Teradata R Package Function Reference - Scale - Teradata R Package - Look here for syntax, methods and examples for the functions included in the Teradata R Package.

Description

The Scale (td_scale_mle) function uses statistical information from the ScaleMap (td_scale_map_mle) function to scale the input data set.

Usage

  td_scale_mle (
      object = NULL,
      data = NULL,
      method = NULL,
      global = FALSE,
      accumulate = NULL,
      multiplier = 1,
      intercept = "0",
      input.columns = NULL,
      object.sequence.column = NULL,
      data.sequence.column = NULL
  )

Arguments

Value

Function returns an object of class "td_scale_mle" which is a named list containing Teradata tbl object.
Named list member can be referenced directly with the "$" operator using name: result.

Examples

    # Get the current context/connection
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("scalemap_example", "scale_housing")
    loadExampleData("scale_example", "scale_stat", "scale_housing_test")
    
    # Create remote tibble objects.
    scale_housing <- tbl(con, "scale_housing")
    scale_housing_test <- tbl(con, "scale_housing_test")
    scale_stat <- tbl(con, "scale_stat")
    
    # Example 1 - This example scales (normalizes) input data using the 
    # midrange method and the default values for the arguments Intercept 
    # and Multiplier (0 and 1, respectively).
    td_scale_map_out <- td_scale_map_mle(data=scale_housing,
                                     input.columns=c('price','lotsize','bedrooms','bathrms','stories')
                                    )
    td_scale_out1 <- td_scale_mle(object=td_scale_map_out,
                              data=scale_housing,
                              method=c("midrange"),
                              accumulate=c("id")
                             )
                         
    # Example 2 - This example uses a tbl_teradata as input for object argument and
    # the Intercept argument has the value "-min" (where min is the global minimum value) 
    # and we also specify different Multiplier values for corresponding columns. 
    td_scale_out2 <- td_scale_mle(object = scale_stat,
                              data = scale_housing,
                              method = c("midrange"),
                              accumulate = c("id"),
                              multiplier = c(1,2,3,4,5),
                              intercept = c("-min")
                             )
    
    # Example 3 - This example creates statistics using ScaleMap on a training data
    # set and then uses these statistics to scale a similar test data set.
    td_scale_out3 <- td_scale_mle(object = scale_stat,
                              data = scale_housing_test,
                              method = c("midrange","mean","maxabs","range"),
                              accumulate = c("id")
                             )
    
    # Example 4 - This example uses the Scale function to scale data (using 
    # the maxabs method) before inputting it to the function KMeans, which 
    # outputs the centroids of the clusters in the dataset.
    loadExampleData("kmeans_example", "computers_train1")
    computers_train1 <- tbl(con, "computers_train1")
    
    td_scale_map_out4 <- td_scale_map_mle(data=computers_train1,
                                            input.columns=c('price','speed','hd','ram'),
                                            miss.value='OMIT'
                                           )
    # Create Table of Scaled Data using Scale function
    td_scale_out4 <- td_scale_mle(object=td_scale_map_out4,
                                     data=computers_train1,
                                     method=c("maxabs"),
                                     accumulate=c("id")
                                    )
    # Use the scaled data as input to KMeans to get clusters
    td_kmeans_out <- td_kmeans_mle(data = td_scale_out4$result,
                                centers = 8,
                                iter.max = 10,
                                threshold = 0.05
                               )