MixfMRI (version 0.1-3)

Plotting: Main plotting function

Description

Main plotting function in MixfMRI.

Usage

plotfclust(da, posterior, main = NULL, xlim = NULL, ylim = NULL)
  plotfclustpv(da, posterior, main = NULL, xlim = NULL, ylim = NULL)
  plotpv(da, posterior, PARAM, zlim = c(0, 0.01), plot.mean = TRUE,
         xlab = "", ylab = "", main = NULL, xlim = NULL, ylim = NULL,
         col = my.YlOrRd(), ignore.bg = FALSE)
  plotpvlegend(zlim = c(0, 0.01), n.level = 20, main = NULL,
               col = my.YlOrRd())

Value

Return plots.

Arguments

da

a data set to be plotted.

posterior

a posterior data set to be plotted.

PARAM

a returning parameter object from fclust().

main

title of the plot.

xlim

limits of x-axis.

ylim

limits of y-axis.

zlim

limits of z-axis.

xlab

labels of x-axis.

ylab

labels of y-axis.

plot.mean

if plotting mean values of each cluster.

col

colors to be drawn.

ignore.bg

if ignoring the background.

n.level

number of levels to be plotted.

Author

Wei-Chen Chen and Ranjan Maitra.

Details

These are example functions to plot results, simulations, and datasets.

References

Chen, W.-C. and Maitra, R. (2021) “A Practical Model-based Segmentation Approach for Accurate Activation Detection in Single-Subject functional Magnetic Resonance Imaging Studies”, arXiv:2102.03639.

See Also

set.global().

Examples

library(MixfMRI, quietly = TRUE)
set.seed(1234)
  
# \donttest{
.rem <- function(){

  ### Check 2d data.
  da <- pval.2d.complex
  id <- !is.na(da)
  PV.gbd <- da[id]
  hist(PV.gbd, nclass = 100, main = "p-value")
  
  ### Test 2d data.
  id.loc <- which(id, arr.ind = TRUE)
  X.gbd <- t(t(id.loc) / dim(da))
  ret <- fclust(X.gbd, PV.gbd, K = 3)
  print(ret)
  
  ### p-values of rest clusters.
  ret.lrt <- lrt(PV.gbd, ret$class, K = 3)
  print(ret.lrt)
  ret.lrt2 <- lrt2(PV.gbd, ret$class, K = 3)
  print(ret.lrt2)
  
  ### Plotting.
  par(mfrow = c(2, 2), mar = c(0, 0, 2, 0))
  plotpv(da, ret$posterior, ret$param,
         zlim = c(0.005, 0.008), main = "Mean of Beta Distribution")
  plotpv(da, ret$posterior, ret$param,
         plot.mean = FALSE, main = "p-value")
  par(mar = c(5.1, 4.1, 4.1, 2.1))
  plotpvlegend(zlim = c(0.005, 0.008), main = "Mean of Beta Distribution")
  plotpvlegend(zlim = c(0, 0.01), main = "p-value")

}
# }