Summary Plots with Adjusted Error Bars

Vignettes

- Package overview
- README.md
- Adding labels to ``superb`` plots"
- (advanced) Alternate ways to decorrelate repeated measures from transformations"
- (advanced) Non-factorial within-subject designs in ``superb``"
- Customizing `superb` plots"
- Generating ready-to-analyze datasets with GRD"
- Plotting Cohen's d with ``superb``"
- Plotting frequencies using ``superb``"
- Plotting proportions with ``superb``"
- Plotting Reference Intervals with ``superb``"
- ``superb`` and SPSS"
- The making-of the figures in the article"
- Three steps to make your plot"
- Unequal variances, Welch test, Tryon adjustment, and ``superb``"
- Using a custom plot layout within ``superb``"
- Using a custom statistic with its error bar within ``superb``"
- Why use correlation-adjusted confidence intervals?"
- Why use difference-adjusted confidence intervals?"

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**biasCorrectionTransform:**bias-correction transform**bootstrapPrecisionMeasures:**Bootstrapped measures of precision**CousineauLaurencelleLambda:**Cousineau-Laurencelle's lambda correction for...**dataFigure1:**Data for Figure 1**dataFigure2:**Data for Figure 2**dataFigure3:**Data for Figure 3**dataFigure4:**Data for Figure 4**geom_superberrorbar:**geom_superberrorbar for expanded error bar displays**GRD:**Generate random data**HyunhFeldtEpsilon:**Hyunh and Feldt's epsilon measure of sphericity**makeTransparent:**makes ggplots with transparent elements**MauchlySphericityTest:**Mauchly's test of Sphericity**measuresWithMissingData:**Measures with missing data**poolSDTransform:**pooled standard deviation transform**precisionMeasures:**Precision measures**precisionMeasureWithCustomDF:**Confidence intervals with custom degree of freedom**runDebug:**runDebug**showSignificance:**Annotate significance of results on plots**ShroutFleissICC1:**Shrout and Fleiss intra-class correlation functions**slope:**Effect description**subjectCenteringTransform:**subject-centering transform**summaryStatistics:**Additional summary statistics**superbData:**Obtain summary statistics with correct error bars.**superb-package:**superb: Summary Plots with Adjusted Error Bars**superbPlot:**summary plot of any statistics with adjusted error bars.**superbPlot.bar:**superbPlot 'bar' layout**superbPlot.boxplot:**superbPlot 'boxplot' layout**superbPlot.halfwidthline:**superbPlot 'halfwidthline' layout**superbPlot.line:**superbPlot 'line' layout**superbPlot.lineBand:**superbPlot 'lineBand' layout**superbPlot.point:**superbPlot 'point' layout**superbPlot.pointindividualline:**superbPlot point and individual-line layout for...**superbPlot.pointjitter:**superbPlot point-and-jitter dots layout**superbPlot.pointjitterviolin:**superbPlot point, jitter and violin plot layout**superbPlot.raincloud:**superbPlot 'raincloud' layout**superbShiny:**User Interface to get summary plot of any statistics with...**superbToWide:**superbToWide: Reshape long data frame to wide, suitable for...**TMB1964r:**Data of Tulving, Mandler, & Baumal, 1964 (reproduction of...**twoStepTransform:**two-step transform for subject centering and bias correction**WelchDegreeOfFreedom:**Welch's rectified degree of freedom**WinerCompoundSymmetryTest:**Winer's test of compound symmetry**Browse all...**

slope | R Documentation |

There is four ways that effects can be defined in GRD. "factor = slope(s)" will vary the means by an amount of s for each step of the factor; "factor = extent(s)" will vary the means uniformly so that there is a difference of s between the first and the last factor level; "factor = custom(a,b,c..)" will alter each means by an amount of a for the first, b for the second, etc. Finally "factor = Rexpression("R code")" will apply R code to all levels of the factors. R code result alters the base mean.

```
slope(s)
extent(s)
custom(...)
Rexpression(str)
```

`s` |
the size of the effect |

`...` |
a sequence with the sizes of the effects |

`str` |
R code string |

These internal functions are not meant to be used in isolation in any meaningful way...

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