get_map: Get MAP estimate of cluster indicators

In carter-allen/spruce: Spatial Random Effects Clustering of Single Cell Data

Get MAP estimate of cluster indicators

Description

Compute maximum aposteriori (MAP) estimate of cluster indicators

Usage

get_map(z)

Arguments

z	All cluster indicator posterior samples from a given cell spot

Value

MAP estimate of cluster labels. Useful applied over columns of posterior samples matrix (see example)

Examples

# parameters
n <- 100 # number of observations
g <- 3 # number of features
K <- 3 # number of clusters (mixture components)
pi <- rep(1/K,K) # cluster membership probability
z <- sample(1:K, size = n, replace = TRUE, prob = pi) # cluster indicators
z <- remap_canonical2(z)

# Cluster Specific Parameters
# cluster specific means
Mu <- list(
  Mu1 = rnorm(g,-5,1),
  Mu2 = rnorm(g,0,1),
  Mu3 = rnorm(g,5,1)
)
# cluster specific variance-covariance
S <- matrix(1,nrow = g,ncol = g) # covariance matrix
diag(S) <- 1.5
Sig <- list(
  Sig1 = S,
  Sig2 = S, 
  Sig3 = S
)

Y <- matrix(0, nrow = n, ncol = g)
for(i in 1:n)
{
  Y[i,] <- mvtnorm::rmvnorm(1,mean = Mu[[z[i]]],sigma = Sig[[z[i]]])
}

# fit model
fit1 <- fit_mvn(Y,3,100,0)

# Apply get_map() to columns of Z (i.e., posterior samples from each cell spot)
z_map <- apply(fit1$Z, 2, get_map)

carter-allen/spruce documentation built on April 6, 2024, 8:34 p.m.