cv.glmreg_fit function

Internal function of cross-validation for glmreg

Internal function to conduct k-fold cross-validation for glmreg, produces a plot, and returns cross-validated log-likelihood values for lambda

cv.glmreg_fit(x, y, weights, offset, lambda=NULL, balance=TRUE, 
              family=c("gaussian", "binomial", "poisson", "negbin"), 
              type=c("loss", "error"), nfolds=10, foldid, plot.it=TRUE,
              se=TRUE, n.cores=2, trace=FALSE, parallel=FALSE, ...)

Arguments

• x: x matrix as in glmreg.

• y: response y as in glmreg.

• 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 glmreg chooses its own sequence

• balance: for family="binomial" only

• family: response variable distribution

• type: cross-validation criteria. For type="loss", loss function (log-negative-likelihood) values and type="error" is misclassification error if family="binomial".

• 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.

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

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

• parallel, n.cores: a logical value, parallel computing or not with 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

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

Details

The function runs glmreg 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.glmreg can be used to search for values for alpha: it is required to call cv.glmreg with a fixed vector foldid for different values of alpha.

Returns

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

• residmat: matrix of log-likelihood values with row values for lambda and column values for kth cross-validation

• cv: The mean cross-validated log-likelihood values - a vector of 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 maximum cv value.

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

References

Zhu Wang, Shuangge Ma, Michael Zappitelli, Chirag Parikh, Ching-Yun Wang and Prasad Devarajan (2014) Penalized Count Data Regression with Application to Hospital Stay after Pediatric Cardiac Surgery, Statistical Methods in Medical Research. 2014 Apr 17. [Epub ahead of print]

Author(s)

Zhu Wang zwang145@uthsc.edu

See Also

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