predict.ecpc: Predict for new samples for 'ecpc' object

In ecpc: Flexible Co-Data Learning for High-Dimensional Prediction

Predict for new samples for ‘ecpc’ object

Description

Predict the response for new samples based on an ‘ecpc’ object.

Usage

## S3 method for class 'ecpc'
predict(object, X2, X=NULL, Y=NULL, ...)

Arguments

object	An 'ecpc' object returned by ecpc.
X2	Independent observed data for which response is predicted.
X	Observed data used in fitting the ‘object’; (nxp)-dimensional matrix (p: number of covariates) with each row the observed high-dimensional feature vector of a sample.
Y	Response data used in fitting the ‘object’; n-dimensional vector (n: number of samples) for linear and logistic outcomes, or Surv object for Cox survival.
...	Other parameters

Value

Vector with predicted values. Note that for Cox response, the relative risks are provided, unless training data X and Y is provided to compute the Breslow estimator.

Examples

 
#####################
# Simulate toy data #
#####################
p<-300 #number of covariates
n<-100 #sample size training data set
n2<-100 #sample size test data set

#simulate all betas i.i.d. from beta_k~N(mean=0,sd=sqrt(0.1)):
muBeta<-0 #prior mean
varBeta<-0.1 #prior variance
indT1<-rep(1,p) #vector with group numbers all 1 (all simulated from same normal distribution)

#simulate test and training data sets:
Dat<-simDat(n,p,n2,muBeta,varBeta,indT1,sigma=1,model='linear') 
str(Dat) #Dat contains centered observed data, response data and regression coefficients

###################
# Provide co-data #
###################
continuousCodata <- abs(Dat$beta) 
Z1 <- cbind(continuousCodata,sqrt(continuousCodata))

#setting 2: splines for informative continuous
Z2 <- createZforSplines(values=continuousCodata)
S1.Z2 <- createS(orderPen=2, G=dim(Z2)[2]) #create difference penalty matrix
Con2 <- createCon(G=dim(Z2)[2], shape="positive+monotone.i") #create constraints

#setting 3: 5 random groups
G <- 5
categoricalRandom <- as.factor(sample(1:G,p,TRUE))
#make group set, i.e. list with G groups:
groupsetRandom <- createGroupset(categoricalRandom)
Z3 <- createZforGroupset(groupsetRandom,p=p)
S1.Z3 <- createS(G=G, categorical = TRUE) #create difference penalty matrix
Con3 <- createCon(G=dim(Z3)[2], shape="positive") #create constraints

#fit ecpc for the three co-data matrices with following penalty matrices and constraints
#note: can also be fitted without paraPen and/or paraCon
Z.all <- list(Z1=Z1,Z2=Z2,Z3=Z3)
paraPen.all <- list(Z2=list(S1=S1.Z2), Z3=list(S1=S1.Z3))
paraCon <- list(Z2=Con2, Z3=Con3)

############
# Fit ecpc #
############
tic<-proc.time()[[3]]
fit <- ecpc(Y=Dat$Y,X=Dat$Xctd,
           Z = Z.all, paraPen = paraPen.all, paraCon = paraCon,
           model="linear",maxsel=c(5,10,15,20),
           Y2=Dat$Y2,X2=Dat$X2ctd)
toc <- proc.time()[[3]]-tic

predictions <- predict(fit, X2=Dat$X2ctd)

ecpc documentation built on March 7, 2023, 6:46 p.m.