calibrate {COMIX} | R Documentation |
This function aligns multiple samples so that their location parameters are equal.
Description
This function aligns multiple samples so that their location parameters are equal.
Usage
calibrate(x, reference.group = NULL)
Arguments
x |
An object of class COMIX. |
reference.group |
An integer between 1 and the number of groups in the data
( |
Value
A named list of 3:
-
Y_cal
: anrow(x$data$Y)
\times
ncol(x$data$Y)
matrix, a calibrated version of the original data. -
calibration_distribution
: anx$pmc$nsave
\times
ncol(x$data$Y)
\times
nrow(x$data$Y)
array storing the difference between the estimated sample-specific location parameter and the group location parameter for each saved step of the chain. -
calibration_median
: anrow(x$data$Y)
\times
ncol(x$data$Y)
matrix storing the median difference between the estimated sample-specific location parameter and the group location parameter for each saved step of the chain. This matrix is equal to the difference between the uncalibrated data (x$data$Y
) and the calibrated data (Y_cal
).
Examples
library(COMIX)
# Number of observations for each sample (row) and cluster (column):
njk <-
matrix(
c(
150, 300,
250, 200
),
nrow = 2,
byrow = TRUE
)
# Dimension of data:
p <- 3
# Scale and skew parameters for first cluster:
Sigma1 <- matrix(0.5, nrow = p, ncol = p) + diag(0.5, nrow = p)
alpha1 <- rep(0, p)
alpha1[1] <- -5
# location parameter for first cluster in first sample:
xi11 <- rep(0, p)
# location parameter for first cluster in second sample (aligned with first):
xi21 <- rep(0, p)
# Scale and skew parameters for second cluster:
Sigma2 <- matrix(-1/3, nrow = p, ncol = p) + diag(1 + 1/3, nrow = p)
alpha2 <- rep(0, p)
alpha2[2] <- 5
# location parameter for second cluster in first sample:
xi12 <- rep(3, p)
# location parameter for second cluster in second sample (misaligned with first):
xi22 <- rep(4, p)
# Sample data:
set.seed(1)
Y <-
rbind(
sn::rmsn(njk[1, 1], xi = xi11, Omega = Sigma1, alpha = alpha1),
sn::rmsn(njk[1, 2], xi = xi12, Omega = Sigma2, alpha = alpha2),
sn::rmsn(njk[2, 1], xi = xi21, Omega = Sigma1, alpha = alpha1),
sn::rmsn(njk[2, 2], xi = xi22, Omega = Sigma2, alpha = alpha2)
)
C <- c(rep(1, rowSums(njk)[1]), rep(2, rowSums(njk)[2]))
prior <- list(zeta = 1, K = 10)
pmc <- list(naprt = 5, nburn = 200, nsave = 200) # Reasonable usage
pmc <- list(naprt = 5, nburn = 2, nsave = 5) # Minimal usage for documentation
# Fit the model:
res <- comix(Y, C, pmc = pmc, prior = prior)
# Relabel to resolve potential label switching issues:
res_relab <- relabelChain(res)
# Generate calibrated data:
cal <- calibrateNoDist(res_relab)
# Compare raw and calibrated data: (see plot in vignette)
# par(mfrow=c(1, 2))
# plot(Y, col = C, xlim = range(Y[,1]), ylim = range(Y[,2]) )
# Get posterior estimates for the model parameters:
res_summary <- summarizeChain(res_relab)
# Check for instance, the cluster assignment labels:
table(res_summary$t)
# Indeed the same as
colSums(njk)
# Or examine the skewness parameter for the non-trivial clusters:
res_summary$alpha[ , unique(res_summary$t)]
# And compare those to
cbind(alpha1, alpha2)
# (see vignette for a more detailed example)