plot.fbst {fbst}

plot.fbst

Description

Plots the results of a Full Bayesian Significance Test.

Usage

## S3 method for class 'fbst'
plot(x, ..., leftBoundary = -100, rightBoundary = 100, type = "contour", parNames = NULL, 
xlimleft = NULL, xlimright = NULL, xlabString = "Parameter", ylabString = NULL)

Arguments

x

An Object of class "fbst".

...

Additional parameters, see "plot(x, ...)".

leftBoundary

x-coordinate for the left boundary to which is used for visualising the evidence in support of the null hypothesis. Defaults to -100.

rightBoundary

x-coordinate for the right boundary to which is used for visualising the evidence in support of the null hypothesis. Defaults to 100.

type

Relevant only if dim=2. Defaults to "contour" which provides a contour plot of the posterior, with a magenta point that shows the supremum over the null set. Alternatively, "persp" provides a 3-dimensional perspective plot of the posterior.

parNames

Vector of two entries which specifies the names for the parameters. Only relevant if dimensionTheta=2.

xlimleft

The left value for the x-axis range for the plot. Defaults to the minimum value provided in the posterior draws stored in the FBST object.

xlimright

The right value for the x-axis range for the plot. Defaults to the maximum value provided in the posterior draws stored in the FBST object.

xlabString

String for the x-axis label. Defaults to "Parameter".

ylabString

String for the y-axis label. Default to "density".

Details

Plots the surprise function, the supremum of the surprise function restricted to the null set (blue point) and visualises the Bayesian e-value against the sharp null hypothesis as the blue shaded area under the surprise function. The Bayesian e-value in favour of the sharp null hypothesis is visualised as the red shaded area under the surprise function.

Value

Returns a plot.

Author(s)

Riko Kelter

References

For a details, see: https://arxiv.org/abs/2001.10577 and https://arxiv.org/pdf/2001.10577.pdf.

Examples

set.seed(57)
grp1=rnorm(50,0,1.5)
grp2=rnorm(50,0.8,3.2)

p = as.vector(BayesFactor::ttestBF(x=grp1,y=grp2, 
  posterior = TRUE, iterations = 3000, 
  rscale = "medium")[,4])

# flat reference function
res = fbst(posteriorDensityDraws = p, nullHypothesisValue = 0, 
dimensionTheta = 2, dimensionNullset = 1)
plot(res)
plot(res, xlimleft = -1.5, xlimright = 0.5)