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Makes predictions for a cross-validated glmtlp model, using
the stored "glmtlp" object, and the optimal value chosen for
lambda.
# S3 method for cv.glmtlp
predict(
object,
X,
type = c("link", "response", "class", "coefficients", "numnzs", "varnzs"),
lambda = NULL,
kappa = NULL,
which = object$idx.min,
...
)# S3 method for cv.glmtlp
coef(object, lambda = NULL, kappa = NULL, which = object$idx.min, ...)
The object returned depends on type.
Fitted "cv.glmtlp" object.
X Matrix of new values for X at which predictions are to be
made. Must be a matrix.
Type of prediction to be made. For "gaussian" models, type
"link" and "response" are equivalent and both give the fitted
values. For "binomial" models, type "link" gives the linear
predictors and type "response" gives the fitted probabilities.
Type "coefficients" computes the coefficients at the provided values
of lambda or kappa. Note that for "binomial"
models, results are returned only for the class corresponding to the second
level of the factor response. Type "class" applies only to
"binomial" models, and gives the class label corresponding to the
maximum probability. Type "numnz" gives the total number of non-zero
coefficients for each value of lambda or kappa. Type
"varnz" gives a list of indices of the nonzero coefficients for
each value of lambda or kappa.
Value of the penalty parameter lambda at which predictions
are to be made Default is NULL.
Value of the penalty parameter kappa at which predictions
are to be made. Default is NULL.
Index of the penalty parameter lambda or kappa
sequence at which predictions are to be made. Default is the idx.min
stored in the cv.glmtp object.
Additional arguments.
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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