predict.lrome: make predictions from a "lrome" object.

In emeryyi/rome: Robust Mean Estimation

Description

Similar to other predict methods, this functions predicts fitted values and class labels from a fitted lrome object.

Usage

## S3 method for class 'lrome'
predict(object, newx, s = NULL, ...)

Arguments

object	fitted lrome model object.
newx	matrix of new values for x at which predictions are to be made. NOTE: newx must be a matrix, predict function does not accept a vector or other formats of newx.
s	value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.
...	Not used. Other arguments to predict.

Details

s is the new vector at which predictions are requested. If s is not in the lambda sequence used for fitting the model, the predict function will use linear interpolation to make predictions. The new values are interpolated using a fraction of predicted values from both left and right lambda indices.

References

Yang, Y. and Zou, H. (2012), "An Efficient Algorithm for Computing The HHSVM and Its Generalizations," Journal of Computational and Graphical Statistics, 22, 396-415.
BugReport: https://github.com/emeryyi/fastcox.git

Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths for generalized linear models via coordinate descent," Journal of Statistical Software, 33, 1.
http://www.jstatsoft.org/v33/i01/

See Also

coef method

Examples

data(FHT)
m1 = lrome(x=FHT$x,y=FHT$y)
print(predict(m1,newx=FHT$x[2:5,]))

emeryyi/rome documentation built on May 6, 2019, 9:53 a.m.