# Predict Method for cglasso and cggm Fits Obtains predictions from an object inheriting class ‘cglasso’ . ```r ## S3 method for class 'cglasso' predict(object, type = c("B", "mu", "Sigma", "Theta"), X.new, lambda.new, rho.new, ...) ## S3 method for class 'cggm' predict(object, X.new, ...) ``` ## Arguments - `object`: an object inheriting class ‘cglasso’ , that is, the output of the model-fitting function ‘cglasso’ or ‘cggm’ . - `type`: a description of prediction required. - `X.new`: matrix of new values for `X` at which predictions are to be made. This argument is used only if ‘type = "mu"’ . - `lambda.new`: value of the tuning parameter $lambda$ at which predictions are required. - `rho.new`: value of the tuning parameter $rho$ at which predictions are required. - ``...``: further arguments passed to or from other methods. ## Details If `object` has S3 class ‘cglasso’ , then for a new pair of the tuning parameters $lambda$ and $rho$, the `predict` function can be used to predict the estimate of the regression coefficient matrix (‘type = "B"’ ), the estimate of the covariance matrix (‘type = "Sigma"’ ) or the estimate of the precision matrix (‘type = "Theta"’ ). If `X.new` is missing and ‘type = "mu"’ , then the `predict` function returns the predicted values using the matrix of predictors `X`, otherwise the predicted fitted values are computed using the matrix `X.new`. For a new pair of the tuning parameters $lambda$ and $rho$, the predicted values are computed using a bilinear interpolation. If the `object` has S3 class ‘cggm’ , then the `predict` function returns only the predicted fitted values using the argument `X.new`. ## Returns The matrix of predicted values. ## Author(s) Luigi Augugliaro (luigi.augugliaro@unipa.it ) ## See Also Model-fitting function `cglasso` and the other accessor functions `coef.cglasso`, `fitted.cglasso`, `residuals.cglasso` and `impute`. ## Examples ```r set.seed(123) # Y ~ N(0, Sigma) and probability of left/right censored values equal to 0.05 n <- 100L 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) rho.new <- mean(out$rho) Theta.pred <- predict(out, rho.new = rho.new, type = "Theta") Theta.pred # Y ~ N(b0 + XB, Sigma) and probability of left/right censored values equal to 0.05 n <- 100L p <- 3L q <- 2 b0 <- runif(p) B <- matrix(runif(q * p), nrow = q, ncol = p) X <- matrix(rnorm(n * q), nrow = n, ncol = q) rho <- 0.3 Sigma <- outer(1L:p, 1L:p, function(i, j) rho^abs(i - j)) Z <- rcggm(n = n, b0 = b0, X = X, B = B, Sigma = Sigma, probl = 0.05, probr = 0.05) out <- cglasso(. ~ ., data = Z) rho.new <- mean(out$rho) lambda.new <- mean(out$lambda) Theta.pred <- predict(out, lambda.new = lambda.new, rho.new = rho.new, type = "Theta") Theta.pred ```

predict.cglasso function