superbData | R Documentation |

The function `suberbData()`

computes 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.

```
superbData(
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"),
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 (x axis, groups, horizontal panels, vertical panels) |

`variables` |
The dependent variable(s) |

`statistic` |
The summary statistic function to use |

`errorbar` |
The function that computes the error bar. Should be "CI" or "SE" or any function name. Defaults to "CI" |

`gamma` |
The coverage factor; necessary when errorbar == "CI". Default is 0.95. |

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

`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 |

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" or "none". Defaults to "none".

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

a list with (1) the summary statistics in summaryStatistics (2) the raw data in long format in rawData (using numeric levels for repeated-measure variables).

```
# Basic example using a built-in dataframe as data;
# by default, the mean is computed and the error bar are 95% confidence intervals
# (it also produces a $rawData dataframe, not shown here)
res <- superbData(ToothGrowth, BSFactors = c("dose", "supp"),
variables = "len")
res$summaryStatistics
# Example introducing adjustments for pairwise comparisons
# and assuming that the whole population is limited to 200 persons
res <- superbData(ToothGrowth, BSFactors = c("dose", "supp"),
variables = "len",
statistic = "median", errorbar = "CI", gamma = .80,
adjustments = list( purpose = "difference", popSize = 200) )
res$summaryStatistics
```

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