Description
This function (td_scale_by_partition_mle)
scales the sequences in each partition
independently, using the same formula as the function Scale(td_scale_mle
).
Usage
td_scale_by_partition_mle (
data = NULL,
method = NULL,
miss.value = "KEEP",
input.columns = NULL,
global = FALSE,
accumulate = NULL,
multiplier = 1,
intercept = "0",
data.sequence.column = NULL,
data.partition.column = NULL
)
Arguments
data |
Required Argument.
Specifies the input tbl_teradata dataset for scale function.
|
data.partition.column |
Required Argument.
Partition By Columns for data.
Values to this argument can be provided as vector, if multiple
columns are used for partition.
|
method |
Required Argument.
Specifies one or more statistical methods to use to scale the data
set. For method values and descriptions. If you specify multiple
methods, the output tbl_teradata includes the column scalemethod
(which contains the method name) and a row for each input-row/method
combination.
Permitted Values: MEAN, SUM, USTD, STD, RANGE, MIDRANGE, MAXABS.
|
miss.value |
Optional Argument.
Specifies how the td_scale_by_partition_mle function is to process NULL
values in input, as follows:
to process NULL values in input, as follows:
KEEP : Keep NULL values,
OMIT : Ignore any row that has a NULL value,
ZERO : Replace each NULL value with zero,
LOCATION : Replace each NULL value with its location value.
Default Value: "KEEP".
Permitted Values: KEEP, OMIT, ZERO, LOCATION.
|
input.columns |
Required Argument.
Specifies the input tbl_teradata columns that contain the attribute
values of the samples.
|
global |
Optional Argument.
Specifies whether all input columns are scaled to the same location
and scale.
Note: Each input column is scaled separately.
Default Value: FALSE.
|
accumulate |
Optional Argument.
Specifies the input tbl_teradata columns to copy to the output table.
By default, the function copies no input tbl_teradata columns to the
output table.
Tip: To identify the sequences in the output, specify the partition
columns in this argument.
|
multiplier |
Optional Argument.
Specifies one or more multiplying factors to apply to the input
variables (multiplier in the following formula):
X' = intercept + multiplier * (X - location)/scale
If you specify only one multiplier, it applies to all columns specified
by the input.columns argument. If you specify multiple multiplying factor,
each multiplier applies to the corresponding input column. For example,
the first multiplier applies to the first column specified by the InputColumns argument,
the second multiplier applies to the second input column, and so on.
Default Value: 1.
|
intercept |
Optional Argument.
Specifies one or more addition factors incrementing the scaled
results-intercept in the following formula:
X' = intercept + multiplier * (X - location)/scale
If you specify only one intercept,
it applies to all columns specified by the input.columns argument. If
you specify multiple addition factors, each intercept applies to the
corresponding input column. The syntax of intercept is: [-]number |
min | mean | max where min, mean, and max are the global minimum,
maximum, mean values in the corresponding columns. The function
scales the values of min, mean, and max. For example, if intercept is
"- min" and multiplier is 1, the scaled result is transformed to a
nonnegative sequence according to this formula, where scaledmin is
the scaled value: X' = -scaledmin + 1 * (X - location)/scale
The Default Value: "0".
|
data.sequence.column |
Optional Argument.
Specifies the vector of column(s) that uniquely identifies each row
of the input argument "data". The argument is used to ensure
deterministic results for functions which produce results that vary
from run to run.
|
Value
Function returns an object of class "td_scale_by_partition_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")
# Create remote tibble objects.
scale_housing_input <- tbl(con, "scale_housing")
# Create remote tibble objects.
# Example 1 - This function scales the sequences on partition cloumn 'lotsize' using
# the same formula as the function td_scale_mle().
td_scale_by_partition_out <- td_scale_by_partition_mle(data=scale_housing_input,
data.partition.column ="lotsize",
input.columns = c("id","price", "lotsize", "bedrooms", "bathrms"),
method = c("maxabs"),
accumulate = c("types")
)