predict.glmtlp | R Documentation |
Predicts fitted values, logits, coefficients and more from a fitted
glmtlp
object.
## S3 method for class 'glmtlp' predict( object, X, type = c("link", "response", "class", "coefficients", "numnz", "varnz"), lambda = NULL, kappa = NULL, which = 1:(ifelse(object$penalty == "l0", length(object$kappa), length(object$lambda))), ... ) ## S3 method for class 'glmtlp' coef( object, lambda = NULL, kappa = NULL, which = 1:(ifelse(object$penalty == "l0", length(object$kappa), length(object$lambda))), drop = TRUE, ... )
object |
Fitted |
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. |
drop |
Whether or not keep the dimension that is of length 1. |
coef(...)
is equivalent to predict(type="coefficients",...)
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.
# Gaussian X <- matrix(rnorm(100 * 20), 100, 20) y <- rnorm(100) fit <- glmtlp(X, y, family = "gaussian", penalty = "l1") predict(fit, X = X[1:5, ]) coef(fit) predict(fit, X = X[1:5, ], lambda = 0.1) # Binomial X <- matrix(rnorm(100 * 20), 100, 20) y <- sample(c(0,1), 100, replace = TRUE) fit <- glmtlp(X, y, family = "binomial", penalty = "l1") coef(fit) predict(fit, X = X[1:5, ], type = "response") predict(fit, X = X[1:5, ], type = "response", lambda = 0.01) predict(fit, X = X[1:5, ], type = "class", lambda = 0.01) predict(fit, X = X[1:5, ], type = "numnz", lambda = 0.01)
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