How to predict quantiles/ntiles of rqlasso with caret?

[John_Rambo]

How to caret predict ntiles of rqlasso?

tried:

Control <- caret::trainControl(method = "cv",number = 5)
myModel <- caret::train(myFormula, df_train,trCtrl= Control,method = "rqlasso",tuneLength=10)
preds   <- predict(myModel, df_test) # , type = "raw"

... but get no quantile/ntiles estimates. Do I need a tau-definition here?

Thank you reader/writer.

[technocrat]

@John_Rambo

myFormula

Hard to say without this. Need a reprex. See the FAQ.

[John_Rambo]

@John_Rambo

Control <- caret::trainControl(method = "cv",number = 5)
myModel <- caret::train(myFormula, df_train,trCtrl= Control,method = "rqlasso",tuneLength=10)
preds   <- predict(myModel, df_test) # , type = "raw"

Complete Example:

`   
suppressPackageStartupMessages(library(caret))
suppressPackageStartupMessages(library(quantreq))

    df <- matrix(rnorm(1000), ncol = 2) %>% 
        as.data.frame() %>%
        dplyr::mutate(y = sample(1000/2))
    colnames(df)<-c('one','two','y')
    
    rownames(df)    <- NULL
    df  <- df %>% dplyr::mutate(id=row_number())
    myLimit     <- base::max(df[['id']])
    myLimit     <- myLimit-30
    
    df_train   <- df %>% filter(id <= myLimit)
    df_test    <- df %>% filter(id > myLimit)
    allData    <- dplyr::bind_rows(df_train,df_test)

    myFormula <- paste0('y ~ one + two')  
    myFormula <- as.formula(myFormula)

    Control <- caret::trainControl(method = "cv",number = 5)
    myModel <- caret::train(myFormula, df_train,trCtrl= Control,method = "rqlasso",tuneLength=10)
    preds   <- predict(myModel, df_test) # , type = "raw"
    
    print('preds')
    print(as_tibble(preds),n=20,max_extra_cols=0,width=110)`

output (no quantile preds):

[1] "preds"
# A tibble: 30 x 1
   value
   <dbl>
 1   250
 2   250
 3   250
 4   250
 5   250
 6   250
 7   250
 8   250
 
output standalone-algo 'quantreg'  :

myModel <- quantreg::rq(myFormula,data=df_train,tau=seq(0.1, 0.9, by = 0.1))
preds <- stats::predict(myModel,newdata=df_test)

print('preds')
print(as_tibble(preds),n=20,max_extra_cols=0,width=110)

[1] "preds"
# A tibble: 30 x 9
   `tau= 0.1` `tau= 0.2` `tau= 0.3` `tau= 0.4` `tau= 0.5` `tau= 0.6` `tau= 0.7` `tau= 0.8` `tau= 0.9`
        <dbl>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>
 1       44.8      102.        163.       204.       256.       289.       331.       393.       439.
 2       33.4       76.4       150.       193.       239.       299.       340.       380.       435.
 3       52.5       84.1       143.       195.       249.       315.       361.       406.       456.
 4       57.7      111.        162.       207.       265.       296.       342.       409.       452.
 5       60.1      117.        166.       209.       270.       293.       339.       412.       453.
 6       49.4      102.