jmv: anovaOneW – R documentation

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anovaOneW

One-Way 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. This 'One-Way ANOVA' is a simplified version of the 'normal' ANOVA, allowing only a single explanatory factor, however also providing a Welch's ANOVA. The Welch's ANOVA has the advantage that it need not assume that the variances of all groups are equal.

Usage

anovaOneW(
  data,
  deps,
  group,
  welchs = TRUE,
  fishers = FALSE,
  miss = "perAnalysis",
  desc = FALSE,
  descPlot = FALSE,
  norm = FALSE,
  qq = FALSE,
  eqv = FALSE,
  phMethod = "none",
  phMeanDif = TRUE,
  phSig = TRUE,
  phTest = FALSE,
  phFlag = FALSE,
  formula
)

Arguments

`data`	the data as a data frame
`deps`	a string naming the dependent variables in `data`
`group`	a string naming the grouping or independent variable in `data`
`welchs`	`TRUE` (default) or `FALSE`, perform Welch's one-way ANOVA which does not assume equal variances
`fishers`	`TRUE` or `FALSE` (default), perform Fisher's one-way ANOVA which assumes equal variances
`miss`	`'perAnalysis'` or `'listwise'`, how to handle missing values; `'perAnalysis'` excludes missing values for individual dependent variables, `'listwise'` excludes a row from all analyses if one of its entries is missing.
`desc`	`TRUE` or `FALSE` (default), provide descriptive statistics
`descPlot`	`TRUE` or `FALSE` (default), provide descriptive plots
`norm`	`TRUE` or `FALSE` (default), perform Shapiro-Wilk test of normality
`qq`	`TRUE` or `FALSE` (default), provide a Q-Q plot of residuals
`eqv`	`TRUE` or `FALSE` (default), perform Levene's test for homogeneity of variances
`phMethod`	`'none'`, `'gamesHowell'` or `'tukey'`, which post-hoc tests to provide; `'none'` shows no post-hoc tests, `'gamesHowell'` shows Games-Howell post-hoc tests where no equivalence of variances is assumed, and `'tukey'` shows Tukey post-hoc tests where equivalence of variances is assumed
`phMeanDif`	`TRUE` (default) or `FALSE`, provide mean differences for post-hoc tests
`phSig`	`TRUE` (default) or `FALSE`, provide significance levels for post-hoc tests
`phTest`	`TRUE` or `FALSE` (default), provide test results (t-value and degrees of freedom) for post-hoc tests
`phFlag`	`TRUE` or `FALSE` (default), flag significant post-hoc comparisons
`formula`	(optional) the formula to use, see the examples

Details

For convenience, this method allows specifying multiple dependent variables, resulting in multiple independent tests.

Note that the Welch's ANOVA is the same procedure as the Welch's independent samples t-test.

Value

A results object containing:

`results$anova`					a table of the test results
`results$desc`					a table containing the group descriptives
`results$assump$norm`					a table containing the normality tests
`results$assump$eqv`					a table of homogeneity of variances tests
`results$plots`					an array of groups of plots
`results$postHoc`					an array of post-hoc tables

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

results$anova$asDF

as.data.frame(results$anova)

Examples

data('ToothGrowth')
dat <- ToothGrowth
dat$dose <- factor(dat$dose)

anovaOneW(formula = len ~ dose, data = dat)

#
#  ONE-WAY ANOVA
#
#  One-Way ANOVA (Welch's)
#  ----------------------------------------
#           F       df1    df2     p
#  ----------------------------------------
#    len    68.4      2    37.7    < .001
#  ----------------------------------------
#

jmv

The 'jamovi' Analyses

v1.2.23

GPL (>= 2)

Authors

Ravi Selker, Jonathon Love, Damian Dropmann

Initial release

2020-06-26