FMCensSkewReg (version 0.1.1)

EM.skewCens.mixR: EM Algorithm for Finite Mixture Censored Regression

Description

Fits finite mixture censored regression models under four families: Normal ("Normal"), Student-t ("T"), Skew-Normal ("SN"), and Skew-t ("ST").

Usage

EM.skewCens.mixR(
  cc,
  y,
  x,
  Abetas = NULL,
  sigma2 = NULL,
  shape = NULL,
  pii = NULL,
  nu = NULL,
  g = NULL,
  get.init = TRUE,
  criteria = TRUE,
  group = FALSE,
  family = "Normal",
  error = 1e-05,
  iter.max = 100,
  obs.prob = FALSE,
  kmeans.param = NULL,
  aitken = TRUE,
  IM = TRUE
)

Value

A list with elements:

Abetas

Estimated regression coefficients (p x g).

sigma2

Estimated variances (length g).

shape

Estimated skewness parameters (length g; SN/ST).

pii

Estimated mixing proportions (length g).

sd

Standard errors (if IM=TRUE).

nu

Estimated/used degrees of freedom (T/ST).

loglik

Final log-likelihood.

loglikT

Log-likelihood trace over iterations.

aic,bic,edc

Information criteria (if criteria=TRUE).

iter

Number of EM iterations.

n

Sample size.

group

Hard labels (if group=TRUE).

Arguments

cc

Integer vector of length n; censoring indicator (1 = censored, 0 = observed).

y

Numeric response vector (univariate).

x

Numeric design matrix (n x p); include intercept column if needed.

Abetas

Optional initial regression coefficient matrix (p x g).

sigma2

Optional initial variance(s), length g.

shape

Optional initial skewness parameter(s), length g (used in SN/ST).

pii

Optional initial mixing proportions, length g, must sum to 1.

nu

Degrees of freedom for T/ST models (scalar).

g

Number of mixture components (\(g \ge 1\)). Required if get.init = TRUE.

get.init

Logical; if TRUE, k-means-based initialization is used.

criteria

Logical; if TRUE, returns AIC/BIC/EDC.

group

Logical; if TRUE, returns hard cluster labels.

family

One of "Normal", "T", "SN", "ST".

error

Convergence tolerance for EM.

iter.max

Maximum number of EM iterations.

obs.prob

Logical; if TRUE, returns posterior membership matrix.

kmeans.param

Optional list for kmeans init.

aitken

Logical; use Aitken acceleration for convergence monitoring.

IM

Logical; if TRUE, compute (robust) standard errors via information matrix.

Details

Left-censoring is indicated by cc[i] = 1 and replacing y[i] by the censoring point. The routine supports Normal, t, Skew-Normal, and Skew-t families with finite mixtures.

Examples

if (FALSE) { # requireNamespace("mixsmsn", quietly = TRUE)
set.seed(1)
n <- 150
X <- cbind(1, runif(n), rnorm(n))
pi <- c(0.6, 0.4); nu <- 4
b1 <- c(0.5, 1.0, -1.0); sigma1 <- 1; shape1 <- 2
b2 <- c(1.0,-0.5, 0.5);  sigma2 <- 2; shape2 <- 3
mu1 <- drop(X %*% b1); mu2 <- drop(X %*% b2)
draw1 <- function(i){
  a1 <- list(mu=mu1[i], sigma2=sigma1, shape=shape1, nu=nu)
  a2 <- list(mu=mu2[i], sigma2=sigma2, shape=shape2, nu=nu)
  mixsmsn::rmix(1, pi, "Skew.t", list(a1,a2), cluster=FALSE)
}
y0 <- vapply(seq_len(n), draw1, numeric(1))
cutoff <- unname(stats::quantile(y0, 0.20))
cc <- as.integer(y0 <= cutoff)
y  <- ifelse(cc == 1, cutoff, y0)
fit <- EM.skewCens.mixR(cc, y, X, g=2, family="Normal", iter.max=50)
fit$loglik
}