selectKPlot: selectKPlot.
In SC.MEB: Spatial Clustering with Hidden Markov Random Field using Empirical Bayes

Description Usage Arguments Details Value Examples

View source: R/SC-MEB.R

The function selectKPlot is used to demonstrate the scatter plot of BIC or Modified BIC vs K for selecting the best K.

1	selectKPlot(SCobject, K_set = 2:10, criterion = "BIC", c = 1)

`SCobject`	is a object generated from SC.MEB function.
`K_set`	is the corresponding K_set used in your previous function SC.MEB.
`criterion`	is a character specifying the criterion for selecting K. The default is BIC, the alternative criterion MBIC can also be used.
`c`	is a positive value in modified BIC. The default is 1. Here we briefly explain how to choose the parameter c in the modified BIC. In general, For the ST or Visium dataset, it often ranges from 0.4 to 1 while for the MERFISH dataset with large number of cells, it often becomes larger, for example 10,20. Most importantly, SC-MEB is fast, scaling well in terms of sample size, which allow the user to tune the c based on their prior knowledge about the tissues or cells.

The function selectKPlot is used to demonstrate the scatter plot of BIC or Modified BIC vs K for selecting the best K.

a ggplot2 object.

y = matrix(rnorm(50, 0, 1), 25,2)
pos = cbind(rep(1:5, each=5), rep(1:5, 5))
Adj_sp = getneighborhood_fast(pos, 1.2)
beta_grid = c(0.5,1)
K_set = 2:3
out = SC.MEB(y, Adj_sp, beta_grid, K_set, TRUE, 2)
selectKPlot(out, K_set)