dot-loss: Cross-validation loss
In palasso: Sparse Regression with Paired Covariates
.loss R Documentation
Cross-validation loss
Description
Calculates mean cross-validated loss
Usage
.loss(y, fit, family, type.measure, foldid = NULL)
Arguments
y
response:
vector of length n
fit
matrix with one row for each sample
("gaussian", "binomial" and "poisson"),
or one row for each fold (only "cox"),
and one column for each lambda
(output from .fit)
family
model family:
character "gaussian", "binomial", "poisson", or "cox"
type.measure
...
loss function:
character "deviance", "mse", "mae", "class", or "auc"
foldid
fold identifiers:
vector of length n,
with entries from 1 to nfolds
Value
Returns list of vectors, one for each model.
Examples
NA
palasso documentation built on Sept. 30, 2024, 9:30 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| .loss | R Documentation |
Cross-validation loss
Description
Calculates mean cross-validated loss
Usage
.loss(y, fit, family, type.measure, foldid = NULL)
Arguments
y |
response:
vector of length |
fit |
matrix with one row for each sample
("gaussian", "binomial" and "poisson"),
or one row for each fold (only "cox"),
and one column for each |
family |
model family: character "gaussian", "binomial", "poisson", or "cox" |
type.measure |
... loss function: character "deviance", "mse", "mae", "class", or "auc" |
foldid |
fold identifiers:
vector of length |
Value
Returns list of vectors, one for each model.
Examples
NA
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.