[R] Glmnet survival cox predict

[Amir Hadanny]

Thank you,
both train and test are originated from the same data object.

attached the missing code:

data<-read.csv("old4.csv", header=TRUE)

library(imputeMissings)
data<-impute(data,object = NULL ,method = "median/mode")

for (i in col[13:68]) {
  data[i]<-lapply(data[i], factor)
}
for (i in col[1:12]) {
  data[i]<-lapply(data[i], numeric)
}

data$TIME<-as.numeric(data$TIME)

  data<-data[-c(61,62,64,65,66,67,68)]
data$TIME<-ceiling(data$TIME/12)
data$TIME[which(data$TIME==37)]<-36

data1 = sort(sample(nrow(data), nrow(data)*.7))
train<-data[data1,]
test<-data[-data1,]


so test should be the exact same, and i still can't find the issue,

thank you
Amir

On Sat, Nov 16, 2019 at 12:00 AM David Winsemius <dwinsemius using comcast.net>
wrote:

>
> On 11/15/19 10:49 AM, Amir Hadanny wrote:
> > Hi all,
> > i'm trying to get the prediction probabilities for a survival elastic
> net.
> > When i use try to predict using the train model on the test set, it
> creates
> > an object with the number rows of the train data (6400 rows) instead of
> the
> > test data (2400 rows). I really don't understand why, and that doesn't
> let
> > me check for performance c-index.
>
>
> If you call most `predict` functions with a second argument that fails
> to contain the predictors in the model, it returns the predictions on
> the original data. The only place where the `test` object appears prior
> to the predict operation is in your call to `predict.coxph`, so my guess
> is that it fails to meet the requirements of the function for a valid
> newdata argument. (Another thought was that maybe `test` didn't exist,
> but that should have thrown an error with the predict call and the nrow
> call.)
>
>
> But since you don't provide code that creates `test` or even an
> unambiguous way of examining its structure, that is entirely a guess.
>
>
> And finally ... Rhelp is a plain text mailing list, so please to read
> the message at the bottom of every transmission from the mailserver ...
> i.e.  read the Posting Guide. (It is not at all difficult to get
> gmail.com to send plain text.)
>
>
> --
>
> David.
>
> > the code:
> >
> > data<-read.csv("old4.csv", header=TRUE)
> > library(imputeMissings)
> > data<-impute(data,object = NULL ,method = "median/mode")
> >
> > trainstatus<-train$DIED1095
> > trainTime<-train$TIME
> > y<-Surv(trainTime,trainstatus)
> >
> > trainX<-train[-c(12,63,64,65,66,67,68,69,70,71)]
> > x<-data.matrix(trainX)
> >
> >
> > library(glmnet)
> > fit <- glmnet(x,Surv(trainTime,trainstatus),family="cox",alpha=0.1,
> > ,maxit=10000)
> > max.dev.index     <- which.max(fit$dev.ratio)
> > optimal.lambda <- fit$lambda[max.dev.index]
> > optimal.beta  <- fit$beta[,max.dev.index]
> > nonzero.coef <- abs(optimal.beta)>0
> > selectedBeta <- optimal.beta[nonzero.coef]
> > selectedTrainX   <- x[,nonzero.coef]
> >
> > coxph.model<- coxph(Surv(train$TIME,train$DIED365) ~x,data=train,
> > init=selectedBeta,iter=0)
> > coxph.predict<-predict(coxph.model,test)
> >
> > nrow(test)
> > 2872
> >
> > nrow(train
> > 6701
> >
> > length(coxph.predict)
> > 6701
> >
> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
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> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>

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