View source: R/cglasso_S3methods.R
predict | R Documentation |
Obtains predictions from an R object inheriting class ‘cglasso
’.
## 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, ...)
object |
an R object inheriting class ‘ |
type |
a description of prediction required. |
X.new |
matrix of new values for |
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. |
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
.
The matrix of predicted values.
Luigi Augugliaro (luigi.augugliaro@unipa.it)
Model-fitting function cglasso
and the other accessor functions coef.cglasso
, fitted.cglasso
, residuals.cglasso
and impute
.
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
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