R: ANCOVA

ancova {jmv} | R Documentation

## ANCOVA

### Description

Analysis of Covariance

### Usage

    ancova(data, dep, factors = NULL, covs = NULL, modelTerms = NULL,
      ss = "3", effectSize = NULL, contrasts = NULL, plotHAxis = NULL,
      plotSepLines = NULL, plotSepPlots = NULL, postHoc = NULL,
      postHocCorr = list("tukey"), descStats = FALSE, homo = FALSE,
      qq = FALSE, plotError = "ci", ciWidth = 95)

### Arguments

`data` | the data as a data frame
`dep` | a string naming the dependent variable from `data`, variable must be numeric
`factors` | a vector of strings naming the fixed factors from `data`
`covs` | a vector of strings naming the covariates from `data`
`modelTerms` | a list of character vectors describing the terms to go into the model
`ss` | `'1'`, `'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
`contrasts` | a list of lists specifying the factor and type of contrast to use, one of `'deviation'`, `'simple'`, `'difference'`, `'helmert'`, `'repeated'` or `'polynomial'`
`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
`postHoc` | a list of terms to perform post-hoc tests on
`postHocCorr` | one or more of `'none'`, `'tukey'`, `'scheffe'`, `'bonf'`, or `'holm'`; provide no, Tukey, Scheffe, Bonferroni, and Holm Post Hoc corrections respectively
`descStats` | `TRUE` or `FALSE` (default), provide descriptive statistics
`homo` | `TRUE` or `FALSE` (default), perform homogeneity tests
`qq` | `TRUE` or `FALSE` (default), provide a Q-Q plot of residuals
`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

### Value

A results object containing:

`results$main` |  |  |  |  | a table of ANCOVA results
`results$model` |  |  |  |  | The underlying `aov` object
`results$assump$homo` |  |  |  |  | a table of homogeneity tests
`results$assump$qq` |  |  |  |  | a q-q plot
`results$contrasts` |  |  |  |  | an array of contrasts tables
`results$postHoc` |  |  |  |  | an array of post-hoc tables
`results$desc` |  |  |  |  | a table of descriptives
`results$descPlot` |  |  |  |  | a descriptives plot
`results$descPlots` |  |  |  |  | an array of descriptives plots

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')

    ancova(ToothGrowth, dep = 'len', factors = 'supp', covs = 'dose')

    #
    #  ANCOVA
    #
    #  ANCOVA
    #  -----------------------------------------------------------------------
    #                 Sum of Squares    df    Mean Square    F        p
    #  -----------------------------------------------------------------------
    #    supp                    205     1          205.4     11.4     0.001
    #    dose                   2224     1         2224.3    124.0    < .001
    #    Residuals              1023    57           17.9
    #  -----------------------------------------------------------------------
    #

[Package jmv version 0.8.6.2 Index]