superbPlot | R Documentation |

The function `suberbPlot()`

plots standard error or confidence interval for various descriptive
statistics under various designs, sampling schemes, population size and purposes,
according to the `suberb`

framework. See \insertCitecgh21superb for more.

```
superbPlot(
data,
BSFactors = NULL,
WSFactors = NULL,
WSDesign = "fullfactorial",
factorOrder = NULL,
variables,
statistic = "mean",
errorbar = "CI",
gamma = 0.95,
adjustments = list(purpose = "single", popSize = Inf, decorrelation = "none",
samplingDesign = "SRS"),
showPlot = TRUE,
plotStyle = "bar",
preprocessfct = NULL,
postprocessfct = NULL,
clusterColumn = "",
...
)
```

`data` |
Dataframe in wide format |

`BSFactors` |
The name of the columns containing the between-subject factor(s) |

`WSFactors` |
The name of the within-subject factor(s) |

`WSDesign` |
the within-subject design if not a full factorial design (default "fullfactorial") |

`factorOrder` |
Order of factors as shown in the graph (in that order: x axis, groups, horizontal panels, vertical panels) |

`variables` |
The dependent variable(s) as strings |

`statistic` |
The summary statistic function to use as a string |

`errorbar` |
The function that computes the error bar. Should be "CI" or "SE" or any function name if you defined a custom function. Default to "CI" |

`gamma` |
The coverage factor; necessary when |

`adjustments` |
List of adjustments as described below.
Default is |

`showPlot` |
Defaults to TRUE. Set to FALSE if you want the output to be the summary statistics and intervals. |

`plotStyle` |
The type of object to plot on the graph. See full list below. Defaults to "bar". |

`preprocessfct` |
is a transform (or vector of) to be performed first on data matrix of each group |

`postprocessfct` |
is a transform (or vector of) |

`clusterColumn` |
used in conjunction with samplingDesign = "CRS", indicates which column contains the cluster membership |

`...` |
In addition to the parameters above, superbPlot also accept a number of optional arguments that will be transmitted to the plotting function, such as pointParams (a list of ggplot2 parameters to input inside geoms; see ?geom_bar2) and errorbarParams (a list of ggplot2 parameters for geom_errorbar; see ?geom_errorbar) |

The possible adjustements are the following

popsize: Size of the population under study. Defaults to Inf

purpose: The purpose of the comparisons. Defaults to "single". Can be "single", "difference", or "tryon".

decorrelation: Decorrelation method for repeated measure designs. Chooses among the methods "CM", "LM", "CA", "UA", or "none". Defaults to "none". "CA" is correlation-adjusted \insertCitec19superb; "UA" is based on the unitary Alpha method (derived from the Cronbach alpha); see \insertCitelc22superb.

samplingDesign: Sampling method to obtain the sample. implemented sampling is "SRS" (Simple Randomize Sampling) and "CRS" (Cluster-Randomized Sampling).

In version 0.97.5, the layouts for plots are the following:

"bar" Shows the summary statistics with bars and error bars;

"line" Shows the summary statistics with lines connecting the conditions over the first factor;

"point" Shows the summary statistics with isolated points

"pointjitter" Shows the summary statistics along with jittered points depicting the raw data;

"pointjitterviolin" Also adds violin plots to the previous layout

"pointindividualline" Connects the raw data with line along the first factor (which should be a repeated-measure factor)

"raincloud" Illustrates the distribution with a cloud (half_violin_plot) and jittered dots next to it. Looks better when coordinates are flipped

`+coord_flip()`

.

a plot with the correct error bars or a table of those summary statistics. The plot is a ggplot2 object with can be modified with additional declarations.

```
# Basic example using a built-in dataframe as data.
# By default, the mean is computed and the error bar are 95% confidence intervals
superbPlot(ToothGrowth, BSFactors = c("dose", "supp"),
variables = "len")
# Example changing the summary statistics to the median and
# the error bar to 80% confidence intervals
superbPlot(ToothGrowth, BSFactors = c("dose", "supp"),
variables = "len", statistic = "median", errorbar = "CI", gamma = .80)
# Example introducing adjustments for pairwise comparisons
# and assuming that the whole population is limited to 200 persons
superbPlot(ToothGrowth, BSFactors = c("dose", "supp"),
variables = "len",
adjustments = list( purpose = "difference", popSize = 200) )
# This example adds ggplot directives to the plot produced
library(ggplot2)
superbPlot(ToothGrowth, BSFactors = c("dose", "supp"),
variables = "len") +
xlab("Dose") + ylab("Tooth Growth") +
theme_bw()
# This example is based on repeated measures
library(gridExtra)
options(superb.feedback = 'none') # shut down 'warnings' and 'design' interpretation messages
# Use the Orange example, but let's define shorter column names...
names(Orange) <- c("Tree","age","circ")
# ... and turn the data into a wide format using superbToWide:
Orange.wide <- superbToWide(Orange, id = "Tree", WSFactors = c("age"), variable = "circ")
# This example contains 5 trees whose diameter (in mm) has been measured at various age (in days):
head(Orange.wide)
# Makes the plots first without decorrelation:
p1=superbPlot( Orange.wide, WSFactors = "age(7)",
variables = c("circ_118","circ_484","circ_664","circ_1004","circ_1231","circ_1372","circ_1582"),
adjustments = list(purpose = "difference", decorrelation = "none")
) +
xlab("Age level") + ylab("Trunk diameter (mm)") +
coord_cartesian( ylim = c(0,250) ) + labs(title="''Standalone'' confidence intervals")
# ... and then with decorrelation (technique Correlation-adjusted CA):
p2=superbPlot( Orange.wide, WSFactors = "age(7)",
variables = c("circ_118","circ_484","circ_664","circ_1004","circ_1231","circ_1372","circ_1582"),
adjustments = list(purpose = "difference", decorrelation = "CA")
) +
xlab("Age level") + ylab("Trunk diameter (mm)") +
coord_cartesian( ylim = c(0,250) ) + labs(title="Decorrelated confidence intervals")
grid.arrange(p1,p2,ncol=2)
```

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