predict.cocktail {fastcox}

make predictions from a "cocktail" object.

Description

Similar to other predict methods, this functions predicts fitted values, link function and more from a fitted cocktail object. This function is modified based on the predict function from the glmnet package.

Usage

## S3 method for class 'cocktail'
predict(object,newx,s=NULL,type=c("link","response","coefficients","nonzero"),...)

Arguments

object

fitted cocktail model object.

newx

matrix of new values for x at which predictions are to be made. Must be a matrix. This argument is not used for type=c("coefficients","nonzero")

s

value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.

type

type of prediction required.

• Type "link" gives the linear predictors for Cox's model.

• Type "response" gives the fitted relative-risk for Cox's model.

• Type "coefficients" computes the coefficients at the requested values for s.

• Type "nonzero" returns a list of the indices of the nonzero coefficients for each value of s.

...

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.

Value

The object returned depends on type.

Author(s)

Yi Yang and Hui Zou
Maintainer: Yi Yang <[e-mail]>

References

Yang, Y. and Zou, H. (2013), "A Cocktail Algorithm for Solving The Elastic Net Penalized Cox's Regression in High Dimensions", Statistics and Its Interface, 6:2, 167-173.
https://github.com/emeryyi/fastcox

Friedman, J., Hastie, T. and Tibshirani, R. (2008) "Regularization Paths for Generalized Linear Models via Coordinate Descent", http://www.stanford.edu/~hastie/Papers/glmnet.pdf
Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010
http://www.jstatsoft.org/v33/i01/

Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011) "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent", Journal of Statistical Software, Vol. 39(5) 1-13
http://www.jstatsoft.org/v39/i05/

See Also

coef method

Examples

data(FHT)
m1<-cocktail(x=FHT$x,y=FHT$y,d=FHT$status,alpha=0.5)
predict(m1,type="nonzero")
predict(m1,newx=FHT$x[1:5,],type="response")