predict.bsrr: make predictions from a "bsrr" object.

In bestridge: A Comprehensive R Package for Best Subset Selection

Description

Returns predictions from a fitted "bsrr" object.

Usage

## 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.

Value

The object returned depends on the types of family.

Author(s)

Liyuan Hu, Kangkang Jiang, Yanhang Zhang, Jin Zhu, Canhong Wen and Xueqin Wang.

See Also

bsrr.

Examples

#-------------------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)

bestridge documentation built on Oct. 10, 2021, 5:06 p.m.