------------------------------------------------------------------------
#