Predict Method for a "cv.glmtlp" Object.

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, ...)

Arguments

object	Fitted "cv.glmtlp" object.
X	X Matrix of new values for X at which predictions are to be made. Must be a matrix.
type	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.
lambda	Value of the penalty parameter lambda at which predictions are to be made Default is NULL.
kappa	Value of the penalty parameter kappa at which predictions are to be made. Default is NULL.
which	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.

Value

The object returned depends on type.

References

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.

See also

print, predict, coef and plot methods, and the cv.glmtlp function.

Author

Chunlin Li, Yu Yang, Chong Wu
Maintainer: Yu Yang yang6367@umn.edu

Examples

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, ])
#> [1] 0.04564887 0.04564887 0.04564887 0.04564887 0.04564887
coef(cv.fit)
#>  intercept         V1         V2         V3         V4         V5         V6 
#> 0.04564887 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 
#>         V7         V8         V9        V10        V11        V12        V13 
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 
#>        V14        V15        V16        V17        V18        V19        V20 
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 
predict(cv.fit, X = X[1:5, ], lambda = 0.1)
#> [1] -0.06388020 -0.09547175  0.26124340 -0.20715326  0.18311460