jmv: ANOVA – R documentation

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ANOVA

Description

The Analysis of Variance (ANOVA) is used to explore the relationship between a continuous dependent variable, and one or more categorical explanatory variables.

Usage

ANOVA(
  data,
  dep,
  factors = NULL,
  effectSize = NULL,
  modelTest = FALSE,
  modelTerms = NULL,
  ss = "3",
  homo = FALSE,
  norm = FALSE,
  qq = FALSE,
  contrasts = NULL,
  postHoc = NULL,
  postHocCorr = list("tukey"),
  postHocES = list(),
  emMeans = list(list()),
  emmPlots = TRUE,
  emmPlotData = FALSE,
  emmPlotError = "ci",
  emmTables = FALSE,
  emmWeights = TRUE,
  ciWidthEmm = 95,
  formula
)

Arguments

`data`	the data as a data frame
`dep`	the dependent variable from `data`, variable must be numeric (not necessary when providing a formula, see examples)
`factors`	the explanatory factors in `data` (not necessary when providing a formula, see examples)
`effectSize`	one or more of `'eta'`, `'partEta'`, or `'omega'`; use eta², partial eta², and omega² effect sizes, respectively
`modelTest`	`TRUE` or `FALSE` (default); perform an overall model test
`modelTerms`	a formula describing the terms to go into the model (not necessary when providing a formula, see examples)
`ss`	`'1'`, `'2'` or `'3'` (default), the sum of squares to use
`homo`	`TRUE` or `FALSE` (default), perform homogeneity tests
`norm`	`TRUE` or `FALSE` (default), perform Shapiro-Wilk tests of normality
`qq`	`TRUE` or `FALSE` (default), provide a Q-Q plot of residuals
`contrasts`	a list of lists specifying the factor and type of contrast to use, one of `'deviation'`, `'simple'`, `'difference'`, `'helmert'`, `'repeated'` or `'polynomial'`
`postHoc`	a formula containing the terms to perform post-hoc tests on (see the examples)
`postHocCorr`	one or more of `'none'`, `'tukey'`, `'scheffe'`, `'bonf'`, or `'holm'`; provide no, Tukey, Scheffe, Bonferroni, and Holm Post Hoc corrections respectively
`postHocES`	a possible value of `'d'`; provide cohen's d measure of effect size for the post-hoc tests
`emMeans`	a formula containing the terms to estimate marginal means for (see the examples)
`emmPlots`	`TRUE` (default) or `FALSE`, provide estimated marginal means plots
`emmPlotData`	`TRUE` or `FALSE` (default), plot the data on top of the marginal means
`emmPlotError`	`'none'`, `'ci'` (default), or `'se'`. Use no error bars, use confidence intervals, or use standard errors on the marginal mean plots, respectively
`emmTables`	`TRUE` or `FALSE` (default), provide estimated marginal means tables
`emmWeights`	`TRUE` (default) or `FALSE`, weigh each cell equally or weigh them according to the cell frequency
`ciWidthEmm`	a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the estimated marginal means
`formula`	(optional) the formula to use, see the examples

Details

ANOVA assumes that the residuals are normally distributed, and that the variances of all groups are equal. If one is unwilling to assume that the variances are equal, then a Welch's test can be used instead (However, the Welch's test does not support more than one explanatory factor). Alternatively, if one is unwilling to assume that the data is normally distributed, a non-parametric approach (such as Kruskal-Wallis) can be used.

Value

A results object containing:

`results$main`					a table of ANOVA results
`results$model`					The underlying `aov` object
`results$assump$homo`					a table of homogeneity tests
`results$assump$norm`					a table of normality tests
`results$assump$qq`					a q-q plot
`results$contrasts`					an array of contrasts tables
`results$postHoc`					an array of post-hoc tables
`results$emm`					an array of the estimated marginal means plots + tables

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

results$main$asDF

as.data.frame(results$main)

Examples

data('ToothGrowth')

ANOVA(formula = len ~ dose * supp, data = ToothGrowth)

#
#  ANOVA
#
#  ANOVA
#  -----------------------------------------------------------------------
#                 Sum of Squares    df    Mean Square    F        p
#  -----------------------------------------------------------------------
#    dose                   2426     2         1213.2    92.00    < .001
#    supp                    205     1          205.4    15.57    < .001
#    dose:supp               108     2           54.2     4.11     0.022
#    Residuals               712    54           13.2
#  -----------------------------------------------------------------------
#

ANOVA(
    formula = len ~ dose * supp,
    data = ToothGrowth,
    emMeans = ~ supp + dose:supp, # est. marginal means for supp and dose:supp
    emmPlots = TRUE,              # produce plots of those marginal means
    emmTables = TRUE)             # produce tables of those marginal means

jmv

The 'jamovi' Analyses

v1.2.23

GPL (>= 2)

Authors

Ravi Selker, Jonathon Love, Damian Dropmann

Initial release

2020-06-26