MANOVA.wide: Tests for Multivariate Data in Semi-Parametric Factorial...

In smn74/MANOVA.RM: Resampling-Based Analysis of Multivariate Data and Repeated Measures Designs

Tests for Multivariate Data in Semi-Parametric Factorial Designs

Description

The MANOVA.wide function calculates the Wald-type statistic (WTS) and a modified ANOVA-type statistic (MATS) as well as resampling versions of these test statistics for semi-parametric multivariate data provided in wide format.

Usage

MANOVA.wide(
  formula,
  data,
  iter = 10000,
  alpha = 0.05,
  resampling = "paramBS",
  para = FALSE,
  CPU,
  seed,
  nested.levels.unique = FALSE,
  dec = 3,
  ...
)

Arguments

formula	A model formula object. The left hand side contains the matrix of response variables and the right hand side contains the factor variables of interest.
data	A data.frame, list or environment containing the variables in formula. Data must be in wide format. Note: Lines containing missing values will be removed.
iter	The number of iterations used for calculating the resampled statistic. The default option is 10,000.
alpha	A number specifying the significance level; the default is 0.05.
resampling	The resampling method to be used, one of "paramBS" (parametric bootstrap approach) and "WildBS" (wild bootstrap approach with Rademacher weights). The Wild Bootstrap is calculated for all test statistics.
para	If parallel computing should be used. Default is FALSE.
CPU	The number of cores used for parallel computing. If not specified, cores are detected via detectCores.
seed	A random seed for the resampling procedure. If omitted, no reproducible seed is set.
nested.levels.unique	A logical specifying whether the levels of the nested factor(s) are labeled uniquely or not. Default is FALSE, i.e., the levels of the nested factor are the same for each level of the main factor. For an example and more explanations see the GFD package and the corresponding vignette.
dec	Number of decimals the results should be rounded to. Default is 3.
...	Not used yet.

Value

See MANOVA

NOTE

The number of resampling iterations has been set to 100 in the examples due to run time restrictions on CRAN. Usually it is recommended to use at least 1000 iterations.

See Also

MANOVA

Examples

#Example on producing plastic film from Krzanowski (1998, p. 381), see \code{\link{manova.summary}}
tear <- c(6.5, 6.2, 5.8, 6.5, 6.5, 6.9, 7.2, 6.9, 6.1, 6.3,
          6.7, 6.6, 7.2, 7.1, 6.8, 7.1, 7.0, 7.2, 7.5, 7.6)
gloss <- c(9.5, 9.9, 9.6, 9.6, 9.2, 9.1, 10.0, 9.9, 9.5, 9.4,
           9.1, 9.3, 8.3, 8.4, 8.5, 9.2, 8.8, 9.7, 10.1, 9.2)
opacity <- c(4.4, 6.4, 3.0, 4.1, 0.8, 5.7, 2.0, 3.9, 1.9, 5.7,
             2.8, 4.1, 3.8, 1.6, 3.4, 8.4, 5.2, 6.9, 2.7, 1.9)
rate     <- gl(2,10, labels = c("Low", "High"))
additive <- gl(2, 5, length = 20, labels = c("Low", "High"))
example <- data.frame(tear, gloss, opacity, rate, additive)
fit <- MANOVA.wide(cbind(tear, gloss, opacity) ~ rate * additive, 
data = example, iter = 1000)
summary(fit)

smn74/MANOVA.RM documentation built on Aug. 30, 2023, 12:01 a.m.