cv.irglmreg_fit function

Internal function of cross-validation for irglmreg

Internal function to conduct k-fold cross-validation for irglmreg, produces a plot, and returns cross-validated loss values for lambda

cv.irglmreg_fit(x, y, weights, offset, lambda=NULL, balance=TRUE, cfun=4, dfun=1, 
                s=1.5, nfolds=10, foldid, type = c("loss", "error"), plot.it=TRUE,
                se=TRUE, n.cores=2, trace=FALSE, parallel=FALSE, ...)

Arguments

• x: x matrix as in irglmreg.

• y: response y as in irglmreg.

• weights: Observation weights; defaults to 1 per observation

• offset: this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases. Currently only one offset term can be included in the formula.

• lambda: Optional user-supplied lambda sequence; default is NULL, and irglmreg chooses its own sequence

• balance: for dfun=4, 5, 6 only

• cfun: a number from 1 to 7, type of convex cap (concave) function

• dfun: a number from 1, 4-7, type of convex downward function

• s: nonconvex loss tuning parameter for robust regression and classification.

• nfolds: number of folds >=3, default is 10

• foldid: an optional vector of values between 1 and nfold

  identifying what fold each observation is in. If supplied, nfold can be missing and will be ignored.

• type: cross-validation criteria. For type="loss", loss function values and type="error" is misclassification error.

• plot.it: a logical value, to plot the estimated log-likelihood values if TRUE.

• se: a logical value, to plot with standard errors.

• n.cores: The number of CPU cores to use. The cross-validation loop will attempt to send different CV folds off to different cores.

• trace: a logical value, print progress of cross validation or not

• parallel: a logical value, parallel computing or not

• ...: Other arguments that can be passed to irglmreg.

Details

The function runs irglmreg nfolds+1 times; the first to compute the lambda sequence, and then to compute the fit with each of the folds omitted. The error or the log-likelihood value is accumulated, and the average value and standard deviation over the folds is computed. Note that cv.irglmreg can be used to search for values for alpha: it is required to call cv.irglmreg with a fixed vector foldid for different values of alpha.

Returns

an object of class "cv.irglmreg" is returned, which is a list with the ingredients of the cross-validation fit. - fit: a fitted irglmreg object for the full data.

• residmat: matrix of loss values or errors with row values for lambda and column values for kth cross-validation

• cv: The mean cross-validated loss values or errors - a vector of length length(lambda).

• cv.error: estimate of standard error of cv.

• foldid: an optional vector of values between 1 and nfold

  identifying what fold each observation is in.

• lambda: a vector of lambda values

• lambda.which: index of lambda that gives minimum cv value.

• lambda.optim: value of lambda that gives minimum cv value.

References

Zhu Wang (2024) Unified Robust Estimation, Australian & New Zealand Journal of Statistics. 66(1):77-102.

Author(s)

Zhu Wang zwang145@uthsc.edu

See Also

irglmreg and plot, predict, and coef methods for "cv.irglmreg" object.