Logistic Example

John Mount

2019-02-24

We can work an example similar to the rquery example using a data.table back-end.

library("rqdatatable")
library("data.table")

## Warning: package 'data.table' was built under R version 3.5.2

# data example
dL <- wrapr::build_frame(
   "subjectID", "surveyCategory"     , "assessmentTotal" |
   1          , "withdrawal behavior", 5                 |
   1          , "positive re-framing", 2                 |
   2          , "withdrawal behavior", 3                 |
   2          , "positive re-framing", 4                 )

scale <- 0.237

# example rquery pipeline
rquery_pipeline <- local_td(dL) %.>%
  extend_nse(.,
             one = 1) %.>%
  extend_nse(.,
             probability =
               exp(assessmentTotal * scale)/
               sum(exp(assessmentTotal * scale)),
             count = sum(one),
             rank = rank(probability, surveyCategory),
             partitionby = 'subjectID') %.>%
  extend_nse(.,
             isdiagnosis = rank == count,
             diagnosis = surveyCategory) %.>%
  select_rows_nse(., 
                  isdiagnosis == TRUE) %.>%
  select_columns(., 
                 c('subjectID', 'diagnosis', 'probability')) %.>%
  orderby(., 'subjectID')

Show expanded form of query tree.

cat(format(rquery_pipeline))

table(dL; subjectID, surveyCategory, assessmentTotal) %.>% extend(., one := 1) %.>% extend(., probability := exp(assessmentTotal * 0.237) / sum(exp(assessmentTotal * 0.237)), count := sum(one), p= subjectID) %.>% extend(., rank := rank(probability, surveyCategory), p= subjectID) %.>% extend(., isdiagnosis := rank == count, diagnosis := surveyCategory) %.>% select_rows(., isdiagnosis == TRUE) %.>% select_columns(., subjectID, diagnosis, probability) %.>% orderby(., subjectID)

Execute the calculation.

ex_data_table(rquery_pipeline)

## subjectID diagnosis probability ## 1: 1 withdrawal behavior 0.6706221 ## 2: 2 positive re-framing 0.5589742