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

Description Usage Arguments Value Author(s) See Also Examples

View source: R/predict.bsrr.R

Description

Returns predictions from a fitted "bsrr" object.

Usage

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## 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

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#-------------------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 Sept. 5, 2021, 5:25 p.m.