predict.cv.glmtlp | R Documentation |
Makes predictions for a cross-validated glmtlp model, using
the stored "glmtlp"
object, and the optimal value chosen for
lambda
.
## S3 method for class 'cv.glmtlp' predict( object, X, type = c("link", "response", "class", "coefficients", "numnzs", "varnzs"), lambda = NULL, kappa = NULL, which = object$idx.min, ... ) ## S3 method for class 'cv.glmtlp' coef(object, lambda = NULL, kappa = NULL, which = object$idx.min, ...)
object |
Fitted |
X |
X Matrix of new values for |
type |
Type of prediction to be made. For |
lambda |
Value of the penalty parameter |
kappa |
Value of the penalty parameter |
which |
Index of the penalty parameter |
... |
Additional arguments. |
The object returned depends on type
.
Chunlin Li, Yu Yang, Chong Wu
Maintainer: Yu Yang yang6367@umn.edu
Shen, X., Pan, W., & Zhu, Y. (2012).
Likelihood-based selection and sharp parameter estimation.
Journal of the American Statistical Association, 107(497), 223-232.
Shen, X., Pan, W., Zhu, Y., & Zhou, H. (2013).
On constrained and regularized high-dimensional regression.
Annals of the Institute of Statistical Mathematics, 65(5), 807-832.
Li, C., Shen, X., & Pan, W. (2021).
Inference for a Large Directed Graphical Model with Interventions.
arXiv preprint arXiv:2110.03805.
Yang, Y., & Zou, H. (2014).
A coordinate majorization descent algorithm for l1 penalized learning.
Journal of Statistical Computation and Simulation, 84(1), 84-95.
Two R package Github: ncvreg and glmnet.
print
, predict
, coef
and plot
methods,
and the cv.glmtlp
function.
X <- matrix(rnorm(100 * 20), 100, 20) y <- rnorm(100) cv.fit <- cv.glmtlp(X, y, family = "gaussian", penalty = "l1") predict(cv.fit, X = X[1:5, ]) coef(cv.fit) predict(cv.fit, X = X[1:5, ], lambda = 0.1)
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