# Prediction methods for ahazpen Compute regression coefficient estimates, linear predictor, cumulative hazard function, or integrated martingale residuals for a fitted penalized semiparametric additive hazards model. ```r ## S3 method for class 'ahazpen' predict(object, newX, type=c("coef","lp","residuals","cumhaz"), lambda=NULL, ...) ## S3 method for class 'ahazpen' coef(object, ...) ``` ## Arguments - `object`: The result of an `ahazpen` fit. - `newX`: New matrix of covariates at which to do predictions. Required unless `type="coef"`. - `lambda`: Value of lambda for at which predictions are to be made. This argument is required for `type="residuals"` and `type="cumhaz"`. Since predictions rely on interpolations between lambda values, it is recommended not to use a lambda-value smaller than the minimum of `object$lambda`. - `type`: The type of prediction. Options are the regression coefficients ("`coef`"), the linear predictors ("`lp`"), the (integrated) martingale residuals ("`residuals`"), or the cumulative hazard ("`cumhaz`") - ``...``: For future methods. ## Details See the details in `predict.ahaz` for information on the different types of predictions. ## Returns For `type="coef"` and `type="lp"`, a matrix of regression coefficients, respectively linear predictions for each value of the penalty parameter. For `type="residuals"`, a matrix of (integrated) martingale residuals associated with the nonzero penalized regression coefficients for a regularization parameter equal to $lambda$. For `type="cumhaz"`, an object with S3 class `"cumahaz"` based on the regression coefficients estimated for a regularization parameter equal to $lambda$, the object containing: - **time**: Jump times for the cumulative hazard estimate. - **cumhaz**: The cumulative hazard estimate. - **event**: Status at jump times (1 corresponds to death, 0 corresponds to entry/exit). ## See Also `ahazpen`, `print.ahazpen`, `plot.ahazpen`, `predict.ahaz`, `plot.cumahaz`. ## Examples ```r data(sorlie) set.seed(10101) # Break ties time <- sorlie$time+runif(nrow(sorlie))*1e-2 # Survival data + covariates surv <- Surv(time,sorlie$status) X <- as.matrix(sorlie[,3:ncol(sorlie)]) # Fit additive hazards regression model w/lasso penalty fit <- ahazpen(surv, X, dfmax=100) # Coefficients beta <- predict(fit,X,lambda=0.08,type="coef") barplot(as.numeric(beta)) # Linear predictions linpred <- predict(fit,X,lambda=0.1,type="lp") riskgrp <- factor(linpred < median(linpred)) plot(survfit(surv~riskgrp)) # Residuals resid <- predict(fit, X, lambda=0.1, type = "residuals") par(mfrow = c(1,2)) hist(resid[,1],main=colnames(resid)[1]) hist(resid[,2],main=colnames(resid)[2]) # Cumulative hazard cumhaz <- predict(fit,X,lambda=0.1,type="cumhaz") plot(cumhaz) ```

predict.ahazpen function