# make predictions from a "bsrr" object. Returns predictions from a fitted "`bsrr`" object. ```r ## S3 method for class 'bsrr' predict(object, newx, type = c("link", "response"), ...) ``` ## Arguments - `object`: Output from the `bsrr` function. - `newx`: New data used for prediction. If omitted, the fitted linear predictors are used. - `type`: `type = "link"` gives the linear predictors for `"binomial"`, `"poisson"` or `"cox"` models; for `"gaussian"` models it gives the fitted values. `type = "response"` gives the fitted probabilities for `"binomial"`, fitted mean for `"poisson"` and the fitted relative-risk for `"cox"`; for `"gaussian"`, `type = "response"` is equivalent to `type = "link"` - ``...``: Additional arguments affecting the predictions produced. ## Returns The object returned depends on the types of family. ## Examples ```r #-------------------linear model----------------------# # Generate simulated data n <- 200 p <- 20 k <- 5 rho <- 0.4 seed <- 10 Tbeta <- rep(0, p) Tbeta[1:k*floor(p/k):floor(p/k)] <- rep(1, k) Data <- gen.data(n, p, k, rho, family = "gaussian", beta = Tbeta, seed = seed) x <- Data$x[1:140, ] y <- Data$y[1:140] x_new <- Data$x[141:200, ] y_new <- Data$y[141:200] lambda.list <- exp(seq(log(5), log(0.1), length.out = 10)) lm.bsrr <- bsrr(x, y, method = "pgsection") pred.bsrr <- predict(lm.bsrr, newx = x_new) #-------------------logistic model----------------------# #Generate simulated data Data <- gen.data(n, p, k, rho, family = "binomial", beta = Tbeta, seed = seed) x <- Data$x[1:140, ] y <- Data$y[1:140] x_new <- Data$x[141:200, ] y_new <- Data$y[141:200] lambda.list <- exp(seq(log(5), log(0.1), length.out = 10)) logi.bsrr <- bsrr(x, y, tune="cv", family = "binomial", lambda.list = lambda.list, method = "sequential") pred.bsrr <- predict(logi.bsrr, newx = x_new) #-------------------coxph model----------------------# #Generate simulated data Data <- gen.data(n, p, k, rho, family = "cox", beta = Tbeta, scal = 10) x <- Data$x[1:140, ] y <- Data$y[1:140, ] x_new <- Data$x[141:200, ] y_new <- Data$y[141:200, ] lambda.list <- exp(seq(log(5), log(0.1), length.out = 10)) cox.bsrr <- bsrr(x, y, family = "cox", lambda.list = lambda.list) pred.bsrr <- predict(cox.bsrr, newx = x_new) #-------------------group selection----------------------# beta <- rep(c(rep(1,2),rep(0,3)), 4) Data <- gen.data(200, 20, 5, rho=0.4, beta = beta, seed =10) x <- Data$x y <- Data$y group.index <- c(rep(1, 2), rep(2, 3), rep(3, 2), rep(4, 3), rep(5, 2), rep(6, 3), rep(7, 2), rep(8, 3)) lm.groupbsrr <- bsrr(x, y, s.min = 1, s.max = 8, group.index = group.index) pred.groupbsrr <- predict(lm.groupbsrr, newx = x_new) ``` ## See Also `bsrr`. ## Author(s) Liyuan Hu, Kangkang Jiang, Yanhang Zhang, Jin Zhu, Canhong Wen and Xueqin Wang.

predict.bsrr function