Obtains predictions at specified boosting steps from a iCoxBoost object fitted by iCoxBoost.
Usage
## S3 method for class 'iCoxBoost'
predict(object,newdata=NULL,
subset=NULL,at.step=NULL,times=NULL,
type=c("lp","logplik","risk","CIF"),...)
Arguments
object
fitted CoxBoost object from a CoxBoost call.
newdata
data frame with new covariate values (for n.new observations). If just prediction for the training data is wanted, it can be omitted. If the predictive partial log-likelihood is wanted (type=logplik), this frame also has to contain the response information.
subset
an optional vector specifying a subset of observations to be used for evaluation.
at.step
scalar or vector of boosting step(s) at which prediction is wanted. If type="risk" is used, only one step is admissible. If no step is given, the final boosting step is used.
times
vector with T time points where prediction is wanted. Only needed for type="risk"
type
type of prediction to be returned: "lp" gives the linear predictor, "logplik" the partial log-likelihood, "risk" the predicted probability of not yet having had the event at the time points given in times, and "CIF" the predicted cumulative incidence function, i.e., the predicted probability of having had the event of interest.
...
miscellaneous arguments, none of which is used at the moment.
Value
For type="lp" and type="logplik" a vector of length n.new (at.step being a scalar) or a n.new * length(at.step) matrix (at.step being a vector) with predictions is returned.
For type="risk" or type="CIF" a n.new * T matrix with predicted probabilities at the specific time points is returned.
n <- 200; p <- 100
beta <- c(rep(1,2),rep(0,p-2))
x <- matrix(rnorm(n*p),n,p)
actual.data <- as.data.frame(x)
real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
cens.time <- rexp(n,rate=1/10)
actual.data$status <- ifelse(real.time <= cens.time,1,0)
actual.data$time <- ifelse(real.time <= cens.time,real.time,cens.time)
# define training and test set
train.index <- 1:100
test.index <- 101:200
# Fit a Cox proportional hazards model by iCoxBoost
cbfit <- iCoxBoost(Surv(time,status) ~ .,data=actual.data[train.index,],
stepno=300,cv=FALSE)
# mean partial log-likelihood for test set in every boosting step
step.logplik <- predict(cbfit,newdata=actual.data[test.index,],
at.step=0:300,type="logplik")
plot(step.logplik)
# names of covariates with non-zero coefficients at boosting step
# with maximal test set partial log-likelihood
print(coef(cbfit,at.step=which.max(step.logplik)-1))
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(CoxBoost)
Loading required package: survival
Loading required package: Matrix
Loading required package: prodlim
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/CoxBoost/predict.iCoxBoost.Rd_%03d_medium.png", width=480, height=480)
> ### Name: predict.iCoxBoost
> ### Title: Predict method for iCoxBoost fits
> ### Aliases: predict.iCoxBoost
> ### Keywords: models regression survial
>
> ### ** Examples
>
> n <- 200; p <- 100
> beta <- c(rep(1,2),rep(0,p-2))
> x <- matrix(rnorm(n*p),n,p)
> actual.data <- as.data.frame(x)
> real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
> cens.time <- rexp(n,rate=1/10)
> actual.data$status <- ifelse(real.time <= cens.time,1,0)
> actual.data$time <- ifelse(real.time <= cens.time,real.time,cens.time)
>
> # define training and test set
>
> train.index <- 1:100
> test.index <- 101:200
>
> # Fit a Cox proportional hazards model by iCoxBoost
>
> ## No test:
> cbfit <- iCoxBoost(Surv(time,status) ~ .,data=actual.data[train.index,],
+ stepno=300,cv=FALSE)
> ## End(No test)
>
> # mean partial log-likelihood for test set in every boosting step
>
> ## No test:
> step.logplik <- predict(cbfit,newdata=actual.data[test.index,],
+ at.step=0:300,type="logplik")
>
> plot(step.logplik)
> ## End(No test)
>
> # names of covariates with non-zero coefficients at boosting step
> # with maximal test set partial log-likelihood
>
> ## No test:
> print(coef(cbfit,at.step=which.max(step.logplik)-1))
V1 V2 V3 V4 V5 V6
1.09211021 0.84349939 0.00000000 0.00000000 0.00000000 0.00000000
V7 V8 V9 V10 V11 V12
0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
V13 V14 V15 V16 V17 V18
0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -0.01223960
V19 V20 V21 V22 V23 V24
0.00000000 0.00000000 0.03937006 0.00000000 0.01192784 0.00000000
V25 V26 V27 V28 V29 V30
0.05123715 -0.04547263 0.00000000 -0.09812249 -0.05796674 0.00000000
V31 V32 V33 V34 V35 V36
0.08551553 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
V37 V38 V39 V40 V41 V42
0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
V43 V44 V45 V46 V47 V48
0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
V49 V50 V51 V52 V53 V54
0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
V55 V56 V57 V58 V59 V60
0.00000000 0.00000000 -0.04847894 0.00000000 0.00000000 0.00000000
V61 V62 V63 V64 V65 V66
0.00000000 0.00000000 0.00000000 0.00000000 0.04079100 0.00000000
V67 V68 V69 V70 V71 V72
0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
V73 V74 V75 V76 V77 V78
0.01317528 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
V79 V80 V81 V82 V83 V84
0.00000000 0.00000000 0.06976195 0.00000000 0.00000000 0.00000000
V85 V86 V87 V88 V89 V90
0.09730206 0.00000000 0.00000000 0.02507007 0.00000000 0.00000000
V91 V92 V93 V94 V95 V96
0.00000000 0.00000000 0.00000000 0.00000000 0.01194670 0.00000000
V97 V98 V99 V100
0.00000000 0.00000000 0.00000000 0.00000000
> ## End(No test)
>
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.