Section author: Ravi Selker, Jonathon Love, Damian Dropmann

One-Way ANOVA (`anovaOneW`)

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, shows `'gamesHowell'` shows Games-Howell post-hoc tests where no equivalence of variances is assumed, and `'tukey'` 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.

Output

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

One-Way ANOVA (anovaOneW)