Description Usage Arguments Value Examples
View source: R/predict_tune_xrnet.R
Extract coefficients or predict response in new data using fitted model from a tune_xrnet
object.
Note that we currently only support returning results that are in the original path(s).
1 2 3 4 5 6 7 8 9 10 |
object |
A |
newdata |
matrix with new values for penalized variables |
newdata_fixed |
matrix with new values for unpenalized variables |
p |
vector of penalty values to apply to predictor variables. Default is optimal value in tune_xrnet object. |
pext |
vector of penalty values to apply to external data variables. Default is optimal value in tune_xrnet object. |
type |
type of prediction to make using the xrnet model, options include:
|
... |
pass other arguments to xrnet function (if needed) |
The object returned is based on the value of type as follows:
response: An array with the response predictions based on the data for each penalty combination
link: An array with linear predictions based on the data for each penalty combination
coefficients: A list with the coefficient estimates for each penalty combination. See coef.xrnet
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | data(GaussianExample)
## 5-fold cross validation
cv_xrnet <- tune_xrnet(
x = x_linear,
y = y_linear,
external = ext_linear,
family = "gaussian",
control = xrnet.control(tolerance = 1e-6)
)
## Get coefficients and predictions at optimal penalty combination
coef_xrnet <- predict(cv_xrnet, type = "coefficients")
pred_xrnet <- predict(cv_xrnet, newdata = x_linear, type = "response")
|
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