spatial2D: Uncertainty plot in localisation probabilities
In pRoloc: A unifying bioinformatics framework for spatial proteomics

Description Usage Arguments Value Author(s) Examples

Produces a pca plot with spatial variation in localisation probabilities

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spatial2D(object, dims = c(1, 2), cov.function = fields::wendland.cov,
  theta = 1, derivative = 2, k = 1, breaks = c(0.99, 0.95, 0.9,
  0.85, 0.8, 0.75, 0.7), aspect = 0.5)

`object`	A valid object of class `MSnset` with mcmc prediction results from `tagmMCMCpredict`
`dims`	The PCA dimension in which to project he data, default is `c(1,2)`
`cov.function`	The covariance function used default is wendland.cov. See `fields` package.
`theta`	A hyperparameter to the covariance function. See `fields` package. Default is 1.
`derivative`	The number of derivative of the wendland kernel. See `fields` package. Default is 2.
`k`	A hyperparamter to the covariance function. See `fields` package. Default is 1.
`breaks`	Probability values at which to draw the contour bands. Default is `c(0.99, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7)`
`aspect`	A argument to change the plotting aspect of the PCA

Used for side effect of producing plot. Invisibily returns an ggplot object that can be further manipulated

Oliver M. Crook <omc25@cam.ac.uk>

## Not run: 
library("pRolocdata")
data("tan2009r1")

tanres <- tagmMcmcTrain(object = tan2009r1)
tanres <- tagmMcmcProcess(tanres)
tan2009r1 <- tagmMcmcPredict(object = tan2009r1, params = tanres, probJoint = TRUE)
spatial2D(object = tan2009r1)

## End(Not run)