predict: Predict Method for cglasso and cggm Fits

Description

Obtains predictions from an R object inheriting class ‘cglasso’.

Usage

# S3 method for cglasso
predict(object, type = c("B", "mu", "Sigma", "Theta"), X.new, lambda.new, rho.new,
        …)
        
# S3 method for cggm
predict(object, X.new, …)

Arguments

object

an R 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 $λ$ at which predictions are required.

rho.new

value of the tuning parameter $ρ$ at which predictions are required.

…

further arguments passed to or from other methods.

Value

The matrix of predicted values.

Details

If object has S3 class ‘cglasso’, then for a new pair of the tuning parameters $λ$ and $ρ$ , 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 $λ$ and $ρ$ , 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.

Examples

Run this code

# NOT RUN {
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(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(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
# }

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