rrpartmlr

How to interplate the different result when using mlr and using other packages like rpart and mboost in R

I am using mlr and other packages to do survival analysis. In mlr, I use surv.rpart and surv.glmboost. I also use the original packages rpart and mboost to do this. I find their results are different. Here is an example:

> myData2 <- data.frame(DaySum=c(3,2,1,6,3,2,2,5,2,7,2),
                        DaysDiff=c(24,4,5,12,3,31,131,6,35,18,19),
                        Status='TRUE')
> myData2$Status <- as.logical(myData2$Status)
> myTrain <- c(1:(nrow(myData2)-1))
> myTest <- nrow(myData2)

When I use surv.rpart in mlr, The result is:

> surv.task <- makeSurvTask(data=myData2,target=c('DaysDiff','Status'))
> surv.lrn <- makeLearner("surv.rpart")
> mod <- train(learner=surv.lrn,task=surv.task,subset=myTrain)
> surv.pred <- predict(mod,task=surv.task,subset=myTest)
> surv.pred
Prediction: 1 observations
predict.type: response
threshold: 
time: 0.00
   id truth.time truth.event response
11 11         19        TRUE        1

If I use the original rpart package, the result is:

> train <- myData2[1:(nrow(myData2)-1),]
> test <- myData2[nrow(myData2),]
> fit <- rpart(DaysDiff~DaySum,data=train)
> predict(fit,newdata=test)
[1] 26.9

How come I have got two different results? It looks like rpart package directly gives me the result I want while the result from mlr have some kind of transformation. The same thing happens when I use surv.glmboost:

> surv.task <- makeSurvTask(data=myData2,target=c('DaysDiff','Status'))
Warning messages:
1: Unknown or uninitialised column: 'Weibull'. 
2: Unknown or uninitialised column: 'Cox'. 
3: Unknown or uninitialised column: 'Month2'. 
4: Unknown or uninitialised column: 'Month2'. 
5: Unknown or uninitialised column: 'Month'. 
6: Unknown or uninitialised column: 'Month'. 
7: Unknown or uninitialised column: 'MonthsDiff'. 
8: Unknown or uninitialised column: 'Weibull'. 
9: Unknown or uninitialised column: 'Cox'. 
> surv.lrn <- makeLearner("surv.glmboost")
> mod <- train(learner=surv.lrn,task=surv.task,subset=myTrain)
Warning message:
In names(data) != all.vars(formula[[2]]) :
  longer object length is not a multiple of shorter object length
> surv.pred <- predict(mod,task=surv.task,subset=myTest)
> surv.pred
Prediction: 1 observations
predict.type: response
threshold: 
time: 0.00
   id truth.time truth.event   response
11 11         19        TRUE -0.1946239

Here is the result using mboost package:

> train <- myData2[1:(nrow(myData2)-1),]
Warning messages:
1: Unknown or uninitialised column: 'Weibull'. 
2: Unknown or uninitialised column: 'Cox'. 
3: Unknown or uninitialised column: 'Month2'. 
4: Unknown or uninitialised column: 'Month2'. 
5: Unknown or uninitialised column: 'Month'. 
6: Unknown or uninitialised column: 'Month'. 
7: Unknown or uninitialised column: 'MonthsDiff'. 
8: Unknown or uninitialised column: 'Weibull'. 
9: Unknown or uninitialised column: 'Cox'. 
> test <- myData2[nrow(myData2),]
> fit <- glmboost(DaysDiff~DaySum,data=train)
> predict(fit,newdata=test)
         [,1]
[1,] 33.08294

This is what I found so far. This could happen to other functions like surv.cforest. my question is: why does this happen? And how I can get the results like rpart and mboost when using mlr package?

Solution

  • Your problem is, that you are not fitting a survival model with rpart and glmboost, but a simple regression model.

    Fitting a survival model in rpart looks like this:

    fit = rpart(Surv(DaysDiff, event = Status) ~ DaySum,data=train, method = "exp")
predict(fit,newdata=test)

    So the complete comparison code gives same results (each one predicts 1):

    library(mlr)
myData2 = data.frame(DaySum=c(3,2,1,6,3,2,2,5,2,7,2),
  DaysDiff=c(24,4,5,12,3,31,131,6,35,18,19),
  Status='TRUE')
myData2$Status = as.logical(myData2$Status)
train = myData2[1:(nrow(myData2)-1),]
test = myData2[nrow(myData2),]
surv.task = makeSurvTask(data=train,target=c('DaysDiff','Status'))
surv.lrn = makeLearner("surv.rpart")
mod = train(learner=surv.lrn,task=surv.task,subset=myTrain)
surv.pred = predict(mod,newdata = test)
surv.pred
library(rpart)
library(survival)
fit = rpart(Surv(DaysDiff, event = Status) ~ DaySum,data=train, method = "exp")
predict(fit,newdata=test)