# Predict Method for a "cv.glmtlp" Object.

`predict.cv.glmtlp.Rd`

Makes predictions for a cross-validated glmtlp model, using
the stored `"glmtlp"`

object, and the optimal value chosen for
`lambda`

.

## 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.

## 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*.

## 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
```