# Extract Model Residuals Extracts model residuals from an object inheriting class ‘cglasso’ . ```r ## S3 method for class 'cglasso' residuals(object, type = c("observed", "working"), lambda.id, rho.id, drop = TRUE, ...) ``` ## Arguments - `object`: an object inheriting class ‘cglasso’ , that is, the output of the model-fitting functions `cglasso` and `cggm`. - `type`: a description of the desired residuals (see section Details ). - `lambda.id`: a vector of integers used to specify the $lambda$-values. - `rho.id`: a vector of integers used to specify the $rho$-values. - `drop`: logical. Dimensions can only be dropped if their extent is one. - ``...``: further arguments passed to or from other methods. ## Details The accessor function ‘residuals’ returns an array storing the observed or working residuals, depending on the argument `type`. The observed residuals are defined as the difference between the observed response values and the corresponding fitted expected values. For missing and censored response values, the observed residuals are set equal to ‘NA’ . The working residuals are obtained as a byproduct of the EM-algorithm used to fit the model. In the E-step, the algorithm computes the working response matrix, that is, the response matrix with missing values replaced by the conditional expected values of the multivariate normal distribution and censored values replaced by the conditional expected values of the multivariate truncated normal distribution. The working residuals are defined as the difference between working responses and fitted expected values. ## Returns The accessor function ‘residuals’ returns an array storing the desired residuals. ## Author(s) Luigi Augugliaro (luigi.augugliaro@unipa.it ) ## See Also Model-fitting functions `cglasso`, `cggm` and the accessor functions `coef.cglasso`, `fitted.cglasso`, `predict.cglasso` and `impute`. ## Examples ```r set.seed(123) # Y ~ N(0, Sigma) and probability of left/right censored values equal to 0.05 n <- 1000L p <- 3L rho <- 0.3 Sigma <- outer(1L:p, 1L:p, function(i, j) rho^abs(i - j)) Z <- rcggm(n = n, Sigma = Sigma, probl = 0.05, probr = 0.05) out <- cglasso(. ~ ., data = Z) residuals(out, type = "observed", rho.id = 3L, drop = TRUE) residuals(out, type = "working", rho.id = 3L, drop = TRUE) # Y ~ N(b0 + XB, Sigma) and probability of left/right censored values equal to 0.05 n <- 1000L 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) out <- cglasso(. ~ ., data = Z) residuals(out, type = "observed", lambda.id = 3L, rho.id = 3L, drop = TRUE) residuals(out, type = "working", lambda.id = 3L, rho.id = 3L, drop = TRUE) ```

residuals.cglasso function