superb: superb

In dcousin3/superb: Summary Plots with Adjusted Error Bars

superb

Description

The function suberb() 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. The functions superb() is now the entry point to realize summary plots. Compared to the previously documented superbPlot(), superb() is based on formula and accept long and wide format.

Usage

superb(
  formula,
  data,
  WSFactors = NULL,
  WSDesign = "fullfactorial",
  factorOrder = NULL,
  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 = NULL,
  ...
)

Arguments

formula	a formula describing the design of the data frame
data	Dataframe in wide or long format
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)
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 errorbar == "CI". Default is 0.95.
adjustments	List of adjustments as described below. Default is adjustments = list(purpose = "single", popSize = Inf, decorrelation = "none", samplingDesign = "SRS")
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)

Details

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", "LDr" (with r an integer) 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). "LDr" is local decorrelation (useful for long time series with autoregressive correlation structures; see \insertCitecppf24superb).

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

The formulas can be for long format data using | notation, e.g.,

• superb( extra ~ group | ID, sleep )

or for wide format, using cbind() or crange() notation, e.g.,

• superb( cbind(DV.1.1, DV.2.1,DV.1.2, DV.2.2,DV.1.3, DV.2.3) ~ . , dta, WSFactors = c("a(2)","b(3)"))

• superb( crange(DV.1.1, DV.2.3) ~ . , dta, WSFactors = c("a(2)","b(3)"))

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()

• "lineband" illustrates the confidence intervals as a band;

• "corset" illustrates within-subject designs with individual lines and clouds.

Personalized layouts can also be created (see Vignette5).

Value

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.

References

\insertAllCited

Examples

######################################################################

# Basic example using a built-in dataframe as data. 
# By default, the mean is computed and the error bar are 95% confidence intervals
superb(len ~ dose + supp, ToothGrowth) 

# Example changing the summary statistics to the median and
# the error bar to 80% confidence intervals
superb(len ~ dose + supp, ToothGrowth,
       statistic = "median", errorbar = "CI", gamma = .80) 

# Example introducing adjustments for pairwise comparisons 
# and assuming that the whole population is limited to 200 persons
superb(len ~ dose + supp, ToothGrowth,  
  adjustments = list( purpose = "difference", popSize = 200) )

# This example adds ggplot directives to the plot produced
library(ggplot2)
superb(len ~ dose + supp, ToothGrowth) + 
xlab("Dose") + ylab("Tooth Growth") +
theme_bw()

######################################################################

# The following examples are based on repeated measures
library(gridExtra)
options(superb.feedback = 'none') # shut down 'warnings' and 'design' interpretation messages

# A simple example: The sleep data
# The sleep data are paired data showing the additional time of sleep with 
# the soporific drug #1 (("group = 1") and with the soporific drug #2 ("group = 2"). 
# There is 10 participants with two measurements.
# sleep is available in long format 

# Makes the plots first without decorrelation:
superb( extra ~ group | ID, sleep )
# As seen the error bar are very long. Lets take into consideration correlation...
# ...  with decorrelation (technique Correlation-adjusted CA):
superb(extra ~ group | ID, sleep, 
  # only difference:
  adjustments = list(purpose = "difference", decorrelation = "CA")
)
# The error bars shortened as the correlation is substantial (r = .795).


######################################################################

# Another example: The Orange data
# This example contains 5 trees whose diameter (in mm) has been measured at various age (in days):
data(Orange)

# Makes the plots first without decorrelation:
p1 <- superb( circumference ~ age | Tree, Orange,
  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 <- superb( circumference ~ age | Tree, Orange,
  adjustments = list(purpose = "difference", decorrelation = "CA")
) + 
  xlab("Age level") + ylab("Trunk diameter (mm)") +
  coord_cartesian( ylim = c(0,250) ) + labs(title="Decorrelated confidence intervals")

# You can present both plots side-by-side
grid.arrange(p1, p2, ncol=2)

######################################################################

dcousin3/superb documentation built on Oct. 29, 2024, 5:28 p.m.