# Choice of penalty parameter in ahazpen Tuning of penalty parameters for the penalized semiparametric additive hazards model via cross-validation - or via non-stochastic procedures, akin to BIC for likelihood-based models. ```r tune.ahazpen(surv, X, weights, standardize=TRUE, penalty=lasso.control(), tune=cv.control(), dfmax=nvars, lambda, ...) ``` ## Arguments - `surv`: Response in the form of a survival object, as returned by the function `Surv()` in the package `survival`. Right-censored and counting process format (left-truncation) is supported. Tied survival times are not supported. - `X`: Design matrix. Missing values are not supported. - `weights`: Optional vector of observation weights. Default is 1 for each observation. - `standardize`: Logical flag for variable standardization, prior to model fitting. Parameter estimates are always returned on the original scale. Default is `standardize=TRUE`. - `penalty`: A description of the penalty function to be used for model fitting. This can be a character string naming a penalty function (currently `"lasso"` or stepwise SCAD, `"sscad"`) or it can be a call to the penalty function. Default is `penalty=lasso.control()`. See `ahazpen.pen.control` for the available penalty functions and advanced options; see also the examples. - `dfmax`: Limit the maximum number of covariates included in the model. Default is `nvars=nobs-1`. Unless a complete regularization path is needed, it is highly recommended to initially choose a relatively smaller value of `dfmax` to reduce computation time and memory usage. - `lambda`: An optional user supplied sequence of penalty parameters. Typical usage is to have the program compute its own `lambda` sequence based on `nlambda` and `lambda.min`. - `tune`: A description of the tuning method to be used. This can be a character string naming a tuning control function (currently `"cv"` or `"bic"`) or a call to the tuning control function. Default is 5-fold cross-validation, `tune=cv.control()`, see `ahaz.tune.control` for more options. See also the examples. - `...`: Additional arguments to be passed to `ahazpen`, see `ahazpen` for options. ## Details The function performs an initial penalized fit based on the penalty supplied in `penalty` to obtain a sequence of penalty parameters. Subsequently, it selects among these an optimal penalty parameter based on the tuning control function described in `tune`, see `ahaz.tune.control`. ## Returns An object with S3 class `"tune.ahazpen"`. - **call**: The call that produced this object. - **lambda**: The actual sequence of `lambda` values used. - **tunem**: The tuning score for each value of `lambda` (mean cross-validated error, if `tune=cv.control()`). - **tunesd**: Estimate of the cross-validated standard error, if `tune=cv.control()`. - **tunelo**: Lower curve = `tunem-tunemsd`, if `tune=cv.control()`. - **tuneup**: Upper curve = `tunem+tunemsd`, if `tune=cv.control()`. - **lambda.min**: Value of `lambda` for which `tunem` is minimized. - **df**: Number of non-zero coefficients at each value of `lambda`. - **tune**: The selected `tune` of S3 class `"ahaz.tune.control"`. - **penalty**: The selected `penalty` of S3 class `"ahazpen.pen.control"`. - **foldsused**: Folds actually used, if `tune=cv.control()`. ## References Gorst-Rasmussen, A. & Scheike, T. H. (2011). **Independent screening for single-index hazard rate models with ultra-high dimensional features.** Technical report R-2011-06, Department of Mathematical Sciences, Aalborg University. ## See Also `ahaz.tune.control`, `plot.tune.ahazpen`, `ahazpen`. ## Examples ```r data(sorlie) # Break ties set.seed(10101) time <- sorlie$time+runif(nrow(sorlie))*1e-2 # Survival data + covariates surv <- Surv(time,sorlie$status) X <- as.matrix(sorlie[,3:ncol(sorlie)]) # Training/test data set.seed(20202) train <- sample(1:nrow(sorlie),76) test <- setdiff(1:nrow(sorlie),train) # Run cross validation on training data set.seed(10101) cv.las <- tune.ahazpen(surv[train,], X[train,],dfmax=30) plot(cv.las) # Check fit on the test data testrisk <- predict(cv.las,X[test,],type="lp") plot(survfit(surv[test,]~I(testrisk

tune.ahazpen function