cglasso (version 2.0.7)

event: Status Indicator Matrix from a ‘datacggm’ Object

Description

The ‘event’ function retrieves the status indicator matrix from an object of class ‘datacggm’.

Usage

event(x, ordered = FALSE)

Value

event returns a \((n\times p)\)-dimensional matrix.

Arguments

x

an object of class ‘datacggm’.

ordered

logical value used to specify if the rows of the status indicator matrix should be ordered according to the patterns of censored/missing values. Default ordered = FALSE.

Author

Luigi Augugliaro ()

Details

The ‘event’ function is used to retrieve the status indicator matrix, denoted by R, from an object of class ‘datacggm’. The entries of the matrix are used to specify the status of the response variable:

• ‘R[i, j] = 0’ means that \(y_{ij}\) is inside the open interval (lo[j], up[j]);

• ‘R[i, j] = -1’ means that \(y_{ij}\) is a left-censored value;

• ‘R[i, j] = +1’ means that \(y_{ij}\) is a right-censored value;

• ‘R[i, j] = +9’ means that \(y_{ij}\) is a missing value.

References

Augugliaro L., Sottile G., Wit E.C., and Vinciotti V. (2023) <tools:::Rd_expr_doi("10.18637/jss.v105.i01")>. cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values. Journal of Statistical Software 105(1), 1--58.

Augugliaro, L., Sottile, G., and Vinciotti, V. (2020) <tools:::Rd_expr_doi("10.1007/s11222-020-09945-7")>. The conditional censored graphical lasso estimator. Statistics and Computing 30, 1273--1289.

Augugliaro, L., Abbruzzo, A., and Vinciotti, V. (2020) <tools:::Rd_expr_doi("10.1093/biostatistics/kxy043")>. \(\ell_1\)-Penalized censored Gaussian graphical model. Biostatistics 21, e1--e16.

See Also

datacggm and rcggm.

Examples

set.seed(123)

# Y ~ N(b0 + XB, Sigma) and
# 1. probability of left/right censored values equal to 0.05
# 2. probability of missing-at-random euqals to 0.05
n <- 100L
p <- 3L
q <- 2
b0 <- runif(p)
B <- matrix(runif(q * p), nrow = q, ncol = p)
X <- matrix(rnorm(n * q), nrow = n, ncol = q)
rho <- 0.3
Sigma <- outer(1L:p, 1L:p, function(i, j) rho^abs(i - j))
Z <- rcggm(n = n, b0 = b0, X = X, B = B, Sigma = Sigma, probl = 0.05, 
           probr = 0.05, probna = 0.05)

# status indicator matrix
event(Z)

# in this case the status indicator matrix is returned with
# rows ordered according to the patterns of missing data
event(Z, ordered = TRUE)