R: Repeated Measures ANOVA

anovaRM {jmv} | R Documentation

## Repeated Measures ANOVA

### Description

Repeated Measures ANOVA

### Usage

    anovaRM(data, rm = list(list(label = "RM Factor 1", levels = list("Level 1",
      "Level 2"))), rmCells = NULL, bs = NULL, cov = NULL, rmTerms = NULL,
      bsTerms = NULL, ss = "3", effectSize = NULL, spherTests = FALSE,
      spherCorr = list("none"), leveneTest = FALSE, contrasts = NULL,
      postHoc = NULL, postHocCorr = list("tukey"), plotHAxis = NULL,
      plotSepLines = NULL, plotSepPlots = NULL, plotError = "ci",
      ciWidth = 95, descStats = FALSE)

### Arguments

`data` | the data as a data frame
`rm` | a list of lists, where each list describes the `label` (as a string) and the `levels` (as vector of strings) of a particular repeated measures factor
`rmCells` | a list of lists, where each list decribes a repeated measure (as a string) from `data` defined as `measure` and the particular combination of levels from `rm` that it belongs to (as a vector of strings) defined as `cell`
`bs` | a vector of strings naming the between subjects factors from `data`
`cov` | a vector of strings naming the covariates from `data`. Variables must be numeric
`rmTerms` | a list of character vectors describing the repeated measures terms to go into the model
`bsTerms` | a list of character vectors describing the between subjects terms to go into the model
`ss` | `'2'` or `'3'` (default), the sum of squares to use
`effectSize` | one or more of `'eta'`, `'partEta'`, or `'omega'`; use eta², partial eta², and omega² effect sizes, respectively
`spherTests` | `TRUE` or `FALSE` (default), perform sphericity tests
`spherCorr` | one or more of `'none'` (default), `'GG'`, or HF; use no p-value correction, the Greenhouse-Geisser p-value correction, and the Huynh-Feldt p-value correction for shericity, respectively
`leveneTest` | `TRUE` or `FALSE` (default), test for equality of variances (i.e., Levene's test)
`contrasts` | in development
`postHoc` | a list of character vectors describing the post-hoc tests that need to be computed
`postHocCorr` | one or more of `'none'`, `'tukey'` (default), `'scheffe'`, `'bonf'`, or `'holm'`; use no, Tukey, Scheffe, Bonferroni and Holm posthoc corrections, respectively
`plotHAxis` | a string naming the variable placed on the horizontal axis of the plot
`plotSepLines` | a string naming the variable represented as separate lines on the plot
`plotSepPlots` | a string naming the variable to separate over to form multiple plots
`plotError` | `'none'`, `'ci'` (default), or `'se'`. Use no error bars, use confidence intervals, or use standard errors on the plots, respectively
`ciWidth` | a number between 50 and 99.9 (default: 95) specifying the confidence interval width
`descStats` | `TRUE` or `FALSE` (default), provide descriptive statistics

### Value

A results object containing:

`results$rmTable` |  |  |  |  | a table
`results$bsTable` |  |  |  |  | a table
`results$assump$spherTable` |  |  |  |  | a table
`results$assump$leveneTable` |  |  |  |  | a table
`results$contrasts` |  |  |  |  | an array of tables
`results$postHoc` |  |  |  |  | an array of tables
`results$descPlot` |  |  |  |  | an image
`results$descPlots` |  |  |  |  | an array of images

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

`results$rmTable$asDF`

`as.data.frame(results$rmTable)`

### Examples

    ## Not run:

    data('bugs', package = 'jmv')

    anovaRM(
        data = bugs,
        rm = list(
            list(
                label = 'Frightening',
                levels = c('Low', 'High'))),
        rmCells = list(
            list(
                measure = 'LDLF',
                cell = 'Low'),
            list(
                measure = 'LDHF',
                cell = 'High')),
        rmTerms = list(
            'Frightening'))

    #
    #  REPEATED MEASURES ANOVA
    #
    #  Within Subjects Effects
    #  -----------------------------------------------------------------------
    #                  Sum of Squares    df    Mean Square    F       p
    #  -----------------------------------------------------------------------
    #    Frightening              126     1         126.11    44.2    < .001
    #    Residual                 257    90           2.85
    #  -----------------------------------------------------------------------
    #    Note. Type 3 Sums of Squares
    #
    #
    #
    #  Between Subjects Effects
    #  -----------------------------------------------------------------
    #                Sum of Squares    df    Mean Square    F    p
    #  -----------------------------------------------------------------
    #    Residual               954    90           10.6
    #  -----------------------------------------------------------------
    #    Note. Type 3 Sums of Squares
    #

    ## End(Not run)

[Package jmv version 0.8.6.2 Index]